## the higher order direct decoupled method (HDD M) - an approach that generates modeled
sensitivities of ozone to emissi ons 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, a llows us to predict temporally- and spatiallyvarying response within an urban area to emissi ons changes and allows us to account for the sensitivity of air quality changes to NOx ve rsus VOC emissions reductions. For example, model-based adjustments account for ozone increa ses 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 in cludes natural and anthropogeni c 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 re ductions, eliminating the need to artificially specify a floor for air quality changes as is required in the quadra tic rollback method. Compared to quadratic rollback, the capabi lities of the HDDM model-based adjustment approach build confidence that the results drawn fr om this type of analys is allow us to more realistically represent th e response in hourly ozone concentrati ons to reductions in emissions for a scenario of just meeting the current and altern ative standard levels. Further analyses will be
conducted to better understand the effect of using the HDDM mode l-based adjustment approach
on the results of the risk and exposure assessment.

Attachment

## I 

# Attachment Model-based rollback using the high er order decoupled direct method (HDDM)

Table of Contents

1. Motivation for a new technique to simulate ozon e concentrations under a lternative standards ............ 1

2. Higher order decoupled direct met hod (HDDM ) .................................................................................. 2

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

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

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

3.1 Conceptual framew ork .......................................................................................................... ...... 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 sensitiv ities to ambi ent data .................................................................. 12

3.2.3 Multi-step application of HDDM sensitiv ities .......................................................................... 12

3.3 Proposed revisions for th e second dr aft REA ........................................................................... 16

4. Using HDDM/CMAQ for SIMULATIONS of Just m eeting alternative stan dards: re sults ................ 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 quad ratic rollback ............................................................................................. 20

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

4.2.1 New distributions of ozone to attain 75 ppb NAAQS: NO x and VOC re ductions .................... 21

4.2.2 Resulting design values: NO x and VOC re ductions .................................................................. 25

4.2.3 Comparison to quad ratic rollback ............................................................................................. 26

5. Recommenda tions ............................................................................................................... ................ 28

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

APPENDIX A. MODEL SET-UP AND EVAL UATION ANALYS ES ...................................................................... 31

A.1. Model set-up an d simulation ................................................................................................... .. 31

A.2. Operational evaluation of modeled ozone co ncentrations in Atla nta and Detroit ..................... 37

A.3. Choice of sensit ivity cutpoints ............................................................................................... ... 42

A.4. Brute force versus HDDM for 1-step and 3-st ep application of HD DM sensitiv ities ............... 43

APPENDIX B. ADDITIONAL PLOT S .............................................................................................................. ......... 48

## II 

# Table of Figures

F IGURE 

### 1: F LOW DIAGRAM DEMONSTRATING 

### DDM MODEL



### ‐ BASED OZONE ADJUSTMENT APPROACH



### ............................................... 6

F IGURE 

### 2: M AP OF OZONE MONITORS IN THE 

### A TLANTA AREA USED FOR THIS ANALYSIS



### .................................................................. 8

F IGURE 

### 3: M AP OF OZONE MONITORS IN THE 

### D ETROIT AREA USED FOR THIS ANALYSIS



### .................................................................. 9

F IGURE 

### 4: P REDICTED CHANGE IN OZONE CONCENTRATIONS 

### ( PPB 

### ) AT 

### A TLANTA SITE 

### 131210055 WITH A 

### 30% DECREASE IN 

### US

ANTHROPOGENIC 

### NO X EMISSIONS IN THE 

### E ASTERN 

### US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS 

### ( LEFT 

### ) AND ON

BINS CREATED FOR LOW OZONE NIGHTS 

### ( RIGHT 

### ). H OURS 

### 25

‐ 29 REPRESENT 

### 1 AM



### – 5 AM THE FOLLOWING MORNING



### . ........... 11

F IGURE 

### 5: C ONCEPTUAL PICTURE OF 

### 3

‐ STEP APPLICATION OF 

### HDDM SENSITIVITIES



### ................................................................... 13

F IGURE 

### 6: E XAMPLE OF 

### 3 STEP APPLICATION OF 

### HDDM SENSITIVITIES FOR 

### 9 AM HOURS AT 

### D ETROIT SITE 

### 261630019 FOR DAYS WITH

MAXIMUM DAILY 

### 8

‐ HR OZONE CONCENTRATIONS GREATER THAN 

### 75 PPB



### . ....................................................................... 15

F IGURE 

### 7: E XAMPLE OF 

### 3 STEP APPLICATION OF 

### HDDM SENSITIVITIES FOR 

### 1 AM HOURS AT 

### D ETROIT SITE 

### 261630001 FOR DAYS WITH

MAXIMUM DAILY 

### 8

‐ HR OZONE CONCENTRATIONS BETWEEN 

### 65 AND 

### 75 PPB



### . ................................................................... 16

F IGURE 

### 8: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR 

### A TLANTA SITE 

### 131210055. C ENTERLINES SHOW MEDIAN

VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE RANGE



### . V ALUES 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

F IGURE 

### 9: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR 

### A TLANTA SITE 

### 132470001. C ENTERLINES SHOW MEDIAN

VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE RANGE



### . V ALUES 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

F IGURE 

### 10: D ISTRIBUTION OF OZONE CONCENTRATIONS FOR ALL 

### A TLANTA SITES 

### (2006

‐ 2008) FOR MEASURED VALUES 

### ( LEFT 

### ), VALUES

CALCULATED USING THE MODEL



### ‐ BASED ADJUSTMENT APPROACH 

### (NO X REDUCTIONS 

### ) ( MIDDLE 

### ) AND VALUES CALCULATED USING

QUADRATIC ROLLBACK 

### ( RIGHT 

### ). C ENTERLINES SHOW MEDIAN VALUES 

### , BOXES COVER 

### 25 TH TO 

### 75 TH PERCENTILE RANGES AND

WHISKERS EXTEND TO 

### 5 TH AND 

### 95 TH PERCENTILE VALUES



### . L EFT PANEL INCLUDES OUTLIER VALUES WHILE RIGHT PANEL FOCUSES ON

THE PORTION OF THE RANGE WHICH INCLUDES THE 

### 5 TH TO 

### 95 TH PERCENTILE VALUES



### . ......................................................... 21

F IGURE 

### 11: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR 

### D ETROIT SITE 

### 260991003. C ENTERLINES SHOW MEDIAN

VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE RANGE



### . V ALUES 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 

### (NO X CUT 

### ) (2006

‐ 2008).

...................... 22

F IGURE 

### 12: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR 

### D ETROIT SITE 

### 260990009. C ENTERLINES SHOW MEDIAN

VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE RANGE



### . V ALUES 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 

### (NO X CUT 

### ) (2006

‐ 2008).

...................... 23

F IGURE 

### 13: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR 

### D ETROIT SITE 

### 260991003. C ENTERLINES SHOW MEDIAN

VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE RANGE



### .

## III V ALUES 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

F IGURE 

### 14: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY FOR 

### D ETROIT SITE 

### 260990009. C ENTERLINES SHOW MEDIAN

VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE RANGE



### . V ALUES 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

F IGURE 

### 15: D ISTRIBUTION OF OZONE CONCENTRATIONS FOR ALL 

### D ETROIT SITES 

### (2006

‐ 2008) FOR MEASURED VALUES 

### ( LEFT 

### ), VALUES

CALCULATED USING 

### HDDM MODEL



### ‐ BASED ADJUSTMENT APPROACH 

### (NO X REDUCTIONS 

### ) (2 ND FROM LEFT 

### ), HDDM MODEL



### ‐

BASED ADJUSTMENT APPROACH 

### (VOC REDUCTIONS 

### ) (2 ND FROM RIGHT 

### ) AND VALUES CALCULATED USING QUADRATIC ROLLBACK 

### ( RIGHT 

### ). C ENTERLINES SHOW MEDIAN VALUES 

### , BOXES COVER 

### 25 TH TO 

### 75 TH PERCENTILE RANGES AND WHISKERS EXTEND TO 

### 5 TH

AND 

### 95 TH PERCENTILE VALUES



### . L EFT PANEL INCLUDES OUTLIER VALUES WHILE RIGHT PANEL FOCUSES ON THE PORTION OF THE

RANGE WHICH INCLUDES THE 

### 5 TH TO 

### 95 TH PERCENTILE VALUES



### . ...................................................................................... 27

F IGURE 

### 16: M AP OF THE 

### CMAQ MODELING DOMAIN



### . T HE 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

F IGURE 

### 17: T IME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT 

### A TLANTA MONITORING SITES FOR 

### J ULY


### 2005. O BSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED



### . ........................................................ 39

F IGURE 

### 18: T IME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT 

### A TLANTA MONITORING SITES FOR 

### A UGUST


### 2005. O BSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED



### . ........................................................ 39

F IGURE 

### 19: T IME SERIES OF MODEL PERFORMANCE FOR 

### 8

‐ HR DAILY MAXIMUM OZONE CONCENTRATIONS AT 

### A TLANTA MONITORING

SITES FOR 

### J ULY AND 

### A UGUST 

### 2005. O BSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED



### . ................. 40

F IGURE 

### 20: T IME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT 

### D ETROIT MONITORING SITES FOR 

### J ULY


### 2005. O BSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED



### . ........................................................ 40

F IGURE 

### 21: T IME SERIES OF MODEL PERFORMANCE FOR HOURLY OZONE CONCENTRATIONS AT 

### D ETROIT MONITORING SITES FOR 

### A UGUST


### 2005. O BSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED



### . ........................................................ 41

F IGURE 

### 22: T IME SERIES OF MODEL PERFORMANCE FOR 

### 8

‐ HR DAILY MAXIMUM OZONE CONCENTRATIONS AT 

### D ETROIT MONITORING SITES

FOR 

### J ULY AND 

### A UGUST 

### 2005. O BSERVED VALUES SHOWN IN BLACK AND MODELED VALUES SHOWN IN RED



### . ......................... 41

F IGURE 

### 23: C OMPARISON OF 

### HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 

### 100% VOC EMISSIONS CUTS AT GRID CELLS

CONTAINING 

### A TLANTA MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS

PLOT



### . ............................................................................................................................... .................................. 44

F IGURE 

### 24: C OMPARISON OF 

### HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 

### 100% VOC EMISSIONS CUTS AT GRID CELLS

CONTAINING 

### D ETROIT MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS

PLOT



### . ............................................................................................................................... .................................. 44

F IGURE 

### 25: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 

### 75% NO X

EMISSIONS CUTS AT GRID CELLS CONTAINING 

### A TLANTA MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT



### . ...................................................................................................................... 45

F IGURE 

### 26: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 

### 75% NO X

EMISSIONS CUTS AT GRID CELLS CONTAINING 

### D ETROIT MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT



### . ...................................................................................................................... 46

F IGURE 

### 27: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 

### 100% NO X

EMISSIONS CUTS AT GRID CELLS CONTAINING 

### A TLANTA MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT



### . ...................................................................................................................... 46

## IV F IGURE 

### 28: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE OZONE PREDICTIONS WITH 

### 100% NO X

EMISSIONS CUTS AT GRID CELLS CONTAINING 

### D ETROIT MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT



### . ...................................................................................................................... 47

F IGURE 

### 29: P REDICTED CHANGE IN OZONE CONCENTRATIONS 

### ( PPB 

### ) AT ALL 

### A TLANTA SITES WITH A 

### 30% DECREASE IN 

### US

ANTHROPOGENIC 

### NO X EMISSIONS IN THE 

### E ASTERN 

### US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS



### . ............... 48

F IGURE 

### 30: P REDICTED CHANGE IN OZONE CONCENTRATIONS 

### ( PPB 

### ) AT ALL 

### A TLANTA SITES WITH A 

### 30% DECREASE IN 

### US

ANTHROPOGENIC 

### NO X EMISSIONS IN THE 

### E ASTERN 

### US DOMAIN BASED ON BINS CREATED FOR LOW OZONE NIGHTS



### . ............... 49

F IGURE 

### 31: P REDICTED CHANGE IN OZONE CONCENTRATIONS 

### ( PPB 

### ) AT ALL 

### D ETROIT SITES WITH A 

### 30% DECREASE IN 

### US

ANTHROPOGENIC 

### NO X EMISSIONS IN THE 

### E ASTERN 

### US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS



### . ............... 50

F IGURE 

### 32: P REDICTED CHANGE IN OZONE CONCENTRATIONS 

### ( PPB 

### ) AT ALL 

### D ETROIT SITES WITH A 

### 30% DECREASE IN 

### US

ANTHROPOGENIC 

### NO X EMISSIONS IN THE 

### E ASTERN 

### US DOMAIN BASED ON BINS CREATED FOR LOW OZONE NIGHTS



### . ............... 51

F IGURE 

### 33: P REDICTED CHANGE IN OZONE CONCENTRATIONS 

### ( PPB 

### ) AT ALL 

### D ETROIT SITES WITH A 

### 30% DECREASE IN 

### US

ANTHROPOGENIC 

### VOC EMISSIONS IN THE 

### E ASTERN 

### US DOMAIN BASED ON BINS CREATED FOR HIGH OZONE NIGHTS



### . .............. 52

F IGURE 

### 34: P REDICTED CHANGE IN OZONE CONCENTRATIONS 

### ( PPB 

### ) AT ALL 

### D ETROIT SITES WITH A 

### 30% DECREASE IN 

### US

ANTHROPOGENIC 

### VOC EMISSIONS IN THE 

### E ASTERN 

### US DOMAIN BASED ON BINS CREATED FOR LOW OZONE NIGHTS



### . ............... 53

F IGURE 

### 35: C OMPARISON 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 

### A TLANTA MONITORING SITES



### . C OLORS DENOTE THE

PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS PLOT



### . ............................................................................ 54

F IGURE 

### 36: C OMPARISON 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 

### D ETROIT MONITORING SITES



### . C OLORS DENOTE THE

PERCENTAGE OF POINTS WHICH FALL IN EACH LOCATION IN THIS PLOT



### . ............................................................................ 54

F IGURE 

### 37: F IGURE 

### 34: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO


### 75% NO X CUTS AT GRID CELLS CONTAINING 

### A TLANTA MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT



### . ...................................................................................................................... 55

F IGURE 

### 38: F IGURE 

### 34: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO


### 75% NO X CUTS AT GRID CELLS CONTAINING 

### D ETROIT MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT



### . ...................................................................................................................... 55

F IGURE 

### 39: F IGURE 

### 34: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO


### 100% NO X CUTS AT GRID CELLS CONTAINING 

### A TLANTA MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH

FALL IN EACH LOCATION IN THIS PLOT



### . ...................................................................................................................... 56

F IGURE 

### 40: C OMPARISON OF 

### 1

‐ STEP 

### ( LEFT 

### ) AND MULTI



### ‐ STEP 

### ( RIGHT 

### ) HDDM TO BRUTE FORCE CHANGES IN OZONE DUE TO 

### 100% NO X

CUTS AT GRID CELLS CONTAINING 

### D ETROIT MONITORING SITES



### . C OLORS DENOTE THE PERCENTAGE OF POINTS WHICH FALL IN EACH

LOCATION IN THIS PLOT



### . ............................................................................................................................... ......... 56

F IGURE 

### 41: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY AT EACH 

### A TLANTA MONITORING SITE



### . C ENTERLINES SHOW

MEDIAN VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE

RANGE



### . V ALUES 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

F IGURE 

### 42: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY AT EACH 

### D ETROIT MONITORING SITE



### . C ENTERLINES SHOW

MEDIAN VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE

RANGE



### . V ALUES 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 

### (NO X CUTS 

### ) (2006

‐ 2008)

...... 58

## V F IGURE 

### 43: D ISTRIBUTION OF OZONE CONCENTRATIONS BY HOUR OF DAY AT EACH 

### D ETROIT MONITORING SITE



### . C ENTERLINES SHOW

MEDIAN VALUES 

### , BOXES DESIGNATE 

### 25 TH TO 

### 75 TH PERCENTILE VALUES AND WHISKERS EXTEND TO 

### 1.5 TIMES THE INTERQUARTILE

RANGE



### . V ALUES 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

T ABLE 

### 1: O BSERVED 

### , MODEL ADJUSTED AND ROLLED



### ‐ BACK DESIGN VALUES FOR 

### A TLANTA SITES



### . U RBAN SITES HIGHLIGHTED IN BLUE



### . . 20

T ABLE 

### 2: O BSERVED 

### , MODEL ADJUSTED 

### , AND ROLLED



### ‐ BACK DESIGN VALUES FOR 

### D ETROIT SITES



### . U RBAN SITES HIGHLIGHTED IN BLUE



### . . 27

T ABLE 

### 3: G EOGRAPHIC ELEMENTS OF DOMAIN USED IN THE 

### CMAQ/HDDM MODELING



### ............................................................ 32

T ABLE 

### 4: V ERTICAL LAYER STRUCTURE FOR 

### MM5 AND 

### CMAQ ( HEIGHTS ARE LAYER TOP 

### )

........................................................... 34

T ABLE 

### 5: S UMMARY OF EMISSIONS TOTALS BY SECTOR FOR THE 

### 12 KM 

### E ASTERN 

### US DOMAIN



### ....................................................... 36

T ABLE 

### 6: P ERFORMANCE STATISTICS OF MODELED OZONE AT 

### 10 A TLANTA MONITORING SITES USED IN THIS ANALYSIS



### ...................... 38

T ABLE 

### 7: P ERFORMANCE STATISTICS OF MODELED OZONE AT 

### 8 D ETROIT MONITORING SITES USED IN THIS ANALYSIS



### ......................... 38

## 1 

# 1. MOTIVATION FOR A NEW TECH NIQUE 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 ha s been adjusted to just meet the current
standard and alternative standards. The firs t draft documents for this review rely upon the quadratic rollback method used in previous re views to adjust or “roll back” hourly ozone
concentrations in urban areas . Although the quadratic rollbac k method simulates historical patterns of air quality changes be tter 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 re quires 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 rollb ack 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” mu st be used to ensure that predicted ozone is not reduced below “background” concentrations 1



## . EPA has received comments during past ozone NAAQS reviews and during the January 9-10, 2012 Clean Air Scientific Advisory Comm ittee (CASAC) meeting for this ozone NAAQS review which encourage the use of alternate met hods 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 spatia l variations in O 3 

## concentrations, and to include information on how NO x 

## versus VOC control strategies might affect risk and exposure to O 3


## . Photochemical modeling can simulate the oz one response to emission reductions while avoiding the limitations presented by the qua dratic rollback method. While there are uncertainties inherent in any modeling exercise due to uncer tainties in inputs and model parameters, the improved characterization of the sp atial and temporal responses of ozone to the

1 

### Background ozone has been charact erized 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 in strumented with the higher order direct decoupled method (HDDM) - an ap proach that generates modele d sensitivities of ozone to emissions changes - to estimate ozone concentrati ons that would occur with the achievement of a 0.075 ppm ozone standard in multiple urban ar eas. This modeling in corporates all known emissions, including emissions from non-anthro pogenic 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 oz one. 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 O 3 

## to estimate how ozone concentrations would respond to changes in anthropogenic emissions within th e U.S. We propose to use this methodology to simulate ozone concentrations meeting the current and alternative standards in the 2 nd 

## drafts of the risk and exposure assessments and the 2 nd 

## draft policy assessment.



# 2. HIGHER ORDER DECOUPLED DIRECT METHOD (HDDM)

Chemical transport models, such as th e Community Multiscale Air Quality Model (CMAQ) (www.cmaq-model.org), ca lculate the effects of physica l 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 th e boundaries of the model domain (i.e. boundary conditions), and a characterization of the 3-D field of chemical c oncentrations 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 ࢚ࣔ 

### ൌ െસ ሺ ࡯࢛ ࢏ 

### ሻ ൅ સ ሺ ࡷસ࡯ ࢏ 

### ሻ ൅ ࡾ ࢏ 

### ൅ ࡱ ࢏ 

### ൅ ڮ 

## Equation (1)

## 3 In this equation (which is defined to vary in both space and time), C i 

## represents the atmospheric concentration field of compound i, u gives the wi nd field, K represents the turbulent diffusivity tensor, R i 

## is a represents the net rate of chemical production (which encompasses gas, aqueous, and particle reactions), E i 

## represents the emissions rate, a nd the ellipsis shows that other processes are also accounted for in the mode l (such as deposition, particle coagulation, and partitioning of semivolatile species between gas a nd particle phases). Solving Equation (1) is complicated by the fact that these models incl ude many chemical species whose concentrations are interconnected through the R i 

## and partitioning terms. Chemical transport models include sources out side of US control which are often termed “background” and explicitly account for transpor t of pollutants from outside the model domain and emission of pollution precursors from natura l and anthropogenic sources within the model domain. Beyond modeling the current concentrati ons of ambient ozone, chemical transport models can be used to estimate the response of ambient ozone concentr ations to changes in
emissions. One technique to model this response, the brute force method, requires the modeler to explicitly model this re sponse by directly altering the emi ssions 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 th e response of ozone to other levels of emissions since the chemistry for oz one formation is nonlinea r. Therefore, when using only brute force technique s 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 partia l derivative of the atmospheric diffusion equations that underly the model calculations, Equations (2) and (3).

࢙ ࢐࢏ 

### ሺ ࢚ ሻ ൌ ࡯ࣔ ࢏ ሺ࢚ሻ ࢖ࣔ

࢐ 

## Equation (2)

ࡿ ࢐࢏ 

### ሺ ࢚ ሻ ࡼ ൌ ෩ ࢐ ࡯ࣔ ࢏ ሻ࢚ሺ ࢖ࣔ

࢐ 

### ࡼ ൌ ෩ ࢐ ࡯ࣔ ࢏ ሻ࢚ሺ
ࣔ ൫ ࣕ ࢐ ࡼ ෩ ࢐ ൯ 

### ൌ ࡯ࣔ ࢏ ሻ࢚ሺ
ࣕࣔ

࢐ 

## Equation (3)

Here, s ij 

## (t), the sensitivity, gives the change in model concentration, C i 

## , (for instance ozone concentration) with an incremental change in any input parameter, p j 

## (in this case emissions). Equation (3) allows us to normali ze the sensitivity coefficient, S ij 

## (t), so that it shows response in relative terms for the input rather th an in absolute units. Therefore, ܲ ෨ ௝ 

## (x,t) is the normalized

## 4 input and ε j 

## is a scaling variable (Yang et al, 1997). In general terms, the sensitivity coefficient tells us how a model output (ozone concentratio n) will change if a model input (emissions of
NO x 

## or VOC) is perturbed. This firs t order sensitivity coefficient, S ij 

## (t) is quite accurate for small perturbations, but gives a lin ear response which is not accura te 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 th e nonlinearities in the response of ozone to emissions changes. Us ing higher order DDM (HDDM) allows for the sensitivities to be accurately a pplied over larger emissions pertur bations. 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 ൅ ∆ࡿࣕ ሺ ૙ ሻ ൅ ∆ࣕ ૛

૛ 

### ࡿ ૛ 

### ሺ ૙ ሻ ൅... ൅ ࣕ∆ ࢔

!࢔ 

### ࡿ ࢔ 

### ሺ ૙ ሻ ൅ ࡾ ࢔ା૚ 

## Equation (4)

Here ∆߳ represents the relative change in emissions (for instance ∆߳ = -0.2 would be equivalent to reducing emissions by 20%), S n 

## (0) is the n-th order sensitivity coefficient, C(0) is the
concentration under baseline conditions (no perturba tion in emissions) and R n+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) predicti ons (Dunker, 1984; Yang et al, 1997; Hakami et al, 2003; Cohan et al, 2005; Napele nok et al, 2006; Koo et al., 2010; Zhang et al, 2012). These implementations allow the modeler to define th e 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 vari ous 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 firs t and second order sensitivities. For the purposes of the ozone NAAQS analys is, HDDM has the potential to provide an improved approach compared to current quadrat ic rollback techniques for several reasons. First, it captures non-linearity of ozone respons e to emissions changes, representing both
increases and decreases in ozone concentrations resulting from emissi ons reductions. Second,

## 5 HDDM characterizes different oz one response at different loca tions (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 “bac kground” ozone as a floor for rollback since predicted sensitivities are based on model formulations that e xplicitly 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 limita tions. 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 mode l 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 sensitivit ies will be capturing the change in ozone that would be predicted by a brute force model simula tion. Several studies have reported reasonable performance of HDDM for ozone up to 50% em issions perturbations (Hakami et al, 2003; Cohan et al., 2005; Hakami et al, 2004), but th e magnitude of pertur bation over which HDDM will give accurate estimates will depend on the specific modeling episode, size of the model domain, emissions and meteorologi cal inputs, and the size of the emissions source to which the sensitivity is being calculated. In this work, we applied sens itivities from simulations done under varying NOx levels (see Sec tion 3.2.3) and found that using th is 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 SIMULA TIONS OF JUST MEETING ALTERNATIVE STANDARDS: METHODOLOGY

3.1 CONCEPTUAL FRAMEWORK This section outlines the methodology app lied to use CMAQ/HDDM to estimate hourly
ozone concentrations that mi ght 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 ozon e 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 dete rmine hourly ozone sensitivities to
NO x 

## 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 th e diurnal sensitivity profiles calculated in Step 2 to each day in the period 2006 through 2008 2 

## 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 STUDIE S 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 ci ties (Atlanta and Detroit) using modeling for July and August 2005 and ambient data for th e 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 chem ical ozone formation regimes 3 

## . 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 i nputs are required to designate model inputs for calculating sensitivit ies. In this analysis, HDDM was set up to calculate the sensitivity of ozone concentr ations to domainwide (within the 12km Eastern domain – see Figure 16) anthropogenic NO x 

## and VOC emissions. US anthr opogenic 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 accounte d for 17,595,000 of the total domainwide 20,729,000 tons per year of NO x 

## emissions. Sensitivities were not dete rmined for biogenic, fire, Canadian, or Mexican emissions. In addition, sensitivi ties were not calculated for any emissions originating from outside the domain (i.e. ente ring through the use of bounda ry concentrations). First and second order hourly oz one sensitivities to VOC and NO x 

## were extracted from the HDDM simulation for model grid cells that contained one of ten ozone monitors near Atlanta, GA and one of eight ozon e 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 200 6-2008. 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 NO x 

## and VOC emissions.

3 

### Different sources of VOC and NO x 

### in the Midwest and Southeastern US lead to different VOC/NO x 

### ratios, ozone formation chemistry, and sensitivity to emi ssions 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 a pplied 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 modeli ng time period covers July and August 2005 but the time period we are analyzing in this proof-o f-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 th e NAAQS standard. In this manner, model predictions are “anchored” to measured ambient values. This is generally not done on a dayspecific basis, but instead averag e response on high modeled days is used for this purpose. This
allows for more confidence in calculated resu lts when “less than ideal model performance [occurs] on individual days” (US EPA, 2007). Sim ilarly, for this analysis we believe it is appropriate to account for th e fact the model does not al ways 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 resour ce 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 se ction 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-spe cific 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 c oncentration. Five classifications were developed for days with 8-hr daily maximu m 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 Figu re 4). From these groupings, five average diurnal sensitivity profiles were created for each monitor location and for each sensitivity type (first order NO x 

## , second order NO x 

## , first order VOC, and second order VOC). These average sensitivities were calculated by taking the median sensitivity of all mode led sensitivities at a particular hour for the group of days falling with in 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 “typi cal” 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, especia lly 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 a nd 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 th e 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 NO x 

## 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 concentrati on) on days with higher 8-hr maximum ozone values. Second, while the model predicts ozone decreases durin g 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 maximu m 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 Figur e 4b are the same for those bins. As explained above, the high ozone nights (Figure 4a) show sma ller 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 mo re response. However, no disbenefits occur in the VOC sensitivities. Figures shown here are for one site and only for response to NO x 

## emissions changes. Figures fo r all other Atlanta sites (NO x 

## sensitivities only) and all Detroit sites (NO x 

## and VOC sensitivities) are shown in Section Appendix B of this document.

Figure 4: Predicted change in ozone concentr ations (ppb) at Atlanta site 131210055 with a 30% decrease in US anthropogenic NO x 

## emissions in the Eastern US domain based on bins created for high ozone nights (lef t) 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 maximu m 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 assi gned to an average diur nal sensitivity profile from the appropriate bin. Note that if no measur ements 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 se nsitivity 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 NO x 

## or VOC emissions could be calculated. To perform the model-based ozone adjustment s, each urban area was treated separately. Within an urban area, we used the binned sens itivities to determine th e impacts on hourly ozone for incrementally increasing emissions reductions (NO x 

## 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 fo r each day. Adjusted design values were then calculated based on the 3-year average of the 4 th 

## highest 8-hr maximum value at each site. The emissions reduction was incrementally increased un til 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 ma x ozone classification), then a missing value was assigned to the adjusted ozone value. Note th at 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 documen t, 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 se nsitivities could replicat e the entire range of emissions reductions. Evaluations of the HDDM estimates compared to brute force zero out model runs in Detroit and Atla nta showed that the HDDM estimat es 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% NO x 

## reduction. Consequently

## 13 two additional CMAQ/HDDM runs were performe d under different levels of NOx emissions reductions. One CMAQ/HDDM run was pe rformed with US anthropogenic NO x 

## cut by 50% in the 12 km Eastern domain. A second a dditional run was performed with a 75% NO x 

## reduction. Emissions of other species were not modified from the base case in these two NOx reduction runs. These addition HDDM simulations give NO x 

## and VOC sensitivities under expected conditions with lower NO x 

## 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 cr eated for the base CMAQ/HDDM run. The 8-hr maximum and high/low nighttime ozone clas sifications were based on modeled ozone concentrations in the base run. This way, th e same hours and days are included in the various bins for all three runs (and fo r the entire ozone adjustment process). To apply these new sensitivities a 3-step methodology wa s adopted. In the first step, the original base sensitivities were applied for NO x 

## emissions reductions less than 40%. If the desired design value was not achieved with 40% NO x 

## reduction then additional emissions reductions were applied using the sensitivities derived from the 50% NO x 

## cut simulation. For instance, to predict the effects of 60% NO x 

## reduction, hourly ozon e concentrations were calculated for 40% NO x 

## 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% NO x 

## reduction simulation. Finally, the sensitivities from the 75% NO x 

## cut simulation were used to estimate the effects of NO x 

## emissions reductions beyond 75%. A conceptual pict ure 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 NO x 

## emissions reductions; for estimates of ozone response to VOC emissions reductions, the base sensitivities were used over the entire range of emissions perturbati ons 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 ૚૙૙ ൌ ࢆ ൅ ࢅ ൅ ࢄ 

## (5) Equation

ࢄ ൌ ૝૙ 

## Equation (6)

ࢅ ൌ ૜૞ 

## Equation (7)

ࢠ ൌ ૛૞ 

## Equation (8)

࢔࢕࢏࢚ࢉ࢛ࢊࢋ࢘ ࢞ࡻࡺ ࢚࢔ࢋࢉ࢘ࢋࡼ ൌ ࡼ 

## (9) Equation

ࡻ∆ ૜ ൞ ൌ ࡿ ൈ ࢇെ ࢋ࢙ࢇ࢈_࢞ࡻࡺ ࢇ ൅ ૛ ࡿ ൈ ࢋ࢙ࢇ࢈_࢞ࡻࡺ ૛ ܺ ൏ ࡼ ࢘࢕ࢌ
ࡿ ൈ ࢇെ ࢋ࢙ࢇ࢈_࢞ࡻࡺ ࢇ ൅ ૛ ࡿ ൈ ࢋ࢙ࢇ࢈_࢞ࡻࡺ ૛ ࡿൈ࢈െ ࢞ࡻࡺ ૙૞ ࢚࢛ࢉ% ࢈ ൅ ૛ ࡿ ൈ ࢞ࡻࡺ ૙૞ ࢚࢛ࢉ% ૛ ሻܻ ൅ ܺሺ ൏ ܲ ൏ ࢄ ࢘࢕ࢌ
ࡿ ൈ ࢇെ ࢋ࢙ࢇ࢈_࢞ࡻࡺ ࢇ ൅ ૛ ࡿ ൈ ࢋ࢙ࢇ࢈_࢞ࡻࡺ ૛ ࡿൈ࢈െ ࢞ࡻࡺ ૙૞ ࢚࢛ࢉ% ࢈ ൅ ૛ ࡿ ൈ ࢞ࡻࡺ ૙૞ ࢚࢛ࢉ% ૛ ࡿൈࢉെ ࢞ࡻࡺ ૞ૠ ࢚࢛ࢉ% ࢉ ൅ ૛ ࡿ ൈ ࢞ࡻࡺ ૞ૠ ࢚࢛ࢉ% ૛ ሻܻ ൅ ܺሺ ൐ ࡼ ࢘࢕ࢌ



## Equation (10) ൌቐࢇ ࡼ
૙૙૚ 

### ܺ ൏ ࡼ ࢘࢕ࢌ

ࢄ
૙૙૚ 

### ࢄ ൒ ࡼ ࢘࢕ࢌ 

## (11) Equation ൌቐ࢈ ൈ૛ ሺ ࢄିࡼ ሻ

૙૙૚ 

### ܻ ൏ ࡼ ࢘࢕ࢌ

ࢅൈ૛
૙૙૚ 

### ࢅ ൒ ࡼ ࢘࢕ࢌ 

## (12) Equation

࡯ ൌ ૝ ൈ ൫ࡼ െ ሺ ࢄ൅ࢅ ሻ ൯ 

## Equation (13)

Figure 6 demonstrates how this three-step application of HDDM sensitivities is implemented. A different version of this figur e 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 ev ery possible change in NO x 

## emissions (%). The segment of the solid line shown in black represents the portion of the NO x 

## reduction spectrum that uses the base sensitivity, the segment shown in blue represents the portion of the NO x 

## emissions reduction spectrum that uses the sensitivity from the 50% NO x 

## cut HDDM run, and the segment shown in green uses the sensitivity from the 75% NO x 

## cut HDDM run. The dotted black line shows the predicted change in ozone if the base sensitivity were us ed for the whole range of NO x 

## emissions reductions and the dotte d blue line show the predicte d change in ozone if only the base and 50% NO x 

## cut sensitivities were used. The tur quoise dots represent change in ozone at 9 am for this locati on estimated by brute force CMAQ runs with 30%, 50%, 75%, and 100% NO x 

## 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-ba sed 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 NO x 

## emissions reduction spectrum, while using the sensitivities from the 50% and 75% NO x 

## reduction simulations lead to pred ictions of ozone decreases when cuts are greater than about 50%. In this case, the use of the three-st ep application of HDDM sensitivities leads to greater predicted ozone d ecreases than would be ach ieved with a two-step application of HDDM sensitivitie s. This behavior is often, bu t not always apparent; there are limited hours/sites/bins for which the three-step es timates lead to smaller ozone decreases than the two-step estimates would. The spread of the tu rquoise dots shows that there is variability in modeled ozone responses even within bins at a single site and at a si ngle time of day. The model-based adjustment approach is supposed to predict “typical” res ponse, but may reduce the amount of variability in model estimated ozone reductions that would be seen in brute force model runs. However, Figure 6 de monstrates that for this instan ce 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 purp le 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 pred icts ozone decreases in response to NO x 

## emissions reductions while on low ozone nights, this met hodology predicts ozone incr eases in response to NO x 

## 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 draf t 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 a nd 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 2 nd 

## draft of the REA. The 2007 model run will include the entire ozone season and, thus, increase availa ble 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 Februa ry 2012. CMAQv5.0 is not yet instrumented with HDDM capabilities, but if th is 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 sensit ivities. Fourth we will expand this analysis to include all 16 citi es evaluated in the exposure assessment, cover ambient data from 2008-2010 and evaluate alte rnate possible new NAAQS in addition to the current 75 ppb standard. Finally, we will extend th e analysis to include the estimation of risk and exposure based on the HDDM adjusted ozone.

## 17 

# 4. USING HDDM/CMAQ FOR SIMULA TIONS 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 c oncentrations is predicted to change by hour of day according to the model-based adjustment approach (NO x 

## 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 NO x 

## cuts. Nighttime ozone concentrations are estimated
to decrease due to the NO x 

## reductions and morning ozone co ncentrations (4 am-7 am) are predicted to increase due to NO x 

## disbenefits during the morning rush hour. This change in ozone response with time of day was not possibl e to replicate with quadr atic 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 fo r the urban site. The HDDM sensitivities are predicting different chemical re gimes during rush hour for the urban and the downwind sites: the urban site is predicted to be NO x 

## 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 NO x 

## reductions at all times. Again, the quadratic rollback technique does not capture the spatial variability in oz one 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 de signate 25th to 75th percentile values and whiskers extend to 1.5 times the interquart ile 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 de signate 25th to 75th percentile values and whiskers extend to 1.5 times the interquart ile 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 e xpected based on the results shown in Figure 8 and Figure 9, the downwind sites show greater overall decreases in ozone with NO x 

## reductions than
the downtown sites due to NO x 

## saturated conditions in the urba n core during some hours of the day. Figure 7 shows that at some times smaller NO x 

## reductions would lead to ozone increases while larger NO x 

## 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 va lue is at the downtow n site, 130890002, after the application of NOx emissions reductions. Ther efore, the spatially a nd temporally varying sensitivities not only lead to differences in re sponse 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 va lues using HDDM model-based adjustment approach versus quadratic rollb ack. The quadratic rollback me thodology requires that ozone at all sites responds the same, so unlike the HDDM a pproach, the quadratic rollback predicts that the relative order of high to low design value sites remains cons tant. Design values at the two downtown sites are lower in the qua dratic rollback case while design values at all other sites are
lower in the model-base ad justment approach case.
Table 1: Observed, model adjusted and rolled-ba ck 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 dist ribution than quadratic rollback (i.e. the 25 th 

## , 50 th



## , 75 th 

## , 95 th 

## percentile values are lower) but that qua dratic rollback reduces the highest hourly
ozone value more.

## 21 Figure 10: Distribution of ozone concentrat ions for all Atlanta sites (2006-2008) for measured values (left), values calculat ed using the model-based adjustment approach (NO x 

## reductions) (middle) and value s calculated using quadratic rollback (right). Centerlines sh ow median values, boxes cover 25 th 

## to 75 th 

## percentile ranges and whiskers extend to 5 th 

## and 95 th 

## percentile values. Left panel includes outlier values while right panel focuses on the portion of the range which includes the 5 th 

## to 95 th 

## percentile values.

4.2 DETROIT RESULTS

4.2.1 New distributions of ozone to attain 75 ppb NAAQS: NO x 

## and VOC reductions Figure 11 and Figure 12 show ozone distribut ions for an urban and downwind Detroit site based on observations (2006-2008) and on th e model-based adjustment approach assuming
NO x 

## reductions. Figure 13 and Figure 14 show th e same information but instead using HDDM
sensitivities to VOC emi ssions reductions. The NO x 

## 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 res ponse during the rest of the day looks some what 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 (a nd 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 adjustme nt case. This indi cates that under a NO x 

## reduction scenario this urban Detroit site is exp ected 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 show s that at the downwind Detroit site, there
are some small NO x 

## disbenefits in the morning and that NO x 

## cuts result in ozone decreases throughout the entire range of ozone for all ot her hours of the day. The ozone design values with VOC emissions reductions (shown in Figur e 13 and Figure 14) show that ozone would be reduced at all hours of the day and for all porti ons 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 NO x 

## reduction. For the VOC reduction case, ozone at the urban site responds more than oz one at the downwind site.

Figure 11: Distribution of ozon e concentrations by hour of day for Detroit site 260991003. Centerlines show median values, boxes de signate 25th to 75th percentile values and whiskers extend to 1.5 times the interquart ile 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 (NO x 

## cut) (2006-2008).

## 23 Figure 12: Distribution of ozon e concentrations by hour of day for Detroit site 260990009. Centerlines show median values, boxes de signate 25th to 75th percentile values and whiskers extend to 1.5 times the interquart ile 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 (NO x 

## cut) (2006-2008).

## 24 Figure 13: Distribution of ozon e concentrations by hour of day for Detroit site 260991003. Centerlines show median values, boxes de signate 25th to 75th pe rcentile values and whiskers extend to 1.5 times the interquart ile range. Values in gray/black show measured ozone distributions (2006-2008) and values in red/pink show predicted ozone distributions based on HDDM mode l-based adjustment approach (VOC cut) (2006-2008).

## 25 Figure 14: Distribution of ozon e concentrations by hour of day for Detroit site 260990009. Centerlines show median values, boxes de signate 25th to 75th percentile values and whiskers extend to 1.5 times the interquart ile range. Values in gray/black show measured ozone distributions (2006-2008) and values in red/pink show predicted ozone distributions based on HDDM mode l-based adjustment approach (VOC cut) (2006-2008).

4.2.2 Resulting design values: NO x 

## 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 Detroi t, 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 th ey do at the other less urbanized sites in Detroit
for the NO x 

## cut case. Conversely, ozone design values decrease the same or slightly more for the urban sites than the upwind and downwind si tes 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
NO x 

## reductions than with VOC reductions: urban site 261630001 is predicte d to have the same

## 26 design value in the NO x 

## and VOC model-based adjustment cases while urban site 2609910003 is predicted to have a hi gher design value with the NO x 

## 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 quadr atic rollback. Again, the quadra tic rollback assumes that all sites will respond the same to a gi ven level of emissions reductions so in the quadratic rollback case the order of high to low design values at De troit sites does not change. However, in both
the NO x 

## and VOC model-based adjustment cases some sites are more responsive than others to
emissions reductions and thus the site with th e highest post-control de sign 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 NO x 

## and VOC emissions reduction cases), and quadratic rollback. Ozone dist ributions for quadratic rollback and model-based adjustment using VOC reductions look similar. Both show small reductions 25 th 

## , 50 th 

## , 75 th 

## , and 95 th 

## percentile values. The distribution of hourly ozone values in the NO x 

## reduction model-based adjustment case shows a la rger decrease in the 95 th 

## percentile value than the VOC model-based adjustment or quadratic ro llback cases. However the NO x 

## reduction model-based adjustment case in Detroit leads to almost no change in the 50 th 

## and 75 th 

## percentile ozone values and a very
small decrease in the 5 th 

## and 25 th 

## percentile ozone values. Therefore, in Detroit the NO x 

## reduction results in lowering th e highest ozone concentrations more than either the VOC reduction or quadratic rollback ca ses 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-ba ck design values for Detroit sites. Urban sites highlighted in blue.

Monitor Measured 2006-2008 DV HDDM Adjusted DV (VOC reductions) HDDM Adjusted DV (NO x 

### 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 concentrat ions for all Detroi t sites (2006-2008) for measured values (left), values calculat ed using HDDM model-based adjustment approach (NO x 

## reductions) (2nd from left), HDDM model-based adjustment approach (VOC reductions) (2nd from righ t) and values calculated using quadratic rollback (right). Centerlines show median values, boxes cover 25 th 

## to 75 th 

## percentile ranges and whiskers extend to 5 th 

## and 95 th 

## percentile values. Left panel includes outlier values while right panel focuses on the portion of the range which includes the 5 th 

## to 95 th 

## percentile values.

## 28 

# 5. RECOMMENDATIONS

Based on the analysis presented here, we belie ve that there are clear benefits for using model-based ozone adjustments for the s econd draft REA. The HDDM model-based adjustment approach, unlike quadratic rollbac k, 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 qual ity changes to NOx versus VOC emissions reductions. For example, the model-based adju stments 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 pr ocesses that lead to ozone formation and transport, and includes both natural and anth ropogenic 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, e liminating the need to artificially specify a floor for air quality changes as is required in the quadratic rollb ack 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 ch anged in both Detroit a nd Atlanta between the
2006-2008 observations and the HDDM model-based oz one estimates. (The application of the quadratic rollback will not change the order of the monitoring site s with respect to magnitude of the design value before versus after rollbac k.) Using the HDDM model-based adjustment approach also had the effect of changing the abso lute 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 a pplication 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 exam ple, the application of HDDM allowed us to compare the effect of reducing NOx versus VOC emissions. Five out of 8 monitoring sites in Detroi t were predicted to have lowe r design values using the HDDM model-based adjustment approach with NOx emissi ons 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 reduc tions versus quadratic rollback. Compared to quadratic rollback, these capab ilities of the HDDM model-base d adjustment approach build confidence that the results drawn from this type of analysis allow us to more realistically represent the response in hourly oz one concentrations to reductions in emissions for a scenario of just meeting the current and alternative standa rd 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 

# 6. REFERENCES

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Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G ., Roselle, S.J. (2008). Evaluation of the community Multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II - particulate matter. Atmospheric Environment, 42: 6057-6066.

Baker, K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S. 12-km Domain Simulation, USEPA/OAQPS, February 2, 2009.

Baker, K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S. 12-km Domain Simulation, USEPA/OAQPS, February 2, 2009.

Baker, K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Continental U.S. 36-km Domain Simulation, USEPA/OAQPS, February 2, 2009.

Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ modeling system, EPA/600/R-99/03 0, Office of Research and Development).

Byun, D., Schere, K.L. (2006). Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews, 59: 51-77.

Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.D., Sarwar, G., Pinder, R.W., Pouliot, G.A., Houyoux, M. (2010). Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environmental Science & Technology, 44: 8553-8560.

Cohan, D.S., Hakami, A., Hu, Y., Russell, A.G. (2005) . Nonlinear response of ozone to emissions: Source apportionment and sensitivity analysis. Environmental Science & Technology, 39: 6739-6748.

Cohan, D.S., Napelenok, S.L. (2011). Air quality response modeling for decision support. Atmosphere, 2: 407-425; doi:10.3390/atmos2030407.

Dunker, A.M. (1984). The decoupled direct method for calculating sensitivity coefficients in chemical kinetics. Journal of Chemical Physics, 81; 2385-2393.

Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Plei m, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O., Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., Bash, J.O. (2010). Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7, Geoscientific Model Development, 3: 205-226.

Grell, G., J. Dudhia, and D. Stauffer (1994). A Descri ption of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.

Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C. (1 989). A photochemical kinetics mechanism for urban and regional scale computer modeling. Journal of Geophysical Research-Atmospheres, 94: 12925-12956.

Hakami, A., Odman, M.T., Russell, A.G. (2003). High-ord er direct sensitivity analysis of multidimensional air quality models. Environmental Science & Technology, 37: 2442-2452.

## 30 Hakami, A., Odman, M.T., Russell, A.G. (2004). Nonlin earity in atmospheric resp onse: A direct sensitivity analysis approach. Journal of Geophysical Research, 109: D15303; doi:10.1029/2003JD004502.

Henze, D.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T-M. Fu, D.J. Jacob, C.L. Heald (2008). Global modeling of secondary organic aerosol formation from aromatic hydrocarbons: High-vs.low-yield pathways. Atmos. Chem. Phys., 8: 2405-2420.

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheel er, N.J.M., Kasibhatla, P.S. (2000). Emission inventorydevelopment and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geophysical Research-Atmospheres, 105: 9079-9090.

Koo, B., Dunker, A.M., Yarwoo d, G. (2007). Implementing the decoupled direct method for sensitivity analysis in a particulate matter air quality model. Environmental Science & Technology, 41: 2847-2854.

Napelenok, S.L., Cohan, D.S., Hu, Y., Russell, A.G. (2006). Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmospheric Environment, 40: 6112-6121.

National Research Council of the National Academies (2008). Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. Th e National Academies Press, Washington, D.C

Nenes, A., Pandis, S.N., Pilinis, C. (1998). ISO RROPIA: A new thermodynamic equilibrium model for multiphasemulticomponent inorganic aerosols. Aquatic Geochemistry, 4: 123-152.

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Seinfeld, J.H. and S.N. Pandis. (1998). Atmospheric chem istry and physics from air po llution to climate change. John Wiley & Sons, Inc, New York, 1998.

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## 31 

# APPENDIX A. MODEL SET-UP AND EV ALUATION 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 ga s-particle partitioning of inorganic species (Nenes et al 1998) and secondary or ganic aerosol treatment as describe d in Carlton et al (2010).

A.1.1. Model domain For this analysis, all CMAQ/HDDM runs we re performed in the Eastern U.S. 12 km modeling domain. As shown in Figure 16, this doma in has a “parent” doma in with a horizontal grid of resolution of 36 km. The air quality pred ictions from the 36 km domain were only used to establish the incoming air quality concentra tions along the boundaries of the 12 km domain. Air quality conditions at the out er 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 co ordinate system. Table 3 provides some basic geographic information rega rding 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 inn er box is the 12 km eastern U.S. fine grid.

Table 3: Geographic elements of doma in 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 mil libar 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 cons idered in the analysis for the HDDM results. Only 3 days were deemed necessary for the ra mp 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 fi elds, emissions, and initial and boundary conditions. The gri dded meteorological input data for the entire year of 2005 were derived from simulations of the Pennsyl vania 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 atmo spheric 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 temper ature 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 10 4 

## for wind fields and temperatures and 1.0 x 10 5 

## for moisture fields. The 12 km domain nudging weighting
factors were 1.0 x 10 4 

## for wind fields and te mperatures and 1.0 x 10 5 

## 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 appr oximately 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 thr ee MM5 runs were processed to create model-ready inputs for CMAQ using the Meteor ology-Chemistry Interfac e 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 m odeling inputs. The 2005 MM5 model performance evaluations used an approach which include d a combination of qualitative and quantitative analyses to assess the adequacy of the MM5 simu lated fields. Qualitative evaluations compared spatial patterns of monthly total rainfall a nd 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 paramete rs. For this portion of the evaluation, five meteorological parameters were investigated : temperature, humidity, shortwave downward radiation, wind speed, and wind direction. The MM 5 evaluations for each domain are described elsewhere (Baker and Dolwick, 2005a,b,c). The resu lts of these analyses i ndicate that the bias
and error values associated with all three se ts 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 th e 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 el ectric generating utilitie s (EGUs) and fires in the United States which are temporalized based on average temporal prof iles from 3 years of data. In addition, US emissions are included from other point sources, ar ea sources, agricultural sources (ammonia only), anthropogenic fugitive dus t 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 versio n 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, NH 3 

## , NO x 

## , PM 10 

## , PM 2.5 

## , SO 2 

## , 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 NH 3 

### NO X 

### PM 10 

### PM 2.5 

### SO 2 

### 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 3,7 23 20,729 12,021 3,840 15,264 41,734



## The lateral boundary and init ial species concentrations for the 36 km domain are provided by a three-dimensional global atmo spheric 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 Godda rd Earth Observing System (GEOS). This model was run for 2005 with a grid resoluti on of 2.0 degree x 2.5 degr ee (latitude-longitude) and 30 vertical layers up to 100 mb. The predic tions were used to pr ovide 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 PM 2.5 

## predictions from the
Eastern 12 km domain can be found in US EP A (2011b). Model evaluation statistics for specific monitors included in this an alysis are provided in this section. Table 6 and Table 7 give performance sta tistics 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 a ddition, data are further disaggregated to show performance when observed hourly ozone or obs erved 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 show n in Figure 20, Figure 21, and Figure 22. These tables and figures show that oz one is overpredicted at Atlanta monitoring sites during July and August 2005. This overprediction occurs both duri ng 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 fluctu ations 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 Atla nta. For the most part, daytime ozone values are well captured in Detroit. The model st ill 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 Atlant a 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 mo nitoring 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 mode l performance for hourly ozone concentrations at Atlanta monitoring sites for July 2005. Observ ed values shown in black and modeled values shown in red.

Figure 18: Time series of mode l performance for hourly ozone concentrations at Atlanta monitoring sites for August 2005. Observ ed values shown in black and modeled values shown in red.

## 40 Figure 19: Time series of model perf ormance for 8-hr daily maximum ozone concentrations at Atlanta monitoring si tes for July and August 2005. Observed values shown in black and modeled values shown in red.

Figure 20: Time series of mode l performance for hourly ozone concentrations at Detroit monitoring sites for July 2005. Observ ed values shown in black and modeled values shown in red.

## 41 Figure 21: Time series of mode l performance for hourly ozone concentrations at Detroit monitoring sites for August 2005. Observ ed values shown in black and modeled values shown in red.

Figure 22: Time series of model perf ormance for 8-hr daily maximum ozone concentrations at Detroit monitoring si tes 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 ad justment technique applies sensitivities from HDDM/CMAQ runs performed under differen t emissions scenarios to cover the entire range of emissions reductions. The base se nsitivity was used over the first 40% of NO x 

## reductions, the sensitivity from a simula tion performed with 50% anthropogenic NO x 

## emissions
was used over the next 35% of NO x 

## reduction and finally the se nsitivity from a simulation performed with 75% anthropogenic NO x 

## emissions was used over the final 25% of NO x 

## reductions. An evaluation was performed to dete rmine the ideal cut points to switch to the sensitivity derived from a different simulation. First, HDDM estimates of ozone concentrations for 50% NO x 

## emissions cuts were compared to brute force simulations in whic h emissions NOx emissions were reduced by 50% and the model was rerun. The HDDM-predicte d ozone concentration was calculated using Equations (5) and (9) – (12), w ith 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 concentrat ions from the brute fo rce 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 Atlant a 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, 99 th 

## percentile bias,
99 th 

## percentile error, correlation coefficient (R), sl ope for the line of best fit, and intercept for
the line of best fit. The maximum and 99 th 

## 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 metric s, the best performance occurs when the value
of the metric is closest to zero. For R, values closest to one in dicate 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 estimat es. 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% NO x 

## cut and 100% NO x 

## 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 ch aracterize how well HDDM
estimates of ozone at these NO x 

## emissions levels compared to brute force model simulations for grid cells in Detroit, Atlanta, and across th e 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 repli cating 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 th at would result from VO C reductions. Figure 23 and Figure 24 show a comparison of predicte d ozone at 100% anthropogenic VOC reductions for the grid cells containing the Atlanta and De troit monitoring sites respectively. These plots are density scatter plots: th e y-axis shows the predicted ozone concentration using HDDM sensitivities, the x-axis shows the predicte d ozone concentration using a brute force CMAQ simulation, and the colors repres ent 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 estima ting 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 fo rce 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 ar e also provided in Section Appendix B.

## 44 Figure 23: Comparison of HDDM to brut e force ozone predictions with 100% VOC emissions cuts at grid cells containing Atla nta monitoring sites. Colors denote the percentage of points which fall in each location in this plot.

Figure 24: Comparison of HDDM to brut e force ozone predictions with 100% VOC emissions cuts at grid cells containing Detr oit 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 NO x 

## reductions for the grid cells containing the Atlanta and Detroit monitoring sites respecti vely. 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% NO x 

## cut case and 3-step HDDM estimates
for the 100% NO x 

## 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 NO x 

## emissions levels provides ozone estimates that are in substantially better ag reement with brute force emissions reductions CMAQ simulations. Even for the hypothetical case in which 100% of the anthropogenic NO x 

## 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 NO x 

## emissions present) to the 100% NO x 

## 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-st ep (left) and multi-step (rig ht) HDDM to brute force ozone predictions with 75% NO x 

## emissions cuts at grid cells containing Atlanta monitoring sites. Colors denote the percent age of points which fall in each location in this plot.

## 46 Figure 26: Comparison of 1-step (left) and multi-step (righ t) HDDM to brute force ozone predictions with 75% NO x 

## emissions cuts at grid cells containing Detroit monitoring sites. Colors denote the percent age of points which fall in each location in this plot.

Figure 27: Comparison of 1-step (left) and multi-step (righ t) HDDM to brute force ozone predictions with 100% NO x 

## emissions cuts at grid cells containing Atlanta monitoring sites. Colors denote the percent age of points which fall in each location in this plot.

## 47 Figure 28: Comparison of 1-step (left) and multi-step (righ t) HDDM to brute force ozone predictions with 100% NO x 

## emissions cuts at grid cells containing Detroit monitoring sites. Colors denote the percent age of points which fall in each location in this plot.

## 48 

# APPENDIX B. ADDITIONAL PLOTS

Figure 29: Predicted change in ozone concentrat ions (ppb) at all Atlanta sites with a 30% decrease in US anthropogenic NOx emission s in the Eastern US domain based on bins created for high ozone nights.

## 49 Figure 30: Predicted change in ozone concentrat ions (ppb) at all Atlanta sites with a 30% decrease in US anthropogenic NOx emission s in the Eastern US domain based on bins created for low ozone nights.

## 50 Figure 31: Predicted change in ozone concentrat ions (ppb) at all Detroit sites with a 30% decrease in US anthropogenic NOx emission s in the Eastern US domain based on bins created for high ozone nights.

## 51 Figure 32: Predicted change in ozone concentrat ions (ppb) at all Detroit sites with a 30% decrease in US anthropogenic NOx emission s in the Eastern US domain based on bins created for low ozone nights.

## 52 Figure 33: Predicted change in ozone concentrat ions (ppb) at all Detroit sites with a 30% decrease in US anthropogenic VOC emission s in the Eastern US domain based on bins created for high ozone nights.

## 53 Figure 34: Predicted change in ozone concentrat ions (ppb) at all Detroit sites with a 30% decrease in US anthropogenic VOC emission s 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% east ern US VOC emissions cuts at grid cells containing Atlanta monitoring sites. Colo rs 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% east ern US VOC emissions cuts at grid cells containing Detroit monitoring sites. Colo rs 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 cu ts at grid cells containing Atlanta monitoring sites. Colors denote the percent age 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 cu ts at grid cells containing Detroit monitoring sites. Colors denote the percent age 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 percent age of points which fall in each location in this plot.

Figure 40: Comparison of 1-st ep (left) and multi-step (r ight) 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 d ay at each Atlanta monitoring site. Centerlines show median values, boxes designate 25th to 75th percentile values and whiskers extend to 1.5 times th e interquartile range. Values in

## 58 gray/black show measured ozone distribut ions (2006-2008) and values in red/pink show predicted ozone distributions ba sed on HDDM model-based adjustment approach (2006-2008)

Figure 42: Distribution of ozone concentrations by hour of d ay at each Detroit monitoring site. Centerlines show median values, boxes designate 25th to 75th percentile values and whiskers extend to 1.5 times th e interquartile range. Values in gray/black show measured ozone distribut ions (2006-2008) and values in red/pink show predicted ozone distributions ba sed on HDDM model-based adjustment approach (NOx cuts) (2006-2008)

## 59 Figure 43: Distribution of ozone concentrations by hour of d ay at each Detroit monitoring site. Centerlines show median values, boxes designate 25th to 75th percentile values and whiskers extend to 1.5 times th e interquartile range. Values in gray/black show measured ozone distribut ions (2006-2008) and values in red/pink show predicted ozone distributions ba sed on HDDM model-based adjustment approach (VOC cuts) (2006-2008)