Final Report



## Ozone Modeling for Maintenance Plan Revision

for Puget Sound in Western Washington

prepared by

Brian Lamb, Guangfeng Jiang, and Hal Westberg

Laboratory for Atmospheric Research

Department of Civil & Environmental Engineering

Was hington State University

Pullman, WA 99164

- 2910

and

Clint Bowman, Michael Boyer, Cristiana Figueroa

- Kaminsky, and Sally Otterson

Washington State Department of Ecology

Lacey, WA

prepared for

Puget Sound Clean Air Agency

Naydene Maykut, Project Of ficer

Seattle, WA

October 15, 2001ii

Summary

To assist in the revision of the ozone maintenance plan for the Puget Sound area, a

photochemical grid modeling study has been completed. The modeling system, developed

during the past several years by Wa shington State University and the Washington Department of

Ecology, consists of the Mesoscale Meteorological Model Version 5 (MM5) coupled with the

photochemical modeling pair, CALMET and CALGRID. In the current study, the modeling

system was employed to simulate an ozone episode that occurred during July 11

- 14, 1996. A

number of improvements were made to the modeling system during this study; the end result was

improved model performance that was within EPA guidelines for acceptable photochemical

ozone m odeling (bias less than  15%, gross error less than 35%). Specifically, the episode

simulation yielded an overall model bias of +5% and an overall gross model error of 26%. The

maximum observed ozone concentration during the episode was 118 ppb at the En umclaw

monitoring site southeast of Seattle; the model predicted maximum concentration at this site was

106 ppb.

In addition to modeling improvements, a unique back

- trajectory source footprint analysis was

developed to show the areas upwind contributing to pollutant concentrations at selected

receptors. A detailed process analysis scheme was also implemented as part of modeling system

to investigate the relative importance of different physical and chemical processes occurring

along the air mass traject ory. These tools showed that differences in ozone simulated at

Enumclaw on different days could be attributed to changes in VOC/NOx ratios along the

transport path. On Friday when the maximum ozone concentrations at Enumclaw reached 90

ppb, the VOC/NOx r atios along the morning trajectory were in the range of 7 to 10 and ozone

production rates were approximately 17 ppb/hr. However, on Sunday when the maximum ozone

concentration reached 118 ppb at Enumclaw, the simulated VOC/NOx ratios were higher than on

Friday and ozone production rates exceeded 25 ppb/hr.

To further investigate ozone formation in the Puget Sound area, a series of model sensitivity tests

was completed. Anthropogenic VOC and NOx emissions were reduced separately by 25%, 50%

and 75%. A s VOC emissions alone are reduced, the peak ozone concentration in the domain

decreases linearly. At 50% VOC reduction, the peak ozone concentration on Sunday decreased

from 106 ppb to 90 ppb. As NOx emissions alone are reduced, the peak ozone concentrat ion in

the domain increases slightly and then, with still greater reduction in NOx emissions, ozone

decreases. The increase in ozone due to moderate decreases in NOx emissions is due to loss of

the titration of ozone from fresh NO emissions. At 50% NOx r eduction, the peak ozone is about

3 ppb less than in the base case.

To examine the sensitivity of ozone formation to the relative impact of different source

categories, model simulations were completed with emissions from each different category

complet ely removed. In terms of the peak ozone concentration simulated on Sunday, removing

mobile emissions had the largest effect with 29 ppb reduction in peak ozone concentration.

Removing all mobile NOx emissions reduced the peak ozone concentration by 26 pp b which

illustrates the critical role that mobile NOx emissions play in the formation of elevated ozone

concentrations in the area. Removing mobile VOC emissions yielded a reduction of 16 ppbiii

which matches the reduction in ozone due to removal of anthropo genic area source emissions.

Removing biogenic emissions caused the smallest effect with the peak ozone concentration

reduced by 12 ppb on Sunday.

The sensitivity of ozone formation to specific VOC was also investigated. The organic emission

with the highest sensitivity was the lumped ARO2 species that represents anthropogenic reactive

aromatic compounds. Ozone sensitivity to ARO2 emissions, measured in terms of change to

peak ozone on Sunday, equaled 7 ppb O3 for a 100% reduction in ARO2 emissions. For

comparison, other lumped anthropogenic organics, ALK1 and ARO1, had sensitivities of

approximately 4 ppb O3 for a 100% reduction in compound emissions. The biogenic species,

isoprene and the terpenes, had sensitivities of 5 ppb O3 and 2 ppb O3, respec tively, for 100%

compound emission reductions.

To provide a foundation for analysis of potential ozone control strategies, the 1996 base case

emission inventory was projected into the future for years 2007 and 2015. These projected

emission inventories were then used with the 1996 meteorological simulation to determine the

impact of emission changes in the future. In the year 2007, emissions are projected to be

reduced by 13% for anthropogenic VOC emissions and 17% for anthropogenic NOx emissions.

In 2015, the emissions are expected to decrease by 22% for anthropogenic VOC emissions and

29% for anthropogenic NOx emissions compared to the 1996 base case. For hourly average

concentrations, the changes in daily maximum predicted ozone from 1996 to the fu ture years is

not consistent from one day to the next. However, for the peak ozone predicted on Sunday

afternoon, the emissions in 2007 produce approximately 2 ppb improvement and in 2015 the

change in emissions decreases peak ozone concentrations by abou t 7 ppb.

Three different control strategies were formulated that emphasized reduction of VOC emissions

for 2007 and 2015. For year 2007, the three cases represent VOC emission reductions of a)

– 16%, b)

– 17%, and c)

– 15% and reduced NOx emissions of

– 18% for all three cases relative to

1996 emissions. These changes represent 2% to 4% more reduction in VOC emissions and 1%

more reduction in NOx emissions than for the 2007 base emissions compared to the 1996 base

emissions. The corresponding change in pea k ozone was a decrease of approximately 2 ppb

relative to the 2007 base case. For year 2015, the VOC emissions were reduced by a)

– 27%, b)

– 29%, and c)

– 27% compared to the 1996 base case and NOx emissions were reduced by 30% for

all three cases relative to 1996 emissions. Relative to the 2015 base case, these changes

represent 5% to 7% more reduction for VOC emissions and 1% more reduction for NOx

emissions. For the 2015 control strategies, peak ozone concentrations decreased by

approximately 1 to 2 ppb compared to the 2015 base case. In each case, the area of reduced

ozone generally follows the area of maximum ozone concentrations. Thus, it appears that

changes in emissions over time from 1996 to 2007 and 2015 will produce a greater change in

maximum ozone than does any of the three control strategies for 2007 or 2015.iv

Table of Contents

Summary ii

Table of Contents iv

List of Tables v

List of Figures vi

Introduction 1

Objectives 1

Study Desig n 1

Modeling System Description 2

Model Domain 2

Episode Selection and Meteorology 2

Boundary Conditions 2

Emission Inventory 6

Modeling System Improvement 9

Chemical Mechanism Update 9

Mixed L ayer Treatment 11

Statistical Model Performance Evaluation 11

Presentation Modeling Analysis and Results 17

Source Footprint and Process Analysis 17

Model Sensitivity Analysis 28

Emission Base Case and Control Strategy Simulat ions 36

References 50

Appendix A: Emission Inventory 51v

List of Tables

Table 1. Initial and boundary condition concentrations for CALGRID simulations.

Previous conditions (Barna et al., 2000) listed in parentheses. 6

Table 2 : Emissions and Strategy Reductions from All Sources 7

Table 3: Emissions and Strategy Reductions from Anthropogenic Sources 8

Table 4. Summary of model performance statistics for the July 11

- 14, 1996 episode in Puget

Sound. 16

Table 5. Summary of overall model performance statistics for Puget Sound and comparison to

results from Barna and Lamb (2000) and to results for UAM

- V and SAQM applications to the

northeastern US (Sistla et al., 2001). 16

Table 6. Effect of removing ind ividual source categories on maximum predicted hourly average

ozone. 35

Table 7. Effect of individual VOC emissions on peak ozone concentration for the case where

each VOC emissions are set to zero. 35

Table 8. Peak hourly ozone con centration (ppb) for years 1996, 2007, and 2015. 36

Table 9. Peak 8

- hour average ozone concentration for years 1996, 2007, and 2015. 43vi

List of Figures

Figure 1. Map of Cascadia model domain. Dashed lines indicate extent of the Puget Sound

ozone modeling domain. Ozone monitoring stations are also shown. For Puget Sound, the

monitoring stations include: Getchel (GE), Lake Sammamish (LS), Enumclaw (EW), Pack

Forest (PF), Paradise (PA), and Packwood (PW). 3

Figure 2. Map of surface winds fr om MM5 observational nudging simulation for Sunday, July

14, 1996 at 1500 PST. 4

Figure 3. Time series of simulated wind direction (top), wind speed (middle), and temperature

(bottom) for urban Seattle during the July 11

- 14, 1996 ozone episode. 5

Figure 4. a) Map of isoprene oxidation using the SAPRC

- 97 chemical mechanism for Sunday,

July 14, 1996 at 2 pm. b) Map of total terpene (OLE3) oxidation rates using the SAPRC

- 97

chemical kinetic mechanism for 2 pm, Sunday, July 14, 1996. 10

Figure 5. Map of mid

- day mixed layer heights derived from the MM5 simulation for Sunday,

July 14, 1996. 12

Figure 6. Time series of observed and predicted ozone concentrations at the Puget Sound

monitoring sites: a) Getchel b) Lake Sammamis h c) Enumclaw d) Paradise e) Pack Forest and f)

Packwood. 13

Figure 7. Schematic of process analysis of terms in the atmospheric diffusion equation applied

to a single grid cell. 18

Figure 8. a) Map of source footprint for back t rajectory arriving at Enumclaw at 3 pm on Friday,

July 12, 1996. Ozone concentrations for indicated times and locations along the trajectory are

also shown, b) map of source footprint and underlying NOx emissions (8 am emissions only),

and c) map of sourc e footprint and underlying VOC emissions (8 am emissions only). 19

Figure 9. a) Map of source footprint for back trajectory arriving at Enumclaw at 3 pm on Friday,

July 12, 1996. NOx concentrations for indicated times and locations along the trajector y are also

shown. b) map of source footprint and VOC/NOx ratios along the trajectory, and c) map of

source footprint and O3 production rates (ppb/hr) along the trajectory. 21

Figure 10. Summary of process analysis along the back trajectory arriving at Enumclaw at 3 pm

on Friday, July 12 1996. 22

Figure 11 a) Map of source footprint for back trajectory arriving at Enumclaw at 3 pm on

Sunday, July 14, 1996. Ozone concentrations for indicated times and locations along the

trajectory are also s hown, b) map of source footprint and underlying NOx emissions (8 am

emissions only), and c) map of source footprint and underlying VOC emissions (8 am emissions

only). 23vii

Figure 12. a) Map of source footprint for back trajectory arriving at En umclaw at 3 pm on

Sunday, July 14, 1996. NOx concentrations for indicated times and locations along the

trajectory are also shown. b) map of source footprint and VOC/NOx ratios along the trajectory,

and c) map of source footprint and O3 production rates (ppb/hr) along the trajectory. 24

Figure 13. Summary of process analysis along the back trajectory arriving at Enumclaw at 3 pm

on Sunday, July 14, 1996. 25

Figure 14. a) Map of source footprint for back trajectory arriving at Paradise at 5 p m on Sunday,

July 14, 1996. Ozone concentrations for indicated times and locations along the trajectory are

also shown, b) map of source footprint and underlying NOx emissions (8 am emissions only),

and c) map of source footprint and underlying VOC emissi ons (8 am emissions only). 26

Figure 15. a) Map of source footprint for back trajectory arriving at Paradise at 5 pm on Sunday,

July 14, 1996. NOx concentrations for indicated times and locations along the trajectory are also

shown, b) map of source f ootprint and VOC/NOx ratio along the trajectory, and c) map of source

footprint and O3 production rates (ppb/hr) along the trajectory. 27

Figure 16. Summary of process analysis along the back trajectory arriving at Paradise at 5 pm on

Sunday, July 14, 1996. 29

Figure 17. Domain wide surface ozone production rates (ppb/hr) on Sunday, July 14, 1996 at 3

pm. 30

Figure 18. Sensitivity of maximum surface ozone concentration downwind of Puget Sound to

changes in NOx and VOC anthropo genic emissions on Friday, July 12, 1996. 31

Figure 19. Sensitivity of maximum surface ozone concentration downwind of Puget Sound to

changes in NOx and VOC anthropogenic emissions on Saturday, July 13, 1996. 32

Figure 20. Sensitivity of maximum surfac e ozone concentration downwind of Puget Sound to

changes in NOx and VOC anthropogenic emissions on Sunday, July 14, 1996. 33

Figure 21. Time series of maximum hourly average surface ozone concentrations downwind of

Puget Sound due to base case emissions for 1996, 2007 and 2015. 37

Figure 22. Time series of maximum 8

- hour average surface ozone concentrations downwind of

Puget Sound due to base case emissions for 1996, 2007 and 2015. 37

Figure 23. Map of surface ozone concentration contours for 3 pm on Sunday, July 14 for 1996

base case emissions. 38

Figure 24. Map of surface ozone concentration contours for 3 pm on Sunday, July 14 for 2007

base case emissions. 39viii

Figure 25. Map of surface ozone concentration contours fo r 3 pm on Sunday, July 14 for 2015

base case emissions. 40

Figure 26. Contour map of differences in surface ozone concentration between 1996 and 2007

(negative numbers indicate a reduction of ozone in 2007 relative to 1996) for 3 pm on Sunday,

July 14. 41

Figure 27. Contour map of differences in surface ozone concentration between 1996 and 2015

(negative numbers indicate a reduction of ozone in 2015 relative to 1996) for 3 pm on Sunday,

July 14. 42

Figure 28. Contour ma p of differences in surface ozone concentration between the 2007 base

case and control strategy 2007A (negative numbers indicate a reduction of ozone in 2015 relative

to 1996) for 3 pm on Sunday, July 14. 44

Figure 29. Contour map of differences i n surface ozone concentration between he 2007 base

case and control strategy 2007B (negative numbers indicate a reduction of ozone in 2015 relative

to 1996) for 3 pm on Sunday, July 14. 45

Figure 30. Contour map of differences in surface ozone con centration between the 2007 base

case and control strategy 2007C (negative numbers indicate a reduction of ozone in 2015 relative

to 1996) for 3 pm on Sunday, July 14. 46

Figure 31. Contour map of differences in surface ozone concentration between the 2015 base

case and control strategy 2015A (negative numbers indicate a reduction of ozone in 2015 relative

to 1996) for 3 pm on Sunday, July 14. 47

Figure 32. Contour map of differences in surface ozone concentration between he 2015 base

case and control strategy 2015B (negative numbers indicate a reduction of ozone in 2015 relative

to 1996) for 3 pm on Sunday, July 14. 48

Figure 33. Contour map of differences in surface ozone concentration between the 2015 base

case and control strat egy 2015C (negative numbers indicate a reduction of ozone in 491

Introduction

The Puget Sound Clean Air Agency is in the process of updating its maintenance plan regarding

the federal ozone standard for the Puget Sound airshed. As part of this effort, nu merical

photochemical air quality modeling has been used to assess the impact of changes in emissions

of ozone precursors upon regional air quality. Results from this modeling analysis are presented

in this report.

The basis for this work is the develop ment of a regional air quality modeling system developed

over the past several years by Washington State University (WSU) and the Washington State

Department of Ecology (Ecology). This system includes the use of the MM5 mesoscale

meteorological model, the CALMET diagnostic wind model, and the CALGRID photochemical

transport model. This system has been used to successfully simulate an ozone event that

occurred during July 11

- 14 (Thurs.

- Sun.), 1996. Details of the development of the modeling

system and in itial applications are described in a series of peer

- reviewed papers in the scientific

literature (Barna et al., 2000; Barna and Lamb, 2000; Barna et al., 2001). The July, 1996 event is

used in this work as the base case for evaluation of emission changes in the Puget Sound region.

The work described in this report was conducted by WSU in collaboration with Ecology and

with assistance from Puget Sound Clean Air Agency.

Objectives

The overall objective of this work was to provide quantitative informat ion to support revision of

the ozone maintenance plan for the Puget Sound airshed. This information was obtained through

an analysis employing a 3

- dimensional photochemical grid modeling system coupled with

projected emissions for future years and potenti al changes in emissions associated with selected

control strategies. The modeling system was evaluated for accuracy in ozone predictions using

data from a historical episode prior to application for control strategy analysis.

Study Design

In this mode ling study, there were five specific tasks to be completed. First, additional work was

completed to improve the simulation of the base case and to develop a better understanding of

the formation of ozone within the Puget Sound airshed during high ozone co nditions. Second,

the base case was simulated and the results provided in a form suitable for comparison to

subsequent control runs. Third, emission projections for model years 2007 and 2015 were

incorporated into the emission inventory and the base case was re

- run using the 2007 and 2015

emissions. Fourth, a series of emission reduction strategy runs were formulated and incorporated

into the emission inventories using the years 2007 and 2015. Fifth, the results of these tasks

were summarized for presen tation in this report.2

Modeling System Description

Model Domain

In the initial model development work as described by Barna et al. (2000), the modeling domain

encompassed the three major metropolitan centers of Vancouver, BC, Seattle, WA, and Portlan d,

OR. For the work presented here, this domain was reduced in size to reduce computer resource

requirements. A map of the domain is shown in Figure 1. The domain extends from the Pacific

Ocean on the west to beyond the crest of the Cascade Mountains on the east and from north of

the Puget Sound area to south of Lewis County. The domain measures 74 by 57 grids

horizontally and employs 5 km x 5 km horizontal grid cells and thirteen vertical layers with

variable spacing as follows: 20 m, 100 m, 220 m, 35 0 m, 450 m, 550 m, 650 m, 750 m, 850 m,

1200 m, 2000 m, 3000 m, and 5000 m

.

Episode Selection and Meteorology

Because of the extensive work already completed for the July, 1996 episode, this case was

selected as the base case for this work. During this episode, maximum daytime ozone

concentrations increased from approximately 80 ppb at Enumclaw on Thursday to 118 ppb on

Sunday. Winds were relatively moderate from the north during the daytime periods and

maximum daytime temperatures in the Puget Sound a rea exceeded 36 o C. Skies were clear

during the episode. A map of the wind vectors that occurred on Sunday, July 14, 1996 is shown

in Figure 2, and the time series of wind speed, wind direction, and temperature for central Puget

Sound are shown in Figure 3.

To simulate this episode, we employed three nested domains for MM5 with grid cell sizes of 45

km, 15 km, and 5 km. The MM5 simulation used 33 vertical layers with variable spacing. The

outer domains employed FDDA analysis nudging and the inner dom ain used observational

nudging with approximately 46 surface meteorological stations. The details of the observational

nudging simulation are given in Barna and Lamb, 2000. In this previous work, the results

demonstrated that the observational nudging ap proach yielded the best overall model

performance for ozone predictions compared to either use of no observations or to interpolation

of observations within the CALMET diagnostic model.

Boundary Conditions

In the course of this work, the boundary and i nitial conditions originally used for the larger

domain were revised to correct a unit conversion error and to account for the new domain

boundary location on the north approximately half way between Vancouver, BC and Seattle,

WA. The revised boundary con ditions are listed in Table 1. These values are based in part on

available volatile organic compound (VOC) measurements in the area (Westberg et al., 1997)

and in part on values from the literature for rural or maritime clean conditions. It should be

rec ognized that no special field measurements were conducted for this episode to establish

boundary condition concentrations. Thus, the specification of boundary conditions remains an

area of uncertainty in this model analysis.3

Figure 1. Map of Cas cadia model domain. Dashed lines indicate extent of the Puget Sound

ozone modeling domain. Ozone monitoring stations are also shown. For Puget Sound, the

monitoring stations include: Getchel (GE), Lake Sammamish (LS), Enumclaw (EW), Pack

Forest (PF), P aradise (PA), and Packwood (PW).4

Figure 2. Map of surface winds from MM5 observational nudging simulation for Sunday, July

14, 1996 at 1500 PST.5

Figure 3. Time series of simulated wind direction (top), wind speed (middle), and temperature

(bottom) for urban Seattle during the July 11

- 14, 1996 ozone episode.6

Table 1. Initial and boundary condition concentrations for CALGRID simulations. Previous

conditions (Barna et al., 2000) listed in parentheses.

Model Species Species Name Ini tial and

Boundary concentration

(ppb)

O3 Ozone 40 (30)

NO Nitrogen oxide 0

NO2 Nitrogen dioxide 1 (0.2)

ALK1 1 st lumped alkanes (k OH < 1.0 E4 ppm

- 1

min

- 1 ) 2.7 (5.75)

ALK2 2 nd lumped alkanes (k OH > 1.0 E4 ppm

- 1

min

- 1 ) 0.5 (0.97)

ARO1 1 st lumped aromat ics (k OH < 2.0 E4 ppm

- 1

min

- 1 ) 0.4 (1.52)

ARO2 2 nd lumped aromatics (k OH > 1.0 E4 ppm

- 1

min

- 1 ) 0.5 (3.17)

ETHE Ethylene 0.5 (0.3)

OLE1 1 st lumped anthropogenic alkenes (k OH <

7.5 E4 ppm

- 1 min

- 1 ) 0.2 (0.33)

OLE2 2 nd lumped anthropogenic alkenes (k OH >

7 .5 E4 ppm

- 1 min

- 1 ) 0.1 (0.0)

OLE3 3 rd lumped biogenic alkenes (iosprene,

terpenes) 0.3 (0.86)

HCHO Formaldehyde 3.0 (0.31)

CCHO Acetaldehyde 1.5 (0.32)

MEK Methyl ethyl ketone 0.8 (0.39)

HNO3 Nitric acid 0.1

CO Carbon monoxide 250 (200)

HO2H Hydroge n peroxide 2

Emission Inventory

An hourly emissions inventory was constructed for the July 11

- 14, 1996 ozone episode using a

consistent methodology over the modeling domain. Emissions of nitrogen oxides (NOx),

volatile organic compounds (VOC), and carb on monoxide (CO) from point, area, mobile and

biogenic sources were included. The inventory was based on the inventory used in the prior

ozone modeling efforts described above. It was updated to include additional point sources in

the central Puget Sound area, incorporate improvements in onroad mobile emissions modeling,

incorporate area and nonroad inventory data prepared by the Puget Sound Clean Air Agency, and

correct errors found in the nitrogen oxide speciation.

Hourly emissions estimates were made for 1996 and 2015 baseline conditions and assigned to

the 5

- kilometer grid system over the modeling domain. A 2007 inventory was constructed using

linear interpolation between the 1996 and 2015 baseline inventories. Three emissions reduction

strategy in ventories for 2015 were formulated from the Puget Sound Clean Air Agency’s three

control option packages. The three control strategy inventories were formulated for 2007 using

linear interpolation between the 1996 baseline and 2015 control strategy invent ories.7

Domain emissions of VOC and NOx for the 1996 baseline and 2007 and 2015 baseline and

control strategy inventories are shown in the Tables 2 and 3 for all sources and anthropogenic

sources only, respectively.

Table 2: Emissions and Strategy Reduc tions from All Sources

Pollutant Year Strategy Emissions in tons per

day Percent change from 1996

baseline

11

- Jul 12

- Jul 13

- Jul 14

-

Jul 11

- Jul 12

- Jul 13

- Jul 14

-

Jul

Thur. Fri. Sat. Sun. Thur. Fri. Sat. Sun.

VOC 1996 Base 2,460 2,867 3 ,214 3,259

VOC 2007 Base 2399 2799 3142 3188

- 2

- 2

- 2

- 2

VOC 2007 A 2377 2774 3118 3172

- 3

- 3

- 3

- 3

VOC 2007 B 2372 2768 3112 3167

- 4

- 3

- 3

- 3

VOC 2007 C 2385 2782 3127 3174

- 3

- 3

- 3

- 3

VOC 2015 Base 2,355 2,750 3,090 3,136

- 4

- 4

- 4

- 4

VOC 2015 A 2,316 2,706 3,048 3,109

- 6

- 6

- 5

- 5

VOC 2015 B 2,308 2,696 3,038 3,100

- 6

- 6

- 5

- 5

VOC 2015 C 2,330 2,721 3,063 3,112

- 5

- 5

- 5

- 5

NOx 1996 Base 703 746 638 540

NOx 2007 Base 558 592 525 455

- 21

- 21

- 18

- 16

NOx 2007 A 553 587 521 452

- 21

- 21

- 18

- 16

NOx 2007 B 553 587 521 452

- 21

- 21

- 18

- 16

NOx 2007 C 553 587 521 452

- 21

- 21

- 18

- 16

NOx 2015 base 452 480 443 394

- 36

- 36

- 31

- 27

NOx 2015 A 444 471 436 388

- 37

- 37

- 32

- 28

NOx 2015 B 444 471 436 388

- 37

- 37

- 32

- 28

NOx 2015 C 444 471 436 388

- 37

- 37

- 32

- 288

Ta ble 3: Emissions and Strategy Reductions from Anthropogenic Sources

Pollutant year Strategy Emissions in tons per

day Percent change from 1996 baseline

11

- Jul 12

- Jul 13

- Jul 14

- Jul 11

- Jul 12

- Jul 13

- Jul 14

- Jul

Thur. Fri. Sat

. Sun. Thur. Fri. Sat. Sun.

VOC 1996 Base 557 598 674 551

VOC 2007 Base 496 531 602 480

- 11

- 11

- 11

- 13

VOC 2007 A 473 505 578 464

- 15

- 15

- 14

- 16

VOC 2007 B 468 500 572 459

- 16

- 16

- 15

- 17

VOC 2007 C 482 514 587 466

- 14

- 14

- 13

- 15

VOC 2015 base 451 482 549 428

- 19

- 19

- 18

- 22

VOC 2015 A 412 438 508 401

- 26

- 27

- 25

- 27

VOC 2015 B 404 428 498 392

- 27

- 28

- 26

- 29

VOC 2015 C 427 453 523 404

- 23

- 24

- 22

- 27

NOx 1996 base 672 711 601 501

NOx 2007 Base 527 557 488 416

- 22

- 22

- 19

- 17

NOx 2007 A 522 552 484 413

- 22

- 22

- 19

- 18

NOx 2007 B 522 552 48 4 413

- 22

- 22

- 19

- 18

NOx 2007 C 522 552 484 413

- 22

- 22

- 19

- 18

NOx 2015 base 422 445 406 355

- 37

- 37

- 32

- 29

NOx 2015 A 413 436 399 349

- 39

- 39

- 34

- 30

NOx 2015 B 413 436 399 349

- 39

- 39

- 34

- 30

NOx 2015 C 413 436 399 349

- 39

- 39

- 34

- 309

Modeling System Improvement

At the onset of this work, it was recognized that the model simulation of the July, 1996 event

could be improved in several ways:

 Updated chemical mechanism for isoprene explicit chemistry

 Modification of the treatment of the mixed layer height

 Correction and revision of the boundary conditions (described above)

 Updated emission inventory with incorporation of additional sources (described above)

In addition to t hese improvements, considerable effort was spent to develop and apply new tools

for understanding the evolution of ozone within the region. In particular, a novel back

- trajectory

source footprint method (O'Neill et al., 2001) was applied to this simulatio n, and a method for

conducting process analyses was implemented within CALGRID. Further, comprehensive

model sensitivity analyses were conducted as part of our overall quality assurance effort and to

guide control strategy development.

Chemical Mechanism Update

The original modeling system employed the SAPRC

- 90 chemical kinetic mechanism (Carter et

al., 1990) which did not treat isoprene as an explicit species. Although biogenic hydrocarbon

emissions in the domain are dominated by terpenes and other VOC, isoprene emissions are a

significant fraction of the total emissions. For the present work, the model was updated to

employ the SAPRC

- 97 chemical kinetic mechanism with isoprene chemistry treated

explicitly(Carter et al., 1997). In this case, isoprene ca n be oxidized to four different reaction

products: MVK (methyl vinyl ketone), METHACRO (Methacrolein), ISOPROD (lumped

reactive species such as hydroxymethacrolein and unsaturated aldehydes), and RCHO (lumped

higher aldehyde species) + RNO 3 (lumped organi c nitrate species). In turn, these products can

undergo further oxidation. The new mechanism has 86 model species versus 50 in SAPRC90,

and the number of reactions was increased from 129 to 197.

The impact of this change on predicted ozone concentratio ns was not large, but it does produce a

more realistic simulation of atmospheric chemistry in the area. In particular, the new mechanism

provides information on the relative importance of isoprene versus terpene emissions within the

domain. As shown in F igure 4a and 4b, the oxidation rate of isoprene ranges from less than 0.2

ppbC/hr to 1 ppbC/hr, while the oxidation rate of OLE3, the lumped terpene species, is

approximately 1 ppbC/hr over most of the domain. Organic oxidation rate is an indirect measure

of ozone production and the results in Figures 4a and 4b suggest that the role of isoprene in

ozone production is of order half that of terpenes within the domain.10

Figure 4a. Map of isoprene oxidation using the SAPRC

- 97 chemical mechanism for Sunday

, July 14, 1996 at 2 pm.

Figure 4b. Map of total terpene (OLE3) oxidation rates using the SAPRC

- 97 chemical kinetic

mechanism for 2 pm, Sunday, July 14, 1996.11

Mixed Layer Treatment

In the work by Barna et al. (2000), it was obvious that CALMET predicted mixed layer heights

grossly overestimated the actual mixed layer depths based upon comparison to available

radiosonde observations in Washington and Oregon. Barna et al. set the maximum mixing

height in CALMET to 1200 m. However, closer examin ation of the spatial patterns of mixed

layer depth obtained from the MM5 simulation showed that mixed layer heights over Puget

Sound were actually much less than 1200 m. Consequently, the mixed layer heights were

adjusted to a maximum allowed height of 6 50 m for the Puget Sound domain. A map of the

mixed layer heights from MM5 for mid day is shown in Figure 5.

Statistical Model Performance Evaluation

To document the effect of model improvements and to provide a foundation for using the system

to eval uate various control strategies, the model performance was evaluated using available

surface ozone observations. Documentation of meteorological model performance has been

presented previously in Barna and Lamb (2000). Within the Puget Sound domain, ther e are six

surface ozone monitors. Several of these are located along the I

- 5 corridor; one is located

immediately next to I

- 90, and the remainder are located in downwind areas (Enumclaw, Pack

Forest) and remote mountain sites (Paradise).

The ozone conc entration time series for each monitoring station are shown in Figures 6a

- f, and

the statistical performance measures are summarized in Tables 4 and 5. In general, model

performance is improved compared to the model simulations prior to this work, and the model

shows good agreement with the observed pattern of ozone at each of the monitoring sites. At

Getchel and Lake Sammamish which are withiin the I

- 5 urban corridor, the model overestimates

nighttime ozone and shows reasonable agreement with daytime pat terns. The nighttime

overestimate is probably associated with O3 titration by nighttime NO emissions into a shallow

surface layer that does not appear to be correctly captured in the model. At Enumclaw, a rural

site, the model shows excellent agreement w ith nighttime O3 concentrations and also yields

excellent agreement with daytime O3 patterns. On Sunday when the maximum observed O3

concentration reached 118 ppb, the model underestimated the peak by approximately 10% (106

ppb). At Paradise on the side of Mt. Rainier, the model predictions were in good agreement with

peak daytime values and the timing of the peak appeared to be captured correctly. However,

Paradise exhibits an elevated background concentration of ozone that increased from

approximately 40 ppb on Thursday to 50 ppb by Sunday. The model underestimates this

increasing background by 10 to 20 ppb. At Pack Forest, another rural downwind site, the model

underestimated peak daytime values during the first two days, but apparently overestimate d

observed values on Sunday. However, it should be noted that observations for all of Saturday

and the first part of Sunday were missing for Pack Forest. At the third rural, downwind site,

Packwood, the model exhibited overall general agreement with the observations. On three days,

the model overestimated peak ozone concentrations, but at night, the model was in close

agreement with the observations.12

Figure 5. Map of mid

- day mixed layer heights derived from the MM5 simulation for Sunday,

July 14, 1996.13

Figure 6a

Figure 6b14

Figure 6c

Figure 6d15

Figure 6e

Figure 6. Time series of observed and predicted ozone concentrations at the Puget Sound

monitoring sites: a) Getchel b) Lake Sammamish c) Enumclaw d) Paradise e) Pack Forest and f)

Packwood.16

EPA guidelines for photochemical modeling shows acceptable model performance when the

normalized bias is within  15% and the normalized gross error is within 35%. The current

simulation is well within these guidelines and shows performanc e equal to or better than that

obtained in Barna and Lamb (2000) and equal to or better than that for application of the UAM

V and SAQM air quality models applied to the eastern US as described by Sistla et al. (2001).

For Puget Sound the overall normaliz ed bias equals +5% and the normalized gross error is 26%.

At Enumclaw where the maximum observed ozone occurred on Sunday, July 14, 1996, the

model performance was very good with a normalized bias of

– 3% and a normalized gross error

of 19%. The maximum o ne hour average ozone concentration for Sunday was predicted to

within 10% at Enumclaw.

Table 4. Summary of model performance statistics for the July 11

- 14, 1996 episode in Puget

Sound.

Monitor Enumclaw Getchel* Lake

Sammamish* Paradise Pack

Forest P ackwood

Avg obs O3 (ppb) 55.4 40.9 57.6 56.0 37.8 38.2

Avg pred O3 (ppb) 51.2 50.0 54.5 44.5 42.2 40.9

Std dev obs O3 (ppb) 25.9 16.3 24.4 8.6 16.3 13.2

Std dev pred O3 (ppb) 23.7 12.2 15.2 13.6 18.9 12.8

Slope 0.85 0.66 0.58 1.2 0.56 0.74

Intercept (ppb) 4.3 23.1 21.0

- 22.3 20.9 12.6

Rmsd (ppb) 10.5 12.1 11.7 14.6 18.3 9.3

Model index 0.95 0.83 0.91 0.63 0.65 0.86

Normalized gross error

(%) 18.8 37.1 25.5 23.7 37.5 22.1

Normalized bias (%)

- 3.4 33.7 8.8

- 21.2 20.9 11.8

July 14, 1996 max obs

O3 p pb 118 67 98 86 no data 55

July 14, 96 max pred

O3 ppb 106 67 79 79 76

* nighttime O3 concentrations less than 10 ppb excluded

Table 5. Summary of overall model performance statistics for Puget Sound and comparison to

results from Barna and Lamb (200 0) and to results for UAM

- V and SAQM applications to the

northeastern US (Sistla et al., 2001).

Performance Measure This

work Barna

and

Lamb

(2000) UAM

- V SAQM EPA

Guidelines

Overall slope 0.68 0.65 na na na

Overall intercept (ppb) 14.1 13.9 na na na

O verall correlation coefficient 0.78 0.65 0.66 0.52 na

Overall normalized gross error (%) 25.8 24.0 20.0 23.0 Withiin 35%

Overall normalized bias (%) 5.0

- 9.8 6 8  15%

na: not available17

Presentation of Modeling Analysis and Results

Source Footprint and Process Analysis

Beyond the statistical performance evaluation, it is also important to investigate, in more detail,

how the model behaves and how it simulates the evolution of ozone in an urban air mass. To

address this aspect of model evaluation, we h ave employed a unique back

- trajectory source

footprint method (Napelenok et al., 2000). In this approach, the MM5/CALMET wind fields are

reversed in direction and inverted in time. These “reversed” winds are then used to drive the

CALPUFF Gaussian puff d ispersion model for a unit “source” located at a selected receptor. In

this reverse dispersion mode, the model yields the probability distribution of upwind source areas

contributing to the concentration at the receptor of interest. In this way, the meth od not only

shows where the air mass originated that affects a receptor, but it also maps the upwind area

— source footprint

— where sources contribute to the receptor concentration. We have recently

presented this method and demonstrated equality between for ward and backward puff modeling

(O’Neill et al., 2001). The source footprint can also be combined with the underlying emission

inventory to develop an emissions weighted footprint so that the relative contribution of upwind

sources is taken into account.

In addition to understanding the area upwind from which air masses have traveled, it is important

to understand how ozone evolves along the air mass path. To do this, we implemented a process

analysis scheme within CALGRID so that the relative contribu tions of various physical and

chemical processes can be determined. Process analysis is simply a way of keeping track of

changes in pollutant concentration due to the different processes or, more simply, it’s a method

for summing the contribution of each term in the underlying atmospheric diffusion equation:

where  C/  t is the pollutant concentration rate of change, and the processes of interest include

horizontal advection (terms 2 and 3), vertical advection (term 4), horizontal diffusion (terms 5

a nd 6), vertical diffusion (term 7), chemical production or loss (R), deposition (D), and source

emissions (S). The concept of process analysis is illustrated in Figure 7. Process analysis can be

applied to a single grid over a period of time to see how t hese various processes change with

time at a given location, or it can be applied using the back

- trajectory method, described above,

to determine the importance of the processes in a Lagrangian sense in an air mass traveling from

source to receptor.

We applied the source footprint and process analysis methods to investigate ozone formation

along trajectories within the modeling domain. First, for the Enumclaw receptor, we examined

the differences in ozone evolution between Friday when peak ozone concent rations exceeded 80

ppb and Sunday when peak ozone concentrations reached 118 ppb. On Friday, the back

trajectory, shown in Figure 8a, shows transport from northwest of Enumclaw with the highest

probability source areas located east of the main urban core as shown in the emission inventory

maps and source footprints in Figures 8b and 8c. At 9 am, the air mass concentrations were

approximately 40 ppb18

Figure 7. Schematic of process analysis of terms in the atmospheric diffusion equation applied

to a s ingle grid cell.19

Figure 8. a) Map of source footprint for back trajectory arriving at Enumclaw at 3 pm on Friday, July 12, 1996. Ozone conce ntrations

for indicated times and locations along the trajectory are also shown, b) map of source footpri nt and underlying NOx emissions (8 am

emissions only), and c) map of source footprint and underlying VOC emissions (8 am emissions only).20

located slightly east of central Seattle, by noon, ozone concentrations in the air mass had

increased to approxim ately 57 ppb, and by 3 pm, the ozone concentrations had reached

93 ppb at the receptor. The corresponding NOx concentrations in the air mass, shown in

Figure 9a, changed from 6 ppb at 9 am to approximately 15 ppb at noon, and then

decreased to approximate ly 13 ppb just upwind of Enumclaw between 1 and 2 pm. At 3

pm at the receptor the NOx concentrations were 7 ppb. The VOC/NOx ratios, shown in

Figure 9b, decreased from 10 at 9 am to 6 at noon east of the urban core, and then

remained near 7 between 1 and 2 pm upwind of Enumclaw. The resulting ozone

production rate, shown in Figure 9c, increased from approximately 4 ppb/hr at 9 am to

approximately 17 ppb/hr along the afternoon transport path. In terms of process analysis

for the Friday trajectory to Enum claw, the air mass ozone concentration steadily

increased due to an approximately steady and positive chemical production that was

offset by continual loss of ozone due to deposition to the surface and vertical diffusion

out of the surface layer as shown i n Figure 10.

We can contrast the behavior observed on Friday when ozone peaked near 80 ppb at

Enumclaw with the behavior observed on Sunday when ozone reached a maximum of

118 ppb at Enumclaw. In this case, the trajectory was similar to the Friday traj ectory as

shown in Figures 11a

- c. Morning ozone concentrations increased from approximately 50

ppb at 9 am to approximately 70 ppb by noon. During the afternoon, ozone

concentrations in the air mass increased to approximately 95 ppb by 2 pm and then in the

last hour of transport increased again to 106 ppb at the receptor. The NOx

concentrations, shown in Figure 12a, were lower along the trajectory compared to Friday.

This is probably due to lower NOx emissions on the weekend, compared to a weekday.

T he VOC/NOx ratios along the air mass path were generally higher and in a range more

favorable for ozone production on Sunday compared to Friday (Figure 12b). In turn,

ozone production rates were higher by several ppb/hr on Sunday. During the afternoon,

o zone production rates increased from approximately 22 ppb/hr to as high as 29 ppb/hr

between 1 and 3 pm immediately upwind of the receptor as shown in Figure 12c. The

process analysis summary for the Sunday trajectory is shown in Figure 13. In this case,

the higher chemical production rate leads to slightly greater loss of ozone due to

deposition and vertical diffusion compared to Friday, but the loss terms are not large

enough to offset the higher production and significantly higher ozone concentrations are

the result.

This same analysis can be applied for other receptors. For example, at Paradise, located

on the side of Mt. Rainier, the source footprint for Sunday afternoon shows a curving

trajectory that originates to the northwest near the south en d of urban Seattle before noon

(Figure 14a). Ozone concentrations increased from approximately 60ppb at 11 am to as

high as 81 ppb along the trajectory by 1 pm, and then decreased slightly to 73 ppb at the

Paradise receptor. Most of this trajectory cover ed rural areas where the emissions were

low as shown in Figures 14b,c, NOx concentrations decreased from 6 pppb before noon

to approximately 1 ppb upwind of Paradise (Figure 15a), and the VOC/NOx ratio was

greater than 15. Ozone chemical production rates reached as high as 28 ppb/hr around

noon, but then decreased significantly to less than 10 ppb/hr in the afternoon prior to the21

Figure 9. a) Map of source footprint for back trajectory arriving at Enumclaw at 3 pm on Friday, July 12, 1996. NOx co ncentrations

for indicated times and locations along the trajectory are also shown. b) map of source footprint and VOC/NOx ratios along t he

trajectory, and c) map of source footprint and O3 production rates (ppb/hr) along the trajectory.22

Figure 10. S ummary of process analysis along the back trajectory arriving at Enumclaw at 3 pm on Friday, July 12 1996.23

Figure 11 a) Map of source footprint for back trajectory arriving at Enumclaw at 3 pm on Sunday, July 14, 1996. Ozone

concentrations for ind icated times and locations along the trajectory are also shown, b) map of source footprint and underlying NOx

emissions (8 am emissions only), and c) map of source footprint and underlying VOC emissions (8 am emissions only).24

Figure 12. a) Map of so urce footprint for back trajectory arriving at Enumclaw at 3 pm on Sunday, July 14, 1996. NOx

concentrations for indicated times and locations along the trajectory are also shown. b) map of source footprint and VOC/NOx ratios

along the trajectory, and c) map of source footprint and O3 production rates (ppb/hr) along the trajectory.25

Figure 13. Summary of process analysis along the back trajectory arriving at Enumclaw at 3 pm on Sunday, July 14, 1996.26

Figure 14. a) Map of source footprint for back trajectory arriving at Paradise at 5 pm on Sunday, July 14, 1996. Ozone concentrations

for indicated times and locations along the trajectory are also shown, b) map of source footprint and underlying NOx emission s (8 am

emissions only), and c) map of source footprint and underlying VOC emissions (8 am emissions only).27

Figure 15. a) Map of source footprint for back trajectory arriving at Paradise at 5 pm on Sunday, July 14, 1996. NOx concen trations

for indicated times and locations along the tr ajectory are also shown, b) map of source footprint and VOC/NOx ratio along the

trajectory, and c) map of source footprint and O3 production rates (ppb/hr) along the trajectory.28

air mass arrival at Paradise (Figure 15b). The evolution of the processes a long the

trajectory is shown in Figure 16. In this case, the ozone production rate after noon is

more than offset by deposition and vertical diffusion so that the ozone concentrations

decreased along the trajectory prior to arrival at Paradise. The horiz ontal advection term

was negative in the first half of the trajectory and then became large and positive in the

second half. At each grid, horizontal advection is determined by differences in

concentrations between the upwind grid and the downwind grid. If ozone concentrations

decrease from one grid to the next along the trajectory, the horizontal advection term will

be positive, ie. the upwind grid is contributing additional ozone to the downwind grid.

For Paradise, the horizontal advection is approxima tely offset by the vertical diffusion

and deposition term.

In summary, for this episode, high concentrations that occurred on Sunday at Enumclaw

appear to be due to differences in weekend vs weekday emissions that resulted in more

favorable VOC/NOx ratio s for ozone production compared to the trajectory for Friday.

At Paradise on Sunday, the air mass experienced high ozone production rates initially,

but the rate decreased significantly as the air mass traveled over relatively clean rural

areas.

The oz one chemical production rates can also be examined on a domain wide basis as

illustrated in Figure 17. This figure shows that the primary chemical production of ozone

takes place along the periphery of the urban core and that the highest ozone production

rates exceeds 28 ppb/hr in these areas. As a result of this pattern of ozone production,

there is a corresponding pattern of horizontal advection of surface ozone where advection

is negative in the high production rate zones as ozone is advected out of the se areas and

positive in outlying receptor regions where production is low and ozone is transported

into these areas.

Model Sensitivity Analysis

Further tests of model behavior and improved understanding of ozone formation in Puget

Sound can be obtained from model sensitivity analyses. In this case, we examine how

changes in emissions perturb maximum predicted ozone concentrations. This analysis

was conducted in several different ways. First, overall changes in anthropogenic NOx

and/or VOC emissions w ere applied (25%, 50%, 100% reductions). Second, changes to

individual source categories were applied including changes to biogenic VOC,

anthropogenic area sources, mobile sources, and point sources. Third, the sensitivity to

changes to emissions of indi vidual VOCs was investigated. The latter analysis yields a

measure of the incremental reactivity of VOCs in the model.

Results are presented in Figures 18

- 20 for Friday, Saturday and Sunday during the

episode in terms of change in the peak simulated ho urly ozone concentration in the

domain as a function of NOx, VOC, and both NOx and VOC anthropogenic emission

reductions for the 1996 base case. For all three days, the overall patterns are similar. As

VOC emissions alone are reduced, the peak ozone conc entration in the domain decreases29

Figure 16. Summary of process analysis along the back trajectory arriving at Paradise at 5 pm on Sunday, July 14, 1996.30

Figure 17. Domain wide surface ozone production rates (ppb/hr) on Sunday, July 14, 1996 at 3 pm.31

Figure 18. Sensitivity of maximum surface ozone concentration downwind of Puget Sound to changes in NOx and VOC

anthropogenic emissions on Friday, July 12, 1996.32

Figure 19. Sensitivity of maximum surface ozone concentration downwind o f Puget Sound to changes in NOx and VOC

anthropogenic emissions on Saturday, July 13, 1996.33

Figure 20. Sensitivity of maximum surface ozone concentration downwind of Puget Sound to changes in NOx and VOC

anthropogenic emissions on Sunday, July 14, 199 6.34

linearly. As NOx emissions alone are reduced, the peak ozone concentration in the

domain increases and then decreases with greater reduction in emissions. For both VOC

and NOx emission reductions, the reduction in peak ozone falls between the curve s for

NOx only and VOC only emission reductions.

On Friday, the peak ozone decreases from 86 ppb to 76 ppb for a 50% reduction in VOC

emissions. On Saturday, peak ozone concentration decreases from 101 ppb to 87 ppb for

a 50% reduction in VOC emissions . Similarly, on Sunday, the peak ozone concentrations

is reduced from 106 ppb to 90 ppb for a 50% reduction in VOC emissions. For NOx

emission reductions, the peak ozone concentration slightly increases on each day for a

25% reduction in NOx emissions. For a 50% reduction in NOx emissions, on Friday the

peak ozone concentration is increased, but on the other two days, the peak ozone

concentration decreases slightly relative to the base case. For a 75% reduction in NOx

emissions, the peak ozone concentra tions decreases substantially on all three days.

Reducing both VOC and NOx emissions by 25% and 50% yields essentially no change in

peak ozone concentration on Friday. However, on the other two days, these emission

reductions do yield decreases in the pe ak ozone concentrations. For all three days,

reducing VOC and NOx emissions by 75% reduces peak ozone concentrations

substantially.

The impact of different source categories was also examined by setting the emissions for

each different category to zero in a model simulation. The results, shown in Table 6,

show that removing point source emissions reduces the peak ozone concentration by

about 2 ppb on each day, while removing anthropogenic area source emissions reduces

the peak ozone concentration by 3 ppb on Friday and 16 ppb on Sunday. A large effect

occurs when mobile emissions are removed. This change reduces the peak ozone

concentration by 5 ppb on Friday and 29 ppb on Sunday. Somewhat surprisingly,

removing only mobile NOx emissions doesn’t chan ge the peak concentration on Friday,

but this change reduces the peak ozone concentration on Sunday by 26 ppb. If only

mobile VOC emissions are removed, the peak ozone concentration drops by 10 ppb on

Friday and by 16 ppb on Sunday. For comparison, remov ing all biogenic emissions

causes the peak ozone concentration to decrease by 9 ppb on Friday and 12 ppb on

Sunday. This set of model runs highlights the fact that mobile NOx emissions play a

critical role in the generation of elevated ozone concentration s in the Puget Sound region.

At the same time, the previous set of sensitivity runs shows that very substantial changes

in NOx emissions are required to have a substantial effect on peak ozone concentrations.35

Table 6. Effect of removing individual so urce categories on maximum predicted hourly

average ozone.

Source Category Friday Saturday Sunday

peak ozone peak ozone peak ozone

Base Case 1996 86 101 106

No Point Sources

Emissions 85 99 104

No Biogenic

Emissions 77 88 94

No Anthr. Area

Emissi ons 83 88 90

No Mobile

Emissions 81 82 77

No Nox Mobile

Emissions 86 85 80

No VOC Mobile

Emissions 76 89 90

The relative importance of individual VOC’s can also be examined by selectively setting

an individual VOC emission rate to zero. Results from these sensitivity runs are shown

in Table 7. Among the compounds tested, ARO2 has the largest impact on peak ozone

concentrations. The sensitivity of peak ozone concentration varies between 5.5 ppb O3

on Friday and 7.7 ppb O3 on Sunday for a 100% reducti on in ARO2 emissions. For

comparison, reducing emissions of ALK1 and ARO1 reduces peak ozone by

approximately 4 ppb on Sunday. Removing biogenic isoprene (ISOP) emissions reduces

the Sunday peak ozone by 5 ppb and removing biogenic terpene (OLE3) emissio ns

reduces the Sunday peak ozone by 2 ppb.

Table 7. Effect of individual VOC emissions on peak ozone concentration for the case

where each VOC emissions are set to zero.

VOC Sensitivity of Peak O3 to VOC

Emissions

(ppb O3/100% VOC reduction)

Friday Saturday Sunday

ALK1 2.9 3.6 4.3

ARO1 2.8 3.4 3.8

ARO2 5.5 7.2 7.6

ISOP 3.0 3.7 5.2

OLE3 2.3 3.0 2.236

Emission Inventory Base Case and Control Strategy Simulations

As indicated in the presentation of the emission inventory, base case emission files were

prepared for 1996, 2007, and 2015. Ozone simulations for these years are shown in terms

of ozone time series in Figure 21 for hourly average concentrations and in Figure 22 in

terms of 8

- hour average ozone concentrations. For hourly average c oncentrations, the

changes from 1996 to the future years is not consistent from one day to the next. As

shown in Table 8, on Friday, the 1996 peak levels are actually less than those for 2007 or

2015. On Saturday, the 1996 peak level is less than for 200 7, but more than in 2015. On

Sunday, when the highest ozone concentrations occurred, ozone for both 2007 and 2015

are less than in 1996. For the peak ozone, the emissions in 2007 produce approximately

2 ppb improvement and in 2015 the change in emissions decrease peak ozone

concentrations by about 7 ppb.

Table 8. Peak hourly ozone concentration (ppb) for years 1996, 2007, and 2015.

Year Friday Saturday Sunday

Maximum predicted

O3, 1996 86 101 106

Maximum predicted

O3, 2007 90 103 104

Maximum predi cted

O3, 2015 91 100 99

The spatial distribution of surface ozone concentrations exhibits some change from 1996

to 2007 and 2015. The surface ozone concentration contours for Sunday afternoon are

shown in Figures 23

- 25 for each year. It is difficult to discern any significant changes

from 1996 to the future years from these maps. The differences are more apparent in the

difference contour maps shown in Figures 26 and 27. In 2007, there is a zone of

enhanced ozone concentration (1 to 5 ppb increase) th at stretches from near Lake

Sammamish to Pack Forest with the largest enhancement (5 to 10 ppb increase)

immediately west of Enumclaw. There are small areas of ozone reduction (

- 5 to

– 10 ppb

decrease) further downwind. In 2015, the area of enhanced ozone concentration remains,

although the area of largest enhancement is greater in 2015 than in 2007. However, there

is also a much larger region of ozone reduction in 2015 that occurs downwind of Pack

forest and in the vicinity of Enumclaw. Thus, changes in emissions from 1996 to 2007

and 2015 generally appear to cause a slight reduction in peak ozone concentrations within

the domain, although there are areas where ozone concentrations increase compared to

1996.37

Figure 21. Time series of maximum hour ly average surface ozone concentrations

downwind of Puget Sound due to base case emissions for 1996, 2007 and 2015.

Figure 22. Time series of maximum 8

- hour average surface ozone concentrations

downwind of Puget Sound due to base case emissions for 1 996, 2007 and 2015.38

Figure 23. Map of surface ozone concentration contours for 3 pm on Sunday, July 14 for

1996 base case emissions.39

Figure 24. Map of surface ozone concentration contours for 3 pm on Sunday, July 14 for

2007 base case emiss ions.40

Figure 25. Map of surface ozone concentration contours for 3 pm on Sunday, July 14 for

2015 base case emissions.41

Figure 26. Contour map of differences in surface ozone concentration between 1996 and

2007 (negative numbers indicate a redu ction of ozone in 2007 relative to 1996) for 3 pm

on Sunday, July 14.42

Figure 27. Contour map of differences in surface ozone concentration between 1996 and

2015 (negative numbers indicate a reduction of ozone in 2015 relative to 1996) for 3 pm

on S unday, July 14.43

Similar results occur for 8

- hr average ozone concentrations as summarized in Table 9. In

2007, there is an increase in peak 8

- hr ozone concentration on Friday, but a reduction of 2

to 3 ppb on the remaining two days. In 2015, there i s a similar increase of 4 ppb in ozone

on Friday and a reduction of 6 to 7 ppb on Saturday and Sunday.

Table 9. Peak 8

- hour average ozone concentration for years 1996, 2007, and 2015.

Friday Saturday Sunday

Maximum predicted

O3 1996 73 82 86

Maxim um predicted

O3 2007 78 79 84

Maximum predicted

O3 2015 78 75 80

Three control strategies were formulated for analysis for years 2007 and 2015. The

changes in emissions associated with these strategies were presented in a previous

section. Maps of the differences in ozone concentration for the Sunday afternoon peak

ozone period are shown in Figures 28

- 30 for 2007 and 31

- 33 for 2007. For all of these

cases, the reduction in maximum ozone is small and within approximately 1 to 2 ppb.

The area of reduce d ozone generally follows the area of maximum ozone concentrations.

Thus, it appears that changes in emissions over time from 1996 to 2007 and 2015 will

produce a greater change in maximum ozone than any of the three control strategies for

2007 or 2015.44

Figure 28. Contour map of differences in surface ozone concentration between the 2007

base case and control strategy 2007A (negative numbers indicate a reduction of ozone in

2015 relative to 1996) for 3 pm on Sunday, July 14.45

Figure 29. Con tour map of differences in surface ozone concentration between he 2007

base case and control strategy 2007B (negative numbers indicate a reduction of ozone in

2015 relative to 1996) for 3 pm on Sunday, July 14.46

Figure 30. Contour map of difference s in surface ozone concentration between the 2007

base case and control strategy 2007C (negative numbers indicate a reduction of ozone in

2015 relative to 1996) for 3 pm on Sunday, July 14.47

Figure 31. Contour map of differences in surface ozone con centration between the 2015

base case and control strategy 2015A (negative numbers indicate a reduction of ozone in

2015 relative to 1996) for 3 pm on Sunday, July 14.48

Figure 32. Contour map of differences in surface ozone concentration between he 2015

base case and control strategy 2015B (negative numbers indicate a reduction of ozone in

2015 relative to 1996) for 3 pm on Sunday, July 14.49

Figure 33. Contour map of differences in surface ozone concentration between the 2015

base case and con trol strategy 2015C (negative numbers indicate a reduction of ozone in50

References

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## Appendix A

Emissions Inventory

An episodic emissions inventory was constructed to support the Puget Sound ozone

modeling effort. Because ozone is a regional issue, this inventory was constructed using

a consistent methodology over the entire modeling domain. The inventory built upon two

prior ozone modeling efforts: the Stage II Vapor Recovery Study an d the Southwest

Washington Ozone Study. i

, ii This study improved upon the prior studies by including

additional point sources in the central Puget Sound area, incorporating improvements in

onroad mobile emissions modeling, incorporating area and nonroad i nventory data

prepared by the Puget Sound Clean Air Agency, and correcting errors found in the

nitrogen oxide speciation.

Large sources of nitrogen oxides (NOx), volatile organic compounds (VOC), and carbon

monoxide (CO) were addressed. Estimates were ma de for 1996 and 2015 for ozone

episode conditions that occurred during July 11

- 14, 1996. Hourly estimates were

assigned to a 5

- kilometer grid system over the modeling domain. Sources and pollutants

are shown in Table 1 below.

I n the initial model development work, the modeling domain encompassed the three

major metropolitan centers of Vancouver, BC, Seattle, WA, and Portland, OR. For the

modeling work, the domain was reduced in size as described in the main body of this

report. Because the decision to reduce the modeling domain was made after most of the

emissions estimates were completed, the inventory was calculated for the original

domain. Emissions for the smaller domain were selected from the larger domain for

modeling. The inventory documentation in this appendix includes all sources in the

original domain. Separate emissions summaries are included at the end of this appendix

for 1) the Puget Sound Clean Air Agency counties, 2) the reduced modeling domain, and

3) the or iginal modeling domain.

Table 1 : Sources and Pollutants Inventoried

Source Type Category CO VOC NOx Puget Sound only

Area Aircraft Refueling X

Area Architectural Coating

- Solvent X

Area Architectural Coating

- Water X

Area Autobody Refinishing X

Area Bakeries X

Area Breweries X

Area Consumer Solvents X

Area Cutback Asphalt X

Area Graphic Arts X

Area Industrial Surface Coating X

Area Gasoline Service Stations X

Area POTWs and TSDFs X52

Source Type Category CO VOC NOx Puget Sound only

Area Restaurants X

Area Rubber and Plastics X

Area Small Point Sources X X X X

Area Surface Cleaning X

Area SVE and SGR X

Area Traffic Marking X

Area Vessel Loading and Unloading X

Biogenic Biogenics X X

Nonroad Aircraft

– Comm ercial X X X X

Nonroad Aircraft

– Military X X X X

Nonroad Airport Service Equipment X X X X

Nonroad Commercial Equipment X X X

Nonroad Commercial Marine Vessels X X X X

Nonroad Construction Equipment X X X

Nonroad Industrial Equipment X X X

Nonr oad Lawn and Garden Equipment X X X

Nonroad Recreational Marine Vessels X X X

Nonroad Recreational Vehicles X X X

Onroad Onroad Mobile Sources X X X

Point Large Point Sources X X X



## Section 1: Emissions Estimation Methods

To estimate emissions , six basic tasks were completed for each source category. The six

tasks were: 1) estimate the 1996 activity level, 2) adjust/allocate the 1996 activity (or

emissions) temporally, 3) allocate the 1996 activity (or emissions) spatially, 4) estimate

1996 em ission rates per the activity, 5) estimate 2015 emission rates, and 6) provide

qualitative observations where applicable. The tasks are described below for each source

category.



### Point Sources

An emissions inventory was developed for major stationary sou rces for western

Washington, Oregon, and British Columbia. To develop an emissions inventory for point

sources, emissions were limited to those sources that met one of the following criteria:

VOC, NOx or SO 2 greater than 40 tons per year; or CO greater t han 100 tons per year.

These point sources were determined by using the Washington Emission Data System

(WEDS) iii and through contact with the Oregon Department of Environmental Quality

and the National Resource Council of Canada.

The point source informat ion had been assembled for prior modeling projects. Two

general updates were incorporated for this effort: 1) additional point sources meeting the

above criteria were identified, and 2) point sources tracked by PSCAA falling below the53

cutoff levels were a lso added. The additions could not easily be added to the point source

modeling file (which incorporates stack parameters). The additions were instead treated

as ground

- level area sources, where emissions were assigned to the appropriate modeling

grid us ing source coordinates.54

Activity Level, Emission Rates and Temporal Adjustments

For this study, hourly emission rates were determined by one of three methods:

1) continuous emission monitoring (CEM) data, 2) amount of raw materials consumed, or

3) repor ted annual emission rate using a facility operating schedule. An attempt was

made to provide the most accurate data possible for the period in question. However, it

was not possible to get continuous emission monitoring (CEM) data from every facility.

Washington Point Sources

CEM data or other daily estimates were obtained for most of the sources regulated by

Ecology (pulp mills and aluminum smelters) and some of the sources regulated by the

Northwest Air Pollution Authority (oil refineries). iv

- xxi Those sources that did not

provide CEM data provided either amount of raw materials consumed or an operating

schedule for the study period. Raw material data was used with emission factors from

AP

- 42 xxii and National Council for Air and Stream Improvement (NCASI) Technical

Bulletins to determine actual emissions. xxiii Operating schedules were used with the

WEDS data to determine more accurate emission rates.

Emissions for Washington point sources where CEM or daily estimates were not

available we re obtained from the WEDS. The annual hours of operation are also reported

to WEDS and these numbers were used with the annual emission rates to determine an

average hourly emission rate.

For the Centralia Power Plant, annual operation was used to determ ine emissions during

upset conditions. The annual reporting showed that upset conditions only occurred over

only three hours during the year. In the modeling, the emissions were mistakenly used

for each hour of the modeling episode, resulting in higher e missions than actually

occurred. This affected NOx emissions. This will be corrected in any future modeling

exercises.

Oregon Point Sources

1996 emissions for 44 facilities in Oregon were provided by the Oregon Department of

Environmental Quality. No data were provided for hours of operation, therefore, each

facility was assumed to operate 24 hours a day for the full year.

Canada Point Sources

Emissions were supplied by the Canadian National Research Council for a specific

weekly event (Sunday

– Friday) in 1993. Average Monday through Friday emissions

were used as estimates for all weekdays. Sunday estimates were used for weekend days.

Stack Parameters55

Some of the point sources did not have stack data either because the sources do not have

a stack or because the data was not available. To estimate stack parameters for these

sources, averages of available stack parameters were calculated by process (defined by

Source Classification Category codes). For processes where stack parameters were no t

available, stack parameters from similar processes were used. These average stack

parameters were applied to these sources dependent on process. For fugitive sources, the

following stack parameters were used:

Temperature = 303 K (30 degrees C)

Hei ght = 0 m

Diameter = 1 m

Velocity = 0.1 m/s

Spatial Adjustments

To determine the location of a point source, coordinates and elevation are needed. The

WEDS, Oregon, and Canada emission inventories provided UTM coordinates for each

point source or fa cility. Sources with missing coordinates were assigned coordinates

using their location address. In some cases, estimates were based on the county and city

of location.

2015 Methodology

The 2015 inventory was assumed identical to the 1996 inventory.



### Onroad Mobile Sources

Onroad mobile source emissions are those generated by operating vehicles on public

roadways. Emissions from fuel combustion and evaporation were estimated.

Activity Level, 1996 and 2015

The activity measurement for onroad mobile sources is the number of miles driven. The

units are typically given in average daily vehicle miles traveled (ADVMT). ADVMT is

normally estimated from traffic counts collected over a sampling area, or through use of

travel demand models, which simulate v ehicle travel patterns based on demographic and

economic parameters. Travel demand models are validated with traffic counts. The

Washington and Oregon Departments of Transportation (WSDOT, ODOT) and several

local transportation planning agencies provide information on the average number of

vehicle miles traveled in an average day (ADVMT). Local agencies track VMT over all

road jurisdictions (state, county, city). The state DOTs only track VMT on state routes.

Wherever possible, local agency travel dema nd model VMT spatially resolved by

roadway segment (link) was obtained to accurately model mobile source emissions. xxiv

,

xxv

, xxvi

, xxvii Where spatially resolved local agency VMT was not available, state route

link VMT was utilized. xxviii

, xxix56

Where state route link AD VMT data was used, it was adjusted to include county and city

jurisdiction ADVMT. To make the adjustment, Highway Performance Monitoring

System (HPMS) data was obtained. HPMS is a traffic counting system used throughout

the nation to track VMT for all ro ad jurisdictions. xxx

, xxxi In 1996, Washington was

divided into twelve HPMS tracking areas. WSDOT provided a breakdown of the HPMS

ADVMT by county. xxxii The total HPMS VMT for a given county was used to increase

the state route link ADVMT for each link in the gi ven county with the following equation:

ADVMT cli x __ HPMS ADVMT

c __ = ADVMT clf



##  ADVMT cli

where ADVMT cli is the initial state route ADVMT for link (l) located in county

(c),



##  ADVMT cli is the sum of all the st ate route link ADVMT in county (c),

HPMS ADVMT

c is the total HPMS ADVMT for county (c), and

ADVMT clf is the final state route ADVMT for link (l) located in county (c).

In Oregon, only statewide HPMS data was available. The state route link ADVMT was

i ncreased by multiplying the state route link ADVMT by the ratio of total state HPMS

ADVMT to the total state route link ADVMT.

Table 2 lists the final sources of ADVMT used in this inventory. This project built upon

prior invent ory projects, and because of resource constraints, updated VMT was only

obtained for the PSRC jurisdiction. The following abbreviations are used for the local

agencies: PSRC (Puget Sound Regional Council), SWRTC (Southwest Washington

Regional Transportati on Council), TRPC (Thurston Regional Planning Council), and

METRO (transportation planning agency for Portland, OR).

Table 2 : ADVMT Sources

Agency Area Year of ADVMT

WSDOT All WA counties except those listed below 1995

ODOT All OR areas except METRO’s planning area 1996

PSRC King, Kitsap, Pierce, Snohomish Counties

– WA 1997

2015 projection

SWRTC Clark County

– WA 1994

TRPC Thurston County

– WA 1994

METRO Greater Portland, OR area 1994

Temporal Adjustments

A nnual, 199657

All 1994 ADVMT was projected to 1995 using the ratio of 1995 to 1994 summary

HPMS ADVMT (1996 HPMS ADVMT for most areas was not available when the

ADVMT estimates were finalized). To project to 1996, WSDOT, PSRC and SWRTC

ADVMT projections were examined. xxxiii

, xxxiv

, xxxv In general, PSRC had the lowest

growth rates and WSDOT the largest. The average of PSRC and SWRTC growth rates

(2%) was used to linearly project 1995 ADVMT to 1996 for all areas except PSRC. The

1997 ADVMT estimate from PSRC was backcast to 1996 using the ratio of 1996/1997

WSDOT county estimates of the 4

- county area VMT (derived from HPMS). xxxii

, xxxvi

1996 ADVMT is listed in Table 2 . The ADVMT listed in the eastern

- most counti es only

represents the ADVMT occurring within the modeling domain (west of UTM zone 10

coordinate 720 km).

A nnual, 2015

PSRC provided a link

- based projection of VMT to 2015 in King, Kitsap, Pierce and

Snohomish Counties. VMT for all other areas was pr ojected from 1996 to 2015 using the

general Puget Sound regional VMT growth rates specified in the Technical Analysis

Protocol (2.5% from 1996 to 2000, 1.6% from 2000 to 2010, and 1.25% from 2010 to

2015). xxxvii

O ther adjustments

Monthly, daily and hourly ad justments were made to the ADVMT based on traffic

recorder statistics supplied by WSDOT. xxxviii

, xxxix July traffic is approximately 7.2%

higher than the annual average. Daily variation shows an increase in traffic beginning

with Monday and peaking on Friday ( Table 3 ). Hourly variation shows morning and

evening peaks during the weekdays. On weekends, the traffic increases during the

morning and remains fairly constant until decreasing in the early evening ( Figure 1 ).

Table 3 : Day

- of

- week Traffic Variation

Sun Mon Tue Wed Thu Fri Sat

0.775 1.013 1.037 1.044 1.052 1.148 0.93358

Figure 1 : Hourly Traffic Variation59

Table 4 : 1996 and 2015 Average Daily Vehicle Miles by County*

Washington Oregon

County 1996 ADVMT 2015 ADVMT County 1996 ADVMT 2015 ADVMT

*Chelan 1,742,772 2,409,125 Benton 1,246,659 1,723,322

Clallam 1,368,402 1,891,615 Clackamas 6,217,7 54 8,595,131

Clark 5,240,672 7,244,458 Clatsop 1,339,824 1,852,109

Cowlitz 2,378,082 3,287,348 Columbia 1,396,361 1,930,264

*Douglas 379,870 525,114 *Crook 294,318 406,851

Grays Harbor 1,644,280 2,272,975 *Deschutes 595,790 823,592

Island 920,235 1,285,556 Hood River 1,063,887 1,470,667

Jefferson 754,675 1,058,182 Jefferson 1,033,630 1,428,842

King 39,197,681 52,447,131 *Lane 69,953 96,700

Kitsap 3,599,891 5,641,577 Lincoln 1,608,477 2,223,483

*Kittitas 2,062,030 2,850,453 Linn 3,778,929 5 ,223,814

*Klickitat 774,754 1,070,984 Marion 6,283,545 8,686,077

Lewis 2,181,664 3,018,231 Multnomah 10,273,247 14,201,253

Mason 759,819 1,080,667 Polk 1,333,983 1,844,036

*Okanogan 121,219 167,567 Sherman 408,368 564,509

Pacific 636,317 879,615 Tillamook 894,375 1,236,342

Pierce 15,266,594 20,717,903 Wasco 1,182,707 1,634,919

Skagit 2,748,326 3,820,938 Washington 6,492,084 8,974,352

Skamania 859,342 1,187,914 Yamhill 2,022,717 2,796,109

Snohomish 13,526,684 19,891,756

Thurston 468,761 648,225

Wahkiakum 145,604 201,277

Whatcom 3,655,238 5,052,829

*Yakima 3,821,669 5,282,895

Total 104,254,581 143,934,335 Total 47,536,610 65,712,372

* only the portion of county VMT that lies within the modeling domain is represented

Spatial Adjustments

Roadway link geographic coordinates were used to spatially allocate the link ADVMT to

the modeling grid system.

Emission Rates, 1996 and 2015 Methodology

VOC (volatile organic compounds) and NOx (nitrogen oxides) emissions were cal culated

with a modified version of EPA’s MOBILE5b xl developed by the Department of Ecology

in 1999 with additional modifications in early 2000. These revised inputs were used to

estimate on

- road mobile source emissions for the purpose of modeling a specifi c 1996

and 2015 ozone (O 3 ) event in western Washington. Ecology modified the MOBILE

model based upon methodologies developed by EPA’s Office of Mobile Sources to60

analyze the benefits for the notice of proposed rulemaking (NPRM) for the Tier 2 Motor

Vehicl e Emissions Standards and National Low Sulfur Gasoline Program. xli These

modifications were intended to produce results similar to a soon to be released

MOBILE6. Documentation on the NPRM, additional Tier 2 modeling results, and

MOBILE6 development have be en posted at the EPA website,

http://www.epa.gov/oms/m6.htm

.

Refiners in western WA will be required to phase

- in the implementation of EPA’s low

sulfur gasoline program beginning in 2004. This phase

- in, combined with the WA fleet’s

previous years of long

- term exposure to high sulfur gasoline, create complexities for

modeling emissions during the early years of this program. For this reason Ecology

specifically developed MOBILE input files for two specific periods: 1) calendar years

prior to 2004, 2) cale ndar years for which the majority of the WA fleet had only been

exposed to low sulfur gasoline (30 parts per million average). A 1996 base year and

2015 future year met these respective criteria.

Three lines of thought primarily drove these MOBILE modi fications:

1. Identify which changes have the most significant impact on modeling MOBILE

emissions.

2. Apply those changes that are clearly documented in the EPA reports.

3. Utilize (1) and (2) to estimate the benefits of Tier 2 Motor Vehicle Emissions

Standards a nd National Low Sulfur Gasoline Program for the future year 2015

relative to the base year 1996.

Modifications meeting the above criteria were most clearly addressed in the EPA

document, “Methodology for Modifying Mobile5b in the Tier 2 Study”, EPA

- 420

- R

- 004,

April 1998, John W. Koupal, Richard A. Rykowski, Office of Mobile Sources, US EPA

(Reference xli ).

Numerous additional EPA MOBILE6 draft reports and memos were reviewed and

provided additional clarifications for the MOBI LE modifications; however, those

documents only utilized for clarification are not included in the bibliography.

EPA’s Tier 2 analysis only addressed MOBILE modifications for hydrocarbons (HC) and

nitrogen oxide (NO

x ) tailpipe emissions. Evaporative emis sions for MOBILE5 were not

modified. Anticipated changes for MOBILE6 carbon monoxide (CO) modifications were

not addressed in the early Tier 2 analysis. Consequently, Ecology did not modify

MOBILE CO emissions. Ancillary CO reduction benefits are expect ed for MOBILE6,

primarily due to more stringent Tier 1 VOC emission standards and Tier 2 NO

x standards. Although not identified as an ozone precursor, CO does play a role in the

atmospheric chemistry for O 3 formation. Ecology estimated these expected an cillary CO

emission reductions for 2015 to be equivalent to the VOC emission reductions for 2015.

Where possible, alternate MOBILE6

- like inputs were substituted in the MOBILE input

files. These included the development of final emission rates (substitute d as alternate61

basic emission rates known as BER), alternate VMT mix, and alternate mileage

accumulation rates. These alternate inputs include substitutions for all of the MOBILE

vehicle classes and include the more stringent emission standards applicable to the

National Low Emission Vehicle Program and the Tier 2 Motor Vehicle Emissions

Standards and National Low Sulfur Gasoline Program. The alternate BER for the

individual vehicle classes include speed correction factors for an average composite

roadway class, temperature correction factors, full usage air conditioning factors, and an

average trip correction factor. Post adjustment factors for occasional light duty vehicle

exposure to refueling with high sulfur fuel and for defeat devices installed on h eavy

- duty

vehicles were additionally applied.

MOBILE model modifications and input parameters are described in more detail below.

VOC and NOx Final Emission Rates

Final emission rates (FER) for light duty gasoline vehicles (LDGV) and light duty

gasolin e trucks (LDGT1 and LDGT2) were developed using Tables F

- 1, F

- 2, F

- 7, and F

8 for 30ppm sulfur content and 330ppm sulfur content. xli Most of the gasoline in Western

Washington is supplied by local refineries t hat primarily process Alaskan crude oil. The

fuel sulfur content of that gasoline may vary on the average from approximately 350ppm

to 450ppm. xlii

, xliii

, xliv The 330ppm final emission rate (FER) tables most closely resemble

the current Western WA fuel sulfur con tent. xli Conventional fuel, No IM FER for LDV

LEV (low emission vehicle), LEV, Tier 1, and Tier 2, no SFTP (Supplemental Federal

Test Procedure) and SFTP were applied to individual model years according to the

appropriate EPA schedules. FER for light duty diesel vehicles (LDDV) were developed

using Tables G

- 12 and G

- 24. xli These FER tables provide zero mileage levels (ZML),

deterioration rates (DR), and flex point s (FLEX) for Tier0, Tier1, LEV, and Tier2 light

duty vehicles and light duty trucks. These FER were substituted as alternate basic

emission rates (BER) in the MOBILE input files. FER for the gasoline and diesel LDT

classes 2, 3, and 4 were combined to cr eate a new LDT2 class. xli FER for all the LDGV,

LDGT1, and LDGT2 classes were adjusted according to phase

- in schedules for the EPA

Tier1, NLEV, and Tier2 programs. FER for heavy

- duty gasoline and diesel trucks

(HDGT and HDDT) were developed using EPA memorandums on modifications to

heavy

- duty gasoline and diesel rates. xlv

, xlvi

Alternate VMT Mix

The EPA web

- site, http://www.epa.gov/otaq/t2models.htm , contains documentation for

MOBILE6 updates used in the Tier 2 F inal Rule Making. VMT mix rates for 1996 and

2015 were extracted from the MOBILE input file Noimcgbs.in.

Vehicle Registration Distribution

Fleet age distributions were constructed using vehicle registration information from the

Washington State Departm ent of Licensing. xlvii

, xlviii Per MOBILE specifications, July62

1995

- June 1996 data was used for the 1996 model runs and July 1997

- June 1998

registration data was used for 2015 model runs.

Alternate Cumulative Mileage Rates

Alternate July 1, cumulative milea ge rates (Table I

- 1, Travel Fractions) were applied to

light duty vehicles for 1995 and newer model years. xli The alternate rates are applicable

to Tier1, LEV and Tier2 vehicles. The Tier1 phase

- in schedule i s 40% for 1994, 80% for

1995, and 100% for 1996. LDT1/2 rates were applied to the LDT1 class and a weighted

average was applied to the LDT2 class (LDT1/2 = 0.695 and LDT3/4 = 0.394). xli

MOBILE6 rates were ap plied to model year 2000 and newer heavy

- duty gasoline and

diesel trucks. xlv

, xlvi

Inspection and Maintenance (I/M) Program

Vehicle I/M programs are operated in the Puget Sound, Vancouver, and Portland areas.

The Portland area also operates an anti

- tampering program. Parameters appropriate for

each area were used in the MOBILE modeling files. xlix

, l

, li The parameters include

program start year, stringency (percent of pre

- 1981 vehicles failing the initial test),

vehicles and model years required to take the test, type and frequency of test(s), waiver

rates, program compliance rates, technician training program, and passing emissions

cutpoints. In 1996, Washington ran a loaded

- idle test. Due to the smaller sample size

used to determine credits for this type of test in the MOBILE model, there was actually

less credit given to it than is given to the 2500/idle test (a lesser test). For this reason,

and at the recomm endation of staff from EPA’s Office of Transportation and Air Quality

(OTAQ) the test type was run as 2500/idle. lii

Default M0BILE5b I/M credits (Basic Program with start

- up cut

- points for Puget Sound

and ASM Program with start

- up cut

- points for Vancouver) , calculated using MOBILE5b

with the alternate FER described in the VOC and NOx Final Emissions Rate section,

were applied to the 1996 base

- year run.

Sierra Research provided the IM data file, Imdata.tox, as a substitute for the IM data file,

Imdata.4. I mdata.tox contains MOBILE6

- like, basic

- idle and IM240 credits that were

applied to the 2015 model run as a second I/M program. Basic

- idle I/M credits were

applied to model years 1991 through 1995, and IM240 credits were substituted for on

board diagnostic credits and applied to model years 1996 through 2010.

Table 5 : I/M Program 1 Parameters, 1996 and 2015

Parameter Puget Sound

Seattle Puget Sound

Tacoma, Everett Portland Vancouver

test type 2500

- idle 2500

- idle 2500

- idle 2500

- idl e

test frequency biennial biennial biennial biennial

start year 1982 1993 1975 1993

stringency 30% 30% 47% 34%63

model yrs tested 96: 1968

- 1994

15: 1991

- 1995 96: 1968

- 1994

15: 1991

- 1995 96: 1975

- 1994

15: 1991

- 1995 96: 1968

- 1994

15: 1991

- 1995

pre ’81 wai vers 4% 4% 0% 2%

post ’80 waivers 9% 9% 0% 4%

compliance rate 95% 95% 90% 93%

cutpoints test default test default test default test default

technician training yes yes no yes64

Table 6 : I/M Program 2 (OBD) Parameters, 2015

P arameter Puget Sound

Seattle Puget Sound

Tacoma, Everett Portland Vancouver

test type IM240 M6alt IM240 M6alt IM240 M6alt IM240 M6alt

test frequency biennial biennial biennial biennial

start year 1982 1993 1975 1993

stringency 30% 30% 47% 34%

model yr s tested 1996

- 2010 1996

- 2010 1996

- 2013 1996

- 2010

pre ’81 waivers 4% 4% 0% 2%

post ’80 waivers 9% 9% 0% 4%

compliance rate 95% 95% 90% 93%

cutpoints 0.80 HC

15.0 CO

2.00 NOx 0.80 HC

15.0 CO

2.00 NOx 0.80 HC

15.0 CO

2.00 NOx 0.80 HC

15.0 CO

2.00 NOx

tec hnician training yes yes no yes

For areas with an inspection and maintenance (I/M) program, emission factors with and

without the I/M program were weighed according to the percentage of vehicles entering

the I/M area from areas where I/M is not required. For the Puget Sound the percentage

was 82.9% I/M, for Vancouver 70.1% I/M, and for Portland 84.1% I/M. liii

, liv

Speed

To simplify modeling of the roadway network of approximately 50,000 links, a single

speed that would produce emissions representative of t he area was utilized for each

pollutant. To determine the speed appropriate for each pollutant, an analysis of link

specific speeds and corresponding emission rates was conducted using the PSRC traffic

network (approx. 15,000 links). Emissions in grams w ere calculated for each link and

summed for an area

- wide total. The total emissions were divided by the total number of

ADVMT to obtain an average emission rate in grams per mile. The average rates were

compared to emission rates for speeds in 1 mph incr ements, and the speeds producing

emission rates closest to the area

- wide average emission rates were chosen. The speeds

were 24 mph for CO, 28 mph for HC, and 50 mph for NOx.

Reid Vapor Pressure (RVP)

The legal summertime RVP limits are 7.8 psi for Ore gon, and 9.0 psi for Washington. lv

Actual Washington RVP for July 1996 was obtained from the Western States Petroleum

Association (WSPA). lvi The WSPA value of 8.7 psi was used for all counties in western

Washington except Clark County. Gasoline used in Cla rk County was assumed to come

from Oregon suppliers, and thus have an RVP of 7.8. lvii65

Hourly Temperatures

Hourly temperatures from the SeaTac Airport were used to calculate hourly emission

rates for all of Washington except in the greater Vancouver area. Portland International

Airport temperatures were used for the greater Vancouver, WA area and all of Oregon. lviii

Table 7 : Minimum and Maximum Temperatures in Fahrenheit

Day SeaTac Portland

min max min max

7/11 56 82 59 91

7/12 61 87 65 95

7/13 66 88 66 99

7/14 70 91 66 97

Month and Year of Evaluation

The MOBILE model can calculate emission factors that represent a January 1 or July 1

registration distribution. All runs were done with a July 1 distribution.

Post NOx Emissio ns Adjustments due to Defeat Devices for Heavy

- duty Diesel

Vehicles

Sierra Research provided tables to calculate the increase in NOx emissions due to the

manufacturers installation of defeat

- devices on heavy

- duty diesel vehicles. lix

Multiplicative, post ad

justment factors of 1.274 and 0.967, weighted by average daily

vehicle miles traveled (ADVMT), functional class, and speed were applied to the

respective 1996 and 2015 runs.

Post Adjustments for Exposure to High Sulfur Fuel

Sulfur in gasoline inhibits t he performance of the catalytic converter and significantly

impacts the emissions generated by motor vehicles. LEV and Tier 2 vehicles are more

greatly impacted than Tier 1 vehicles, which are more greatly impacted than Tier 0

vehicles. A motor vehicle’s catalytic converter exposed to a high fuel sulfur content may

suffer temporary, or possibly permanent, impairment such that the emission control

system never again performs at the optimal design level. VOC and NOx emissions

adjustments for vehicle exposu re to high sulfur gasoline for calendar year 2015 were

provided by Sierra Research. lx

Table 8 : High Sulfur Fuel Post Adjustments

Pollutant LDGV LDGT1 LDGT2

VOC 1.05 1.05 1.05

NOx 1.25 1.19 1.066667

Observations

Ecology’s modific ations to the MOBILE model are based upon EPA’s early Tier 2

modeling analysis in their Notice of Proposed Rule Making (NPRM). Subsequent EPA

modeling for the Tier 2 Final Rule Making (FRM) and Environmental Impact Analysis

included additional MOBILE modi fications combined with substantial MOBILE6

- like

methodology changes. EPA has made additional changes to MOBILE6 that most likely

will generate emissions different than the MOBILE changes used for the NPRM and

FRM.



### Nonroad Mobile Sources

Activity Level and Emission Rates

PSCAA provided 1996 emission estimates of VOC, CO and NOx in tons per year for

nonroad source categories in their jurisdiction: aircraft service equipment, construction

equipment, commercial equipment, industrial equipment, lawn and ga rden equipment,

recreational vehicles, recreational boats, commercial marine vessels, and aircraft. lxi

, lxii

VOC estimates for gasoline powered equipment were split into exhaust and evaporative

emissions using ratios from an EPA nonroad study. lxiii

, lxiv

The annual emissions estimates for construction equipment, commercial equipment,

industrial equipment, lawn and garden equipment, and recreational vehicles were

converted to g/person using population. Annual estimates for recreational boats were

converted to g/boat using boat population provided by PSCAA. To calculate emissions

throughout the modeling domain, the emission rates in g/person and g/boat were

multiplied by human and boat populations, respectively. lxi

, lxv

, lxvi Oregon water coverage

and boating registrations necessary to calculate and spatially allocate recreational boat

emissions were not obtained in the time allowed for the analysis; therefore, recreational

boat emissions were not calculated for Oregon.

Emi ssion rates for aircraft, airport service equipment, and commercial marine vessels

were provided by individual airport and seaport for the Puget Sound area by PSCAA.68

Table 9 : Population* and Boat Registration Estimates, 1996

Wash ington Oregon

County population boats County population

*Chelan 62,104 3,244 Benton 74,338

Clallam 62,459 3,757 Clackamas 341,041

Clark 302,995 9,330 Clatsop 35,037

Cowlitz 88,693 4,017 Columbia 42,147

*Douglas 17,511 1,504 *Crook 14,225

Gray s Harbor 65,436 3,005 *Deschutes 22,149

Island 68,538 4,025 Hood River 21,484

Jefferson 23,905 1,892 Jefferson 16,252

King 1,574,120 49,630 *Lane 1,370

Kitsap 222,649 9,870 Lincoln 44,730

*Kittitas 30,389 973 Linn 100,958

*Klickitat 14,488 484 Marion 266,100

Lewis 62,683 2,235 Multnomah 602,989

Mason 43,707 3,707 Polk 51,347

*Okanogan 3,546 1,283 Sherman 1,822

Pacific 19,415 1,020 Tillamook 24,007

Pierce 650,582 23,146 Wasco 23,588

San Juan 11,456 1,859 Washington 378,397

Skagit 91 ,231 6,143 Yamhill 78,967

Skamania 8,667 281

Snohomish 585,461 20,870

Thurston 190,568 8,599

Wahkiakum 3,357 286

Whatcom 149,370 6,473

*Yakima 171,425 5,826

total 4,524,755 173,459 total 2,140,947

* only the portion of cou nty population that lies within the modeling domain is

represented69

Table 10 : 1996 Nonroad Mobile Source Emission Rates

Category Emission Rates Units

NOx VOC

exhaust VOC

evap CO

Diesel Construction Equipment 2585 329 0 1438 g/ person

Diesel Commercial Equipment 51 8 0 32 g/person

Diesel Industrial Equipment 338 39 0 146 g/person

Diesel Lawn and Garden Equipment 12 6 0 7 g/person

Diesel Recreational Vehicles 0 0 0 0 g/person

Gas Construction Equipment 11 72 15 1128 g/person

Gas Commercial Equipment 9 244 59 7115 g/person

Gas Industrial Equipment 156 121 44 2755 g/person

Gas Lawn and Garden Equipment 7 1537 276 8549 g/person

Gas Recreational Vehicles 2 295 34 1183 g/person

Diesel Recreational Boats 394 193 0 421 g/ boat

Gas Recreational Boats 4557 34216 7911 208759 g/boat

Temporal Adjustments

The EPA study estimates of summer activity were used to seasonally adjust the emission

rates. lxiii Hourly adjustments were ma de using EPA adjustment factors. lxvii

Table 11 : Nonroad Mobile Source Temporal Adjustments

Category Summer

Activity (%) Day of Week and Hourly Adjustments

Boats 57 30% M

- F, 70% S

- S, hours 06

- 18, 7 d/w

(added one hour to the evening)

Construction Equipment 38 uniform hrs 06

- 18 M

- F, 06

- 11 Sat

Commercial Equipment 38 uniform hrs 06

- 18 M

- F, 06

- 11 Sat

(used construction equipment as surrogate)

Industrial Equipment 25 80% hrs 06

- 18, 20% hrs 19

- 23, 6 d/w

Lawn and Garden Equipment 40 50% M

- F, 50% S

- S, hours 08

- 18

Recreational Vehicles 43 30% M

- F, 70% S

- S, hours 08

- 18

Commercial Marine Vessels 25 uniform hrs 00

- 23

Aircraft 25 uniform hrs 00

- 23

Airport Service Equipment 25 uniform hrs 00

- 2370

Spatial Adjustments

Emissions from constru ction equipment, commercial equipment, industrial equipment,

lawn and garden equipment, and recreational vehicles were assigned to grids based on

grid

- allocated 1990 population Census data. The 1990 population was projected to 1996

using 1996 county growt h rates before emissions were calculated. A GIS surface water

overlay was used to identify grids containing surface waters. Total recreational boat

emissions by county (calculated using county boat registrations) were divided evenly

among the county’s su rface water grids. Aircraft, airport service equipment, and

commercial marine vessel emissions were assigned to the grids containing the individual

airports/seaports.

2015 Methodology

The methodology for estimating emissions in 2015 was almost identical to that used for

the base year 1996 inventory above. Projected county population for 2015 was used to

estimate emissions for all of the nonroad mobile sources. lxviii

, lxix Projected increases in

county population ranged from 70% (Deschutes County) to 8% (Wasco County) with an

average of 30%. In Puget Sound, the average was 27%. For boats, the number of

registrations estimated in 2015 was calculated by multiplying the ratio of 2015 projected

county population to 1996 county population by the number of registrat ions in 1996.

Emission rate adjustments for new nonroad engine VOC and NOx standards were

provided by PSCAA and Sierra Research. lxx The new standards are expected to result in

decreased NOx emissions from heavy duty diesel engines, and decreased VOC emissi ons

from gasoline engines. The new standards are expected to increase NOx emissions from

gasoline engines.71

Table 12 : Population* and Boat Registration Estimates, 2015

Washington Oregon

County population boats County populatio n

*Chelan 82,117 4,289 Benton 86,323

Clallam 75,232 4,525 Clackamas 464,336

Clark 400,404 12,329 Clatsop 39,910

Cowlitz 120,302 5,449 Columbia 47,037

*Douglas 24,655 2,118 *Crook 18,743

Grays Harbor 77,727 3,569 *Deschutes 37,594

Island 97,464 5,724 Hood River 26,857

Jefferson 37,919 3,001 Jefferson 27,349

King 1,865,132 58,805 *Lane 1,774

Kitsap 309,274 13,710 Lincoln 52,176

*Kittitas 38,923 1,246 Linn 120,095

*Klickitat 18,261 610 Marion 361,601

Lewis 81,054 2,890 Multnomah 688,5 10

Mason 60,653 5,144 Polk 64,897

*Okanogan 4,380 1,585 Sherman 2,065

Pacific 24,594 1,292 Tillamook 28,920

Pierce 844,484 30,045 Wasco 25,400

San Juan 17,709 2,874 Washington 547,414

Skagit 131,558 8,858 Yamhill 111,266

Skamania 10,729 348

Snohomish 851,987 30,371

Thurston 291,569 13,156

Wahkiakum 4,461 380

Whatcom 203,568 8,822

*Yakima 210,774 7,163

total 5,884,929 228,304 total 2,752,267

* only the portion of county population that lies within the modeling dom ain is

represented72

Table 13 : 1996 to 2015 Nonroad Emission Rate Adjustment Factors

Category 2015 Adjustment Factor

NOx VOC

exhaust VOC

evap CO

Diesel Construction Equipment 0.37 0.20 1.00 1.00

Diesel Commercial Equipment 1.00 1.00 1.00 1.00

Diesel Industrial Equipment 0.44 0.26 1.00 1.00

Diesel Lawn and Garden Equipment 1.00 1.00 1.00 1.00

Diesel Recreational Vehicles 1.00 1.00 1.00 1.00

Gas Construction Equipment 1.03 0.20 0.20 1.00

Gas Commercial Equipment 1.11 0.20 0.2 0 1.00

Gas Industrial Equipment 1.00 0.26 0.26 1.00

Gas Lawn and Garden Equipment 1.064 0.31 0.31 1.00

Gas Recreational Vehicles 1.00 1.00 1.00 1.00

Diesel Recreational Boats 1.00 1.00 1.00 1.00

Gas Recreational Boats 1.25 0.65 0.65 1.00

Obse rvations

The method of spatially allocating nonroad sources by population may overestimate

emissions in highly populated areas. The emission rate changes in 2015 are based on

new and proposed federal regulations. Actual emission rate changes could be d ifferent

from the estimated changes.



### Stationary Area Sources (except Gasoline Service Stations)

Activity Level and Emission Rates

PSCAA provided 1996 emission estimates of VOC in tons per year for stationary area

source categories in their jurisdiction (see Table 1 ). lxi The annual emissions estimates

were converted to g/person using population. To calculate emissions throughout the

modeling domain, the emission rates in g/person w ere multiplied by 1996 population (see

Table 9 ).73

Table 14 : 1996 Stationary Area Source VOC Emission Rates

Category g/pers

- yr Category g/pers

- yr

Aircraft Refueling 22 Industrial Surface 3794

Architec tural Solvent 1035 POTW and TSDF 14

Architectural Water 681 Restaurants 28

Autobody 236 Rubber and Plastics 35

Bakery 270 Surface Clean 773

Breweries 15 SVE and SGR 8

Commercial/Consumer Prod. 2905 Traffic Marking 80

Cutback Asphalt 168 Vessel Load and Unload 71

Graphic Arts 521

Temporal Adjustments

Hourly adjustments were made using EPA adjustment factors. lxvii Operations were

considered uniform where no information was readily available.

Table 15 : Stationary Area Source Temporal Adjustments

Category Summer

Activity (%) Day of Week and Hourly Adjustments

Aircraft Refueling 25 uniform hrs 00

- 23, 7 d/w

Architectural Solvent 33 uniform hrs 06

- 18, 7 d/w

Architectural Water 33 uniform hrs 06

- 18, 7 d/w

Autobody 25 80% hrs 06

- 18, 20% hrs 19

- 23, Mon

- Sat

(used degreasing as surrogate)

Bakery 25 uniform hrs 00

- 23, 7 d/w

Breweries 25 uniform hrs 00

- 23, 7 d/w

Comm/Cons 25 80% hrs 06

- 18, 20% hrs 19

- 23, 7 d/w

Cutback Aspha lt 43 uniform hrs 07

- 18, Mon

- Fri

Graphic Arts 25 90% hrs 06

- 18, 10% hrs 19

- 23, Mon

- Sat

(used commercial fuel use as surrogate)

Industrial Surface 25 80% hrs 06

- 18, 20% hrs 19

- 23, Mon

- Sat

(used degreasing as surrogate)

POTW and TSDF 25 uniform hrs 00

- 23 , 7 d/w

Restaurants 25 uniform hrs 00

- 23, 7 d/w

Rubber and Plastics 25 uniform hrs 00

- 23, 7 d/w

Surface Clean 25 80% hrs 06

- 18, 20% hrs 19

- 23, 6 d/w

SVE and SGR 25 uniform hrs 00

- 23, 7 d/w

Traffic Marking 43 uniform hrs 07

- 18, Mon

- Fri

Vessel Load and Unload 25 uniform hrs 00

- 23, 7 d/w74

Spatial Adjustments

Emissions from area sources were assigned to grids based on grid

- allocated 1990

population Census data. The 1990 population was projected to 1996 using 1996 county

growth rates before emissions w ere calculated.

2015 Methodology

The methodology for estimating emissions in 2015 was almost identical to that used for

the base year 1996 inventory above. Projected county population for 2015 was used to

estimate emissions for all of the stationary are a sources (see Table 12 ). No changes were

made to the temporal allocation of activity.

Emission rates in grams per person for all stationary area sources except architectural

surface coating, autobody refinishing and commercial /consumer solvent use were held to

the 1996 values. The emission rates for architectural surface coating and

commercial/consumer products were reduced by 20% and autobody refinishing was

reduced by 33%. The reductions were based on Maximum Achievable Con trol

Technology (MACT) projections lxxi and were recommended by Sierra Research and

PSCAA. lxx

Observations

The method of spatially allocating area sources by population may overestimate

emissions in highly popula ted areas. The emission rate changes in 2015 are based on

new and proposed federal regulations. Actual emission rate changes could be different

from the estimated changes.



### Stationary Area Sources

- Gasoline Service Stations

Activity Level

Activity le vel is measured in gallons of gasoline pumped or in VMT if gallons are not

available. For Washington, individual gasoline station gallons information was obtained

from the local air authorities for all counties in Washington. lxxii

, lxxiii

, lxxiv

, lxxv

, lxxvi They

track info rmation on stage I controls, stage II controls, and gasoline throughput. In

Oregon, VMT was used as a surrogate for gasoline gallons pumped, assuming an average

fuel economy of 20 miles per gallon. xl

The Sout hwest Clean Air Agency (SWCAA) and Olympic Air Pollution Control

Authority (OAPCA) track exact throughput. PSCAA, the Northwest Air Pollution

Authority (NWAPA) and Ecology's Northwest Regional Office (NWRO) track

throughput classes. Where only classes we re tracked, the midpoint of the throughput75

ranges were used for each station for ranges below 1.2 million gallons per year. For

throughput over 1.2 million gallons per year, estimates were made. PSCAA compared

counties within their jurisdiction to Dept. of Licensing (DOL) fuel tax data lxxvii and

estimated 2.2 million gallons as an average. In Island county, the use of 1.2 million

gallons resulted in higher estimates than DOL data, so 1.2 millions gallons was assumed.

In Skagit and Whatcom Counties, using an average of 2 million gallons resulted in

estimates that were within 2% of the DOL estimates. Gallons of gasoline are shown in

the table below for each of the local jurisdictions in Washington.

Table 16 : 1995 Gasoline Gallons

Loc al Gallons

(in thousands)

OAPCA 189,907

PSCAA 1,136,260

NWAPA 170,125

SWCAA 210,260

NWRO 2,307

total 1,708,859

Temporal Adjustments

Annual gasoline station throughput was adjusted from 1995 to 1996 using the same

method that was used to project ADVMT. Monthly, day of week and hour of day

adjustments were made using WSDOT VMT adjustment factors, assuming that vehicle

refueling activity would follow VMT patterns. xxxviii

Spatial Adjustments

The loca l authorities do not track coordinates for service stations. A computer program

was used to assign coordinates to the stations based upon their addresses. lxxviii The match

rate was 78%. Stations not matched by their address were assigned coordinates of their

zip code zone centroid.

Emission Rates

Emission rates were calculated for 1) underground tank filling (stage I), 2) underground

tank breathing, and 3) vehicle tank filling (stage II). All of the air authorities track the

use of both stage I and stage II vapor recovery equipment. In Washington, AP42

emission factors were used for underground tank filling and breathing. lxxix Emission rates

in grams per gallon for vehicle tank refueling were generated using the model

MOBILE5b. The control efficiency rate was assumed to be 86%. lxxx

For Oregon, emissions were calculated using an alternate method available through

MOBILE5b. Emission factors in grams per gallon calculated above for Clark County

were converted to grams per mile using MOBILE5b's average of 20 miles per gallon.76

Emission controls were assumed to be similar to those in Clark County (98% stage 1

controlled, 74% stage 2 controlled), except that stage 1 control was assumed 100% to

ease calculations. The emission factors in grams per mile were combined wi th Oregon's

VMT data to calculate hourly emissions.77

2015 Methodology

The estimates calculated for 1996 were used for 2015 due to modeling difficulties.



### Incorporation of Canadian Area and Mobile Source Inventories

Information for on

- road mobile and ar ea sources for southern British Columbia were

obtained from the Canadian National Research Council. The emissions were adjusted

from their base year of 1993 to 1996 using population, vehicle miles traveled, and vehicle

emission rate projections. The Augu st 4th, 1993 Canadian on

- road mobile file was

adjusted, and used for all 1996 case days because the temperature range for that day was

close to the temperature ranges experienced in the 1996 episodes. The Canadian area

files were matched with 1996 case da ys based on day of week, and then adjusted for

population growth between 1993 and 1996 by multiplying by 1.05 (ratio of 1996 to 1993

Washington State population). lxvi The 1996 estimates were used directly for the 2015

inventory (without accounting for growth) due to time and resource constraints.



### Biogenic Sources

Biogenic emissions are natural emissions that result from biological activity. For this

inventory, emissions from vegetation (the predominant biog enic source of VOC) and soil

microbial activity (NOx emissions), were addressed. Much of the information here was

excerpted from work done by Washington State University for an ozone formation study

conducted for the Dept. of Ecology. i

Biogenic emissions were calculated for isoprene, terpenes, other VOCs and nitrogen

oxide. For each of these compound classes, the following formulation was used to

calculate the emission flux (mass/area/time) for a given land u se of vegetation class:

F = Fs x CL x CT

where Fs is the class specific emission flux for the specified compound class at 30  C and

1000  mol/m2/sec PAR (photosynthetically active radiation), CL is a light correction

factor, and CT is a temperat ure correction factor. To perform the emissions calculations,

the standard emission factors and correction factors were combined with vegetation

species distributions. Each is discussed below.

Activity Level

Activity data for biogenic sources is the ar ea of land covered by each of several major

vegetative species groups. Recent US Forest Service tree inventory data were obtained

for western Washington, and these data were used to determine the distribution of forest

species within the modeling domain. These data included tree inventory information for

approximately 2,000 plots within the state. For each plot the data included number of78

trees, species types, tree sizes, and crown area. Plots represented approximately 3 mi. x 3

mi. areas. The informat ion from the plots was used to determine the species distributions

for each grid where a plot occurred and to determine the fraction of the grid area

accounted for by the total crown area. The overall averages of these plot data are listed in

Table 17

.

The plot data was combined with land use data obtained from the National Research

Council of Canada. These data were the dominant land use type in each grid for the land

use types ( Table 18 ).

To combine the land use and species plot data, for each grid with a plot, the species

distribution was used directly and the balance of the land area within the grid was

represented by the land use type. These plot data were then interpolated using an inverse

distan ce squared routine where the existing land use type in a grid was modified

according to the interpolation results for that grid. For example, given a grid with land

use type equal to rangeland, the areal contribution of species from nearby plots was

calcu lated and the rangeland area was reduced proportionally. The interpolation scheme

is essentially identical to that used in the CALMET wind field interpolation. The

weighting factor was taken to equal 5 km and the maximum distance for interpolation

was 40 km.

Table 17 : Tree Species Distribution

Code Genus Examples Percent of Crown Area

11 Abies (fir) 5.3

17 Abies (fir) 1.6

22 Abies (fir) 0.2

42 Larix (larch) 0.0

98 Picea (spruce) 0.9

108 Pinus (pine) 0.2

119 Pinus (pine) 0.0

122 Pinus (pine) 2.5

202 Pseudotsuga (douglas fir) 38.5

231 Taxodium (cypress) 0.0

242 Thuja (W.red cedar) 5.9

263 Tsuga (Eastern hemlock) 21.5

264 Tsuga (Eastern hemlock) 0.7

312 Acer (maple) 4.3

351 Alnus (European alder) 14.3

361 Alnus (Europe an alder) 1.2

376 Betula (birch) 0.8

492 Cornus (dogwood) 0.0

542 Fraxinus (ash) 0.2

746 Populus (aspen) 0.0

747 Populus (aspen) 1.279

Code Genus Examples Percent of Crown Area

760 Prunus (cherry) 0.2

815 Quercus (oak) 0.1

999 Misc crops 0.2

Table 18 : Land Use Dis tribution

Land use Code Percent Distribution

urban 1100

agriculture 1200 6.6 (combined ag & urban)

irrigated agriculture

- 1200

rangeland 1300 20.6

forest 1400 34.4

water 1500 37.0

wetland 1600 0.0

barren 1700 0.9

tundra 1800 0.4

ice 1900 0.2

Emission Rates

Emission factors for isoprene, terpenes, other volatile organic compounds and nitrogen

oxide from the Biogenic Emission Inventory System 2 (BEIS2) are given below for

standard conditions of 30  C and 1000  mol/m2/sec PAR. lxxxi Only the major s pecies and

land use types for western Washington are shown in the table. Emission factors for

western Washington were defined using the BEIS2 factors as a basis for assignments. lxxxii

An adjustment was made to the NO factors. The standard factors given in BE IS2

represent conditions where the soil temperature is 30  C, not the ambient air temperature;

however, corrections for variations in temperature are based on air temperature.

Therefore, adjustments were made to standardize the factors to 30  C air temperat ure

using the air temperature to soil temperature equations in the PC

- BEIS2 manual. lxxxi

Table 19 : Emission Factors at Standard Conditions in  g/m 2 hr

Species Isop Terp OVOC NO

bigleaf maple 42.5 680 693.7 4.2

conif. forest 524.2 1672 1781.2 4.2

Douglas fir 170 2720 2775 4.2

grassland 37.8 94.5 56.7 52.9

mixed ag & forest 265.9 845.5 896.3 8.1

pacific silver fir 170 5100 2775 4.2

red alder 42.5 42.5 693.7 4.280

western hemlock 79.3 158.7 1295 4.2

western red cedar 170 1020 2775 4.281

Temporal Adjustments

The formulations for CL and CT are given in BEIS2. Corrections for light and

temperature were made to the standard emission factors using a model developed by Dr.

Brian Lamb of WSU. lxxxiii In the model, microclimate solar radiation levels and leaf

temperatures are estimated as a function of height within the canopy using a simple forest

canopy model and leaf energy balance. During daylight hours, the effects are to attenuate

solar ra diation downward through the canopy, and to decrease leaf temperatures due to

shading in the lower sections of the canopy. In the BEIS2 model, solar radiation is

attenuated through the canopy, but leaf temperatures are assumed to equal above canopy

temper atures. Hourly meteorological information from SeaTac and Portland

International Airports was used to calculate CL and CT. lviii SeaTac data was used north

of UTM 5140 km, and Portland data south of UTM 5140 k m. In general, Portland

temperatures were warmer, resulting in higher emissions.

Spatial Adjustments

Spatial adjustments were not necessary. Data were provided by 1

- km grid.

2015 Methodology

The 2015 inventory was assumed identical to the 1996 invent ory.

Observations

Some land use/species distribution changes will occur between 1996 and 2015. No

analysis was done to assess the effect of such changes. Differences in meteorology may

have a greater effect on biogenic emissions than changes in land us e/species distribution.

A historical study of biogenic emissions was completed by holding land use/species

distribution constant and calculating emissions based on daily meteorological differences in

temperature and light conditions for summers between 19 75 and 1996. lxxxiv The highest

emissions were 25% higher than the lowest emissions.



## Section 2: Hydrocarbon and Nitrogen Oxide Speciation

Hydrocarbon and nitrogen oxide emissions were speciated for input into the

photochemical model CALGRID. The SAPRC97 c hemical mechanism was used to lump

speciated hydrocarbon emissions into several different reactivity categories. Nitrogen

oxide emissions were speciated into nitrogen oxide (NO) and nitrogen dioxide (NO 2 ).82



### Hydrocarbon Speciation

Speciation was accompli shed in one of four ways:

 profiles from the SPECIATE database lxxxv

 profiles from NCASI Technical Bulletin No. 701, Tables 3, 5, 8, 12A, 13A, 15A,

17, and 19A. lxxxvi

 profiles provided by the facility (point sources)

 profiles from the Emission Inventory Improvement P rogram documents lxxxvii

Each pollutant in a profile was assigned to a lumping category according to the

SAPRC97 lumping scheme.



### Nitrogen Oxide Speciation

NOx emissions from combustion sources were assumed to be comprised of 90% nitrogen

oxide (NO) and 10% ni trogen dioxide (NO 2 )



## Section 3: Control Strategy Modeling

To ensure future ozone levels remain under the standard, PSCAA wished to explore

additional emissions control strategies that could be implemented in addition to the new

federal rules for onroad , nonroad, and MACT sources. The additional controls were

viewed as providing a larger safety margin than would occur through the implementation

of the federal regulations. PSCAA convened an advisory stakeholder committee made up

of people representing v arious interests in the Puget Sound region. The committee

developed three control strategy packages, mainly targeting VOC. The strategies are

described below

Package A

Reid Vapor Pressure Lowered to 8.2 psi

Vapor Recovery Enhanced Enforcement

Californi a LEV II, No ZEV

I/M ASM2525, Gas cap check, On

- board diagnostics

Paint Rule

– Voluntary

Package B

Reid Vapor Pressure Lowered to 7.8 psi

Vapor Recovery Enhanced Enforcement

California LEV II, No ZEV

I/M ASM2525, Gas cap check, On

- board diagnostics

Paint Rule

– Mandatory83

Package C

Reid Vapor Pressure Lowered to 8.2 psi

Vapor Recovery Enhanced Enforcement

California LEV II, No ZEV

I/M ASM2525, Gas cap check, On

- board diagnostics

PSCAA, through their contractor (Sierra Research), provided expected reduc tions from

each of the strategies by individual source in tons per day. lxxxviii

, lxxxix The estimates were

converted to a percent reduction and applied to the individual source baseline emissions

estimates. An exception was made to this method in the fuel strategy. The fuel strategy

reductions had been calculated using a baseline RVP of 9.0 psi instead of the actual RVP

value of 8.7 psi. Using the MOBILE5b model, expected emissions reductions from an

8.7 psi baseline to both 8.2 and 7.8 psi control RVPs were calcul ated for each gasoline

vehicle type. The calculated reductions were converted to percent reductions and applied

to the individual vehicle type baseline emissions estimates. The reductions calculated

were also applied to lawn/garden and boat evaporative e missions.

PSCAA provided reductions for both 2007 and 2015; however, modeling difficulties

prevented generating specific 2007 emissions files. Percent reductions for 2007 were

generally comparable to those estimated for 2015. Percent reductions from the 2015

baseline are shown in the table below for individual source categories. The strategy

reductions apply only to the 4

- county PSCAA region.84

Table 20 : Strategy Percent Reductions from 2015 Baseline

Group Category pollutant* Str ategy 1 Strategy 2 Strategy 3

Area Architectural Solvents VOC 23.08 23.08 0.00

Area Industrial Surface Coating VOC 23.08 23.08 0.00

Area WA Gasoline Stage II VOC 54.78 66.27 54.78

Nonroad Gasoline Boats HCEVAP 14.78 26.27 14.78

Nonroad Gasoline Lawn/G arden HCEVAP 14.78 26.27 14.78

Onroad Light Duty Gas Vehicle CO 0.23 0.23 0.23

Onroad Light Duty Gas Vehicle HCEVAP 25.20 35.16 25.20

Onroad Light Duty Gas Vehicle HCEXH 10.68 10.68 10.68

Onroad Light Duty Gas Vehicle HCREST 10.68 10.68 10.68

Onroad L ight Duty Gas Vehicle HCRUN 29.77 45.23 29.77

Onroad Light Duty Gas Vehicle NOx 9.41 9.41 9.41

Onroad Light Duty Gas Truck 1 CO 0.23 0.23 0.23

Onroad Light Duty Gas Truck 1 HCEVAP 22.46 31.89 22.46

Onroad Light Duty Gas Truck 1 HCEXH 10.68 10.68 10.68

Onroad Light Duty Gas Truck 1 HCREST 10.68 10.68 10.68

Onroad Light Duty Gas Truck 1 HCRUN 30.38 46.37 30.38

Onroad Light Duty Gas Truck 1 NOx 9.41 9.41 9.41

Onroad Light Duty Gas Truck 2 CO 0.23 0.23 0.23

Onroad Light Duty Gas Truck 2 HCEVAP 22.49 31 .51 22.49

Onroad Light Duty Gas Truck 2 HCEXH 10.68 10.68 10.68

Onroad Light Duty Gas Truck 2 HCREST 10.68 10.68 10.68

Onroad Light Duty Gas Truck 2 HCRUN 30.62 46.69 30.62

Onroad Light Duty Gas Truck 2 NOx 9.41 9.41 9.41

Onroad Heavy Duty Gas Truck C O 0.00 0.00 0.00

Onroad Heavy Duty Gas Truck HCEVAP 13.77 23.14 13.77

Onroad Heavy Duty Gas Truck HCEXH 0.00 0.00 0.00

Onroad Heavy Duty Gas Truck HCREST 0.00 0.00 0.00

Onroad Heavy Duty Gas Truck HCRUN 19.82 35.63 19.82

Onroad Heavy Duty Gas Truck NO x 0.00 0.00 0.00

Onroad Motorcycle CO 0.00 0.00 0.00

Onroad Motorcycle HCEVAP 11.46 19.80 11.46

Onroad Motorcycle HCEXH 0.00 0.00 0.00

Onroad Motorcycle HCREST 0.00 0.00 0.00

Onroad Motorcycle HCRUN 0.00 0.00 0.00

Onroad Motorcycle NOx 0.00 0.00 0.00

* VOC emissions from onroad consist of 4 components (HCEXH, HCEVAP, HCRUN,

HCREST). While the general breakdown of VOC contribution from the components

varies depending on temperature and vehicle type, it is approximately 41% exhaust, 32%

evaporative, 24% running loss and 3% resting loss.85



## Section 4: Emissions Summaries

Emissions estimates in pounds per day were calculated for 45 individual source

categories for each modeling day and control scenario. The individual source categories

were grouped by major source type (point, area, onroad, nonroad, biogenic) in the

summaries presented below. Summaries are included for 1) the Puget Sound Clean Air

Agency counties, 2) the reduced modeling domain, and 3) the original modeling domain.

The estimates for the individual source categories are available in spreadsheet or database

format upon request.

Table 21 : VOC Emissions and Strategy Reductions, Puget Sound

year strategy Category Emissions in tons per day Percent c hange from 1996 baseline

7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su) 7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su)

1996 base Area 124 125 116 59 na na na na

1996 base Biogenic 433 516 577 618 na na na na

1996 base Nonroad 60 60 153 145 na na na na

1996 base Onroad 192 222 1 86 160 na na na na

1996 base Point 20 20 20 20 na na na na

total 829 943 1051 1002 na na na na

2015 base Area 142 143 132 61 14 14 14 4

2015 base Biogenic 433 516 577 618 0 0 0 0

2015 base Nonroad 38 38 111 109

- 37

- 37

- 27

- 25

2015 b ase Onroad 109 130 109 95

- 43

- 41

- 41

- 41

2015 base Point 20 20 20 20 0 0 0 0

total 742 846 949 904

- 11

- 10

- 10

- 10

2015 A Area 122 122 112 54

- 2

- 2

- 3

- 8

2015 A Biogenic 433 516 577 618 0 0 0 0

2015 A Nonroad 37 37 109 107

- 38

- 38

- 2 9

- 26

2015 A Onroad 91 107 90 78

- 53

- 52

- 52

- 51

2015 A Point 20 20 20 20 0 0 0 0

total 703 802 908 877

- 15

- 15

- 14

- 12

2015 B Area 121 121 112 54

- 3

- 3

- 3

- 9

2015 B Biogenic 433 516 577 618 0 0 0 0

2015 B Nonroad 37 37 108 105

- 38

- 38

- 30

- 27

2015 B Onroad 84 98 82 71

- 56

- 56

- 56

- 56

2015 B Point 20 20 20 20 0 0 0 0

total 695 792 898 868

- 16

- 16

- 15

- 13

2015 C Area 137 137 127 57 10 9 10

- 3

2015 C Biogenic 433 516 577 618 0 0 0 0

2015 C Nonroad 37 37 109 107

- 3 8

- 38

- 29

- 26

2015 C Onroad 91 107 90 78

- 53

- 52

- 52

- 51

2015 C Point 20 20 20 20 0 0 0 0

total 717 817 923 880

- 13

- 13

- 12

- 1286

Table 22 : NOx Emissions and Strategy Reductions, Puget Sound

year strategy Category Emissions in tons per day Percent c hange from 1996 baseline

7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su) 7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su)

1996 base Area 9 9 7 7 na na na na

1996 base Biogenic 8 9 9 10 na na na na

1996 base Nonroad 135 135 117 90 na na na n a

1996 base Onroad 321 346 278 230 na na na na

1996 base Point 7 7 7 6 na na na na

total 480 506 418 343 na na na na

2015 base Area 9 9 7 7 0 0 0 0

2015 base Biogenic 8 9 9 10 0 0 0 0

2015 base Nonroad 111 111 110 95

- 18

- 18

- 6 6

20 15 base Onroad 144 156 126 104

- 55

- 55

- 55

- 55

2015 base Point 7 7 7 6 0 0 0 0

total 279 291 259 223

- 42

- 42

- 38

- 35

2015 A Area 9 9 7 7 0 0 0 0

2015 A Biogenic 8 9 9 10 0 0 0 0

2015 A Nonroad 111 111 110 95

- 18

- 18

- 6 6

2015 A Onroa d 135 147 119 98

- 58

- 58

- 57

- 57

2015 A Point 7 7 7 6 0 0 0 0

total 270 282 252 217

- 44

- 44

- 40

- 37

2015 B Area 9 9 7 7 0 0 0 0

2015 B Biogenic 8 9 9 10 0 0 0 0

2015 B Nonroad 111 111 110 95

- 18

- 18

- 6 6

2015 B Onroad 135 147 119 98

- 58

- 58

- 57

- 57

2015 B Point 7 7 7 6 0 0 0 0

total 270 282 252 217

- 44

- 44

- 40

- 37

2015 C Area 9 9 7 7 0 0 0 0

2015 C Biogenic 8 9 9 10 0 0 0 0

2015 C Nonroad 111 111 110 95

- 18

- 18

- 6 6

2015 C Onroad 135 147 119 98

- 58

- 58

- 57

- 57

2015 C Point 7 7 7 6 0 0 0 0

total 270 282 252 217

- 44

- 44

- 40

- 3787

Table 23 : VOC Emissions and Strategy Reductions, Reduced Modeling Domain

year strategy Category Emissions in tons per day Percent c hange from 1996 baseline

7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su) 7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su)

1996 base Area 167 168 157 80 na na na na

1996 base Biogenic 1904 2268 2540 2708 na na na na

1996 base Nonroad 79 79 215 206 na na na na

1996 base Onroad 265 305 256 2 20 na na na na

1996 base Point 46 46 46 46 na na na na

total 2460 2867 3214 3259 na na na na

2015 base Area 194 195 182 84 16 16 16 5

2015 base Biogenic 1904 2268 2540 2708 0 0 0 0

2015 base Nonroad 49 49 160 157

- 38

- 38

- 26

- 23

2015 base Onroad 162 192 162 140

- 39

- 37

- 37

- 36

2015 base Point 46 46 46 46 0 0 0 0

total 2355 2750 3090 3136

- 4

- 4

- 4

- 4

2015 A Area 174 175 162 78 4 4 4

- 3

2015 A Biogenic 1904 2268 2540 2708 0 0 0 0

2015 A Nonroad 49 49 158 155

- 38

- 3 8

- 27

- 24

2015 A Onroad 144 169 142 123

- 46

- 45

- 44

- 44

2015 A Point 46 46 46 46 0 0 0 0

total 2316 2706 3048 3109

- 6

- 6

- 5

- 5

2015 B Area 173 174 162 77 4 3 3

- 4

2015 B Biogenic 1904 2268 2540 2708 0 0 0 0

2015 B Nonroad 48 48 156 1 54

- 39

- 39

- 27

- 25

2015 B Onroad 137 160 134 116

- 48

- 47

- 48

- 47

2015 B Point 46 46 46 46 0 0 0 0

total 2308 2696 3038 3100

- 6

- 6

- 5

- 5

2015 C Area 189 189 177 80 13 12 13 0

2015 C Biogenic 1904 2268 2540 2708 0 0 0 0

2015 C Nonroad 49 49 158 155

- 38

- 38

- 27

- 24

2015 C Onroad 144 169 142 123

- 46

- 45

- 44

- 44

2015 C Point 46 46 46 46 0 0 0 0

total 2330 2721 3063 3112

- 5

- 5

- 5

- 588

Table 24 : NOx Emissions and Strategy Reductions, Reduced Modeling Domain

year strategy Category Emissions in tons per day Percent c hange from 1996 baseline

7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su) 7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su)

1996 base Area 9 9 7 7 na na na na

1996 base Biogenic 31 35 37 39 na na na na

1996 bas

e Nonroad 155 155 132 95 na na na na

1996 base Onroad 433 467 375 310 na na na na

1996 base Point 75 81 87 89 na na na na

total 703 746 638 540 na na na na

2015 base Area 9 9 7 7 0 0 0 0

2015 base Biogenic 31 35 37 39 0 0 0 0

2015 ba se Nonroad 123 123 124 103

- 21

- 21

- 6 9

2015 base Onroad 215 233 188 156

- 50

- 50

- 50

- 50

2015 base Point 75 81 87 89 0 0 0 0

total 452 480 443 394

- 36

- 36

- 31

- 27

2015 A Area 9 9 7 7 0 0 0 0

2015 A Biogenic 31 35 37 39 0 0 0 0

2015 A Nonroad 123 123 124 103

- 21

- 21

- 6 9

2015 A Onroad 206 224 181 150

- 52

- 52

- 52

- 52

2015 A Point 75 81 87 89 0 0 0 0

total 444 471 436 388

- 37

- 37

- 32

- 28

2015 B Area 9 9 7 7 0 0 0 0

2015 B Biogenic 31 35 37 39 0 0 0 0

2015 B Nonroad 123 123 124 103

- 21

- 21

- 6 9

2015 B Onroad 206 224 181 150

- 52

- 52

- 52

- 52

2015 B Point 75 81 87 89 0 0 0 0

total 444 471 436 388

- 37

- 37

- 32

- 28

2015 C Area 9 9 7 7 0 0 0 0

2015 C Biogenic 31 35 37 39 0 0 0 0

2015 C Nonroad 123 123 124 103

- 21

- 21

- 6 9

2015 C Onroad 206 224 181 150

- 52

- 52

- 52

- 52

2015 C Point 75 81 87 89 0 0 0 0

total 444 471 436 388

- 37

- 37

- 32

- 2889

Table 25 : VOC Emissions and Strategy Reductions, Original Modeling Domain

year st rategy Category Emissions in tons per day Percent c hange from 1996 baseline

7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su) 7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su)

1996 base Area 268 270 253 129 na na na na

1996 base Biogenic 4198 5026 5713 5831 na na na na

1996 base Nonroad 109 109 281 265 na na na na

1996 base Onroad 435 501 425 358 na na na na

1996 base Point 78 79 79 79 na na na na

total 5089 5985 6752 6662 na na na na

2015 base Area 311 313 294 136 16 16 16 5

2015 base Biogenic 419 8 5026 5713 5831 0 0 0 0

2015 base Nonroad 62 62 200 196

- 43

- 43

- 29

- 26

2015 base Onroad 256 302 259 219

- 41

- 40

- 39

- 39

2015 base Point 78 79 79 79 0 0 0 0

total 4906 5782 6545 6460

- 4

- 3

- 3

- 3

2015 A Area 291 292 274 129 8 8 8 0

2 015 A Biogenic 4198 5026 5713 5831 0 0 0 0

2015 A Nonroad 62 62 198 193

- 43

- 43

- 30

- 27

2015 A Onroad 237 279 240 202

- 45

- 44

- 44

- 44

2015 A Point 78 79 79 79 0 0 0 0

total 4867 5738 6504 6434

- 4

- 4

- 4

- 3

2015 B Area 290 291 274 128 8 8 8

- 1

2015 B Biogenic 4198 5026 5713 5831 0 0 0 0

2015 B Nonroad 62 62 196 192

- 44

- 44

- 30

- 28

2015 B Onroad 230 270 232 194

- 47

- 46

- 46

- 46

2015 B Point 78 79 79 79 0 0 0 0

total 4859 5728 6494 6425

- 5

- 4

- 4

- 4

2015 C Area 306 307 289 132 14 14 14 2

2015 C Biogenic 4198 5026 5713 5831 0 0 0 0

2015 C Nonroad 62 62 198 193

- 43

- 43

- 30

- 27

2015 C Onroad 237 279 240 202

- 45

- 44

- 44

- 44

2015 C Point 78 79 79 79 0 0 0 0

total 4881 5753 6519 6437

- 4

- 4

- 3

- 390

Table 26 : NOx Emissions and Strategy Reductions, Original Modeling Domain

year strategy Category Emissions in tons per day Percent c hange from 1996 baseline

7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su) 7/ 11 (Th) 7/ 12 (Fr) 7/ 13 (Sa) 7/ 14 (Su)

1996 b ase Area 9 9 7 7 na na na na

1996 base Biogenic 54 60 65 66 na na na na

1996 base Nonroad 200 200 156 97 na na na na

1996 base Onroad 703 757 609 504 na na na na

1996 base Point 119 123 130 132 na na na na

total 1084 1150 967 807 na na na na

2015 base Area 9 9 7 7 0 0 0 0

2015 base Biogenic 54 60 65 66 0 0 0 0

2015 base Nonroad 147 147 139 106

- 27

- 27

- 11 10

2015 base Onroad 346 375 303 252

- 51

- 50

- 50

- 50

2015 base Point 119 123 130 132 0 0 0 0

total 674 714 644 563

- 38

- 38

- 33

- 30

2015 A Area 9 9 7 7 0 0 0 0

2015 A Biogenic 54 60 65 66 0 0 0 0

2015 A Nonroad 147 147 139 106

- 27

- 27

- 11 10

2015 A Onroad 337 366 296 246

- 52

- 52

- 51

- 51

2015 A Point 119 123 130 132 0 0 0 0

total 666 705 637 557

- 39

- 39

- 34

- 31

2015 B Area 9 9 7 7 0 0 0 0

2015 B Biogenic 54 60 65 66 0 0 0 0

2015 B Nonroad 147 147 139 106

- 27

- 27

- 11 10

2015 B Onroad 337 366 296 246

- 52

- 52

- 51

- 51

2015 B Point 119 123 130 132 0 0 0 0

total 666 705 637 557

- 39

- 39

- 34

- 31

2015 C Area 9 9 7 7 0 0 0 0

2015 C Biogenic 54 60 65 66 0 0 0 0

2015 C Nonroad 147 147 139 106

- 27

- 27

- 11 10

2015 C Onroad 337 366 296 246

- 52

- 52

- 51

- 51

2015 C Point 119 123 130 132 0 0 0 0

total 666 705 637 557

- 39

- 39

- 34

- 3191



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