georgia environmental protection division impacts of modeling choices on relative response factors...

Georgia Environmental Protection Division

IMPACTS OF MODELING CHOICES ON RELATIVE RESPONSE FACTORS

IN ATLANTA, GA

Byeong-Uk Kim, Maudood Khan, Amit Marmur, and James Boylan

6th Annual CMAS ConferenceChapel Hill, NCOctober 2, 2007

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Objective• Investigate the effects of modeling

choices on Relative Response Factors (RRFs) in Atlanta, GA– Horizontal grid resolution: 4 km and

12 km

– Chemical Transport Model: CMAQ and CAMx

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Approach• Exercising typical SIP modeling

– Model Performance Evaluation (MPE)• Measures and methods following the EPA’s guidance

document (EPA, 2007)

– Modeled Attainment Test• Relative Response Factors

• Additional analyses– MPE with graphical measures

• Partial implementation of PROMPT (Kim and Jeffries, 2006)

– Investigation of day-by-day and site-by-site variation of model predictions

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Modeled Attainment Test• Future Attainment Status is determined

by Future Design Value (DVf)– DVf should be less than 0.85 ppm.

• DVf = RRF x DVbWhere,

DVb is Baseline Design Value and

RRF is Relative Response Factor defined as

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8-Hour Ozone Attainment Status in GA

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Modeling System Setup• Base case modeling

period– May 21, 2002 ~ Sep

13, 2002 UTC (3 spin-up days )

• MM5 (v 3.x)– Pleim-Xiu model for

Land-Surface interaction

– Asymmetric Convective Mixing

• SMOKE (v 2.x)– VISTAS Base G

version 2 inventory• CMAQ and CAMx

– Inputs made to be close to each model for a same grid configuration.

Georgia

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12 km

4 km

7x7arrayfor 4-km runs

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MPE with statistical metrics

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Time series

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Time series

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Time series

O3

O3

Mon Tue Wed Thur Fri Sat Sun

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Time seriesNO2

ETH

Mon Tue Wed Thur Fri Sat Sun

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Time seriesO3

O3

Mon Tue Wed Thur Fri Sat Sun

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NO2

ETH

Mon Tue Wed Thur Fri Sat Sun

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CAMx

CAMx-CMAQ

2002-06-12

Spatial distribution (12km)Daily Max 8-hr O3

2002-07-23 2002-07-24

ppb

ppb

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Relative Response Factors• Two possible methods

to calculate RRFs• Max value in

“nearby” grid cell arrays

• Value at each monitoring site grid cell

• Spatially averaged RRFs vary from 0.891 to 0.897 by modeling choices• If DVb = 100 ppb,

0.001 difference in RRF will result in 0.1 ppb in DVf.

RRFs from max O3 nearby grid cell arrays

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Conclusion (1)• Reasonable performance with respect to statistical

metrics by all four models, CMAQ and CAMx with 4-km and 12-km grids– 4-km emissions had 11% lower NOx in non-attainment areas– 4-km MM5 runs showed poor nighttime performance.

• Higher biases during nighttime by CMAQ and during daytime by CAMx– Gross overestimation of ozone by CAMx for several days

• Lower biases from 4-km simulations– Probably due to emission discrepancies in 4-km inputs

compared with 12-km emissions.

• No significant daytime NOx biases

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Conclusion (2)• Stable or insensitive RRFs

– Due to higher absolute concentrations predicted by CAMx, CAMx might show quite lower RRFs than CMAQ.

– Max-Value based RRFs fell within 0.863 ~ 0.914 for all simulations.

• Effect of RRF calculation methods– Despite of noticeable differences between 4-km and 12-km

modeling inputs, Max-Value based RRFs does not reflect this fact significantly.

– Cell-Value based RRF distinguished grid configuration differences.

– For all 11 monitoring sites, maximum RRF difference due to model choices were 0.036 and 0.033 by Max-Value based and Cell-Value based RRF calculation.

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Future Work• Process Analysis to explain large

variation of predicted ozone concentrations with similar modeling inputs

• Detail study on the relationship between model performance including day-by-day and site-by-site meteorological model performance and RRFs

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Acknowledgement

• ENVIRON International Corporation– Ralph Morris for CMAQ-to-CAMx

utilities

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Byeong-Uk Kim, Ph.D.Georgia Environmental Protection Division

4244 International Parkway, Suite 120Atlanta, GA 30354

Byeong_Kim@dnr.state.ga.us 404-362-2526

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