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


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


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


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


8-Hour Ozone Attainment Status in GA


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


12 km

4 km

7x7arrayfor 4-km runs


MPE with statistical metrics


Time series


Time series

O3

O3

Mon Tue Wed Thur Fri Sat Sun


Time seriesNO2

ETH



Time seriesO3

O3



NO2

ETH



CAMx

CAMx-CMAQ

2002-06-12

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

2002-07-23 2002-07-24

ppb

ppb


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


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


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.


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


Acknowledgement

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

utilities


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

4244 International Parkway, Suite 120Atlanta, GA 30354

[email protected] 404-362-2526

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