the higher order direct decoupled method (HDDM) - an approach that generates modeled 

sensitivities of ozone to emissions changes - to estimate ozone concentrations that would occur 

with the achievement of a 0.075 ppm ozone standard in multiple urban areas. 

Based on the analysis presented here, we believe that there are clear benefits for using the 

model-based adjustments for the second draft REA. For example, the HDDM model-based 

adjustment approach, unlike quadratic rollback, allows us to predict temporally- and spatiallyvarying response within an urban area to emissions changes and allows us to account for the 

sensitivity of air quality changes to NOx versus VOC emissions reductions. For example, 

model-based adjustments account for ozone increases with NOx reductions which may occur at 

times and locations where NOx to VOC ratios are high and total ozone concentrations are low. 

This approach also directly accounts for physical and chemical processes that lead to ozone 

formation and transport and includes natural and anthropogenic sources of ozone precursors 

from sources both within and outside of the U.S. Consequently, it is more straightforward to 

isolate the ozone response to U.S. emission reductions, eliminating the need to artificially 

specify a floor for air quality changes as is required in the quadratic rollback method. 

Compared to quadratic rollback, the capabilities of the HDDM model-based adjustment 

approach build confidence that the results drawn from this type of analysis allow us to more 

realistically represent the response in hourly ozone concentrations to reductions in emissions for 

a scenario of just meeting the current and alternative standard levels. Further analyses will be 

conducted to better understand the effect of using the HDDM model-based adjustment approach 

on the results of the risk and exposure assessment. 

Attachment 
 I 

Attachment

Model-based rollback using the higher order decoupled direct method 

(HDDM) 

Table of Contents

1. Motivation for a new technique to simulate ozone concentrations under alternative standards ............ 1

2. Higher order decoupled direct method (HDDM) .................................................................................. 2

2.1 Capabilities ................................................................................................................................. 4

2.2 Limitations .................................................................................................................................. 5

3. Using HDDM/CMAQ for SIMULATIONS of Just meeting alternative standards: methodology ........ 5

3.1 Conceptual framework ................................................................................................................ 5

3.2 Application to case studies in Atlanta and Detroit ...................................................................... 7

3.2.1 Binning of HDDM sensitivities .................................................................................................. 7

3.2.2 Application of binned sensitivities to ambient data .................................................................. 12

3.2.3 Multi-step application of HDDM sensitivities .......................................................................... 12

3.3 Proposed revisions for the second draft REA ........................................................................... 16

4. Using HDDM/CMAQ for SIMULATIONS of Just meeting alternative standards: results ................ 17

4.1 Atlanta results ........................................................................................................................... 17

4.1.1 New distributions of ozone to attain 75 ppb NAAQS ............................................................... 17

4.1.2 Resulting design values ............................................................................................................ 19

4.1.3 Comparison to quadratic rollback ............................................................................................. 20

4.2 Detroit results............................................................................................................................ 21

4.2.1 New distributions of ozone to attain 75 ppb NAAQS: NOx and VOC reductions .................... 21

4.2.2 Resulting design values: NOx and VOC reductions .................................................................. 25

4.2.3 Comparison to quadratic rollback ............................................................................................. 26

5. Recommendations ............................................................................................................................... 28

6. References ........................................................................................................................................... 29

APPENDIX A. MODEL SET-UP AND EVALUATION ANALYSES ...................................................................... 31

A.1. Model set-up and simulation ..................................................................................................... 31

A.2. Operational evaluation of modeled ozone concentrations in Atlanta and Detroit ..................... 37

A.3. Choice of sensitivity cutpoints .................................................................................................. 42

A.4. Brute force versus HDDM for 1-step and 3-step application of HDDM sensitivities ............... 43

APPENDIX B. ADDITIONAL PLOTS ....................................................................................................................... 48
 II 

Table of Figures 

FIGURE 1: FLOW DIAGRAM DEMONSTRATING DDM MODEL‐BASED OZONE ADJUSTMENT APPROACH............................................... 6

FIGURE 2: MAP OF OZONE MONITORS IN THE ATLANTA AREA USED FOR THIS ANALYSIS.................................................................. 8

FIGURE 3: MAP OF OZONE MONITORS IN THE DETROIT AREA USED FOR THIS ANALYSIS .................................................................. 9

FIGURE 4: PREDICTED CHANGE IN OZONE CONCENTRATIONS (PPB) AT ATLANTA SITE 131210055 WITH A 30% DECREASE IN US

ANTHROPOGENIC NOX EMISSIONS IN THE EASTERN US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS (LEFT) AND ON

BINS CREATED FOR LOW OZONE NIGHTS (RIGHT).  HOURS 25‐29 REPRESENT 1 AM – 5 AM THE FOLLOWING MORNING............ 11

FIGURE 5: CONCEPTUAL PICTURE OF 3‐STEP APPLICATION OF HDDM SENSITIVITIES ................................................................... 13

FIGURE 6: EXAMPLE OF 3 STEP APPLICATION OF HDDM SENSITIVITIES FOR 9AM HOURS AT DETROIT SITE 261630019 FOR DAYS WITH

MAXIMUM DAILY 8‐HR OZONE CONCENTRATIONS GREATER THAN 75 PPB........................................................................ 15

FIGURE 7: EXAMPLE OF 3 STEP APPLICATION OF HDDM SENSITIVITIES FOR 1AM HOURS AT DETROIT SITE 261630001 FOR DAYS WITH

MAXIMUM DAILY 8‐HR OZONE CONCENTRATIONS BETWEEN 65 AND 75 PPB.................................................................... 16

FIGURE 8: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR ATLANTA SITE 131210055.  CENTERLINES SHOW MEDIAN

VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE RANGE.  

VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW PREDICTED

OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (2006‐2008)....................................... 18

FIGURE 9: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR ATLANTA SITE 132470001.  CENTERLINES SHOW MEDIAN

VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE RANGE.  

VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW PREDICTED

OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (2006‐2008)....................................... 19

FIGURE 10: DISTRIBUTION OF OZONE CONCENTRATIONS FOR ALL ATLANTA SITES (2006‐2008) FOR MEASURED VALUES (LEFT), VALUES

CALCULATED USING THE MODEL‐BASED ADJUSTMENT APPROACH (NOX REDUCTIONS) (MIDDLE) AND VALUES CALCULATED USING

QUADRATIC ROLLBACK (RIGHT).  CENTERLINES SHOW MEDIAN VALUES, BOXES COVER 25TH TO 75TH PERCENTILE RANGES AND

WHISKERS EXTEND TO 5TH AND 95TH PERCENTILE VALUES.  LEFT PANEL INCLUDES OUTLIER VALUES WHILE RIGHT PANEL FOCUSES ON

THE PORTION OF THE RANGE WHICH INCLUDES THE 5TH TO 95TH PERCENTILE VALUES. ......................................................... 21

FIGURE 11: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR DETROIT SITE 260991003.  CENTERLINES SHOW MEDIAN

VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE RANGE.  

VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW PREDICTED

OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (NOX CUT) (2006‐2008). ...................... 22

FIGURE 12: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR DETROIT SITE 260990009.  CENTERLINES SHOW MEDIAN

VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE RANGE.  

VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW PREDICTED

OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (NOX CUT) (2006‐2008). ...................... 23

FIGURE 13: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR DETROIT SITE 260991003.  CENTERLINES SHOW MEDIAN

VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE RANGE.  
 III 

VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW PREDICTED

OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (VOC CUT) (2006‐2008). ..................... 24

FIGURE 14: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR DETROIT SITE 260990009.  CENTERLINES SHOW MEDIAN

VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE RANGE.  

VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW PREDICTED

OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (VOC CUT) (2006‐2008). ..................... 25

FIGURE 15: DISTRIBUTION OF OZONE CONCENTRATIONS FOR ALL DETROIT SITES (2006‐2008) FOR MEASURED VALUES (LEFT), VALUES

CALCULATED USING HDDM MODEL‐BASED ADJUSTMENT APPROACH (NOX REDUCTIONS) (2ND FROM LEFT), HDDM MODEL‐

BASED ADJUSTMENT APPROACH (VOC REDUCTIONS) (2ND FROM RIGHT) AND VALUES CALCULATED USING QUADRATIC ROLLBACK

(RIGHT). CENTERLINES SHOW MEDIAN VALUES, BOXES COVER 25TH TO 75TH PERCENTILE RANGES AND WHISKERS EXTEND TO 5TH

AND 95TH PERCENTILE VALUES.  LEFT PANEL INCLUDES OUTLIER VALUES WHILE RIGHT PANEL FOCUSES ON THE PORTION OF THE

RANGE WHICH INCLUDES THE 5TH TO 95TH PERCENTILE VALUES....................................................................................... 27

FIGURE 16: MAP OF THE CMAQ MODELING DOMAIN.  THE BLACK OUTER BOX DENOTES THE 36 KM NATIONAL MODELING DOMAIN AND

THE BLUE INNER BOX IS THE 12 KM EASTERN U.S. FINE GRID......................................................................................... 32

FIGURE 17: TIME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT ATLANTA MONITORING SITES FOR JULY

2005.  OBSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED. ........................................................ 39

FIGURE 18: TIME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT ATLANTA MONITORING SITES FOR AUGUST

2005.  OBSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED. ........................................................ 39

FIGURE 19: TIME SERIES OF MODEL PERFORMANCE FOR 8‐HR DAILY MAXIMUM OZONE CONCENTRATIONS AT ATLANTA MONITORING

SITES FOR JULY AND AUGUST 2005.  OBSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED. ................. 40

FIGURE 20: TIME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT DETROIT MONITORING SITES FOR JULY

2005.  OBSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED. ........................................................ 40

FIGURE 21: TIME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT DETROIT MONITORING SITES FOR AUGUST

2005.  OBSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED. ........................................................ 41

FIGURE 22: TIME SERIES OF MODEL PERFORMANCE FOR 8‐HR DAILY MAXIMUM OZONE CONCENTRATIONS AT DETROIT MONITORING SITES

FOR JULY AND AUGUST 2005.  OBSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED.......................... 41

FIGURE 23: COMPARISON OF HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 100% VOC EMISSIONS CUTS AT GRID CELLS

CONTAINING ATLANTA MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS

PLOT.................................................................................................................................................................. 44

FIGURE 24: COMPARISON OF HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 100% VOC EMISSIONS CUTS AT GRID CELLS

CONTAINING DETROIT MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS

PLOT.................................................................................................................................................................. 44

FIGURE 25:  COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 75% NOX

EMISSIONS CUTS AT GRID CELLS CONTAINING ATLANTA MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT. ...................................................................................................................... 45

FIGURE 26: COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 75% NOX

EMISSIONS CUTS AT GRID CELLS CONTAINING DETROIT MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT. ...................................................................................................................... 46

FIGURE 27: COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 100% NOX

EMISSIONS CUTS AT GRID CELLS CONTAINING ATLANTA MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT. ...................................................................................................................... 46
 IV 

FIGURE 28: COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 100% NOX

EMISSIONS CUTS AT GRID CELLS CONTAINING DETROIT MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT. ...................................................................................................................... 47

FIGURE 29: PREDICTED CHANGE IN OZONE CONCENTRATIONS (PPB) AT ALL ATLANTA SITES WITH A 30% DECREASE IN US

ANTHROPOGENIC NOX EMISSIONS IN THE EASTERN US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS................ 48

FIGURE 30: PREDICTED CHANGE IN OZONE CONCENTRATIONS (PPB) AT ALL ATLANTA SITES WITH A 30% DECREASE IN US

ANTHROPOGENIC NOX EMISSIONS IN THE EASTERN US DOMAIN BASED ON BINS CREATED FOR LOW OZONE NIGHTS. ............... 49

FIGURE 31: PREDICTED CHANGE IN OZONE CONCENTRATIONS (PPB) AT ALL DETROIT SITES WITH A 30% DECREASE IN US

ANTHROPOGENIC NOX EMISSIONS IN THE EASTERN US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS................ 50

FIGURE 32: PREDICTED CHANGE IN OZONE CONCENTRATIONS (PPB) AT ALL DETROIT SITES WITH A 30% DECREASE IN US

ANTHROPOGENIC NOX EMISSIONS IN THE EASTERN US DOMAIN BASED ON BINS CREATED FOR LOW OZONE NIGHTS. ............... 51

FIGURE 33: PREDICTED CHANGE IN OZONE CONCENTRATIONS (PPB) AT ALL DETROIT SITES WITH A 30% DECREASE IN US

ANTHROPOGENIC VOC EMISSIONS IN THE EASTERN US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS. .............. 52

FIGURE 34: PREDICTED CHANGE IN OZONE CONCENTRATIONS (PPB) AT ALL DETROIT SITES WITH A 30% DECREASE IN US

ANTHROPOGENIC VOC EMISSIONS IN THE EASTERN US DOMAIN BASED ON BINS CREATED FOR LOW OZONE NIGHTS................ 53

FIGURE 35: COMPARISON OF 1‐STEP HDDM TO BRUTE FORCE OZONE CONCENTRATIONS (LEFT) AND CHANGES IN OZONE (RIGHT)  DUE

TO 50% EASTERN US VOC EMISSIONS CUTS AT GRID CELLS CONTAINING ATLANTA MONITORING SITES.  COLORS DENOTE THE

PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS PLOT............................................................................. 54

FIGURE 36: COMPARISON OF 1‐STEP HDDM TO BRUTE FORCE OZONE CONCENTRATIONS (LEFT) AND CHANGES IN OZONE (RIGHT)  DUE

TO 50% EASTERN US VOC EMISSIONS CUTS AT GRID CELLS CONTAINING DETROIT MONITORING SITES.  COLORS DENOTE THE

PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS PLOT............................................................................. 54

FIGURE 37: FIGURE 34: COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO

75% NOX CUTS AT GRID CELLS CONTAINING ATLANTA MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT. ...................................................................................................................... 55

FIGURE 38: FIGURE 34: COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO

75% NOX CUTS AT GRID CELLS CONTAINING DETROIT MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT. ...................................................................................................................... 55

FIGURE 39: FIGURE 34: COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO

100% NOX CUTS AT GRID CELLS CONTAINING ATLANTA MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT. ...................................................................................................................... 56

FIGURE 40: COMPARISON OF 1‐STEP (LEFT) AND MULTI‐STEP (RIGHT) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO 100% NOX

CUTS AT GRID CELLS CONTAINING DETROIT MONITORING SITES.  COLORS DENOTE THE PERCENTAGE OF POINTS WHICH FALL IN EACH

LOCATION IN THIS PLOT. ........................................................................................................................................ 56

FIGURE 41: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY AT EACH ATLANTA MONITORING SITE.  CENTERLINES SHOW

MEDIAN VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE

RANGE.  VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW

PREDICTED OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (2006‐2008) ....................... 57

FIGURE 42: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY AT EACH DETROIT MONITORING SITE.  CENTERLINES SHOW

MEDIAN VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE

RANGE.  VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW

PREDICTED OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (NOX CUTS) (2006‐2008)...... 58
 V 

FIGURE 43: DISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY AT EACH DETROIT MONITORING SITE.  CENTERLINES SHOW

MEDIAN VALUES, BOXES DESIGNATE 25TH TO 75TH PERCENTILE VALUES AND WHISKERS EXTEND TO 1.5 TIMES THE INTERQUARTILE

RANGE.  VALUES IN GRAY/BLACK SHOW MEASURED OZONE DISTRIBUTIONS (2006‐2008) AND VALUES IN RED/PINK SHOW

PREDICTED OZONE DISTRIBUTIONS BASED ON HDDM MODEL‐BASED ADJUSTMENT APPROACH (VOC CUTS) (2006‐2008)...... 59

 
 VI 

Table of Tables 

TABLE 1: OBSERVED, MODEL ADJUSTED AND ROLLED‐BACK DESIGN VALUES FOR ATLANTA SITES.  URBAN SITES HIGHLIGHTED IN BLUE. . 20

TABLE 2: OBSERVED, MODEL ADJUSTED, AND ROLLED‐BACK DESIGN VALUES FOR DETROIT SITES.  URBAN SITES HIGHLIGHTED IN BLUE. . 27

TABLE 3: GEOGRAPHIC ELEMENTS OF DOMAIN USED IN THE CMAQ/HDDM MODELING ............................................................ 32

TABLE 4: VERTICAL LAYER STRUCTURE FOR MM5 AND CMAQ (HEIGHTS ARE LAYER TOP) ........................................................... 34

TABLE 5: SUMMARY OF EMISSIONS TOTALS BY SECTOR FOR THE 12KM EASTERN US DOMAIN....................................................... 36

TABLE 6: PERFORMANCE STATISTICS OF MODELED OZONE AT 10 ATLANTA MONITORING SITES USED IN THIS ANALYSIS ...................... 38

TABLE 7: PERFORMANCE STATISTICS OF MODELED OZONE AT 8 DETROIT MONITORING SITES USED IN THIS ANALYSIS ......................... 38

 
 1 

1. MOTIVATION FOR A NEW TECHNIQUE TO SIMULATE OZONE 

CONCENTRATIONS UNDER ALTERNATIVE STANDARDS 

As part of the reviews of the National Ambient Air Quality Standards (NAAQS) for 

ozone, EPA estimates health risks after ozone has been adjusted to just meet the current 

standard and alternative standards. The first draft documents for this review rely upon the 

quadratic rollback method used in previous reviews to adjust or “roll back” hourly ozone 

concentrations in urban areas. Although the quadratic rollback method simulates historical 

patterns of air quality changes better than some alternative methods (e.g. simply shaving peak 

concentrations off at the NAAQS level), its implementation requires some assumptions that 

may not always hold true. First, the quadratic rollback method requires that all monitors in an 

urban area exhibit the same response to emissions changes not allowing for temporally varying 

response depending on time of day. In addition, it assumes that ozone concentrations never 

increase in response to emissions reductions. However, during NOx-saturated (VOC limited) 

conditions, NOx reductions can result in ozone increases (Seinfeld and Pandis, 1998). Finally, 

since the quadratic rollback method is purely a mathematical technique and does not account 

for physical and chemical atmospheric processes or the sources of emissions precursors that 

lead to ozone formation, a backstop or “floor” must be used to ensure that predicted ozone is 

not reduced below “background” concentrations1

. 

EPA has received comments during past ozone NAAQS reviews and during the January 

9-10, 2012 Clean Air Scientific Advisory Committee (CASAC) meeting for this ozone NAAQS 

review which encourage the use of alternate methods to quadratic rollback. In addition, the 

National Research Council of the National Academies (NRC, 2008) recommended that EPA 

explore how emissions reductions might effect temporal and spatial variations in O3

concentrations, and to include information on how NOx versus VOC control strategies might 

affect risk and exposure to O3. 

Photochemical modeling can simulate the ozone response to emission reductions while 

avoiding the limitations presented by the quadratic rollback method. While there are 

uncertainties inherent in any modeling exercise due to uncertainties in inputs and model 

parameters, the improved characterization of the spatial and temporal responses of ozone to the 

 

1

 Background ozone has been characterized in previous reviews of the ozone NAAQS as “policy relevant 

background” or PRB, defined as ozone concentrations that would exist in the absence of North American 

anthropogenic emissions. In the current review, we have refined the concept of background ozone to recognize 

that there are several possible definitions of background ozone, reflecting both the geographic source of emissions, 

e.g. U.S., North American, Global non-U.S., and whether emissions are anthropogenic or natural in origin. In the 

cases described in this document, “background” refers to ozone that would exist in absence of U.S. anthropogenic 

emissions. 
 2 

reductions in emissions that would be needed to just meet the standards justify these additional 

uncertainties. In this document we present a model-based ozone adjustment approach. This 

analysis uses the CMAQ photochemical model instrumented with the higher order direct 

decoupled method (HDDM) - an approach that generates modeled sensitivities of ozone to 

emissions changes - to estimate ozone concentrations that would occur with the achievement of 

a 0.075 ppm ozone standard in multiple urban areas. This modeling incorporates all known 

emissions, including emissions from non-anthropogenic sources and anthropogenic emissions 

from sources in and outside of the U.S. As a result, the need to specify values for U.S. 

background concentrations is not necessary, as it is incorporated in the modeling directly. 

Because the simulations focus on reductions in U.S. anthropogenic emissions while holding 

constant those emissions that influence U.S. background, all changes in ozone will be relative 

to U.S. background. This does not mean that the background ozone concentrations will be 

constant between recent ambient ozone conditions and after just meeting the current standards, 

because of nonlinearities in the formation of ozone. In simulations of just meeting the 

standards used to inform the exposure and risk assessment, HDDM sensitivities can be applied 

relative to ambient measurements of O3 to estimate how ozone concentrations would respond to 

changes in anthropogenic emissions within the U.S. We propose to use this methodology to 

simulate ozone concentrations meeting the current and alternative standards in the 2nd drafts of 

the risk and exposure assessments and the 2nd draft policy assessment. 

2. HIGHER ORDER DECOUPLED DIRECT METHOD (HDDM) 

Chemical transport models, such as the Community Multiscale Air Quality Model 

(CMAQ) (www.cmaq-model.org), calculate the effects of physical and chemical processes in 

the atmosphere to predict 3-D gridded pollutant concentrations (Foley et al, 2011, Appel et al, 

2008, Appel et al, 2007, Byun and Schere, 2006). These models account for the impacts of 

emissions, transport, chemistry, and deposition on spatially and temporally varying pollutant 

concentrations. Required model inputs include time-varying emissions and meteorology fields, 

time varying concentrations of pollutants at the boundaries of the model domain (i.e. boundary 

conditions), and a characterization of the 3-D field of chemical concentrations to initialize the 

model (i.e. initial conditions). A simplified version of the atmospheric diffusion equation 

solved by such models is given in Equation (1) (Cohan and Napelenok, 2011): 

࢏࡯ࣔ

࢚ࣔ

ൌ െસሺ࢏࡯࢛ሻ ൅ સሺࡷસ࢏࡯ሻ ൅ ࢏ࡾ ൅ ࢏ࡱ ൅ ڮ Equation (1) 
 3 

In this equation (which is defined to vary in both space and time), Ci represents the atmospheric 

concentration field of compound i, u gives the wind field, K represents the turbulent diffusivity 

tensor, Ri is a represents the net rate of chemical production (which encompasses gas, aqueous, 

and particle reactions), Ei represents the emissions rate, and the ellipsis shows that other 

processes are also accounted for in the model (such as deposition, particle coagulation, and 

partitioning of semivolatile species between gas and particle phases). Solving Equation (1) is 

complicated by the fact that these models include many chemical species whose concentrations 

are interconnected through the Ri and partitioning terms. 

Chemical transport models include sources outside of US control which are often termed 

“background” and explicitly account for transport of pollutants from outside the model domain 

and emission of pollution precursors from natural and anthropogenic sources within the model 

domain. Beyond modeling the current concentrations of ambient ozone, chemical transport 

models can be used to estimate the response of ambient ozone concentrations to changes in 

emissions. 

One technique to model this response, the brute force method, requires the modeler to 

explicitly model this response by directly altering the emissions inputs in the model simulation. 

This technique provides an accurate estimate of the ozone concentration at the altered emission 

level, but often does not provide accurate information regarding the response of ozone to other 

levels of emissions since the chemistry for ozone formation is nonlinear. Therefore, when 

using only brute force techniques a new model simulation would need to be performed for 

every emissions scenario in question. 

Other analytical techniques have been developed to estimate the ozone response to 

emissions perturbations without rerunning the entire simulation. One such method is termed 

the decoupled direct method (DDM) (Dunker 1984). DDM, solves for sensitivity coefficients 

which are defined as the partial derivative of the atmospheric diffusion equations that underly 

the model calculations, Equations (2) and (3). 

࢐࢏࢙ሺ࢚ሻ ൌ ࢏࡯ࣔሺ࢚ሻ

࢐࢖ࣔ

 Equation (2) 

࢐෩ࡼ ൌ ሻ࢚ሺ࢐࢏ࡿ

ሻ࢚ሺ࢏࡯ࣔ

࢐࢖ࣔ

࢐෩ࡼ ൌ

ሻ࢚ሺ࢏࡯ࣔሻ࢚ሺ࢏࡯ࣔ ൌ ൯࢐෩ࡼ࢐ࣕ൫ࣔ

࢐ࣕࣔ

 Equation (3) 

Here, sij(t), the sensitivity, gives the change in model concentration, Ci, (for instance ozone 

concentration) with an incremental change in any input parameter, pj (in this case emissions). 

Equation (3) allows us to normalize the sensitivity coefficient, Sij(t), so that it shows response in 

relative terms for the input rather than in absolute units. Therefore, ܲ෨

௝ (x,t) is the normalized 
 4 

input and εj is a scaling variable (Yang et al, 1997). In general terms, the sensitivity coefficient 

tells us how a model output (ozone concentration) will change if a model input (emissions of 

NOx or VOC) is perturbed. This first order sensitivity coefficient, Sij(t) is quite accurate for 

small perturbations, but gives a linear response which is not accurate for large perturbations in 

very nonlinear relationships. Second (and third) order derivatives can be taken to give higher 

order sensitivity coefficients (Hakami et al, 2003). Higher order sensitivity coefficients give the 

curvature and inflection points for the response curve and can capture the nonlinearities in the 

response of ozone to emissions changes. Using higher order DDM (HDDM) allows for the 

sensitivities to be accurately applied over larger emissions perturbations. Hakami et al. (2003) 

report that for an application in California, HDDM gave good approximations of ozone changes 

for perturbations of emissions up to 50% using the first three terms of the Taylor series 

expansion, Equation (4). 

࡯ሺ൅∆ࣕሻ ൌ ࡯ሺ૙ሻ ൅ ∆ࡿࣕሺ૙ሻ ൅ ∆ࣕ૛

૛

࢔ࣕ∆ ൅... ൅ ሻ૙ሺ૛ࡿ

!࢔

࢔ࡿሺ૙ሻ ൅ ࢔ࡾା૚ Equation (4) 

Here ∆߳ represents the relative change in emissions (for instance ∆߳ = -0.2 would be equivalent 

to reducing emissions by 20%), Sn(0) is the n-th order sensitivity coefficient, C(0) is the 

concentration under baseline conditions (no perturbation in emissions) and Rn+1 is a remainder 

term. 

2.1 CAPABILITIES 

DDM and HDDM have been implemented into several chemical transport models for 

both ozone and particulate matter (PM) predictions (Dunker, 1984; Yang et al, 1997; Hakami et 

al, 2003; Cohan et al, 2005; Napelenok et al, 2006; Koo et al., 2010; Zhang et al, 2012). These 

implementations allow the modeler to define the parameters for which sensitivities will be 

calculated. For instance, the sensitivity can be calculated for emissions from a specific source 

type, for emissions in a specific geographic region, and for emissions of a single ozone 

precursor or for multiple ozone precursors. In addition, sensitivities can be calculated to 

boundary conditions, initial conditions, and various other model inputs. Sensitivities to 

different sets of parameters can be calculated in a single model simulation but computation time 

increases as the number of sensitivities increases. Outputs from an HDDM simulation consist 

of time varying 3-D fields of first and second order sensitivities. 

For the purposes of the ozone NAAQS analysis, HDDM has the potential to provide an 

improved approach compared to current quadratic rollback techniques for several reasons. 

First, it captures non-linearity of ozone response to emissions changes, representing both 

increases and decreases in ozone concentrations resulting from emissions reductions. Second, 
 5 

HDDM characterizes different ozone response at different locations (downtown urban versus 

downwind suburban) and at different times of day to emissions reductions allowing us to 

incorporate temporal and spatial variations in response into the ozone adjustment methodology. 

Finally, HDDM eliminates the need to use “background” ozone as a floor for rollback since 

predicted sensitivities are based on model formulations that explicitly account for background 

sources. 

2.2 LIMITATIONS 

In addition to the many potential benefits of using HDDM to understand ozone response 

to emissions changes, there are several limitations. First, HDDM encompasses all of the 

uncertainties of the base model formulation and inputs. So uncertainties in how the physical 

and chemical processes are treated in the model and in the model inputs propagate to the 

HDDM results. Also, HDDM can capture response to larger emissions perturbations than 

DDM but it is still most accurate for small perturbations. The larger the relative change in 

emissions, the less likely that the HDDM sensitivities will be capturing the change in ozone that 

would be predicted by a brute force model simulation. Several studies have reported reasonable 

performance of HDDM for ozone up to 50% emissions perturbations (Hakami et al, 2003; 

Cohan et al., 2005; Hakami et al, 2004), but the magnitude of perturbation over which HDDM 

will give accurate estimates will depend on the specific modeling episode, size of the model 

domain, emissions and meteorological inputs, and the size of the emissions source to which the 

sensitivity is being calculated. In this work, we applied sensitivities from simulations done 

under varying NOx levels (see Section 3.2.3) and found that using this technique we were able 

to replicate brute force estimates using HDDM sensitivities for up to 75% NOx or VOC 

reductions with a normalized mean bias of less than 10% and a normalized mean error of less 

than 15%. 

3. USING HDDM/CMAQ FOR SIMULATIONS OF JUST MEETING 

ALTERNATIVE STANDARDS: METHODOLOGY 

3.1 CONCEPTUAL FRAMEWORK 

This section outlines the methodology applied to use CMAQ/HDDM to estimate hourly 

ozone concentrations that might result from meeting the current and possible new ozone 

NAAQS. Note that model results are not used in an absolute sense, but instead are applied to 

ambient measurements, thus tying predicted ozone distributions more directly to measured 
 6 

values. The basic steps are outlined below and in Figure 1. Details are given in section 3.2 and 

Appendix A. 

• Step 1: Run CMAQ simulation with HDDM to determine hourly ozone sensitivities to 

NOx emissions and VOC emissions for the grid cells containing monitoring sites in an 

urban area. 

• Step 2: For each monitoring site, group days by predicted daily 8-hr max ozone and 

predicted high versus low nighttime ozone. Calculate average diurnal profiles for 

sensitivities in each group. 

• Step 3: For each monitoring site, assign one of the diurnal sensitivity profiles calculated 

in Step 2 to each day in the period 2006 through 20082

 based on measured 8-hr max 

ozone and measured nighttime ozone. 

• Step 4: Adjust measured hourly ozone concentrations for incrementally increasing 

levels of emissions reductions using assigned sensitivities and then recalculate design 

values until all monitors in an urban area are in attainment of current and proposed 

alternative levels of the standard. 

Figure 1: Flow diagram demonstrating DDM model-based ozone adjustment approach 

 

2

 The first draft of the Health Risk and Exposure Assessment for the 2013 ozone NAAQS review (US 

EPA, 2012) estimated the health impacts of meeting various levels of the NAAQS for two three-year periods: 

2006-2008 and 2008-2010. In this analysis we focus on the first of these three-year periods. 
 7 

3.2 APPLICATION TO CASE STUDIES IN ATLANTA AND DETROIT 

In this case study, we illustrate the model-based adjustment approach for attainment of 

the 75 ppb NAAQS. The analysis covers two cities (Atlanta and Detroit) using modeling for 

July and August 2005 and ambient data for the years 2006-2008. Atlanta and Detroit were 

chosen for this analysis because they are both cities included in exposure analysis and because 

they represent different chemical ozone formation regimes3

. These case studies were selected 

as a proof-of-concept for the application of the HDDM model-based ozone adjustment 

approach. In addition, these case studies have helped us identify a number of challenges and 

the benefits of this type of approach. 

3.2.1 Binning of HDDM sensitivities 

When running CMAQ with HDDM additional inputs are required to designate model 

inputs for calculating sensitivities. In this analysis, HDDM was set up to calculate the 

sensitivity of ozone concentrations to domainwide (within the 12km Eastern domain – see 

Figure 16) anthropogenic NOx and VOC emissions. US anthropogenic emissions were defined 

as all emissions in the following sectors: nonpt, nonroad, onroad, alm_no_c3, ptipm, ptnonipm, 

and seca_C3 (see Table 5) and accounted for 17,595,000 of the total domainwide 20,729,000 

tons per year of NOx emissions. Sensitivities were not determined for biogenic, fire, Canadian, 

or Mexican emissions. In addition, sensitivities were not calculated for any emissions 

originating from outside the domain (i.e. entering through the use of boundary concentrations). 

First and second order hourly ozone sensitivities to VOC and NOx were extracted from 

the HDDM simulation for model grid cells that contained one of ten ozone monitors near 

Atlanta, GA and one of eight ozone monitors near Detroit, MI. Maps of the Atlanta and Detroit 

area ozone monitor locations are shown in Figure 2 and Figure 3. The 18 monitors within these 

two areas are being used for this analysis because data availability was such that design values 

could be calculated for the years 2006-2008. Note that sites131210055 and 130890002 in 

Atlanta are contained in the same model grid cell and therefore are modeled as having the same 

sensitivities to NOx and VOC emissions. 

 

3

 Different sources of VOC and NOx in the Midwest and Southeastern US lead to different VOC/NOx

ratios, ozone formation chemistry, and sensitivity to emissions changes in these two regions. Generally the large 

sources of biogenic VOC emissions in the Southeastern US have a large regional impact of ozone formation. 
 8 

Figure 2: Map of ozone monitors in the Atlanta area used for this analysis 
 9 

Figure 3: Map of ozone monitors in the Detroit area used for this analysis 

Extracted data included modeled sensitivities at monitor locations for all hours in July 

and August 2005. These sensitivities cannot be applied directly to observed values for two 

reasons 1) high modeled ozone days/hours do not always occur concurrently with high 

observed ozone days/hours and 2) the modeling time period covers July and August 2005 but 

the time period we are analyzing in this proof-of-concept analysis is 2006-2008. As to the first 

point, photochemical models are generally used in a relative sense for purposes of projecting 

design values to assess attainment with the NAAQS standard. In this manner, model 

predictions are “anchored” to measured ambient values. This is generally not done on a dayspecific basis, but instead average response on high modeled days is used for this purpose. This 

allows for more confidence in calculated results when “less than ideal model performance 

[occurs] on individual days” (US EPA, 2007). Similarly, for this analysis we believe it is 

appropriate to account for the fact the model does not always perfectly agree with 
 10 

measurements and that sensitivities from a low-ozone modeled day would not be appropriate to 

apply to a high-ozone measured day (and vice-versa) even if they occur on the same calendar 

day. For the second point, due to current resource and time constraints we were only able to 

model two months in 2005 for the purposes of developing and testing the methodology. 

However, the ozone exposure analysis evaluates the effects of ozone decreases for two 3-year 

periods in 2006-2008 and 2008-2010. The analysis presented in this section only examines 

2006-2008 ambient data, but this is still outside the modeled time period. For both of these 

reasons, a method was developed to generalize the modeled hour-specific sensitivities so that 

they could be applied to ambient data. 

For each grid cell containing a monitor, each modeled day was classified based on its 

CMAQ-simulated daily 8-hr maximum ozone concentration. Five classifications were 

developed for days with 8-hr daily maximum ozone concentrations < 45 ppb, 45-55 ppb, 55-65 

ppb, 65-75 ppb, and 75 ppb: each bin includes values at the lower limit but not at the upper 

limit (i.e. for the 44-55 bin, days are included that are 45 and < 55). The rational for this 

binning system is that the response of ozone to emissions reductions will be greater when ozone 

concentrations themselves are higher (see Figure 4). From these groupings, five average 

diurnal sensitivity profiles were created for each monitor location and for each sensitivity type 

(first order NOx, second order NOx, first order VOC, and second order VOC). These average 

sensitivities were calculated by taking the median sensitivity of all modeled sensitivities at a 

particular hour for the group of days falling within a bin (i.e. the median for all 8 am hours on 

days < 45 ppb would be used as a “typical” 8 am response for this lowest bin, the median for all 

9 am hours on those days would be used as a “typical” 9 am response, etc.). Days were defined 

as running from 6 am to 5 am so that the 1 am – 5 am sensitivities were binned based on the 

previous day’s 8-hr maximum ozone concentration. 

The median values described above are meant to capture the “typical” response at each 

site based on 8-hr maximum ozone. However, there is variation among the sensitivities from 

the days that comprise each binned group, especially during nighttime hours. In general, nights 

with higher ozone concentrations within each bin are also more responsive to emissions 

reductions. Consequently, nighttime hours within each bin are further classified by “high” 

versus “low” nighttime ozone. For the purposes of this analysis, nights with at least one hourly 

ozone concentration above 50 ppb (between 7 pm and 5 am) are classified as high ozone nights. 

Sensitivities for nighttime hours (7 pm to 5 am) use median values from only “high” or “low” 

ozone nights within the original bin, while sensitivities for daytime hours (6 am to 6 pm) use 

median values from all days in the original bin. For bins and sites that contain only “high” or 

only “low” nights, sensitivities for nighttime values are also calculated as the median from all 

nighttime hours in the original bin. 
 11 

Figure 4a presents the diurnal profiles for site 13121005 in downtown Atlanta on high 

ozone nights and shows the calculated change in ozone for each bin that would occur with a 

30% decrease in NOx emissions (using both the first and second order sensitivity terms). Figure 

4b shows the same information as Figure 4a but for low ozone nights. Several features are 

evident in these plots. First, as described above, there is more ozone response (larger decrease 

in predicted ozone concentration) on days with higher 8-hr maximum ozone values. Second, 

while the model predicts ozone decreases during daytime hours, there are clear disbenefits 

(ozone increases) during morning and evening rush hour times (although the degree of this 

disbenefit and the length of time it lasts depend on the ozone bin). For this site, the days in the 

highest and lowest 8-hr maximum ozone bins also have higher peak ozone increases during 

morning and evening hours than is seen on mid-range ozone days. Also, for site 131210055 

there were no high ozone nights in the <45, 45-55, and 55-65 ppb bins so the nighttime 

responses in Figure 4a and Figure 4b are the same for those bins. As explained above, the high 

ozone nights (Figure 4a) show smaller disbenefits and/or larger ozone decreases than low ozone 

nights (Figure 4b) between 7 pm and 5 am. This behavior varies by site. VOC response is 

similar in that high ozone bins generally have more response. However, no disbenefits occur in 

the VOC sensitivities. Figures shown here are for one site and only for response to NOx

emissions changes. Figures for all other Atlanta sites (NOx sensitivities only) and all Detroit 

sites (NOx and VOC sensitivities) are shown in Section Appendix B of this document. 

Figure 4: Predicted change in ozone concentrations (ppb) at Atlanta site 131210055 with a 

30% decrease in US anthropogenic NOx emissions in the Eastern US domain based 

on bins created for high ozone nights (left) and on bins created for low ozone nights 

(right). Hours 25-29 represent 1 am – 5 am the following morning. 
 12 

3.2.2 Application of binned sensitivities to ambient data 

To apply the binned sensitivity profiles to ambient data, each day from each monitor for 

2006-2008 was grouped by measured 8-hr maximum ozone and high versus low nighttime 

ozone. The binning criteria were identical to that used for creating the average diurnal 

sensitivity profiles except that ambient ozone values were used in place of modeled ozone 

values. Each day at each monitor was then assigned to an average diurnal sensitivity profile 

from the appropriate bin. Note that if no measurements were taken for a particular day or if not 

enough hourly ozone concentrations were measured to calculate a valid maximum daily 8-hr 

ozone concentration then no sensitivity profile was assigned (for 1 am – 5 am the 8-hr 

maximum ozone concentration was needed from the previous day). Once each hour at each 

monitor was assigned an average sensitivity, the response at that monitor to changes in NOx or 

VOC emissions could be calculated. 

To perform the model-based ozone adjustments, each urban area was treated separately. 

Within an urban area, we used the binned sensitivities to determine the impacts on hourly ozone 

for incrementally increasing emissions reductions (NOx or VOC). The same emissions 

reductions were applied to the sensitivity profiles for all monitors within the urban area and 

over all hours for each day. Each incremental reduction leads to a new set of hourly ozone 

concentrations for all days from 2006-2008 at each monitor. After each increment, 8-hr 

maximum daily ozone values were recalculated for each day. Adjusted design values were then 

calculated based on the 3-year average of the 4th highest 8-hr maximum value at each site. The 

emissions reduction was incrementally increased until all monitors within an urban area were 

predicted to have design values below 75 ppb. If no sensitivity was assigned to a particular 

hour and monitor (due to lack of 8-hr daily max ozone classification), then a missing value was 

assigned to the adjusted ozone value. Note that since 1 am-5 am hours depend on the presence 

of an 8-hr maximum ozone concentration from the previous day, there may be more missing 

values in the HDDM-based adjusted ozone values than in the measured ozone values or in the 

quadratic rollback case. 

3.2.3 Multi-step application of HDDM sensitivities 

As discussed in Section 2.2 of this document, HDDM has been reported to reasonably 

replicate brute force emissions reductions up to a 50% change in emissions. For this analysis, it 

was desirable to have confidence that the HDDM sensitivities could replicate the entire range of 

emissions reductions. Evaluations of the HDDM estimates compared to brute force zero out 

model runs in Detroit and Atlanta showed that the HDDM estimates of ozone response to VOC 

emissions reductions are accurate down to 100% emissions reduction (see evaluation in section 

A.4 of this document). However, this was not the case for 100% NOx reduction. Consequently 
 13 

two additional CMAQ/HDDM runs were performed under different levels of NOx emissions 

reductions. One CMAQ/HDDM run was performed with US anthropogenic NOx cut by 50% in 

the 12 km Eastern domain. A second additional run was performed with a 75% NOx reduction. 

Emissions of other species were not modified from the base case in these two NOx reduction 

runs. These addition HDDM simulations give NOx and VOC sensitivities under expected 

conditions with lower NOx emissions in the Eastern US. 

Binned average diurnal sensitivity profiles were created for these two additional runs in the 

same manner as the sensitivity profiles were created for the base CMAQ/HDDM run. The 8-hr 

maximum and high/low nighttime ozone classifications were based on modeled ozone 

concentrations in the base run. This way, the same hours and days are included in the various 

bins for all three runs (and for the entire ozone adjustment process). To apply these new 

sensitivities a 3-step methodology was adopted. In the first step, the original base sensitivities 

were applied for NOx emissions reductions less than 40%. If the desired design value was not 

achieved with 40% NOx reduction then additional emissions reductions were applied using the 

sensitivities derived from the 50% NOx cut simulation. For instance, to predict the effects of 

60% NOx reduction, hourly ozone concentrations were calculated for 40% NOx emissions 

reductions using the base sensitivities. Then the change in ozone beyond the 40% reduction in 

NOx was estimated using the sensitivities from the 50% NOx reduction simulation. Finally, the 

sensitivities from the 75% NOx cut simulation were used to estimate the effects of NOx

emissions reductions beyond 75%. A conceptual picture of this process is provided in Figure 5. 

Equations (5)-(13) show how these calculations were performed. Note that this multi-step 

procedure was only performed when evaluating ozone response to NOx emissions reductions; 

for estimates of ozone response to VOC emissions reductions, the base sensitivities were used 

over the entire range of emissions perturbations since this method is shown to accurately 

replicate ozone predictions from the 100% VOC cut brute force simulations (Figure 23 and 

Figure 24). 

Figure 5: Conceptual picture of 3-step application of HDDM sensitivities 
 14 

 

 (5) Equation ૚૙૙ ൌ ࢆ ൅ ࢅ ൅ ࢄ

ࢄ ൌ ૝૙ Equation (6) 

ࢅ ൌ ૜૞ Equation (7) 

ࢠ ൌ ૛૞ Equation (8) 

 

 (9) Equation ࢔࢕࢏࢚ࢉ࢛ࢊࢋ࢘ ࢞ࡻࡺ ࢚࢔ࢋࢉ࢘ࢋࡼ ൌ ࡼ

 

ࢋ࢙ࢇ࢈_࢞ࡻࡺࡿ ൈ૛ ࢇ ൅ ࢋ࢙ࢇ࢈_࢞ࡻࡺࡿ ൈ ࢇെ ൞ ൌ૜ ࡻ∆

ܺ ൏ ࡼ ࢘࢕ࢌ ૛

ࢋ࢙ࢇ࢈_࢞ࡻࡺࡿ ൈ૛ ࢇ ൅ ࢋ࢙ࢇ࢈_࢞ࡻࡺࡿ ൈ ࢇെ

ሻܻ ൅ ܺሺ ൏ ܲ ൏ ࢄ ࢘࢕ࢌ ૛ ࢚࢛ࢉ%૙૞࢞ࡻࡺࡿ ൈ૛ ࢈ ൅ ࢚࢛ࢉ%૙૞࢞ࡻࡺࡿൈ࢈െ૛ 

ࢋ࢙ࢇ࢈_࢞ࡻࡺࡿ ൈ૛ ࢇ ൅ ࢋ࢙ࢇ࢈_࢞ࡻࡺࡿ ൈ ࢇെ

ሻܻ ൅ ܺሺ ൐ ࡼ ࢘࢕ࢌ ૛ ࢚࢛ࢉ%૞ૠ࢞ࡻࡺࡿ ൈ૛ ࢉ ൅ ࢚࢛ࢉ%૞ૠ࢞ࡻࡺࡿൈࢉെ ૛ ࢚࢛ࢉ%૙૞࢞ࡻࡺࡿ ൈ૛ ࢈ ൅ ࢚࢛ࢉ%૙૞࢞ࡻࡺࡿൈ࢈െ૛ 

Equation (10) 

ࡼ ൌቐࢇ

ܺ ൏ ࡼ ࢘࢕ࢌ ૙૙૚

ࢄ

 (11) Equation ࢄ ൒ ࡼ ࢘࢕ࢌ ૙૙૚

ሻࢄିࡼൈሺ૛ൌቐ࢈

ܻ ൏ ࡼ ࢘࢕ࢌ ૙૙૚

ࢅൈ૛

 (12) Equation ࢅ ൒ ࡼ ࢘࢕ࢌ ૙૙૚

 

࡯ ൌ ૝ ൈ ൫ࡼ െ ሺࢄ൅ࢅሻ൯ Equation (13) 

Figure 6 demonstrates how this three-step application of HDDM sensitivities is 

implemented. A different version of this figure could be created for each hour, each monitoring 

site, and each 8-hr maximum ozone bin. The solid lines in the figure show the change in ozone 

concentrations (ppb) for every possible change in NOx emissions (%). The segment of the solid 

line shown in black represents the portion of the NOx reduction spectrum that uses the base 

sensitivity, the segment shown in blue represents the portion of the NOx emissions reduction 

spectrum that uses the sensitivity from the 50% NOx cut HDDM run, and the segment shown in 

green uses the sensitivity from the 75% NOx cut HDDM run. The dotted black line shows the 

predicted change in ozone if the base sensitivity were used for the whole range of NOx

emissions reductions and the dotted blue line show the predicted change in ozone if only the 

base and 50% NOx cut sensitivities were used. The turquoise dots represent change in ozone at 

9 am for this location estimated by brute force CMAQ runs with 30%, 50%, 75%, and 100% 

NOx cuts for all days in June and July 2005 that had modeled 8-hr daily maximum ozone values 

greater than 75 ppb. 

Several features of the 3 step HDDM model-based adjustment approach are apparent from 

this figure. First, for this particular site/hour/bin, the base sensitivity would lead to net ozone 
 15 

increases at all points in the NOx emissions reduction spectrum, while using the sensitivities 

from the 50% and 75% NOx reduction simulations lead to predictions of ozone decreases when 

cuts are greater than about 50%. In this case, the use of the three-step application of HDDM 

sensitivities leads to greater predicted ozone decreases than would be achieved with a two-step 

application of HDDM sensitivities. This behavior is often, but not always apparent; there are 

limited hours/sites/bins for which the three-step estimates lead to smaller ozone decreases than 

the two-step estimates would. The spread of the turquoise dots shows that there is variability in 

modeled ozone responses even within bins at a single site and at a single time of day. The 

model-based adjustment approach is supposed to predict “typical” response, but may reduce the 

amount of variability in model estimated ozone reductions that would be seen in brute force 

model runs. However, Figure 6 demonstrates that for this instance the three-step application of 

HDDM sensitivities does a better job of capturing an average trend in ozone response than the 

one- or two-step application of HDDM sensitivities. Again, this holds true in most cases but 

may not be the case at every site for every hour and every ozone bin. Figure 7 demonstrates the 

same information as Figure 6 except that it shows a nighttime hour. In Figure 7, the black, 

blue, and green lines (turquoise dots) are used to demonstrate estimated ozone response on a 

high ozone night while the red, magenta, and purple lines (pink asterisks) show response on a 

low ozone night. In this case, it can be seem that on high ozone nights, the 3-step HDDM 

application of HDDM sensitivities predicts ozone decreases in response to NOx emissions 

reductions while on low ozone nights, this methodology predicts ozone increases in response to 

NOx emissions reductions. 

Figure 6: Example of 3 step application of HDDM sensitivities for 9am hours at Detroit 

site 261630019 for days with maximum daily 8-hr ozone concentrations greater 

than 75 ppb. 
 16 

Figure 7: Example of 3 step application of HDDM sensitivities for 1am hours at Detroit 

site 261630001 for days with maximum daily 8-hr ozone concentrations between 65 

and 75 ppb. 

3.3 PROPOSED REVISIONS FOR THE SECOND DRAFT REA 

Several updates to this analysis are planned for the second draft of the REA. First, EPA’s 

Office of Air Quality Planning and Standards is currently developing a 2007-based modeling 

platform which includes 2007 meteorology and a combination of 2007 and 2008 emissions 

inputs. These more recent emissions are more relevant to this analysis which focuses on 

ambient data from 2006-2008. It is anticipated that this new modeling platform will be 

available in time for use in preparing the 2nd draft of the REA. The 2007 model run will include 

the entire ozone season and, thus, increase available data points for creating binned sensitivities 

compared to the 2005-based test cases. Second, EPA’s Office of Research and Development 

released a newer version of the CMAQ model (v5.0) in February 2012. CMAQv5.0 is not yet 

instrumented with HDDM capabilities, but if this capability is available in time, we will 

consider using this most recent version of CMAQ. Third, we will explore other approaches for 

grouping modeled and observed days into bins and calculating average sensitivities. Fourth we 

will expand this analysis to include all 16 cities evaluated in the exposure assessment, cover 

ambient data from 2008-2010 and evaluate alternate possible new NAAQS in addition to the 

current 75 ppb standard. Finally, we will extend the analysis to include the estimation of risk 

and exposure based on the HDDM adjusted ozone. 
 17 

4. USING HDDM/CMAQ FOR SIMULATIONS OF JUST MEETING 

ALTERNATIVE STANDARDS: RESULTS 

4.1 ATLANTA RESULTS 

4.1.1 New distributions of ozone to attain 75 ppb NAAQS 

Figure 8 shows how the distribution of ozone concentrations is predicted to change by 

hour of day according to the model-based adjustment approach (NOx reductions only) for a 

downtown site. The model-based adjustment approach predicts that ozone will respond 

differently at different times of day. For mid-morning and afternoon hours, adjusted ozone 

concentrations are lower than observed values. During evening hours, the HDDM sensitivities 

show very little response of ozone to NOx cuts. Nighttime ozone concentrations are estimated 

to decrease due to the NOx reductions and morning ozone concentrations (4 am-7 am) are 

predicted to increase due to NOx disbenefits during the morning rush hour. This change in 

ozone response with time of day was not possible to replicate with quadratic rollback. Figure 9 

shows the same information as Figure 8 except for a downwind monitoring location. At the 

downwind site, ozone decreases in the adjusted case occur at all hours and the response in the 

evening hours is much larger than it was for the urban site. The HDDM sensitivities are 

predicting different chemical regimes during rush hour for the urban and the downwind sites: 

the urban site is predicted to be NOx saturated while the downwind site is not. Consequently, 

the urban site shows increases or lack of response during the rush hour times while the 

downwind site shows response of ozone to NOx reductions at all times. Again, the quadratic 

rollback technique does not capture the spatial variability in ozone response within an urban 

area that is exhibited in the HDDM-based results. 
 18 

Figure 8: Distribution of ozone concentrations by hour of day for Atlanta site 131210055. 

Centerlines show median values, boxes designate 25th to 75th percentile values and 

whiskers extend to 1.5 times the interquartile range. Values in gray/black show 

measured ozone distributions (2006-2008) and values in red/pink show predicted 

ozone distributions based on HDDM model-based adjustment approach (2006-

2008). 
 19 

Figure 9: Distribution of ozone concentrations by hour of day for Atlanta site 132470001. 

Centerlines show median values, boxes designate 25th to 75th percentile values and 

whiskers extend to 1.5 times the interquartile range. Values in gray/black show 

measured ozone distributions (2006-2008) and values in red/pink show predicted 

ozone distributions based on HDDM model-based adjustment approach (2006-

2008). 

4.1.2 Resulting design values 

The resulting design values for all sites in Atlanta with the model-based adjustment 

approach are shown in Table 1. The highest monitor in the observed data is 132470001 which 

is a site downwind of Atlanta. As might be expected based on the results shown in Figure 8 and 

Figure 9, the downwind sites show greater overall decreases in ozone with NOx reductions than 

the downtown sites due to NOx saturated conditions in the urban core during some hours of the 

day. Figure 7 shows that at some times smaller NOx reductions would lead to ozone increases 

while larger NOx reductions would lead to ozone decreases at some urban sites. This trend can 

be seen in the observed and HDDM adjusted design values. The two urban sites (131510002 
 20 

and 132470001) show the smallest ozone decrease. Consequently, the model-based adjustment 

approach predicts that the highest design value is at the downtown site, 130890002, after the 

application of NOx emissions reductions. Therefore, the spatially and temporally varying 

sensitivities not only lead to differences in response during rush-hour times (as discussed in 

section 4.1.1), but also lead to difference in response of 8-hr daily maximum ozone values. 

4.1.3 Comparison to quadratic rollback 

Table 1 also compares predicted design values using HDDM model-based adjustment 

approach versus quadratic rollback. The quadratic rollback methodology requires that ozone at 

all sites responds the same, so unlike the HDDM approach, the quadratic rollback predicts that 

the relative order of high to low design value sites remains constant. Design values at the two 

downtown sites are lower in the quadratic rollback case while design values at all other sites are 

lower in the model-base adjustment approach case. 

Table 1: Observed, model adjusted and rolled-back design values for Atlanta sites. Urban 

sites highlighted in blue. 

Monitor Measured 2006-2008 

DV HDDM Adjusted DV Quadratic Rolled-back DV 

130670003 85 65 69 

130770002 84 63 68 

130890002 93 75 73 

130970004 87 66 70 

131130001 86 65 69 

131210055 91 73 72 

131350002 88 65 71 

131510002 94 73 74 

132230003 80 59 66 

132470001 95 71 75 

Figure 10 shows the distribution of hourly ozone concentrations at all Atlanta sites for 

the observed values, the model-based adjustment approach case, and the quadratic rollback 

case. This comparison shows that the model-based adjustment approach reduces the hourly 

ozone values more through most of the distribution than quadratic rollback (i.e. the 25th, 50th, 

75th, 95th percentile values are lower) but that quadratic rollback reduces the highest hourly 

ozone value more. 
 21 

Figure 10: Distribution of ozone concentrations for all Atlanta sites (2006-2008) for 

measured values (left), values calculated using the model-based adjustment 

approach (NOx reductions) (middle) and values calculated using quadratic 

rollback (right). Centerlines show median values, boxes cover 25th to 75th

percentile ranges and whiskers extend to 5th and 95th percentile values. Left panel 

includes outlier values while right panel focuses on the portion of the range which 

includes the 5th to 95th percentile values. 

4.2 DETROIT RESULTS 

4.2.1 New distributions of ozone to attain 75 ppb NAAQS: NOx and VOC reductions 

Figure 11 and Figure 12 show ozone distributions for an urban and downwind Detroit 

site based on observations (2006-2008) and on the model-based adjustment approach assuming 

NOx reductions. Figure 13 and Figure 14 show the same information but instead using HDDM 

sensitivities to VOC emissions reductions. The NOx cut HDDM adjustment case for the Detroit 

urban site shows the same morning disbenefits (ozone increase) as was seen at the Atlanta 

downtown site. However, the response during the rest of the day looks somewhat different than 

the downtown Atlanta site. At the site shown in Figure 11, the high end of the ozone 

distribution (shown as the upper black and pink whiskers for observed and adjusted ozone) 

increases during the morning rush hour times (and one evening hour) but decreases at all other 

times of day in the model-based adjustment case. The median ozone values, however, increase 

throughout the day in the model-based adjustment case. This indicates that under a NOx

reduction scenario this urban Detroit site is expected to see decreases in design values but will 

also see increases in 8-hr daily maximum ozone on days below the standard. Note that the 

median observed values are quite low (less then 40 ppb) so these increases in ozone 
 22 

concentrations occur on low ozone days even though they are in the middle of the observed 

ozone distribution for this site (i.e., this site has a larger number of low ozone days despite 

having a design value above 75 ppb). Figure 12 shows that at the downwind Detroit site, there 

are some small NOx disbenefits in the morning and that NOx cuts result in ozone decreases 

throughout the entire range of ozone for all other hours of the day. The ozone design values 

with VOC emissions reductions (shown in Figure 13 and Figure 14) show that ozone would be 

reduced at all hours of the day and for all portions of the distribution. However, the high end 

(top whiskers) of the ozone concentrations are not reduced as dramatically in VOC reduction 

for these two sites as it is with NOx reduction. For the VOC reduction case, ozone at the urban 

site responds more than ozone at the downwind site. 

Figure 11: Distribution of ozone concentrations by hour of day for Detroit site 260991003. 

Centerlines show median values, boxes designate 25th to 75th percentile values and 

whiskers extend to 1.5 times the interquartile range. Values in gray/black show 

measured ozone distributions (2006-2008) and values in red/pink show predicted 

ozone distributions based on HDDM model-based adjustment approach (NOx cut) 

(2006-2008). 
 23 

Figure 12: Distribution of ozone concentrations by hour of day for Detroit site 260990009. 

Centerlines show median values, boxes designate 25th to 75th percentile values and 

whiskers extend to 1.5 times the interquartile range. Values in gray/black show 

measured ozone distributions (2006-2008) and values in red/pink show predicted 

ozone distributions based on HDDM model-based adjustment approach (NOx cut) 

(2006-2008). 
 24 

Figure 13: Distribution of ozone concentrations by hour of day for Detroit site 260991003. 

Centerlines show median values, boxes designate 25th to 75th percentile values and 

whiskers extend to 1.5 times the interquartile range. Values in gray/black show 

measured ozone distributions (2006-2008) and values in red/pink show predicted 

ozone distributions based on HDDM model-based adjustment approach (VOC cut) 

(2006-2008). 
 25 

Figure 14: Distribution of ozone concentrations by hour of day for Detroit site 260990009. 

Centerlines show median values, boxes designate 25th to 75th percentile values and 

whiskers extend to 1.5 times the interquartile range. Values in gray/black show 

measured ozone distributions (2006-2008) and values in red/pink show predicted 

ozone distributions based on HDDM model-based adjustment approach (VOC cut) 

(2006-2008). 

4.2.2 Resulting design values: NOx and VOC reductions 

Table 2 shows the resulting design values from the two adjustment scenarios for each 

monitor for the 75 ppb attainment case. In Detroit, there are four sites which are located in 

heavily urbanized areas (261630001, 261250001, 260991003, and 261630019). Ozone design 

values at these four sites decrease less than they do at the other less urbanized sites in Detroit 

for the NOx cut case. Conversely, ozone design values decrease the same or slightly more for 

the urban sites than the upwind and downwind sites for the VOC reduction case. However, all 

but two sites are predicted to have lower design values when the 75 ppb standard is met with 

NOx reductions than with VOC reductions: urban site 261630001 is predicted to have the same 
 26 

design value in the NOx and VOC model-based adjustment cases while urban site 2609910003 

is predicted to have a higher design value with the NOx reduction case than with the VOC 

HDDM reduction case. 

4.2.3 Comparison to quadratic rollback 

Table 2 also shows a comparison of both HDDM model-based adjustment cases to the 

predicted design values with quadratic rollback. Again, the quadratic rollback assumes that all 

sites will respond the same to a given level of emissions reductions so in the quadratic rollback 

case the order of high to low design values at Detroit sites does not change. However, in both 

the NOx and VOC model-based adjustment cases some sites are more responsive than others to 

emissions reductions and thus the site with the highest post-control design value is different 

from the site with the highest measured design value for 2006-2008. Figure 15 shows the 

distribution of hourly ozone concentrations for all sites in Detroit from 2006-2008 based on 

observations, model-based adjustments (both NOx and VOC emissions reduction cases), and 

quadratic rollback. Ozone distributions for quadratic rollback and model-based adjustment 

using VOC reductions look similar. Both show small reductions 25th, 50th, 75th , and 95th

percentile values. The distribution of hourly ozone values in the NOx reduction model-based 

adjustment case shows a larger decrease in the 95th percentile value than the VOC model-based 

adjustment or quadratic rollback cases. However the NOx reduction model-based adjustment 

case in Detroit leads to almost no change in the 50th and 75th percentile ozone values and a very 

small decrease in the 5th and 25th percentile ozone values. Therefore, in Detroit the NOx

reduction results in lowering the highest ozone concentrations more than either the VOC 

reduction or quadratic rollback cases but results in lowering the mid range ozone concentrations 

less than the VOC reduction and quadratic rollback cases. 
 27 

Table 2: Observed, model adjusted, and rolled-back design values for Detroit sites. Urban 

sites highlighted in blue. 

Monitor Measured 2006-2008 

DV 

HDDM Adjusted DV 

(VOC reductions) 

HDDM Adjusted 

DV 

(NOx reductions) 

Quadratic Rolledback DV 

261630001 71 65 65 65 

261610008 74 68 65 68 

260910007 75 71 63 69 

261250001 77 72 70 70 

261470005 78 74 67 71 

260991003 80 73 75 74 

260990009 81 75 72 74 

261630019 82 74 71 75 

Figure 15: Distribution of ozone concentrations for all Detroit sites (2006-2008) for 

measured values (left), values calculated using HDDM model-based adjustment 

approach (NOx reductions) (2nd from left), HDDM model-based adjustment 

approach (VOC reductions) (2nd from right) and values calculated using quadratic 

rollback (right). Centerlines show median values, boxes cover 25th to 75th percentile 

ranges and whiskers extend to 5th and 95th percentile values. Left panel includes 

outlier values while right panel focuses on the portion of the range which includes 

the 5th to 95th percentile values. 
 28 

5. RECOMMENDATIONS 

Based on the analysis presented here, we believe that there are clear benefits for using 

model-based ozone adjustments for the second draft REA. The HDDM model-based 

adjustment approach, unlike quadratic rollback, allows us to predict the temporally- and 

spatially-varying response of ozone within an urban area to emissions changes and allows us to 

account for the sensitivity of these air quality changes to NOx versus VOC emissions 

reductions. For example, the model-based adjustments account for ozone increases with NOx 

emissions reductions which may occur at times and locations where NOx to VOC concentration 

ratios are high and total ozone concentrations are low. The model-based methodology also 

directly accounts for physical and chemical processes that lead to ozone formation and 

transport, and includes both natural and anthropogenic sources of ozone precursors from 

sources both within and outside of the U.S. Consequently, it is more straightforward to isolate 

the ozone response to U.S. emission reductions, eliminating the need to artificially specify a 

floor for air quality changes as is required in the quadratic rollback method. The effect of these 

capabilities is clearly shown in this work where the order of the monitoring sites with respect to 

the highest to lowest ozone design values changed in both Detroit and Atlanta between the 

2006-2008 observations and the HDDM model-based ozone estimates. (The application of the 

quadratic rollback will not change the order of the monitoring sites with respect to magnitude of 

the design value before versus after rollback.) Using the HDDM model-based adjustment 

approach also had the effect of changing the absolute predicted magnitude of the design values 

after simulating just meeting the current standard in the two case study areas, when compared to 

the application of the quadratic rollback. For example, the application of the model-based 

adjustments in Atlanta resulted in 8 out of 10 monitoring sites with lower design values than 

those estimated by using quadratic rollback. In the Detroit case study example, the application 

of HDDM allowed us to compare the effect of reducing NOx versus VOC emissions. Five out 

of 8 monitoring sites in Detroit were predicted to have lower design values using the HDDM 

model-based adjustment approach with NOx emissions reductions versus quadratic rollback but 

only 2 out of 8 sites were predicted to have lower design values using HDDM model-based 

adjustment approach with VOC emissions reductions versus quadratic rollback. Compared to 

quadratic rollback, these capabilities of the HDDM model-based adjustment approach build 

confidence that the results drawn from this type of analysis allow us to more realistically 

represent the response in hourly ozone concentrations to reductions in emissions for a scenario 

of just meeting the current and alternative standard levels. Further analyses will be conducted to 

better understand the effect of using the HDDM model-based adjustment approach on the 

results of the risk and exposure assessment. 
 29 

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 31 

APPENDIX A. MODEL SET-UP AND EVALUATION ANALYSES 

A.1. MODEL SET-UP AND SIMULATION 

The air quality modeling underlying this analysis was performed using CMAQv4.7.1 

with HDDM for ozone (www.cmaq-model.org). CMAQ was run using the carbon bond 2005 

(CB05) gas-phase chemical mechanism (Grey et al 1989; Yarwood et al, 2005) and the AERO5 

aerosol module which includes ISORROPIA for gas-particle partitioning of inorganic species 

(Nenes et al 1998) and secondary organic aerosol treatment as described in Carlton et al (2010). 

 

A.1.1. Model domain 

For this analysis, all CMAQ/HDDM runs were performed in the Eastern U.S. 12 km 

modeling domain. As shown in Figure 16, this domain has a “parent” domain with a horizontal 

grid of resolution of 36 km. The air quality predictions from the 36 km domain were only used 

to establish the incoming air quality concentrations along the boundaries of the 12 km domain. 

Air quality conditions at the outer boundary of the 36 km domain were taken from a global 

model. For both domains, the model extends vertically from the surface to 100 millibars 

(approximately 15 km) using a sigma-pressure coordinate system. Table 3 provides some basic 

geographic information regarding the CMAQ domains. 
 32 

Figure 16: Map of the CMAQ modeling domain. The black outer box denotes the 36 km 

national modeling domain and the blue inner box is the 12 km eastern U.S. fine 

grid. 

Table 3: Geographic elements of domain used in the CMAQ/HDDM modeling 

CMAQ Modeling Configuration 

National Grid Eastern U.S. Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 

Coordinate Center 97 deg W, 40 deg N 

True Latitudes 33 deg N and 45 deg N 

Dimensions 148 x 112 x 14 279 x 240 x 14 

Vertical extent 14 Layers: Surface to 100 millibar level (see Table II-3) 

A.1.2. Model time period 

The CMAQ/HDDM modeling domain was simulated for July and August of 2005. The 

simulation included at 3 day “ramp-up” period from June 28-June 30 to minimize the effects of 

initial conditions. The ramp-up days were not considered in the analysis for the HDDM results. 

Only 3 days were deemed necessary for the ramp up because the initial concentrations are 

derived from the 36 km parent simulation. 

36km Domain Boundary

12km East Domain Boundary
 33 

A.1.3. Model Inputs: meteorology, emissions, initial and boundary conditions 

CMAQ model simulations require inputs of meteorological fields, emissions, and initial 

and boundary conditions. The gridded meteorological input data for the entire year of 2005 

were derived from simulations of the Pennsylvania State University / National Center for 

Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5, is a 

limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and 

thermodynamic equations which govern atmospheric motions (Grell et al., 1994). 

Meteorological model input fields were prepared separately for each of the two domains shown 

in Figure 16 using MM5 version 3.7.4. The MM5 simulations were run on the same map 

projection as CMAQ. 

The 36 km and 12 km meteorological model runs configured similarly. The selections 

for key MM5 physics options are shown below: 

 Pleim-Xiu PBL and land surface schemes 

 Kain-Fritsh 2 cumulus parameterization 

 Reisner 2 mixed phase moisture scheme 

 RRTM longwave radiation scheme 

 Dudhia shortwave radiation scheme 

Three dimensional analysis nudging for temperature and moisture was applied above the 

boundary layer only. Analysis nudging for the wind field was applied above and below the 

boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104

 for wind fields 

and temperatures and 1.0 x 105 for moisture fields. The 12 km domain nudging weighting 

factors were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields. 

The 36 km and 12 km domain model runs were conducted in 5.5 day segments with 12 

hours of overlap for spin-up purposes. Both meteorological modeling domains contained 34 

vertical layers with an approximately 38 m deep surface layer and a 100 millibar top. The 

MM5 and CMAQ vertical structures are shown in Table 4 and do not vary by horizontal grid 

resolution. 
 34 

Table 4: Vertical layer structure for MM5 and CMAQ (heights are layer top) 

CMAQ Layers MM5 Layers Sigma P 

Approximate 

Height (m) 

Approximate 

Pressure (mb) 

0 0 1.000 0 1000 

1 1 0.995 38 995 

2 2 0.990 77 991 

3 

3 0.985 115 987 

4 0.980 154 982 

4 

5 0.970 232 973 

6 0.960 310 964 

5 

7 0.950 389 955 

8 0.940 469 946 

6 

9 0.930 550 937 

10 0.920 631 928 

11 0.910 712 919 

7 

12 0.900 794 910 

13 0.880 961 892 

14 0.860 1,130 874 

8 

15 0.840 1,303 856 

16 0.820 1,478 838 

17 0.800 1,657 820 

9 

18 0.770 1,930 793 

19 0.740 2,212 766 

10 

20 0.700 2,600 730 

21 0.650 3,108 685 

11 

22 0.600 3,644 640 

23 0.550 4,212 595 

12 

24 0.500 4,816 550 

25 0.450 5,461 505 

26 0.400 6,153 460 

13 

27 0.350 6,903 415 

28 0.300 7,720 370 

29 0.250 8,621 325 

30 0.200 9,625 280 

14 

31 0.150 10,764 235 

32 0.100 12,085 190 

33 0.050 13,670 145 

34 0.000 15,674 100 
 35 

The 2005 meteorological outputs from all three MM5 runs were processed to create 

model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP), 

version 3.4 (Byun and Ching, 1999). 

Before initiating the air quality simulations, it is important to identify the biases and 

errors associated with the meteorological modeling inputs. The 2005 MM5 model performance 

evaluations used an approach which included a combination of qualitative and quantitative 

analyses to assess the adequacy of the MM5 simulated fields. Qualitative evaluations compared 

spatial patterns of monthly total rainfall and monthly maximum modeled planetary boundary 

layer (PBL) heights. The operational evaluation included statistical comparisons of 

model/observed pairs (e.g., mean normalized bias, mean normalized error, root mean square 

errors, etc.) for multiple meteorological parameters. For this portion of the evaluation, five 

meteorological parameters were investigated: temperature, humidity, shortwave downward 

radiation, wind speed, and wind direction. The MM5 evaluations for each domain are described 

elsewhere (Baker and Dolwick, 2005a,b,c). The results of these analyses indicate that the bias 

and error values associated with all three sets of 2005 meteorological data were generally 

within the range of past meteorological modeling results that have been used for air quality 

applications. 

The emissions data used are based on the 2005 v4.3 platform. Emissions are processed 

to photochemical model inputs with the SMOKE emissions modeling system (Houyoux et al., 

2000). This platform includes emissions from electric generating utilities (EGUs) and fires in 

the United States which are temporalized based on average temporal profiles from 3 years of 

data. In addition, US emissions are included from other point sources, area sources, agricultural 

sources (ammonia only), anthropogenic fugitive dust sources, nonroad mobile sources, onroad 

mobile sources, and biogenic sources. Onroad mobile sources were created using EPA’s 

MOVES model (www.epa.gov/otaq/models/moves). Biogenic emissions were estimated using 

the Biogenic Emissions Inventory System version 3.14 (BEISv3.14) (Pierce et al, 1998). Other 

North American emissions are based on a 2006 Canadian inventory and 1999 Mexican 

inventory. Emissions totals within the 12 km Eastern domain are summarized in Table 5 for 

CO, NH3, NOx, PM10, PM2.5, SO2, VOC. More details on the emission used for this modeling 

can be found in US EPA (2011a). 
 36 

Table 5: Summary of emissions totals by sector for the 12km Eastern US domain 

Sector 

Name Sector description 

Emissions (1000 tons/year) 

CO NH3 NOX PM10 PM2.5 SO2 VOC 

afdust Anthropogenic fugitive dust 7,916 924 

ag Agricultural sources 2,874 

alm_no_c3 

Air, locomotive, and marine 

mobile sources 

(except C3 marine) 224 1 1,614 49 47 110 56 

avefire Average year fire emissions 4,664 19 103 417 357 25 1,068 

nonpt Area sources 6,124 109 1,460 1,082 871 1,095 6,350 

nonroad Off road equipment 16,766 2 1,679 175 167 176 2,318 

onroad Onroad mobile vehicles 34,793 117 6,866 307 251 151 2,715 

othar 

Canada and Mexico area 

sources 2,552 411 476 783 236 104 997 

othon 

Canada and Mexico onroad 

mobile sources 3,249 16 375 12 9 7 237 

othpt 

Canada and Mexico point 

sources 634 12 537 108 84 1,161 214 

ptipm 

Point sources: electric 

generation units 567 20 3,419 570 472 10,171 38 

ptnonipm 

Point sources other than electric 

generating units 2,802 143 1,979 555 378 1,891 1,166 

seca_c3 C3 marine vessels 48 578 48 44 371 20 

beis Biogenic emissions 5,401 1,644 26,555 

total US 

anthro 

Total US anthropogenic 

emissions used in HDDM (NOx 

and VOC only) 17,595 12,662 

total Domainwide total 77,822 3,723 20,729 12,021 3,840 15,264 41,734 

The lateral boundary and initial species concentrations for the 36 km domain are 

provided by a three-dimensional global atmospheric chemistry model, the GEOS-CHEM 

(Yantosca, 2004) model (standard version 7-04-11(Henze et al, 2008)). The global GEOSCHEM model simulates atmospheric chemical and physical processes driven by assimilated 

meteorological observations from the NASA’s Goddard Earth Observing System (GEOS). This 

model was run for 2005 with a grid resolution of 2.0 degree x 2.5 degree (latitude-longitude) 

and 30 vertical layers up to 100 mb. The predictions were used to provide one-way dynamic 

boundary conditions at three-hour intervals and an initial concentration field for the 36 km 

CMAQ simulations. The outputs from the 36 km modeling were used to develop the 

initial/boundary concentrations for the subsequent 12 km Eastern domain model simulation. 
 37 

A.2. OPERATIONAL EVALUATION OF MODELED OZONE CONCENTRATIONS 

IN ATLANTA AND DETROIT 

An overall model performance evaluation for ozone and PM2.5 predictions from the 

Eastern 12 km domain can be found in US EPA (2011b). Model evaluation statistics for 

specific monitors included in this analysis are provided in this section. 

Table 6 and Table 7 give performance statistics for modeled ozone versus measured 

ozone at the Atlanta and Detroit monitoring sites respectively. Performance statistics are given 

both for hourly ozone (all hours of the day) and 8-hr daily maximum ozone. Aggregate 

statistics are given for both metrics. In addition, data are further disaggregated to show 

performance when observed hourly ozone or observed 8-hr daily maximum ozone is above a 

threshold of 60 ppb. Time series of measured and modeled hourly values in Atlanta are shown 

in Figure 17 and Figure 18, while a time series of 8-hr daily maximum ozone values is shown in 

Figure 19. The time series for Detroit are shown in Figure 20, Figure 21, and Figure 22. These 

tables and figures show that ozone is overpredicted at Atlanta monitoring sites during July and 

August 2005. This overprediction occurs both during daytime peak hours and at night. The 8-

hr daily max ozone is also overpredicted in Atlanta (Figure 19), however the model shows skill 

in capturing the day-to-day fluctuations in ozone levels. This model overprediction makes the 

binning process especially important since it matches similar modeled and measured days 

instead of relying on perfect model performance. For sites in Detroit the predictions are more 

closely aligned with the observations than in Atlanta. For the most part, daytime ozone values 

are well captured in Detroit. The model still slightly overpredicts low nighttime ozone 

concentrations in Detroit, but not nearly to the extent seen in Atlanta. Although daytime ozone 

predictions are fairly accurate in Detroit, the binning methodology still prevents the application 

of sensitivities derived from high modeled ozone days to low observed ozone days and vice 

versa since there are still some overpredicted and some underpredicted daytime ozone values. 
 38 

Table 6: Performance statistics of modeled ozone at 10 Atlanta monitoring sites used in 

this analysis 

Metric Hourly ozone 8-hr daily maximum ozone 

All values  60 ppb All values  60 ppb 

Number 14,560 1533 606 166 

Mean Obs (ppb) 27.0 72.5 49.2 71.3 

Mean Mod (ppb) 45.1 79.7 64.1 81.2 

Mean Bias (ppb) 18.0 7.2 14.9 9.9 

Mean Error (ppb) 19.3 13.3 15.9 12.9 

Normalized mean 

bias (%) 66.6 9.9 30.4 13.9 

Normalized mean 

error (%) 71.5 18.3 32.3 18.1 

Table 7: Performance statistics of modeled ozone at 8 Detroit monitoring sites used in this 

analysis 

Metric Hourly ozone 8-hr daily maximum ozone 

All values  60 ppb All values  60 ppb 

Number 11,329 1362 295 88 

Mean Obs (ppb) 33.6 71 53.7 71.7 

Mean Mod (ppb) 37.9 63.2 55.9 67.7 

Mean Bias (ppb) 4.4 -7.8 2.2 -4.1 

Mean Error (ppb) 11.2 11.7 7.8 7.7 

Normalized mean 

bias (%) 13.0 -10.9 4.1 -5.7 

Normalized mean 

error (%) 33.4 16.6 14.5 10.7 
 39 

Figure 17: Time series of model performance for hourly ozone concentrations at Atlanta 

monitoring sites for July 2005. Observed values shown in black and modeled 

values shown in red. 

Figure 18: Time series of model performance for hourly ozone concentrations at Atlanta 

monitoring sites for August 2005. Observed values shown in black and modeled 

values shown in red. 
 40 

Figure 19: Time series of model performance for 8-hr daily maximum ozone 

concentrations at Atlanta monitoring sites for July and August 2005. Observed 

values shown in black and modeled values shown in red. 

Figure 20: Time series of model performance for hourly ozone concentrations at Detroit 

monitoring sites for July 2005. Observed values shown in black and modeled 

values shown in red. 
 41 

Figure 21: Time series of model performance for hourly ozone concentrations at Detroit 

monitoring sites for August 2005. Observed values shown in black and modeled 

values shown in red. 

Figure 22: Time series of model performance for 8-hr daily maximum ozone 

concentrations at Detroit monitoring sites for July and August 2005. Observed 

values shown in black and modeled values shown in red. 
 42 

A.3. CHOICE OF SENSITIVITY CUTPOINTS 

As described in Section 3.2.3, the multi-step adjustment technique applies sensitivities 

from HDDM/CMAQ runs performed under different emissions scenarios to cover the entire 

range of emissions reductions. The base sensitivity was used over the first 40% of NOx

reductions, the sensitivity from a simulation performed with 50% anthropogenic NOx emissions 

was used over the next 35% of NOx reduction and finally the sensitivity from a simulation 

performed with 75% anthropogenic NOx emissions was used over the final 25% of NOx

reductions. An evaluation was performed to determine the ideal cut points to switch to the 

sensitivity derived from a different simulation. 

First, HDDM estimates of ozone concentrations for 50% NOx emissions cuts were 

compared to brute force simulations in which emissions NOx emissions were reduced by 50% 

and the model was rerun. The HDDM-predicted ozone concentration was calculated using 

Equations (5) and (9) – (12), with P = 50%. This analysis was repeated 51 times with X 

varying between 0 and 50 and Y= 100 –X (e.g. X=0 and Y = 50; X= 1 and Y = 49; X = 2 and Y 

= 48 etc). For each of the 51 iterations, the HDDM estimates of hourly ozone concentrations 

were compared to the modeled ozone concentrations from the brute force simulation. For each 

comparison a variety of performance metrics were calculated at the grid cells containing the 

eight Detroit monitoring sites, at the grid cells containing the ten Atlanta monitoring sites, and 

at the grid cells containing all AQS sites in the Eastern modeling domain. The calculated 

metrics include: mean bias, mean error, maximum bias, maximum error, 99th percentile bias, 

99th percentile error, correlation coefficient (R), slope for the line of best fit, and intercept for 

the line of best fit. The maximum and 99th percentile bias and error metrics were included to 

characterize the worst performance for HDDM when compared to brute force model 

simulations. For each of the bias and error metrics, the best performance occurs when the value 

of the metric is closest to zero. For R, values closest to one indicate the best performance. And 

for the line of best fit, a slope close to one and intercept close to zero indicates good agreement 

between HDDM predictions and brute force estimates. After evaluating the performance based 

on these nine metrics for these three subsets of model grid cells, it was determined that X=40% 

provided HDDM estimates that were closest to brute force estimates across multiple locations. 

To determine best values for Y and Z, a similar analysis was done in which HDDM and 

brute force estimates of ozone at 75% NOx cut and 100% NOx cut conditions were compared. 

In both sets of analyses, X=40%, Y varied between 10% and 35%, and Z was set to 100 – 

(X+Y). Again, the nine metrics listed above were calculated to characterize how well HDDM 

estimates of ozone at these NOx emissions levels compared to brute force model simulations for 

grid cells in Detroit, Atlanta, and across the eastern United States. Based on the relative 
 43 

performance for the 9 metrics in these comparisons, Y = 35% and Z = 25% were deemed the 

most appropriate values for replicating brute force CMAQ simulations. 

A.4. BRUTE FORCE VERSUS HDDM FOR 1-STEP AND 3-STEP APPLICATION OF 

HDDM SENSITIVITIES 

As discussed previously, only the sensitivities from the base CMAQ/HDDM simulation 

were used to estimate ozone concentrations that would result from VOC reductions. Figure 23 

and Figure 24 show a comparison of predicted ozone at 100% anthropogenic VOC reductions 

for the grid cells containing the Atlanta and Detroit monitoring sites respectively. These plots 

are density scatter plots: the y-axis shows the predicted ozone concentration using HDDM 

sensitivities, the x-axis shows the predicted ozone concentration using a brute force CMAQ 

simulation, and the colors represent the percentage of points which fall at any location on the 

plot. When the majority of points fall on the 1-to-1 line, as in Figure 23 and Figure 24, this 

indicates that the two techniques are estimating the same values. Figure 23 and Figure 24 

demonstrate an excellent ability of the HDDM to replicate 100% VOC cuts from the brute force 

simulation. Plots comparing HDDM to brute force for the 50% VOC cut show even better 

agreement and are provided in Section Appendix B. Plots showing the change in ozone from 

the base simulation (100% of anthropogenic VOC emissions present) to 50% or 100% VOC cut 

scenarios also show similarly good agreement and are also provided in Section Appendix B. 
 44 

Figure 23: Comparison of HDDM to brute force ozone predictions with 100% VOC 

emissions cuts at grid cells containing Atlanta monitoring sites. Colors denote the 

percentage of points which fall in each location in this plot. 

Figure 24: Comparison of HDDM to brute force ozone predictions with 100% VOC 

emissions cuts at grid cells containing Detroit monitoring sites. Colors denote the 

percentage of points which fall in each location in this plot. 
 45 

Figure 25a, Figure 26a, Figure 27a, and Figure 28a show comparisons of predicted 

ozone using HDDM with sensitivities from the base runs only (1-step HDDM) to brute force 

CMAQ estimates at 75% and 100% anthropogenic NOx reductions for the grid cells containing 

the Atlanta and Detroit monitoring sites respectively. These plots show considerably more 

scatter than the VOC reduction plots for 50% or 100% emissions cuts. Figure 25b, Figure 26b, 

Figure 27b, and Figure 28b show the same comparison except using multi-step HDDM 

calculations (2-step HDDM estimates for the 75% NOx cut case and 3-step HDDM estimates 

for the 100% NOx cut case). Comparing Figure 25a, Figure 26a, Figure 27a, and Figure 28a 

with Figure 25b, Figure 26b, Figure 27b, and Figure 28b shows that using sensitivities from 

multiple CMAQ/HDDM simulations over varying NOx emissions levels provides ozone 

estimates that are in substantially better agreement with brute force emissions reductions 

CMAQ simulations. Even for the hypothetical case in which 100% of the anthropogenic NOx

emissions were eliminated, the 3-step HDDM ozone estimates only have a normalized mean 

bias of -15% in Atlanta and -0.9% in Detroit and normalized mean error of 31% in Atlanta and 

13% in Detroit. Plots showing the change in ozone from the base simulation (100% of 

anthropogenic NOx emissions present) to the 100% NOx cut scenario are provided in Appendix 

B and show similarly improved performance when the 3-step application of HDDM 

sensitivities is used. 

Figure 25: Comparison of 1-step (left) and multi-step (right) HDDM to brute force ozone 

predictions with 75% NOx emissions cuts at grid cells containing Atlanta 

monitoring sites. Colors denote the percentage of points which fall in each location 

in this plot. 
 46 

Figure 26: Comparison of 1-step (left) and multi-step (right) HDDM to brute force ozone 

predictions with 75% NOx emissions cuts at grid cells containing Detroit 

monitoring sites. Colors denote the percentage of points which fall in each location 

in this plot. 

Figure 27: Comparison of 1-step (left) and multi-step (right) HDDM to brute force ozone 

predictions with 100% NOx emissions cuts at grid cells containing Atlanta 

monitoring sites. Colors denote the percentage of points which fall in each location 

in this plot. 

 
 47 

Figure 28: Comparison of 1-step (left) and multi-step (right) HDDM to brute force ozone 

predictions with 100% NOx emissions cuts at grid cells containing Detroit 

monitoring sites. Colors denote the percentage of points which fall in each location 

in this plot. 
 48 

APPENDIX B. ADDITIONAL PLOTS 

Figure 29: Predicted change in ozone concentrations (ppb) at all Atlanta sites with a 30% 

decrease in US anthropogenic NOx emissions in the Eastern US domain based on 

bins created for high ozone nights. 
 49 

Figure 30: Predicted change in ozone concentrations (ppb) at all Atlanta sites with a 30% 

decrease in US anthropogenic NOx emissions in the Eastern US domain based on 

bins created for low ozone nights. 
 50 

Figure 31: Predicted change in ozone concentrations (ppb) at all Detroit sites with a 30% 

decrease in US anthropogenic NOx emissions in the Eastern US domain based on 

bins created for high ozone nights. 
 51 

Figure 32: Predicted change in ozone concentrations (ppb) at all Detroit sites with a 30% 

decrease in US anthropogenic NOx emissions in the Eastern US domain based on 

bins created for low ozone nights. 
 52 

Figure 33: Predicted change in ozone concentrations (ppb) at all Detroit sites with a 30% 

decrease in US anthropogenic VOC emissions in the Eastern US domain based on 

bins created for high ozone nights. 
 53 

Figure 34: Predicted change in ozone concentrations (ppb) at all Detroit sites with a 30% 

decrease in US anthropogenic VOC emissions in the Eastern US domain based on 

bins created for low ozone nights. 
 54 

Figure 35: Comparison of 1-step HDDM to brute force ozone concentrations (left) and 

changes in ozone (right) due to 50% eastern US VOC emissions cuts at grid cells 

containing Atlanta monitoring sites. Colors denote the percentage of points which 

fall in each location in this plot. 

Figure 36: Comparison of 1-step HDDM to brute force ozone concentrations (left) and 

changes in ozone (right) due to 50% eastern US VOC emissions cuts at grid cells 

containing Detroit monitoring sites. Colors denote the percentage of points which 

fall in each location in this plot. 
 55 

Figure 37: Figure 34: Comparison of 1-step (left) and multi-step (right) HDDM to brute 

force changes in ozone due to 75% NOx cuts at grid cells containing Atlanta 

monitoring sites. Colors denote the percentage of points which fall in each location 

in this plot. 

Figure 38: Figure 34: Comparison of 1-step (left) and multi-step (right) HDDM to brute 

force changes in ozone due to 75% NOx cuts at grid cells containing Detroit 

monitoring sites. Colors denote the percentage of points which fall in each location 

in this plot. 
 56 

Figure 39: Figure 34: Comparison of 1-step (left) and multi-step (right) HDDM to brute 

force changes in ozone due to 100% NOx cuts at grid cells containing Atlanta 

monitoring sites. Colors denote the percentage of points which fall in each location 

in this plot. 

Figure 40: Comparison of 1-step (left) and multi-step (right) HDDM to brute force 

changes in ozone due to 100% NOx cuts at grid cells containing Detroit monitoring 

sites. Colors denote the percentage of points which fall in each location in this plot.
 57 

Figure 41: Distribution of ozone concentrations by hour of day at each Atlanta monitoring 

site. Centerlines show median values, boxes designate 25th to 75th percentile 

values and whiskers extend to 1.5 times the interquartile range. Values in 
 58 

gray/black show measured ozone distributions (2006-2008) and values in red/pink 

show predicted ozone distributions based on HDDM model-based adjustment 

approach (2006-2008)

Figure 42: Distribution of ozone concentrations by hour of day at each Detroit monitoring 

site. Centerlines show median values, boxes designate 25th to 75th percentile 

values and whiskers extend to 1.5 times the interquartile range. Values in 

gray/black show measured ozone distributions (2006-2008) and values in red/pink 

show predicted ozone distributions based on HDDM model-based adjustment 

approach (NOx cuts) (2006-2008) 
 59 

Figure 43: Distribution of ozone concentrations by hour of day at each Detroit monitoring 

site. Centerlines show median values, boxes designate 25th to 75th percentile 

values and whiskers extend to 1.5 times the interquartile range. Values in 

gray/black show measured ozone distributions (2006-2008) and values in red/pink 

show predicted ozone distributions based on HDDM model-based adjustment 

approach (VOC cuts) (2006-2008)