EPA/600/A-97/097
      9A.15             ESTIMATING THE AREA OF INFLUENCE OF OZONE PRODUCED
                         BY LOCAL PRECURSOR EMISSIONS FOR A SUMMER PERIOD
                            WITH A RANGE OF PHOTOCHEMICAL ACTIVITY

                           Gail S. Tonnesen1, Robin L Dennis8, Gerald L Gipson
              Atmospheric Modeling Division, National Exposure Research Laboratory, U.S, EPA
                                       Research Triangle Park, NC
1. INTRODUCTION

Regional transport of ozone (O3) and its precursors is
suspected to significantly affect O3 control strategies and
the effectiveness of local controls throughout much of the
eastern U.S. A Federal Advisory Committee Act (FACA)
work group is studying the  identification of Areas of
influence (AOt's), essentially O3 airsheds, around which
to design controls for subregions of the eastern U.S. It is
difficult, however, to quantify the production, life-time and
effect of transport on regional/urban O3 concentrations
([O3]). It is also difficult to quantify the sensitivity of the
transported O3 to hydrocarbon (VOC) or nitrogen oxide
(NOx) emissions or to emissions reductions in any given
region   or  urban   area.  At  least  two   types  of
methodologies have been proposed to evaluate AOIs.
They include sensitivity studies to evaluate the change in
[03] in the AOI when emissions in the source region are
changed (Yang et  al, 1997); and Process and Mass
tracking studies to evaluate the actual transport of mass
and the production of O3 on route from the source region
to the AOI (Lo and Jeffries, 1996; Yarwood et al., 1997).
Each  of  these approaches has  limitations: sensitivity
studies have predictive value but they lack explanatory
value; ie, they do not provide adequate insights into the
processes and feedbacks that cause the change in [03]
in the AOI. They may ignore feedbacks in  the system
which effectively buffer [O3], thereby underestimating the
size of the AOI. Further, computed sensitivities may vary
considerably with the base case scenario conditions, so
h is unclear how sensitivities should be calculated or
applied in light of changes In future scenario conditions.
Process and Mass tracking studies do have considerable
explanatory value; they can explain in great detail how
precursor and O3 transport determine [O3] in the AOI for
a particular  scenario. They  do not, however, have
predictive power; ie, they do not predict exactly how
emissions reductions will affect  [O3] in the  AOI. We
propose that these  methods   have complementary
strengths and must be used together both to define the
AOI  and  to understand  the   Impact of emissions
 'Gail S. Tonnesen, U.S. EPA, MD-84, RTP, NC
 27711; tonnesen@olympus.epa.gov.

 'Atmospheric Sciences Modeling Division, National
  Oceanic and Atmospheric Administration, RTP, NC
 27711, on assignment to NERL, U.S. EPA.
reductions in the AOI. In this study, we use sensitivity
simulations to evaluate the AOI of precursor emissions
in  selected source regions, and we  use a process
analysis to explain  the results of those  sensitivity
simulations.

2,  MODEL AND SCENARIO CONDITIONS.! 995

We used  a version  of  the Regional Acid Deposition
Model  (RADM) (Chang et al., 1990),  a complex,
three-dimensional  grid  model,  that we  modified  to
calculate and output integrated reaction and process
rates (Tonnesen and Dennis, 1997). We used 21 vertical
layers and a 20 km grid resolution in a domain extending
from Kentucky to Maine, one-way nested in an 80 km
grid  that  extended  from  southern  Texas to  New
Brunswick, Canada. Meteorological Inputs are described
in  Li et at. (in this volume). We conducted a series of
emissions source modulation experiments using a 25-day
base case scenario during July and August, 1988, that
includes two  frontal  passages which  clean out the
system, and two periods of stagnant conditions in which
peak  model [O3]  exceeds 200  ppb.  For the  source
modulations we reduced either NOx emissions,  VOC
emissions, or both NOx  and VOC emissions by 50% in
selected model grid cells. We then evaluated the area of
influence of these source cells by calculating changes in
[O3] and its precursor concentrations, and the change in
the sensitivity of [O3] to precursors In areas downwind of
the source cell.  For example, we calculated the change
in hourly [O3] using:  Delta = Modulation-Base Case, so
negative deltas represent decreases relative to the base
case. We conducted individual source modulations in the

Figure 1. Model domain and areas of source modulation.

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four urban areas shown in Figure 1: a 40 by 120 km
area around Pittsburgh,  a 60 by 80 km area near
Baltimore., a 40 by 60 km area near Philadelphia, and a
60  by 80  km  area around  New York  City;  and a
combined modulation using all four areas.

3. RESULTS.

We evaluated the response of [03]  and  odd oxygen
production. P(0x), by examining the hourly perturbation
in the plumes moving downwind from the urban areas.
We discuss briefly here the results for the combined 50%
VOC and NOx  reduction. The size of the AOI will be
determined in part  by the magnitude of  perturbation
defined to  be  significant. Considerable  study is  still
required  to  determine what  constitutes a significant
response in relation to control strategy development, but
for this exploratory study we used a  change  in hourly
[O3] of more than 2 ppb as a significant response, and
then observed the [03] response plume for the 25 day
simulation period. In all  model  simulations with NOx
emissions  reductions,  nighttime  and  morning  [O3]
increased near urban  centers and  other large NOx
sources because  of decreased titration of 03 by  the
reduced NO emissions. As a result, early morning [03]
typically increased by several ppb from levels  below 20
ppb in  the base case. Then, as the day progressed, the
increases in urban [O3] gradually became smaller, and
then switched to [03] decreases by late morning to mid
afternoon. (The only exception to this was several cells
in the center of NY  City for which  [03] increased at all
hours.) Daytime [O3] levels always  decreased at greater
distances downwind of the source modulation areas;
maximum reductions occurred late in the afternoon at the
time of peak [O3]. The [03] reductions did not persist
through the night-time in the surface layer because of the
effects of titrstion, deposition, and chemical reactions of
[O3], but the [O3] reductions did persist through  the
nighttime above the surface layer. As the  nighttime
inversion broke up in the mid-morning, the [03] reduction
became evident again in the surface layer.

We describe here the results of the combined  VOC and
NOx reduction for a simulation from July 28 to  August 1,
1988, This was  one  of the stagnant high [03] episodes
with light south-westerly to north-westerly winds. During
this period, the Pittsburgh AOI (defined by  a plume of  2
ppb  reduction  in  hourly   [O3])   first   extended
south-eastward through most of Maryland on July 28 and
29; then north-eastward to about  200 km north of NY
City on July 30 and 31; and finally south-eastward to the
North Carolina border on August 1. The plumes of 2 ppb
[03] reduction from Baltimore, Philadelphia and NY Ctty
showed similar spatial extent and temporal variability.
The plume for the combined 4 area source modulation
extended from  NC,  across the eastern boundary over
the Atlantic Ocean, and north into Maine, tt is convenient
to show the AOI over a period  of several  days by
integrating the [03] response plume over time to show
cumulative response. Figure  2a shows the integrated
[03] response as ppb-hours  for the four  urban areas
combined. The dark shades over the urban areas show
net increases in  [03]. The maximum [03] increase was
1840  ppb-hours  over southern  Long  Island;  this
represents an average hourly [03] increase of 18 ppb. tt
is useful to determine if this increase is primarily due to
low [03] levels increasing to values still below the [03]
standard, or more problematic increases in high [03], so
we also calculated the response using cumulative hours
of [03] above 80 ppb (not shown here); in this case
there  was  no  increase  in   [03]  for   Pittsburgh,
Philadelphia,  or  Baltimore.  The increased  [03] was
limited to a smaller area around New York City, and was
again largest in the south  Long Island cell, with  an
average hourly [03] increase of 9 ppb. This shows that
most  of the increases in [O3] occurred  during early
morning hours and was caused by reduced titration of
03 resulting from the NO emissions reductions, and was
primarily an increase from near zero to less than 80 ppb.

The white areas in Figure 2a show areas wifi integrated
[03] reductions  greater than 100 ppb-hours. We note
that there may be considerable temporal variation in the
hourly [03] that is not evident in the integrated 108 hours
response. The largest integrated [03] reductions, greater
than  500 ppb.  occurred over  Rhode  Island. The
cumulative [03]  plume extended from northern Virginia
to central Maine, and beyond the eastern boundary over
the Atlantic Ocean. High  [03] levels result from local
P(0x) and from transport of 03 produced in upwind cells.
An evaluation of the P(Ox) response plume, or the P(0x)
AOI,  is useful in explaining  the [O3] AOI. Rgure 2b
shows the cumulative P(0x) response for the same time
period as Rgure 2a. The change in [O3] shown in Figure
2a results directly from the change fn P(Ox) in Figure 2b.
Production rates are integrated over five days, so a -60
ppb response indicates an average reduction in P(Ox) of
12 ppb/day. For each of the four urban areas, the P(Ox)
response plume extended approximately 200 km from
the source modulation region.  For  the most part, the
plumes did not  overlap,  so there was  little interaction
between P(0x)  plumes. If the plumes did overlap,  we
would expect non-linear interactions ia P(0x) for the
combined modulation.  The precursor response plumes
are not shown, but they extended over approximately the
same area as did P(Ox). Rgure 2b shows that the AOI
of [03] is significantly enlarged because P(0x) rtsetf has
an AOI that  extends about 200 km downwind from the
source modulations. As a result, the [O3] AOI would be
some 200 km larger than that of a  reacting tracer that
decays with the same loss rate  as [03].

Figure 3 shows a 36 hour time-series for a 400 by 400
km area around the source modulation areas. Figure 3a
shows average reductions in [O3] of 2 to 3 ppb at 6 PM,
and reductions  in P(0x)  of 0.8  ppb/hr  at  noon.  In
general, reductions in P(0x) are caused by reductions in

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ppb hours
  '«                 August 1.1S88 23:00:00
  UcyHC      Miru=-581 JS5 at (62,3?), Max=1843 JO at (53,31)
                                                                        August 1,1388 23:05:00
                                                             Mins-3020.14? at (57,35), Max=218J692 at (28,28)
    HCNC
                       August 2,1388 0:00:00
               Min=-260 J at (43,27), Max* 12 at (63,50)
                                                                        August 2,1188 0:00:00
                                                                Min=-9633 at (51,32) Mix= OJ8 at (65,5?)
Figure 2. Cumulative response to the combined VOC and NOx source modulation integrated over 108 hours for two
different base case scenarios:  on the left, (a) and (b) show tie cumulative [O3] and P(0x) response for the high O3
scenario; on the right, (c) and (d) show the cumulative [03] and P(0x) response for the tow O3 base case scenario.

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                High O8 Base Case
                                                           (o)
            Low O3 Base Case
                                                             B AM      6 PM       6 AM
                   a  AP
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accompanied  by  compensating  increases  in  03
production  efficiency downwind, so we expect [O3]
sensitivity studies will underestimate the [O3] AOI. We
tested this hypothesis by evaluating the AOI using a low
[03] base case scenario with full biogenic emissions but
zero anthropogenic emissions. This base case had high
O3 production efficiency, low O3 loss rates, IO3] levels
ranging from 30 to 80 ppb. We then performed a source
modulation for  the same source areas and adding
anthropogenic emissions of the same magnitude as for
the reduction described in Section 3.1 above. Rgure 2c
shows the cumulative [O3] response calculated using:
Delta = Low 03 Base  Case -  Modulation. Comparing
Figure 2c to 2a, we note first that the AOI is considerable
larger, extending from North Carolina throughout Maine.
The maximum  [O3] reduction is also larger, 3020
ppb-hours in the low O3 Base  Case compared to 582
ppb-hours in standard Base Case. Finally, with the
exception of one Pittsburgh cell, the large [O3] increases
due to early morning titratjon effects were compensated
by larger decreases in P(Ox) and [O3] later in the day,

Rgure 2d shows the cumulative P(Ox) response for the
low O3 Scenario. Comparing Rgure 2d to Rgure 2b, we
see that the same emissions modulation produced very
different  AOls  for  P(0x).  The  largest reduction  in
cumulative P(Ox) in  the base  scenario was 260  ppb
compared to 963 ppb in the low 03 scenario. The P(Ox)
response plumes also overlap for Philadelphia, Baltimore,
and New York City; this suggests that there will be
non-linear interactions  in the  P(0x) response in the
plume. Rgure 3c shows the [03] and P(0x) time-series
for the 400 by 400 km sub-domain. Reductions are about
a factor of 4 larger than in the high 03 base case. Rgure
3d  shows a large reduction in  radical initiation due to
lower O3 photolysis which amplified the [03] reduction
caused by precursor reductions. Finally, Rgure 4 shows
that the low O3 source modulation had  a much higher
O3 production efficiency, 28 ppb/ppb,  compared to 9
        in the high O3 base case.
V   K.


I   «

I
I   »


&   J»
IU
1?
0
    ,2
B Htofl O3 B»»« C*»»
» Mfch OS Bouro* Mod
O Low OS B*»« C«M
         8AM
                    BPM
                              BAM
                                         BPM
 Figure 4. O3 production efficiency for a 36 hour time-
 series, averaged over a 400 by 400 km area.
4. CONCLUSIONS.

The  AOI of precursor emissions  is determined by a
combination of P(Ox) and transport of [O3]. Transport of
precursor emissions creates  an P(Ox) AOI  that can
extend on the order of hundreds of km downwind from
the source area even under conditions of  stagnant
meteorology and light winds. The P(0x) AOI is also
affected by feedbacks from the [O3] response that affect
radical initiation rates. The [O3] AOI extends on the order
of hundreds of km beyond the P(0x) AOI. The area and
size  of the  AOI depends on wind speed and  direction,
but also varies depending on the methodology and level
of precursor  emissions  used.  A  combination   of
methodologies and a range of scenarios should be used
to evaluate AOIs for the full range of conditions for which
changes in  air quality are likely to occur.
5. REFERENCES.

Chang, J. S, R. A. Brost, 1. S. A. Isaksen, P. Middleton,
W. R, Stockwell, and C. J. Walcek, A three-dimensional
Eulerian Acid Deposition model: Physical concepts and
formulation, J. Geophys. Res., 92,14.681-14,700,1987.

Lo  Cha-Yang  S. and H.  E.  Jeffries,  A  quantitative
technique for assessing pollutant source location and
process composition in photochemical grid models, 90th
AWMA Meeting, Toronto, Ontario, CA, 1997.

Li, Y., R. L Dennis, G. S. Tonnesen, J. E. Pleim, and D.
Byun, Effects of uncertainty in meteorological inputs on
03 concentration, O3 production  efficiency, and O3
sensitivity to emissions reductions in the Regional Acid
Deposition Model, (in this volume).

Tonnesen, G. S., and R. L Dennis, Analysis of Radical
Propagation Efficiency to Assess Ozone Sensitivity to
Hydrocarbons  and NOx.  Part  1:  Local Indicators of
Instantaneous Odd  Oxygen  Production  Sensitivity,
(submitted to J. Geophys. Res., 1997).

Yang Y.-J., J. Wilkinson, anf A. G. Russell, Fast, direct
sensitivity analysis of multidimensional photochemical
models, Env. Sci. Techno!,, (submitted) 1997.

Yarwood, G., R.E. Morris, MA Yocke. H. Hobo and T.
Chico, Development of a methodology  for  source
apportionment of ozone concentration estimates from a
photochemical grid model, 89th AWMA Annual Meeting.
Nashville, TN. June 23-28, 1996.

DISCLAIMER: This  paper has  been reviewed  in
accordance with the U.S. Environmental Protection
Agency's  peer  review  policies  and   approved  for
presentation and publication. Mention of trade names or
commercial products does not constitute endorsement or
recommendation for use.

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                                   TECHNICAL  REPORT  DATA
1.  REPORT NO,
 EPA/600/A-97/097
2.
4,  TITLE AND SUBTITLE
Estimating  the area of  influence  of  ozone
produced  by local  precursor  emissions  for  a
summer  period  with a  range of photochemical
activity
3.REC
                                   5.REPORT  DATE
                                   6.PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
G.S. Tonncsen
National Exposure Research Laboratory
Research Triangle Park, NC 27711

R.L. Dennis
Atmospheric Modeling Division
National Exposure Research Laboratory
Research Triangle Park, NC 27711

G.L, Gipson
National Exposure Research Laboratory
Research Triangle Park, NC 27711
                                   8.PERFORMING ORGANIZATION
                                   REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS

Same as block 12.
                                   10.PROGRAM ELEMENT NO.
                                                                 11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS

National Exposure Research Laboratory
Office of Research and Development
U. S. Environmental Protection Agency
Research Triangle Park, NC 27711
                                   13.TYPE OF REPORT AND PERIOD COVERED

                                   Preprints, lOih Joint Conference on the Applications of Ajr
                                   Pollulion Meteorology wilh the A&WMA, January 11-16,
                                   1998, Phoenix, Arizona
                                                                 14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT

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       Regional transport of ozone (O3) and its precursors is suspected to significantly affect O3 control
strategies and the effectiveness of local controls throughout much of the eastern U.S. A Federal
Advisory Committee Act (FACA) work group is studying the identification of Areas of Influence
(AOI's), essentially O3 airsheds, around which to design controls for subregions of the eastern U.S. It is
difficult, however, to quantify the production, life-time and effect of transport on regional/urban O3
concentrations ([O3]). It is also difficult to quantify the sensitivity of the transported O3 to hydrocarbon
(VOC) or nitrogen oxide (NOx) emissions or to emissions reductions in any given region or urban area.
At least two types of methodologies have been proposed to evaluate AOIs.  They include sensitivitiy
studies to evaluate the change in [03] in the AOI when emissions in the source region are changed
(Yang et al., 1997); and Process and Mass tracking studies to evaluate the actual transport of mass and
the production of O3 on route from the source region to the AOI (Lo and Jeffries, 1996; Yarwood et al.,
1997). Each of these approaches has limitations: senesitivity studies have predictive value but they lack
explanatory value; ie, they do not provide adequate insights into the processes and feedbacks that cause
the change in [03] in the AOI.  They may ignore feedbacks in the system which effectively buffer [03],
thereby underestimating the size of the AOI. Further, computed sensitivities may vary considerably with
the base case scenario conditions, so it is unclear how sensitivities should be calculated or applied in
light of changes in future scenario conditions.  Process and Mass tracking studies do have considerable
explanatory value; they can explain in great detail how precursor and O3  transport determine [03] in the
AOI for a particular scenario.  They do not, however, have predictive power, ie, they do not predict
exactly how emissions reductions will affect [03] in the AOI.  We propose that these methods have
complementary strengths and must be used together both to define the AOI and  to understand the impact
of emissions reductions in the AOL In this study, we use sensitivitiy simulations to evaluate  the AOI of
precursor emissions in selected source regions, and we use a process analysis to explain the results of
those sensitivity simulations.
17.
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