ozone transport analysis using back-trajectories and camx probing tools

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Ozone Transport Analysis Using Back-Trajectories and

CAMx Probing Tools

Sue Kemball-Cook, Greg Yarwood, Bonyoung Koo and Jeremiah Johnson, ENVIRONJim Price and Mark Estes, TCEQ

2010 CMAS Conference, October 11-13, 2010Chapel Hill, North Carolina

Ozone Transport Presentation for CMAS

Background

• Lowering the 8-hour standard increases the importance of background ozone and transport

• Simulation of ozone transport in photochemical models will be critical for development of ozone control strategies

• CAMx probing tools can be used to quantify background ozone


Objectives

• Investigate ozone transport into Texas– Which source regions contribute to high ozone in Texas?– What effect do emissions changes in those source regions

have on Texas ozone?

• Use a suite of independent tools to analyze ozone transport– HYSPLIT back trajectories

Show pathways for air arriving at Texas monitors on high ozone days

New MM5CAMx-to-ARL tool reformats CAMx winds for input to HYSPLIT and calculates vertical velocities using CAMx algorithm

– CAMx probing tools APCA (Anthropogenic Precursor Culpability Assessment) HDDM (Higher order Decoupled Direct Method)


Using APCA and HDDM to Study Modeled Transport

• CAMx APCA and HDDM probing tools provide complementary information for transport analysis

• APCA– Can quantify contributions by source region and source

category to ozone at a given receptor and time– Does not give information about how such a contribution may

change if emissions change

• HDDM– Can provide estimates of model response to changes in

emissions


APCA/HDDM Source Region Maps

• APCA emissions source regions shown in red

• HDDM emissions source regions shaded– Ohio and Tennessee

Valleys (blue)– Southeastern U.S.

(pink)

• 3 emissions source categories – Elevated anthropogenic

emissions– Biogenic emissions– All other emissions

• Buffer around Texas, roughly corresponding to 12 km Texas domain


Episode Selection

• Modeled three Texas high ozone episodes during 2005-6

• Episode selection based on monitoring network and model performance evaluation results– More rural ozone monitors in Texas during 2006 than 2005

• Focus on periods of high diagnosed and modeled transport from Ohio and Tennessee Valleys and Southeastern U.S. – June 13-15, 2006

Good model performance in both 12 km grid and in source regions in 36 km grid; transport from OH-TN Valleys and SE diagnosed using HYSPLIT back trajectories


June 13-15, 2006 Transport Episode

• Back trajectories from TCEQ analysis using EDAS meteorological inputs to HYSPLIT

• Transport from OH-TN Valleys and SE into Texas

7

June 13 June 14 June 15

Ozone Transport Presentation for CMAS 8

June 9-15, 2006: MPE for Source Regions

• Good model performance with small positive bias

Ohio/Tennessee Valleys

SoutheastNormalized Bias

-40

-30

-20

-10

0

10

20

30

40

6/9

6/10

6/11

6/12

6/13

6/14

6/15

(%)

SE

+15%

-15%

Normalized Bias

-40

-30

-20

-10

0

10

20

30

40

6/9

6/10

6/11

6/12

6/13

6/14

6/15

(%)

OH+TN

+15%

-15%

Transport


Monitors Used in APCA/HDDM Analysis

• Used rural monitors sited upwind of non-attainment areas

• Selected monitors based on location and MPE

• During transport episodes, evaluated source contributions with APCA at these receptors at the time of daily max 8-hour average ozone (DM8)


San Augustine, TX: June 14, 2006

• Largest contributions from Louisiana, Tennessee, BCs, other states in OH-TN Valley source region

• APCA and HYSPLIT back trajectories are consistent in assessment of source regions


Ozone Sensitivity to Change in Elevated Anthro NOx Emissions in

the Source Regions• Sensitivity of DM8 to elevated anthropogenic NOx

(eaNOx) emissions in the OH-TN and SE source regions when > 60 ppb

• For eaNOx only, calculate sensitivities

S1OH-TN~∂O3 _____, S1

SE ~ ∂O3_____,

∂(eaNOxOH-TN) ∂(eaNOxSE)

S2

OH-TN~ ∂2O3________, S2SE ~ ∂2O3 ____,

∂(eaNOxOH-TN)2 ∂(eaNOxSE)2

where OT=Ohio and Tennessee Valleys, SE=Southeastern U.S.

• How sensitive is East Texas ozone to emissions in these source regions?


June 13-15, 2006: Average S1OT

and S2

OT

• S1OT generally positive and largest in source regions

– Emissions increase generally would increase ozone; emissions reduction would decrease ozone

– S1OT negative near large NOx sources; i.e. NOx reduction increases ozone (NOx disbenefit)

• East-west S1OT gradient across Texas, 1-4 ppb on average over episode in East TX

• S2OT largest in vicinity of large coal-fired power plants along the Ohio River


Sensitivity of Texas Ozone to Reductions in Source Region eaNOx

• For a given monitor, calculate ozone change from emissions perturbation Δε using Taylor series expansion about unperturbed state, C(0)

• C=ozone concentration, S(i) are sensitivities

• Where Δε=-0.20 for 20% emissions reduction in the source region, for example, and C(0)=DM8 at the monitor in the unperturbed case

– Neglecting higher order terms Rn+1

• Plot emissions change Δε versus change in ozone C(Δε)-C(0) at the monitor (next slide)


Change in Texas Ozone from Emissions Reduction in OH-TN Valley

Source Region

• Shows change in DM8 at rural TX monitors that would result from reducing eaNOx in OH-TN source region

• NEWT and SAGA show largest ozone reductions

-8

-7

-6

-5

-4

-3

-2

-1

0

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

ge in

Dai

ly M

ax 8

-Hou

r O

zone

(pp

b)

Karnack

Temple

Greenville

Newton

San Augustine

June 14, 2006

Change in Daily 8-Hour Max Ozone with NOx Emission Reduction in

OH-TN Valley Elevated Anthro NOx

% Reduction in OH-TN Valley Elevated Anthro NOx Emissions


Zero-Out Contribution

• HDDM coefficients can be used to estimate effect on ozone of removing (zeroing out) one or more emissions sources

• For 2 emitters j and k, zero out contribution (ZOC) is calculated

• For the OH-TN and SE source regions, ZOC is given by

ZOC(OT+SE)=(S(1)OT-½S(2)

OT,OT)+ (S(1)SE-½S(2)

SE,SE)- S(2)OT,SE

• Here, we calculate components of ZOC for OH-TN and SE eaNOx emissions and present them separately, as cross term was negligible


Comparison of APCA and HDDM ZOC Estimates for eaNOx

• The APCA and HDDM tools agree on the relative importance of these two source regions in contributing to high ozone in Texas

• APCA and HDDM consistent with the HYSPLIT back trajectories

0

2

4

6

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10

12

APCA HDDM

Ozo

ne (p

pb)

Comparison of APCA Source Region Contributions and HDDM Zero-Out Contribution: San Augustine at Time of 8-Hour Max

ea NOx SE

ea NOx OT

June 14, 2006

HYSPLIT Back Trajectories

APCA/HDDM


Conclusions

• HYSPLIT, APCA and HDDM provide complementary information on – model winds that define transport pathways from source

regions to receptor regions– ozone source apportionment– sensitivity of receptors to emissions changes in the source

regions

• Because their formulations are independent of one another, each of these tools can serve as a way to evaluate information provided by the others

• Used in combination, these tools can provide a valuable resource for control strategy development.


Questions?

ozone transport analysis using back-trajectories and camx probing tools

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