# 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

# 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 Institut e of Technology


### Submitted to: Patricia F. Brewer ** Technical Coordinator Visibility Improvement St ate and Tribal Associ ation 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

### Contents Chapter 1: A Variable Time-step Algorithm for Air Quality Models Chapter 2: Determining the Sources of Regi onal Haze in the Southeastern United States Using the CMAQ Model Chapter 3: Quantifying the Sources of Ozone, Fine Part iculate Matter, and Regional Haze in the Southeastern U.S. Chapter 4: Impact of NO x Emission Reductions on PM Levels in the VISTAS Region



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

ii

## A Variable Time-step Algori thm for Air Quality Models

M. Talat Odman and Yongtao Hu School of Civil and Environmental Engi neering, 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

1M. T. Odman and Y. Hu

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 pr ocesses. Usually, the characteristic time for advection, or the grid length divided by the wi nd 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 lead s to computational inefficiency in cells where process characteristic times are much longer than the global time step. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 A variable time-step algorithm is developed that allows each grid cell to have its own time step. Concentrations in cells with shorte r 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. C oncentrations 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. PM 2.5 estimates obtained by using variable time steps are, on average, within 4 % of those obtained by usi ng a single time step. Larger differences are observed for ozone with 11 % hi gher concentrations dur ing a winter episode. The computation time decreases by 25 % but th is can be increased through more efficient programming.

Keywords: time-splitting, process splitting, computational efficiency

2A Variable time-step algorithm

1 2 3 4 5 1. Introduction

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

## ( ) ScRc c t c ++∇⋅∇=⋅∇+ ∂ ∂ )()( Ku . (1) 6

7 8 9 10 11 12 13 14 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 part icular, 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 = ∂ ∂ (2) 17

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 syst em is enormous due to the stiffness caused by the wide range of characteristic times. In addition, various numerical difficulties associated with special require ments of each transport and chem istry process must be dealt with. Therefore, in AQMs, Eq. 2 is divided into smaller pieces. A common approach is

3M. T. Odman and Y. Hu

process splitting [ Blom and Verwer , 2000] where F is split into functions representing different processes: 1 2 

### )()()()( wFwFwFwF t w R D A 

### ++≡= ∂ ∂ (3) 3

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 The function F A contains horizontal and ve rtical advection terms. A dvection is the dominant horizontal transport process; ve rtical advection is usually le ss important. Some models, e.g., the Urban-to-Regional Multiscale (URM) model [ Boylan, et al. , 2002], combine horizontal diffusion with advection while others, includ ing the Community Multiscale Air Quality (CMAQ) model [ Byun and Ching , 1999], keep them separate. F D and F R contain the vertical diffusion (and horizontal diffusion, if not already included in F A ) 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. Howe ver, emissions should be considered as a source term and a difficult choice is whether to make emissions part of F D or F R . If they are made part of F D 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 F R they would participate in chemi cal 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 pl ace and time to time. The URM model solves the problem by combining F D and F R into a single diffusion-chemistry function. Note that not all processes modeled in current AQMs ar e shown in Eq. (3). Complex aerosol and

4A Variable time-step algorithm

1 2 3 4 5 6 7 8 9 10 11
12
13
14
15
16 17 18 19 20 cloud processes are applied to the pollutant concentration fi elds through process splitting and other processes can be added to the list. Splitting drastically reduces the computationa l resources required by Eq. (2). It also allows using custom-built numerical solvers fo r each piece or process. It is much easier to deal with process-specific problems individua lly 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 F A , F D and F R 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 sa tisfied in any realistic AQM, the question is not whether there would be spli tting 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 splitti ng 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 Φ A , Φ D and Φ R are the integrators for F A , F D and F R in Equation 3 respectively; for simplicity other processes of CMAQ (see Sec tion 3 below) are not shown. Strang splitting [ Strang , 1968], which is believed to be second orde r, became very popular in AQMs after being adopted by McRae, et al. [1982]. It advances the solu tion 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= + + + . (5) 21

5M. T. Odman and Y. Hu

Ν ote that the tran sport operators Φ A and Φ D are applied for one half of the splitting time step, ∆ t , symmetrically around the chemistry operator Φ 1
2
3
4 5 6 7 8 9 10
11
12
13
14 15
16 17 18 19 20 21 22 23 R . Recently, Sportisse [2000] argued that, for R linear in c , Strang splitting is only first order unless the stiff operator Φ R is applied last. The splitting time step determines the fr equency by which processes of differing characteristic times are “synchronized” or “couple d.” 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 char acteristic 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 ope rators. 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 gr id 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 wa ter 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 lo w. 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 orde rs of magnitude, the inefficien cy of using a global time step is even more significant. In th is paper, a variable time-step algorithm is described. Its accuracy and computation-time benefits are evaluated in comparison to using a global time step.

6A Variable time-step algorithm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19
20 21 2. Methodology

The variable time-step algorithm (VARTS TEP) is developed with the objective of enabling local time steps in AQMs. VARTSTEP a llows 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 verti cal column. To our knowledge, loca l time steps have only been used in the vertical direction th erefore 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 therefor e 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. Th e user is allowed to specify th e 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 st ep capability in the ve rtical direction ispreserved in the VARTSTEP version of CMAQ, which will be referred to as VARTSTEPCMAQ hereafter. VARTSTEP assigns every vertical column its own time step, ∆ t i , 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 tw o conditions. The first condition is

1 max ≤ ∆ ∆

i ii x tu (5) 22

7M. T. Odman and Y. Hu

where is the maximum wind speed in vertical column i (up to the user-specified altitude) and ∆ x max i u 1
2
3 4 5 6
7
8 9 10 11 12 13 14
15
16
17
18
19

20

21 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 al so assures that fresh emissions ar e not transported by a horizontal distance longer than the grid length before other processes are appl ied at least once. The second condition requires that ∆ t i must be an integer multiple of the global time step, ∆ t , and an integer divisor of the output time step. Th e global time step is equal to the smallest ∆ t i in the entire domain. This condition assures s ynchronization of processes among all the grid cells before the results are written out. CM AQ, like most other AQMs, generates outputs typically once every hour. Theref ore, assuming an output time st ep of 60 min, if the global time step is 5 min, the local tim e 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 determ ine 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, ∆ t i , (i.e., concentrations are advanced by ∆ t i ) only if the clock time is an integer multiple of ∆ t i (i.e.,

t = N ×∆ t i ). 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

tttttt w
);();();( );();();(

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

∆Φ∆Φ∆Φ ∆Φ∆Φ∆Φ ∆ Φ∆ Φ∆ Φ=

+ + + −+ −+ −+ +

K K

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

8A Variable time-step algorithm

∆ t i = m ×∆ t as 1
2
3
4
5
6
7 8 9 10 11 12 (7) n n D n A n R mn wtmttmttmtw );();();( ∆Φ∆Φ∆Φ= + Since there is a single time step for each vertical column, there are no difficulties involved in doing this with th e vertical diffusion operator, Φ D . The chemical reaction operator, Φ R , does not pose any problems either since it is applied to one grid cell at a time. However, the horizontal transport operator, Φ A , requires special atten tion since neighboring grid cells in the horizontal may have different time steps. Extension to three dimensions would necessitate similar considerations for Φ D 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 (a dvective 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 concentr ations (i.e., when ). On the other hand, if 13
14

2 t ∆×= Nt 12 t t ∆ > ∆ and if 2 t ∆ × ≠ Nt any possible horizontal flux
from Cell 1 to Cell 2 is directed to a reser voir. This is shown in Figure1 by the arrow marked “Store”. In other words, concentra tions in cells with longer time steps remain constant until the time of update and flux calculati ons that require the co ncentrations of such cells would use their non-upda ted values. When the time comes for updating Cell 2 concentrations (i.e., when 15
16 17 18 19

2 t ∆ ×= Nt ) 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 20
21

9M. T. Odman and Y. Hu

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 by the arrow marked “Flush” in Figure 1. Si nce all fluxes are eventually added to the appropriate cell, the algorithm is strictly mass conservative. Above, we ignored any other neighboring ce lls 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 po ssibility 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 li mited 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 with in 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 processe s are called is as follo ws: vertical diffusion, which includes emissions and dry deposition; horizontal advection; vertical advection; horizontal diffusion; cloud proces ses; gas-phase chemistry; and aerosol processes. The CGRID array is four-dimensional consisting of 3-D concentration fields for each and every

10A Variable time-step algorithm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 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 VAR TSTEP assigns a local time step to each vertical column. In VARTSTEP-CMAQ, pro cesses other than horizontal transport (advection and diffusion) are app lied 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 ti me step of that vertical co lumn 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 vertic al 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 pr ocesses. 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 dime nsions of the horizontal grid. In a 3-D implementation every grid cell would be assi gned 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 cel ls 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

11M. T. Odman and Y. Hu

updated once per local time step . Calculation of fluxes that w ill not be used immediately may be unnecessary in other models. Howeve r, 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 interp olation between their hour ly values. Evaluating flux parameters at different times may lead to mass conservation problems. For example, the wind velocity at a cell boundary ev aluated at the middle of the lo cal time step of the cell to one side of the boundary might yi eld a different flux leaving (or entering) that cell than the flux entering (or leaving) th e cell on the other side of th e 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 violat e mass conservation. If the flux parameters were constant during the hour, then horizontal transport coul d also be applied for the duration of the local time step. Even if this wa s the case, there is little to be gained in CMAQ by implementing a local time step in hor izontal transport. In CMAQ, horizontal advection is split into one-dimensional operators along or thogonal directions. The fluxes are calculated along west-to-east and so uth-to-north directions, in a lternating order, inside loops over layers and pollutant species. Therefore, th e 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. No te 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

12A Variable time-step algorithm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 time in velocity direction checks that are need ed for flux calculations . This looping order would also reduce the overhead of deciding whet her 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 st ep and whose concentrations are not being updated immediately. This array, FGRID, fulf ills the duty of the reservoir described in Section 2 above. In CMAQ, horizontal trans port processes (advection or diffusion) are applied sequentially to all horiz ontal planes through a loop over vertical layers. For example, horizontal diffusion is applied firs t to the surface laye r, 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 encapsulati on, and considering the possibility of a 3-D implementation of VARTS TEP 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 di fferent reservoir, we made th e FGRID array four-dimensional, where the fourth dimension is indexed by polluta nt species, just like the CGRID array. Note that if the loops over vertical layers are taken out of horiz ontal process modules and these processes are called in a sequen ce 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 storag e requirements considerably for this 2-D implementation of VARTSTEP. For mass conservation, horizontal transport sc hemes are usually in flux form therefore the calculation of horizontal fluxes is rou tine in most AQMs. In CMAQ, the preferred advection scheme is the Pi ecewise Parabolic Method of Collela and Woodward [1984]. In this scheme, advective fluxes are calculated by fitting piecewise parabolas to the cell

13M. T. Odman and Y. Hu

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 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, adv ective flux in the same direction du ring a previous global time-step, or diffusive flux) or influxes may have accumu lated 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 updati ng 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 th e clock time, the cell concentrations in CGRID must be updated using the horizontal fluxes stored in FG RID. This can be done after the horizontal transport modules. In VARTSTE P-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 ho rizontal 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 incl udes emissions and dry depositi on, is the first process in CMAQ and it is followed by horizontal transport. The reason for this is most likely the

14A Variable time-step algorithm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 desire to start each process cycle with fres h 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 othe r hand, there is no reas on for applying vertical diffusion with the frequency of the global time step just to re main 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 proce ss cycle. Note that gas-phase chemistry, aerosol and cloud processes are app lied when the local time step evenly divides the clock time therefore they e ffectively take place afte r horizontal transport. Note that this is not an issue in models where vertical diffu sion and emissions are applied after horizontal transport. The last important implementation issue is the time interpolati on of meteorological parameters and emissions. In CMAQ, these para meters 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 meteorologi cal parameters and emission rates at a different time can violate mass cons ervation. For example, the total amount of emissions into cells with l onger local time steps may be ove r/under-estimated. One way to

15M. T. Odman and Y. Hu

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 meteorol ogical parameters and emission rates constant at their hourly-average values. Instead, a new interpolator was developed here for VARTSTEP-CMAQ that can evaluate time-depende nt 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 am bient fine particulate matter (PM 2.5 ) and ozone (O 3 ) 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 wi nter episode, while the savings were only 16 % for the summer episode (Table 1). This differ ence in savings is largely due to the load reduction of the aerosol module from 39 % for the winter episode to 22 % for the summer

16A Variable time-step algorithm

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 diffusi on, 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 ch emical kinetics solver whose computation time does not respond too much to changes in th e 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. Th e increase in the computati on 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 orig inal 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 unavoidabl e 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 fl ux 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

17M. T. Odman and Y. Hu

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 significan tly improve its computational efficiency. Theoretical considerations base d 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 co mputation time. The question now is whether it can produce reliable air quality results. Daily average PM 2.5 concentrations for 17 January 2002 resulting from the two simulations are compared in Figure 2. This is the day when domain average difference between PM 2.5 concentrations is second largest. Both simulations produced a similar PM 2.5 distribution over the south eastern U.S. with a peak near Pensacola, Florida. The value of the peak is 66.8 μ g m -3 according to the benchmark
simulation and 62.9 μ g m -3 according to the VARTSTEP-CMAQ simulation. Note that some portion of the differences stem from the diffe rence in the vertical advection modules as explained above. However, this portion is e xpected to be small except over mountainous regions of the domain. Daily average observations of PM 2.5 and its composition are available through the Interagency Monitoring of Protected Visu al Environments (IMPROVE) network. The

18A Variable time-step algorithm
network covers areas designated as “Class-I” for visibility prot ection such as national parks and wilderness areas and there are 22 such obser vation sites in the region shown in Figure 2. Eight of these sites are cluste red along the Southern Appalach ian Mountains, six sites are distributed along the coastline and the remain ing 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 performan ce 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 betwee n 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 sa fe to say that VARTSTEP-CMAQ produces PM 0.98R

2 =

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 PM 2.5 but its components as well. It is well known that different components of PM 2.5 affect light extinction differently. Contributions to li ght extinction of sulfate and nitrate particles can be equally substantial in the presence of water vapor. Elemental carbon can also be an important visib ility degradation agent while other particles are relatively less important [ Malm, et al. , 2000]. Daily average concentrations of sulfate, nitrate, ammonium, soil, elemental car bon and organic carbon components of PM 2.5 were

19M. T. Odman and Y. Hu

1 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 scat ter 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, elementa l carbon and organic carbon for both episodes. For the winter episode, while su lfate concentrations are, on average, 8.5 % higher, the concentratio ns of all the other PM 2 

### R

2.5 components calculated by VARTSTEP are lower than the benchmark (Figure 4). This results in PM 2.5 being much more comparable than its major components. It should be noted that sulfate is only the third largest component of PM 2.5 during this period. In add ition, relative humidity is also low in winter therefore, compared to summer time, sulfate has a much smaller contribution to light extinction. Nitrate, the larges t component of PM 2.5 , is 8.8 % lower. This a nd the high bias in sulfate suggest that oxi dants prefer SO 2 over NO x in VARTSTEP-CMAQ . So il, the second largest component of PM 2.5 , and ammonium are lower by 5.8 % a nd 2.0 %, respectively. Elemental carbon, an important cont ributor to light extinction, is lower by 5.1 % while organic carbon which is the fourth largest component of PM 2.5 is only 2.9 % lower. Based on these results, one may expect VARTSTEP-CMAQ light extinc tion to be about 5 % lower than the benchmark during the 1-20 January 2002 episode. For the summer episode, the concentrations of all the PM 2.5 components calculated by VARTSTEP are lower than the benchmark (Figur e 5). Sulfate is the largest component of
PM 2.5 during this period and, with high summertime relative humidity, it has the largest contribution to light extinction. Its concentr ations are, on average, 3.7 % lower. Organic carbon, ammonium and soil, which are the sec ond, third and fourth largest components of

20A Variable time-step algorithm

PM 2.5 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 co mponent 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 we re also compared at the 22 Class-I areas (Figure 6). The correlation between the benc hmark and the results obtained with the VARTSTEP-CMAQ is strong fo r 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 0.98R 2 = 0.87R

2 =

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 a nd the frequency of the calls to the process modules is decreased in VARTSTEP, turbulen t mixing and dry deposition, which are both performed in the vertical diffu sion 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 NO x 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 PM 2.5 and its components. As for th e increased leve ls of sulfate during the winter episode, this is because of the enhanced formation through the aqueous reaction of SO 2 with ozone. Finally, note that some of the differences in concentrations are because of the different approaches taken for vertical advection.

21M. T. Odman and Y. Hu

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 synchr onize different processes for all the grid cells, at least in a horizontal pl ane. When the characteristic tim e of a process is longer than this global time step, computations are ineffici ent 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 computa tional 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 computationa lly intensive summertime episode). A factorof-two speedup is likely if the algorithm is implemented different ly, in a different model, or using coding practices more targeted to in creasing computational efficiency. The Adaptive Grid Model of Odman, et al. [2002] is expected to experien ce 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 PM 2.5 components (excluding summertime nitrate, which was on average 12 %, or less than 0.15 μ g m -1 , lower). For ozone, the differences are 11 % in winter but only 2.5 % in summer, when this pollu tant is of concern. These differences should not significan tly affect model performance nor should they change the conclusions drawn from the model.

22A Variable time-step algorithm

Acknowledgements. This research was supported by the Visibility Improvement State and Tribal Association of the S outheast (VISTAS) under contra ct number V-2003-12 to Georgia Institute of Technology. 1 2 3

23M. T. Odman and Y. Hu

REFERENCES

Anderson, D. A., et al. ( 1984), Computational Fluid Mech anics and Heat Transfer, Hemisphere, New York. Blom, J. G., and J. G. Verwer (2000), A comp arison 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 pe rformance on microprocessor based parallel computer system, paper presented at EPA Workshop on the Multimedia Integrated Modeling System, Research Triang le Park, NC, August 15-17 2000, http://www.baronams.com/projects/ppar/hpcc_models.html

. Collela, P., and P. R. Woodward (1984), Th e piecewise parabolic method (PPM) for gasdynamical simulations, J. Comput. Phys., 54. Hu, Y., et al. (2006), Mass c onservation in the Community Mu ltiscale Air Quality model, Atmos. Environ., 40, 1199-1204. Lanser, D., and J. G. Verwer (1999), Analys is of operator splitting for advection-diffusionreaction problems from air pollution modeling, J. Com put. Appl. Math., 111, 201-216.

24A Variable time-step algorithm

Malm, W. C., et al. (2000), Spatial and Seas onal 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 soluti on 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 an d Validation, Techni cal report, 83 pp., ENV-96TR002, MCNC–North Carolina Supe rcomputing Center, Research Triangle Park, North Carolina. Odman, M. T., et al. (2002), In itial application of the adaptiv e 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), Ma ss 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 co mparison of difference schemes, SIAM J. Numer. Anal, 5.

25M. T. Odman and Y. Hu

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

26A Variable time-step algorithm

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

Figure 1. Concept of a reservoir used in VARTS TEP 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 PM 2.5 concentrations ( μ g m -3 ) over southeastern U.S. on 17 January 2002 as calculated by the benchmark CMAQ si mulation (left) and the VARTSTEP-CMAQ simulation (right).

Figure 3. Daily average PM 2.5 concentrations ( μ g m -3 ) at 22 Class-I areas in southeastern U.S. during 1-20 January 2002 (lef t) and 12-27 July 2001 (right): Comparison of the results obtained from the variable tim e-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 ( μ g m -3 ) calculated by VARTSTEPCMAQ ( y -axis) with the benchmark ( x -axis) for various components of PM 2.5 at 22 Class-I areas in southeastern U.S. during 1-20 Ja nuary 2002: (a) Sulfate, (b) Nitrate, (c) Ammonium, (d) Soil, (e) Elemental Carbon, (f) Organic Carbon.

27M. T. Odman and Y. Hu

Figure 5. Comparison of daily average concentrations ( μ g m -3 ) calculated by VARTSTEPCMAQ ( y -axis) with the benchmark ( x -axis) for various components of PM 2.5 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 O 3 concentrations (ppm) at 22 Class-I areas in southeastern U.S. during 1-20 January 2002 (lef t) and 12-27 July 2001 (right): Comparison of the results obtained from the variable tim e-step version of CMAQ ( y -axis) with the benchmark obtained from Version 4.3 of CMAQ ( x -axis).

28A Variable time-step algorithm

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.

29M. T. Odman and Y. Hu

Figure 2. Daily average PM 2.5 concentrations ( μ g m -3 ) over southeastern U.S. on 17 January 2002 as calculated by the benchmark CMAQ si mulation (left) and the VARTSTEP-CMAQ simulation (right).

30A Variable time-step algorithm

y = 0.9658x R 2 = 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 4.3VARTSTEP y = 0.9608x R 2 = 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 4.3VARTSTEP

Figure 3. Daily average PM 2.5 concentrations ( μ g m -3 ) at 22 Class-I areas in southeastern U.S. during 1-20 January 2002 (left) and 12-27 Ju ly 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).

31M. T. Odman and Y. Hu

(a) (b)

y = 1.0851x R 2 = 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 7.0VARTSTEP y = 0.9121x R 2 = 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 R 2 = 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 3VARTSTEP

. 5 y = 0.9419x R 2 = 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 R 2 = 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 4.3VARTSTEP y = 0.9706x R 2 = 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 ( μ g m -3 ) calculated by VARTSTEPCMAQ ( y -axis) with the benchmark ( x -axis) for various components of PM 2.5 at 22 Class-I areas in southeastern U.S. during 1-20 Ja nuary 2002: (a) Sulfate, (b) Nitrate, (c) Ammonium, (d) Soil, (e) Elemental Carbon, (f) Organic Carbon.

32A Variable time-step algorithm

(a) (b)

y = 0.963x R 2 = 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 30.0VARTSTEP y = 0.8789x R 2 = 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 R 2 = 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 6.0VARTSTEP y = 0.9493x R 2 = 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 R 2 = 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 4.3VARTSTEP y = 0.9732x R 2 = 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 ( μ g m -3 ) calculated by VARTSTEPCMAQ ( y -axis) with the benchmark ( x -axis) for various components of PM 2.5 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.

33M. T. Odman and Y. Hu

y = 1.1137x R 2 = 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 4.3VARTSTEP y = 1.0253x R 2 = 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 4.3VARTSTEP

Figure 6. Daily maximum 8-hr O 3 concentrations (ppm) at 22 Class-I areas in southeastern U.S. during 1-20 January 2002 (lef t) and 12-27 July 2001 (right): Comparison of the results obtained from the variable time-step versio n of CMAQ (y-axis) with the benchmark obtained from Version 4.3 of CMAQ (x-axis).

34Determining the Sources of Regional Haze in the Southeastern United States Using the CMAQ Model
M. T ALAT O DMAN , Y ONGTAO H U , A LPER U NAL ,* AND A RMISTEAD G. R USSELL School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia J AMES W. B OYLAN 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 fromvarious 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 (SO 2 ), 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 SO 2 emissions was the most effective control strategy. In the second tier, SO 2 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 SO 2 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 PM 2.5 to denote particle sizes smaller than 2.5  m) is a mixture of suspended solid particles and liquid droplets and canimpact both human health and the environment. In addition to being seen as a reason for numerous health problems such as asthma, PM 2.5 is also a major cause of visibility impairment. PM 2.5 originates from manysources 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 theatmosphere 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 N OVEMBER 2007 ODMAN ET AL. 1731

DOI: 10.1175/2007JAMC1430.1 © 2007 American Meteorological Society

JAM2557sual Environments 2000). In particular, PM 2.5 can scatter and absorb light very efficiently. The contributionsof 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 rulerequires that reasonable progress should be made toward reducing visibility impairment on the 20% worstvisibility days. In addition, the rule requires no worsening of visibility on the 20% best visibility days. Every10 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 Associationof 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 thesoutheastern 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 modelsimulations: 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 andAerosol Scalable Unified System to model ozone sensitivities to nitrogen oxides (NO x ) and volatile organiccompound (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 Thailandwith 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/AtmosphericUtility 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 hasfound 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 (SO 2 ), NO x , ammonia (NH 3 ), 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 V OLUME 46categories. At the time this project had started, the implementation of DDM for PM sensitivities in theCMAQ 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 PM 2.5 and a coarse mode. PM 2.5 consists of sulfate, nitrate, ammonium, elemental carbon (EC), organic carbon (OC), and soil components; the coarse modeincludes 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 PM 2.5 predictions (Bhave et al. 2004); the particular version used here was version 4.3. a. Simulation of PM 2.5 levelsTo model the seasonal variation of PM concentrations, a summer (12

– 28 July 2001) and a winter (1

– 21January 2002) episode were simulated with CMAQ. Inputs to CMAQ were generated using the fifth-generation Pennsylvania State University

– National Center forAtmospheric Research Mesoscale Model for meteorology (Grell et al. 1994) and the Sparse Matrix OperatorKernel 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 was12 km over the domain shown in Fig. 1. Nineteen unF IG . 1. Modeling domain, 10 VISTAS states, and locations of class I areas. N OVEMBER 2007 ODMAN ET AL. 1733equally 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 projectedout 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. Ineach simulation, emissions from a particular source category were reduced by 30% from its base-case valueand 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 themost 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 B ext (Mm
1 ) was calculated as a linear combination of specific pollutant concentrations ( 

g m 3 ), component-specific light scattering/absorption coefficients (m 2 g 1 ), and a dimensionless relative humidity adjustment factor f (RH) using the following equation (Interagency Monitoring of Protected Visual Environments 2000):

B ext  3  f  RH    SO 4 2

  NO 3  NH 4   4   OC  10   EC  1   soil  0.6   CM  B Rayleigh .  1  Here, [SO 2

4 ], [NO
3 ], [NH 4 ], [OC], [EC], and [soil] are the concentrations of PM 2.5 associated with sulfate, nitrate, ammonium, organic carbon, elemental carbon, and soils, respectively; [CM] is the concentration of coarse PM; and B Rayleigh is the Rayleigh scattering (10 Mm
1 ). Note that [NH 4 ] is set equal to 0.375  [SO 2

4 ] 0.29  [SO 2

4 ] in Eq. (1) assuming ammonium completely neutralizes sulfate and nitrate to form SO 4 (NH 4 ) 2 and NO 3 NH 4 (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 ( B ext ) by the following equation: HI  10  ln  B ext  B Rayleigh  ,  2  where HI is zero for an unpolluted atmosphere andeach deciview increase in HI approximates a perceptible decrease in visibility.

The Interagency Monitoring of Protected Visual Environments (IMPROVE) network routinely monitors the composition of PM 2.5 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 areavailable 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 leavingvery few days. There are 1–5 simulated days that represent the 20% best/worst visibility days at each siteconsidered here (Table 1). Classification and Regression Tree (CART) analysis was used to group the meteorological conditions that lead to good/bad visibilityat each class I area and to determine the representativeness of simulated days (Douglas et al. 2006). Eachday was assigned a weight that characterizes the fre1734 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY V OLUME 46quency 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 thesimulated 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 scalingthe modeled future response (i.e., difference in concentrations between the base and reduced emissions cases)

C modeled

future by the ratio of currently observed concentrations C observed current to the modeled current concentrations C modeled

current :

C future  C current observed C current

modeled C future modeled

.  3  Note that this scaling was applied to each PM 2.5 speciesseparately. 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 berealized 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 PM 2.5 to domainwide emission reductions (Odman et al. 2002; Hakami et al. 2003; Hakami et al. 2004); however, this might not be true for subdomainreductions. Extrapolation beyond 30% is highly discouraged. In addition, extreme caution should be usedin 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 directionalsense as to where to look for emission reductions. However, they should not be used as a substitute for simuT ABLE 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. N OVEMBER 2007 ODMAN ET AL. 1735lations 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 PMwere evaluated using observations from various monitoring networks, including IMPROVE. Among the PM 2.5 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 C modeled current in Eq. (3) above for scaling modeled responses. a. Visibility simulated for 2018

Domainwide SO 2 , NO x , NH 3 , 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 NO x emissions (38% in January and 43% in July). The second largest relative reduction is in SO 2 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, NO x emissions are reduced, most notably along the Ohio River valley, but not everywhere. SO 2 emissionsare 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 reductionsdue to the implementation of the clean air interstate rule (CAIR). In comparison with the OTB scenario, reductions of SO 2 emissions almost double (48% reduction both in January and July when compared with their typical baseline values). Winter and summer NO x 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 baselinevalues. 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 B ext range from 30 to 260 Mm 1 at the class I areas of the modeling domain (Fig. 2a). Figure 2b shows the expected changes in light extinction B ext on the 20% best and 20% worst visibility days under the OTB and OTW scenarios in 2018. Negative B ext , plotted upward from the origin indicates an improvement in visibility while positive B ext signals visibility degradation. The range and the average of the values of

B ext are shown for each one of the three categories of class I areas: coastal, mountain, and non-VISTAS. Under the OTB scenario, B ext on the 20% best visibility days range from 2.76 to

9.00 with an average of

0.83 Mm 1 at coastal sites. Visibility is expected to degrade at Cape Romain, the Everglades, and T ABLE 2. Domainwide daily average emissions (Mg) for typical baseline and 2018-OTB and 2018-OTW* scenarios.

SO 2 NO x NH 3 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. 1736 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY V OLUME 46Okefenokee 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. SO 2 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 PM 2.5 and degradation of visibility. At mountain sites, where the best visibility days generally occur in winter, B ext range from 1.71 to

2.92 with an average of

0.72 Mm 1 . Visibility degradations are expected at the Great Smoky Mountains and, to asmaller 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 B ext range from 1.37 to

5.25 with an average of

2.37 Mm 1 ; visibility is expected to degrade at Breton and Mingo. F IG . 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. N OVEMBER 2007 ODMAN ET AL. 1737Under the more stringent controls of the OTW scenario, no mountain or non-VISTAS site is expected toexperience 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 B ext decreases rise to 3.53, 3.26, and 4.03 Mm 1 for the coastal, mountain, and non-VISTAS categories, respectively.

On the 20% worst visibility days, visibility in 2018improves 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, B ext ranges from

1.63 to

52.7 with an average of

24.3 Mm 1 at coastal sites, from

18.5 to

70.8 with an average of

41.9 Mm 1 at mountain sites, and from

1.72 to

26.8 with an average of

18.9 Mm 1 at non-VISTAS sites. These improvements are largely because of decreases in sulfate and ammonium levels as a result of SO 2 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 levelsand 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 SO 2 reductions. Shenandoah experiences the largest decrease in nitrate levels among all sites.

Under the OTW scenario, all sites experience greaterimprovements in visibility on the 20% worst days. Average B ext rise to

39.7,

81.2, and

35.6 Mm 1 atthe 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 casefor 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, SO 2 , NO x , NH 3 , VOC (both manmade and biogenic), and PC emissions were reduced by30%, from their 2018-OTB levels (Table 2), individually and uniformly over the entire domain. During the summer period, SO 2 emission reduction was the most effective control strategy in reducing PM 2.5 levels because of a large decrease in sulfate concentrations. The SO 2 reduction was also the most beneficial strategy inimproving visibility because sulfate is the largest summertime contributor to light extinction in this region. In winter, NH 3 reduction was the most effective strategy because of large reductions in nitrate and ammonium, followed by NO x 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, NO x controls increased the sulfate and ammonium levels. This was largest at Cohutta, where

B ext from 30% NO x reduction was 3.75 Mm 1 but not sufficient to consume the margin of 5.46 Mm 1 between the typical baseline and 2018-OTW values of B ext .

The contributions of PM 2.5 components in the response of B ext 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% SO 2 reductions amounts to an increase in B ext of 3.34 Mm 1 but, afterconsidering the decrease in sulfate and ammonium levels, the net result is a B ext of

33.1 Mm 1 . The reason

F IG . 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 SO 2 , NO x , NH 3 , 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. 1738 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY V OLUME 46for the nitrate increase is the availability of more ammonium to form ammonium nitrate (instead of ammonium sulfate) in response to SO 2 reductions. Reduction of NO x emissions by 30% reduces all PM 2.5 components and is the second most effective in improving visibility at this site ( B ext  6.82 Mm 1 ). Ammonia reductions primarily decrease nitrate levels and are less effective ( B ext  2.96 Mm 1 ) than the same relativeamount of PC reductions, which lower OC and EC levels ( B ext  4.53 Mm 1 ). Note that VOC reductionsincrease sulfate, nitrate, and ammonium levels and despite decreases in OC levels their net effect is visibilitydegradation. Reductions in VOC emissions lead to increased ozone and other atmospheric oxidants at rural class I areas that are NO x limited. Increased atmospheric oxidants lead to increased sulfate and nitrateformation and higher ammonium sulfate and ammonium nitrate levels.

Figure 4 shows the response of light extinction totier-1 reductions on the 20% worst visibility days. Reducing SO 2 by 30% improves visibility at all sites, most notably at mountain sites. The minimum, maximum, and average B ext decreases are, respectively, 4.80, 21.1, and 11.6 Mm 1 for coastal; 10.6, 47.8, and 28.9 Mm 1 for mountain; and 3.53, 23.0, and 15.3 Mm 1 for nonVISTAS sites. The second largest average B ext decreases result from reducing PC emissions at coastal (3.83 Mm
1 ) and non-VISTAS sites (5.45 Mm
1 ) and NO x emissions at mountain sites (5.85 Mm
1 ). The NH 3 reduction results in the third largest average B ext decrease at coastal and non-VISTAS sites but it is not very effective at mountain sites. As an exception, the largest decrease in B ext is the result of the 30% NH 3 emission reduction at Brigantine, (9.61 Mm
1 ), larger than the effect of PC reduction (7.37 Mm
1 ) and almost three times the effect of SO 2 reduction (3.53 Mm
1 ). Note that anthropogenic VOC reductions do not result in any noticeable change but biogenic VOC reductions slightly increase B ext in most instances.

An additional question was addressed in this tier. The effects of combined reductions in SO 2 , NO x , and NH 3 emissions were compared with the effects of reducing these emissions individually. It was found thatadding individual responses may result in an overestimation of the combined response by about 10%.

In the second tier, SO 2 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. ElevatedVISTAS sources were further broken into three categories: elevated but not electric generating unit (nonEGU), coal-fired power plant (CPP), and other powerplant (OPP). During the summer period, the daily average SO 2 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 (2960Mg). Of the elevated-source portion, 48% are from outside the VISTAS region (8905 Mg). In the VISTAS region, 26% of elevated SO 2 emissions are from non-EGU sources (2540 Mg). Approximately 98% of the EGU SO 2 emissions can be categorized as CPP (7165 Mg).

Because most of the worst visibility days occur duringsummer, with the exception of a few coastal and nonVISTAS class I areas, only the 12

– 28 July period was simulated with the 30% SO 2 emission reductions in thisand 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 suchday 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 onemaking the estimated responses considerably less reliable. At sites with worst visibility days in winter, newweights 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 B ext resulting from tier-2 reductions at class I areas of the domain. Not counting the total elevated emission category, reduction of CPP emissionsof the VISTAS region had the largest impact on visibility at mountain sites: the average decrease in B ext was 16.0 Mm 1 . The largest decrease was observed at Mammoth Cave (30.3 Mm
1 ) and the smallest decreases ocF IG . 4. Response of light extinction to a 30% domainwide decrease in SO 2 , NO x , NH 3 , 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. N OVEMBER 2007 ODMAN ET AL. 1739curred 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 visibilityat Mammoth Cave. Non-VISTAS and VISTAS nonEGU sources almost tied for the second largest impact at mountain sites. The B ext decreases had a slightly larger average for non-VISTAS (4.89 Mm
1 ) but a wider range for non-EGU sources. At coastal sites, the average decreases in B ext were almost identical and about 5.35 Mm 1 for non-VISTAS elevated and VISTAS CPP SO 2 emission reductions. The range was larger for the CPP category. For non-VISTAS sites the range and average of B ext 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 SO 2 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 SO 2 and sulfate by 30%. On average, the response of B ext is a decrease of 3.41 Mm 1 , a value very similar to the average decrease due to VISTAS non-EGU emission reduction (3.46 Mm 1 ). 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 SO 2 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 SO 2 emission reductions from the VISTAS states on the 20% worst visibility days. Negative B ext , plotted upward from the origin, indicates a benefit from the reduction. The impact of reductions from each state are color coded and stackedfor 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 ( B ext  10.1 Mm 1 ) and then from TN ( B ext  7.59 Mm 1 ) and GA ( B ext  6.58 Mm 1 ). As a counterintuitive result, reduction of NC CPP emissions by 30% slightly increased sulfate levels and B ext by 0.20 Mm 1 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 toemission perturbations is smaller than the level of numerical errors in the base model. Other states contributed less than 1.5 Mm 1 to visibility improvement. The net change in B ext (i.e., the result of adding the contributions of all 10 VISTAS states) was

29.1 Mm 1 , thelargest 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 hazeproblem 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 SO 2 controls are expected at southern mountain sites when compared with non-VISTAS, coastal,

T ABLE 3. CPP and non-EGU elevated SO 2 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

F IG . 5. Response of light extinction to the 30% SO 2 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 SO 2 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 V OLUME 46and northern mountain (Shenandoah and Dolly Sods) sites.

The visibility improvements due to non-EGU SO 2 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 SO 2 reductions were, on average, about 36% of the impact of CPP SO 2 reductions, commensurate with the ratio of emissions reduced (35%). Cohutta showed the greatest net benefit with the largest contribution from SC ( B ext  1.95 Mm 1 ) followed by VA ( B ext 

1.79 Mm 1 ) and NC ( B ext  1.37 Mm 1 ). GA ’ s contribution ( B ext  0.76 Mm 1 ) was even less than WV ( B ext  0.98 Mm 1 ). 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/worstvisibility metrics. Because only two episodes, one summer and one winter, were simulated not all key meteorological conditions leading to the best/worst visibility F IG . 6. Response of light extinction to the 30% reduction in SO 2 emissions from (a) CPPsand (b) elevated but non-EGU sources in 10 VISTAS states at coastal, mountain, and nonVISTAS class I areas in the domain. N OVEMBER 2007 ODMAN ET AL. 1741

Fig 6 live 4/Cwere 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, SO 2 controls andincreasing 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 sourcecategory were reduced by 30% and the visibility response was calculated using model estimated PM concentrations at each class I area. First, domainwide reductions of SO 2 , NO x , NH 3 , VOC, and PC emissions were considered. SO 2 reductions showed the largest benefits at the majority of class I areas on the 20% worst visibility days followed by NO x reductions at mountain sites and PC reductions at other sites. NH 3 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 SO 2 reductions was conducted using the summer episode only. This did not change the number of days considered in the annual20% 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 SO 2 emissions were significantly more effective than the 30% ground-level SO 2 reductions. Of these elevated SO 2 sources, coal-fired power plants in the VISTAS regiongenerally 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, theimpact 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 natureof the haze problem and calls for regional control strategies. Quantitative estimates provided here, especially sensitivities to emission reductions from specific statescan 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 byVISTAS under Contract V-2003-12 to the Georgia Institute of Technology.

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– 2868. N OVEMBER 2007 ODMAN ET AL. 1743

## Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern U.S.

M. Talat Odman 1 , Yongtao Hu, Armistead G. Russell School of Civil and Environmental Engi neering, Georgia Institute of Technology

Asude Hanedar Department of Environmental Engineer ing, Istanbul Technical University

James W. Boylan Georgia Department of Natural Resour ces, Environm ental Protection Division

Patricia F. Brewer Visibility Improvement State and Tr ibal Association of the Southeast

1 Corresponding author, 311 Ferst Drive, Atlanta, GA 30332-0512, odman@gatech.eduAbstract A detailed sensitivity analysis was conducte d to quantify the contributions of various emission sources to ozone, fine particulate matter with aerodynamic diameter less than 2.5 μ m (PM 2.5 ), and regional haze in Southeastern United States. Ozone (O 3 ) 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 pr ojections 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 we ll 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 wi nter (November 19 – December 19, 2002), were simulated to capture the seasonal variat ion of the responses. The impacts of SO 2, primary carbon (PC), NH 3 , NO x and VOC emissions reductions were analyzed. Some of these emissions were split into source categories, a nd further into geographic regions to understand the local and regional impacts of emissions reductions on ozone, PM 2.5 , and light extinction. Ammonium sulfate is the largest contributor to summertime PM 2.5 and visibility impairment in the Southeastern US and therefore SO 2 emission reduction is the most beneficial control strategy in reducing summertime PM 2.5 levels and improving visibility in the Southeast. Electric generating utilities (EGU) are the single largest source category for SO 2 in the region. The impacts of reducing EGU and non-EGU point source SO 2 emissions from 10 southeastern states as well as 3 neighboring regions ( portions of central, midwestern and northeastern U.S. that are

2within the modeling domain) were quantified. Co ntrolling PC emissions was found to be an effective local strategy to decrease PM 2.5 levels, especially in winter . Fire-related PC emissions were differentiated from other PC emission sour ces due to potential limitations in controlling these emissions. Finally, reducing NH 3 emissions was considered as an effective wintertime strategy to reduce ammonium nitrate levels and consequently improve visibility. Wintertime NO x emissions reductions were less effective than NH 3 emissions reductions in reducing wintertime ammonium nitrate. PM 2.5 was not responsive to cha nges in anthropogenic VOC. The responses of ozone to NO x emission reductions from ground- level and, separately, from elevated sources of each southeastern state we re 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 NO x controls are generally more beneficial than elevated NO x 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 PM 2.5 to denote particles a nd liquid droplets with sizes smaller than 2.5 μ m) can have a negative impact on human health as well as the environment. In addition to being seen as a reas on for numerous health problems such as asthma,
PM 2.5 is also a major cause of visibility impairment. PM 2.5 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

3atmosphere are coined as “primary” while the ones formed in the atmosphere are referred to as “secondary”. PM 2.5 can travel long distances and aff ect very large geographic areas. Since the enforcements of the Acid Rain Pr ogram, New Source Review and other programs, as ruled according to the US Clean Air Act and its amendments, the SO 2 and NO x emissions (and other criteria pollutants) leading to ozone and PM 2.5 have been considerably reduced nationwide from their peaks in 1970s [ Chestnut and Mills , 2005; U.S.EPA , 2003]. More recen t rules by the US Environmental Protection Agency (EPA), such as the NO x SIP Call finalized in 1998 and the Clean Air Interstate Rule (CAIR) proposed in 2004, have been implemented to further reduce/limit SO 2 and NO x emissions [ Evans, et al. , 2008; Palmer, et al. , 2007; U.S.EPA , 1998]. Benefits from the decline of these emissions we re significant during the past decade in most areas of the eastern US, however were not appr eciable 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 PM 2.5 and O 3 are tightened [ U.S.EPA , 2006, 2008], more urban areas are beco ming 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 th e Southeast (VISTAS) is the Regional Planning Organization set up by EPA to in itiate 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). VI STAS is conducting technical analyses supporting state regulatory plans to improve air qualit y in Southeastern United States.

4This paper reports a detailed sensitivity analysis conducted as part of that study to quantify the contributions of various em ission sources to ozone, PM 2.5 , and regional haze. The responses are calculated by taking the difference between two air quality model simulations: one with basecase 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 th e air quality is the Community Multiscale Air Quality (CMAQ) model (Byun and Ching 1999). Brute force sensitivity analysis is commonly used in the desi gn of control strategies. In a previous study, Odman et al. (2007) used this method to quan tify the impacts of various emission control options in terms of pot ential visibility improvements at Class I national parks and wilderness areas of the VISTAS region. We w ill continue to use the same method here. However, note that a more efficient method for ca lculating several sensitiv ities in a single model run, the decoupled direct method (DDM), is now av ailable for ozone and PM sensitivities in the CMAQ model (Napelenok et al. 200 6; 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 logno rmal modes: the nucleation and accumulation modes for PM 2.5 and a coarse mode for larger particles. PM 2.5 is speciated as sulfate, nitrate, ammonium, elemental carbon (EC), organic carbo n (OC) and soil components while the coarse mode includes sea sa lt 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

5reactions form secondary PM such as sulfate, nitrate, ammonium and or ganic particles. CMAQ’s aerosol module is continuously updated to improve the accuracy of its PM 2.5 predictions (Bhave et al. 2004); the particular ve rsion used here was version 4.4 with secondary organic aerosol (SOA) updates to include SOA formation from ses quiterpenes and isoprene (Morris et al, 2006).

a. Simulation of O 3 and PM 2.5 levels To model the seasonal variation of PM con centrations, a summer (June 1 – July 10, 2002) and a winter (November 19 – December 19, 2002) pe riod were simulated with CMAQ. Inputs to CMAQ were generated using th e Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) for meteorology (Grell et al. 1994) and the Sparse Matrix Operat or 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 th e surface extended to a height of approximately 14 km. The lowest four la yers were 36 meters thick. Earlier evaluations of the effects of the number of la yers and layer thicknesse s showed that this setup is adequate for modeling surface layer concentrations and depos ition 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 (M ACTEC, 2007); Integrated Planni ng Model (IPM) was used to predict emission rates and contro ls at all electric generating units (EGU). Biogenic emissions were assumed to remain constant in the future. In emission reduction simulations, SO 2 emissions were reduced separately for E GU and non-EGU point sources. NO x emissions were reduced separately from ground-level (on-road and non-road mobile sources, area sources, and area fires)

6and elevated (EGUs, non-EGU point sources, and poi nt fires) source cate gories. Primary Carbon (PC) emissions were reduced separately for ground-level, elevated, and fire emissions. NH 3 and VOC emissions were reduced from al l 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 me teorological inputs deve loped for the year 2002 were also used for the 2009 simulations. Init ial 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 c onditions were kept the same in emission reduction runs. The effects of SO 2 and sulfate boundary conditions were investigated in separate sensitivity analyses a nd it was found that reducing SO 2 and sulfate boundary conditions by 30% substantially reduced PM 2.5 , 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 valu es were recorded as responses of O 3 and PM 2.5 concentrations to these reductions. Overall, around 100 emission reduction scenarios were considered. Responses were calculated and visu alized for the entire domain but quantitative analysis focused on ozone and PM 2.5 non-attainment areas. Ozone c oncentrations are routinely monitored by the State and Local Air Monito ring Stations (SLAMS) network at locations throughout the US; results of reduction scenarios we re 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 stat es (Figure 1). Speciated composition of PM 2.5 is monitored by the Speciated Trends Network (S TN) (now called Chemical Speciation Network); results of reduction scenarios were projected for 65 STN sites in the VISTAS states (4 in FL, 6 in

7MS, 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 PM 2.5 concentrations monitored by the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in Class I national parks and wilderness areas

. Responses to emissions reduc tions were projected for 16 IMPROVE monitors in the VISTAS states (9 cate gorized here as mountain sites and 7 as coastal sites) and 6 more in the rest of the domain (F igure 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 concen trations compared to the model estimates for the year 2009 as a result of 30% emissions redu ctions. 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 emi ssion change (i.e., reduction) 2009 30% reduced EEE −=∆
E ∆

. All responses were derived from hourly modele d 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 “summe r” (June 1 – July 10) or “winter” (November 19 – December 19) responses. Pollutant emissions for the 2009 base case are given in Table 1.

8All reductions are 30% of these figures for the pollutant ty pe and source categories listed under each state, three region outside th e 10 VISTAS states distinguis hed as Central (CEN), Midwest (MW) and Northeast (NE), or th e entire domain. The 12-km mode ling domain includes portions, but not the entire areas of the CEN, MW, and NE regional planning or ganizations; emissions from the more distant portions of these regions are included in the boundar y 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 NO x and PC emissions VISTAS states defined specific counties for whic h emissions were reduced (Figure 2), based on expected contributi ons to ozone or PM 2.5 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 NO x emissions and PC emissions and some states included areas in neighboring

states. The emissions that were re duced by 30% are as follows:

• SO 2 from EGU sources in each VISTAS stat e and 3 other regions in the domain,

• SO 2 from non-EGU point sources in each VISTAS state and other regions,

• NO x 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,

• NO x from elevated (EGU plus non-EGU point) s ources in each VISTAS state and other regions,

• Primary Carbon from all ground-level source s 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,

9• Primary Carbon from all fires in each VISTAS state and other regions,

• NH 3 from all sources in VI STAS and other regions,

• VOC from anthropogenic and bioge nic sources in the domain. Light extinction, B ext (Mm -1 ), was calculated as a linear co mbination of specific pollutant concentrations ( μ g m -3 ), component-specific light sca ttering/absorption coefficients (m 2 g -1 ), and a dimensionless relative humidity adjustment factor, f (RH), using the following equation (IMPROVE 2000):
B ext = 3 × f

(RH) × 

### { [SO 4 2- ] + [NO 3 - ] + [NH 4 + ] 

### } + 4 × [OC] + 10 × [EC]

+ 1 × [Soil] + 0.6 × [CM] + B rayleigh (3) Here, [SO 4 2- ], [NO 3 - ], [NH 4 + ], [OC], [EC], [Soil] are the concentrations of PM 2.5 respectively associated with sulfate, nitrate, ammonium, organic carbon, elemental carbon, and soils; [CM] is the concentration of coarse PM and B rayleigh is the Rayleigh scattering (10 Mm -1 ). Note that [NH 4 + ] is set equal to 0.375 × [SO 4 2- ] + 0.29 × [SO 4 2- ] in Equation 3 assuming ammonium completely neutralizes sulfate and nitrate to form SO 4 (NH 4 ) 2 and NO 3 NH 4 (Boylan et al 2006). Site specific monthly values base d 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 re gion. If the results are used to estimate the response to a smaller reduction by sca ling, it should be realized that an error is intr oduced due to the non-linearity of atmospheric response. Extra polation beyond 30% is hi ghly discouraged. In addition, extreme caution should be used in supe rposing the sensitivities to emissions from different geographic areas. Such deductions can be used duri ng the development of control strategies to give an estimate d magnitude and some directional sense as to where to look for

10emission 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 fr om various monitoring networks. In general, modeled ozone shows the best agre ement with observations. Among the PM 2.5 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 car bon 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 uncertainti es 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 PM 2.5 especially in winter. Nitrate constitutes a larger fraction (2-15%) of PM 2.5 in winter than in summer (1-2% of PM 2.5 ) and PM 2.5 responses to wintertime NO x and NH 3 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 PM 2.5 sites (a subset of all correspondi ng monitors in that state, focused on non-attainment areas) we refer to

11this 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 un it mass of each pollutant they can be used in the development of efficient control strategies. Howe ver the extent of the benefits is limited by the amount of controllable emissions given in Tabl e 1. For example, as we will see below, PM 2.5 sensitivities to PC emissions ar e much larger than those to SO 2 emissions. But controlling 30% (6 Mg) of ground-level PC in GA (s ensitivity to which is 19.4 ng m -3 Mg -1 ) in summer amounts to an average PM 2.5 decrease of only 0.12 μ g m -3 in GA while controlling 30% (285 Mg) of EGU
SO 2 emissions in GA (sensitivity to which is 0.75 ng m -3 Mg -1 ) results in an average PM 2.5 decrease of 0.21 μ g 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 NH 3 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 sensi tivities are not. Similarly, for emissions reduced in non-attainment areas only (ground-level NO x and ground level PC) it is more meaningful to calculate an average over the s ites in non-attainment areas shown in Figure 2. However this is

12not done here therefore special care should be ex ercised when comparing state-mean sensitivities to ground-level NO x and ground level PC reductions with th e state-mean sensit ivities to other emission types.

a. O 3 responses Since wintertime ozone is usually not an issue, only summertime responses will be discussed here. State mean ozone responses to 30% emi ssions reductions are larger for elevated NO x than ground-level NO x , except in MS, GA and TN (Figures 3a and 3b) where larger geographic areas of these states were included in the ground-level NO x reduction (Figure 2). In these states and in NC and VA maximum responses to ground-level NO x emissions are larger than maximum responses to elevated NO x . The largest respons e to ground-level NO x reductions is an O 3 decrease of 5.0 ppb in Jackson MS and Atlant a GA (Figure S-20). The largest response to elevated NO x reductions is -3.5 ppb in Aiken SC and Helena AL (Figure S-22). The largest contribution to the state-mean O 3 response is generally from the ground-level NO x 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 infl uences of MW and NE emissions, O 3 responses are smaller in KY, WV and VA compared to southern VISTAS states where the ozone forming potentials are larger. Responses to elevated NO x emissions have a slightly le ss localized pattern (Figure 4b). With the exception of FL, AL, SC and WV the se lf contributions are sm aller than those for ground-level NO x . Responses to a 30% domain-wide anthropogenic VOC reduction are much smaller compared to responses to NO x reductions (Figure 3c). They generally result in an O 3 increase, more so in southern VISTAS states than KY, WV and VA (Figure S-24). On the other hand, a 30% domain-

13wide biogenic VOC decrease, while infeasible, co uld significantly reduce ozone, especially in northern VISTAS states where these respons es can even exceed the responses to NO x reductions. b. O 3 sensitivities Since NO x emission reductions were applied to individual state emissions, NO x sensitivities are averaged over each state’s select ed ozone sites (a subset of a ll 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 aver aged over all the sites in the domain. In the VISTAS region, state-mean summer time ozone sensitivities to ground-level NO x emissions are larger than those to elevated NO x , except in FL where O 3 is more sensitive to GA’s NO x emissions (both ground-level and elevated) than FL’s own. In other st ates, the sensitivity to their own ground-level emissions range between 7.0 (VA) and 85 ppt Mg -1 (AL). State-mean O 3 in KY is more sensitive to ground-level NO x emissions from WV than those from KY. The sensitivities to elevated NO x emissions of their own state are always larger than the sensitivities to elevated NO x emissions of neighboring states and range between 6.0 (VA) and 19 ppt Mg -1 (AL). Note that these state-mean sens itivities to elevated and ground level NO x are 2-3 orders of magnitude larger than the domain-mean sensitiv ities to VOC emissions. When the comparison is made to sensitivities calculated by using total NO x emissions reductions from the entire VISTAS region, and averaged over all the VISTAS site s, VOC sensitivities are still very small. The sensitivity of ozone to domain wide VOC ar e larger in winter but wintertime ozone is not of interest here. In summer, the sensitivit y 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.

14c. PM 2.5 responses Responses to fire PC reductions (Figures S7-S 8) are excluded from the following discussion because there is no distinction be tween prescribed and natural fire s here; therefore the control of fire emissions is not trivial. In summer, responses to 30% emissi ons reductions are largest for EGU SO 2 emissions (Figure 5) with a maximum decrease of 1.12 μ g 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 10 90 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 th e same latitudes (note that, in Figure 5, the states are ordered from west to east starti ng from the very south and moving north). The decreasing influence of MW reductions can expl ain the southward decrease but the easterly decrease is more complicated. Note the eastern neighboring state contri butions 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 contribu tions 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 μ g 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 em ission 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 SO 2 reductions, except in FL where th e mean response to ground-level PC reductions are larger than responses to non-EGU SO 2 . The next largest responses are to ground-

15level PC reductions followed by those to elevat ed PC reductions, except in AL where the order switches. Responses to elevated and ground-level NO x reductions are smalle r than responses to SO 2 and PC. Responses to non-EGU SO 2 reductions display a geographic pattern very similar to the responses to EGU SO 2 reductions, i.e. decreasing from north to south and from west to east (Figure 5d). The self contri butions in some states are larger compared to EGU SO 2 (AL, SC, TN, VA) while they are smaller in others (GA, KY) (Figure 6d). The impacts of ground-level PC emissions are highly localized (Figur e 6a) and there is no clear geographic pattern of the responses (Figur e 5a). The state-mean responses range between -0.27 (KY) and -0.11 μ g m -3 (SC). The largest reduction is -0.69 μ g 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 PM 2.5 decreases. An increase from east to west is observed in re sponses 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 SO 2 , are impacted by larger southern or northern contributions compared to their eastern nei ghbors. Summertime responses to elevated and ground level NO x emissions are similar in magnitude a nd pattern (Figures 5e and 5f) and are smaller than responses to PC and SO 2 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 SO 2 is larger in AL and the mean response to elevated NO x is larger in SC. This reflects the fact that pr imary carbon emissions increase in winter while sulfate formation from SO 2 emissions is less efficient and ammonium nitrate formation is favored at colder

16temperatures. Individual sites may have very diff erent orderings (Figures S-3, S-5, S-9, S-11, S-13, and S-15). The maximum response to grou nd-level PC is once again in Arlington Co. VA (Figure S-9) where PM 2.5 decreases by 1.61 μ g m -3 due 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 μ g

m -3 . The state-mean responses range between -0.43 (KY) and -0.16 μ g 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 contri bution is much larger th an other contributions (Figure 8a) indicating th e highly localized nature of the responses. In order of decreasing state- mean wintertime responses, ground-level PC reductions are generally followed by EGU SO 2 , elevated NO x , ground-level NO x , elevated PC and non-EGU SO 2 reductions, but there are many exceptions. The maximum response to 30% EGU SO 2 reductions is a decrease of 0.32 μ g m -3 in Athens GA (Figure S-13) . The mean responses are largest in FL and GA (both -0.26 μ g m -3 ) and they decrease in northern VISTAS states (Figure 7c). This pattern is the opposite of the southward d ecrease observed in summer. The contribution of the CEN region becomes significant even in ea sternmost states (Figure 8c) suggesting strong westerly flow. The c ontributions of EGU SO 2 emissions from the MW region are comparable to their summertime values but due to colder temperat ures they don’t form as much sulfate as they did in summer therefore wintertime PM 2.5 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 NO x reductions, it should be remembered that the model overestimated wintertime nitrate by 50 to 100%. Ther efore, these responses are most likely also subject to large uncertainties. Wintertime responses to elevated NO x reductions show much

17larger variability than responses to EGU SO 2 and state-maximum responses are larger than those to EGU SO 2 reductions, except in FL (Fi gure 7f). Recall that ammonium nitrate is a much larger fraction of the total PM 2.5 in the winter than in the summer episode. The largest response to 30% elevated NO x reductions is -0.35 μ g m -3 in Athens GA (Figure S-5). The self contributions of the states are much smaller than what they are for EGU SO 2 reductions (Figure 8f). Responses to ground-level NO x emissions reductions are similar to responses to elevated NO x both in magnitude and geographic pattern (Figure 7e). Once again the larg est response is in Athens GA (-0.29 μ g 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 PM 2.5 decrease of 0.27
μ g 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 cont ributions are not as large as in the case of ground-level PC reductions but much larger wh en compared to other emission reductions. Responses to non-EGU SO 2 are largest in FL and smallest in WV following a clear trend of decrease northward and eastward (Figure 7d). Note that the c ontributions of the CEN region decreases in the same directions (Figure 8d). The EGU SO 2 contributions from the MW region dominate in KY and WV but the relatively small PM 2.5 responses in those states show that MW SO 2 emissions do not have as much sulfate forming potential as they did in summer. Wintertime PM 2.5 levels can be reduced by 0.75 μ g m -3 on average in VISTAS states by reducing NH 3 emissions from those states by 30% or by 350 Mg day -1 . When NH 3 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 μ g m -3 (SC) (Figure 9b). The maximum response to 30% NH 3 reductions is a PM 2.5 decrease of 1.69 μ g m -3 in Columbia SC (Figure S-1). The contribution of

18the VISTAS states is larger than 72% in AL , GA and SC where the responses are the largest (Figure 9d). Summertime NH 3 emissions are almost twice as large as in wintertime but the responses are much smaller in summer (Figure 9a). This is be cause ammonium nitrate is a very small fraction of PM 2.5 in summer. The largest response is a PM 2.5 decrease of 0.52 μ g m -3 at Hancock, WV (Figure S-2). The contribution of the MW regi on dominates in KY and WV where the responses are the largest (Figure 9c). d. PM 2.5 sensitivities In an attempt to rank the NH 3 sensitivities among sens itivities to other t ypes 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 re gion are smaller than those for individual states for the same class of emissions. This is simply b ecause of the larger cont ribution of other states to total emission reductions than to total re sponses. The states are generally the largest contributor to the PM 2.5 response of their own sites and when they are not, the percent contribution of other states to the response is usually smaller than thei r contributi on to total emissions. On average, sensitivity to NH 3 ranked second after sensitivity to ground-level PC in winter. In summer, the sensitivity to NH 3 was larger than the sensit ivities to ground and elevated NO x emissions. Summertime sensitivities to NH 3 emissions are small: the average sensitivity at VISTAS sites to VISTAS NH 3 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 NH 3 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.

19Similarly, to rank the VOC sensitivities, we normalized all the responses with total emissions reductions over the domain. The sensitivity to bi ogenic VOCs was larger than the sensitivities to NO x in summer and sensitivities to NO x and SO 2 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 individua l VISTAS states in this study, PM 2.5 sensitivity is generally largest to ground-level PC (PC-G) followed by elevated PC (PC-E), non-EGU SO 2 , EGU SO 2 , ground-level NO x (NO x -G) and elevated NO x (NO x -E) both in summer and in winter. However, there are several exceptions to this order. In summer, the sensitivity to EGU SO 2 is larger than the sensitivity to non-EGU SO 2 (Table 2) in NC. In MS, TN, KY and VA the sensitivity to elevated NO x is larger than the se nsitivity to ground-level NO x . In winter, SO 2 and NO x sensitivities are of similar magnitudes and thei r ranking varies from state to state (Table 3).Sensitivities to fire PC are difficult to compar e 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 sens itivity to its own emissions but this is not necessarily the largest sensitivity in that state fo r that class of emissions. As a result of the proximity of the sites in one st ate 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 NO x emissions from WV than to those from KY and the mean sensitivity in FL is larger to AL and GA’s elevated NO x emissions than to FL’s. In winter, sensitivities to

20emissions 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 SO 2 in NC (TN); 2) EGU SO 2 in SC (GA) and KY (TN); 3) Ground-level NO x in FL (AL and GA) and GA (AL and SC); and 4) Elevated NO x 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 NO x 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 NO x emissions.

e. B ext responses The mountain Class I areas experience their wo rst visibility days in summer. Because SO 4 dominates visibility impairment, the largest summertime B ext responses are to 30% EGU SO 2 reductions both at mountain and coastal s ites, followed by responses to non-EGU SO 2 reductions (Figures 10a and 10b). The responses to other emission reductions are insignificant. The maximum responses to EGU SO 2 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 ma ximum response to non-EGU SO 2 emissions among mountain sites is al so in Mammoth Cave KY (-3.4 Mm -1 ) but in Okefenokee GA (-2.0 Mm -1 ) among coastal sites. In Everglades FL the respons es to both types of SO 2 reductions are almost zero because SO 4 has a smaller contribution to haze at Everglades. Some coastal Class I areas experience their wo rst visibility days in winter. Wintertime responses are generally largest for 30% NH 3 reductions with a ma ximum decrease in B ext of 12.6
Mm -1 in Mammoth Cave KY and 9.4 Mm -1 in Cape Romain SC. The impacts of SO 2 reductions are much smaller in winter. The maximum response to EGU SO 2 is at Chassahowitzka FL (-3.3

21Mm 1). The responses to NO x reductions become larger then wh at they were in summer but they are small compared to responses to NH 3 reductions.



### Conclusions The CMAQ model was used to estimate th e responses and sensitivities (responses normalized by emissions reduced) of ozone and PM 2.5 levels, as well as B ext calculated from
PM 2.5 components, to SO 2 , NO x , NH 3 , VOC, and PC emissions in Southeastern U.S. for the year 2009. The analysis focused on poten tial non-attainment areas of the region and the findings have direct implications to the identificat ion of region-wide control strategies. The sensitivity of ozone to VOC emissions is 2-3 orders of magnit ude smaller than the sensitivities to elev ated and ground level NO x therefore VOC controls would be much less effective than NO x controls for reducing ozone levels in southeastern U.S. Although sensitivities to ground-level NO x emissions from non-attainment counties are larger, state-wide reductions of elevated NO x emissions are generally more benefici al strategies for summertime ozone. Reducing one state’s ground-level NO x emissions generally produces the largest effect in that state. Elevated NO x reductions have a less localized and more regional effect. Responses are larger in southern VISTAS states where ozone forming potentials are great er. It is possible to achieve as much as 3.5 to 5 ppb decreases in 2009 ozone levels by reducing ground-level or elevated NO x emissions by 30% throughout the VISTAS region. In the VISTAS states, sensitivity of PM 2.5 is generally largest to PC emissions (sensitivity to ground-level sources being larger than elevated sources), followed by SO 2 emissions (sensitivity to non-EGU being larger than EGU emissions), and NO x emissions (sensitivity to ground-level being larger than elevated sour ces). There are several exceptions to this rule depending on the

22season 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 PM 2.5 reductions. Since PC emissions are limited but there is ample SO 2 emissions that can be controlled, responses to 30% SO 2 reductions are generally larger than responses to 30% PC reductions. In this respect, SO 2 reductions are ultimately more “beneficial” in summer. As much as a 1.12 μ g m -3 decrease in 2009 PM 2.5 levels can be achieved by reducing EGU SO 2 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-attainme nt areas of the region is the reduction of non-EGU SO 2 emissions, followed by ground-level and elevated PC controls , respectively. The impacts of PC reductions are highly localized; as much as a 0.69 μ g m -3 decrease in PM 2.5 levels can be expected. The impacts of elevated and ground-level NO x controls are generally smaller. In winter, ground-level PC controls are generally more bene ficial than EGU SO 2 controls.
PM 2.5 levels can be decreased by as much as 1.61 μ g m -3 by reducing the ground-level PC emissions by 30%. The maximum response to 30% EGU SO 2 reductions is a PM 2.5 decrease of
0.32 μ g m -3 . Colder temperatures in northern VISTAS states limit secondary sulfate formation and the potential benefits of SO 2 emissions reductions. It is possibl e to achieve greater impacts in some states by reducing elevated NO x emissions than by reducing EGU SO 2 emissions (however note that the uncertainties in sensitivities to NO x are larger than those to SO 2 ). Another interesting result that was not show n here is the contribution of SO 2 emissions from outside the modeling domain. This contributi on was emulated by reducing the SO 2 and sulfate boundary conditions. It was found that continental and/or intercontinental transport of SO 2 emissions can

23have impacts comparable to or, in some cases gr eater than the impacts from local sources of SO 2 or NO x or even PC. When PM 2.5 responses are normalized by total emissi ons reductions over the VISTAS region, sensitivity to NH 3 emissions ranks second after sensitivit y to ground-level PC in winter and is larger than sensitivities to ground and elevated NO x emissions in summer. In southeastern U.S., ammonium nitrate (NO 3 NH 4 ) levels are generally limited by the availability of NH 3 , so reducing
NH 3 emissions can be a more effective strategy to reduce NO 3 NH 4 than reducing NO x emissions. Wintertime PM 2.5 levels can be reduced by as much as 0.75 μ g m -3 on average in VISTAS states by reducing NH 3 emissions from those states by 30%. VOC controls would have no significant impact on PM 2.5 levels in this region. At the mountain Class I areas, the greatest visi bility benefits would likely be from further EGU SO 2 reductions within the VISTAS states. At th e costal Class I areas, reducing ammonia emissions would be more benefici al for reducing ammonium nitrat e contributions to visibility impairment in wintertime than further reduci ng 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 strate gy for reducing regional haze in winter. Controlling manmade VOC emissions would have little if any visi bility 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 co ntributor 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.

24Quantitative estimates provided here, especially sensitivities to emission reductions from specific states can be used as guidance in the de sign of emission control strategies. However, the effectiveness of a control strategy should always be ch ecked with simulations.

Acknowledgements This research was supported by the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) under c ontract number V-2003-12 to Geor gia Institute of Technology.

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same state. State PC-G PC-E SO 2 -nonEGU SO 2 -EGU NO x -G NO x -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 SO 2 -nonEGU SO 2 -EGU NO x -G NO x -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 0.10Figure 1. Modeling domain, 10 VISTAS states and lo cations of ozone (blue), PM (red) and combined ozone and PM (black) monitors, and Class I areas (green).(a)

(b) Figure 2. The extent of ground-level emission reductions: (a) NO x and (b) primary carbon.

30(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 O 3 (ppb) in each southeastern st ate to 30 % reduction of (a) ground-level NO x , (b) elevated NO x , (c) anthropogenic VOC, and (d) biogenic VOC, emissions. Diamonds denote the average over non-attainment areas while bars mark the range.

31(a) (b)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution by State or Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution 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 O 3 responses in each southeastern state (Figure 3 ) to 30 % reduction of (a) ground-level NO x , and (b) elevated NO x emissions. Contributions le ss than 5% are not shown.

32(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∆PM2.5(μg/m3) -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∆PM2.5(μg/m3)

(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∆PM2.5(μg/m3) -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∆PM2.5(μg/m3)

(e) (f)

-0.20

-0.16

-0.12

-0.08

-0.04

0.00 FL MS AL GA SC TN NC KY WV VA∆PM2.5(μg/m3) -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∆PM2.5(μg/m3)

Figure 5. Response of summertime PM 2.5 ( μ g m -3 ) in each southeastern state to 30 % reduction of (a) ground-level PC, (b ) elevated PC, (c) EGU SO 2 , (d) non-EGU SO 2 , (e) ground-level NO x , and (f) elevated NO x emissions. Diamonds denote the aver age over non-attainment areas while bars mark the range.

33(a) (b)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution by State or Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution 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 VAContribution by State or Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution 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 VAContribution by State or Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution 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 PM 2.5 responses in each southeastern state (Figure 5 ) to 30 % reduction of (a) ground-lev el PC, (b) elevated PC, (c) EGU SO 2 , (d) non-EGU SO 2 , (e) ground-level NO x , and (f) elevated NO x emissions. Contributions less than 5% are not shown.

34(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∆PM2.5(μg/m3) -0.30

-0.25

-0.20

-0.15
-0.10

-0.05

0.00 FL MS AL GA SC TN NC KY WV VA∆PM2.5(μg/m3)

(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∆PM2.5(μg/m3) -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∆PM2.5(μg/m3)

(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∆PM2.5(μg/m3) -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∆PM2.5(μg/m3)

Figure 7. Response of wintertime PM 2.5 ( μ g m -3 ) in each southeastern st ate to 30 % reduction of (a) ground-level PC, (b) elevated PC, (c) EGU SO 2 , (d) non-EGU SO 2 , (e) ground-level NO x , and (f) elevated NO x emissions. Diamonds denote the aver age over non-attainment areas while bars mark the range.

35(a) (b)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution by State or Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution 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 VAContribution by State or Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution by State or Region

(e) (f)

-20%

0% 20% 40% 60% 80% 100%

FL MS AL GA SC TN NC KY WV VAContribution by State or Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution 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 PM 2.5 responses in each southeastern state (Figure 7 ) to 30 % reduction of (a) ground-lev el PC, (b) elevated PC, (c) EGU SO 2 , (d) non-EGU SO 2 , (e) ground-level NO x , and (f) elevated NO x emissions. Contributions less than 5% are not shown.

36(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∆PM2.5(μg/m3) -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∆PM2.5(μg/m3)

(c) (d)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution by Region

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

FL MS AL GA SC TN NC KY WV VAContribution by Region

VIS CEN MW NE Figure 9. Responses of summertime PM 2.5 ( μ g m -3 ) in each VISTAS stat e to 30 % reduction of NH 3 emissions (a) in summer and (b) in winter (d iamonds denote the mean while bars mark the range), and contributions (%) by region to mean responses (c) in summer and (d) in winter.

37(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- N O

x N H 3 A - VO

C 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-

S O2 n - SO

2 G - N O

x E-NOx NH3 A-V O C 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-

S O2 n - S O2 G - N O

x E- N O

x NH 3 A-V O C G -P

C E-PC F-P C∆Bext(Mm-1) -10.0
-8.0
-6.0
-4.0
-2.0
0.0
2.0

E

- S O 2 n-SO2 G- N Ox E - NO

x NH3 A - V O C G -PC E - PC F- P C∆Bext(Mm-1)

Figure 10. Responses of B ext (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 co astal sites. Diamonds denote the mean while bars mark the range.

38

## Technical Report



## Impact of NO x 

## 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 Engi neering, Georgia Institute of Technology

Prepared for: Patricia Brewer Visibility Improvement State and Tr ibal Association of the Southeast and James W. Boylan Georgia Department of Natural Resour ces, Environm ental Protection Division

September 30, 2008Abstract A detailed sensitivity analysis was conducted to quantify the impact of NO x emission reductions to fine particulate matter (PM 2.5 ) levels in Southeastern United States. Changes in
PM 2.5 concentrations were estimated using the Community Multiscale Air Quality (CMAQ) model. First, the base case was established us ing the emission projections for the year 2009. Then, NO x emissions from six southeas tern states (AL, GA, KY, TN, VA, WV) were reduced by 100 % and the responses of PM 2.5 concentrations were calculated by taking the difference from the base case. To investigate the non-linear ity of the sensitivit y, 70% reduction of NO x 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 NO x 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 prio r modeling over the continental U.S. An entire year was simulated using the meteorology of the year 2002 to capture the a nnual variation of the sensitivities. The analysis focuses on PM 2.5 non-attainment areas of the VISTAS region. Reducing total NO x emissions by 100% from individual VIST AS states resulted in a decrease of the PM 2.5 design values between 0.1 and 1.60 μ g m -3 . When 70% NO x reduction in AL and GA was compared to 100% reductions it wa s found that the relationship between PM 2.5 and NO x emissions is highly non-linear for severely reduced NO x emissions. The last 30% reduction results in more than 60% of the full benefits. 100% re duction of mobile NO x emissions from non-attainment counties in WV and KY di d not have a significant impact on PM 2.5 design values. Additional reductions in the ra nge of 70 to 100% of all NO x emissions are not currently feasible. Given the non-linearity of the respons es and how quickly they diminish going from

2100% to 70% reductions, the response of PM 2.5 to feasible NO x controls would be most likely insignificant in the southeastern U.S. Quantitative estimates provided here can be used as guidance in the design of emissi on control strategies. However, the effectiveness of a control strategy should always be checked with simulations.



### Introduction Fine particulate matter (often referred to as PM 2.5 to denote particle sizes smaller than 2.5
μ m) is a mixture of suspended solid particle s and liquid droplets a nd can impact both human health and the environment. In addition to bei ng seen as a reason for nu merous health problems such as asthma, PM 2.5 is also a major cause of visibility impairment. PM 2.5 originates from many sources such as fossil fuel combustion, bioma ss burning, vegetative emissions, sea salt, windblown dust, and fugitive dust (e.g., road dust, co nstruction, grinding, and agriculture), or it is formed in the atmosphere through chemical transf ormation of precursor gase s. It can travel long distances and affect very large geographic areas. Reduction of nitrogen oxides (NO x ) emissions generally results in a decrease of PM 2.5 nitrate concentrations. However, in some cases where ox idant formation is sensitive to volatile organic compounds (VOC), nitrate concentrations may actua lly increase, for example if the increase in oxidant concentrations exceeds the decrease in nitrogen dioxide (NO 2 ) concentrations (Pun et al. 2008; Lane et al. 2008). The effect of NO x reductions on PM 2.5 is not limited to nitrate. NO x (and VOC) emissions are precursors to many oxidants such as O 3 and OH. These oxidants are involved in the formation of numerous condensable products, therefore they can potentially affect all secondary PM 2.5 species. Different VOC and NO x chemical regimes lead to different amounts of volatile products re lative to condensable products (e.g., organic nitrates vs. HNO 3 ).

3As a result, PM 2.5 response to NO x emission reductions can be highl y nonlinear and even in the undesired direction. Pun et al. 2008 investigated the response of PM 2.5 concentrations to various precursor emissions over the entire U.S., including the sout heast, However, this investigation was limited to a 10-day summer episode. PM 2.5 responses to NO x emissions over the southeastern U.S. have not been systematically investigated for an enti re year. The national ambient air quality standards for PM 2.5 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 a nd state emissions control programs are reducing emissions of NO x , sulfur dioxide (SO 2 ), and primary PM 2.5 ; further reductions will likely be needed in some urban areas to a ttain the standard. The objective of this study was to evaluate the benefits of additional NO x controls to annual average PM 2.5 for attainment planning purposes. Visibility Improvement – State and Tribal Association of th e Southeast (VISTAS) is the Regional Planning Organization set up by EPA to in itiate 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 impl ementation planning with the goal of lowering PM 2.5 levels and improving visibility in Southeastern United Stat es. This paper reports a detailed sensitivity analysis conducted as part of that study to quantif y the contributions of NO x emissions to PM 2.5 levels throughout the region. In the analysis here, the sensitivities are ca lculated by taking the di fference between two air quality model simulations: one w ith base case and anot her with reduced emission inputs. This method is known as the “brute-force” method (Hwa ng et al. 1997). The model used to simulate

4the 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 PM 2.5 levels in urban areas and potential visibility improvements at cla ss I national parks and wilderness areas of the VISTAS region. A more efficient method for calcula ting several sensitivities in a single model run, the decoupled direct method (DDM), is now available for ozone and PM 2.5 sensitivities in the CMAQ model (Napelenok et al. 2006; Napelenok et al. 2008 but it has not been used here.



### Method

a. Simulation of PM 2.5 levels To model the annual variation of PM 2.5 concentrations, an entire year (2002) was simulated with CMAQ. The aerosol module of CMAQ is desi gned to estimate size resolved and speciated
PM 2.5 concentrations (Binkowski and Rose lle 2003). In this module, the PM 2.5 size distribution is represented by the superposition of three logno rmal modes: the nucleation and accumulation modes for PM 2.5 and a coarse mode for larger particles. As for speciation, PM 2.5 is broken into sulfate, nitrate, ammonium, el emental carbon (EC), organic car bon (OC) and crustal components while the coarse mode includes sea salt and du st. The dynamical processe s considered in the CMAQ aerosol module include primary emissions, new particle formation by nucleation, particle growth, coagulation, evaporation and condensa tion, dry deposition, and scavenging by clouds. Chemical reactions form secondary PM 2.5 such as sulfate, nitrate, ammonium and organic particles. CMAQ’s aerosol module is continuous ly updated to improve the accuracy of its PM 2.5

5predictions (Bhave et al. 2004); the particular version used he re was version 4.5 with VISTASmodified secondary organic aerosol (SOA) module (Mo rris et al. 2006). Inputs to CMAQ were generated using the Fi fth-Generation NCAR/P enn State Mesoscale Model (MM5) for meteorology (Grell et al. 1994) and the Sparse Matrix Operator Kernel Emissions (SMOKE) model for emissions (CEM PD 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. Th e 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 genera ting units (EGU). Biogenic emissions were assumed to remain constant in the future. Manmade NO x emissions that were reduced in sensitivity runs include ground level and elevated source categories. Gro und-level sources includ e on-road and non-road mobile sources, area sources and area fires. Elev ated 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 me teorological inputs deve loped for the year 2002 were also used for the 2009 simulations. Init ial 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 PM 2.5 concentrations from the base case were recorded. In

6each simulation, NO x emissions from a particular southeas tern state were reduced by either 70 % or 100% (i.e., zeroing out of NO x emissions) from its base-case value. While a statewide zeroing out of NO x emissions is impossible to accomplish in reality, it represents the maximum conceptual effect of NOx emissions on PM 2.5 . 70% NO x reduction statewide is a dramatic scenario which allows for some O 3 formation and oxidation proce sses. In addition, to study the impact of mobile emissions, the on-road mobile NO x emissions from the PM 2.5 non-attainment areas of KY and WV were reduced by 100%. Over all, 10 annual emission reduction simulations were conducted. Responses were calc ulated and visualized for the en tire domain but quantitative analysis focused on responses at PM 2.5 non-attainment areas.

b. Estimation of future PM 2.5 concentrations and responses to emission reductions The modeled concentrations were used in a relati ve rather than absolute fashion to reduce the effects of modeling erro rs. 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 future C observed current C

modeled current C :

modeled future modeled current observed current future C C C C ×= (1)
Responses were then calculated by taking the difference between s caled future concentrations (i.e., reduced emissions cases minus 2009 base ca se). Taking the absolute difference of the modeled future concentrations would be suscep tible to modeling errors. For example, if the currently observed nitrate concentration is ze ro but the model predicts a non-zero nitrate concentration and a future reduction then scal ing would set the respons e to zero. Absolute response on the other hand would suggest a negative n itrate concentration which is impossible

7and 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 current modeled current modeled future future C C C C C ×=×= RRF (2)
The observations needed for scaling were obt ained from the Speciated Trends Network (STN). STN routinely mon itors the composition of PM 2.5 at urban areas of the region shown in Figure 1. The design value (DV) at e ach STN site is defined as the 98 th percentile of the daily average concentration of PM 2.5 , averaged over three consecutive years. DVs were available from 2000 to 2004 therefore DVs for the current year ( 2002), or DVCs, were calcu lated as a weighted average of 5 years. As recommended by the US EP A (2007), scaling was app lied separately to each PM 2.5 species, namely sulfates, nitrat es, EC, OC, and crustal matter (ammonium and water were treated in a special way). First, quarter ly averaged concentrations were calculated from modeled hourlyaveraged concentrations of PM 2.5 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 curre nt concentrati on (for 2002). 

## [ ] [ ] Q current Q future Q Comp Comp Comp RRF = (3)
A future quarterly design value for each com ponent was calculated from the quarterly current value of the design value for that component as follows: (4) Q Comp Q Comp Q Comp DVC RRF DVF ×= Here, the component design value is calculated from the quarterly PM 2.5 design value using the component fractions from the observed data as

8

## ( ) Q Q Comp DVC Comp fraction 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 asso ciated with sulfate and nitrate 1 . 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 ammonium DVF DVF DON DVF ×+ ×= 29. 0 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 desi gn values of PBW were calculated from the quarterly future design values of sulfat e, nitrate and ammonium as follows:
0.980314 0.260011- 0.000784--0.002618 Q ammonium Q nitrate Q sulfate Q PBW DVF DVF DVF DVF × + × × = 

# ( ) ( ) ( ) 0.159452- 0.153894 0.048352- 2 2 2 Q ammonium Q nitrate Q sulfate DVF DVF DVF × × + ×

Q ammonium Q nitrate Q ammonium Q sulfate Q nitrate Q sulfate DVF DVF DVF DVF DVF DVF

×× ×× +×× + 0.356957- 0.212891 0.044366 (7)
Total organic matter (OC) was split into its primary and secondary (polymerized, biogenic and anthropogenic) components. Since no observa tions are available for the components of OC, the modeled current year fractions were used

9

## [ ] [ ] Q OC Q current Q current Q compOC DVC OC compOC DVC × = _

_ (8)
and the quarterly future design values of OC components were calculated as 

## [ ] [ ] [ ] [ ] [ ] [ ] Q OC Q current Q future Q OC Q current Q current Q current Q future Q compOC Q compOC Q compOC

DVC OC compOC DVC OC compOC compOC compOC DVC RRF DVF

× = × × = × =

_ _

_ _

_ _ _ (9)

This assures that the sum of the future OC com ponent design values is equal to the future OC design values. The characterization of PM 2.5 components used here aims to provide a complete mass balance. It does not have any unknown mass whic h is sometimes presented as the difference between measured PM 2.5 mass and the characterized chemical components derived from routine speciation measurements. EPA r ecommended methods are expected to yield more accurate assessments of future PM 2.5 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 va lues were summed to get the future PM 2.5 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% NO x reductions, to get the response of the design value. A nega tive response implies a decrease and a positive response implies an increas e of the design value. The results presented here are most accurate fo r the actual emission re duction used (100% or 70%) of the specified emission category (total NO x or on-road mobile NO x ) 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 NH 4 NO 3 is approximately 29% of the mass of nitrate.

10due 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 simu lations 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 observation s from various monitoring networks, including FRM, IMPROVE, and STN monitors. Among the PM 2.5 components, sulfate ag rees the best with observations: its bias is within ±20% both in su mmer and in winter. Nitrate concentrations are overestimated by 50 to 100% in winter and orga nic carbon is underestimated by 80 to 100% in summer (Morris et al 2005; Tesche et al. 2006). Here, the final m odeling setup used by Morris et al. (2007, 2008) was used in all simulations. A second set of simulations of the same episode s were conducted using the “typical” baseline emissions. The difference in these emissions fr om the actual case is that the EGU and fire emissions were averaged over a longer time pe riod 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 current C In the following discussion the sensitivities to statewide emission reductions are averaged over each state’s PM 2.5 (FRM and STN) sites (31 in AL, 29 in GA, 28 in TN, 28 in KY, 17 in WV, and 21 in VA). The sensitiv ities to on-road mobile NO x emissions from the PM 2.5 non-

11attainment areas of KY and WV are averagedover the FRM and STN sites in those nonattainment areas only (27 in KY and 17 in WV n on-attainment areas). Site specific sensitivities can be found at http://vistas.ce.gatech.edu/xcel/2009_NOx_zero-outs 2

.

a. PM 2.5 simulated for the year 2009 and comparison to 2002 Domain-wide SO 2 , NO x , NH 3 , PC and VOC emissions for 20 09 are compared to their 2002 (2000-2004 average) levels in Table 1. Th e largest relative reductions are in SO 2 emissions (30%). The second largest re lative reduction is in NO x 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 ch anges is highly non-uniform: the magnitude as well as the direction of the change varies from one place to another. SO 2 and NO x emissions reductions are most notably along the Ohio River Valley. While NO x emissions increases are rare SO 2 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 PM 2.5 range from 17.06 to 8.40
μ g m -3 at FRM and STN sites in the modeling domain (Figure 1). Wh en the design values of the year 2009 are compared to 2002 (Figure 2), it is f ound that design values d ecrease over time but their distributions over state st ations remain almost the same. The largest state-mean PM 2.5 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 obs erved 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 exampl e, 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_Sensitiv ities spreadsheet are for site number 01-073 -0023. For a different site enter the site index in cell E1 (e.g., 7 for site 01-0 69-0002) and press enter. The charts w ill be automatically updated for that site.

12values. These PM 2.5 reductions are primarily due to reductions in SO 4 ; with small reductions in OC, EC, and NO 3 . b. Sensitivity of PM 2.5 to NO x emission reductions The 2009 emissions were taken as the base case for the sensitivity analysis. First, total NO x emissions in six southeastern states (AL, GA, KY, TN, VA, and WV) we re reduced individually by 100%, from their 2009 levels (Table 2). Next, 70% reduction of total NO x emissions were considered for two states, AL and GA, to investigate the non-linearity of the sensitivity. Finally, on-road mobile NO x emissions in the non-attainment ar eas of KY and WV (Table 2) were reduced individually by 100% to in vestigate the sensitivity to m obile emissions. These on-road mobile NO x reductions represent 8.5% and 6.6 % of total NO x emissions in 2009 in KY and WV, respectively. In each case, comparison of PM 2.5 concentrations to the base case leads to the desired sensitivities. In Figure 3, PM 2.5 design values are given for 2009 base case, and the NO x 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 reducti ons in all states. When PM 2.5 concentrations are compared between base values and 100% reduction of NO x 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 PM 2.5 concentrations for 100% reduction of NO x emissions stayed around half of th ese values in the other states. 

### Non-linearity of Sensitivity It can be seen from Figure 3 that there is an important non-linearit y in sensitivity. For example, when comparing the 2009 base PM 2.5 values to the NO x emission reduced values for

13AL, PM 2.5 concentration for 100% NO x decreases by 0.83 μ g/m 3 but this value is only 0.38

μ g/m 3 for 70% NO x reduction case for the mean values. Note that there are larger differences betw een 100% and 70% reduction cases in GA. Total
PM 2.5 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 NO x . The annual average response quickly reduces to -0.6 μg m -3 for 70% reduction of the GA NO x . Figure 4 shows the decreases of PM 2.5 in each state in response to statewide reductions of NO x emissions. It can be clearly seen from this figure that there were large differences in PM 2.5 values between 100% and 70% NO x emission reductions for AL a nd GA. The distribution bar for base-100% formed only one box for KY. Lower lin e of this box is the median value and upper line is 75 th percentile value in this case. Median and 25 th percentile values are equal in this distribution. This means that the decreases have almost the same va lue in most of sampling sites. Figure 5 shows the distributi on of the decreases in PM 2.5 at non-attainment areas of KY and WV in response to reductions of on-road mobile NO x emissions from those areas. The mean value of the decrease is almost zero in both KY and WV. In these two states, on-road NO x emissions are less than 10% of the total NO x emissions and the response of PM 2.5 to on-road NO x emission reductions is very small. The minimum value of PM 2.5 decrease is -0.1 μg m -3 for KY and there are no percentile boxes on the distribution bar. This mean s that the decreases were zero at most sampling sites and there is an increase in PM 2.5 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 PM 2.5 values at some of the sites.

14

### Comparison to annual PM 2.5 

### standard
Table 3 lists the sites whose 2009 base case design values exceed the annual PM 2.5 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 whic h are expected to exceed the standard in the 2009 base case. At one of these site s (Jefferson Co. 2; 15.6 μg m -3 ), the only scenario that reduces the design value below the annual standard is the 100% NO x 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 PM 2.5 standard for the 2009 base case. The 100% and 70% NO x reductions in GA reduce the design va lue 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% NO x 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% NO x reduction in WV reduces the design value below the standard at one of these sites (Brooke Co.) but not at the othe r site (Hancock Co.). It can be seen from Table 3 that PM 2.5 in a state decreases not only by NO x reductions in that state but by NO x reductions in adjacent states as well. For example, 100% NO x reductions in AL, TN and KY decrease PM 2.5 below the annual standard at sites in GA (Clayton Co.), KY (Jefferson Co.) and WV (Brooke Co.), respectivel y. Although Ohio (OH) is not included in the NO x reduction areas; the design values at some OH sites are lowered below the annual PM 2.5 standard by 100% NO x reductions in KY and TN.

15NO x reductions are projected to reduce PM 2.5 but are not sufficient to bring all areas into attainment. Continued atte ntion to controls of SO 2 and PM 2.5 are also warranted.



### PM 2.5 

### fractions In Table 4, state-mean PM 2.5 fraction design values are given for all NO x reduction cases. It can be seen from this table that crustal and elem ental carbon fractions do not change at all. OC fractions of PM 2.5 have the biggest response to NO x reductions and more th an half of the total response occurs in OC fraction for all states. Responses of PM 2.5 fractions, in decreasing order, are as follows: OC, SO 4 , NO 3 , NH 4 , and PBW. The largest response is observe d for GA 100% reduction NO x 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% NO x reductions. The smallest respons e in OC fraction among 100% re duction 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 NO x 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% NO x 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 NH 4 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 PM 2.5 fractions: 0.03 μg m -3 or less for all states for 100% NO x reductions. Figure 6 shows the responses of seasonal and annual PM 2.5 fractions in all states to all reduction cases of NO x . Negative ∆ PM 2.5 , plotted upward from the or igin indicates a decrease

16while positive ∆ PM 2.5 represents an increase. As expected, the maximum decreases were achieved for the 100% NO x reduction cases. WV has the sm allest response among all of the states for all PM 2.5 fractions. OC fractions of PM 2.5 display the largest responses for all states. The OC fraction of PM 2.5 has been further split into four sub-fractions as primary organic matter (POM), polymerized secondary organi c matter (PSOM), biogeni c 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 PM 2.5 fractions to NO x reductions are given in Table 5. Since there is no cha nge in the POM fraction only SOMA, SOMB and PSOM responses are tabulated. The largest responses are always obs erved in the SOMB fraction. The maximum SOMB response is -1.14 μg m -3 in Q3 (-0.73 μg m -3 annually) to 100% NO x reduction in GA. Figure 7 shows the seasonal an d annual responses of OC fractions to 100% NO x reductions in TN. The seasonal distribution of the SOMB respons e in Figure 7 is typical for 100% NO x reduction cases in all the states as well as the 70% NO x 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% NO x reduction in KY are notably smaller than responses in AL, GA, and TN, despite KY having larger NO x emissions in 2009 than TN and AL. This may be because of relatively cool er temperatures in KY that w ould suppress the formation of secondary organic aerosols. SO MB annual response to 100% NO x reduction is twice as large as

17the response to 70% NO x reduction in AL. In GA, the response to 100% NO x reduction is 3 times the response to 70% NO x reduction. These proportions clearl y show the high non-linearity in the SOMB response to NO x reductions. In general, the second largest re sponse 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% NO x 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 re sponse is -0.03 μg m -3 in Q2 (-0.02 μg m -3 annually) to 100% NO x 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 NO x 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 simila r level of decrease in OC (about 0.45 μg m -3 ) for a July 1999 episode as a resu lt of 50% reduction of NO x emissions. Accompanied by decreases in sulfate and nitrate, thei r prediction of average PM 2.5 decrease in the southeastern US is approximately 1.5 μg m -3

. Odman et al. (2008) present responses to 30% NO x reductions from elevated sources (electric generating utilities, or EGU, and non- EGU point sources) statewide for each VISTAS state as well as 30% NO x 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

18state-mean responses in PM 2.5 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 si milar to the one used here, performed simulations with 10, 30, 50, 70, 90, and 100% reductions of all NO x emissions in the 20-county Atlanta nonattainment area from their 2009 base case levels. The 10, 30, and 50% NO x reductions resulted in little to no annual average response in PM 2.5 . The nonlinearity of the response is evident in Figure 8 which shows noticeable decreases in PM 2.5 after 70% reduction, reaching an annual average of -0.5 μg m -3 at 100% reduction level, primarily du e to a large OC response. The statemean PM 2.5 response for GA to 100% NO x reduction predicted here is about -1.0 μg m -3 and Atlanta sites are generally in upper percentil es 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 con ducted with emission projections, PM 2.5 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 SO 2 , NH 3 and PC controls may be effective in reducing PM 2.5 levels. Here, the focus is placed on NO x emission reductions and the responses of
PM 2.5 design values at FRM and STN sites that are likely to remain in non-attainment. Using 2009 emission projections as the base case, NO x emission reductions from various southeastern states were simula ted using the CMAQ model. Future design values were calculated by scaling the predicted concentrations with current design values for each PM 2.5 component. The difference between the PM 2.5 design values of a reduced emission case and the base case

19gives the PM 2.5 response to that emission reduction. Zeroing out (100% reduction) of total NO x emissions from individual states resulted in a decrease of the PM 2.5 design values between 0.1 and 1.60 μ g m -3 at FRM and STN sites in the modeling dom ain. This scenario may be impossible to accomplish in practice but it represents the maximum theoretical effect of NO x emissions on
PM 2.5 . When a 70% NO x reduction scenario was tried in AL and GA and compared to 100% reduction it was found that the relationship between PM 2.5 and NO x emissions is highly nonlinear for severely reduced NO x emissions. Reducing the last 30% of the emissions generates more than 60% of the full benefi ts. 100% reduction of mobile NO x emissions from nonattainment counties in WV did not have a significant impact on PM 2.5 design values (less than
0.10 μ g m -3 decrease) and, in KY, it actually increased them (as much as 0.10 μ g m -3 ). The largest response to NO x reductions among PM 2.5 components is observed in OC. In southern VISTAS states (AL, GA and TN) OC re sponse is about 75% of the total response while it decreases to 50% in northern VISTAS stat es (KY, VA and WV). The OC component was further split into primary a nd 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 res ponses 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 st ates where they are almost zero. The highly non-linear relation between PM 2.5 and NO x emissions suggests that reductions below the 70% level investigated here would resu lt in responses in a much smaller scale. Thus moderate NO x controls may only reduce PM 2.5 levels slightly in southeastern U.S. and reductions of other types of emissions should be c onsidered as well fo r attainment of PM 2.5 standards.

20Quantitative estimates provided here can be used as guidance in the design of emission control strategies. However, the effectiveness of a c ontrol strategy should alwa ys be checked with simulations. The results here may have been affected by model uncertainty. Most of the PM 2.5 response to NO x reductions may be coming from over pr ediction of secondary organic carbon, which may respond much more modestly under normally observed levels. Additional investigation of the role of NO x in secondary organic aerosol formation is warranted.

Acknowledgements This research was supported by th e Visibility Improvement Stat e and Tribal Association of the Southeast (VISTAS) under c ontract number V-2003-12 to Geor gia Institute of Technology.

21References 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 NO x to PM 2.5 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 fo r 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 Mode ls 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 adv ection schemes in air quality models Atmos. Environ. , 31 879-888.

22MACTEC, 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. Pandi s, 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 evalua tion and model sensitiv ity 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 sensitiv ity 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.

23Napelenok, S. L., D. S. Cohan, M. T. Odman, and S. Tonse, 2008: Exte nsion and evaluation of sensitivity analysis capabilitie s 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. Ha nedar, J. W. Boylan, and P. Brewer, 2008: Quantifying the sources of smog and regi onal haze in the S outheastern U.S., in preparation

. Olerud D.; Sims, A., 2003: MM5 Sensitivity Mode ling in Support of VISTAS. Draft Report for VISTAS, 112 pp. Pun, B. K., C. Seigneur, E. M. Bailey, L. L. Gaut ney, S. G. Douglas, J. L. Haney, and N. Kumar, 2008: Response of atmospheric particulate ma tter 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. Mc Nally, J. Boylan, P. Brewer, 2006: CMAQ/CAMx annual 2002 performance eval uation over the eastern US. Atmos. Environ ., 40 , 4906– 4919.

24Figure Captions Figure 1. Modeling domain, 10 VISTAS states and locations of STN sites. Figure 2. Comparison of PM 2.5 design value distributions for the years 2002 and 2009 ( ◊ : average value; lower line is 25 th percentile, upper line is me dian; upper line is 75 th percentile; in error bars, upper cap is max value, lower cap is min value) Figure 3. Distribution of design values fo r 2009 base case and 100% and 70% NO x reductions in AL. GA, KY, TN, VA and WV. Figure 4. Decreases of PM 2.5 (μg m -3 ) in each state in response to reductions of NO x emissions Figure 5. Decreases of PM 2.5 (μg m -3 ) at non-attainment areas of KY and WV in response to onroad mobile NO X reductions. Figure 6. Responses of seasonal and annual PM 2.5 fractions (μg m -3 ) in all states to all reduction cases of NO x Figure 7. Response of seasonal and annual organic PM 2.5 fractions (μg m -3 ) in Tennessee to 100% reduction of NO x

25Figure 1. Modeling domain, 10 VISTAS states, and locations of Federal Reference Method (FRM) and Speciated Tre nd Network (STN) sites.

26Figure 2. Comparison of PM 2.5 design value distributions in AL. GA, KY, TN, VA and WV for the years 2002 and 2009 ( ◊ : average value; lower line is 25 th percentile, upper line is median; upper line is 75 th percentile; in error bars, upper cap is max value, lower cap is min value)

6 8 10 12 14 16 18PM2.5Concentration(μg/m3)

Figure 3. Distribution of design values fo r 2009 base case and 100% and 70% NO x reductions in AL. GA, KY, TN, VA and WV.

270.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

A L ( B A SE

‐ 100% ) AL( B ASE ‐ 70%) GA(BASE ‐ 100%) GA (B

A SE

‐ 70

%) KY(B A SE

‐ 1 00%) TN(BASE ‐ 1 00% ) VA(B A SE

‐ 1 00

% ) WV(B A SE

‐ 10

0%)-∆PM2..5Concentration (μg m-3)

Figure 4. Decreases of PM 2.5 (μg m -3 ) in each state in response to statewide reductions of NO x emissions.

‐ 0.2 0 0.2

KY(BASE

‐ onroa d ) WV(BASE

‐ on roa d )-∆PM2..5Concentration (μg m-3)

Figure 5. Decreases of PM 2.5 (μg m -3 ) at non-attainment areas of KY and WV in response to onroad mobile NO X reductions.

28‐ 1.30

‐ 1.10

‐ 0.90

‐ 0.70

‐ 0.50

‐ 0.30

‐ 0.10
0.10100%‐Base70%‐Base100%‐Base70%‐Base100%‐BaseOnroad‐Base100%‐Base100%‐Base100%‐BaseOnroad‐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 PM 2.5 fractions (μg m -3 ) in all states to all reduction cases of NO x

‐ 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 PM 2.5 fractions (μg m -3 ) in Tennessee to 100% reduction of NO x
29

### -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/m3) 

### Soils EC OC H2O NO3 NH4 SO4

Figure 8. Annual average PM 2.5 responses to 10, 30, 50, 70, 90 and 100% NO x emissions reductions from 2009 base case in 20 counties of th e Atlanta nonattainment area (Source: Boylan 2008).

3031 Table 1. Domain-wide annual emissions (Gg) for 2002 and 2009.
SO 2 NO X NH 3 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 NO x 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.Table 3.2009 design values exceedingthe annual standard (15μg m-3) and how they change with emission reductions.ALGAKYTNVAWVStateCounty2009 DV100 %NOx70%NOx100 %NOx70%NOx100%NOx100%NOx100%NOx100%NOxJefferson. 117.115.7(↓)16.6(↓)16.9(↓)16.9(↓)17.117.0(↓)17.0(↓)17.0(↓)ALJefferson 215.614.215.0(↓)15.5(↓)15.5(↓)15.615.5(↓)15.615.6Clayton15.114.915.0(↓)13.814.715.115.115.115.1GAFulton16.416.2 (↓)16.3 (↓)15.0(↓)16.1(↓)16.416.3(↓)16.416.4KYJefferson15.015.015.015.015.014.414.915.015.0Hamilton 115.815.815.815.815.815.4(↓)15.815.815.8Hamilton 215.215.215.215.215.214.815.215.215.2Hamilton 315.415.415.415.415.415.0(↓)15.415.415.4OHScioto15.015.015.015.015.014.614.915.014.8Brooke15.015.015.015.015.014.915.015.014.6WVHancock15.615.615.615.615.615.615.615.615.3(↓)32(↓) indicates a decrease but the design value remains above the limit.Boldindicates a decrease below the limit value33 Table 4. PM 2.5 fractions of the state-mean design values for 2009 base and NO x 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 2009(100%) 0.87 0.58 4.28 3.79 0.15 1.06 0.95 AL 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 2009(100%) 0.73 0.55 4.92 3.69 0.12 0.99 0.87 GA 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 2009(100%) 0.60 0.49 3.78 3.70 0.65 1.35 1.14 KY 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 2009(100%) 0.65 0.48 4.53 3.60 0.66 1.25 1.03 WV 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 emissionsTable 5.Seasonal and annual changes in OC fractions ofthe state-mean design values for statewide NOxreduction cases (μg m-3)Q1Q2Q3Q4AnnualStateReductionSOMASOMBPSOMSOMASOMBPSOMSOMASOMBPSOMSOMASOMBPSOMSOMASOMBPSOM100% AL NOx-0.02-0.27-0.02-0.01-0.81-0.05-0.01-0.89-0.04-0.01-0.36-0.03-0.01-0.58-0.04AL70% AL NOx0.00-0.04-0.01-0.01-0.42-0.03-0.01-0.50-0.030.00-0.08-0.010.00-0.26-0.02100% GA NOx-0.02-0.34-0.03-0.03-1.07-0.07-0.02-1.14-0.06-0.01-0.37-0.03-0.02-0.73-0.05GA70% GA NOx0.01-0.02-0.01-0.01-0.43-0.03-0.01-0.51-0.030.01-0.020.000.00-0.24-0.02KY100% KY NOx0.010.000.00-0.01-0.27-0.02-0.02-0.45-0.030.010.010.000.00-0.18-0.01TN100% TN NOx0.00-0.050.00-0.02-0.47-0.03-0.02-0.64-0.040.00-0.10-0.01-0.01-0.31-0.02VA100% VA NOx0.010.000.00-0.02-0.35-0.02-0.01-0.44-0.030.00-0.020.00-0.01-0.20-0.01WV100% WV NOx0.01-0.020.00-0.01-0.43-0.03-0.01-0.45-0.040.01-0.010.000.00-0.23-0.0234