The Atlanta Eight-Hour Ozone Attainment Demonstration 

Appendix M 

Georgia Environmental Protection 

Division Comments on: 

"Guidance on the Use of Models and 

Other Analyses for Demonstrating 

Attainment of Air Quality Goals for 

Ozone, PM2.5 and Regional Haze" 

October 27, 2006
Georgia Environmental Protection Division Comments on: 

“Guidance on the Use of Models and Other Analyses for Demonstrating Attainment 

of Air Quality Goals for Ozone, PM2.5 and Regional Haze” 

October 27, 2006 

Grid Cells Used to Calculate RRFs

Pages 24-27 describe the use of cells “near” a monitor rather than the cell containing the 

monitor in the modeled attainment test. Although three reasons are listed why this is 

appropriate, we feel that the use of the cell containing the monitor is more appropriate 

and will lead to more realistic future DVs at the monitor locations. We find it hard to 

justify using unmonitored grid cells near the monitor to describe what is happening at the 

monitor. Model performance evaluations (MPE) are only conducted for the grid cells 

containing monitors; therefore, we feel that the model attainment test should remain 

consistent with the MPE and only use these grid cells. In addition, calculating RRFs 

based on nearby cells that change locations between the baseline simulation and future 

simulation (not paired in space) can lead to erroneous and misleading conclusions. 

 

To address any potential issues arising from predicted peak “migration”, we propose that 

the cells “near” the monitor undergo the “unmonitored area analysis” described in 

Section 3.4 on Page 28. Although these grid cells will not be used in the actual modeled 

attainment test to project future design values, they would still be examined along with 

the other unmonitored areas. As part of the WOE, an overall exposure metric (weighted 

by population) could be evaluated in the array of nearby grid cells to quantify reductions 

in exposure. 

Uncertainty in the formulation of the model and the model inputs is not a good reason to 

use an array of nearby grid cells instead of the cell containing the monitor. There can be 

very large (and very real) differences in precursor emissions, pollutant 

formation/deposition processes, and resulting pollutant concentrations between 

neighboring grid cells within the array. Arbitrarily taking the maximum value or the 

average value is not an accurate way to characterize the changes occurring at the monitor. 

If the model performance at the grid cell with the monitor is extremely poor on a specific 

day, consideration should be given to removing this day from the RRF calculation. 

To diminish the likelihood of inappropriate results occurring from the geometry of the 

superimposed grid system, we feel the best solution is to find the exact location of the 

monitors in the grid cells. If a monitor is near a grid border (e.g., offset from the center 

of grid by more than 25%), multiple grid cells could be averaged by bi-linear 

interpolation instead of just using the cell containing the monitor. 

The use of nearby grid cells is especially inappropriate in situations where there are major 

emission reductions from large point sources or significant area/mobile sources located 

within the array of nearby cells. For example, if we simulate the installation of SCRs at a 

large utility located two grid cells to the left of the monitor (assume 7x7 array for 4-km 
grid), the model might predict lower future ozone concentrations in the array even when 

the wind is blowing in the completely opposite direction from the monitor. 

Application of Dispersion Models

Section 5.3 outlines a procedure for using a dispersion model for primary PM2.5 in an 

attainment test. However, the procedure is not clearly described and may not be feasible. 

For example, it is not clear how to accurately identify primary versus secondary 

components of PM2.5 at the monitor (Step 2, Page 61) or the percentage of total primary 

PM2.5 at the monitor due to local sources (Step 3, Page 61). How do you accurately split 

OC into primary versus secondary components? What if there is no speciation 

information at the monitor in question? The guidance should give more details on how 

these questions should be approached. For example: 

• If speciation data exists, use the EC tracer approach of CMB to identify SOA. 

Next, define primary PM as: PM2.5 - sulfate - nitrate - ammonium - SOA. 

• If speciation data is not available, but there is a nearby speciation monitor, 

estimate total primary PM by the difference between total PM2.5 at the site in 

question and all secondary components at the speciation monitor. Again, this 

would require an EC tracer or CMB analysis for the speciation monitor. 

Alternatively, a much easier and straightforward approach would be to use the absolute 

response from the dispersion model instead of the relative response. Absolute 

concentration differences resulting from the control of these local sources would simply 

be subtracted from the future DVs calculated using the photochemical model. This 

approach may be especially appropriate and introduce the least amount of uncertainty at 

sites that do not have any speciated monitoring data. 

Model Performance

Not all days simulated by the model will be used in the RRF calculations. For example, 

only ozone days over 85 ppb will be used for ozone projections and only the 20% best 

and worst days will be used for regional haze projections. The guidance should stress 

that the model performance on these days should be examined in more detail with 

specific emphasis on precursor performance and indicator ratios. In addition, a second 

set of performance statistics should be developed based on the limited set of modeled 

days used in the RRF calculations. Finally, the guidance should make allowances for 

removing poor performing days from the RRF calculations as part of WOE. 

Specific Comments

Pages v – viii: Page numbers listed in the “Table of Contents” do not match those in the 

text starting at page 28. The page discrepancies vary between one and three pages 

throughout the document. 

RRF - The document first introduces the concept of “RRF” as “relative reduction factor” 

and then later switches to “relative response factor”. A consistent definition should be 

used throughout the document. 
Page 30, Section 3.4.2: The first time “CAIR” is mentioned, the acronym should be 

written out and a reference added. 

Page 196, Section on Direct Decoupled Method (DDM): DDM stands for “Decoupled 

Direct Method”, not “Direct Decoupled Method”. Note that DDM is a “direct method” 

of sensitivity analysis that is “decoupled” from the system. Here is the original citation: 

• Dunker, A.M. (1984) The Decoupled Direct Method For Calculating Sensitivity 

Coefficients In Chemical-Kinetics. J. Chem. Phys. 81, 2385-2393. 

One of the first published applications of DDM in an advanced 3-dimensional Eulerian 

grid model was back in 1997. A reference to this paper should be added in this section: 

• Yang Y.J., Wilkinson J.W. and Russell A.G. (1997) Fast, direct sensitivity 

analysis of multidimensional photochemical models. Environ. Sci. Technol. 31, 

2859-2868. 

Note that the above reference is incorrectly listed in the “Reference” section as “in 

press”. Also, it should also be noted that DDM has been implemented in CMAQ (as well 

as CAMx) and that the CMAQ version is capable of calculating higher order ozone 

sensitivities which can capture the non-linear ozone responses to precursor emissions. 

Here are some additional references: 

• Cohan, D. S., Hakami, A., Hu, Y. T., and Russell, A. G. (2005). Nonlinear 

Response of Ozone to Emissions: Source Apportionment and Sensitivity 

Analysis. Environ. Sci. Technol. 39, 6379-6748. 

• Hakami, A., Odman, M. T., and Russell, A. G. (2003). High-order, direct 

sensitivity analysis of multidimensional air quality models. Environ. Sci. Technol.

37, 2442-2452. 

• Hakami, A., Odman, M. T., and Russell, A. G. (2004). Nonlinearity in 

atmospheric response: A direct sensitivity analysis approach. J. Geophys. Res.

109, D15303. 

Pages 191-192: New reference for “bugle plots”: 

• Boylan, J.W. and Russell, A.G (2006). PM and Light Extinction Model 

Performance Metrics, Goals, and Criteria for Three-Dimensional Air Quality 

Models. Atmos. Environ. 40, 4946-4959.