Appendix N 

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Appendix N 

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Grid Resolution Journal Article Appendix N i 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone April 5, 2007 

Pre-Hearing Draft –North Carolina Attainment Demonstration 
Atmospheric Environment 40 (2006) 5010–5026

A quantitative assessment of the influence of grid resolution on

predictions of future-year air quality in North Carolina, USA

Saravanan Arunachalama,, Andrew Hollanda, Bebhinn Dob,

Michael Abraczinskasb

a

Carolina Environmental Program, University of North Carolina at Chapel Hill, Bank of America Plaza, CB #6116, 137 E. Franklin St.,

Chapel Hill, NC 27599-6116, USA b

Division of Air Quality, North Carolina Department of Environment and Natural Resources, 1641 Mail Service Center,

Raleigh, NC 27699-1641, USA

Received 14 February 2005; received in revised form 5 January 2006; accepted 5 January 2006

Abstract

Increased focus has been directed at fine-scale modeling for improving the ability of air quality modeling systems to

capture local phenomena. While numerous studies have investigated model performance at finer resolution (4–5 km), there

is relatively limited information available for choosing the optimum grid resolution for predicting future air quality in

attainment demonstration studies. We demonstrate an evaluation of the MM5–SMOKE–MAQSIP modeling system for

four 8-h ozone episodes in the summers of 1995, 1996 and 1997 in North Carolina using a one-way nested 36/12/4-km

application. After establishing acceptable base-case model performance for ozone predictions during each episode, we

developed future-year emissions control scenarios for 2007 and 2012, and finally computed relative reduction factors

(RRFs) using model outputs from each of the three grid resolutions. Our analyses, based upon qualitative as well as

quantitative approaches like the Student’s t-test, indicate that RRFs computed at specific monitoring locations—and hence

predicted future-year air quality—are not very different between the 4- and 12-km results, while the differences are slightly

larger between the 4- and 36-km results. The results imply that grid resolution contributes to a variability of about 1–3 ppb

in the projected future-year design values; this variability needs to be incorporated into policy-relevant decision-making.

Since this assessment was performed for four different episodes under diverse meteorological, physical and chemical

regimes, one can generalize the results from this study. They are also relevant for regional modeling applications that are

currently ongoing for studying PM2.5 nonattainment issues, where the need for annual base-year and future-year

simulations for demonstrating attainment may place a large demand on computing resources. Based upon the results from

this study, future studies may consider using results from 12-km modeling to address future-year air quality goals for ozone

and PM2.5 and its components, and then incorporate grid-resolution uncertainties into the computed results.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Emissions strategies; CMAQ; Student’s t-test; O3 NAAQS; Attainment

1. Introduction

In 1997, the US Environmental Protection

Agency (US EPA) revised the National Ambient

ARTICLE IN PRESS

www.elsevier.com/locate/atmosenv

1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.atmosenv.2006.01.024

Corresponding author. Tel.: +1 919 966 2126;

fax: +1 919 843 3113.

E-mail address: sarav@email.unc.edu (S. Arunachalam).

Grid Resolution Journal Article 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone

Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 1 

April 5, 2007
Air Quality Standard (NAAQS) for ozone from a

1-h-based standard of 0.120 ppm to 0.080 ppm

measured over an 8-h period; this is called the 8-h

O3 standard (Federal Register, 1997). The new US

NAAQS is met at an ambient air quality monitoring

site when the 3-year average of the annual fourthhighest daily maximum 8-h average O3 concentration is p0.080 ppm (i.e., the site is said to be in

attainment). This revised NAAQS for O3 is more

stringent than the 1-h O3 standard and more

protective of public health. Based upon 8-h O3

measurements from 2001 to 2003, the US EPA

designated a total of 474 counties in the US as not

attaining the 8-h O3 standard in 2004 (Federal

Register, 2004). In North Carolina alone, 32

counties were designated nonattainment. The Clean

Air Act Amendments of 1990 established selected

comprehensive, three-dimensional (3-D) photochemical air quality simulation models as the required

regulatory tools for analyzing the urban and

regional problem of high ambient O3 levels across

the US (US EPA, 1991). These models are currently

applied to study and establish strategies for meeting

the NAAQS for O3 nonattainment areas, and State

Implementation Plans resulting from these efforts

must be submitted to the US EPA. The US EPA has

developed a draft modeling guidance document (US

EPA, 1999) for demonstrating attainment of the

NAAQS for 8-h O3. In this document, the US EPA

recommends a methodology for applying air quality

models to generate predictions that indicate whether

a given set of emissions controls is likely to

demonstrate attainment in the future. This involves

establishing acceptable base-case model performance for the area of interest, then using the same

system to model future-year emissions reductions

strategies to test for attainment.

With this background, the use of grid-based

photochemical models has expanded dramatically

to include spatial scales varying from urban to

regional. Users selecting a modeling system for a

given application need to fully understand the

structural and algorithmic differences between

models and of their possible impacts on both

operational and scientific inferences that may be

drawn from the models’ respective simulations

(Russell and Dennis, 2000). These differences could

significantly affect the development of emissions

control strategies for attaining the O3 NAAQS.

Sillman et al. (1990) suggest that degrading the

model horizontal resolution can cause a systematic

positive bias in O3 simulation, since the artificial

dilution of NOx emissions over relatively large grid

volumes can increase the O3 production efficiency

per unit of NOx oxidized (Liu et al., 1987). Similar

positive bias in predicted O3 resulting from degradation of horizontal grid resolution has been

reported in simulations over southern California

by Kumar et al. (1994). Jang et al. (1995a, b) showed

that grid resolution significantly influences both

rates of chemistry and vertical transport processes,

and that coarser grids tend to underpredict ozone

maxima over major source areas. Through boxmodel calculations of O3 production efficiency for a

variety of conditions, Liang and Jacobson (2000)

suggest that coarsely resolved models might underpredict or overpredict O3 production because they

are unable to accurately represent the blending of

air masses of different origins. Recently, an operational assessment of the application of the draft

guidance in demonstrating attainment of the O3

NAAQS has shown that one needs to account for

model-to-model uncertainty in the attainment demonstration (Sistla et al., 2004). The authors

demonstrate that the choice of modeling system

may bring an area into attainment or not, depending upon the estimated response of the chosen

modeling system. Here, we present a case that the

choice of grid resolution also needs to be judiciously

made, as the results from our future-year modeling

show different sites being in attainment or nonattainment of the O3 NAAQS depending upon the

chosen model grid resolution.

2. Modeling systems

In the modeling performed to study O3 nonattainment issues in North Carolina, we identified

four ozone episodes that occurred during the

summers of 1995, 1996 and 1997 and developed

base-case applications using the MM5–SMOKE–

MAQSIP modeling system. An iterative procedure

was adopted to refine the base-case model performance for each episode; each time, the overall

modeling system was improved by either changing

the model configuration or by refining the meteorological and/or emissions inputs. Once acceptable

base cases were developed per the US EPA guidance

(US EPA, 1991, 1999; Russell and Dennis, 2000), we

performed numerous emissions sensitivity simulations to develop future-year 2007 and 2012 attainment scenarios. We modeled a total of 16 episode

days spanning the three summer periods: 12–15 July

1995, 21–24 and 27–30 June 1996 and 12–15 July

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Grid Resolution Journal Article 

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Appendix N 2 

April 5, 2007
1997. These episodes were chosen based upon

extensive analyses of historical episodes in North

Carolina (STI, 1998); they represented typical

meteorological conditions for the region, and

included high 8-h ozone episodes closer to current

ozone design values. For each episode, we performed simulations from 2 days prior to the actual

episode start date, to provide ramp-up of the initial

and boundary conditions (obtained from climatological data). The modeling domain (Fig. 1a) used

has a nested system of 36/12/4-km grids centered

over North Carolina. Fig. 1b shows the zoomed-in

4-km modeling domain and the location of the air

quality monitors that are the focus of this study.

The various models used are described below.

2.1. Mesoscale meteorological model

The meteorological inputs for this study were

derived from the Fifth-Generation Penn State/

ARTICLE IN PRESS

Fig. 1. (a) Nested air quality modeling domain (36-, 12- and 4-km grids) and (b) O3 monitors in the 4-km grid (state boundaries shown in

thicker/blue lines).

5012 S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026

Grid Resolution Journal Article 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone

Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 3 

April 5, 2007
NCAR mesoscale model (MM5) (Grell et al., 1994).

We used MM5 V1, V2.12 and V3.3 for the 1995,

1996 and 1997 episodes, respectively, since these

were the latest versions that were available when we

developed the base cases. We used one-way nesting

in MM5, and the choices of cloud scheme and

planetary boundary layer (PBL) scheme used for

each grid resolution are shown in Table 1. We

employed observational nudging of winds (using

surface reports, profilers and soundings when

available) at 12- and 4-km resolutions, and we used

grid nudging at 108/36/12-km resolutions. An

iterative procedure was applied to each episode to

generate the best meteorological inputs possible. We

changed either the PBL scheme and/or the cloud

parameterization scheme during these iterations.

The meteorological model performance was evaluated each time to measure improvements, by

looking at the model’s capability to predict key

meteorological variables such as wind speed, wind

direction, PBL heights, temperature and moisture

(CEP, 2005).

The model was vertically resolved into 26 layers,

and the top of the domain extends up to 100 mb. No

interpolations were necessary from MM5 to the

Multiscale Air Quality Simulation Platform (MAQSIP) model in the horizontal or vertical because

the models used the same coordinate systems and

grid resolutions. MAQSIP-ready meteorological

inputs were derived using the meteorology-coupler

(MCPL) (Coats et al., 1998). MCPL is a drop-in

MM5 output module designed for coupling MM5

to other environmental models, using the Models-3

Input/Output Applications Programming Interface

(Coats, 2005).

2.2. Sparse matrix operator kernel emissions

(SMOKE) modeling system

Emissions inputs were developed using the

SMOKE (Houyoux and Vukovich, 1999; Houyoux

et al., 2000) processing system. We used the beta

version of SMOKE V2.0 for all emissions modeling

presented here. Speciation was performed using the

carbon bond mechanism-IV (CBM-IV) (Gery et al.,

1989). Nonroad emissions estimates were obtained

using the US EPA’s NONROAD model. Biogenic

emissions were processed using an implementation

of the Biogenic Emissions Inventory System-3

(Vukovich and Pierce, 2002) within SMOKE. All

episode-specific emissions inputs to the model for

each of the four periods studied were continuously

refined during development of the base-case model

applications. Once acceptable model performance

was established for the four episodes, we developed

current-year emissions inputs (2000) and future-year

emissions inputs (2007 and 2012). Emissions control

strategies modeled for 2007 and 2012 include several

Federal and State-specific control measures already

in place or being implemented over the next few

years for each state in the modeling domain. These

measures will significantly reduce emissions from

point, on-road mobile and nonroad mobile sources

in North Carolina and neighboring states. Details of

the actual emissions controls strategies modeled for

the future years are discussed in NCDENR (2004).

Fig. 2 shows changes in NOx emissions in North

Carolina by source category from 2000 to 2007 to

2012 for a typical episode day in 1996. Since the

volatile organic compound (VOC) emissions estimates are dominated by biogenic sources and do not

change much in the future years, only 2000 baseyear estimates for a typical 1996 episode day are

shown in Fig. 2.

2.3. Multiscale air quality simulation platform

MAQSIP (Mathur et al., 2004) is a comprehensive urban- to intercontinental-scale atmospheric

chemistry-transport model, developed in collaboration with the US EPA; the model also served as a

prototype for the US EPA’s community multiscale

air quality (CMAQ) modeling system (Dennis et al.,

1996; Byun and Ching, 1999). The MM5 model is

used to provide meteorological inputs to MAQSIP.

In its current form, MAQSIP has been applied to

study issues related to tropospheric O3, acidic

substances, and aerosol formation and pollutant

ARTICLE IN PRESS

Table 1

Cloud scheme and PBL scheme used in MM5 for each episode/

grid resolution

Episode

year

36 km 12 km 4 km

Cloud

Scheme

1995 KainFritschKainFritschExplicit

1996 KainFritschKainFritschExplicit

1997 Grell Grell Explicit

PBL

Scheme

1995 Blackadar GaynoSeamanGaynoSeaman

1996 Blackadar Blackadar Blackadar

1997 Blackadar Blackadar Blackadar

S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026 5013

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Appendix N 4 

April 5, 2007
distribution on urban, regional and intercontinental

scales for various geographic areas of the world.

While MAQSIP can emulate many existing atmospheric chemistry-transport models, its modular and

flexible structure facilitates incorporation of new,

improved process representations and algorithms.

This capability allows the system to be used as a

comprehensive test-bed environment for exploring

different process and algorithmic representations in

situ in a 3-D modeling framework with other

interacting physical and chemical phenomena. Its

modularity also facilitates adaptation of the model

to address a broad range of atmospheric chemistry

and transport problems. MAQSIP is formulated

with a generalized coordinate system to better

interface the chemistry-transport calculations with

various meteorological models. The formulation of

MAQSIP supports multiple nesting of grids for

efficient resolution of smaller-scale phenomena,

and allows for one-way or two-way interaction

between disparate spatial scales. Thus, the model

can be adapted to a variety of spatial domains

ranging from urban to interregional with flexible

grid resolution; simulations to date have used 4 to

108-km grid resolutions. The model also has the

flexibility to use different chemical mechanisms for

representing gas-phase chemistry or for incorporating explicit chemical schemes. We modeled only gasphase chemistry for this study, and used CBM-IV

with updated isoprene chemistry (Carter and

Atkinson, 1996). The effects of cloud transport on

the vertical distribution of trace species in the

atmosphere are represented depending on the scale

of model application and on the scheme used in

MM5 (Mathur et al., 2004). The modular design of

MAQSIP permits a number of choices in the

algorithm used for each modeled process. For this

study, we modeled horizontal and vertical advection

using the Bott scheme, horizontal diffusion using a

constant Kh, and vertical diffusion based upon

K-theory. We used one-way nesting in MAQSIP for

all the episodes, and did not use plume-in-grid

technique for any of the three resolutions. Several of

these modules have been evaluated in detail during

the course of the model development, and for

subsequent other model applications; descriptions

and results of the evaluations can be found

separately (Kasibhatla et al., 1997; Kasibhatla and

Chameides, 2000; Hogrefe et al., 2001; Fiore et al.,

2003; Mathur and Dennis, 2003; Mathur et al.,

1998, 2004, 2005).

2.4. Model performance evaluation

The US EPA has developed guidance documents

for photochemical model performance evaluation

(US EPA, 1991, 1999) that suggest specific tests

and comparisons, recommend graphical methods

for use in interpreting and displaying results, and

identify potential issues or problems that may arise.

We performed extensive evaluation of model

performance for each of the episodes using observed

data from the US EPA’s aerometric information

retrieval system (AIRS) at these sites. The list of

ozone monitors is shown in Table 2. As a result

of the iterative procedures adopted during the

base-case development phase, we were able to

achieve acceptable model performance for ozone

on all 16 episode days. Fig. 3 shows daily mean

normalized bias (acceptable performance is when

ARTICLE IN PRESS

2000 NOx Emissions (2,280 Tons/day)

0

200

400

600

800

1000

1200

1400

Point Onroad Mobile Nonroad

Mobile

Area

Source Category

NOx Emissions (Tons/day)

2000

2007

2012

2000 VOC Emissions (14,028 Tons/day)

3%

2%

2%

3%

90%

Area Point Nonroad Mobile Onroad Mobile Biogenics

(a) (b)

Fig. 2. NOx (left) and VOC emissions (right) for a typical 2000 day in North Carolina.

5014 S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026

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Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 5 

April 5, 2007
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Table 2

O3 monitoring sites in North Carolina and DVCs (shown in ppm)

Region AIRS ID ID Site name Site in

4 km

Ht

(m)

DVCa DVCb Max(DVC)

Charlotte 37-119-1009 CTYL County Line Yes 216 0.101 0.098 0.101

37-159-0021 ROCK Rockwell Yes 240 0.098 0.100 0.100

37-159-0022 ENVL Enochville Yes 270 0.099 0.099 0.099

37-119-0041 PLZA Garinger (Plaza) Yes 239 0.098 0.096 0.098

37-109-0004 CRSE Crouse Yes 270 0.091 0.092 0.092

37-119-1005 ARWD Arrowood Yes 195 0.092 0.084 0.092

37-179-0003 MONR Monroe Yes 200 0.087 0.088 0.088

Triangle (Raleigh–Durham–Chapel Hill) 37-183-0014 MLBK Millbrook Yes 100 0.094 0.092 0.094

37-077-0001 BTNR Butner Yes 91 0.092 0.094 0.094

37-183-0015 STAG St. Augustine Yes 127 0.093 0.091 0.093

37-145-0003 BSHF Bushy Fork Yes 198 0.089 0.091 0.091

37-069-0001 FRKL Franklinton Yes 135 0.086 0.090 0.090

37-063-0013 DUKE Duke St. Yes 390 0.087 0.089 0.089

37-183-0017 WRAL Tower Yes 320 0.088 0.085 0.088

37-183-0016 FUQV Fuquay-Varina Yes 117 0.086 0.088 0.088

37-101-0002 WJOH W. Johnston Yes 127 0.087 0.085 0.087

37-037-0004 PITT Pittsboro Yes 400 0.081 0.082 0.082

Triad (Greensboro–Winston–

Salem–High Point)

37-059-0002 DAVI Cooleemee Yes 219 0.096 0.093 0.096

37-067-0022 HTAV Hattie Ave. Yes 287 0.094 0.093 0.094

37-067-1008 UCRS Union Cross Yes 241 0.093 0.089 0.093

37-157-0099 BETH Bethany Yes 277 0.085 0.091 0.091

37-033-0001 CHGR Cherry Grove Yes 285 0.090 0.088 0.090

37-081-0011 MLVL McLeansville Yes 229 0.090 0.089 0.090

37-067-0028 SHIL Shiloh Church Yes 294 0.089 0.088 0.089

37-151-0004 SOPH Sophia Yes 250 0.085 0.085 0.085

37-067-0027 POLL Pollirosa Yes 271 0.082 0.082 0.082

Asheville 37-199-0003 MTMI Mt. Mitchell Yes 1982 0.089 0.083 0.089

37-087-0035 FRYP Fry Pan Yes 1585 0.087 0.082 0.087

37-087-0036 PKNO Purchase Knob No 1494 0.087 0.085 0.087

37-099-0005 BKNO Barnet Knob No 1433 0.085 0.084 0.085

37-021-0030 BENT Bent Creek Yes 675 0.083 0.078 0.083

37-087-0004 WAYN Waynesville Yes 802 0.080 0.079 0.080

Greenville, Rocky Mount and

Wilson (Down East)

37-065-0099 LEGT Leggett Yes 18 0.087 0.089 0.089

37-147-0099 FARM Farmville Yes 26 0.084 0.082 0.084

37-107-0004 KINS L. College No 29 0.082 0.081 0.082

37-061-0002 KVIL Kenansville Yes 34 0.082 0.079 0.082

37-117-0001 JVIL Jamesville No 6 0.080 0.081 0.081

Fayetteville 37-051-0008 WADE Wade Yes 43 0.088 0.086 0.088

37-051-1003 HOPE Golfview Yes 68 0.086 0.087 0.087

NW Piedmont (Hickory) 37-003-0003 ALEX Taylorsville Yes 339 0.087 0.088 0.088

37-027-0003 LENR Lenoir Yes 366 0.087 0.084 0.087

Various areas 37-131-0002 GAST Gaston Yes 40 0.082 0.084 0.084

37-029-0099 CAMD Camden No 3 0.080 0.081 0.081

37-011-0002 LINV Linville Yes 987 0.078 0.078 0.078

37-129-0002 WILM Castle Hayne Yes 12 0.075 0.078 0.078

CASTNET Sites 37-123-8001 CAND Candor Yes n/a 0.086 n/a 0.086

37-011-8001 CRAN Cranberry Yes 1219 0.083 n/a 0.083

37-113-8001 COWE Coweeta No 686 0.077 n/a 0.077

37-031-8001 BEAU Beaufort No n/a 0.076 n/a 0.076

Note: DVCs40.084 ppm are shown in bold font.

S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026 5015

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Appendix N 6 

April 5, 2007
MNBo715%) and mean normalized gross error

(acceptable performance is when MNGEo35%)

(Russell and Dennis, 2000) for 8-h O3 computed at

North Carolina sites in each of the three model grid

resolutions. These metrics correspond to the final

base-case modeling that used episode-specific emissions. In computing these metrics, we used all

hourly 8-h ozone modeled predictions that corresponded to an observed threshold of 0.060 ppm.

Traditionally, for attainment demonstration purposes, the focus has been on using this threshold to

evaluate the model’s capability to predict values

that are closer to the NAAQS (US EPA, 1991), and

to help reduce apparent model errors (due to

overpredictions of very low observations). However,

we also computed MNB and MNGE for 8-h ozone

using an observed threshold of 0.005 ppm (shown in

Fig. 4), and one can see the relatively large bias and

large errors when using low concentrations. Additional results from model performance evaluation

performed for these episodes are also available

(Arunachalam et al., 2001, 2002; CEP, 2005;

NCDENR, 2004).

3. Attainment demonstration

This paper focuses on an evaluation of the

MM5–SMOKE–MAQSIP modeling system for

ARTICLE IN PRESS

Mean Normalized Bias for 8-hr O3 (Obs Thresh = 60 ppb)

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

950712950713950714950715960621960622960623960624960627960628960629960630970712970713970714970715

Episode Date

% MNB

4-km 12-km 36-km

(a)

Mean Normalized Gross Error for 8-hr O3 (Obs Thresh = 60 ppb)

0%

10%

20%

30%

40%

50%

950712950713950714950715960621960622960623960624960627960628960629960630970712970713970714970715

Episode Date

% MNGE

4-km 12-km 36-km

(b)

Fig. 3. Mean normalized bias and mean normalized gross error for 8-h O3 using an observed threshold of 60 ppb.

5016 S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026

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Appendix N 7 

April 5, 2007
predicting future-year air quality using a demonstration of the US EPA’s modeled attainment

test (US EPA, 1999) in the North Carolina

modeling domain (Fig. 1a and b). The attainment

test to determine whether a given monitor meets

the NAAQS or not by the mandated future year,

recommends using modeled predictions in a

relative sense rather than an absolute sense. A key

assumption underlying the use of the relative

reduction factor (RRF) in demonstrating attainment is that the uncertainty in the difference

between the ozone maximum predictions for the

base and emissions control scenarios is less than the

uncertainty in the ozone maximum predictions

themselves (Hanna et al., 2001). Further, this

attainment test is closely tied to the form of the

NAAQS for 8-h ozone.

3.1. Modeled attainment test

For this study, we applied the modeled attainment test only to North Carolina O3 monitors in the

ARTICLE IN PRESS

Mean Normalized Gross Error for 8-hr O3 (Obs Thresh = 5 ppb)

0%

20%

40%

60%

80%

100%

120%

140%

Episode Date

% MNGE

4-km 12-km 36-km

950712950713

950714950715960621960622960623960624960627960628960629960630970712970713970714970715

(b)

Mean Normalized Bias for 8-hr O3 (Obs Thresh = 5 ppb)

0%

20%

40%

60%

80%

100%

120%

140%

Episode Date

% MNB

4-km 12-km 36-km

950712950713950714950715960621960622960623960624960627960628960629960630970712970713970714970715

(a)

Fig. 4. Mean normalized bias and mean normalized gross error for 8-h O3 using an observed threshold of 5 ppb.

S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026 5017

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Appendix N 8 

April 5, 2007
4-km grid (see Fig. 1b). The site-specific future-year

air quality is computed as follows:

1. Calculate the site-specific current design values

(DVCs) from monitored data.

To be consistent with the form of the NAAQS,

we calculated the DVC at each site by using the

fourth-highest daily maximum 8-h O3 concentration in each of three consecutive years (chosen

as discussed next), and then finding their

arithmetic mean. Because there is likely to be

some variability in observed design values due

to meteorological variations, the higher of the

following two values was chosen: (a) average

design value from the 3-year period ‘‘straddling’’

the year represented by the most recent available

emissions inventory (1999–2001), indicated by

DVCa in Table 2; and (b) average design value

from the 3-year period (2001–2003) used to

designate an area ‘‘nonattainment’’, shown as

DVCb in Table 2. Using the maximum of these

two values ensures that DVCs are not underestimated.

Table 2 lists the monitors in North Carolina

grouped by urban area, along with various site

characteristics, including the DVC for each site.

From Table 2 and from Fig. 1b, one can see

that there is a very extensive monitoring network

in North Carolina, and in some instances, the

monitors are located very close to each other

(within a few kilometers).

2. Use air quality modeling results to estimate sitespecific RRFs.

In this step, we identified surface grid cells that

were considered to be ‘‘near’’ a monitoring site,

rather than just the cell containing the monitor.

Since it is hypothesized that RRFs can be

underestimated due to potential migration of

the predicted O3 peak that results from a chosen

control strategy, we identified a 15-km radius as

being ‘‘near’’ a site, i.e., as an area consistent

with the intended representativeness for urbanscale O3 monitors (US EPA, 1999). The size of

the array of ‘‘nearby’’ cells around each site

varied for each grid resolution. For the 36-km

resolution, it was a 1  1 array; for the 12 km, a

3  3 array; and for the 4 km, a 7  7 array. The

spatial extents of these grid-cell clusters are

shown in Fig. 5 using a typical episode day’s

daily maximum 8-h O3 predictions in the baseyear, i.e., for 22 June 2000. This figure also shows

the spatial variability of the daily maximum

values within the ‘‘nearby’’ cells at each site for

each of the three grid resolutions. It can be

hypothesized that the monitor is at the center of

the cell in which it is located, and that this cell is

at the center of the appropriate array (e.g., at

the center of a 7  7 array of ‘‘nearby’’ cells

in the case of the 4-km grid). The elevation of

the monitor is not a factor while defining the

‘‘nearby’’ cells.

We computed the site-specific RRFs for each grid

resolution as follows:

(a) Compute the daily maximum 8-h O3 concentration in every grid cell for each modeled

episode day in the base case. (After establishing model performance for the base case with

episodic emissions inputs, MAQSIP was run

using the ‘‘current-year’’ emissions [i.e., 2000]

for use in the base-year calculations.)

(b) From these values, find each day’s highest

predicted daily maximum 8-h O3 concentration.

(c) Compute the average of the highest values for

all modeled episode days, using only those

days when the highest was X0.070 ppm, to

get a site-specific mean base-year value

(Mean_BY). The threshold of 0.070 ppm is

applied to exclude low-value days that would

cause overestimation of future-year design

values (calculated in step 3 below). In some

instances, this led to using fewer than 16

episode days in computing Mean_BY.

(d) Repeat steps (a)–(c) for the future-year

projected case, using the same days to

average in the future-year calculation as were

used in the base-year calculation, to get a sitespecific mean future-year value (Mean_FY).

Note that on any given day, the grid cell

chosen for the future-year case need not be

the same as the one chosen in the base-year

case, and this helps to capture the ‘migration’

of a predicted peak from the base-year to the

future-year case.

(e) Calculate the relative reduction factor:

RRF 1⁄4 Mean_FY=Mean_BY.

3. Compute site-specific future-year design values

(DVFs): DVF 1⁄4 DVC  RRF.

4. Compare all DVFs to NAAQS for 8-h O3.

If all the calculated DVFs are p0.084 ppm, the

modeled attainment test has been passed, and the

modeling domain is set to be in attainment of

the US NAAQS for 8-h O3 in the modeled future

year. Although the level of the 8-h O3 NAAQS is

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5018 S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026

Grid Resolution Journal Article 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone

Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 9 

April 5, 2007
ARTICLE IN PRESS

Fig. 5. ‘‘Nearby’’ grid cells around the monitors in 36- (top), 12- (middle) and 4-km (bottom) grid resolutions shown for a typical baseyear day’s daily maximum 8-h O3 in 2000.

S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026 5019

Grid Resolution Journal Article 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone

Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 10 

April 5, 2007
0.080 ppm, the third decimal digit, in ppm, is

rounded. Hence, 0.084 ppm is the largest concentration that is p0.080 ppm.

4. Results and discussion

Using each episode’s meteorology, we performed

current-year (2000) and future-year simulations for

2007 and 2012. We then postprocessed the model

results as discussed above to compute Mean_BY

and Mean_FY values (from the 16 episode days)

and then finally the RRFs and DVFs for each of

2007 and 2012. Table 3 shows the 2007 and 2012

DVFs for all North Carolina ozone monitors.

Fig. 6 (left column) shows the variation of DVCs

and 2007 RRFs and DVFs vs. Mean_BY O3

computed using model outputs from each of the

36-, 12- and 4-km grid resolutions. All North

Carolina monitors in each of these grid resolutions

are shown in these plots, and one should note that

the 4-km domain consisted of less number of

monitors than the other domains. Hence, the 36-

and 12-km plots show more data points than the

4-km plot. A 0.085 ppm line is also shown as the

level of the 8-h O3 NAAQS exceedance. When we

use the 36-km computed RRFs, only two sites—

County Line (DVF of 0.086 ppm) and Enochville

(0.085 ppm) in the Charlotte region—are still shown

to be in nonattainment. Using the 12-km computed

RRFs, four sites—County Line (0.087 ppm), Rockville (0.087 ppm), Enochville (0.086 ppm) and Plaza

(0.085 ppm) in the Charlotte region—are shown to

be in nonattainment. The 4-km computed RRFs

yield an identical DVF of 0.087 ppm at all of these

four sites. Similar metrics for the 2012 future-year

scenario (Fig. 6, right column) indicate that all sites

are in attainment of the 8-h O3 NAAQS using

RRFs from each of the three grid resolutions. As in

2007, the 2012 DVFs are also mostly similar

between the 12- and 4-km resolutions, but show

slightly larger differences between the 36- and 4-km

modeled outputs. Interestingly, for both 2007 and

2012, the four Charlotte-region sites just referenced

have higher base-year ozone concentrations with the

12-km grid than with the 36- or 4-km grids. Note

that although the circles representing DVCs (a yaxis variable) change location as the grid-resolution

changes, it is actually the change in Mean_BY

values (x-axis) that cause the DVC circles to move.

The DVCs themselves remain constant across all

grid resolutions.

To facilitate comparisons, the variations of the

36-, 12- and 4-km 2007 and 2012 RRFs vs. the

corresponding DVCs are shown on the same plot in

Fig. 7, while Fig. 8 shows similar plots of DVFs vs.

DVCs. These plots illustrate the almost identical

response of the modeling system in the 12 km when

compared to the 4 km. This seems to indicate that

the artificial dilution of emissions within coarse-grid

resolutions plays a relatively bigger impact in

predicting model responses at 36 km than at 12-

km grid resolution. Thus, the underlying combination of physical and chemical processes that

contribute to ozone exceedances and their reductions is similar in the 12- and 4-km grid resolutions,

but not at 36 km. Table 4 gives the number of

monitors that show differences in RRFs when

comparing 4-km model outputs to 12- or 36-km

outputs. For 2007, the RRFs computed using 4-km

outputs at 38 out of 42 sites have either 0% or o3%

difference compared to the 12-km outputs. Not a

single site shows a difference of 45%. However,

comparing the 4-km RRFs to the 36-km RRFs,

only 26 sites have either 0% or o3% differences,

and six sites have differences of 45%. These

differences in RRFs translate to DVF differences

on the order of 1–3 ppb at most of these sites. These

results corroborate the findings by Jones et al.

(2005) where they showed that modeled DVFs can

have uncertainties of about 2–4 ppb just through

differences in alternate options to modeling.

For the 2012 4-km RRFs, 37 and 21 sites have

differences of o3% when compared to the 12- and

36-km RRFs, respectively. Only one site (Fry Pan in

the Asheville high-elevation region) shows 45%

difference for the 2012 4-km RRF vs. 12-km RRF.

Interestingly, all six sites (Bushy Fork, Pittsboro,

Cherry Grove, Bent Creek, Waynesville and Castle

Hayne) that have 45% difference in 2007 RRFs

(4 vs. 36 km) show 45% differences in the 2012

RRFs (4 vs. 36 km) too. Also, all four sites (Pittsboro,

Fry Pan, Bent Creek and Waynesville) that show

differences of 43% in 2007 RRFs (4 vs. 12 km) also

have differences of 43% in 2012 RRFs (4 vs. 12 km),

with Fry Pan showing a notable increase from 3.4%

(in 2007) to 7.1% (in 2012) difference in RRF.

4.1. Statistical significance

To further quantify the differences (or similarities) between the model responses in different grid

resolutions, we used the Student’s t-test (Steel et al.,

1997) for the second part of the analyses. As we

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5020 S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026

Grid Resolution Journal Article 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone

Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 11 

April 5, 2007
ARTICLE IN PRESS

Table 3

O3 design values and p-values from Student’s t-tests

Region ID DVC 2007 DVF 2012 DVF p-Value (12 vs. 4)

36 km 12 km 4 km 36 km 12 km 4 km 2007 2012

Charlotte CTYL 0.101 0.086 0.087 0.087 0.079 0.080 0.081 0.79 0.57

ROCK 0.100 0.084 0.087 0.087 0.077 0.080 0.080 0.75 0.90

ENVL 0.099 0.085 0.086 0.087 0.078 0.080 0.081 0.39 0.47

PLZA 0.098 0.084 0.085 0.087 0.077 0.080 0.080 0.82 0.85

CRSE 0.092 0.078 0.079 0.080 0.072 0.074 0.076 0.56 0.38

ARWD 0.092 0.079 0.082 0.082 0.073 0.077 0.078 0.77 0.79

MONR 0.088 0.074 0.077 0.075 0.070 0.071 0.070 0.10 0.24

Triangle (Raleigh–Durham–Chapel Hill) MLBK 0.094 0.080 0.080 0.080 0.074 0.073 0.075 0.85 0.76

BTNR 0.094 0.078 0.079 0.079 0.072 0.075 0.074 0.57 0.78

STAG 0.093 0.079 0.080 0.080 0.073 0.074 0.074 0.87 0.84

BSHF 0.091 0.070 0.077 0.077 0.066 0.073 0.072 0.45 0.47

FKL 0.090 0.077 0.077 0.078 0.071 0.071 0.072 0.70 0.54

DUKE 0.089 0.076 0.077 0.075 0.070 0.070 0.070 0.76 0.97

WRAL 0.088 0.075 0.076 0.075 0.068 0.070 0.068 0.50 0.65

FUQV 0.088 0.075 0.073 0.075 0.070 0.068 0.070 0.54 0.46

WJOH 0.087 0.074 0.075 0.074 0.068 0.068 0.068 0.83 0.97

PITT 0.082 0.067 0.068 0.071 0.060 0.063 0.065 0.15 0.04

Triad (Greensboro–Winston Salem–High Point) DAVI 0.096 0.080 0.083 0.084 0.073 0.076 0.079 0.36 0.20

HTAV 0.094 0.078 0.079 0.080 0.073 0.074 0.075 0.93 0.82

UCRS 0.093 0.078 0.079 0.079 0.071 0.073 0.073 0.92 1.00

BETH 0.091 0.076 0.076 0.076 0.071 0.073 0.071 0.72 0.79

CHGR 0.090 0.070 0.074 0.076 0.064 0.071 0.072 0.68 0.57

MLVL 0.090 0.075 0.077 0.076 0.068 0.071 0.071 1.00 0.93

SHIL 0.089 0.073 0.078 0.076 0.068 0.072 0.072 0.26 0.49

SOPH 0.085 0.071 0.072 0.072 0.065 0.066 0.067 0.97 0.75

POLL 0.082 0.068 0.069 0.069 0.063 0.064 0.065 0.78 0.62

Asheville MTMI 0.089 0.072 0.074 0.075 0.067 0.070 0.071 0.58 0.58

FRYP 0.087 0.073 0.074 0.077 0.069 0.068 0.073 0.03 0.02

PKNO 0.087 0.073 0.075 n/a 0.066 0.070 n/a n/a n/a

BKNO 0.085 0.070 0.073 n/a 0.066 0.068 n/a n/a n/a

BENT 0.083 0.068 0.071 0.074 0.063 0.067 0.069 0.02 0.03

WAYN 0.080 0.067 0.068 0.071 0.060 0.064 0.067 0.20 0.10

Greenville, Rocky Mount and Wilson (Down East) LEGT 0.089 0.080 0.076 0.077 0.074 0.070 0.072 0.59 0.38

FARM 0.084 0.073 0.074 0.073 0.069 0.070 0.068 0.62 0.77

KINS 0.082 0.072 0.073 n/a 0.068 0.069 n/a n/a n/a

KVIL 0.082 0.071 0.072 0.072 0.066 0.067 0.068 0.83 0.94

JVIL 0.081 0.073 0.073 n/a 0.070 0.070 n/a n/a n/a

Fayetteville WADE 0.088 0.077 0.077 0.078 0.070 0.071 0.073 0.90 0.70

HOPE 0.087 0.077 0.077 0.077 0.070 0.071 0.072 0.84 0.53

NW Piedmont (Hickory) ALEX 0.088 0.074 0.075 0.075 0.069 0.069 0.069 0.62 0.81

LENR 0.087 0.073 0.074 0.073 0.068 0.069 0.068 0.91 0.73

Various areas GAST 0.084 0.073 0.073 0.073 0.069 0.069 0.069 0.95 0.76

CAMD 0.081 0.073 0.076 n/a 0.071 0.074 n/a n/a n/a

LINV 0.078 0.065 0.066 0.067 0.060 0.061 0.063 0.94 0.75

WILM 0.078 0.065 0.069 0.069 0.062 0.065 0.066 0.70 0.93

CASTNET Sites CAND 0.086 0.074 0.073 0.073 0.069 0.068 0.068 0.39 0.61

CRAN 0.083 0.068 0.069 0.070 0.063 0.065 0.066 0.77 0.99

COWE 0.077 0.066 0.066 n/a 0.062 0.060 n/a n/a n/a

BEAU 0.076 0.066 0.067 n/a 0.063 0.064 n/a n/a n/a

Note: Design values40.084 ppm are shown in bold font.

S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026 5021

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Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 12 

April 5, 2007
discussed earlier, the only factor that changes DVFs

(between future years or between grid resolutions) is

the RRFs. Since DVCs remain constant for all grid

resolutions, for the Student’s t-test it was appropriate to focus on RRFs, the key determinant

for attainment. The test was to determine whether

the daily RRFs for the 12-km grid are equal to

the daily RRFs for the 4-km grid, for each

monitoring site. The null hypothesis was defined

as no difference between the mean daily RRFs

from the 12-km grid and the 4-km grid, i.e., H0:

m12 m4 1⁄4 0 (or m12 1⁄4 m4) and H1: m12 m4a0

(or m12am4). Assuming that the two populations

from which the samples were drawn have unequal

ARTICLE IN PRESS

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110 0.115 0.120

0.70

0.75

0.80

0.85

0.90

0.95

1.00

 DVC or DVF (ppm) 

 Relative Reduction Factor (RRF) 

 Mean_BY O3 (ppm) 

DVF

DVC

RRF 0.085 ppm

(a)

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110 0.115 0.120

0.70

0.75

0.80

0.85

0.90

0.95

1.00

 DVC or DVF (ppm) 

 Relative Reduction Factor (RRF) 

 Mean_BY O3 (ppm)

DVF

DVC

RRF 0.085 ppm

(b)

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110 0.115 0.1200.70

0.75

0.80

0.85

0.90

0.95

1.00

 DVC or DVF (ppm) 

 Relative Reduction Factor (RRF) 

 Mean_BY O3 (ppm) 

DVF

DVC

RRF 0.085 ppm

(c)

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110 0.115 0.120

0.70

0.75

0.80

0.85

0.90

0.95

1.00

 DVC or DVF (ppm) 

 Relative Reduction Factor (RRF) 

 Mean_BY O3 (ppm) 

DVF

DVC

RRF 0.085 ppm

(d)

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110 0.115 0.120

0.70

0.75

0.80

0.85

0.90

0.95

1.00

 DVC or DVF (ppm) 

 Relative Reduction Factor (RRF)

 Mean_BY O3 (ppm) 

DVF

DVC

RRF 0.085 ppm

(e)

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110 0.115 0.120

0.70

0.75

0.80

0.85

0.90

0.95

1.00

 DVC or DVF (ppm) 

 Relative Reduction Factor (RRF) 

 Mean_BY O3 (ppm) 

DVF

DVC

RRF 0.085 ppm

(f)

Fig. 6. Mean base-year O3 vs. DVCs, RRFs and DVFs from 36- (top), 12- (middle) and 4-km (bottom) grid resolutions, shown for 2007

(left column) and 2012 (right column).

5022 S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026

Grid Resolution Journal Article 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone

Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 13 

April 5, 2007
variances ðs2

12as24Þ, the appropriate variance for use

in calculating t-statistics is computed as

sðY ̄ 12Y ̄ 4Þ 1⁄4

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ðs12Þ

2

n12

þ ðs4Þ

2

n4

s

,

where n12 and n4 are the number of episode days

used in calculating the mean daily RRFs for the 12-

and 4-km grids, and hence had a maximum value of

16, ðs12Þ

2 and ðs4Þ2 are the sample variances for the

12- and 4-km grids, and Y ̄ 12 and Y ̄ 4 are the

monitor-specific mean daily RRFs for the 12- and

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0.70

0.75

0.80

0.85

0.90

0.95

1.00

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110

 Relative Reduction Factor (RRF) 

 Current-Year Design Value (DVC) (ppm)

4-km

12-km

36-km

0.70

0.75

0.80

0.85

0.90

0.95

1.00

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110

 Relative Reduction Factor (RRF) 

 Current-Year Design Value (DVC) (ppm)

4-km

12-km

36-km

(a) (b)

Fig. 7. Variation of DVCs vs. 2007 RRFs (left) and 2012 RRFs (right).

0.050

0.055

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110

 Future-Year Design Value (DVF) (ppm) 

 Current-Year Design Value (DVC) (ppm) 

4-km

12-km

36-km 0.085 ppm

0.050

0.055

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110

 Future-Year Design Value (DVF) (ppm) 

 Current-Year Design Value (DVC) (ppm) 

4-km

12-km

36-km 0.085 ppm

(a) (b)

Fig. 8. Variation of DVCs vs. 2007 DVFs (left) and 2012 DVFs (right).

Table 4

Number of sites that have differences in 12- and 36-km RRFs compared to 4 km

Magnitude of RRF differences (%) 2007 2007 2012 2012

4 vs. 12 km 4 vs. 36 km 4 vs. 12 km 4 vs. 36 km

0 15 7 11 9

o3 23 19 26 12

3–5 4 10 4 12

45 0619

S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026 5023

Grid Resolution Journal Article 

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Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 14 

April 5, 2007
4-km grids. The corresponding t-statistic is then

defined as

t

0 1⁄4 Y ̄ 12 Y ̄ 4

sY ̄ 12Y ̄ 4

with an effective degree of freedom equal to

df 1⁄4 ðs2

12=n12Þþðs24=n4Þ

1⁄2ðs2

12=n12Þ=ðn12 1Þ þ 1⁄2ðs24=n4Þ=ðn4 1Þ .

One should note that the t-statistic presented in

the paper is t

0

. The prime indicates that, due to the

test setup, the t-statistic is not distributed strictly as

a Student’s t but as an approximation to it. The

approximate nature dictates that we use an effective

degree of freedom with the tabulated t as opposed

to a more common calculation of the degrees of

freedom.

Table 3 also shows the results of the t-test by

presenting the p-values for each monitor, when

comparing the RRFs from the 12 and 4 km for each

of the two future years. Using a significance level of

a 1⁄4 0:05, any p-value o0.05 would be significant,

and the null hypothesis is rejected in favor of

the alternative hypothesis. A low p-value for the

statistical test indicates rejection of the null hypothesis because it indicates how unlikely it is that a test

statistic as extreme as, or more extreme than the one

given by these data will be observed from this

population if the null hypothesis is true. For

example, if p 1⁄4 0:012, this means that if the

population means were equal as hypothesized

(under the null), there is a 12 in 1000 chance that

a more extreme test statistic would be obtained

using data from this population. If one agrees that

there is enough evidence to reject the null hypothesis, one may conclude that there is significant

evidence to support the alternative hypothesis.

Applying the Student’s t-test on a per-monitor basis

helps isolate areas (or monitors) that may benefit

from the finer grid resolution.

Of all the North Carolina sites analyzed, Bent

Creek and Fry Pan accept H1, i.e., the 12- and 4-km

RRFs are different for both 2007 and 2012 futureyear emissions scenarios. Pittsboro rejected H0 only

for 2012. For all the other sites in North Carolina,

the differences between the RRFs from the 12 and

4 km were statistically insignificant. Additional

examination of the sites that reject H0 reveal that

all three sites have DVCs which are near attainment

(p0.087 ppm), and DVFs well below the NAAQS

(p0.077 ppm). Both Bent Creek and Fry Pan are

situated at high elevations, and we believe that

topography at these sites may contribute to

significant differences in RRFs between the two

grid resolutions; at these locations, the models are

blending air masses of different origins and the 12-

km grid artificially dilutes NOx emissions over a

relatively larger grid volume. Pittsboro is a special

case since it is a rural site that lies at the edge of the

Triangle urban area, and may also be affected by a

major NOx source (Cape Fear power plant) in its

vicinity. Given these two reasons, the emissions

gradient and hence the individual values in the daily

maxima array in the ‘nearby’ cells in each of the

4- and 12-km grids may undergo a rapid transition

and hence contribute to different RRFs. Furthermore, the effect of NOx titration from the Cape

Fear source may also lead to differences in the

RRFs between the two grid resolutions.

The US EPA draft guidance for demonstrating

attainment of the NAAQS for PM2.5 nonattainment

areas (US EPA, 2001) recommends a similar

approach for computing RRFs and then DVFs

for future years. For urban areas, the US EPA

recommends that states use a 12-km grid resolution

for estimating RRFs for secondary particulate

matter, but a much finer resolution of 5 km or less

for primary particulate matter. Since there is an

annual standard for PM2.5, states may resort to

performing annual base-year and future-year simulations to compute RRFs and DVFs; this will

impose a huge burden on computational resources,

especially for fine-grid modeling of longer periods.

Based upon our application of the draft guidance

for O3 attainment purposes, we recommend that

similar model evaluation be performed for PM2.5

nonattainment areas to obtain ranges of variability

for future-year PM2.5 concentrations at various

grid resolutions. States could then consider the use

of coarse-grid resolutions, say 12 km for annual

modeling to compute RRFs and DVFs for primary

particulate matter as well, and then use the

information gained from benchmark studies like

these as bounds of variability to supplement the

modeling results.

5. Conclusions

We used the MM5–SMOKE–MAQSIP modeling

system to study future-year attainment of the 8-h O3

NAAQS in several urban areas of North Carolina

using a diverse set of meteorological (four different

episodes in 1995, 1996 and 1997), chemical (two

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5024 S. Arunachalam et al. / Atmospheric Environment 40 (2006) 5010–5026

Grid Resolution Journal Article 

The Charlotte-Gastonia-Rock Hill, NC-SC 8-Hour Ozone

Pre-Hearing Draft –North Carolina Attainment Demonstration

Appendix N 15 

April 5, 2007
different future-year emissions scenarios—2007 and

2012) and physical (in three different model grid

resolutions—36, 12 and 4 km) conditions. Using

extensive model performance evaluation, we first

established that the modeling systems performed

well in capturing historical O3 exceedances before

using them in a relative sense for the future years.

Using an illustration of the US EPA’s guidance

for 8-h O3 attainment demonstration in North

Carolina, the results (focused on predicted RRFs

and DVFs) show the potential uncertainty in

modeled results due to grid resolution. Specifically,

out of 42 sites analyzed in North Carolina, 38 (in

the case of 2007) and 37 (in the case of 2012) sites

have 12-km RRFs that differ by only o3% when

compared to the 4-km RRFs. However, only 26 (in

the case of 2007) and 21 (in the case of 2012) sites

show o3% differences in the 36-km RRFs vs. 4-km

RRFs, which indicates the sensitivity of the RRFs

to the grid resolution modeled. In general, the

coarser the grid resolution, the greater is the model

response, and hence slightly lower are the RRFs.

We were able to quantify the grid-resolution

variability using the Student’s t-test, which showed

that the differences between the 4- and 12-km

computed RRFs were statistically insignificant

except for three sites. These differences in RRFs

(between the 36- and 4-km predictions) translate to

an average difference of 1–3 ppb in the DVFs for

some sites, and we recommend that this estimate of

1–3 ppb be considered as the estimate of variability

due to grid resolution in the modeling. Based upon

these results, future attainment demonstration

studies (for ozone and perhaps PM2.5) may consider

using RRFs and DVFs from 12-km modeling and

incorporate variability estimates into them.

Acknowledgments

The authors would like to thank the Division of

Air Quality (DAQ) under the North Carolina

Department of Environment and Natural Resources for funding this work, under Modeling

Assistance Contracts EA2012, EA03017 and

EA05009. We also gratefully acknowledge Donald

Olerud of Baron Advanced Meteorological Systems

for the meteorological modeling and evaluation, the

emissions modeling staff at DAQ for preparing

the emissions inputs, Zac Adelman, Jeff Vukovich

and Kim Hanisak of the Carolina Environmental

Program for their assistance with preparing various

other inputs, and George Bridgers of the DAQ for

his contributions throughout this project. We also

thank Jeanne Eichinger for her assistance with

editing the final version of the manuscript. We

finally thank the two anonymous reviewers for their

valuable comments and suggestions that helped

improve the overall quality of this paper.

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