Appendix H 

Air Quality Modeling Sensitivity and Source 

Apportionment

1. A Variable Time-step Algorithm for Air Quality Models 

2. Determining the Sources of Regional Haze in the Southeastern 

United States Using the CMAQ Model 

3. Quantifying the Sources of Ozone, Fine Particulate Matter, and 

Regional Haze in the Southeastern U.S. 

4. Impact of NOx Emission Reductions on PM Levels in the VISTAS 

Region 

Appendix H 

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Sensitivity Analysis and 

Source Apportionment for VISTAS 

Final Report to

Visibility Improvement State and 

Tribal Association of the Southeast (VISTAS) 

Prepared by: 

M. Talat Odman* and Armistead G. Russell 

School of Civil and Environmental Engineering 

Georgia Institute of Technology 

Submitted to: 

Patricia F. Brewer**

Technical Coordinator 

Visibility Improvement State and Tribal Association of the Southeast 

Submission Date: 

September 30, 2008 

 

*

 M. Talat Odman: 311 Ferst Drive, Atlanta, GA 30332-0512 

Phone: 404-894-2783; Fax: 404-894-8266; E-mail: talat.odman@ce.gatech.edu 

**

 Patricia F. Brewer: 2090 US Highway 70, Swannanoa, NC 28778 

Phone: 828-296-4541; E-mail: pat.brewer@ncmail.net 

Appendix H 

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Contents 

Chapter 1: A Variable Time-step Algorithm for Air Quality Models 

Chapter 2: Determining the Sources of Regional Haze in the Southeastern United 

States Using the CMAQ Model 

Chapter 3: Quantifying the Sources of Ozone, Fine Particulate Matter, and Regional 

Haze in the Southeastern U.S. 

Chapter 4: Impact of NOx Emission Reductions on PM Levels in the VISTAS Region 

Website 

http://vistas.ce.gatech.edu/ 

 ii 

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A Variable Time-step Algorithm for Air Quality Models 

M. Talat Odman and Yongtao Hu 

School of Civil and Environmental Engineering, Georgia Institute of Technology 

Correspondence 

M. Talat Odman 

School of Civil and Environmental Engineering 

311 Ferst Drive 

Atlanta, GA 30332-0512 

Telephone: 404-894-2783 

Fax: 404-894-8266 

Email: talat.odman@ce.gatech.edu

Submitted for publication in 

Journal of Geophysical Research - Atmospheres 

June 2006 

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Abstract. In current air quality models, distinct process operators are applied sequentially 

to pollutant concentration fields. Each process has its own internal time step but there is a 

common step used to synchronize all the processes. Usually, the characteristic time for 

advection, or the grid length divided by the wind speed, is selected as the common step. 

Since a single time step is used for the entire domain, its size is determined by maximum 

wind speed and minimum grid length. This leads to computational inefficiency in cells 

where process characteristic times are much longer than the global time step. 

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A variable time-step algorithm is developed that allows each grid cell to have its own 

time step. Concentrations in cells with shorter time steps are updated using fluxes from cells 

with longer time steps. Fluxes from cells with shorter time steps to cells with longer time 

steps are kept in reservoirs. Concentrations in cells with longer time steps remain constant 

until the time levels are synchronized; then the mass in each reservoir is added to the 

appropriate cell. 

A two-dimensional implementation of the algorithm that uses the same time step 

through each vertical column is described. PM2.5 estimates obtained by using variable time 

steps are, on average, within 4 % of those obtained by using a single time step. Larger 

differences are observed for ozone with 11 % higher concentrations during a winter episode. 

The computation time decreases by 25 % but this can be increased through more efficient 

programming. 

Keywords: time-splitting, process splitting, computational efficiency 

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1. Introduction 

Air quality models (AQMs) are based on the atmospheric transport and chemistry 

equation: 

( ) c ScRc

t

c ++∇⋅∇=⋅∇+

∂

∂ 6 Ku )()( . (1) 

7 

8 

9 

10 

11 

12 

13 

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15 

16 

Here c is a vector of pollutant concentrations, u is the wind field, K represents 

parameterized atmospheric turbulence, R denotes chemical production (or loss) which is a 

nonlinear function of c and S includes various sources (e.g., emissions) and sinks (e.g., 

deposition). The dependence of the variables on the spatial variable x and time t is not 

shown here for simplicity. Both u and K are given (usually provided by a prognostic 

meteorological model) so that the problem is linear with respect to the transport part. 

Characteristic times differ from one process to another. In particular, the range of 

characteristic times for chemical reactions in R spans several orders of magnitude. 

After spatial discretization of Eq. (1) a semi-discrete system of the following form is 

obtained: 

wF )(

t

w = ∂

∂ 17 (2) 

18 

19 

20 

21 

22 

The new variable w consists of c and some other parameters and F is a vector function. The 

computational power required to solve this system is enormous due to the stiffness caused 

by the wide range of characteristic times. In addition, various numerical difficulties 

associated with special requirements of each transport and chemistry process must be dealt 

with. Therefore, in AQMs, Eq. 2 is divided into smaller pieces. A common approach is 

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process splitting [Blom and Verwer, 2000] where F is split into functions representing 

different processes: 

1 

2 

wFwFwFwF )()()()(

t

w

A D ++≡= R ∂

∂ 3 (3) 

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The function FA contains horizontal and vertical advection terms. Advection is the dominant 

horizontal transport process; vertical advection is usually less important. Some models, e.g., 

the Urban-to-Regional Multiscale (URM) model [Boylan, et al., 2002], combine horizontal 

diffusion with advection while others, including the Community Multiscale Air Quality 

(CMAQ) model [Byun and Ching, 1999], keep them separate. FD and FR contain the vertical 

diffusion (and horizontal diffusion, if not already included in FA) and chemical reaction 

terms, respectively. Deposition processes are not included in the discussion here because dry 

deposition is generally treated as a boundary condition and wet deposition is assigned its 

own function separate from the ones above. However, emissions should be considered as a 

source term and a difficult choice is whether to make emissions part of FD or FR. If they are 

made part of FD then fresh emission plumes would be diffused before they had a chance to 

react with radicals in the environment. This is the choice made in the CMAQ model. On the 

other hand, if made part of FR they would participate in chemical reactions before they had a 

chance to diffuse into the surrounding air. It is obvious that emissions should be made part 

of the process with shorter characteristic time but the relative magnitudes of the 

characteristic times may differ from place to place and time to time. The URM model solves 

the problem by combining FD and FR into a single diffusion-chemistry function. Note that 

not all processes modeled in current AQMs are shown in Eq. (3). Complex aerosol and 

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cloud processes are applied to the pollutant concentration fields through process splitting 

and other processes can be added to the list. 

Splitting drastically reduces the computational resources required by Eq. (2). It also 

allows using custom-built numerical solvers for each piece or process. It is much easier to 

deal with process-specific problems individually rather than trying to develop a general 

solver. For example, the advection operator is usually made nonlinear to achieve positivity 

either through filtering or flux-limiting. The only disadvantage of splitting is that it 

introduces an error into the solution unless FA, FD and FR commute with each other [Lanser 

and Verwer, 1999]. The conditions for this are that u, K, and R do not vary spatially, and 

that R is linear in c. Since these conditions are not satisfied in any realistic AQM, the 

question is not whether there would be splitting errors but how large they are. 

Splitting methods are classified as first or second order based on the order in ∆t of the 

splitting error that they introduce. Most splitting methods are first order (i.e., their splitting 

error is second order in ∆t) such as the one used in CMAQ: 

 (4) nn

D

n

A

n

R

n );();();( wttttttw 1 ∆Φ∆Φ∆Φ= +

Here the ΦA, ΦD and ΦR are the integrators for FA, FD and FR in Equation 3 respectively; for 

simplicity other processes of CMAQ (see Section 3 below) are not shown. Strang splitting 

[Strang, 1968], which is believed to be second order, became very popular in AQMs after 

being adopted by McRae, et al. [1982]. It advances the solution in time by the following 

sequence of operators: 

nn

A

n

D

n

R

n

D

n

A

n w

t

t

t

ttt

t

t

t

tw ) 2;() 2;();() 2;() 2;( 2

1

2

1

1 ∆ Φ ∆ Φ∆Φ ∆ Φ ∆ Φ= + + + 21 . (5) 

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Νote that the transport operators ΦA and ΦD are applied for one half of the splitting time 

step, ∆t, symmetrically around the chemistry operator Φ

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R. Recently, Sportisse [2000] argued 

that, for R linear in c, Strang splitting is only first order unless the stiff operator ΦR is 

applied last. 

The splitting time step determines the frequency by which processes of differing 

characteristic times are “synchronized” or “coupled.” In general, the characteristic time for 

advection, which is equal to the grid size divided by the wind speed, is selected as the 

splitting time step. Processes with shorter characteristic times are advanced in several 

internal steps through a process called “sub-cycling” but, to simplify the discussion, this is 

considered to be a part of the process operators. Current AQMs use a global (splitting is 

dropped from hereon) time step, ∆t, which is determined by the maximum wind speed in the 

entire domain. In general, a large number of grid cells would have characteristic times larger 

than ∆t. This is often the case when the modeling domain includes a body of water or 

extends to altitudes above the boundary layer. High wind speeds over water or aloft dictate 

small time steps over the entire domain even if the local wind speeds over a large fraction of 

the domain may be very low. Time stepping with a ∆t much smaller than the characteristic 

time step usually does not change the results by much, therefore using a single time step for 

the entire domain is computationally inefficient. If the grid is non-uniform such as in the 

URM model [Boylan, et al., 2002], or the Adaptive Grid Model [Odman, et al., 2002] where 

the grid sizes can differ by orders of magnitude, the inefficiency of using a global time step 

is even more significant. In this paper, a variable time-step algorithm is described. Its 

accuracy and computation-time benefits are evaluated in comparison to using a global time 

step. 

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2. Methodology 

The variable time-step algorithm (VARTSTEP) is developed with the objective of 

enabling local time steps in AQMs. VARTSTEP allows each grid cell to advance by its own 

time step. The version of the algorithm described here is two-dimensional: it uses the same 

time step for an entire vertical column. To our knowledge, local time steps have only been 

used in the vertical direction therefore this is the first attempt at using local time steps in the 

horizontal domain of an AQM. Extension of the algorithm to third dimension should be 

straightforward. Here, VARTSTEP is implemented in the CMAQ model [Byun and Ching, 

1999]. CMAQ is a uniform grid AQM therefore the maximum wind speed determines the 

global time step. Version 4.3 of CMAQ introduced a limited local time-step capability in the 

vertical direction. The user is allowed to specify the layer up to which wind speeds are 

considered in determining the global time step, and stronger winds aloft are ignored. 

Advection is applied multiple times to the upper layers with a shorter time step that 

guaranties stability. This limited local time step capability in the vertical direction is 

preserved in the VARTSTEP version of CMAQ, which will be referred to as VARTSTEPCMAQ hereafter. 

VARTSTEP assigns every vertical column its own time step, ∆ti, where the subscript i

is used to denote the local character of the time step that is specific to the vertical column. 

The local time step must satisfy two conditions. The first condition is 

1

max

≤

∆

∆

i

ii

x

tu 22 (5) 

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where is the maximum wind speed in vertical column i (up to the user-specified 

altitude) and ∆x

max 1 ui

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i is the horizontal grid size for that vertical column (subscript i is used 

considering that grid size may be non-uniform). This is the well known Courant, Friedrichs, 

and Lewy (CFL) stability condition for advection [Anderson, et al., 1984]. In process 

splitting, this condition also assures that fresh emissions are not transported by a horizontal 

distance longer than the grid length before other processes are applied at least once. The 

second condition requires that ∆ti must be an integer multiple of the global time step, ∆t, and 

an integer divisor of the output time step. The global time step is equal to the smallest ∆ti in 

the entire domain. This condition assures synchronization of processes among all the grid 

cells before the results are written out. CMAQ, like most other AQMs, generates outputs 

typically once every hour. Therefore, assuming an output time step of 60 min, if the global 

time step is 5 min, the local time step can be 5, 10, 15, 20, 30, or 60 min. However, it cannot 

be 25 min, for example, since 25 min does not divide 60 min evenly. 

The model clock, which is used to determine when to integrate, is advanced in 

increments equal to the global time step, ∆t. Operators are applied to the grid cell 

concentrations of vertical column i for the duration of the local time step, ∆ti, (i.e., 

concentrations are advanced by ∆ti) only if the clock time is an integer multiple of ∆ti (i.e., 

t = N×∆ti). In other words, instead of advancing the solution in m steps with the global time 

step ∆t as 

 (6) 

nn

D

n

A

n

R

kn

D

kn

A

kn

R

mn

D

mn

A

mn

R

mn

wtttttt

tttttt

w tttttt

);();();(

);();();(

);();();( 1 1 1

∆Φ∆Φ∆Φ

∆Φ∆Φ∆Φ

Φ= Φ∆ Φ∆ ∆

+ + +

+ −+ −+ −+

K

K

where 1 < k < m −2, VARTSTEP advances it in one step with the local time step 

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A Variable time-step algorithm 

1 ∆ti = m×∆t as 

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12 

 (7) n n

D

n

A

n

R

mn ∆Φ∆Φ∆Φ= );();();( wtmttmttmtw +

Since there is a single time step for each vertical column, there are no difficulties 

involved in doing this with the vertical diffusion operator, ΦD. The chemical reaction 

operator, ΦR, does not pose any problems either since it is applied to one grid cell at a time. 

However, the horizontal transport operator, ΦA, requires special attention since neighboring 

grid cells in the horizontal may have different time steps. Extension to three dimensions 

would necessitate similar considerations for ΦD since vertical layers would also have 

different time steps. 

Concentrations in grid cells with shorter local time steps are updated as usual by adding 

the horizontal fluxes (advective and diffusive) coming from cells with longer time steps. 

This situation is shown by the arrow marked “Pass” in Figure 1. For simplicity, let us 

assume that there are no other cells but Cell 1 and Cell 2. If 12 ∆ < ∆t t , any flux from Cell 1 

must be passed to Cell 2 when it’s time to update Cell 2 concentrations (i.e., when 

). On the other hand, if 

13 

14 

2 Nt ∆×= t 12 ∆ > ∆t t and if 2 ≠ Nt × ∆t any possible horizontal flux 

from Cell 1 to Cell 2 is directed to a reservoir. This is shown in Figure1 by the arrow 

marked “Store”. In other words, concentrations in cells with longer time steps remain 

constant until the time of update and flux calculations that require the concentrations of such 

cells would use their non-updated values. When the time comes for updating Cell 2 

concentrations (i.e., when 

15 

16 

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19 

2 Nt ×= ∆t ) not only any possible flux from Cell 1 is passed to 

Cell 2, but the mass accumulated in the reservoir is also added to Cell 2. This is represented 

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by the arrow marked “Flush” in Figure 1. Since all fluxes are eventually added to the 

appropriate cell, the algorithm is strictly mass conservative. 

Above, we ignored any other neighboring cells to simplify the description of the 

algorithm. In practice, since there are always other neighboring cells, some with shorter 

local time steps, there may always be some mass accumulated in the reservoir. The actual 

implementation of VARTSTEP accounts for this possibility and always flushes the reservoir 

when it is updating grid cell concentrations. Finally, note that VARTSTEP increases 

memory requirements, due to its need to store fluxes. 

3. Major Implementation Issues 

In this section, the two-dimensional (2-D) implementation of VARTSTEP in CMAQ is 

described. The discussion is limited to general issues and is intended to be useful for 

implementation in other air quality models. Also, the possibility of a three-dimensional (3-

D) implementation is borne in mind. 

CMAQ, like most AQMs, uses time splitting to advance the pollutant concentrations in 

time. Various process modules are called within a time-step loop. After the process is 

applied for the duration of a global time step, each module updates the concentration array, 

CGRID. The specific order in which the processes are called is as follows: vertical diffusion, 

which includes emissions and dry deposition; horizontal advection; vertical advection; 

horizontal diffusion; cloud processes; gas-phase chemistry; and aerosol processes. The 

CGRID array is four-dimensional consisting of 3-D concentration fields for each and every 

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pollutant species. The global time step is set to the minimum characteristic time for 

advection among all grid cells. 

As mentioned before, the 2-D version of VARTSTEP assigns a local time step to each 

vertical column. In VARTSTEP-CMAQ, processes other than horizontal transport 

(advection and diffusion) are applied to vertical columns for the duration of the local time 

step. The clock time is always advanced by the global time step, TSTEP. The decision to 

update pollutant concentrations in a vertical column as a result of a particular process is 

made based on whether the local time step of that vertical column evenly divides the clock 

time. The criterion is slightly different for vertical diffusion as will be explained below. The 

potential for computational time savings of VARTSTEP lies in processes that are applied 

less frequently and for longer periods to vertical columns with local time steps longer than 

the global time step. In VARTSTEP-CMAQ, this is the case for vertical diffusion, cloud 

processes, gas-phase chemistry and aerosol processes. An additional array, LOCSTEP, is 

needed to hold the local time steps and to pass this information to the processes that need it. 

This 2-D array is indexed with the dimensions of the horizontal grid. In a 3-D 

implementation every grid cell would be assigned its own time step hence LOCSTEP would 

be 3-D. 

The local time steps of neighboring cells in a horizontal plane can be different therefore 

the implementation of VARTSTEP for horizontal transport is more complex compared to 

vertical transport where all the cells in a vertical column share the same local time step. In a 

3-D implementation, vertical transport would have to be treated similarly to the way 

horizontal transport is dealt with here. In VARTSTEP-CMAQ, the fluxes in and out of grid 

cells must be computed every global time step even though the cell concentrations are 

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updated once per local time step. Calculation of fluxes that will not be used immediately 

may be unnecessary in other models. However, in CMAQ, flux parameters, namely wind 

velocity for advective flux and eddy diffusivity for diffusive flux, are evaluated at the 

middle of the time step through linear interpolation between their hourly values. Evaluating 

flux parameters at different times may lead to mass conservation problems. For example, the 

wind velocity at a cell boundary evaluated at the middle of the local time step of the cell to 

one side of the boundary might yield a different flux leaving (or entering) that cell than the 

flux entering (or leaving) the cell on the other side of the boundary since the latter flux 

would be calculated using the wind velocity at the same boundary but at a different time 

(i.e., at the middle of the local time step of the second cell). Having different fluxes on 

opposite sides of the cell boundaries would violate mass conservation. If the flux parameters 

were constant during the hour, then horizontal transport could also be applied for the 

duration of the local time step. Even if this was the case, there is little to be gained in 

CMAQ by implementing a local time step in horizontal transport. In CMAQ, horizontal 

advection is split into one-dimensional operators along orthogonal directions. The fluxes are 

calculated along west-to-east and south-to-north directions, in alternating order, inside loops 

over layers and pollutant species. Therefore, the decision of whether to calculate the fluxes 

would have to be made twice for each cell, once for each direction, inside the innermost 

loop. The overhead of this frequent decision making would considerably tax the gains from 

skipping the flux calculations. Note that this may not be the case for other models. For 

example, a real-time version of MAQSIP [Odman and Ingram, 1996], MAQSIP-RT, has the 

layers and species loops inside the loops over horizontal dimensions [Coats, 2000] to save 

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time in velocity direction checks that are needed for flux calculations. This looping order 

would also reduce the overhead of deciding whether to execute or skip the flux calculations. 

VARTSTEP requires another array to store the fluxes into or out of cells whose local 

time steps are longer than the global time step and whose concentrations are not being 

updated immediately. This array, FGRID, fulfills the duty of the reservoir described in 

Section 2 above. In CMAQ, horizontal transport processes (advection or diffusion) are 

applied sequentially to all horizontal planes through a loop over vertical layers. For example, 

horizontal diffusion is applied first to the surface layer, then to the other layers, from second 

layer all the way to the top layer, before CGRID is updated and handed over to another 

process. To be consistent with this spirit of process encapsulation, and considering the 

possibility of a 3-D implementation of VARTSTEP in the future, we chose to store the 

fluxes of each pollutant in a three-dimensional array. Further, since the fluxes of each 

pollutant must be kept in a different reservoir, we made the FGRID array four-dimensional, 

where the fourth dimension is indexed by pollutant species, just like the CGRID array. Note 

that if the loops over vertical layers are taken out of horizontal process modules and these 

processes are called in a sequence within a layers loop, then only the horizontal fluxes of 

each layer would have to be stored in a 2-D array for each pollutant therefore FGRID can be 

a 3-D array. This would reduce the storage requirements considerably for this 2-D 

implementation of VARTSTEP. 

For mass conservation, horizontal transport schemes are usually in flux form therefore 

the calculation of horizontal fluxes is routine in most AQMs. In CMAQ, the preferred 

advection scheme is the Piecewise Parabolic Method of Collela and Woodward [1984]. In 

this scheme, advective fluxes are calculated by fitting piecewise parabolas to the cell 

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concentrations. Horizontal advection modules of CMAQ were modified not to update cell 

concentrations but to store already calculated lateral fluxes in the FGRID array. In CMAQ, 

advective outfluxes are limited such that they do not generate negative concentrations. No 

flux is allowed to remove more than the pollutant mass content of a cell. Note that some of 

the cell content may already be committed to other outfluxes (e.g., advective flux in the 

orthogonal direction, advective flux in the same direction during a previous global time-step, 

or diffusive flux) or influxes may have accumulated in the reservoir. Therefore the outflux 

must be limited by the cell content plus the fluxes (positive or negative) stored in FGRID up 

to that point. The horizontal diffusion module was also modified to store the fluxes 

calculated by central differencing without updating concentrations. In addition, flux limiting 

was added to this module to take into account fluxes that are already committed. 

When the local time step evenly divides the clock time, the cell concentrations in 

CGRID must be updated using the horizontal fluxes stored in FGRID. This can be done after 

the horizontal transport modules. In VARTSTEP-CMAQ, instead of creating a separate 

module, the task was assigned to the vertical advection module, which already contains an 

update of CGRID based on LOCSTEP as well as flux limiting. This results in computational 

time savings by reducing the total number of conditional operations. However, the original 

process order had to be changed such that horizontal diffusion takes place before vertical 

advection. Note that VARTSTEP-CMAQ uses a different vertical advection module that 

adjusts vertical velocities for strict mass conservation [Hu, et al., 2006; Odman and Russell, 

2000]. 

Vertical diffusion, which includes emissions and dry deposition, is the first process in 

CMAQ and it is followed by horizontal transport. The reason for this is most likely the 

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desire to start each process cycle with fresh emissions and to account for relatively short 

characteristic times of vertical diffusion. In VARTSTEP-CMAQ, if vertical diffusion were 

applied when the local time step evenly divides the clock time, in cells with local time steps 

longer than the global time step, horizontal transport would start before emissions were 

injected and vertically diffused. On the other hand, there is no reason for applying vertical 

diffusion with the frequency of the global time step just to remain faithful to the original 

process order. Therefore, vertical diffusion was applied to each vertical column for the 

duration of the local time step at the beginning of the process cycle. Note that gas-phase 

chemistry, aerosol and cloud processes are applied when the local time step evenly divides 

the clock time therefore they effectively take place after horizontal transport. Note that this 

is not an issue in models where vertical diffusion and emissions are applied after horizontal 

transport. 

The last important implementation issue is the time interpolation of meteorological 

parameters and emissions. In CMAQ, these parameters are evaluated at the middle of the 

global time step. The reading of hourly values and interpolation takes place for all the grid 

cells at once. Using this global interpolator may have some undesirable consequences. For 

example, assuming a 10 min global time-step, between 07:00 and 08:00, the interpolator 

would interpolate the parameters to 07:05, 07:15, 07:25, and so on. For a concentration 

update at 7:30, a cell with a local time step of 30 min should have its parameters at 07:15, 

which is the middle of the time step from 7:00 to 7:30, but the last available parameters from 

the global interpolator would be at 7:25. Having meteorological parameters and emission 

rates at a different time can violate mass conservation. For example, the total amount of 

emissions into cells with longer local time steps may be over/under-estimated. One way to 

15

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1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

avoid these consequences is to keep meteorological parameters and emission rates constant 

at their hourly-average values. Instead, a new interpolator was developed here for 

VARTSTEP-CMAQ that can evaluate time-dependent parameters for each grid cell at a 

different time. This way all the cell parameters can be evaluated at the middle of the local 

time step of that cell. 

4. Results and Discussion 

Results presented here are from air quality simulations in southeastern U.S. during a 

winter episode (1-20 January 2002) and a summer episode (12-27 July 2001). Version 4.3 of 

CMAQ was used for calculating the ambient fine particulate matter (PM2.5) and ozone (O3) 

concentrations. A coding error was discovered in the vertical advection module of CMAQ 

Version 4.3: the code assumed uniform grid spacing despite the non-uniform vertical layer 

structure in CMAQ [Hu, et al., 2006]. This was corrected in the version used here. The 

horizontal grid resolution was 12-km over the entire domain shown in Figure 2. CMAQ uses 

a global time step which varied between the user-specified minimum of 5 min and the CFL 

condition imposed maximum of 7.5 min during the winter episode and 10 min during the 

summer episode. In addition to these “benchmark” simulations, a second set of simulations 

was conducted with VARTSTEP-CMAQ. 

The total computation time for the simulation with VARTSTEP-CMAQ was 26 % less 

than the benchmark CMAQ simulation for the winter episode, while the savings were only 

16 % for the summer episode (Table 1). This difference in savings is largely due to the load 

reduction of the aerosol module from 39 % for the winter episode to 22 % for the summer 

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1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

episode in CMAQ simulations. The times spent in the vertical diffusion, clouds, chemistry, 

and aerosol modules were reduced by 55 %, 5 %, 10 %, and 66 %, respectively for the 

winter episode and by 59 %, 8 %, 9 % and 51 % for the summer episode. The gas-phase 

chemistry module of CMAQ uses an implicit chemical kinetics solver whose computation 

time does not respond too much to changes in the time step. A different solver with larger 

overhead could have benefited more from less frequent calls to the chemistry module. 

Since horizontal transport modules are called every global time step, no benefit of 

VARTSTEP should be expected. The increase in the computation time of the horizontal 

advection module is due to the flux limiting that accounts for committed fluxes in FGRID. 

The relatively larger increase in the computation time of the horizontal diffusion module is 

due to the fact that the diffusive fluxes needed by VARTSTEP were not computed and 

stored in the original module. The VARTSTEP burden in vertical advection for the winter 

episode and the relatively small benefit for the summer episode are due to our pursuit of 

strict mass conservation. In CMAQ Version 4.3, to deal with unavoidable inconsistencies in 

the input wind fields, pollutant concentrations are renormalized according to the difference 

between input and advected air densities. This leads to mass conservation problems 

especially over complex terrain [Hu, et al., 2006]. Instead of the concentration 

renormalization approach, we chose to adjust the vertical velocities [Hu, et al., 2006; Odman 

and Russell, 2000] because VARTSTEP is based on a flux concept that requires strict mass 

conservation. The vertical velocity adjustment takes more time than the concentration 

renormalization and sometimes the difference can outweigh the benefits of using a local 

time step in the vertical advection module. During the winter episode horizontal wind 

velocities are generally larger than the summer episode (this is also the reason for shorter 

17

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1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

global time steps above); this results in more intensive vertical velocity adjustment 

computations. Note that since there are some arguments against the velocity adjustment 

approach (e.g., velocity adjustment alters plume trajectories however this was not a 

significant factor in the cases studied by Hu et al. [2006]), we decided to keep the 

concentration renormalization approach in CMAQ Version 4.3. 

Note that, as discussed in Section 3, VARTSTEP could have been implemented and 

coded in such a way that would significantly improve its computational efficiency. 

Theoretical considerations based on wind-speed distributions in the domain of application 

lead to the expectation of a factor-of-two speedup (i.e., 50 % decrease of the total 

computation time). In an adaptive grid model where the grid sizes differ by two orders of 

magnitude a factor of 10 to 100 speedup can be expected. 

Obviously, VARTSTEP can cut down the computation time. The question now is 

whether it can produce reliable air quality results. Daily average PM2.5 concentrations for 17 

January 2002 resulting from the two simulations are compared in Figure 2. This is the day 

when domain average difference between PM2.5 concentrations is second largest. Both 

simulations produced a similar PM2.5 distribution over the southeastern U.S. with a peak 

near Pensacola, Florida. The value of the peak is 66.8 μg m-3 according to the benchmark 

simulation and 62.9 μg m-3 according to the VARTSTEP-CMAQ simulation. Note that some 

portion of the differences stem from the difference in the vertical advection modules as 

explained above. However, this portion is expected to be small except over mountainous 

regions of the domain. 

Daily average observations of PM2.5 and its composition are available through the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) network. The 

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network covers areas designated as “Class-I” for visibility protection such as national parks 

and wilderness areas and there are 22 such observation sites in the region shown in Figure 2. 

Eight of these sites are clustered along the Southern Appalachian Mountains, six sites are 

distributed along the coastline and the remaining eight sites are scattered throughout the 

domain. The objective here is not to evaluate modeled concentrations vis-à-vis observations 

but to perform a detailed comparison of the VARTSTEP-CMAQ results with benchmark 

CMAQ results at these 22 sites where performance is of utmost concern. Figure 3 compares 

the two sets of model results for daily average PM

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

2.5 concentrations at these 22 sites during 

the 1-20 January 2002 and 12-27 July 2001periods. Note that all comparisons provided here 

are for the surface layer. The differences between the two model versions are largest at the 

surface and decrease with altitude. 

There is strong correlation ( ) between the benchmark and the results obtained 

with VARTSTEP-CMAQ with the exception of a few outliers. VARTSTEP-CMAQ results 

are lower than the benchmark, on average, by about 3 to 4 %. With such high correlation 

and low level of bias, it is probably safe to say that VARTSTEP-CMAQ produces PM

0.98R2 =

2.5

results that are virtually identical to those of CMAQ. 

Visibility is the primary concern in this study therefore it is important to accurately 

model not just the total PM2.5 but its components as well. It is well known that different 

components of PM2.5 affect light extinction differently. Contributions to light extinction of 

sulfate and nitrate particles can be equally substantial in the presence of water vapor. 

Elemental carbon can also be an important visibility degradation agent while other particles 

are relatively less important [Malm, et al., 2000]. Daily average concentrations of sulfate, 

nitrate, ammonium, soil, elemental carbon and organic carbon components of PM2.5 were 

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2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

also compared at the 22 Class-I areas. The scatter plots in Figures 4 (1-20 January 2002) and 

5 (12-27 July 2001) have the concentrations calculated by VARTSTEP-CMAQ on the y-axis 

and those calculated by CMAQ Version 4.3 on the x-axis. 

The two model results are highly correlated: is at least 0.95 (in the case of 

wintertime sulfate) and 0.99 in the case of soils, elemental carbon and organic carbon for 

both episodes. For the winter episode, while sulfate concentrations are, on average, 8.5 % 

higher, the concentrations of all the other PM

2 R

2.5 components calculated by VARTSTEP are 

lower than the benchmark (Figure 4). This results in PM2.5 being much more comparable 

than its major components. It should be noted that sulfate is only the third largest component 

of PM2.5 during this period. In addition, relative humidity is also low in winter therefore, 

compared to summer time, sulfate has a much smaller contribution to light extinction. 

Nitrate, the largest component of PM2.5, is 8.8 % lower. This and the high bias in sulfate 

suggest that oxidants prefer SO2 over NOx in VARTSTEP-CMAQ . Soil, the second largest 

component of PM2.5, and ammonium are lower by 5.8 % and 2.0 %, respectively. Elemental 

carbon, an important contributor to light extinction, is lower by 5.1 % while organic carbon 

which is the fourth largest component of PM2.5 is only 2.9 % lower. Based on these results, 

one may expect VARTSTEP-CMAQ light extinction to be about 5 % lower than the 

benchmark during the 1-20 January 2002 episode. 

For the summer episode, the concentrations of all the PM2.5 components calculated by 

VARTSTEP are lower than the benchmark (Figure 5). Sulfate is the largest component of 

PM2.5 during this period and, with high summertime relative humidity, it has the largest 

contribution to light extinction. Its concentrations are, on average, 3.7 % lower. Organic 

carbon, ammonium and soil, which are the second, third and fourth largest components of 

20

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PM2.5 by small margins, are lower by 2.7 %, 4.9 % and 5.1 %, respectively. Nitrate is 12 % 

lower but its concentrations are much smaller in summer, making it only the fifth largest 

component. Elemental carbon, the smallest component but an important light extinction 

agent, is lower by 3.5 %. Based on these results, one may expect VARTSTEP-CMAQ light 

extinction to be about 4 % lower than the benchmark during the 12-27 July 2001 episode. 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

Daily maximum 8-hr ozone concentrations were also compared at the 22 Class-I areas 

(Figure 6). The correlation between the benchmark and the results obtained with the 

VARTSTEP-CMAQ is strong for the summer episode ( ) but not as strong for the 

winter episode( ). In fact, the wintertime ozone correlation is weaker than any 

correlation obtained for PM

0.98R2 =

0.87R2 =

2.5 or its components. On average, the VARTSTEP-CMAQ 

ozone concentrations are 2.5 % higher in summer and 11 % higher in winter compared to the 

benchmark. Since the time steps are increased and the frequency of the calls to the process 

modules is decreased in VARTSTEP, turbulent mixing and dry deposition, which are both 

performed in the vertical diffusion module, can operate longer on freshly emitted pollutants 

(emissions are also part of vertical diffusion) before they have a chance to take part in 

photochemical reactions in the chemistry module. This leads to a more uniform vertical 

distribution of NOx in the boundary layer. Meanwhile, more ozone is brought over from 

aloft due to the increased time for mixing. The combined result of these processes is more 

ozone being left over near the surface after photochemistry. Note that enhanced mixing also 

leads to lower levels of PM2.5 and its components. As for the increased levels of sulfate 

during the winter episode, this is because of the enhanced formation through the aqueous 

reaction of SO2 with ozone. Finally, note that some of the differences in concentrations are 

because of the different approaches taken for vertical advection. 

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1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

5. Conclusions 

Current AQMs use a global time step to synchronize different processes for all the grid 

cells, at least in a horizontal plane. When the characteristic time of a process is longer than 

this global time step, computations are inefficient since a result in similar accuracy could 

have been obtained by using a longer time step. The variable time-step algorithm, 

VARTSTEP, was developed to improve computational efficiency by using local time steps 

that are more in tune with characteristic process times of each grid cell. A two-dimensional 

version of the algorithm was implemented in CMAQ; it reduced the computation time by as 

much as 25 % (15 % for the less computationally intensive summertime episode). A factorof-two speedup is likely if the algorithm is implemented differently, in a different model, or 

using coding practices more targeted to increasing computational efficiency. The Adaptive 

Grid Model of Odman, et al. [2002] is expected to experience a factor of 10 to 100 speedup 

from VARTSTEP. The differences between concentrations of the VARTSTEP-CMAQ and 

benchmark CMAQ are less than 9 % for PM2.5 components (excluding summertime nitrate, 

which was on average 12 %, or less than 0.15 μg m-1, lower). For ozone, the differences are 

11 % in winter but only 2.5 % in summer, when this pollutant is of concern. These 

differences should not significantly affect model performance nor should they change the 

conclusions drawn from the model. 

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Acknowledgements. This research was supported by the Visibility Improvement State and 

Tribal Association of the Southeast (VISTAS) under contract number V-2003-12 to Georgia 

Institute of Technology.

1 

2 

3 

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REFERENCES 

Anderson, D. A., et al. (1984), Computational Fluid Mechanics and Heat Transfer, 

Hemisphere, New York. 

Blom, J. G., and J. G. Verwer (2000), A comparison of integration methods for atmospheric 

transport-chemistry problems, J. Comput. Appl. Math., 126, 381-396. 

Boylan, J. W., et al. (2002), Development of a comprehensive multiscale one atmosphere 

modeling system: application to the southern Appalachian mountains, Atmos. Environ., 

36, 3721-3734. 

Byun, D. W., and J. K. S. Ching (1999), Science Algorithms of the EPA Models 3 

Community Multiscale Air Quality (CMAQ) Modeling System, edited, U. S. 

Environmental Protection Agency, Washington, DC. 

Coats, C. J. (2000), Model design for high performance on microprocessor based parallel 

computer system, paper presented at EPA Workshop on the Multimedia Integrated 

Modeling System, Research Triangle Park, NC, August 15-17 2000, 

http://www.baronams.com/projects/ppar/hpcc_models.html. 

Collela, P., and P. R. Woodward (1984), The piecewise parabolic method (PPM) for gasdynamical simulations, J. Comput. Phys., 54. 

Hu, Y., et al. (2006), Mass conservation in the Community Multiscale Air Quality model, 

Atmos. Environ., 40, 1199-1204. 

Lanser, D., and J. G. Verwer (1999), Analysis of operator splitting for advection-diffusionreaction problems from air pollution modeling, J. Comput. Appl. Math., 111, 201-216. 

24

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Malm, W. C., et al. (2000), Spatial and Seasonal Patterns and Temporal Variability of Haze 

and its Constituents in the United States, Report III; ISSN: 0737-5352-47, Colorado 

State University: Fort Collins, CO. 

McRae, G. J., et al. (1982), Numerical solution of the atmospheric diffusion equation for 

chemically reacting flows, J. Comput. Phys., 45, 1-42. 

Odman, M. T., and C. L. Ingram (1996), Multiscale Air Quality Simulation Platform 

(MAQSIP): Source Code Documentation and Validation, Technical report, 83 pp., 

ENV-96TR002, MCNC–North Carolina Supercomputing Center, Research Triangle 

Park, North Carolina. 

Odman, M. T., et al. (2002), Initial application of the adaptive grid air pollution model, in 

Air Pollution Modeling and its Application XV, edited by C. Borrego and G. Schayes, 

pp. 319-328, Academic/Plenum Publishers, New York. 

Odman, M. T., and A. G. Russell (2000), Mass conservative coupling of non-hydrostatic 

meteorological models with air quality models, in Air Pollution Modeling and its 

Application XIII, edited by S.-E. Gryning and E. Batchvarova, pp. 651-660, Kluwer 

Academic/Plenum Publishers, New York. 

Sportisse, B. (2000), An Analysis of Operator Splitting Techniques in the Stiff Case, J. 

Comput. Phys., 161, 140-168. 

Strang, G. (1968), On the construction and comparison of difference schemes, SIAM J. 

Numer. Anal, 5. 

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Table 1. CPU times broken down by process: CMAQ (Version 4.3), VARTSTEPCMAQ and the fraction of the total VARTSTEP savings attributed to each process. 

CMAQ 

(s) 

VARTSTEP-CMAQ

(s) 

Fraction of Savings 

(or Burden) 

(%) 

Module 

Winter 

episode 

Summer 

episode 

Winter 

episode 

Summer 

episode 

Winter 

episode 

Summer 

episode 

Vertical Diffusion 82,464 54,004 37,005 22,364 21.13 43.37

Horizontal Advection 141,163 73,064 175,266 82,621 (15.85) (13.10)

Horizontal Diffusion 9,647 6,469 30,340 18,545 (9.62) (16.55)

Vertical Advection 40,255 23,039 42,513 22,813 (1.05) 0.31

Clouds 11,146 8,267 10,570 7,622 0.27 0.88

Chemistry 178,232 153,856 161,214 139,676 7.91 19.44

Aerosols 322,536 97,605 109,377 47,586 99.09 68.57

Other 50,476 28,365 54,512 30,492 (1.88) (2.92)

TOTAL 835,919 444,669 620,798 371,719 100 100

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Figure Captions (note that all figures, except Figure 2, will be reproduced in black and 

white)

Figure 1. Concept of a reservoir used in VARTSTEP algorithm. The flux from Cell 1 is 

either passed directly to Cell 2 or stored in a reservoir which is flushed when it is time to 

update Cell 2 concentrations. 

Figure 2. Daily average PM2.5 concentrations (μg m-3) over southeastern U.S. on 17 January 

2002 as calculated by the benchmark CMAQ simulation (left) and the VARTSTEP-CMAQ 

simulation (right). 

Figure 3. Daily average PM2.5 concentrations (μg m-3) at 22 Class-I areas in southeastern 

U.S. during 1-20 January 2002 (left) and 12-27 July 2001 (right): Comparison of the results 

obtained from the variable time-step version of CMAQ (y-axis) with the benchmark 

obtained from Version 4.3 of CMAQ (x-axis). 

Figure 4. Comparison of daily average concentrations (μg m-3) calculated by VARTSTEPCMAQ (y-axis) with the benchmark (x-axis) for various components of PM2.5 at 22 Class-I 

areas in southeastern U.S. during 1-20 January 2002: (a) Sulfate, (b) Nitrate, (c) 

Ammonium, (d) Soil, (e) Elemental Carbon, (f) Organic Carbon. 

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Figure 5. Comparison of daily average concentrations (μg m-3) calculated by VARTSTEPCMAQ (y-axis) with the benchmark (x-axis) for various components of PM2.5 at 22 Class-I 

areas in southeastern U.S. during 12-27 July 2001: (a) Sulfate, (b) Nitrate, (c) Ammonium, 

(d) Soil, (e) Elemental Carbon, (f) Organic Carbon. 

Figure 6. Daily maximum 8-hr O3 concentrations (ppm) at 22 Class-I areas in southeastern 

U.S. during 1-20 January 2002 (left) and 12-27 July 2001 (right): Comparison of the results 

obtained from the variable time-step version of CMAQ (y-axis) with the benchmark 

obtained from Version 4.3 of CMAQ (x-axis). 

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Figure 1. Concept of a reservoir used in VARTSTEP algorithm. The flux from cell 1 is 

either passed directly to cell 2 or stored in reservoir R2 which is flushed when it is time to 

update cell 2 concentrations. 

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Figure 2. Daily average PM2.5 concentrations (μg m-3) over southeastern U.S. on 17 January 

2002 as calculated by the benchmark CMAQ simulation (left) and the VARTSTEP-CMAQ 

simulation (right). 

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A Variable time-step algorithm 

y = 0.9658x

R2 = 0.9835

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0

CMAQ 4.3

VARTSTEP

y = 0.9608x

R2

 = 0.9766

0.0

10.0

20.0

30.0

40.0

50.0

0.0 10.0 20.0 30.0 40.0 50.0

CMAQ 4.3

VARTSTEP

Figure 3. Daily average PM2.5 concentrations (μg m-3) at 22 Class-I areas in southeastern 

U.S. during 1-20 January 2002 (left) and 12-27 July 2001 (right): Comparison of the results 

obtained from the variable time-step version of CMAQ (y-axis) with the benchmark 

obtained from Version 4.3 of CMAQ (x-axis). 

31

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 M. T. Odman and Y. Hu

 (a) (b) 

y = 1.0851x

R2

 = 0.9492

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

VARTSTEP

y = 0.9121x

R2

 = 0.9761

0.0

2.0

4.0

6.0

8.0

10.0

0.0 2.0 4.0 6.0 8.0 10.0

 (c) (d) 

y = 0.9797x

R2

 = 0.9809

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3

VARTSTEP

. 5 

y = 0.9419x

R2

 = 0.9922

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

 (e) (f) 

y = 0.9495x

R2

 = 0.9937

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

CMAQ 4.3

VARTSTEP

 

y = 0.9706x

R2

 = 0.9904

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

CMAQ 4.3

Figure 4. Comparison of daily average concentrations (μg m-3) calculated by VARTSTEPCMAQ (y-axis) with the benchmark (x-axis) for various components of PM2.5 at 22 Class-I 

areas in southeastern U.S. during 1-20 January 2002: (a) Sulfate, (b) Nitrate, (c) 

Ammonium, (d) Soil, (e) Elemental Carbon, (f) Organic Carbon. 

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 (a) (b) 

y = 0.963x

R2

 = 0.9734

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0

VARTSTEP

y = 0.8789x

R2

 = 0.9657

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.2 0.4 0.6 0.8 1.0 1 .2

 (c) (d) 

y = 0.9506x

R2

 = 0.9732

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0

VARTSTEP

 

y = 0.9493x

R2

 = 0.9887

0.0

1.0

2.0

3.0

4.0

5.0

0.0 1.0 2.0 3.0 4.0 5.0

 (e) (f) 

y = 0.9654x

R2

 = 0.9895

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

CMAQ 4.3

VARTSTEP

 

y = 0.9732x

R2

 = 0.9888

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

CMAQ 4.3

Figure 5. Comparison of daily average concentrations (μg m-3) calculated by VARTSTEPCMAQ (y-axis) with the benchmark (x-axis) for various components of PM2.5 at 22 Class-I 

areas in southeastern U.S. during 12-27 July 2001: (a) Sulfate, (b) Nitrate, (c) Ammonium, 

(d) Soil, (e) Elemental Carbon, (f) Organic Carbon. 

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 M. T. Odman and Y. Hu

y = 1.1137x

R2

 = 0.8652

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070

CMAQ 4.3

VARTSTEP

y = 1.0253x

R2

 = 0.9799

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080

CMAQ 4.3

VARTSTEP

Figure 6. Daily maximum 8-hr O3 concentrations (ppm) at 22 Class-I areas in southeastern 

U.S. during 1-20 January 2002 (left) and 12-27 July 2001 (right): Comparison of the results 

obtained from the variable time-step version of CMAQ (y-axis) with the benchmark 

obtained from Version 4.3 of CMAQ (x-axis). 

34

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Determining the Sources of Regional Haze in the Southeastern United States Using the

CMAQ Model

M. TALAT ODMAN, YONGTAO HU, ALPER UNAL,* AND ARMISTEAD G. RUSSELL

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia

JAMES W. BOYLAN

Georgia Department of Natural Resources Environmental Protection Division, Atlanta, Georgia

(Manuscript received 15 February 2006, in final form 2 October 2006)

ABSTRACT

A detailed sensitivity analysis was conducted to help to quantify the impacts of various emission control

options in terms of potential visibility improvements at class I national parks and wilderness areas in the

southeastern United States. Particulate matter (PM) levels were estimated using the Community Multiscale

Air Quality (CMAQ) model, and light extinctions were calculated using the modeled PM concentrations.

First, likely changes in visibility at class I areas were estimated for 2018. Then, using emission projections

for 2018 as a starting point, the sensitivity of light extinction was evaluated by reducing emissions from

various source categories by 30%. Source categories to be analyzed were determined using a tiered approach: any category that showed significant impact in one tier was broken into subcategories for further

analysis in the next tier. In the first tier, sulfur dioxide (SO2), nitrogen oxides, ammonia, volatile organic

compound, and primary carbon emissions were reduced uniformly over the entire domain. During summer,

when most class I areas experience their worst visibility, reduction of SO2 emissions was the most effective

control strategy. In the second tier, SO2 sources were separated as ground level and elevated. The elevated

sources in 10 southeastern states were differentiated from those in the rest of the domain and broken into

three subcategories: coal-fired power plant (CPP), other power plant, and other than power plant [i.e.,

non–electric generating unit (non EGU)]. The SO2 emissions from the CPP subcategory had the largest

impact on visibility at class I areas, followed by the non-EGU subcategory. In the third tier, emissions from

these two subcategories were further broken down by state. Most class I areas were affected by the

emissions from several states, indicating the regional nature of the haze problem. Here, the visibility

responses to all of the aforementioned emission reductions are quantified and deviations from general

trends are identified.

1. Introduction

Fine particulate matter (often referred to as PM2.5 to

denote particle sizes smaller than 2.5 m) is a mixture

of suspended solid particles and liquid droplets and can

impact both human health and the environment. In addition to being seen as a reason for numerous health

problems such as asthma, PM2.5 is also a major cause of

visibility impairment. PM2.5 originates from many

sources such as fossil fuel combustion, sea salt, windblown dust, and fugitive dust (e.g., road dust, construction, grinding, and agriculture), and it is formed in the

atmosphere through chemical transformation of precursor gases. It can travel long distances and affect very

large geographic areas. In certain parts of the United

States the visual range has been reduced considerably

from natural conditions.

Visibility is a measure of the clearness of the atmosphere. Regional haze is the impairment of visibility

caused by the presence of PM in the atmosphere and is

usually expressed in term of light extinction or haze

index. It has been determined that particles of different

size and chemical composition can affect visibility very

differently (Interagency Monitoring of Protected Vi-

* Current affiliation: MACTEC Federal Programs, Inc., Trenton, New Jersey.

Corresponding author address: M. T. Odman, Georgia Institute

of Technology, 311 Ferst Dr., Atlanta, GA 30332-0512.

E-mail: odman@gatech.edu

NOVEMBER 2007 ODMAN ET AL. 1731

DOI: 10.1175/2007JAMC1430.1

© 2007 American Meteorological Society

JAM2557

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sual Environments 2000). In particular, PM2.5 can scatter and absorb light very efficiently. The contributions

of fine sulfate and nitrate particles to haze can be substantial especially in the presence of water vapor. Elemental and organic carbon is also important in visibility degradation while most other particles are viewed as

being relatively less important.

The objective of the regional haze rule from the Environmental Protection Agency (EPA 1999) is to

achieve natural visibility conditions (no man-made

impairment) at national parks and wilderness areas

known as class I areas (EPA 2002) by 2064. The rule

requires that reasonable progress should be made toward reducing visibility impairment on the 20% worst

visibility days. In addition, the rule requires no worsening of visibility on the 20% best visibility days. Every

10 years, progress toward these goals must be demonstrated. The first regional haze state implementation

plans (due by December 2007) must demonstrate

progress toward natural conditions by evaluating the

change in visibility between current conditions (2000–

04) and the first progress check point (2014–18).

Progress can be measured by comparing the projected

visibility on the 20% worst days to a straight line drawn

between current visibility (2000–04) and natural conditions (2064) on a haze index versus time graph. However, achieving such a uniform rate of progress at every

check point is neither a requirement nor a standard for

demonstrating reasonable progress.

Visibility Improvement State and Tribal Association

of the Southeast (VISTAS) is the regional planning organization set up by EPA to initiate and coordinate

activities associated with the management of regional

haze in 10 southeastern states: Alabama (AL), Florida

(FL), Georgia (GA), Mississippi (MS), Kentucky (KY),

North Carolina (NC), South Carolina (SC), Tennessee (TN), Virginia (VA), and West Virginia (WV).

VISTAS is conducting a modeling study with the goal

of lowering PM levels and improving visibility in the

southeastern United States. This paper reports a detailed sensitivity analysis conducted as part of that

study to quantify the contributions of various emission

sources to regional haze. The sensitivities are calculated

by taking the difference between two air quality model

simulations: one with base case and another with reduced emission inputs. This method is known as the

“brute force” method (Hwang et al. 1997). The model

used here to simulate the air quality is the Community

Multiscale Air Quality (CMAQ) model (Byun and

Ching 1999).

Brute-force sensitivity analysis has been widely used

to aid the design of control strategies for ozone. For

example, recently, Marr and Harley (2002) studied the

impact of motor vehicle emissions on ozone levels in

central California using the California Institute of

Technology (CIT) air shed model (Harley et al. 1993).

Stein and Lamb (2002), using CMAQ, modeled regional ozone sensitivities over northeastern America.

Chang et al. (2002) used the Comprehensive Air Quality Model with Extensions (CAMx; ENVIRON 2006)

to estimate the sensitivity of ozone to chlorine emissions in Houston, Texas. Schmidt and Martin (2003)

used “CHIMERE,” a chemistry-transport model designed for the simulation of photo-oxidant concentrations within the atmospheric boundary layer (Vautard

et al. 2001), to calculate ozone sensitivities in Paris,

France. Later, Pison and Menut (2004) used the same

model, to assess the impact of aircraft emissions on

ozone in Paris. Jiang and Fast (2004) used the Pacific

Northwest National Laboratory Eulerian Gas and

Aerosol Scalable Unified System to model ozone sensitivities to nitrogen oxides (NOx) and volatile organic

compound (VOC) emissions in Houston. Fast and Heilman (2005) used the same model to calculate ozone

sensitivities in the Great Lakes region. Oanh and

Zhang (2004) studied ozone sensitivities in Thailand

with the aid of the Urban Airshed Model (System Applications International, Inc., 1999). Lin et al. (2005)

conducted an ozone sensitivity analysis in Texas using

CMAQ. Distinct from these studies, Pai et al. (2000)

used the brute-force method to calculate the sensitivities of PM, as well as ozone, in central California using

the SARMAP Air Quality Model (SARMAP denotes

the San Joaquin Valley Air Quality Study/Atmospheric

Utility Signatures, Predictions, and Experiments Regional Model Adaptation Project).

A number of other sensitivity analysis methods have

been tested and applied in 3D air quality models

(AQMs). Automatic differentiation in FORTRAN has

found limited use in AQMs (Hwang et al. 1997; Carmichael et al. 1997). Recently, the adjoint method was

tried in 3D models such as CHIMERE and Polair3D to

conduct ozone sensitivity analyses in Europe (Menut et

al. 2000; Mallet and Sportisse 2005). The decoupled

direct method (DDM), which provides a very efficient

way of calculating several sensitivities in a single model

run, became very popular after its implementation in

the CIT model (Yang et al. 1997). Odman et al. (2004)

used DDM in the Urban to Regional Multiscale model

(Boylan et al. 2002) to study the sensitivity of ozone and

PM levels to sulfur dioxide (SO2), NOx, ammonia

(NH3), and VOC emissions in the southeastern United

States. Cohan et al. (2005) implemented DDM in

CMAQ to study ozone sensitivities to various source

1732 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 46

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categories. At the time this project had started, the

implementation of DDM for PM sensitivities in the

CMAQ model was under way. Later, the implementation was completed (Napelenok et al. 2006) but not in

time to be used here.

2. Method

The aerosol module of CMAQ is designed to estimate size resolved and speciated PM concentrations

(Binkowski and Roselle 2003). The PM size distribution is represented by the superposition of three lognormal modes: the nucleation and accumulation modes

for PM2.5 and a coarse mode. PM2.5 consists of sulfate,

nitrate, ammonium, elemental carbon (EC), organic

carbon (OC), and soil components; the coarse mode

includes sea salt and dust. The aerosol dynamical processes considered in CMAQ include primary emissions,

new particle formation by nucleation, particle growth,

coagulation, evaporation/condensation, dry deposition,

and scavenging by clouds. Chemical reactions form

secondary PM such as sulfate, nitrate, ammonium,

and organic compounds. CMAQ is continuously updated to improve the accuracy of its PM2.5 predictions

(Bhave et al. 2004); the particular version used here was

version 4.3.

a. Simulation of PM2.5 levels

To model the seasonal variation of PM concentrations, a summer (12–28 July 2001) and a winter (1–21

January 2002) episode were simulated with CMAQ. Inputs to CMAQ were generated using the fifth-generation Pennsylvania State University–National Center for

Atmospheric Research Mesoscale Model for meteorology (Grell et al. 1994) and the Sparse Matrix Operator

Kernel Emissions model for emissions (Center for Environmental Modeling for Policy Development 2004).

Details of the meteorological and emissions modeling

can be found elsewhere (Olerud and Sims 2003; Morris

et al. 2004).

The horizontal resolution of the CMAQ grid was

12 km over the domain shown in Fig. 1. Nineteen unFIG. 1. Modeling domain, 10 VISTAS states, and locations of class I areas.

NOVEMBER 2007 ODMAN ET AL. 1733

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equally spaced vertical layers with finer resolution

near the surface extended to a height of approximately

14 km. The lowest four layers were 36 m thick. Earlier

evaluations of the effects of the number of layers and

layer thicknesses showed that this setup is adequate for

modeling surface layer concentrations and deposition

fluxes even during stable nighttime conditions (Morris

et al. 2004).

Anthropogenic emissions were considered in groundlevel and elevated source categories. Ground-level

sources include on-road and nonroad mobile sources,

area sources, and area fires. Elevated sources include

electric generating units (EGUs), other point sources,

and point fires. All man-made emissions were projected

out to 2018 using growth and control factors; the Integrated Planning Model was used to predict emission

rates and controls at all EGUs. Two scenarios were

developed to reflect VISTAS’s assumptions about future growth and the implementation of regulations and

incentives: “on the books” (OTB) and “on the way”

(OTW). The main difference between these scenarios is

that 2018-OTW includes the clean air interstate rule.

It was assumed that any potential feedback of future

land use and emissions on the meteorology would be

negligible. Therefore meteorological inputs developed

for the July 2001 and January 2002 periods were also

used for the 2018 simulations. Biogenic emissions were

also assumed to remain constant in the future. Initial

and boundary conditions were derived from a 36-km

resolution simulation of 2018 over North America. A

two-day ramp-up period was used before each episode

to dampen the effect of initial conditions.

Using emission projections for 2018 as the base case,

emission-reduction simulations were performed and

the differences from the base case were recorded. In

each simulation, emissions from a particular source category were reduced by 30% from its base-case value

and responses of PM concentrations and light extinctions to these reductions were calculated. A tiered approach was used to identify the most important emission species and emission source categories that could

be controlled to achieve maximum visibility benefits.

This approach first identified the importance of individual emission species (tier 1). Then, the impact of the

most important emission source categories were examined for important emission species (tier 2). Finally, the

most important emission source categories were further

broken down into geographic regions (tier 3). Overall,

more than 100 model simulations were conducted. Responses were calculated and visualized for the entire

domain but quantitative analysis focused on responses

at class I areas.

b. Sensitivity analysis of visibility to emission

reductions

Light extinction Bext (Mm1) was calculated as a linear combination of specific pollutant concentrations

(g m3

), component-specific light scattering/absorption

coefficients (m2 g1

), and a dimensionless relative humidity adjustment factor f(RH) using the following

equation (Interagency Monitoring of Protected Visual

Environments 2000):

Bext  3  fRH  SO4

2 	 NO3 	 NH4	 

	 4  OC 	 10  EC 	 1  soil 	 0.6

 CM 	 BRayleigh. 1

Here, [SO2

4 ], [NO3 ], [NH	4 ], [OC], [EC], and [soil] are

the concentrations of PM2.5 associated with sulfate, nitrate, ammonium, organic carbon, elemental carbon,

and soils, respectively; [CM] is the concentration of

coarse PM; and BRayleigh is the Rayleigh scattering

(10 Mm1

). Note that [NH	

4 ] is set equal to 0.375 

[SO2

4 ] 	 0.29  [SO24 ] in Eq. (1) assuming ammonium completely neutralizes sulfate and nitrate to form

SO4(NH4)2 and NO3NH4 (Boylan et al. 2006). Sitespecific monthly values based on climatology were used

for f(RH). The haze index (HI) is typically expressed in

units of deciviews and can be calculated directly from

the total light extinction (Bext) by the following equation:

HI  10  lnBextBRayleigh, 2

where HI is zero for an unpolluted atmosphere and

each deciview increase in HI approximates a perceptible decrease in visibility.

The Interagency Monitoring of Protected Visual Environments (IMPROVE) network routinely monitors

the composition of PM2.5 at the class I areas of the

region shown in Fig. 1. The sites are categorized as

mountain, coastal, or non-VISTAS in Table 1. The

measurements of daily average concentrations are

available at each site every third day; therefore, visibility can be calculated only on about 1⁄3 of the simulated

days. Also, because the regional haze rule is concerned

only with the 20% best and 20% worst visibility days,

only those days were considered in the analysis leaving

very few days. There are 1–5 simulated days that represent the 20% best/worst visibility days at each site

considered here (Table 1). Classification and Regression Tree (CART) analysis was used to group the meteorological conditions that lead to good/bad visibility

at each class I area and to determine the representativeness of simulated days (Douglas et al. 2006). Each

day was assigned a weight that characterizes the fre1734 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 46

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quency of occurrence of its meteorological conditions

among the 20% best or 20% worst visibility days. Note

that the weights are site specific and their sum over the

simulated days is unity. Hereinafter, when used in connection with a metric (e.g., light extinction), “20% best

or 20% worst visibility” refers to an approximation to

that metric obtained by taking the inner product of the

CART weights with the values on simulated days.

The modeled concentrations were used in a relative

rather than absolute fashion to account for under- and

overpredictions by the model. This was done by scaling

the modeled future response (i.e., difference in concentrations between the base and reduced emissions cases)


Cmodeled

future by the ratio of currently observed concentrations Cobserved

current to the modeled current concentrations

Cmodeled

current :

Cfuture  Ccurrent

observed

Ccurrent

modeled Cfuture

modeled. 3

Note that this scaling was applied to each PM2.5 species

separately. As an example, let us assume that the observed nitrate concentration is zero but the model predicts a nonzero nitrate concentration and a future reduction. In this case, because there is no observed nitrate to be reduced, Eq. (3) will set the response to zero.

The scaling was not used in some rare cases when small

modeled concentrations unrealistically amplified the

response.

The results presented here are only valid for 30%

reduction of the specified emission category from the

specified region. If one wants to estimate the response

to a smaller reduction by scaling the results, it should be

realized that an error is introduced because of the nonlinearity of atmospheric response. Prior work has

shown that this error would be small for the responses

of ozone and PM2.5 to domainwide emission reductions

(Odman et al. 2002; Hakami et al. 2003; Hakami et al.

2004); however, this might not be true for subdomain

reductions. Extrapolation beyond 30% is highly discouraged. In addition, extreme caution should be used

in superposing the sensitivities to emissions from different geographic regions or sensitivities to emissions

from different source categories. Such deductions can

be used during the development of control strategies to

give an estimated magnitude and some directional

sense as to where to look for emission reductions. However, they should not be used as a substitute for simuTABLE 1. Class I areas in the modeling domain, their categorization as coastal (C), mountain (M), or non-VISTAS (N), and their

number of the simulated 20% best and 20% worst visibility days. The number of the worst visibility days during summer is shown in

parentheses.

Site State Abbreviation Class

No. of days

Best Worst (summer)

1 Sipsey AL SIPS M 2 3 (3)

2 Chassahowitzka FL CHAS C 4 2 (1)

3 Everglades FL EVER C 6 1 (0)

4 St. Marks FL SAMA C 4 2 (2)

5 Cohutta GA COHU M 1 2 (2)

6 Okefenokee GA OKEF C 5 4 (3)

7 Wolf Island* GA WOLF C — —

8 Mammoth Cave KY MACA M 3 4 (4)

9 Linville Gorge NC LIGO M 4 2 (2)

10 Shining Rock NC SHRO M 4 2 (2)

11 Swanquarter NC SWAN C 5 3 (2)

12 Cape Romain SC ROMA C 5 2 (1)

13 Great Smoky Mountains TN GRSM M 4 2 (2)

14 James River Face VA JARI M 5 1 (1)

15 Shenandoah VA SHEN M 3 3 (3)

16 Dolly Sods WV DOSO M 5 3 (3)

17 Caney Creek AR CACR N 4 5 (5)

18 Upper Buffalo AR UPBU N 3 5 (5)

19 Breton LA BRET N 3 2 (2)

20 Hercules Glades MO HEGL N 2 5 (4)

21 Mingo MO MING N 1 2 (1)

22 Brigantine NJ BRIG N 4 2 (1)

* No visibility data are available at Wolf Island.

NOVEMBER 2007 ODMAN ET AL. 1735

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lations that demonstrate the effectiveness of a given

emission control strategy.

3. Results and discussion

The July 2001 and January 2002 episodes were simulated by Morris et al. (2004) using “actual” emissions.

Model estimates for gaseous precursors, ozone, and PM

were evaluated using observations from various monitoring networks, including IMPROVE. Among the

PM2.5 components, sulfate agrees the best with observations: its bias is within 20% both in summer and in

winter. Nitrate concentrations are overestimated by

50%–100% in winter and organic carbon is underestimated by 80%–100% in summer (Morris et al. 2005;

Tesche et al. 2006). Here, the final modeling setup recommended by Morris et al. (2004) was used in all simulations.

A second set of simulations of the same episodes

were conducted using the “typical” baseline emissions.

The difference in these emissions from the actual case is

that the EGU and fire emissions were averaged over a

longer time period (e.g., 3 yr for EGUs) to remove

anomalies and make them more representative of the

2000–04 baseline period. The results of these typical

baseline simulations were used as Cmodeled

current in Eq. (3)

above for scaling modeled responses.

a. Visibility simulated for 2018

Domainwide SO2, NOx, NH3, VOC, and primary carbon (PC) emissions under the 2018-OTB and OTW

scenarios are compared with their typical baseline

(2000–04) levels in Table 2. Under the OTB scenario,

the largest relative reductions are in NOx emissions

(38% in January and 43% in July). The second largest

relative reduction is in SO2 emissions (24% in January

and 28% in July) followed by anthropogenic VOC

emissions (20% in January and 18% in July). The PC

emissions increase by 4% in this case. The spatial distribution of these emission changes is highly nonuniform: the magnitude as well as the direction of the

change varies from one place to another. In general,

NOx emissions are reduced, most notably along the

Ohio River valley, but not everywhere. SO2 emissions

are also reduced in the same region, with some exceptions, but increases are dominant in some southeastern

states and along the Gulf of Mexico coast.

The 2018-OTW scenario includes further reductions

due to the implementation of the clean air interstate rule (CAIR). In comparison with the OTB scenario, reductions of SO2 emissions almost double (48%

reduction both in January and July when compared

with their typical baseline values). Winter and summer

NOx emissions are, respectively, 47% and 45% less

than their typical baseline values. No further controls

are applied to anthropogenic VOC emissions. The PC

emissions are assumed to remain near their baseline

values. Without further change under the OTW scenario ammonia emissions increase by 21% in all cases

and biogenic VOC emissions are assumed to remain

constant.

Typical baseline (2000–04) values of total light extinction Bext range from 30 to 260 Mm1 at the class I

areas of the modeling domain (Fig. 2a). Figure 2b

shows the expected changes in light extinction 
Bext on

the 20% best and 20% worst visibility days under the

OTB and OTW scenarios in 2018. Negative 
Bext, plotted upward from the origin indicates an improvement

in visibility while positive 
Bext signals visibility degradation. The range and the average of the values of


Bext are shown for each one of the three categories

of class I areas: coastal, mountain, and non-VISTAS.

Under the OTB scenario, 
Bext on the 20% best visibility days range from 	2.76 to 9.00 with an average

of 0.83 Mm1 at coastal sites. Visibility is expected

to degrade at Cape Romain, the Everglades, and

TABLE 2. Domainwide daily average emissions (Mg) for typical baseline and 2018-OTB and 2018-OTW* scenarios.

SO2 NOx NH3 PC

Anthropogenic

VOC

Biogenic

VOC

Typical baseline (2000–04)

Jan 30 800 35 400 4770 2820 38 400 9500

Jul 30 100 36 200 4850 2770 37 700 113 500

2018-OTB

Jan 23 300 22 000 5750 2920 30 600 9500

Jul 21 700 21 200 5850 2870 30 800 113 500

2018-OTW

Jan 16 100 18 600 5750 2790 30 600 9500

Jul 15 600 20 000 5850 2760 30 800 113 500

* The 2018-OTW scenario assumes the implementation of the CAIR in addition to all the assumptions of 2018-OTB.

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Okefenokee as a result of increases in the levels of

nitrate, EC, OC, and coarse matter. Note that most of

the best visibility days occur during summer at these

sites. SO2 controls and increasing ammonia emissions

(Table 2) can result in increased nitrate levels as a result of more ammonia becoming available to react with

nitric acid and form nitrate aerosols (Odman et al.

2004). At these sites, the increases in nitrate levels are

larger than the decreases in sulfate levels resulting in a

net increase of PM2.5 and degradation of visibility.

At mountain sites, where the best visibility days generally occur in winter, 
Bext range from 	1.71 to 2.92

with an average of 0.72 Mm1

. Visibility degradations

are expected at the Great Smoky Mountains and, to a

smaller extent, at Linville Gorge and Dolly Sods because of local increases in sulfate levels on the 20% best

visibility days. Among the non-VISTAS sites 
Bext

range from 	1.37 to 5.25 with an average of 2.37

Mm1

; visibility is expected to degrade at Breton and

Mingo.

FIG. 2. (a) Typical baseline (TYP) values of light extinction and (b) its expected changes

under the 2018-OTB and 2018-OTW scenarios on the 20% best and 20% worst visibility days

at coastal (C), mountain (M), and non-VISTAS (N) class I areas. Diamonds denote the

average for each category while bars mark the range.

NOVEMBER 2007 ODMAN ET AL. 1737

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Under the more stringent controls of the OTW scenario, no mountain or non-VISTAS site is expected to

experience visibility degradation in 2018. The difference that CAIR makes is sufficient to put almost all of

the sites in compliance with the regional haze rule (i.e.,

no visibility degradation on the 20% best visibility

days); therefore, there is no need for additional controls

on the best visibility days except at Cape Romain and

the Everglades. Average Bext decreases rise to 3.53,

3.26, and 4.03 Mm1 for the coastal, mountain, and

non-VISTAS categories, respectively.

On the 20% worst visibility days, visibility in 2018

improves at all sites under both OTB and OTW scenarios. Note that the scale for these days is shown on

the right side of Fig. 2b. Worst visibility days generally

occur during summer except at some coastal sites,

which may experience their poorest visibility in winter.

Under the OTB scenario, 
Bext ranges from 1.63 to

52.7 with an average of 24.3 Mm1 at coastal sites,

from 18.5 to 70.8 with an average of 41.9 Mm1 at

mountain sites, and from 1.72 to 26.8 with an average of 18.9 Mm1 at non-VISTAS sites. These improvements are largely because of decreases in sulfate

and ammonium levels as a result of SO2 emission reductions. The largest improvements in visibility are expected at mountain sites. The decreases in sulfate levels

are generally larger in southern (e.g., Cohutta and

Great Smoky Mountains) than western (e.g., Mammoth

Cave) and northern (e.g., Dolly Sods) class I areas of

the VISTAS region. Some mountain (e.g., Great

Smoky Mountains) and coastal (e.g., Okefenokee) sites

in the South will experience increases in nitrate levels

and some coastal (e.g., Chassahowitzka) and nonVISTAS sites (e.g., Upper Buffalo) will experience increases in OC levels, but these increases are not sufficient to counter the benefits of SO2 reductions. Shenandoah experiences the largest decrease in nitrate levels

among all sites.

Under the OTW scenario, all sites experience greater

improvements in visibility on the 20% worst days. Average 
Bext rise to 39.7, 81.2, and 35.6 Mm1 at

the coastal, mountain, and non-VISTAS sites, respectively. Responses to further emission reductions will be

analyzed next.

b. Sensitivity of visibility to emission reductions

The 2018-OTB emissions were taken as the base case

for the sensitivity analysis. CAIR was promulgated after the analysis had started. Given that 2018-OTW scenario might be more realistic, the first tier described

below was repeated with 2018-OTW emissions as the

base case. However, because the sensitivities for both

scenarios were very similar, the decision was made to

continue the analysis, which was well under way, using

the 2018-OTB emissions.

In the first tier, SO2, NOx, NH3, VOC (both manmade and biogenic), and PC emissions were reduced by

30%, from their 2018-OTB levels (Table 2), individually and uniformly over the entire domain. During the

summer period, SO2 emission reduction was the most

effective control strategy in reducing PM2.5 levels because of a large decrease in sulfate concentrations. The

SO2 reduction was also the most beneficial strategy in

improving visibility because sulfate is the largest summertime contributor to light extinction in this region. In

winter, NH3 reduction was the most effective strategy

because of large reductions in nitrate and ammonium,

followed by NOx and PC reductions. None of the reductions simulated here made visibility so much worse

that the estimated value of light extinction exceeded

its current (2000–04) value. At some coastal and most

mountain sites, NOx controls increased the sulfate and

ammonium levels. This was largest at Cohutta, where


Bext from 30% NOx reduction was 	3.75 Mm1 but not

sufficient to consume the margin of 5.46 Mm1 between

the typical baseline and 2018-OTW values of Bext.

The contributions of PM2.5 components in the response of Bext to various 30% domainwide emission

reductions are shown in Fig. 3 for the Great Smoky

Mountains as a typical mountain site. All ammonium

contributions were calculated by assuming that sulfates

and nitrates are fully neutralized by ammonium. The

increase in nitrate as a result of 30% SO2 reductions

amounts to an increase in Bext of 3.34 Mm1 but, after

considering the decrease in sulfate and ammonium levels, the net result is a 
Bext of 33.1 Mm1

. The reason

FIG. 3. Contributions of sulfate, nitrate, ammonium (where the

asterisk indicates the assumption that all ammonium is in the form

of ammonium sulfate and ammonium nitrate), OC, and EC in the

response of light extinction to a 30% domainwide reduction of

SO2, NOx, NH3, anthropogenic (M)VOC, biogenic (B)VOC, and

PC emissions on the 20% worst visibility days at Great Smoky

Mountains, TN, as a typical mountain class I area.

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for the nitrate increase is the availability of more ammonium to form ammonium nitrate (instead of ammonium sulfate) in response to SO2 reductions. Reduction

of NOx emissions by 30% reduces all PM2.5 components

and is the second most effective in improving visibility

at this site (
Bext  6.82 Mm1

). Ammonia reductions primarily decrease nitrate levels and are less effective (
Bext  2.96 Mm1

) than the same relative

amount of PC reductions, which lower OC and EC levels (
Bext  4.53 Mm1

). Note that VOC reductions

increase sulfate, nitrate, and ammonium levels and despite decreases in OC levels their net effect is visibility

degradation. Reductions in VOC emissions lead to increased ozone and other atmospheric oxidants at rural

class I areas that are NOx limited. Increased atmospheric oxidants lead to increased sulfate and nitrate

formation and higher ammonium sulfate and ammonium nitrate levels.

Figure 4 shows the response of light extinction to

tier-1 reductions on the 20% worst visibility days. Reducing SO2 by 30% improves visibility at all sites, most

notably at mountain sites. The minimum, maximum,

and average Bext decreases are, respectively, 4.80, 21.1,

and 11.6 Mm1 for coastal; 10.6, 47.8, and 28.9 Mm1

for mountain; and 3.53, 23.0, and 15.3 Mm1 for nonVISTAS sites. The second largest average Bext decreases result from reducing PC emissions at coastal

(3.83 Mm1

) and non-VISTAS sites (5.45 Mm1) and

NOx emissions at mountain sites (5.85 Mm1

). The

NH3 reduction results in the third largest average Bext

decrease at coastal and non-VISTAS sites but it is not

very effective at mountain sites. As an exception, the

largest decrease in Bext is the result of the 30% NH3

emission reduction at Brigantine, (9.61 Mm1), larger

than the effect of PC reduction (7.37 Mm1

) and almost

three times the effect of SO2 reduction (3.53 Mm1

).

Note that anthropogenic VOC reductions do not result

in any noticeable change but biogenic VOC reductions

slightly increase Bext in most instances.

An additional question was addressed in this tier.

The effects of combined reductions in SO2, NOx, and

NH3 emissions were compared with the effects of reducing these emissions individually. It was found that

adding individual responses may result in an overestimation of the combined response by about 10%.

In the second tier, SO2 sources were first differentiated as ground level and elevated. Then, elevated

sources in the VISTAS region were differentiated from

elevated sources in the rest of the domain. Elevated

VISTAS sources were further broken into three categories: elevated but not electric generating unit (nonEGU), coal-fired power plant (CPP), and other power

plant (OPP). During the summer period, the daily average SO2 emissions add up to 21 700 Mg domain wide

according to the 2018-OTB scenario. Only 14% of

these emissions are from ground-level sources (2960

Mg). Of the elevated-source portion, 48% are from outside the VISTAS region (8905 Mg). In the VISTAS

region, 26% of elevated SO2 emissions are from

non-EGU sources (2540 Mg). Approximately 98% of

the EGU SO2 emissions can be categorized as CPP

(7165 Mg).

Because most of the worst visibility days occur during

summer, with the exception of a few coastal and nonVISTAS class I areas, only the 12–28 July period was

simulated with the 30% SO2 emission reductions in this

and the next tier. In Table 1, the number of summertime worst visibility days is shown in parentheses. Note

that there are no wintertime 20% worst visibility days

at any of the mountain sites and there is only one such

day at five coastal and three non-VISTAS sites. However, at four of these sites the number of worst visibility

days drops from two (an already small number) to one

making the estimated responses considerably less reliable. At sites with worst visibility days in winter, new

weights were applied to summer days for the calculation of the annual 20% worst visibility metrics. Because

the only worst visibility day at the Everglades was in

winter, no response estimates will be reported for this

site.

Figure 5 shows 
Bext resulting from tier-2 reductions

at class I areas of the domain. Not counting the total

elevated emission category, reduction of CPP emissions

of the VISTAS region had the largest impact on visibility at mountain sites: the average decrease in Bext was

16.0 Mm1

. The largest decrease was observed at Mammoth Cave (30.3 Mm1

) and the smallest decreases ocFIG. 4. Response of light extinction to a 30% domainwide decrease in SO2, NOx, NH3, anthropogenic (M)VOC, biogenic

(B)VOC, and PC emissions on the 20% worst visibility days at

coastal (C), mountain (M), and non-VISTAS (N) class I areas.

Diamonds denote the average for each category while bars mark

the range.

NOVEMBER 2007 ODMAN ET AL. 1739

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curred at the northern sites (i.e., Shenandoah and Dolly

Sods). A number of coal-fired power plants within the

VISTAS region are likely responsible for poor visibility

at Mammoth Cave. Non-VISTAS and VISTAS nonEGU sources almost tied for the second largest impact

at mountain sites. The Bext decreases had a slightly

larger average for non-VISTAS (4.89 Mm1

) but a

wider range for non-EGU sources. At coastal sites,

the average decreases in Bext were almost identical

and about 5.35 Mm1 for non-VISTAS elevated and

VISTAS CPP SO2 emission reductions. The range was

larger for the CPP category. For non-VISTAS sites the

range and average of 
Bext from VISTAS CPP and

non-VISTAS elevated sources were very similar but

their individual values differed at each site. In general,

the response to 30% ground-level SO2 reductions was

small at each site and the response to OPP reductions

was negligible. Also shown in Fig. 5 is the impact of

reducing the boundary values of SO2 and sulfate by

30%. On average, the response of Bext is a decrease of

3.41 Mm1

, a value very similar to the average decrease

due to VISTAS non-EGU emission reduction (3.46

Mm1

). This significant contribution can be attributed

to sources outside the domain shown in Fig. 1 and indicates how important it is to set the right boundary

conditions. Recall that the boundary conditions used

(and reduced) here were produced by simulating the

same period over continental United States with 36-km

grid resolution and 2018-OTB emissions.

In the third tier, CPP and non-EGU emissions of the

VISTAS region were further broken down by state as

shown in Table 3. The CPP emissions from GA, WV,

and AL combined exceed the total of the other seven

VISTAS states. Non-EGU SO2 emissions are more uniformly distributed among the states than the CPP emissions. WV, AL, KY, and VA emit more than the others.

Figure 6a shows the response of light extinction at

each class I area to 30% CPP SO2 emission reductions

from the VISTAS states on the 20% worst visibility

days. Negative 
Bext, plotted upward from the origin,

indicates a benefit from the reduction. The impact of

reductions from each state are color coded and stacked

for comparison. The sites are grouped as coastal, mountain, and non-VISTAS and, within each group, ordered

from south to north. At Mammoth Cave, for example,

the largest contribution to visibility improvement

comes from AL (
Bext  10.1 Mm1

) and then from

TN (
Bext  7.59 Mm1

) and GA (
Bext  6.58

Mm1

). As a counterintuitive result, reduction of NC

CPP emissions by 30% slightly increased sulfate levels

and Bext by 0.20 Mm1 at Mammoth Cave. This phenomenon is most likely due to numerical errors rather

than any modeled process. The brute-force method can

lead to such peculiarities when the level of response to

emission perturbations is smaller than the level of numerical errors in the base model. Other states contributed less than 1.5 Mm1 to visibility improvement. The

net change in Bext (i.e., the result of adding the contributions of all 10 VISTAS states) was 29.1 Mm1

, the

largest decrease at any site. In general, the largest impacts were attributed to the CPP emissions of the nearest states; however, the regional nature of the haze

problem cannot be overlooked. For example, Chassahowitzka showed the greatest visibility improvement

when WV CPP emissions, a large but very distant

source, were reduced. Greater improvements because

of CPP SO2 controls are expected at southern mountain

sites when compared with non-VISTAS, coastal,

TABLE 3. CPP and non-EGU elevated SO2 emissions from the VISTAS states in the 2018-OTB scenario for an average summer

day (Mg).

AL FL GA KY MS NC SC TN VA WV

CPP 1078 576 1373 757 94 315 505 761 408 1298

non-EGU 364 192 224 306 166 201 182 225 301 381

FIG. 5. Response of light extinction to the 30% SO2 emission

reductions from domainwide ground-level (GL) and elevated

(El.) sources, elevated sources in non-VISTAS portion of the domain (non-V), elevated but non-EGU sources in 10 VISTAS

states, CPP and OPP in VISTAS states, and the response to the

30% decrease in SO2 and sulfate boundary conditions (BCs) on

the 20% worst visibility days at coastal (C), mountain (M), and

non-VISTAS (N) class I areas. Diamonds denote the average for

each category while bars mark the range. The combined average

of non-V, non-EGU, CPP, and OPP is the same as the El. average.

1740 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 46

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and northern mountain (Shenandoah and Dolly Sods)

sites.

The visibility improvements due to non-EGU SO2

reductions from each individual VISTAS state show

contributions that are more regional in nature (Fig. 6b).

At each class I area visibility was affected at a different

level by these reductions. The impact of non-EGU

SO2 reductions were, on average, about 36% of the

impact of CPP SO2 reductions, commensurate with the

ratio of emissions reduced (35%). Cohutta showed the

greatest net benefit with the largest contribution from

SC (
Bext  1.95 Mm1

) followed by VA (
Bext 

1.79 Mm1

) and NC (
Bext  1.37 Mm1). GA’s

contribution (
Bext  0.76 Mm1

) was even less than

WV (
Bext  0.98 Mm1

). Once again, southern

mountain sites experienced the greatest benefits but the

difference from other sites decreased in general.

4. Conclusions

The CMAQ model was used to estimate PM and

visibility levels at class I areas of the southeastern

United States in the year 2018. The analysis focused on

a limited number of simulated days at each class I area

in an effort to approximate the annual 20% best/worst

visibility metrics. Because only two episodes, one summer and one winter, were simulated not all key meteorological conditions leading to the best/worst visibility

FIG. 6. Response of light extinction to the 30% reduction in SO2 emissions from (a) CPPs

and (b) elevated but non-EGU sources in 10 VISTAS states at coastal, mountain, and nonVISTAS class I areas in the domain.

NOVEMBER 2007 ODMAN ET AL. 1741

Fig 6 live 4/C Appendix H 

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Page 48 of 122
were represented. Therefore, the results of this study

should be viewed with some caution.

According to the 2018 on-the-way scenario, which

includes the already promulgated clean air interstate

rule, only a couple of coastal class I areas in the region

are expected to experience visibility degradation on

20% best visibility days. However, SO2 controls and

increasing ammonia emissions may increase nitrate levels at a number of sites. Additional controls may be

necessary for reasonable progress toward the goal of

achieving natural visibility conditions on the 20% worst

visibility days by 2064.

Using 2018 emission projections as the base case,

various emission reduction options were simulated using the CMAQ model to evaluate each option in terms

of visibility improvement on the 20% worst visibility

days and degradation, if any, on the 20% best visibility

days. In each option, emissions from a certain source

category were reduced by 30% and the visibility response was calculated using model estimated PM concentrations at each class I area. First, domainwide reductions of SO2, NOx, NH3, VOC, and PC emissions

were considered. SO2 reductions showed the largest

benefits at the majority of class I areas on the 20%

worst visibility days followed by NOx reductions at

mountain sites and PC reductions at other sites. NH3

reductions were somewhat effective at coastal sites and

at class I areas outside the VISTAS region but not at

mountain sites, and VOC reductions were ineffective.

None of the reductions made visibility worse than current conditions at any class I area.

More detailed analysis of the SO2 reductions was

conducted using the summer episode only. This did not

change the number of days considered in the annual

20% worst visibility metric at mountain sites, but reduced it by one at a few other sites making the results

less reliable. The 30% reductions in elevated SO2 emissions were significantly more effective than the 30%

ground-level SO2 reductions. Of these elevated SO2

sources, coal-fired power plants in the VISTAS region

generally had the largest effect on visibility but the impact of elevated sources outside this region were larger

for some coastal and non-VISTAS class I areas. Smaller

than the impact of these two source categories, the

impact of sources outside the domain, simulated by reducing the boundary conditions of sulfur by 30%,

were almost as large as the impact of emissions from

elevated non–electric generating units (non-EGU) in

the VISTAS region.

When the coal-fired power plants of the VISTAS

region were broken down by state, visibility at each

class I area was affected at a different level by emissions

from different states. In general, the largest impacts

could be attributed to the closest states; however, the

contributions of distant states were often significant.

When the non-EGU emissions were broken down by

state, the contribution from distant sources became

more obvious. This result points to the regional nature

of the haze problem and calls for regional control strategies. Quantitative estimates provided here, especially

sensitivities to emission reductions from specific states

can be used as guidance in the design of emission control strategies. However, the effectiveness of a control

strategy should always be checked with simulations.

Acknowledgments. This research was supported by

VISTAS under Contract V-2003-12 to the Georgia Institute of Technology.

REFERENCES

Bhave, P. V., S. J. Roselle, F. S. Binkowski, C. G. Nolte, S. Yu,

G. L. Gipson, and K. L. Schere, 2004: CMAQ aerosol module

development: Recent enhancements and future plans. Extended

Abstracts, Third Annual CMAS Models-3 Users’ Conf., Chapel

Hill, NC, Community Modeling and Analysis System (CMAS)

Center. [Available online at http://www.cmascenter.org/

conference/2004/archive.html.]

Binkowski, F. S., and S. J. Roselle, 2003: Models-3 Community

Multiscale Air Quality (CMAQ) model aerosol component.

1. Model description. J. Geophys. Res., 108, 4183, doi:10.1029/

2001JD001409.

Boylan, J. W., M. T. Odman, J. G. Wilkinson, A. G. Russell, K. G.

Doty, W. B. Norris, and R. T. McNider, 2002: Development

of a comprehensive multiscale “one atmosphere” modeling

system: Application to the southern Appalachian Mountains.

Atmos. Environ., 36, 3721–3734.

——, ——, ——, and ——, 2006: Integrated assessment modeling

of atmospheric pollutants in the Southern Appalachian

Mountains: Part II—PM2.5 and visibility. J. Air Waste Manage. Assoc., 56, 12–22.

Byun, D. W., and J. K. S. Ching, Eds., 1999: Science algorithms of

the EPA Models-3 Community Multiscale Air Quality system. U.S. Environmental Protection Agency, Rep. EPA-600/

R-99/030, 768 pp.

Carmichael, G. R., A. Sandu, and F. A. Potra, 1997: Sensitivity

analysis for atmospheric chemistry models via automatic differentiation. Atmos. Environ., 31, 475–489.

Center for Environmental Modeling for Policy Development,

2004: SMOKE version 2.1 user’s manual. [Available online at

http://cf.unc.edu/cep/empd/products/smoke/version2.1/

manual.pdf.]

Chang, S. Y., E. McDonald-Buller, Y. Kimura, G. Yarwood, J.

Neece, M. Russell, P. Tanaka, and D. Allen, 2002: Sensitivity

of urban ozone formation to chlorine emission estimates. Atmos. Environ., 36, 4991–5003.

Cohan, D. S., A. Hakami, Y. T. Hu, and A. G. Russell, 2005: Nonlinear response of ozone to emissions: Source apportionment

and sensitivity analysis. Environ. Sci. Technol., 39, 6739–6748.

Douglas, S., B. Hudischewskyj, and Y. Wei, 2006: Characterization of meteorology and its relationships to fine particulate mass and visibility in the VISTAS region. Draft Rep. to

VISTAS, Swannanoa, NC, 298 pp.

1742 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 46

Appendix H 

Regional Haze SIP for Florida

Page 49 of 122
ENVIRON, 2006: User’s guide: Comprehensive Air Quality

Model with extensions, version 4.40, ENVIRON, 261 pp.

[Available online at http://www.camx.com.]

EPA, 1999: Regional haze regulations: Final rule (Federal Register, 40 CFR Part 51). U.S. Environmental Protection Agency,

Washington, DC, 164 pp.

——, 2002: List of 156 mandatory class I federal areas (Federal

Register, 40 CFR Part 81). U.S. Environmental Protection

Agency, Washington, DC. [Available online at www.epa.gov/

oar/vis/class1.html.]

Fast, J. D., and W. E. Heilman, 2005: Simulated sensitivity of seasonal ozone exposure in the Great Lakes region to changes in

anthropogenic emissions in the presence of interannual variability. Atmos. Environ., 39, 5291–5306.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of

the fifth-generation Penn State/NCAR Mesoscale Model

(MM5). NCAR/TN-398	STR, NCAR, 122 pp.

Hakami, A., M. T. Odman, and A. G. Russell, 2003: High-order,

direct sensitivity analysis of multidimensional air quality

models. Environ. Sci. Technol., 37, 2442–2452.

——, ——, and ——, 2004: Nonlinearity in atmospheric response:

A direct sensitivity analysis approach. J. Geophys. Res., 109,

D15303, doi:10.1029/2003JD004502.

Harley, R. A., A. G. Russell, G. J. McRae, G. R. Cass, and J. H.

Seinfeld, 1993: Photochemical modeling of the Southern California Air Quality Study. Environ. Sci. Technol., 27, 378–388.

Hwang, D., D. W. Byun, and M. T. Odman, 1997: An automatic

differentiation technique for sensitivity analysis of numerical

advection schemes in air quality models. Atmos. Environ., 31,

879–888.

Interagency Monitoring of Protected Visual Environments, 2000:

Spatial and seasonal patterns and temporal variability of haze

and its constituents in the United States, Rep. III, 384 pp.

Jiang, G. F., and J. D. Fast, 2004: Modeling the effects of VOC

and NOX emission sources on ozone formation in Houston

during the TexAQS 2000 field campaign. Atmos. Environ.,

38, 5071–5085.

Lin, C. J., T. C. Ho, H. W. Chu, H. Yang, S. Chandru, N. Krishnarajanagar, P. Chiou, and J. R. Hopper, 2005: Sensitivity

analysis of ground-level ozone concentration to emission

changes in two urban regions of southeast Texas. J. Environ.

Manage., 75, 315–323.

Mallet, V., and B. Sportisse, 2005: A comprehensive study of

ozone sensitivity with respect to emissions over Europe with

a chemistry-transport model. J. Geophys. Res., 110, D22302,

doi:10.1029/2005JD006234.

Marr, L. C., and R. A. Harley, 2002: Modeling the effect of weekday-weekend differences in motor vehicle emissions on photochemical air pollution in central California. Environ. Sci.

Technol., 36, 4099–4106.

Menut, L., R. Vautard, M. Beekmann, and C. Honore, 2000: Sensitivity of photochemical pollution using the adjoint of a simplified chemistry-transport model. J. Geophys. Res., 105,

15 379–15 402.

Morris, R. E., and Coauthors, 2004: Model performance evaluation and model sensitivity tests for three Phase I episodes.

ENVIRON to VISTAS, 112 pp. [Available online at http://

pah.cert.ucr.edu/vistas/reports/Final_Phase1.]

——, D. E. McNally, T. W. Tesche, G. Tonnesen, J. W. Boylan,

and P. Brewer, 2005: Preliminary evaluation of the Community Multiscale Air Quality Model for 2002 over the Southeastern United States. J. Air Waste Manage. Assoc., 55, 1694–

1708.

Napelenok, S. L., D. S. Cohan, Y. Hu, and A. G. Russell, 2006:

Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmos. Environ., 40, 6112–6121.

Oanh, N. T. K., and B. N. Zhang, 2004: Photochemical smog modeling for assessment of potential impacts of different management strategies on air quality of the Bangkok Metropolitan Region, Thailand. J. Air Waste Manage. Assoc., 54, 1321–

1338.

Odman, M. T., and Coauthors, 2002: Integrated modeling for air

quality assessment: The Southern Appalachian Mountains

Initiative project. J. Phys. IV France, 12, 211–234.

——, A. G. Russell, and J. W. Boylan, 2004: Estimates of PM2.5

levels in the southeastern United States for the year 2010:

What else can be done? Fuel Process. Technol., 85, 631–639.

Olerud, D., and A. Sims, 2003: MM5 sensitivity modeling in

support of VISTAS. Draft Rep. for VISTAS, 112 pp. [Available online at http://www.baronams.com/projects/VISTAS/

reports/VISTAS_TASK2e_draft.pdf.]

Pai, P., K. Vijayaraghavan, and C. Seigneur, 2000: Particulate matter modeling in the Los Angeles basin using SAQM-AERO.

J. Air Waste Manage. Assoc., 50, 32–42.

Pison, I., and L. Menut, 2004: Quantification of the impact of

aircraft traffic emissions on tropospheric ozone over Paris

area. Atmos. Environ., 38, 971–983.

System Applications International, Inc., 1999: User’s Guide to the

Variable-Grid Urban Airshed Model (UAM-V). System

Applications International, Inc., San Rafael, CA, 415/507-

7100, 177 pp. [Available online at http://uamv.saintl.com/

documents/uam-v_1.31_user’s_guide.pdf.]

Schmidt, H., and D. Martin, 2003: Adjoint sensitivity of episodic

ozone in the Paris area to emissions on the continental scale.

J. Geophys. Res., 108, 8561, doi:10.1029/2001JD001583.

Stein, A. F., and D. Lamb, 2002: Chemical indicators of sulfate

sensitivity to nitrogen oxides and volatile organic compounds.

J. Geophys. Res., 107, 4449, doi:10.1029/2001JD001088.

Tesche, T. W., R. Morris, G. Tonnesen, D. McNally, J. Boylan,

and P. Brewer, 2006: CMAQ/CAMx annual 2002 performance evaluation over the eastern U.S. Atmos. Environ., 40,

4906–4919.

Vautard, R., M. Beekmann, J. Roux, and D. Gombert, 2001: Validation of a hybrid forecasting system for the ozone concentration over the Paris area. Atmos. Environ., 35, 2449–2461.

Yang, Y. J., J. G. Wilkinson, and A. G. Russell, 1997: Fast, direct

sensitivity analysis of multidimensional photochemical models. Environ. Sci. Technol., 31, 2859–2868.

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Quantifying the sources of ozone, fine particulate matter, 

and regional haze in the Southeastern U.S. 

M. Talat Odman1

, Yongtao Hu, Armistead G. Russell 

School of Civil and Environmental Engineering, Georgia Institute of Technology 

Asude Hanedar 

Department of Environmental Engineering, Istanbul Technical University 

James W. Boylan 

Georgia Department of Natural Resources, Environmental Protection Division 

Patricia F. Brewer 

Visibility Improvement State and Tribal Association of the Southeast 

 

1

 Corresponding author, 311 Ferst Drive, Atlanta, GA 30332-0512, odman@gatech.edu 

Appendix H 

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Abstract 

A detailed sensitivity analysis was conducted to quantify the contributions of various 

emission sources to ozone, fine particulate matter with aerodynamic diameter less than 2.5 μm 

(PM2.5), and regional haze in Southeastern United States. Ozone (O3) and particulate matter (PM) 

levels were estimated using the Community Multiscale Air Quality (CMAQ) model and light 

extinction values were calculated from modeled PM concentrations. First, the base case was 

established using the emission projections for the year 2009. Then, in each model run, emissions 

from a particular source category in a certain geographic area were reduced by 30 % and the 

responses of ozone and PM concentrations as well as light extinction were determined by taking 

the difference between the results of the reduced emission case and the base case. 

The resolution of the grid was 12 km over the modeling domain whose boundary conditions 

were provided by prior 36-km modeling over the continental U.S. Two meteorological periods, 

one summer (June 1 – July 10, 2002) and one winter (November 19 – December 19, 2002), were 

simulated to capture the seasonal variation of the responses. The impacts of SO2, primary carbon 

(PC), NH3, NOx and VOC emissions reductions were analyzed. Some of these emissions were 

split into source categories, and further into geographic regions to understand the local and 

regional impacts of emissions reductions on ozone, PM2.5, and light extinction. 

Ammonium sulfate is the largest contributor to summertime PM2.5 and visibility impairment 

in the Southeastern US and therefore SO2 emission reduction is the most beneficial control 

strategy in reducing summertime PM2.5 levels and improving visibility in the Southeast. Electric 

generating utilities (EGU) are the single largest source category for SO2 in the region. The 

impacts of reducing EGU and non-EGU point source SO2 emissions from 10 southeastern states 

as well as 3 neighboring regions (portions of central, midwestern and northeastern U.S. that are 

 2 

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within the modeling domain) were quantified. Controlling PC emissions was found to be an 

effective local strategy to decrease PM2.5 levels, especially in winter. Fire-related PC emissions 

were differentiated from other PC emission sources due to potential limitations in controlling 

these emissions. Finally, reducing NH3 emissions was considered as an effective wintertime 

strategy to reduce ammonium nitrate levels and consequently improve visibility. Wintertime NOx

emissions reductions were less effective than NH3 emissions reductions in reducing wintertime 

ammonium nitrate. PM2.5 was not responsive to changes in anthropogenic VOC. 

The responses of ozone to NOx emission reductions from ground-level and, separately, from 

elevated sources of each southeastern state were quantified. Also the impacts of domain-wide 

VOC reductions from biogenic and anthropogenic sources were estimated. The sensitivity of 

ozone to VOC emissions is small in southeastern U.S. Ground-level NOx controls are generally 

more beneficial than elevated NOx controls. The results presented here will help the development 

of specific emission control strategies for future attainment of the national ambient air quality 

standards by the VISTAS states. 

Introduction 

Fine particulate matter (often referred to as PM2.5 to denote particles and liquid droplets with 

sizes smaller than 2.5 μm) can have a negative impact on human health as well as the 

environment. In addition to being seen as a reason for numerous health problems such as asthma, 

PM2.5 is also a major cause of visibility impairment. PM2.5 originates from many sources such as 

fossil fuel combustion, biomass burning, sea salt, wind-blown dust, and fugitive dust (e.g., road 

dust, construction, grinding, and agriculture). It can also be formed in the atmosphere through 

chemical transformation of precursor gaseous emissions. Particles directly emitted into the 

 3 

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atmosphere are coined as “primary” while the ones formed in the atmosphere are referred to as 

“secondary”. PM2.5 can travel long distances and affect very large geographic areas. 

Since the enforcements of the Acid Rain Program, New Source Review and other programs, 

as ruled according to the US Clean Air Act and its amendments, the SO2 and NOx emissions (and 

other criteria pollutants) leading to ozone and PM2.5 have been considerably reduced nationwide 

from their peaks in 1970s [Chestnut and Mills, 2005; U.S.EPA, 2003]. More recent rules by the 

US Environmental Protection Agency (EPA), such as the NOx SIP Call finalized in 1998 and the 

Clean Air Interstate Rule (CAIR) proposed in 2004, have been implemented to further 

reduce/limit SO2 and NOx emissions [Evans, et al., 2008; Palmer, et al., 2007; U.S.EPA, 1998]. 

Benefits from the decline of these emissions were significant during the past decade in most 

areas of the eastern US, however were not appreciable in the southern Appalachian Mountain 

region [Anderson, et al., 2006]. Slower decrease of emissions in the Southeastern US is partly 

due to the rapid growth in this region during the past decade. As this fast growth continues and 

the National Ambient Air Quality Standards (NAAQS) of both PM2.5 and O3 are tightened 

[U.S.EPA, 2006, 2008], more urban areas are becoming non-compliant with NAAQS. It is 

becoming more evident that regional strategies are/will be required to improve the air quality in 

non-attainment areas in this region. 

Visibility Improvement – State and Tribal Association of the Southeast (VISTAS) is the 

Regional Planning Organization set up by EPA to initiate and coordinate activities associated 

with the management of regional haze in 10 southeastern states: Alabama (AL), Florida (FL), 

Georgia (GA), Mississippi (MS), Kentucky (KY), North Carolina (NC), South Carolina (SC), 

Tennessee (TN), Virginia (VA), and West Virginia (WV). VISTAS is conducting technical 

analyses supporting state regulatory plans to improve air quality in Southeastern United States. 

 4 

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This paper reports a detailed sensitivity analysis conducted as part of that study to quantify the 

contributions of various emission sources to ozone, PM2.5, and regional haze. The responses are 

calculated by taking the difference between two air quality model simulations: one with base 

case emissions and another with reduced emission inputs. This method is known as the “bruteforce” method (Hwang et al. 1997). The model used here to simulate the air quality is the 

Community Multiscale Air Quality (CMAQ) model (Byun and Ching 1999). 

Brute force sensitivity analysis is commonly used in the design of control strategies. In a 

previous study, Odman et al. (2007) used this method to quantify the impacts of various emission 

control options in terms of potential visibility improvements at Class I national parks and 

wilderness areas of the VISTAS region. We will continue to use the same method here. 

However, note that a more efficient method for calculating several sensitivities in a single model 

run, the decoupled direct method (DDM), is now available for ozone and PM sensitivities in the 

CMAQ model (Napelenok et al. 2006; Napelenok et al. 2008). 

Method 

The aerosol module of CMAQ is designed to estimate size resolved and speciated PM 

concentrations (Binkowski and Roselle 2003). In this module, the PM size distribution is 

represented by the superposition of three lognormal modes: the nucleation and accumulation 

modes for PM2.5 and a coarse mode for larger particles. PM2.5 is speciated as sulfate, nitrate, 

ammonium, elemental carbon (EC), organic carbon (OC) and soil components while the coarse 

mode includes sea salt and dust. The dynamical processes considered in the CMAQ aerosol 

module include primary emissions, new particle formation by nucleation, particle growth, 

coagulation, evaporation and condensation, dry deposition, and scavenging by clouds. Chemical 

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reactions form secondary PM such as sulfate, nitrate, ammonium and organic particles. CMAQ’s 

aerosol module is continuously updated to improve the accuracy of its PM2.5 predictions (Bhave 

et al. 2004); the particular version used here was version 4.4 with secondary organic aerosol 

(SOA) updates to include SOA formation from sesquiterpenes and isoprene (Morris et al, 2006). 

a. Simulation of O3 and PM2.5 levels 

To model the seasonal variation of PM concentrations, a summer (June 1 – July 10, 2002) 

and a winter (November 19 – December 19, 2002) period were simulated with CMAQ. Inputs to 

CMAQ were generated using the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) 

for meteorology (Grell et al. 1994) and the Sparse Matrix Operator Kernel Emissions (SMOKE) 

model for emissions (CEMPD 2004). Details of the meteorological and emissions modeling can 

be found elsewhere (Olerud and Sims 2003; Morris et al. 2004). 

The horizontal resolution of the CMAQ grid was 12 km over the domain shown in Figure 1. 

Nineteen unequally spaced vertical layers with finer resolution near the surface extended to a 

height of approximately 14 km. The lowest four layers were 36 meters thick. Earlier evaluations 

of the effects of the number of layers and layer thicknesses showed that this setup is adequate for 

modeling surface layer concentrations and deposition fluxes even during stable nighttime 

conditions (Morris et al. 2004). 

All manmade emissions were projected from the 2002 base year to the year 2009 using 

growth and control factors (MACTEC, 2007); Integrated Planning Model (IPM) was used to 

predict emission rates and controls at all electric generating units (EGU). Biogenic emissions 

were assumed to remain constant in the future. In emission reduction simulations, SO2 emissions 

were reduced separately for EGU and non-EGU point sources. NOx emissions were reduced 

separately from ground-level (on-road and non-road mobile sources, area sources, and area fires) 

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and elevated (EGUs, non-EGU point sources, and point fires) source categories. Primary Carbon 

(PC) emissions were reduced separately for ground-level, elevated, and fire emissions. NH3 and 

VOC emissions were reduced from all source categories as a whole. 

It was assumed that any potential feedback of future land use and emissions on the 

meteorology would be negligible. Therefore meteorological inputs developed for the year 2002 

were also used for the 2009 simulations. Initial and boundary conditions were derived from a 

36-km resolution simulation of the year 2009 over North America. A two-day ramp-up period 

was used to dampen the effect of initial conditions. Boundary conditions were kept the same in 

emission reduction runs. The effects of SO2 and sulfate boundary conditions were investigated in 

separate sensitivity analyses and it was found that reducing SO2 and sulfate boundary conditions 

by 30% substantially reduced PM2.5, particularly in western and northern parts of the domain. 

Emission reduction simulations were based on 2009 emission projections. In each simulation, 

emissions from a selected emission category and geographic region were reduced by 30% and 

the differences from the base-case values were recorded as responses of O3 and PM2.5

concentrations to these reductions. Overall, around 100 emission reduction scenarios were 

considered. Responses were calculated and visualized for the entire domain but quantitative 

analysis focused on ozone and PM2.5 non-attainment areas. Ozone concentrations are routinely 

monitored by the State and Local Air Monitoring Stations (SLAMS) network at locations 

throughout the US; results of reduction scenarios were projected for the 54 sites in the VISTAS 

region (5 in FL, 5 in MS, 2 in AL, 7 in GA, 5 in SC, 5 in TN, 5 in NC, 6 in KY, 7 in WV, and 7 

in VA) and 15 outside of the 10 VISTAS states (Figure 1). Speciated composition of PM2.5 is 

monitored by the Speciated Trends Network (STN) (now called Chemical Speciation Network); 

results of reduction scenarios were projected for 65 STN sites in the VISTAS states (4 in FL, 6 in 

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MS, 6 in AL, 8 in GA, 5 in SC, 5 in TN, 9 in NC, 8 in KY, 8 in WV, 6 in VA) and 14 in the rest 

of the domain (Figure 1). Light extinction is calculated from PM2.5 concentrations monitored by 

the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in Class I 

national parks and wilderness areas. Responses to emissions reductions were projected for 16 

IMPROVE monitors in the VISTAS states (9 categorized here as mountain sites and 7 as coastal 

sites) and 6 more in the rest of the domain (Figure 1). Only results for VISTAS sites will be 

discussed here. 

b. Sensitivity analysis 

Responses were calculated as the difference in concentrations between the reduced and base 

case emissions: 

 (1) 2009

30%

reduced −=∆ CCC

A negative response indicates a decrease in concentrations compared to the model estimates for 

the year 2009 as a result of 30% emissions reductions. As a first-order approximation to the 

sensitivities, these responses were normalized by the emissions reductions: 

 E

S

∆

∆ ≈ C (2) 

where . A positive sensitivity corresponds to a decrease of concentrations in 

response to the negative emission change (i.e., reduction) 

2009

30%

reduced −=∆ EEE

∆E . 

All responses were derived from hourly modeled concentrations at the locations shown in 

Figure 1 and 24-hour averages were calculated. Then these daily averages were further averaged 

over the simulation periods and reported as “summer” (June 1 – July 10) or “winter” (November 

19 – December 19) responses. Pollutant emissions for the 2009 base case are given in Table 1. 

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All reductions are 30% of these figures for the pollutant type and source categories listed under 

each state, three region outside the 10 VISTAS states distinguished as Central (CEN), Midwest 

(MW) and Northeast (NE), or the entire domain. The 12-km modeling domain includes portions, 

but not the entire areas of the CEN, MW, and NE regional planning organizations; emissions 

from the more distant portions of these regions are included in the boundary conditions for the 

12-km domain. Responses to reductions in these regions as reported here consider only those 

portions of the regions that are in the 12-km domain. For ground-level NOx and PC emissions 

VISTAS states defined specific counties for which emissions were reduced (Figure 2), based on 

expected contributions to ozone or PM2.5 in nearby urban areas. Only MS was interested in the 

impact of its sources statewide. Note that there are differences between the areas selected for 

ground-level NOx emissions and PC emissions and some states included areas in neighboring 

states. 

The emissions that were reduced by 30% are as follows: 

• SO2 from EGU sources in each VISTAS state and 3 other regions in the domain, 

• SO2 from non-EGU point sources in each VISTAS state and other regions, 

• NOx from ground-level sources (on-road plus off-road plus area) in selected counties of 

each VISTAS state, and 3 other regions in the domain, 

• NOx from elevated (EGU plus non-EGU point) sources in each VISTAS state and other 

regions, 

• Primary Carbon from all ground-level sources in selected counties of each VISTAS 

state, and 3 other regions in the domain, 

• Primary Carbon from all elevated sources in each VISTAS state and other regions, 

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• Primary Carbon from all fires in each VISTAS state and other regions, 

• NH3 from all sources in VISTAS and other regions, 

• VOC from anthropogenic and biogenic sources in the domain. 

Light extinction, Bext (Mm-1), was calculated as a linear combination of specific pollutant 

concentrations (μg m-3), component-specific light scattering/absorption coefficients (m2

 g-1), and 

a dimensionless relative humidity adjustment factor, f(RH), using the following equation 

(IMPROVE 2000): 

 Bext = 3×f(RH)×{[SO4

2-] + [NO3-

] + [NH4

+

]} + 4×[OC] + 10×[EC] 

 + 1×[Soil] + 0.6×[CM] + Brayleigh (3)

Here, [SO4

2-], [NO3-

], [NH4

+

], [OC], [EC], [Soil] are the concentrations of PM2.5 respectively 

associated with sulfate, nitrate, ammonium, organic carbon, elemental carbon, and soils; [CM] is 

the concentration of coarse PM and Brayleigh is the Rayleigh scattering (10 Mm-1). Note that 

[NH4

+

] is set equal to 0.375×[SO4

2-] + 0.29×[SO42-] in Equation 3 assuming ammonium 

completely neutralizes sulfate and nitrate to form SO4(NH4)2 and NO3NH4 (Boylan et al 2006).

Site specific monthly values based on climatology were used for f(RH). 

The sensitivities presented here are most accurate for 30% reduction of the specified 

emission type and category from the specified region. If the results are used to estimate the 

response to a smaller reduction by scaling, it should be realized that an error is introduced due to 

the non-linearity of atmospheric response. Extrapolation beyond 30% is highly discouraged. In 

addition, extreme caution should be used in superposing the sensitivities to emissions from 

different geographic areas. Such deductions can be used during the development of control 

strategies to give an estimated magnitude and some directional sense as to where to look for 

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emission reductions. However, they should not be used as a substitute for simulations that 

demonstrate the effectiveness of a given emission control strategy. 

Results and discussion 

The year 2002 was simulated by Morris et al. (2005) using “actual” emissions. Model 

estimates were evaluated using observations from various monitoring networks. In general, 

modeled ozone shows the best agreement with observations. Among the PM2.5 components, 

sulfate agrees the best with observations: its bias is within ±20% both in summer and in winter 

(Morris et al 2005; Tesche et al. 2006). Nitrate concentrations are overestimated by 50 to 100% 

in winter. Summertime organic carbon used to be underestimated by 80 to 100% but the addition 

of SOA formation from sesquiterpenes and isoprene reduced this bias to near zero (Morris et al, 

2006). Here, the final modeling setup recommended by Morris et al. (2005) was used in all 

simulations. The performance of the model in 2002 simulations gives an indication of the 

uncertainties in future year simulation and sensitivity analysis results. It is generally assumed 

that the levels of the uncertainties in modeled responses would be similar to those in evaluated 

model results (Pun et al, 2008). Therefore, the responses below are probably most accurate for 

ozone while the uncertainties may be larger for PM2.5 especially in winter. Nitrate constitutes a 

larger fraction (2-15%) of PM2.5 in winter than in summer (1-2% of PM2.5) and PM2.5 responses 

to wintertime NOx and NH3 reductions may be overestimates because of the model’s nitrate 

overestimation. 

In the following discussion, responses (∆C) and sensitivities (∆C/∆E) are presented as values 

averaged over several sites. When values are averaged over a state’s ozone or PM2.5 sites (a 

subset of all corresponding monitors in that state, focused on non-attainment areas) we refer to 

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this as the “state-mean” value. All responses (∆C) are presented as state-mean values. The range 

of the values for sampled sites is given in the figures as error bars around the average. Values for 

individual sites are presented as supplemental information (Figures S-1 through S-24), which is 

helpful for example in identifying where the state-maximum and minimum values occur. 

Sensitivities (∆C/∆E) provide a means for comparing the benefits of controlling different 

pollutants. Along with the cost of controls per unit mass of each pollutant they can be used in the 

development of efficient control strategies. However the extent of the benefits is limited by the 

amount of controllable emissions given in Table 1. For example, as we will see below, PM2.5

sensitivities to PC emissions are much larger than those to SO2 emissions. But controlling 30% 

(6 Mg) of ground-level PC in GA (sensitivity to which is 19.4 ng m-3 Mg-1) in summer amounts 

to an average PM2.5 decrease of only 0.12 μg m-3 in GA while controlling 30% (285 Mg) of EGU 

SO2 emissions in GA (sensitivity to which is 0.75 ng m-3 Mg-1) results in an average PM2.5

decrease of 0.21 μg m-3. 

When reductions are applied to an individual state’s emissions it makes sense to compare the 

state-mean sensitivities. However, if reductions are applied over a larger region (for example, 

VOC reductions are applied domain wide and NH3 reductions are applied over the entire 

VISTAS region) the region-mean sensitivity is more meaningful. State-mean sensitivities should 

not be compared to region-mean sensitivities. For reductions applied to individual states, a 

region-mean sensitivity can be calculated by summing the responses to individual state 

reductions and dividing the sum to the sum of the emission reductions from individual states. 

Note that the responses are additive but the sensitivities are not. Similarly, for emissions reduced 

in non-attainment areas only (ground-level NOx and ground level PC) it is more meaningful to 

calculate an average over the sites in non-attainment areas shown in Figure 2. However this is 

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not done here therefore special care should be exercised when comparing state-mean sensitivities 

to ground-level NOx and ground level PC reductions with the state-mean sensitivities to other 

emission types. 

a. O3 responses 

Since wintertime ozone is usually not an issue, only summertime responses will be discussed 

here. State mean ozone responses to 30% emissions reductions are larger for elevated NOx than 

ground-level NOx, except in MS, GA and TN (Figures 3a and 3b) where larger geographic areas 

of these states were included in the ground-level NOx reduction (Figure 2). In these states and in 

NC and VA maximum responses to ground-level NOx emissions are larger than maximum 

responses to elevated NOx. The largest response to ground-level NOx reductions is an O3

decrease of 5.0 ppb in Jackson MS and Atlanta GA (Figure S-20). The largest response to 

elevated NOx reductions is -3.5 ppb in Aiken SC and Helena AL (Figure S-22). 

The largest contribution to the state-mean O3 response is generally from the ground-level 

NOx emissions of the same state, except in FL where the self contribution is less than 5%, SC 

where NC contribution is slightly larger, and VA where the contribution of the NE region is 10% 

larger (Figure 4a). Despite the influences of MW and NE emissions, O3 responses are smaller in 

KY, WV and VA compared to southern VISTAS states where the ozone forming potentials are 

larger. Responses to elevated NOx emissions have a slightly less localized pattern (Figure 4b). 

With the exception of FL, AL, SC and WV the self contributions are smaller than those for 

ground-level NOx. 

Responses to a 30% domain-wide anthropogenic VOC reduction are much smaller compared 

to responses to NOx reductions (Figure 3c). They generally result in an O3 increase, more so in 

southern VISTAS states than KY, WV and VA (Figure S-24). On the other hand, a 30% domain-

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wide biogenic VOC decrease, while infeasible, could significantly reduce ozone, especially in 

northern VISTAS states where these responses can even exceed the responses to NOx reductions. 

b. O3 sensitivities 

Since NOx emission reductions were applied to individual state emissions, NOx sensitivities 

are averaged over each state’s selected ozone sites (a subset of all ozone monitors in the states, 

focused on ozone non-attainment areas). VOC reductions, on the other hand, were applied 

domain wide hence VOC sensitivities are averaged over all the sites in the domain. 

In the VISTAS region, state-mean summertime ozone sensitivities to ground-level NOx

emissions are larger than those to elevated NOx, except in FL where O3 is more sensitive to GA’s 

NOx emissions (both ground-level and elevated) than FL’s own. In other states, the sensitivity to 

their own ground-level emissions range between 7.0 (VA) and 85 ppt Mg-1 (AL). State-mean O3

in KY is more sensitive to ground-level NOx emissions from WV than those from KY. The 

sensitivities to elevated NOx emissions of their own state are always larger than the sensitivities 

to elevated NOx emissions of neighboring states and range between 6.0 (VA) and 19 ppt Mg-1

(AL). Note that these state-mean sensitivities to elevated and ground level NOx are 2-3 orders of 

magnitude larger than the domain-mean sensitivities to VOC emissions. When the comparison is 

made to sensitivities calculated by using total NOx emissions reductions from the entire VISTAS 

region, and averaged over all the VISTAS sites, VOC sensitivities are still very small. 

The sensitivity of ozone to domain wide VOC are larger in winter but wintertime ozone is 

not of interest here. In summer, the sensitivity to biogenic VOC emissions is generally larger 

than the sensitivity to anthropogenic VOC but even that is less than 0.12 ppt Mg-1 (maximum 

sensitivity is in Arlington County VA). Summertime sensitivities to anthropogenic VOC are 

negative at several AL, GA, NC, SC and TN sites. 

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c. PM2.5 responses 

Responses to fire PC reductions (Figures S7-S8) are excluded from the following discussion 

because there is no distinction between prescribed and natural fires here; therefore the control of 

fire emissions is not trivial. 

In summer, responses to 30% emissions reductions are largest for EGU SO2 emissions 

(Figure 5) with a maximum decrease of 1.12 μg m-3 in Cincinnati OH (Figure S-14), which is 

considered part of KY non-attainment areas here. This is in part due to the large (33%) 

contribution of MW region (Figure 6c) where 1090 Mg are reduced compared to 265 Mg in KY 

(Table 1). For the same reason, the average response in WV is also large. The responses decrease 

moving southward and from west to east along the same latitudes (note that, in Figure 5, the 

states are ordered from west to east starting from the very south and moving north). The 

decreasing influence of MW reductions can explain the southward decrease but the easterly 

decrease is more complicated. Note the eastern neighboring state contributions in GA (SC), AL 

(GA) and KY (WV). This is not the case for TN or WV where the contributions from NC and 

VA are less than 5%. However, AL and GA contributions are much larger in TN than NC and 

the contribution of the MW region is much larger in WV than in VA. The smallest response is a 

decrease of 0.20 μg m-3 in Tampa FL. Being the southernmost state, FL is affected only 40% by 

the reductions in other states. Self contributions are around 30% for AL and GA and smaller for 

more northern VISTAS states. 

The order of summertime responses to other emission reductions varies from state to state 

and site to site (Figures S-4, S-6, S-10, S-12, and S-16). The second largest state-mean responses 

are to non-EGU SO2 reductions, except in FL where the mean response to ground-level PC 

reductions are larger than responses to non-EGU SO2. The next largest responses are to ground-

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level PC reductions followed by those to elevated PC reductions, except in AL where the order 

switches. Responses to elevated and ground-level NOx reductions are smaller than responses to 

SO2 and PC. 

Responses to non-EGU SO2 reductions display a geographic pattern very similar to the 

responses to EGU SO2 reductions, i.e. decreasing from north to south and from west to east 

(Figure 5d). The self contributions in some states are larger compared to EGU SO2 (AL, SC, TN, 

VA) while they are smaller in others (GA, KY) (Figure 6d). 

The impacts of ground-level PC emissions are highly localized (Figure 6a) and there is no 

clear geographic pattern of the responses (Figure 5a). The state-mean responses range between 

-0.27 (KY) and -0.11 μg m-3 (SC). The largest reduction is -0.69 μg m-3 in Arlington Co. VA 

(Figure S-10), in part, due to the contribution from reductions in the NE region. Large 

metropolitan areas such as Atlanta GA, Charlotte NC and Cincinnati OH respond to ground-level 

PC reductions with large PM2.5 decreases. 

An increase from east to west is observed in responses to elevated PC reductions (Figure 5b) 

as the contributions from eastern neighbors factor in (Figure 6b), except in TN and WV which, 

as in the case of responses to EGU SO2, are impacted by larger southern or northern 

contributions compared to their eastern neighbors. Summertime responses to elevated and 

ground level NOx emissions are similar in magnitude and pattern (Figures 5e and 5f) and are 

smaller than responses to PC and SO2 in the same state.In winter, responses to 30% ground-level 

PC reductions are generally the largest (Figure 7), with some exceptions. For example, the mean 

response to EGU SO2 is larger in AL and the mean response to elevated NOx is larger in SC. 

This reflects the fact that primary carbon emissions increase in winter while sulfate formation 

from SO2 emissions is less efficient and ammonium nitrate formation is favored at colder 

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temperatures. Individual sites may have very different orderings (Figures S-3, S-5, S-9, S-11, 

S-13, and S-15). The maximum response to ground-level PC is once again in Arlington Co. VA 

(Figure S-9) where PM2.5 decreases by 1.61 μg m-3due to a large contribution (34%) from the NE 

region. The decreases in Atlanta GA, Charlotte NC and Cincinnati OH are all larger than 0.86 μg 

m-3. The state-mean responses range between -0.43 (KY) and -0.16 μg m-3 (AL). Except in KY 

and WV where MW contributions weigh in (reductions are 114 Mg in the MW region and only 2 

Mg in each of KY and WV), each state’s self contribution is much larger than other contributions 

(Figure 8a) indicating the highly localized nature of the responses. 

In order of decreasing state-mean wintertime responses, ground-level PC reductions are 

generally followed by EGU SO2, elevated NOx, ground-level NOx, elevated PC and non-EGU 

SO2 reductions, but there are many exceptions. The maximum response to 30% EGU SO2

reductions is a decrease of 0.32 μg m-3 in Athens GA (Figure S-13). The mean responses are 

largest in FL and GA (both -0.26 μg m-3) and they decrease in northern VISTAS states (Figure 

7c). This pattern is the opposite of the southward decrease observed in summer. The contribution 

of the CEN region becomes significant even in easternmost states (Figure 8c) suggesting strong 

westerly flow. The contributions of EGU SO2 emissions from the MW region are comparable to 

their summertime values but due to colder temperatures they don’t form as much sulfate as they 

did in summer therefore wintertime PM2.5 responses in KY, WV and VA are much smaller. The 

contributions from western and southern neighbors play a much more important role compared 

to the summertime contributions. 

Before discussing the responses to NOx reductions, it should be remembered that the model 

overestimated wintertime nitrate by 50 to 100%. Therefore, these responses are most likely also 

subject to large uncertainties. Wintertime responses to elevated NOx reductions show much 

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larger variability than responses to EGU SO2 and state-maximum responses are larger than those 

to EGU SO2 reductions, except in FL (Figure 7f). Recall that ammonium nitrate is a much larger 

fraction of the total PM2.5 in the winter than in the summer episode. The largest response to 30% 

elevated NOx reductions is -0.35 μg m-3 in Athens GA (Figure S-5). The self contributions of the 

states are much smaller than what they are for EGU SO2 reductions (Figure 8f). Responses to 

ground-level NOx emissions reductions are similar to responses to elevated NOx both in 

magnitude and geographic pattern (Figure 7e). Once again the largest response is in Athens GA 

(-0.29 μg m-3). The state-mean responses are dominated by contributions from the CEN and MW 

regions except for FL, SC and NC (Figure 8e). 

The largest wintertime response to 30% elevated PC reductions is a PM2.5 decrease of 0.27 

μg m-3 in Birmingham AL (Figure S-11). Similar to the summertime responses, a decrease from 

west to east to is observed (Figure 7b). States’ self contributions are not as large as in the case of 

ground-level PC reductions but much larger when compared to other emission reductions. 

Responses to non-EGU SO2 are largest in FL and smallest in WV following a clear trend of 

decrease northward and eastward (Figure 7d). Note that the contributions of the CEN region 

decreases in the same directions (Figure 8d). The EGU SO2 contributions from the MW region 

dominate in KY and WV but the relatively small PM2.5 responses in those states show that MW 

SO2 emissions do not have as much sulfate forming potential as they did in summer. 

Wintertime PM2.5 levels can be reduced by 0.75 μg m-3 on average in VISTAS states by 

reducing NH3 emissions from those states by 30% or by 350 Mg day-1. When NH3 reductions are 

extended to the CEN, MW and NE regions of the domain, the state-mean responses range 

between -0.91 (FL) and -1.46 μg m-3 (SC) (Figure 9b). The maximum response to 30% NH3

reductions is a PM2.5 decrease of 1.69 μg m-3 in Columbia SC (Figure S-1). The contribution of 

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the VISTAS states is larger than 72% in AL, GA and SC where the responses are the largest 

(Figure 9d). 

Summertime NH3 emissions are almost twice as large as in wintertime but the responses are 

much smaller in summer (Figure 9a). This is because ammonium nitrate is a very small fraction 

of PM2.5 in summer. The largest response is a PM2.5 decrease of 0.52 μg m-3 at Hancock, WV 

(Figure S-2). The contribution of the MW region dominates in KY and WV where the responses 

are the largest (Figure 9c). 

d. PM2.5 sensitivities 

In an attempt to rank the NH3 sensitivities among sensitivities to other types of emissions, we 

normalized all the responses with total emissions reductions over the VISTAS region. The values 

of the sensitivities so obtained for the VISTAS region are smaller than those for individual states 

for the same class of emissions. This is simply because of the larger contribution of other states 

to total emission reductions than to total responses. The states are generally the largest 

contributor to the PM2.5 response of their own sites and when they are not, the percent 

contribution of other states to the response is usually smaller than their contribution to total 

emissions. On average, sensitivity to NH3 ranked second after sensitivity to ground-level PC in 

winter. In summer, the sensitivity to NH3 was larger than the sensitivities to ground and elevated 

NOx emissions. Summertime sensitivities to NH3 emissions are small: the average sensitivity at 

VISTAS sites to VISTAS NH3 reductions is 2.0×10-1 ng m-3 Mg-1 with a maximum of 3.9×10-1

ng m-3 Mg-1 in Birmingham AL and Atlanta GA. In winter, the sensitivity to NH3 increases by an 

order of magnitude to an average of 2.1 ng m-3 Mg-1 and a maximum of 4.2 ng m-3 Mg-1 in 

Columbia SC. 

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Similarly, to rank the VOC sensitivities, we normalized all the responses with total emissions 

reductions over the domain. The sensitivity to biogenic VOCs was larger than the sensitivities to 

NOx in summer and sensitivities to NOx and SO2 in winter. The sensitivity to domain-wide 

biogenic VOC emissions is 2.3×10-2 ng m-3 Mg-1 on average in summer. It is much larger in 

winter, with an average of 8.9×10-2 ng m-3 Mg-1, when biogenic VOC emissions are smaller by 

an order of magnitude. Sensitivities to domain-wide anthropogenic VOC emissions are near-zero 

at all VISTAS sites in both winter and summer. 

Among the emissions reduced in individual VISTAS states in this study, PM2.5 sensitivity is 

generally largest to ground-level PC (PC-G) followed by elevated PC (PC-E), non-EGU SO2, 

EGU SO2, ground-level NOx (NOx-G) and elevated NOx (NOx-E) both in summer and in winter. 

However, there are several exceptions to this order. In summer, the sensitivity to EGU SO2 is 

larger than the sensitivity to non-EGU SO2 (Table 2) in NC. In MS, TN, KY and VA the 

sensitivity to elevated NOx is larger than the sensitivity to ground-level NOx. In winter, SO2 and 

NOx sensitivities are of similar magnitudes and their ranking varies from state to state (Table 

3).Sensitivities to fire PC are difficult to compare with sensitivities to other pollutant emissions 

because fire emissions are from episodic events while emissions from other categories are more 

continuous. 

So far, we looked at each state’s mean sensitivity to its own emissions but this is not 

necessarily the largest sensitivity in that state for that class of emissions. As a result of the 

proximity of the sites in one state to upwind emission sources in another (neighboring) state, the 

sensitivity to emissions from other states can be larger. In summer, the mean sensitivity in KY is 

larger to ground-level NOx emissions from WV than to those from KY and the mean sensitivity 

in FL is larger to AL and GA’s elevated NOx emissions than to FL’s. In winter, sensitivities to 

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emissions from other states, listed in parenthesis, are larger than the sensitivities to in-state 

emissions for the following emissions categories in the following states: 1) Non-EGU SO2 in NC 

(TN); 2) EGU SO2 in SC (GA) and KY (TN); 3) Ground-level NOx in FL (AL and GA) and GA 

(AL and SC); and 4) Elevated NOx in FL (AL and GA), GA (AL), TN (MS and AL), KY (TN), 

WV (TN, KY and VA) and VA (NC and TN). Note that the negative sensitivities to in-state 

ground-level NOx emissions in KY, WV and VA (Table 3) may be due to large uncertainties 

associated with wintertime ammonium nitrate predictions and consequently with wintertime 

sensitivities to NOx emissions. 

e. Bext responses 

The mountain Class I areas experience their worst visibility days in summer. Because SO4

dominates visibility impairment, the largest summertime Bext responses are to 30% EGU SO2

reductions both at mountain and coastal sites, followed by responses to non-EGU SO2 reductions 

(Figures 10a and 10b). The responses to other emission reductions are insignificant. The 

maximum responses to EGU SO2 are in Mammoth Cave KY (-14.3 Mm-1), a mountain site, and 

in Saint Marks FL (-5.3 Mm-1), a coastal site. The maximum response to non-EGU SO2

emissions among mountain sites is also in Mammoth Cave KY (-3.4 Mm-1) but in Okefenokee 

GA (-2.0 Mm-1) among coastal sites. In Everglades FL the responses to both types of SO2

reductions are almost zero because SO4 has a smaller contribution to haze at Everglades. 

Some coastal Class I areas experience their worst visibility days in winter. Wintertime 

responses are generally largest for 30% NH3 reductions with a maximum decrease in Bext of 12.6 

Mm-1 in Mammoth Cave KY and 9.4 Mm-1 in Cape Romain SC. The impacts of SO2 reductions 

are much smaller in winter. The maximum response to EGU SO2 is at Chassahowitzka FL (-3.3 

 21 

Appendix H 

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Mm1). The responses to NOx reductions become larger then what they were in summer but they 

are small compared to responses to NH3 reductions. 

Conclusions 

The CMAQ model was used to estimate the responses and sensitivities (responses 

normalized by emissions reduced) of ozone and PM2.5 levels, as well as Bext calculated from 

PM2.5 components, to SO2, NOx, NH3, VOC, and PC emissions in Southeastern U.S. for the year 

2009. The analysis focused on potential non-attainment areas of the region and the findings have 

direct implications to the identification of region-wide control strategies. 

The sensitivity of ozone to VOC emissions is 2-3 orders of magnitude smaller than the 

sensitivities to elevated and ground level NOx therefore VOC controls would be much less 

effective than NOx controls for reducing ozone levels in southeastern U.S. Although sensitivities 

to ground-level NOx emissions from non-attainment counties are larger, state-wide reductions of 

elevated NOx emissions are generally more beneficial strategies for summertime ozone. 

Reducing one state’s ground-level NOx emissions generally produces the largest effect in that 

state. Elevated NOx reductions have a less localized and more regional effect. Responses are 

larger in southern VISTAS states where ozone forming potentials are greater. It is possible to 

achieve as much as 3.5 to 5 ppb decreases in 2009 ozone levels by reducing ground-level or 

elevated NOx emissions by 30% throughout the VISTAS region. 

In the VISTAS states, sensitivity of PM2.5 is generally largest to PC emissions (sensitivity to 

ground-level sources being larger than elevated sources), followed by SO2 emissions (sensitivity 

to non-EGU being larger than EGU emissions), and NOx emissions (sensitivity to ground-level 

being larger than elevated sources). There are several exceptions to this rule depending on the 

 22 

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season of the year, magnitude of emissions from different source categories in each state, and the 

proximity of the sources to the sampling sites. Sensitivities provide a means of comparing the 

controls of different pollutant on a per-unit-mass basis. Ultimately, the amounts of controllable 

emissions determine the achievable PM2.5 reductions. Since PC emissions are limited but there is 

ample SO2 emissions that can be controlled, responses to 30% SO2 reductions are generally 

larger than responses to 30% PC reductions. In this respect, SO2 reductions are ultimately more 

“beneficial” in summer. As much as a 1.12 μg m-3 decrease in 2009 PM2.5 levels can be achieved 

by reducing EGU SO2 emissions by 30% throughout the VISTAS region. The benefits generally 

decrease from north to south and from west to east. The second most beneficial summertime 

strategy at most non-attainment areas of the region is the reduction of non-EGU SO2 emissions, 

followed by ground-level and elevated PC controls, respectively. The impacts of PC reductions 

are highly localized; as much as a 0.69 μg m-3 decrease in PM2.5 levels can be expected. The 

impacts of elevated and ground-level NOx controls are generally smaller. 

In winter, ground-level PC controls are generally more beneficial than EGU SO2 controls. 

PM2.5 levels can be decreased by as much as 1.61 μg m-3 by reducing the ground-level PC 

emissions by 30%. The maximum response to 30% EGU SO2 reductions is a PM2.5 decrease of 

0.32 μg m-3. Colder temperatures in northern VISTAS states limit secondary sulfate formation 

and the potential benefits of SO2 emissions reductions. It is possible to achieve greater impacts in 

some states by reducing elevated NOx emissions than by reducing EGU SO2 emissions (however 

note that the uncertainties in sensitivities to NOx are larger than those to SO2). Another 

interesting result that was not shown here is the contribution of SO2 emissions from outside the 

modeling domain. This contribution was emulated by reducing the SO2 and sulfate boundary 

conditions. It was found that continental and/or intercontinental transport of SO2 emissions can 

 23 

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have impacts comparable to or, in some cases greater than the impacts from local sources of SO2

or NOx or even PC. 

When PM2.5 responses are normalized by total emissions reductions over the VISTAS region, 

sensitivity to NH3 emissions ranks second after sensitivity to ground-level PC in winter and is 

larger than sensitivities to ground and elevated NOx emissions in summer. In southeastern U.S., 

ammonium nitrate (NO3NH4) levels are generally limited by the availability of NH3, so reducing 

NH3 emissions can be a more effective strategy to reduce NO3NH4 than reducing NOx emissions. 

Wintertime PM2.5 levels can be reduced by as much as 0.75 μg m-3 on average in VISTAS states 

by reducing NH3 emissions from those states by 30%. VOC controls would have no significant 

impact on PM2.5 levels in this region. 

At the mountain Class I areas, the greatest visibility benefits would likely be from further 

EGU SO2 reductions within the VISTAS states. At the costal Class I areas, reducing ammonia 

emissions would be more beneficial for reducing ammonium nitrate contributions to visibility 

impairment in wintertime than further reducing nitrogen oxide emissions from either groundlevel or elevated sources. This is consistent with Odman et al. (2007), which suggests ammonia 

control can be a very effective strategy for reducing regional haze in winter. 

Controlling manmade VOC emissions would have little if any visibility benefits at Class I 

areas. Reducing primary carbon from elevated or ground-level sources is projected to have small 

to no visibility benefit. This is consistent with the monitoring data that shows that most of 

measured organic carbon is secondary in origin and primary carbon is only a small fraction of the 

total measured carbon. Sulfate is the dominant contributor to visibility impairment on the worst 

visibility days at mountain sites, and ammonium nitrate may be important for coastal sites where 

the worst visibility days occur in the winter. 

 24 

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Quantitative estimates provided here, especially sensitivities to emission reductions from 

specific states can be used as guidance in the design of emission control strategies. However, the 

effectiveness of a control strategy should always be checked with simulations. 

Acknowledgements 

This research was supported by the Visibility Improvement State and Tribal Association of 

the Southeast (VISTAS) under contract number V-2003-12 to Georgia Institute of Technology. 

References 

Anderson, J., et al. (2006), Trends in cloud water sulfate and nitrate as measured at two mountain 

sites in the Eastern United States: Regional contributions and temporal changes compared 

with regional changes in emissions, 1986-1999, Atmospheric Environment, 40, 4423-

4437. 

Bhave, P. V., S. J. Roselle, F. S. Binkowski, C. G. Nolte, S. Yu, G. L. Gipson, and K. L. Schere, 

2004: CMAQ aerosol module development: Recent enhancements and future plans. 3rd 

Annual CMAS Models-3 Users' Conference, Chapel Hill, NC. 

Binkowski, F. S. and S. J. Roselle, 2003: Models-3 community multiscale air quality (CMAQ) 

model aerosol component - 1. Model description J. Geophys. Res.-Atmospheres 108, Art. 

No. 4183. 

Boylan, J. W., M. T. Odman, J. G. Wilkinson, and A. G. Russell, 2006: Integrated assessment 

modeling of atmospheric pollutants in the Southern Appalachian Mountains: Part II – 

PM2.5 and visibility. J. Air & Waste Manage. Assoc., 56, 12-22. 

Byun, D. W. and J. K. S. Ching, Eds., 1999: Science algorithms of the EPA Models-3 community 

multiscale air quality system. U.S. Environmental Protection Agency. 

CEMPD (Center for Environmental Modeling for Policy Development): SMOKE V2.1 User’s 

Manual. [Available online from 

http://cf.unc.edu/cep/empd/products/smoke/version2.1/manual.pdf.] 

Chestnut, L., and D. Mills (2005), A fresh look at the benefits and costs of the US Acid Rain 

Program, Journal of Environmental Management, 77, 252-266. 

Evans, D., et al. (2008), Modeling the effects of changes in new source review on national SO2 

and NOx emissions from electricity-generating units, Environmental Science & 

Technology, 42, 347-353. 

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn 

State/NCAR mesoscale model (MM5) NCAR/TN-398+STR, 122 pp. 

Hwang, D., D. W. Byun, and M. T. Odman, 1997: An automatic differentiation technique for 

sensitivity analysis of numerical advection schemes in air quality models Atmos. 

Environ., 31 879-888. 

 25 

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 26 

IMPROVE (Interagency Monitoring of Protected Visual Environments), 2000: Spatial and 

Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United 

States, Report III. ISSN: 0737-5352-47. 

MACTEC, Inc. 2008: Documentation of the Base G2 and Best and Final 2002 Base Year, 2009, 

and 2018 Emissions Inventories for VISTAS. [Available online from http://www.vistassesarm.org/documents/VISTABF2003-20-2008.pdf] 

Morris, R. E., B. Koo, S. Lau, T. W. Tesche, D. McNally, C. Loomis, G. Stella, G. Tonnesen, 

and Z. Wang, 2004: Model performance evaluation and model sensitivity tests for three 

Phase I episodes. [Available online from http://pah.cert.ucr.edu/vistas/docs.shtml.] 

Morris, R.E., D.E. McNally, T.W. Tesche, G. Tonnesen, J.W. Boylan, P. Brewer, 2005: 

Preliminary Evaluation of the Community Multiscale Air Quality Model for 2002 over 

the Southeastern United States. J. Air & Waste Manage. Assoc., 55, 1694–1708. 

Morris, R.E.; Koo, B.; Guenther, A.; Yarwood, G.; McNally, D.; Tesche, T.W.; Tonnesen, G.; 

Boylan, J.; and Brewer, P. 2006: Model sensitivity evaluation for organic carbon using 

two multi-pollutant air quality models that simulate regional haze in the southeastern 

United States. Atmospheric Environment, 40, 4960-4973. 

Napelenok, S., D. S. Cohan, Y. Hu, and A. G. Russell, 2006: Decoupled direct 3D sensitivity 

analysis for particulate matter (DDM-3D/PM). Atmospheric Environment, 40, 6112-6121. 

Napelenok, S. L., D. S. Cohan, M. T. Odman, and S. Tonse, 2008: Extension and evaluation of 

sensitivity analysis capabilities in a photochemical model. Environmental Modelling & 

Software, 23, 994-999. 

Odman, M. T., Y. Hu, A. Unal, A. G. Russell, and J. W. Boylan, 2007: Determining the sources 

of regional haze in the southeastern United States using the CMAQ model. Journal of 

Applied Meteorology and Climatology, 46, 1731-1743. 

Palmer, K., et al. (2007), The benefits and costs of reducing emissions from the electricity sector, 

Journal of Environmental Management, 83, 115-130. 

Pun, B. K., C. Seigneur, E. M. Bailey, L. L. Gautney, S. G. Douglas, J. L. Haney, and N. Kumar, 

2008: Response of atmospheric particulate matter to changes in precursor emissions: A 

comparison of three air quality models. Environmental Science & Technology, 42, 831-

837. 

Tesche, T.W., R. Morris, G. Tonnesen, D. McNally, J. Boylan, P. Brewer, 2006: CMAQ/CAMx 

annual 2002 performance evaluation over the eastern US. Atmos. Environ., 40, 4906–

4919. 

U.S.EPA (1998), Finding of Significant Contribution and Rulemaking for Certain States in the 

Ozone Transport Assessment Group Region for Purposes of Reducing Regional 

Transport of Ozone, 40 CFR Parts 51, 72, 75, and 96, edited, Environmental Protection 

Agency. 

U.S.EPA (2003), National Air Quality and Emissions Trends Report: 2003 Special Studies 

Edition, report, EPA 454/R-03-005, USEPA Office of Air Quality Planning and 

Standards, Emissions Monitoring and Analysis Division and Air Quality Trends Analysis 

Group, Research Triangle Park, NC. 

U.S.EPA (2006), National Ambient Air Quality Standards for Particulate Matter; Final Rule, 40 

CFR Part 50, edited, Environmental Protection Agency. 

U.S.EPA (2008), National Ambient Air Quality Standards for Ozone; Final Rule, 40 CFR Parts 

50 and 58, edited, Environmental Protection Agency. 

Appendix H 

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Table 1. Daily average emissions (Mg) by state, region, and/or domain wide for summer and winter episodes. 

Summer AL FL GA KY MS NC SC TN VA WV VIS CEN MAVOC BVOC 

NH3 2235 2596 2

NOx Ground 71 303 329 116 371 135 117 476 237 72 2228 3520 3

NOx Point 397 573 301 282 264 274 232 250 293 295 3160 2352 2

PC Fires 3 20 20 6 15 25 0 11 27 4 130 47 

PC Ground 4 16 20 5 19 20 5 21 13 3 126 222 

PC Point 21 23 34 16 17 17 18 27 14 18 206 146 

SO2 EGU 751 520 949 883 278 301 358 646 290 692 5668 1818 3

SO2 non-EGU 244 194 137 88 94 133 167 209 187 139 1592 1325 1

Winter AL FL GA KY MS NC SC TN VA WV VIS CEN MAVOC BVOC 

NH3 1174 1580 1

NOx Ground 83 283 341 118 357 135 115 477 244 76 2231 3375 3

NOx Point 601 509 448 585 254 296 266 524 331 773 4588 2629 3

PC Fires 8 37 58 8 16 26 0 14 28 5 201 62 

PC Ground 6 23 32 6 29 20 10 30 22 6 183 239 

PC Point 21 40 32 16 17 17 18 29 15 20 226 145 

SO2 EGU 823 476 890 875 298 350 371 685 391 795 5953 1974 3

SO2 non-EGU 244 193 141 92 88 130 155 206 186 134 1569 1319 1

Appendix H Regional Haze SIP for Florida
Table 2. Summertime mean sensitivities (ng m-3 Mg-1) in each VISTAS state to emissions in the 

same state. 

State PC-G PC-E SO2-nonEGU SO2-EGU NOx-G NOx-E 

FL 28.1 7.58 1.50 1.36 0.02 0.01 

MS 18.4 10.1 1.41 1.27 0.08 0.11 

AL 62.1 18.0 1.20 0.90 0.51 0.26 

GA 19.4 5.21 1.26 0.75 0.33 0.26 

SC 41.1 10.8 1.39 1.22 0.17 0.14 

TN 22.3 9.93 1.22 0.70 0.21 0.22 

NC 25.3 6.50 0.89 1.04 0.25 0.12 

KY 55.7 7.32 1.25 0.89 0.18 0.20 

WV 36.0 8.84 1.23 0.86 0.39 0.30 

VA 19.2 8.39 0.76 0.71 0.10 0.11 

Table 3. Wintertime mean sensitivities (ng m-3 Mg-1) in each VISTAS state to emissions in the 

same state. 

State PC-G PC-E SO2-nonEGU SO2-EGU NOx-G NOx-E 

FL 39.0 4.47 0.44 0.31 0.11 0.15 

MS 14.2 5.65 0.34 0.23 0.38 0.27 

AL 48.3 13.3 0.28 0.21 0.31 0.24 

GA 21.6 3.91 0.23 0.34 0.23 0.20 

SC 46.0 9.33 0.25 0.21 0.81 0.33 

TN 18.3 9.86 0.36 0.21 0.19 0.11 

NC 40.8 6.11 0.16 0.29 0.95 0.30 

KY 55.6 5.86 0.16 0.08 -0.16 0.04 

WV 32.7 5.54 0.18 0.08 -0.04 0.01 

VA 27.4 6.54 0.16 0.14 -0.10 0.10 

Appendix H 

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Figure 1. Modeling domain, 10 VISTAS states and locations of ozone (blue), PM (red) and 

combined ozone and PM (black) monitors, and Class I areas (green). 

Appendix H 

Regional Haze SIP for Florida

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(a)

(b)

Figure 2. The extent of ground-level emission reductions: (a) NOx and (b) primary carbon. 

 30 

Appendix H 

Regional Haze SIP for Florida

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 (a) (b) 

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

FL MS AL GA SC TN NC KY WV VA

∆

O3 (ppb)

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

FL MS AL GA SC TN NC KY WV VA

∆

O3 (ppb)

 (c) (d) 

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

FL MS AL GA SC TN NC KY WV VA

∆ O3 (ppb)

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

FL MS AL GA SC TN NC KY WV VA

∆ O3 (ppb)

Figure 3. Response of summertime O3 (ppb) in each southeastern state to 30 % reduction of (a) 

ground-level NOx, (b) elevated NOx, (c) anthropogenic VOC, and (d) biogenic VOC, emissions. 

Diamonds denote the average over non-attainment areas while bars mark the range.

 31 

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 (a) (b) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

AL FL GA KY MS NC SC TN VA WV CEN MW NE

Figure 4. Contributions (%) by state or region to summertime average O3 responses in each 

southeastern state (Figure 3) to 30 % reduction of (a) ground-level NOx, and (b) elevated NOx

emissions. Contributions less than 5% are not shown.

 32 

Appendix H 

Regional Haze SIP for Florida

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 (a) (b) 

-0.80

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

 (c) (d) 

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

 (e) (f) 

-0.20

-0.16

-0.12

-0.08

-0.04

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

-0.16

-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

Figure 5. Response of summertime PM2.5 (μg m-3) in each southeastern state to 30 % reduction 

of (a) ground-level PC, (b) elevated PC, (c) EGU SO2, (d) non-EGU SO2, (e) ground-level NOx, 

and (f) elevated NOx emissions. Diamonds denote the average over non-attainment areas while 

bars mark the range. 

 33 

Appendix H 

Regional Haze SIP for Florida

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 (a) (b) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

 (c) (d) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

 (e) (f) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

AL FL GA KY MS NC SC TN VA WV CEN MW NE

Figure 6. Contributions (%) by state or region to summertime average PM2.5 responses in each 

southeastern state (Figure 5) to 30 % reduction of (a) ground-level PC, (b) elevated PC, (c) EGU 

SO2, (d) non-EGU SO2, (e) ground-level NOx, and (f) elevated NOx emissions. Contributions 

less than 5% are not shown. 

 34 

Appendix H 

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 (a) (b) 

-1.80

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

 (c) (d) 

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

-0.16

-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

 (e) (f) 

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

Figure 7. Response of wintertime PM2.5 (μg m-3) in each southeastern state to 30 % reduction of 

(a) ground-level PC, (b) elevated PC, (c) EGU SO2, (d) non-EGU SO2, (e) ground-level NOx, 

and (f) elevated NOx emissions. Diamonds denote the average over non-attainment areas while 

bars mark the range. 

 35 

Appendix H 

Regional Haze SIP for Florida

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 (a) (b) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

 (c) (d) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

 (e) (f) 

-20%

0%

20%

40%

60%

80%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by State or Region

AL FL GA KY MS NC SC TN VA WV CEN MW NE

Figure 8. Contributions (%) by state or region to wintertime average PM2.5 responses in each 

southeastern state (Figure 7) to 30 % reduction of (a) ground-level PC, (b) elevated PC, (c) EGU 

SO2, (d) non-EGU SO2, (e) ground-level NOx, and (f) elevated NOx emissions. Contributions 

less than 5% are not shown. 

 36 

Appendix H 

Regional Haze SIP for Florida

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 (a) (b) 

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

FL MS AL GA SC TN NC KY WV VA

∆ PM 2.5 (μ g/m 3

)

 (c) (d) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by Region

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FL MS AL GA SC TN NC KY WV VA

Contribution by Region

VIS CEN MW NE

Figure 9. Responses of summertime PM2.5 (μg m-3) in each VISTAS state to 30 % reduction of 

NH3 emissions (a) in summer and (b) in winter (diamonds denote the mean while bars mark the 

range), and contributions (%) by region to mean responses (c) in summer and (d) in winter. 

 37 

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 (a) (b) 

-16.0

-14.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

E-SO2

n-SO2

G-NOx

E-NOx

NH3

A-VOC

G-PC

E-PC

F-PC

∆ Bext (Mm -1)

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

E-SO2

n-SO2

G-NOx

E-NOx

NH3

A-VOC

G-PC

E-PC

F-PC

∆ Bext (Mm -1)

 (c) (d) 

-14.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

E-SO2

n-SO2

G-NOx

E-NOx

NH3

A-VOC

G-PC

E-PC

F-PC

∆ Bext (Mm -1)

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

E-SO2

n-SO2

G-NOx

E-NOx

NH3

A-VOC

G-PC

E-PC

F-PC

∆ Bext (Mm -1)

Figure 10. Responses of Bext (Mm-1) to 30 % reductions in summer (a) at mountain sites; (b) at 

coastal sites, and in winter (c) at mountain sites and (d) at coastal sites. Diamonds denote the 

mean while bars mark the range. 

 38 

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Technical Report 

Impact of NOx emission reductions on PM levels in the 

VISTAS region 

Prepared by: 

M. Talat Odman, Asude Hanedar, Yongtao Hu, Armistead G. Russell 

School of Civil and Environmental Engineering, Georgia Institute of Technology 

Prepared for: 

Patricia Brewer 

Visibility Improvement State and Tribal Association of the Southeast 

and 

James W. Boylan 

Georgia Department of Natural Resources, Environmental Protection Division 

September 30, 2008 

Appendix H 

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Abstract 

A detailed sensitivity analysis was conducted to quantify the impact of NOx emission 

reductions to fine particulate matter (PM2.5) levels in Southeastern United States. Changes in 

PM2.5 concentrations were estimated using the Community Multiscale Air Quality (CMAQ) 

model. First, the base case was established using the emission projections for the year 2009. 

Then, NOx emissions from six southeastern states (AL, GA, KY, TN, VA, WV) were reduced by 

100 % and the responses of PM2.5 concentrations were calculated by taking the difference from 

the base case. To investigate the non-linearity of the sensitivity, 70% reduction of NOx emissions 

were also considered for two states (AL and GA). In addition, the sensitivity to mobile emissions 

was also calculated for two states (KY and WV) by reducing the on-road NOx emissions from 

non-attainment areas by 100%. 

The resolution of the CMAQ grid was 36 km over the modeling domain whose boundary 

conditions were provided by prior modeling over the continental U.S. An entire year was 

simulated using the meteorology of the year 2002 to capture the annual variation of the 

sensitivities. The analysis focuses on PM2.5 non-attainment areas of the VISTAS region. 

Reducing total NOx emissions by 100% from individual VISTAS states resulted in a decrease 

of the PM2.5 design values between 0.1 and 1.60 μg m-3. When 70% NOx reduction in AL and 

GA was compared to 100% reductions it was found that the relationship between PM2.5 and NOx

emissions is highly non-linear for severely reduced NOx emissions. The last 30% reduction 

results in more than 60% of the full benefits. 100% reduction of mobile NOx emissions from 

non-attainment counties in WV and KY did not have a significant impact on PM2.5 design values. 

Additional reductions in the range of 70 to 100% of all NOx emissions are not currently 

feasible. Given the non-linearity of the responses and how quickly they diminish going from 

 2 

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100% to 70% reductions, the response of PM2.5 to feasible NOx controls would be most likely 

insignificant in the southeastern U.S. Quantitative estimates provided here can be used as 

guidance in the design of emission control strategies. However, the effectiveness of a control 

strategy should always be checked with simulations. 

Introduction 

Fine particulate matter (often referred to as PM2.5 to denote particle sizes smaller than 2.5 

μm) is a mixture of suspended solid particles and liquid droplets and can impact both human 

health and the environment. In addition to being seen as a reason for numerous health problems 

such as asthma, PM2.5 is also a major cause of visibility impairment. PM2.5 originates from many 

sources such as fossil fuel combustion, biomass burning, vegetative emissions, sea salt, windblown dust, and fugitive dust (e.g., road dust, construction, grinding, and agriculture), or it is 

formed in the atmosphere through chemical transformation of precursor gases. It can travel long 

distances and affect very large geographic areas. 

Reduction of nitrogen oxides (NOx) emissions generally results in a decrease of PM2.5 nitrate 

concentrations. However, in some cases where oxidant formation is sensitive to volatile organic 

compounds (VOC), nitrate concentrations may actually increase, for example if the increase in 

oxidant concentrations exceeds the decrease in nitrogen dioxide (NO2) concentrations (Pun et al. 

2008; Lane et al. 2008). The effect of NOx reductions on PM2.5 is not limited to nitrate. NOx (and 

VOC) emissions are precursors to many oxidants such as O3 and OH. These oxidants are 

involved in the formation of numerous condensable products, therefore they can potentially 

affect all secondary PM2.5 species. Different VOC and NOx chemical regimes lead to different 

amounts of volatile products relative to condensable products (e.g., organic nitrates vs. HNO3). 

 3 

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As a result, PM2.5 response to NOx emission reductions can be highly nonlinear and even in the 

undesired direction. 

Pun et al. 2008 investigated the response of PM2.5 concentrations to various precursor 

emissions over the entire U.S., including the southeast, However, this investigation was limited 

to a 10-day summer episode. PM2.5 responses to NOx emissions over the southeastern U.S. have 

not been systematically investigated for an entire year. The national ambient air quality standards 

for PM2.5 are set at an annual average level of 15.0 μg m-3 and many places in southeastern U.S. 

currently exceed this limit. Current federal and state emissions control programs are reducing 

emissions of NOx, sulfur dioxide (SO2), and primary PM2.5; further reductions will likely be 

needed in some urban areas to attain the standard. The objective of this study was to evaluate the 

benefits of additional NOx controls to annual average PM2.5 for attainment planning purposes. 

Visibility Improvement – State and Tribal Association of the Southeast (VISTAS) is the 

Regional Planning Organization set up by EPA to initiate and coordinate activities associated 

with the management of regional haze in 10 southeastern states: Alabama (AL), Florida (FL), 

Georgia (GA), Mississippi (MS), Kentucky (KY), North Carolina (NC), South Carolina (SC), 

Tennessee (TN), Virginia (VA), and West Virginia (WV). VISTAS is conducting a modeling 

study to support state implementation planning with the goal of lowering PM2.5 levels and 

improving visibility in Southeastern United States. This paper reports a detailed sensitivity 

analysis conducted as part of that study to quantify the contributions of NOx emissions to PM2.5

levels throughout the region. 

In the analysis here, the sensitivities are calculated by taking the difference between two air 

quality model simulations: one with base case and another with reduced emission inputs. This 

method is known as the “brute-force” method (Hwang et al. 1997). The model used to simulate 

 4 

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the air quality is the Community Multiscale Air Quality (CMAQ) model (Byun and Ching 1999). 

Brute force sensitivity analysis is commonly used in the design of control strategies. In previous 

studies Odman et al. 2007;Odman et al. 2008 used this method to quantify the impacts of various 

emission control options in terms of possible improvements in ozone and PM2.5 levels in urban 

areas and potential visibility improvements at class I national parks and wilderness areas of the 

VISTAS region. A more efficient method for calculating several sensitivities in a single model 

run, the decoupled direct method (DDM), is now available for ozone and PM2.5 sensitivities in 

the CMAQ model (Napelenok et al. 2006; Napelenok et al. 2008 but it has not been used here. 

Method 

a. Simulation of PM2.5 levels 

To model the annual variation of PM2.5 concentrations, an entire year (2002) was simulated 

with CMAQ. The aerosol module of CMAQ is designed to estimate size resolved and speciated 

PM2.5 concentrations (Binkowski and Roselle 2003). In this module, the PM2.5 size distribution is 

represented by the superposition of three lognormal modes: the nucleation and accumulation 

modes for PM2.5 and a coarse mode for larger particles. As for speciation, PM2.5 is broken into 

sulfate, nitrate, ammonium, elemental carbon (EC), organic carbon (OC) and crustal components 

while the coarse mode includes sea salt and dust. The dynamical processes considered in the 

CMAQ aerosol module include primary emissions, new particle formation by nucleation, particle 

growth, coagulation, evaporation and condensation, dry deposition, and scavenging by clouds. 

Chemical reactions form secondary PM2.5 such as sulfate, nitrate, ammonium and organic 

particles. CMAQ’s aerosol module is continuously updated to improve the accuracy of its PM2.5

 5 

Appendix H 

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predictions (Bhave et al. 2004); the particular version used here was version 4.5 with VISTASmodified secondary organic aerosol (SOA) module (Morris et al. 2006). 

Inputs to CMAQ were generated using the Fifth-Generation NCAR/Penn State Mesoscale 

Model (MM5) for meteorology (Grell et al. 1994) and the Sparse Matrix Operator Kernel 

Emissions (SMOKE) model for emissions (CEMPD 2004). Details of the meteorological and 

emissions modeling can be found elsewhere (Olerud and Sims 2003; Morris et al. 2004, Morris et 

al. 2007). The horizontal resolution of the CMAQ grid was 36 km over the domain shown in 

Figure 1. Nineteen unequally spaced vertical layers with finer resolution near the surface 

extended to a height of approximately 14 km. The lowest four layers were 36 meters thick. 

All manmade emissions were projected out to 2009 using growth and control factors 

(MACTEC 2007); Integrated Planning Model (IPM) was used to predict emission rates and 

controls at all electric generating units (EGU). Biogenic emissions were assumed to remain 

constant in the future. Manmade NOx emissions that were reduced in sensitivity runs include 

ground level and elevated source categories. Ground-level sources include on-road and non-road 

mobile sources, area sources and area fires. Elevated sources include EGUs, other point sources, 

and point fires. 

It was assumed that any potential feedback of future land use and emissions on the 

meteorology would be negligible. Therefore meteorological inputs developed for the year 2002 

were also used for the 2009 simulations. Initial and boundary conditions were derived from a 

36-km resolution simulation of the year 2009 over North America. A five-day ramp-up period 

was used to dampen the effect of initial conditions. 

Using emission projections for the year 2009 as the base case, emission reduction simulations 

were performed and the differences of PM2.5 concentrations from the base case were recorded. In 

 6 

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each simulation, NOx emissions from a particular southeastern state were reduced by either 70 % 

or 100% (i.e., zeroing out of NOx emissions) from its base-case value. While a statewide zeroing 

out of NOx emissions is impossible to accomplish in reality, it represents the maximum 

conceptual effect of NOx emissions on PM2.5. 70% NOx reduction statewide is a dramatic 

scenario which allows for some O3 formation and oxidation processes. In addition, to study the 

impact of mobile emissions, the on-road mobile NOx emissions from the PM2.5 non-attainment 

areas of KY and WV were reduced by 100%. Overall, 10 annual emission reduction simulations 

were conducted. Responses were calculated and visualized for the entire domain but quantitative 

analysis focused on responses at PM2.5 non-attainment areas. 

b. Estimation of future PM2.5 concentrations and responses to emission reductions 

The modeled concentrations were used in a relative rather than absolute fashion to reduce the 

effects of modeling errors. Modeled future concentrations (i.e., 2009 base and reduced emissions 

cases), , were scaled by the ratio of currently observed concentrations, , to the 

modeled current concentrations, 

modeled Cfuture

observed Ccurrent

modeled C : current

modeled

modeled future

current

observed

current

future C

C

C C ×= (1) 

Responses were then calculated by taking the difference between scaled future concentrations 

(i.e., reduced emissions cases minus 2009 base case). Taking the absolute difference of the 

modeled future concentrations would be susceptible to modeling errors. For example, if the 

currently observed nitrate concentration is zero but the model predicts a non-zero nitrate 

concentration and a future reduction then scaling would set the response to zero. Absolute 

response on the other hand would suggest a negative nitrate concentration which is impossible 

 7 

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and obviously due to modeling errors. Note that, after rearrangement of Equation 1, the future 

concentration can be expressed in terms of a relative response factor (RRF) as 

observed

current

observed

modeled current

current

modeled

future

future C C

C

C C RRF ×=×= (2) 

The observations needed for scaling were obtained from the Speciated Trends Network 

(STN). STN routinely monitors the composition of PM2.5 at urban areas of the region shown in 

Figure 1. The design value (DV) at each STN site is defined as the 98th percentile of the daily 

average concentration of PM2.5, averaged over three consecutive years. DVs were available from 

2000 to 2004 therefore DVs for the current year (2002), or DVCs, were calculated as a weighted 

average of 5 years. 

As recommended by the US EPA (2007), scaling was applied separately to each PM2.5

species, namely sulfates, nitrates, EC, OC, and crustal matter (ammonium and water were treated 

in a special way). First, quarterly averaged concentrations were calculated from modeled hourlyaveraged concentrations of PM2.5 components. Then a quarterly relative response factor was 

calculated for each component by taking the ratio of the quarterly averaged modeled future 

concentration (for 2009) to the quarterly averaged modeled current concentration (for 2002). 

[ ]

[ ]Q

current

Q

Q future

Comp Comp

Comp RRF = (3) 

A future quarterly design value for each component was calculated from the quarterly current 

value of the design value for that component as follows: 

 (4) Q

Comp

Q

Comp

Q DVFComp RRF ×= DVC

Here, the component design value is calculated from the quarterly PM2.5 design value using the 

component fractions from the observed data as 

 8 

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( ) Q Q DVCComp = fraction Comp × DVC (5) 

Note that the sum of the fractions for sulfate, nitrate, OC, EC, crustal, ammonium and particle 

bound water (PBW) is unity. 

EPA recommends reconstruction of measured species component mass so that they add up to 

the measured Federal Reference Method (FRM) mass. Due to uncertainties associated with 

ammonium measurements, one of the methods used to achieve the goal above is to calculate the 

ammonium mass from ammonium associated with sulfate and nitrate1

. The quarterly future 

design values of ammonium were calculated from the quarterly future design values of sulfate 

and nitrate using the degree of neutralization of sulfate (DON), which is obtained from the 

observed data for any given site. 

 (6) Q

nitrate

Q

sulfate

Q DVFammonium DON ×= DVF 0 29. ×+ DVF

EPA also recommends calculating the PBW mass to reflect the water included in gravimetric 

FRM measurements. Since ammoniated sulfate and ammonium nitrate are hygroscopic, the 

retained sulfate and nitrate mass will include water. PBW mass can be estimated using an 

equilibrium model. The quarterly future design values of PBW were calculated from the 

quarterly future design values of sulfate, nitrate and ammonium as follows: 

0.000784--0.002618 0.260011- 0.980314 Q

ammonium

Q

nitrate

Q

sulfate

Q DVFPBW = × DVF × DVF + × DVF

0.048352- ( ) 0.153894 ( ) 0.159452- ( ) 2 2 Q 2

ammonium

Q

nitrate

Q × DVFsulfate + × DVF × DVF

Q

ammonium

Q

nitrate

Q

ammonium

Q

sulfate

Q

nitrate

Q

sulfate

DVF DVF

DVF DVF DVF DVF

××

+ +×× ××

0.356957- 

0.044366 0.212891 

 (7) 

Total organic matter (OC) was split into its primary and secondary (polymerized, biogenic 

and anthropogenic) components. Since no observations are available for the components of OC, 

the modeled current year fractions were used 

 9 

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[ ]

[ ]

Q

Q OC

current

Q

Q current

compOC DVC

OC

compOC DVC = × _ _ (8) 

and the quarterly future design values of OC components were calculated as 

[ ]

[ ]

[ ]

[ ]

[ ]

[ ]

Q

Q OC

current

Q

future

Q

Q OC

current

Q

current

Q

current

Q

Q future

compOC

Q

compOC

Q

compOC

DVC

OC

compOC

DVC

OC

compOC

compOC

compOC DVF RRF DVC

= ×

= × = × ×

_

_

_

_ _ _ _

(9) 

This assures that the sum of the future OC component design values is equal to the future OC 

design values. 

The characterization of PM2.5 components used here aims to provide a complete mass 

balance. It does not have any unknown mass which is sometimes presented as the difference 

between measured PM2.5mass and the characterized chemical components derived from routine 

speciation measurements. EPA recommended methods are expected to yield more accurate 

assessments of future PM2.5 concentrations as measured by FRM monitors. 

The quarterly future design values for each component were averaged to find an annual 

future component design value and these values were summed to get the future PM2.5 design 

value. Responses were calculated by taking the difference of the future design values: the 2009 

base case was subtracted from the 2009 control strategy, for example 100% NOx reductions, to 

get the response of the design value. A negative response implies a decrease and a positive 

response implies an increase of the design value. 

The results presented here are most accurate for the actual emission reduction used (100% or 

70%) of the specified emission category (total NOx or on-road mobile NOx) from the specified 

region. The results should not be used to estimate the response to a smaller reduction by scaling, 

 

1

 The mass of ammonium in NH4NO3 is approximately 29% of the mass of nitrate. 

 10 

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due to the non-linearity of atmospheric response. In addition, extreme caution should be used in 

superposing the sensitivities to emissions from different geographic regions (i.e., states). Such 

deductions can be used during the development of control strategies to give an estimated 

magnitude and some directional sense as to where to look for emission reductions. However, 

they should not be used as a substitute for simulations that demonstrate the effectiveness of a 

given emission control strategy. 

Results and discussion 

The year 2002 was simulated by Morris et al. (2005, 2007, 2008) using “actual” emissions. 

Model estimates were evaluated using observations from various monitoring networks, including 

FRM, IMPROVE, and STN monitors. Among the PM2.5 components, sulfate agrees the best with 

observations: its bias is within ±20% both in summer and in winter. Nitrate concentrations are 

overestimated by 50 to 100% in winter and organic carbon is underestimated by 80 to 100% in 

summer (Morris et al 2005; Tesche et al. 2006). Here, the final modeling setup used by Morris et 

al. (2007, 2008) was used in all simulations. 

A second set of simulations of the same episodes were conducted using the “typical” baseline 

emissions. The difference in these emissions from the actual case is that the EGU and fire 

emissions were averaged over a longer time period to remove anomalies and make them more 

representative of the 2000-2004 baseline period. The results of these typical baseline simulations 

were used as in Equation 1 above for scaling modeled responses. modeled Ccurrent

In the following discussion the sensitivities to statewide emission reductions are averaged 

over each state’s PM2.5 (FRM and STN) sites (31 in AL, 29 in GA, 28 in TN, 28 in KY, 17 in 

WV, and 21 in VA). The sensitivities to on-road mobile NOx emissions from the PM2.5 non-

 11 

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attainment areas of KY and WV are averaged over the FRM and STN sites in those nonattainment areas only (27 in KY and 17 in WV non-attainment areas). Site specific sensitivities 

can be found at http://vistas.ce.gatech.edu/xcel/2009_NOx_zero-outs2

. 

a. PM2.5 simulated for the year 2009 and comparison to 2002 

Domain-wide SO2, NOx, NH3, PC and VOC emissions for 2009 are compared to their 2002 

(2000-2004 average) levels in Table 1. The largest relative reductions are in SO2 emissions 

(30%). The second largest relative reduction is in NOx emissions (27%) followed by VOC 

emissions (6%). PC emissions decrease by 3% in this case. Ammonia emissions increase by 

10%. The spatial distribution of these emission changes is highly non-uniform: the magnitude as 

well as the direction of the change varies from one place to another. SO2 and NOx emissions 

reductions are most notably along the Ohio River Valley. While NOx emissions increases are 

rare SO2 emissions increase at a number of sources in the midwest and southeast and along the 

coast of the Gulf of Mexico. 

Current design values (2002 as an average of 2000-2004) of PM2.5 range from 17.06 to 8.40 

μg m-3 at FRM and STN sites in the modeling domain (Figure 1). When the design values of the 

year 2009 are compared to 2002 (Figure 2), it is found that design values decrease over time but 

their distributions over state stations remain almost the same. The largest state-mean PM2.5

concentration for 2002 is observed in WV with a value of 14.97 μg m-3. In 2009, GA has the 

largest state-mean value with 13.50 μg m-3. The largest changes between two years are observed 

for VA with a 2.05 μg m-3 decrease in mean value and a 2.41 μg m-3 decrease in median value. 

The smallest decreases are observed for AL with 1.41 μg m-3 in mean and 1.48 μg m-3 in median 

 

2

 Download the file for a particular state. For example, for the 100% and 70% NOx reductions in Alabama, 

download AL_NOx_Sensitivities_with_OC_breakdown.zip. Unzip the file and open with Microsoft Excel. The 

charts on the AL_NOx_Sensitivities spreadsheet are for site number 01-073-0023. For a different site enter the site 

index in cell E1 (e.g., 7 for site 01-069-0002) and press enter. The charts will be automatically updated for that site. 

 12 

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values. These PM2.5 reductions are primarily due to reductions in SO4; with small reductions in 

OC, EC, and NO3. 

b. Sensitivity of PM2.5 to NOx emission reductions 

The 2009 emissions were taken as the base case for the sensitivity analysis. First, total NOx

emissions in six southeastern states (AL, GA, KY, TN, VA, and WV) were reduced individually 

by 100%, from their 2009 levels (Table 2). Next, 70% reduction of total NOx emissions were 

considered for two states, AL and GA, to investigate the non-linearity of the sensitivity. Finally, 

on-road mobile NOx emissions in the non-attainment areas of KY and WV (Table 2) were 

reduced individually by 100% to investigate the sensitivity to mobile emissions. These on-road 

mobile NOx reductions represent 8.5% and 6.6 % of total NOx emissions in 2009 in KY and WV, 

respectively. In each case, comparison of PM2.5 concentrations to the base case leads to the 

desired sensitivities. 

In Figure 3, PM2.5 design values are given for 2009 base case, and the NOx reductions 

applied to the same year’s emissions. As can be seen from this figure, distributions of 

concentrations are almost the same as the 2009 base case for all reductions in all states. 

When PM2.5 concentrations are compared between base values and 100% reduction of NOx

emissions, the biggest change was observed for GA (1.05 μg m-3 decrease in mean value) and 

AL (0.83 μg m-3 decrease in mean value). The decreases in mean PM2.5 concentrations for 100% 

reduction of NOx emissions stayed around half of these values in the other states. 

Non-linearity of Sensitivity 

It can be seen from Figure 3 that there is an important non-linearity in sensitivity. For 

example, when comparing the 2009 base PM2.5 values to the NOx emission reduced values for 

 13 

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AL, PM2.5 concentration for 100% NOx decreases by 0.83 μg/m3 but this value is only 0.38 

μg/m3

 for 70% NOx reduction case for the mean values. 

Note that there are larger differences between 100% and 70% reduction cases in GA. Total 

PM2.5 decrease achieved by 100% reduction is 1.05 μg m-3. Only 36.5% of this decrease (0.38 μg 

m-3) occurred in 70% reduction case. The maximum decrease in the design value is 1.6 μg m-3

observed at Gwinnett GA (13-135-0002) for 100% reduction of GA NOx. The annual average 

response quickly reduces to -0.6 μg m-3 for 70% reduction of the GA NOx. 

Figure 4 shows the decreases of PM2.5 in each state in response to statewide reductions of 

NOx emissions. It can be clearly seen from this figure that there were large differences in PM2.5 

values between 100% and 70% NOx emission reductions for AL and GA. The distribution bar for 

base-100% formed only one box for KY. Lower line of this box is the median value and upper 

line is 75th percentile value in this case. Median and 25th percentile values are equal in this 

distribution. This means that the decreases have almost the same value in most of sampling sites. 

Figure 5 shows the distribution of the decreases in PM2.5 at non-attainment areas of KY and 

WV in response to reductions of on-road mobile NOx emissions from those areas. The mean 

value of the decrease is almost zero in both KY and WV. In these two states, on-road NOx

emissions are less than 10% of the total NOx emissions and the response of PM2.5 to on-road NOx

emission reductions is very small. The minimum value of PM2.5 decrease is -0.1 μg m-3 for KY 

and there are no percentile boxes on the distribution bar. This means that the decreases were zero 

at most sampling sites and there is an increase in PM2.5 values (as implied by the negative sign) 

at one or more sites. There is a similar situation in WV but in this case there are no increases but 

small decreases in PM2.5 values at some of the sites. 

 14 

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Comparison to annual PM2.5 standard 

Table 3 lists the sites whose 2009 base case design values exceed the annual PM2.5

standard of 15 μg m-3. The changes in design values of these sites after various emission 

reductions can also be seen in this table. There are two sites in AL which are expected to exceed 

the standard in the 2009 base case. At one of these sites (Jefferson Co. 2; 15.6 μg m-3), the only 

scenario that reduces the design value below the annual standard is the 100% NOx reduction in 

AL. At the other site (Jefferson Co. 1, mass), none of the scenarios reduces the design value 

below the standard. 

There are two sites in GA which exceed the annual PM2.5 standard for the 2009 base case. 

The 100% and 70% NOx reductions in GA reduce the design value below the standard at one of 

these sites (Clayton Co.), but not at the other site (Fulton Co.). 

There is only one site in KY (Jefferson Co.) that equals the annual standard in the 2009 

base case. The 100% NOx reduction in KY brings the design value below the standard. In WV, 

there are two STN sites which equal or exceed the standard in the 2009 base case. The 100% 

NOx reduction in WV reduces the design value below the standard at one of these sites (Brooke 

Co.) but not at the other site (Hancock Co.). 

It can be seen from Table 3 that PM2.5 in a state decreases not only by NOx reductions in that 

state but by NOx reductions in adjacent states as well. For example, 100% NOx reductions in AL, 

TN and KY decrease PM2.5 below the annual standard at sites in GA (Clayton Co.), KY 

(Jefferson Co.) and WV (Brooke Co.), respectively. Although Ohio (OH) is not included in the 

NOx reduction areas; the design values at some OH sites are lowered below the annual PM2.5

standard by 100% NOx reductions in KY and TN. 

 15 

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NOx reductions are projected to reduce PM2.5 but are not sufficient to bring all areas into 

attainment. Continued attention to controls of SO2 and PM2.5 are also warranted.

PM2.5 fractions 

In Table 4, state-mean PM2.5 fraction design values are given for all NOx reduction cases. It 

can be seen from this table that crustal and elemental carbon fractions do not change at all. OC 

fractions of PM2.5 have the biggest response to NOx reductions and more than half of the total 

response occurs in OC fraction for all states. Responses of PM2.5 fractions, in decreasing order, 

are as follows: OC, SO4, NO3, NH4, and PBW. 

The largest response is observed for GA 100% reduction NOx in OC fraction with a 

decrease of 0.81 μg m-3. This is followed by AL (-0.63 μg m-3) and TN (-0.34 μg m-3) 100% NOx

reductions. The smallest response in OC fraction among 100% reduction cases is observed for 

KY (-0.20 μg m-3). Clearly, responses do not follow the same order as the amounts of emission 

reductions because KY has the second largest NOx emissions and AL has the second smallest 

(Table 2). 

For the sulfate fraction, the largest response was observed for GA (-0.11 μg m-3) and AL (-

0.10 μg m-3) 100% NOx reduction cases. For nitrate fractions, the largest decrease was observed 

for KY with 0.12 μg m-3 in state-mean value. VA and TN followed this state with 0.09 and 0.06 

μg m-3 decreases in mean values respectively. The largest NH4 response was achieved in KY 

with -0.05 μg m-3 in mean value. Not counting crustal and elemental carbon, PBW fraction has 

the smallest decrease in all of the PM2.5 fractions: 0.03 μg m-3 or less for all states for 100% NOx

reductions. 

Figure 6 shows the responses of seasonal and annual PM2.5 fractions in all states to all 

reduction cases of NOx. Negative ∆PM2.5, plotted upward from the origin indicates a decrease 

 16 

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while positive ∆PM2.5 represents an increase. As expected, the maximum decreases were 

achieved for the 100% NOx reduction cases. WV has the smallest response among all of the 

states for all PM2.5 fractions. OC fractions of PM2.5 display the largest responses for all states. 

The OC fraction of PM2.5 has been further split into four sub-fractions as primary organic 

matter (POM), polymerized secondary organic matter (PSOM), biogenic secondary organic 

matter (SOMB), and anthropogenic secondary organic matter (SOMA). Since the largest 

responses were achieved for OC fractions, the sub-fractions of OC in each state were evaluated 

in detail in following sections. The variations of the fractions were evaluated seasonally (as 

quarters 1 through 4) and annually. 

OC fractions

The seasonal and annual state-mean responses of organic PM2.5 fractions to NOx reductions 

are given in Table 5. Since there is no change in the POM fraction only SOMA, SOMB and 

PSOM responses are tabulated. The largest responses are always observed in the SOMB fraction. 

The maximum SOMB response is -1.14 μg m-3 in Q3 (-0.73 μg m-3 annually) to 100% NOx

reduction in GA. Figure 7 shows the seasonal and annual responses of OC fractions to 100% 

NOx reductions in TN. The seasonal distribution of the SOMB response in Figure 7 is typical for 

100% NOx reduction cases in all the states as well as the 70% NOx reduction cases in AL and 

GA. The largest state-mean SOMB response is observed in Q3, then in Q2, followed by the 

response in either Q4 or Q1. 

The SOMB responses to 100% NOx reduction in KY are notably smaller than responses in 

AL, GA, and TN, despite KY having larger NOx emissions in 2009 than TN and AL. This may 

be because of relatively cooler temperatures in KY that would suppress the formation of 

secondary organic aerosols. SOMB annual response to 100% NOx reduction is twice as large as 

 17 

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the response to 70% NOx reduction in AL. In GA, the response to 100% NOx reduction is 3 times 

the response to 70% NOx reduction. These proportions clearly show the high non-linearity in the 

SOMB response to NOx reductions. 

In general, the second largest response is in the PSOM fraction, but it is much smaller than 

the SOMB response. The maximum seasonal response is -0.07 μg m-3 in Q2 (-0.05 μg m-3

annually) to 100% NOx reduction in GA. Once again, the response to 100% reduction is twice 

the response to 70% reduction in AL, and almost 3 (2.5) times in GA. In AL and GA the largest 

seasonal responses are in Q2 while in KY, TN, VA and WV they are in Q3 but with very small 

margins. SOMA responses are the smallest. The maximum seasonal response is -0.03 μg m-3 in 

Q2 (-0.02 μg m-3 annually) to 100% NOx reduction in GA. Note that some increases in SOMA 

fraction are seen for some of the cases in Q1 and Q4 but they are very small (+0.01 μg m-3). 

Corroborative studies 

Lane et al. (2008) predict a decrease of the average SOMB by 0.4-0.6 μg m-3 in the 

southeast US as a result of a 50% reduction in NOx emissions during a July 2001 episode. This is 

primarily because of a decrease in ozone and the ozonolysis of the monoterpenes which forms 

SOA. Pun et al. (2008) predict a very similar level of decrease in OC (about 0.45 μg m-3) for a 

July 1999 episode as a result of 50% reduction of NOx emissions. Accompanied by decreases in 

sulfate and nitrate, their prediction of average PM2.5 decrease in the southeastern US is 

approximately 1.5 μg m-3. 

Odman et al. (2008) present responses to 30% NOx reductions from elevated sources 

(electric generating utilities, or EGU, and non-EGU point sources) statewide for each VISTAS 

state as well as 30% NOx reductions from ground-level sources (on-road plus off-road mobile 

plus area sources) in selected counties of each VISTAS state. For each type of reduction, the 

 18 

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state-mean responses in PM2.5 are less than -0.10 μg m-3 in summer and -0.23 μg m-3 or less in 

winter. 

Boylan (2008), using a model similar to the one used here, performed simulations with 10, 

30, 50, 70, 90, and 100% reductions of all NOx emissions in the 20-county Atlanta nonattainment area from their 2009 base case levels. The 10, 30, and 50% NOx reductions resulted 

in little to no annual average response in PM2.5. The nonlinearity of the response is evident in 

Figure 8 which shows noticeable decreases in PM2.5 after 70% reduction, reaching an annual 

average of -0.5 μg m-3 at 100% reduction level, primarily due to a large OC response. The statemean PM2.5 response for GA to 100% NOx reduction predicted here is about -1.0 μg m-3 and 

Atlanta sites are generally in upper percentiles with values as much as -1.5 μg m-3. Despite this 

difference in magnitude of the response, the degree of its non-linearity agrees with what is 

suggested by Boylan (2008). 

Conclusions 

Based on air quality simulations conducted with emission projections, PM2.5 concentrations 

at a number of places in southeastern U.S. will likely remain above the national standards in 

2009. Previous studies have shown that further SO2, NH3 and PC controls may be effective in 

reducing PM2.5 levels. Here, the focus is placed on NOx emission reductions and the responses of 

PM2.5 design values at FRM and STN sites that are likely to remain in non-attainment. 

Using 2009 emission projections as the base case, NOx emission reductions from various 

southeastern states were simulated using the CMAQ model. Future design values were calculated 

by scaling the predicted concentrations with current design values for each PM2.5 component. 

The difference between the PM2.5 design values of a reduced emission case and the base case 

 19 

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gives the PM2.5 response to that emission reduction. Zeroing out (100% reduction) of total NOx

emissions from individual states resulted in a decrease of the PM2.5 design values between 0.1 

and 1.60 μg m-3 at FRM and STN sites in the modeling domain. This scenario may be impossible 

to accomplish in practice but it represents the maximum theoretical effect of NOx emissions on 

PM2.5. 

When a 70% NOx reduction scenario was tried in AL and GA and compared to 100% 

reduction it was found that the relationship between PM2.5 and NOx emissions is highly nonlinear for severely reduced NOx emissions. Reducing the last 30% of the emissions generates 

more than 60% of the full benefits. 100% reduction of mobile NOx emissions from nonattainment counties in WV did not have a significant impact on PM2.5 design values (less than 

0.10 μg m-3 decrease) and, in KY, it actually increased them (as much as 0.10 μg m-3 ). 

The largest response to NOx reductions among PM2.5 components is observed in OC. In 

southern VISTAS states (AL, GA and TN) OC response is about 75% of the total response while 

it decreases to 50% in northern VISTAS states (KY, VA and WV). The OC component was 

further split into primary and three secondary fractions: anthropogenic, biogenic and 

polymerized. Secondary OC with biogenic origin (SOMB) is responsible for more than 90% of 

the total OC response. The largest SOMB responses are observed in Q3 (July-September) 

followed by Q2 (April-June). The responses in Q4 and Q1 (October-March) are much smaller 

especially in the northern VISTAS states where they are almost zero. 

The highly non-linear relation between PM2.5 and NOx emissions suggests that reductions 

below the 70% level investigated here would result in responses in a much smaller scale. Thus 

moderate NOx controls may only reduce PM2.5 levels slightly in southeastern U.S. and reductions 

of other types of emissions should be considered as well for attainment of PM2.5 standards. 

 20 

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Quantitative estimates provided here can be used as guidance in the design of emission control 

strategies. However, the effectiveness of a control strategy should always be checked with 

simulations. The results here may have been affected by model uncertainty. Most of the PM2.5

response to NOx reductions may be coming from over prediction of secondary organic carbon, 

which may respond much more modestly under normally observed levels. Additional 

investigation of the role of NOx in secondary organic aerosol formation is warranted. 

Acknowledgements 

This research was supported by the Visibility Improvement State and Tribal Association of 

the Southeast (VISTAS) under contract number V-2003-12 to Georgia Institute of Technology. 

 21 

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References 

Bhave, P. V., S. J. Roselle, F. S. Binkowski, C. G. Nolte, S. Yu, G. L. Gipson, and K. L. Schere, 

2004: CMAQ aerosol module development:Recent enhancements and future plans. 3rd 

Annual CMAS Models-3 Users' Conference, Chapel Hill, NC. 

Binkowski, F. S. and S. J. Roselle, 2003: Models-3 community multiscale air quality (CMAQ) 

model aerosol component - 1. Model description J. Geophys. Res.-Atmospheres 108, Art. 

No. 4183 

Boylan, J., 2008. Insignificance of NOx to PM2.5 Formation in Georgia. Presentation to EPA on 

June 18, 2008. 

Byun, D. W. and J. K. S. Ching, Eds., 1999: Science algorithms of the EPA Models-3 community 

multiscale air quality system. U.S. Environmental Protection Agency. 

CEMPD (Center for Environmental Modeling for Policy Development): SMOKE V2.1 User’s 

Manual. [Available online from 

http://cf.unc.edu/cep/empd/products/smoke/version2.1/manual.pdf.] 

EPA, U.S., 2007. Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. Office of Air 

Quality Planning and Standards, Air Quality Analysis Division, Air Quality Modeling 

Group, Research Triangle Park, North Carolina, 454/B-07-002. 

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn 

State/NCAR mesoscale model (MM5) NCAR/TN-398+STR, 122 pp. 

Hwang, D., D. W. Byun, and M. T. Odman, 1997: An automatic differentiation technique for 

sensitivity analysis of numerical advection schemes in air quality models Atmos. 

Environ., 31 879-888. 

 22 

Appendix H 

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MACTEC, 2007. VISTAS Emissions Inventories 2002, 2009, and 2018. http://www.vistassesarm.org/final work products. 

IMPROVE (Interagency Monitoring of Protected Visual Environments), 2000: Spatial and 

Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United 

States, Report III. ISSN: 0737-5352-47. 

Lane, T. E., N. M. Donahue, and S. N. Pandis, 2008: Effect of NOx on Secondary Organic 

Aerosol Concentrations. Environ. Sci. Technol., 42, 6022-6027. 

Morris, R. E., B. Koo, S. Lau, T. W. Tesche, D. McNally, C. Loomis, G. Stella, G. Tonnesen, 

and Z. Wang, 2004: Model performance evaluation and model sensitivity tests for three 

Phase I episodes. [Available online from http://pah.cert.ucr.edu/vistas/docs.shtml.] 

Morris, R.E., D.E. McNally, T.W. Tesche, G. Tonnesen, J.W. Boylan, P. Brewer, 2005: 

Preliminary Evaluation of the Community Multiscale Air Quality Model for 2002 over 

the Southeastern United States. J. Air & Waste Manage. Assoc., 55, 1694–1708. 

Morris, R. E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T. W. Tesche, G. Tonnesen, J. 

Boylan, and P. Brewer, 2006: Model sensitivity evaluation for organic carbon using two 

multi-pollutant air quality models that simulate regional haze in the southeastern United 

States. Atmospheric Environment, 40, 4960-4972. 

Morris et al. 2007. VISTAS Technical Support Document. http://www.vistas-sesarm.org/final 

work products. 

Morris et al. 2008 ASIP Technical Support Document. http://www.vistas-sesarm.org/final work 

products. 

 23 

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Napelenok, S. L., D. S. Cohan, M. T. Odman, and S. Tonse, 2008: Extension and evaluation of 

sensitivity analysis capabilities in a photochemical model. Environmental Modelling & 

Software, 23, 994-999. 

Odman, M. T., Y. Hu, A. Unal, A. G. Russell, and J. W. Boylan, 2007: Determining the sources 

of regional haze in the southeastern United States using the CMAQ model. Journal of 

Applied Meteorology and Climatology, 46, 1731-1743. 

Odman, M. T., Y. Hu, A. G. Russell, A. Hanedar, J. W. Boylan, and P. Brewer, 2008: 

Quantifying the sources of smog and regional haze in the Southeastern U.S., in 

preparation. 

Olerud D.; Sims, A., 2003: MM5 Sensitivity Modeling in Support of VISTAS. Draft Report for 

VISTAS, 112 pp. 

Pun, B. K., C. Seigneur, E. M. Bailey, L. L. Gautney, S. G. Douglas, J. L. Haney, and N. Kumar, 

2008: Response of atmospheric particulate matter to changes in precursor emissions: A 

comparison of three air quality models. Environmental Science & Technology, 42, 831-

837. 

Tesche, T.W., R. Morris, G. Tonnesen, D. McNally, J. Boylan, P. Brewer, 2006: CMAQ/CAMx 

annual 2002 performance evaluation over the eastern US. Atmos. Environ., 40, 4906–

4919. 

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Figure Captions 

Figure 1. Modeling domain, 10 VISTAS states and locations of STN sites. 

Figure 2. Comparison of PM2.5 design value distributions for the years 2002 and 2009 

(◊: average value; lower line is 25th percentile, upper line is median; upper line is 75th

percentile; in error bars, upper cap is max value, lower cap is min value) 

Figure 3. Distribution of design values for 2009 base case and 100% and 70% NOx reductions in 

AL. GA, KY, TN, VA and WV. 

Figure 4. Decreases of PM2.5 (μg m-3) in each state in response to reductions of NOx emissions 

Figure 5. Decreases of PM2.5 (μg m-3) at non-attainment areas of KY and WV in response to onroad mobile NOX reductions. 

Figure 6. Responses of seasonal and annual PM2.5 fractions (μg m-3) in all states to all reduction 

cases of NOx 

Figure 7. Response of seasonal and annual organic PM2.5 fractions (μg m-3) in Tennessee to 

100% reduction of NOx 

 25 

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Figure 1. Modeling domain, 10 VISTAS states, and locations of Federal Reference Method 

(FRM) and Speciated Trend Network (STN) sites. 

 26 

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Figure 2. Comparison of PM2.5 design value distributions in AL. GA, KY, TN, VA and WV for 

the years 2002 and 2009 (◊: average value; lower line is 25th percentile, upper line is 

median; upper line is 75th percentile; in error bars, upper cap is max value, lower cap is min 

value) 

6

8

10

12

14

16

18

PM2.5 Concentration (μg/m3)

Figure 3. Distribution of design values for 2009 base case and 100% and 70% NOx reductions in 

AL. GA, KY, TN, VA and WV. 

 27 

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0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

AL(BASE‐100%)

AL(BASE‐70%)

GA(BASE‐100%)

GA(BASE‐70%)

KY(BASE‐100%)

TN(BASE‐100%)

VA(BASE‐100% )

WV(BASE‐100%)

-∆PM2..5 Concentration (μg m -3 )

Figure 4. Decreases of PM2.5 (μg m-3) in each state in response to statewide reductions of NOx

emissions. 

‐0.2

0

0.2

KY(BASE‐onroa d ) WV(BASE‐on roa d )

-∆PM2..5 Concentration (μg m -3 )

Figure 5. Decreases of PM2.5 (μg m-3) at non-attainment areas of KY and WV in response to onroad mobile NOX reductions. 

 28 

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‐1.30

‐1.10

‐0.90

‐0.70

‐0.50

‐0.30

‐0.10

0.10

100%‐Base

70%‐Base

100%‐Base

70%‐Base

100%‐Base

Onroad‐Base

100%‐Base100%‐Base100%‐Base

Onroad‐Base

AL GA KY TN VA WV

∆PM2.5 Concentrations (μg/m3)

PBW

NH4

NO3

SO4

OC

EC

Crustal

Figure 6. Responses of seasonal and annual PM2.5 fractions (μg m-3) in all states to all reduction 

cases of NOx 

‐0.80

‐0.70

‐0.60

‐0.50

‐0.40

‐0.30

‐0.20

‐0.10

0.00

100%‐base 100%‐base 100%‐base 100%‐base 100%‐base

Q1 Q2 Q3 Q4 Annual

∆PM2.5 (μg/m3)

POM

PSOM

SOMB

SOMA

Figure 7. Response of seasonal and annual organic PM2.5 fractions (μg m-3) in Tennessee to 

100% reduction of NOx 

 29 

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-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

10% 30% 50% 70% 90% 100%

Atlanta Sensitivity to NOx

∆ Conc. (

μg/m

3

)

Soils

EC

OC

H2O

NO3

NH4

SO4

Figure 8. Annual average PM2.5 responses to 10, 30, 50, 70, 90 and 100% NOx emissions 

reductions from 2009 base case in 20 counties of the Atlanta nonattainment area (Source: Boylan 

2008). 

 30 

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 31 

Table 1. Domain-wide annual emissions (Gg) for 2002 and 2009. 

SO2 NOX NH3 PC VOC**

2002* 8,870 11,790 1,990 5,510 30,300

2009 6,260 8,580 2,180 5,540 28,460 ∗

 Typical 2000-2004 baseline 

** Manmade and biogenic combined 

Table 2. Annual total or on-road mobile NOx emissions (Gg) by state for 2009. 

Domain AL GA KY TN VA WV 

Total 8,583 290 408 387 338 322 243 

Mobile* 33 16 ∗

 On-road mobile from non-attainment counties only. 

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Table 3. 2009 design values exceeding the annual standard (15 μg m-3) and how they change with emissi

State County 2009 DV AL GA KY TN 

100 % 

NOx

70% 

NOx

100 % 

NOx

70% 

NOx

100% 

NOx

100% 

NOx

Jefferson. 1 17.1 15.7(↓) 16.6(↓) 16.9(↓) 16.9(↓) 17.1 17.0(↓) 

AL Jefferson 2 15.6 14.2 15.0(↓) 15.5(↓) 15.5(↓) 15.6 15.5(↓) 

Clayton 15.1 14.9 15.0(↓) 13.8 14.7 15.1 15.1 

GA Fulton 16.4 16.2 (↓) 16.3 (↓) 15.0(↓) 16.1(↓) 16.4 16.3(↓) KY Jefferson 15.0 15.0 15.0 15.0 15.0 14.4 14.9 

Hamilton 1 15.8 15.8 15.8 15.8 15.8 15.4(↓) 15.8 Hamilton 2 15.2 15.2 15.2 15.2 15.2 14.8 15.2 Hamilton 3 15.4 15.4 15.4 15.4 15.4 15.0(↓) 15.4 

OH 

Scioto 15.0 15.0 15.0 15.0 15.0 14.6 14.9 Brooke 15.0 15.0 15.0 15.0 15.0 14.9 15.0 

WV Hancock 15.6 15.6 15.6 15.6 15.6 15.6 15.6 

 32 (↓) indicates a decrease but the design value remains above the limit. Bold indicates a decrease below the limit valueAppendix H Regional Haze SIP for Florida
 33 

Table 4. PM2.5 fractions of the state-mean design values for 2009 base and NOx reduction cases 

(μg m-3) 

States Crustal 

Elemental 

Carbon 

Organic 

Carbon Sulfate Nitrate Ammonium PBW* 

2009(base) 0.87 0.58 4.91 3.89 0.20 1.10 0.97 

AL 2009(100%) 0.87 0.58 4.28 3.79 0.15 1.06 0.95 

2009(70%) 0.87 0.58 4.63 3.84 0.17 1.08 0.96 

2009(base) 0.73 0.55 5.73 3.80 0.19 1.04 0.90 

GA 2009(100%) 0.73 0.55 4.92 3.69 0.12 0.99 0.87 

2009(70%) 0.73 0.55 5.46 3.75 0.15 1.02 0.89 

2009(base) 0.60 0.49 3.98 3.76 0.77 1.40 1.17 

KY 2009(100%) 0.60 0.49 3.78 3.70 0.65 1.35 1.14 

2009(onroad%) 0.60 0.49 3.98 3.76 0.76 1.40 1.17 

2009(base) 0.71 0.47 4.82 3.60 0.39 1.17 1.02 TN 2009(100%) 0.71 0.47 4.48 3.55 0.33 1.14 1.01 

2009(base) 0.60 0.48 3.75 3.56 0.47 1.20 1.04 VA 2009(100%) 0.60 0.48 3.53 3.49 0.38 1.16 1.02 

2009(base) 0.65 0.48 4.78 3.67 0.71 1.28 1.06 

WV 2009(100%) 0.65 0.48 4.53 3.60 0.66 1.25 1.03 

2009(onroad%) 0.65 0.48 4.77 3.67 0.70 1.28 1.06 

2009(base): base design values for 2009; 2009(100%): 2009 design values after reducing 100% of NOx emissions; 

2009(70%)2009 design values after reducing 70% of NOx emissions; 2009(on-road); 2009 design values after 

reducing 100% of on-road NOx emissions 

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Table 5. Seasonal and annual changes in OC fractions of the state-mean design values for statewide NOx reducti

Q1 Q2 Q3 Q4 

State Reduction

SOMA SOMB PSOM SOMA SOMB PSOM SOMA SOMB PSOM SOMA SOMB 

100% AL NOx -0.02 -0.27 -0.02 -0.01 -0.81 -0.05 -0.01 -0.89 -0.04 -0.01 -0.36 

AL 

 70% AL NOx 0.00 -0.04 -0.01 -0.01 -0.42 -0.03 -0.01 -0.50 -0.03 0.00 -0.08 

100% GA NOx -0.02 -0.34 -0.03 -0.03 -1.07 -0.07 -0.02 -1.14 -0.06 -0.01 -0.37 

GA 

 70% GA NOx 0.01 -0.02 -0.01 -0.01 -0.43 -0.03 -0.01 -0.51 -0.03 0.01 -0.02 

KY 100% KY NOx 0.01 0.00 0.00 -0.01 -0.27 -0.02 -0.02 -0.45 -0.03 0.01 0.01 

TN 100% TN NOx 0.00 -0.05 0.00 -0.02 -0.47 -0.03 -0.02 -0.64 -0.04 0.00 -0.10 

VA 100% VA NOx 0.01 0.00 0.00 -0.02 -0.35 -0.02 -0.01 -0.44 -0.03 0.00 -0.02 

WV 100% WV NOx 0.01 -0.02 0.00 -0.01 -0.43 -0.03 -0.01 -0.45 -0.04 0.01 -0.01 

 34 Appendix H Regional Haze SIP for Florida