### Adopted August 2 0 th 

### , 2010



# Alaska Department of Environmental Conservation



# Amendments to:

State Air Qua l ity Control Plan

Vol. III: Appendices

Appendices to:

Volume II, Section II. Air Quality Control Program; Volume II, Section III.A Statewide C arbon Monoxide Control Program ; Volume II, Section III.B Anchorage Transportation Control Program ; and Volume II, Section III .D.2 Eagle River Limited Maintenance Plan



# Adopted

August 2 0 th 

# , 20 1 0

### Adopted August 20 th 

### , 2010



### The State of Alaska’s State Air Quality Control Plan Volume III

, Appendix to Volume II of this

p lan , is amended to include the following documents:

Volume II, Section II. Air Quality Control Program is amended by removing the following regulations:

• 18 AAC 5 0 Air Quality Control as amended through April 1 st 

### , 2010; and replacing them

with the following regulations:

• 18 AAC 50 Air Quality Control as amended through August 20 th 

### , 2010.

Volume II, Section III.B Anchorage Transportation Control Program, adopted into the State Air Quality Control Plan August 20 th 

### , 2010, is amended as follows:

• Appendix III.B.1 is amended by adding the following documents:

· Anchorage Assembly Resolution No. 2010-174, dated June 8, 2010, a resolution of

the Municipality of Anchorage adopting revisions to the Anchorage Carbon

Monoxide Maintenance Plan (dated May 13, 2010 ) that delete s the commitment to I/M as a primary CO control measure.

• Appendix III.B.3 is amended by adding the following document:

· Anchorage 2007 Carbon Monoxide Emission Inventory and 2007-2023 Emission

Projections, prepared by the Municipality of Anchorage, dated March 2010.

• Appendix III.B.6 is amended by adding the following document:

· Analysis of Probability of Complying with the N ational Ambient Air Quality

Standard for Carbon Monoxide in Anchorage between 2007 and 2023, prepared by the Municipality of Anchorage, dated March 2010.

• Appendix III.B.9 is amended by adding the following document:

· Municipality of Anchorage Ordina nce No. 20 10-35(S), adopted by the Anchorage

Assembly May 11 , 2010 ; a n ordinance repealing Anchorage Municipal Code Chapter s 15.80 and 15.85, relating to motor vehicle emissions inspection and maintenance (I/M) program; amending Chapter 15.80 and 15.85 in the interim to revise existing fees and program administration; amending section 9.30.155 to repeal reference to I/M certification; and amending the fine schedule at section 14.60.030.

• Appendix III.B.10 is amended by adding the following documents:

· Estimation of Background Carbon Monoxide Concentrations for Anchorage ProjectLev el Conformity Analyses, no date; and

# Alaska Department of Environmental Conservation

Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III. B.1

“Anchorage Assembly Resolution No. 2010-174”



# Adopted

August 20 th 

# , 20 10

# Alaska Department of Environmental Conservation



# Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III. B. 3

“ Anchorage 2007 Carbon Monoxide Emission Inventory and 2007-2023 Emission Projections , dated March 2010”



# Adopted

August 20 th 

# , 20 10

### A dopted A ugust 20 th 

### , 2010



# Anchorage 200 7 Carbon Monoxide Emission Inventory and
200 7

– 2023 Emission Projections

Municipality of Anchorage Air Quality Program

Environmental Services Division Department of Health and Human Services

March 2010

### A dopted A ugust 20 th 

### , 2010



## Preface

This document discusses the methodology used to prepare the base year 200 7 CO emission inventory and emission projections for the 200 7 – 2023 period covered by the Anchorage maintenance plan.

### A dopted A ugust 20 th 

### , 2010

i 

### Table of Contents



### INTRODUCTION

................................................................................................ .................................. 1

OVERVIEW HYBRID EMIS SION ESTIMATION METHODOLOGY

................................................. 1

INVENTORY BOUNDARY

.................................................................................................................. 1

ANCHORAGE TRANSPORTATION MODEL AND INVENTORY GRID SYSTEM

......................... 3

TIME

- OF

- DAY ESTIMATES OF CO EMISSIONS

............................................................................. 5

MOTOR VEHICLE EMISSI ONS

......................................................................................................... 6

Estimation of Warm

- up Idle Emissions

....................................................................................................................... 7

Estimation of On

- Road Travel Emissions

................................................................................................................. 13



### AIRCRAFT OPERATION E MISSIONS

............................................................................................ 18

RESIDENTIAL WOOD BUR NING EMISSIONS

.............................................................................. 20

EMISSIONS FROM NATUR AL GAS COMBUSTION FO R SPACE HEATING

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

OTHER MISCELLANEOUS SOURCES

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

Use of NONROAD to Estimate Emissions from Snowmobiles, Snow Blowers, Welders, Air Compressors

and Other Miscellaneous Sources

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

Railroad Emissions

................................................................................................ ..................................................... 25

Marine Vessel Emissions

................................................................................................ ........................................... 26



### EMISSIONS FROM POINT SOURCES

............................................................................................ 27

Source Descriptions and Emission Estimation Information

..................................................................................... 28

Summary of Point Source Emissions

........................................................................................................................ 28



### EMISSIONS SUMMARY

................................................................................................................... 29

2 007 Base year Area

- wide CO Inventory 29

Projected Area

- wide CO Emissions (2007

- 2023) ...............

...... .........................................................29



### COMPILATION OF MICRO

- AREA INVENTORY FOR T URNAGAIN MONITORING STATION

. 31

REFERENCES

................................................................................................ .......................................

### A dopted A ugust 20 th 

### , 2010

i List of Figures

Figure 1 Anchorage Nonattainment Boundary Compared with Expanded Inventory Area

........................ 2

Figure 2 Anchorage Inventory Grid System

.................................................................................................. 4

Figure 3 MOBILE6 Emission Factors as a Function of Speed and Thermal State

.................................. 16

Figure 4 Projected Area

- wide CO Emissions (2007

- 2023)

..................................................................... 30

Figure 5 CO emissions distribution in Anchorage

..................................................................................... 32

Figure 6 Aerial photo of Turnagain micro

- inventory area boundary

........................................................ 33

Figure 7 Projected CO Emissions in Turnagain CO Micro

- inventory Area 2007 – 2023

........................ 35

Figure 8 CO emission rate by time

- of

- day in Turnagain CO micro

- inventory area (2007 )

...................... 36

### A dopted A ugust 20 th 

### , 2010

ii List of Tables

Table 1 CO Emission Inventory Time Periods and Apportionment of Characteristic Source Activity 5

Table 2 Factors involved in computation of thermal state of vehicle at critic al points in a vehicle trip. 7

Table 3 Assumed warm

- up idle duration by trip purpose and origin 8

Table 4 Average soak time prior to trip start 9

Table 5 Idle emission look up table for calendar year 2000 10

Table 6 Idle CO adjustment factors 1 1

Tab le 7 Block heater plug

- in rates (%) by time

- of

- day, trip origin and trip purpose after media campaign promoting block heater use 1 2

Table 8 Estimated warm

- up idle emissions by time

- of

- day Anchorage inventory area 1 3

Table 9 Speed Adjustment Factors 15

Table 1 0 Soak distributions used in MOBILE6 modeling with comparable operating mode fractions used in MOBILE5b/Cold CO modeling 16

Table 1 1 On

- road travel emissions by time

- of

- day 1 8

Table 12 24

- hour CO emissions estimates from aircraft at ANC and Mer rill Field in 2002 19

Table 1 3 Projected aircraft operations and CO emissions at ANC 19

Table 1 4 Projected Aircraft Operations and CO Emissions at Merrill Field Airport 2 0

Table 1 5 Estimation of residential wood burning CO emission factors for Anchorage 21

Table 16 Estimated Anchorage

- wide 24

- hour CO emissions from residential wood burning 21

Table 17 Methods of Home Heating in Anchorage (ASK Marketing & Research, 1990) 22

Table 18 Peak winter season natural gas consumption rates and CO emission rates in Anchorage (1990) 2 2

Table 19 CO Emissions from natural gas combustion (excludes power generation) 2 3

Table 20 Estimation of NONROAD CO emissions in 2007 24

Table 21 CO emissions from NONROAD sources (2007

- 2023) 25

Table 22 Alaska Railroad emission estimates 2007

- 2023 2 6

Table 23 Estimated CO emissions from the Port of Anchorage 2 7

Table 24 Point Source CO Emissions Summary 2 8

Table 25 Sources of Anchorage CO emissions in 2007 base year in Anchorage inventory area 29

Table 26 Total CO emitted during typical 24

- hour winter day in the Anchorage bowl inventory area 30

Table 27 Sources of CO Emissions in Turnagain Micro

- inventory Area

2007 Base Year 34

Table 28 Total CO emitted during typical 24

- hour winter day when CO is elevated in Turnagain mi cro

- inventory area 34

### Adopted August 20 th 

### , 2010

1 

### Introduction

T his document provide s technical support and justification for the methods used to prepare the

maintenance demonstration for Anchorage

, submitted as a revision to the Alaska State Implementation Plan (SIP).

As part of the plan revision, a comprehensive inventory of the sources of CO emissions for base year

200 7 was compiled

. Historically

, violations of the CO NAAQS have occur red most often on winter weekdays, therefore a 24

- hour inventory was prepared that reflects ambient temperatures, traffic volumes and other emission source activity levels experienced on a typical winter “ design day” in 200 7.

In April 2007 an air quality conformity analysis was prepared when the Anchorage Long Range Transportation Plan was amende d to include the Knik Arm Crossing. The most recent population, employment, and land use assumptions and forecasts were used in the development of this analysis. Specific forecasts were developed for analysis years 2007, 2017 and 2027. This demographic data was used to generate the 2007 base year CO inventory for the maintenance plan revisions. In addition this data was used directly or interpolated to generate forecasts for 200 9 , 20 11 , 2013, 2015, 2017, 2019,

2021 and 2023

. The methodology employed to develop the 200 7 base year emission inventory and projections through 2023 was very similar to that employed to develop previous emission inventor ies for the CO attainment plan in 2000 and the maintenance plan in 2004.



### Inventory Boundary

The Anchorage nonattainment area boundary was established in 1978. Upon EPA ’s approval of the maintenance plan in 2004

, the area encompassed by this boundary became the maintenance area. The inventory boundary contains this maintenance area plus some additional area t o the south and west where significant residential and commercial growth has occurred over the past two decades. For this reason, the inventory area was expanded slightly to encompass areas not included in the nonattainment

area. The boundar y of the main tenance area is shown along with the expanded inventory area in Figure 1. The inventory area encompasses approximately 200 square kilometers of the Anchorage

Bowl.

### A dopted A ugust 20 th 

### , 2010

2 Figure 1.

Anchorage Maintenance Area Boundary with Expanded Inventory Area

### A dopted A ugust 20 th 

### , 2010

3 

### Anchorag e Transportation Model and Inventory Grid System

The CO inventor y w as based in large part on traffic activity outputs from the Anchorage Transportation Model. The Anchorage Transportation Model is used by AMATS * 

### and the Municipality of Anchorage to

evalua te transportation plans and programs. It was validated against measured traffic volumes in base year 2002 and utilizes the latest planning assumptions to forecast future travel activity

. . The model was developed using TransCAD travel demand modeling software. Because TransCAD is a GIS

- based model, post

- processing software could be used to overlay a grid system on the inventory area. The
post

- processor was used to disaggregate the inventory area into grid cells, each one square kilometer in

size.

Transpo rtation activity estimates (e.g., vehicle miles of travel, number of trip starts, and vehicle speeds) were produced for each of the cells. The grid location of every roadway link in the transportation network is known. Thus, the attributes of a particular roadway link (e.g., traffic volume, speed, and prior travel time) c ould be assigned to a particular grid. If a roadway link crosse d the boundary between two or more grids, its attributes were assigned to the appropriate grid in relation to the proportio n of the length of link contained in each grid. In other words, if 80% of a roadway link lies within a particular grid, 80% of the vehicle travel is assigned to that grid and 20% to the other grid.

Demographic information (population, number of dwelling units, income, and employment information) is collected by census tract. Because most census tracts in Anchorage are larger in size than the one kilometer grids, the demographic characteristics of a particular grid had to be estimated from lower

resoluti on census tract data. If, for example, a particular census tract was comprised of three one kilometer grids, the population and employment in that census tract was divided equally among the three grids contained in the census tract. This demographic information was helpful in developing gridded

estimates of non

- vehicular source activities, like wood burning and space heating where the amount of activity (i.e. wood burning or residential space heating) was assumed to be related to the number of dwellings i n a grid.

Emissions from other area sources such as Ted Stevens Anchorage International Airport, Merrill Field, marine vessel operations at the Port of Anchorage and railroad activity in the rail yard and haul routes were assigned to the grids where the activity takes place. Similarly, emissions from point sources such as electrical power plants were assigned to the grid where the source is located.

The Anchorage emission inventory grid system is shown in Figure 2.

* AMATS stands for Anchorage Metropolitan Area Transportation Solutions. AMATS is the designated metropolitan planning organization for the Municipality of Anchorage. It is responsible for prioritizing federal transportation funding. It is also responsible for air quality planning in the Municipality.

### A dopted A ugust 20 th 

### , 2010

4 Figure 2

Anchorage Inventory Grid Syst em

### A dopted A ugust 20 th 

### , 2010

5 

### Overview of Hybrid Emission Estimation Methodology

Between 1997 – 2003, the Municipality of Anchorage (MOA), Fairbanks North Star Borough and Alaska Department of Environmental Conservation (ADEC) invested a great deal of effort quantifying the

so urces of CO emissions in Anchorage and Fairbanks, particularly those from cold starts and warm

- up idling. Sierra Research, working under contract with ADEC, performed cold temperature emission tests

on 35 vehicle s in Anchorage and Fairbanks during the win ters of 1998

- 99 and 2000

- 2001. This testing

showed that cold start /warm

- up idle emissions are a very important source of CO emissions and us ing engine block heaters is an effective way to reduce emissions.

MOBILE6 alone would ordinarily be used to quantify vehicle emissions. However, a conventional MOBILE6 approach to computing vehicle emission rates does not adequately address the emissions impact of extended warm

- up idling at the beginning of a trip nor does it provide a means to estimate the

emissio n reductions resulting from engine block heater use. To address these limitations, a “hybrid” approach was developed to quantify motor vehicle emissions. This hybrid approach utilizes idle emissions data generated from the Sierra Research emission testin g 1



### The MOBILE6 model was run with supplemental speed (SFTP) correction factors disabled. The purpose of the SFTP speed correction fact ors is to reflect the increase in emissions that occur during aggressive driving (e.g. hard accelerations and decelerations). During the winter of 1999

- 2000, Sierra Research performed a study in Anchorage and Fairbanks that showed that winter driving in Alaska had almost none of the high speed, high acceleration rate driving that is represented by the SFTP speed

correction factors. to estimate warm

- up idle emissions while MOBILE6 is used to estimate the emissions that occur during the travel mode.

2 

### For this reason, MOBILE6 was run with these correction factors disabled



### Time

- of

- Day Estimates of CO Emissions

Separate es timates of mobile CO emissions were prepared for the morning commute ( 7 a. m. – 9 a.m.), the evening commute (3 p.m.
– 6 p.m.) and combined off

- peak periods (6 p.m.
– 7 a. m. and 9 a.m . – 3 p.m.) . These estimates relied on time

- of

- day activity estimates (e .g., number of trip starts and VMT) generated by the Anchorage Transportation Model. A 24

- hour inventory was compiled by summing the separate emission contributions from each time period.

Activity estimates for non

- vehicular sources were available on a 24

- hour basis only, however. Time

- of

day estimates had to be developed from these 24

- hour values. For some sources (e.g. airport, natural gas combustion), activity was assumed to be continuous throughout the day and emissions were apportioned accordingly

. F ireplace and wood stove usage is more likely to occur in the evening after 6

p.m. For this reason, 9 0% of all wood burning activity was assumed to take place during the off peak time period.

Table 1 shows the specific time periods inventoried and gives examples of the types and levels of activity characteristic of those time periods. (Note that the 2

- hour AM peak comprises 8.3% of a 24

- hour day, the 3

- hour PM peak comprises 12.5% of the day, and the 19

- hour off peak period comprise 79.2% of the day. )

### A dopted A ugust 20 th 

### , 2010

6 Table 1. CO emission inventory time periods and apportionment of characteristic source activity % of activity occurring within each time period

Source Category AM Peak.

7 a. m.

– 9 a.m. PM Peak. 3 p.m.

– 6 p.m. Off

- Peak periods

9 a.m. – 3 p.m

. 6 p.m

. – 7 a. m. Comments

motor vehicle idle and

travel emissions From model (~16%) From model (~27%) From model (~57%) Travel activity higher in AM and PM peak periods

Residential wood burning 3 .0% 7 .0% 9 0.0% Most burning in evening

space heating 8.3 % 12

. 5 % 79.2 % Evenly

distributed through day

Ted Stevens Int'l Airport 8.3% 12.5% 79.2 % Evenly

distributed through day

Merrill Field 8.3% 12.5% 79.2 % Evenly

distributed through day

Miscellaneous /

Other * 8.3% 12.5% 79.2% Evenly

distributed through day

Poi nt Sources 8.3% 12.5% 79.2% Evenly

distributed through day

* Miscellaneous/other emissions are comprised largely of sources related to construction and

industrial activity like generator sets, welding activities, and pumps.



### Motor Vehicle Emissions

A great deal of effort was devoted to developing a credible highway motor vehicle emissions inventory

that reflect ed real world conditions and driver behavior in Anchorage. Unlike the inventories prepared as part of previous air quality attainment plans, this inventory explicitly quantifies the CO emissions that occur during cold starts and lengthy warm

- up idles that precede many vehicle trips. Separate estimates

were made of the emissions associated with the initial warm

- up idle period and the after

- idl e, “on

- road” trip period. Sample calculations for warm

- up idle and on

- road emissions are available by request along with copies of the MOBILE6 input files used to compute on

- road emission factors for analys is years

. As discussed earlier, a hybrid approach utilizing locally

- generated cold temperature idle emission data in

combination with the MOBILE 6 model was employed to compute motor vehicle emissions. An essential element of this hybrid approach is the use of “thermal state tracking” to determine how wa rmed up a vehicle is at three critical points in the vehicle trip. These three critical points and the important factors involved in computing the thermal state of the vehicles operating in each of these three point s in the trip

are described in Table 2.

### A dopted A ugust 20 th 

### , 2010

7 Table 2. Factors involved in computation of thermal state of vehicle at critical points in a vehicle trip.



### Critical point in trip Factors involved in computation of

thermal state of vehicle

1. Immediately prior to start

- up H ow long, and at what temperature the vehicle has been parked before it was started (i.e. length of cold soak)

2. After warm

- up idle, immediately prior to travel portion of trip Length of cold soak and subsequent idle

3. During travel portion of trip (within grid of interest) Durati on of prior cold soak and warm

- up idle, length of trip (miles) and average speed.

Intuitively, the effect of each of the three factors on the thermal state or degree of warmth of a vehicle is fairly obvious. One would expect that vehicles that are parked for long periods of time would be in a colder thermal state than those parked for short periods; a long warm

- up idle period would result in a warmer thermal state than a short idle; and long travel time at a high rate of speed would result in a

warmer ve hicle than a short trip at slow speeds

. An elaborate spreadsheet was developed that incorporates the results of the thermal state calculations described above along with post processor outputs from the Anchorage Transportation Model, outputs from the MOBI LE6 model

, warm

- up idle emission data from research conducted in Anchorage and Fairbanks and from locally

- derived information on driver idling behavior. This spreadsheet allowed for separate computation of warm

- up idle emissions and on

- road trip emissions

.

Estimation of Warm

- up Idle Emissions

Three key sources of information were required to estimate idle emissions: (1) the duration of the idle period preceding the trip; (2) the amount of time since the vehicle last operated and has been cooling or

“soak ing” in ambient conditions; and (3) the idle emission rate. The idle emission rate is largely a function of engine and catalyst temperature and thus is dependent on idle duration and soak time.

Idle Duration

Idle duration was quantified by the MOA Air Quality Program during the winter of 1997

- 98 as part of the Anchorage Driver Behavior Study. 3 

### The objective of this field study was to observe and document winter season driver idling behavior prior to the beginning of a trip. Over 1300 start up idles were observed and documented at various times and locations in Anchorage. In addition to documenting the duration of each of the idles, the trip origin (e.g., home, work, shopping, etc.), time of day, ambient temperature, weather and windshield icing conditions were also recorded. One important objective of the study was to develop estimates of median idle duration by trip purpose *

* The Anchorage Transportation model now categorizes all travel into eight trip purposes instead of three. The original three trip categori es (HBW

=:home

- based work , HBO =home based other , and NHB = non home

- based have been expanded into seven separate categories. The model now provides estimates of the number of trip starts in the following categories: (1) HBW = home

- based work, (2) HB SCH = home

- based school, (3) HBS = home

- based shopping, (4) HBO = home

- based other, (5) NHBW = non home

- based work, (6) NHBNW = non home

- based non

- work ; and (7) TRK = truck . 

### and time

- of

- day. Because drivers were not questioned, the trip purpose was not known. Nevertheless, a methodology was developed to use data collected in the study to estimate idle duration for home

- based work (HBW),

home

- based other (HBO) and non home

- based (NHB) trips for each time

- of

- day. The methodology used to develop these estimates is described in Appendices A and C of the Anchorage Driver Behavior Study. The idle duration assumptions used to develop CO inventories for 200 7, 200 9 , 20 11 , 2013,

### A dopted A ugust 20 th 

### , 2010

8 

### 2015, 2017, 2 019, 2021 and 20 2 3 are shown in Table 3 . The longest idle duration was associated with

home

- based trips (work, school and shopping) during the 7 a. m. – 9 a.m. time period. *

Table 3.

Assumed warm

- up idle duration by trip purpose and origin (in minutes)

Trip Type Trip origin AM Peak

7 a. m.

– 9 a.m. PM Peak 3 p.m.

– 6 p.m. Off

- Peak P eriods

9 a.m. – 3 p.m

. 6 p.m.

– 7 a. m.

H ome

- based

work home 7 3 3

work 3 1 3

Home

- based

school home 7 2 2

school 1 1 1

Home

- based shopping home 7 2 1
shopping 1 1 1

Home

- based

other home 7 2 2

other 1 1 1

Non home

- based

work NA 3 3 2

Non home

based, non

- work NA 1 1 1

Truck NA 3 3 1



### It should be noted that during the ten years since this survey data was collected, a number of changes have occurred that could have changed idling behavior among Anchorage drivers. One change of particular note is the increasing proliferation of remote “auto start devices” that allow drivers to start their vehicles remotely. Recent survey data suggest that approximately 27% of Anchorage vehicles are now equipped with such devices. The effect of auto starts on idle times in Anchorage has not been

studi ed. Even if the use of auto starts has increased average idle duration, the effect on overall CO emissions is likely small. A 2001 study performed by Sierra Research examined the effect of idle

duration on the CO emissions that occur over the course of a typical vehicle trip of 7.3 miles. 4



### Soak Time Sierra found that overall CO emissions for trips preceded by a 2

- minute idle (281.4 grams) were greater than those preceded by a 15

- minute idle (246.7 grams). Thus, it is possible that the use of remote starters

may a ctually reduce overall CO emissions is the idle time following a cold start is limited to 15 minutes

or less

. Overall trip emissions would increase, however, if idle times following an auto start were extended to 20 minutes or more. More recently S ierra examined the possible impact of auto starts on CO emissions in Fairbanks, Alaska where the proportion of vehicle equipped with these devices approaches 50%. They concluded that if drivers opted to use these devices for extended idling (20 minutes or longer) CO emissions could increase by 0.18 tons per day. This amounts to an increase of

about 0.5% in total CO emissions in Fairbanks.

Vehicle emissions of CO are highest just after startup and decrease rapidly as the engine warms. The emissions that occur during start up are largely a function of how long the engine has been shut off and cooling at ambient temperatures. Because these data suggest that soak time is a critical factor in determining vehicle CO emissions, it was important to develop credible estimates of soak times in Anchorage as part of the CO emission inventory preparation.

Fortunately, information was available from a local travel survey that allowed average vehicle soak times to be estimated for the a.m., mid

- day, p.m. and night periods by trip purpose. Hellenthal and Associates

* 35% of home

- based trips were assumed to begin with cars parked in garages and 65% outside. Warm

- up idle time for cars parked inside was not quantified in the idling study but was assumed to be 30 seconds. The idle times shown in Table 3 reflect the weighted av erage of idle times for
garage and outside

- parked vehicles.

### A dopted A ugust 20 th 

### , 2010

9 

### conducted a household travel behavior survey of 1,548 Anchorage households between February 25 and April 12, 1992. 5

Table 4. Average soak time prior to trip start (in hours) 

### Soak times were estimated by examining travel logs from the survey. Drivers

recorded the time when each trip began and ended. The time elapsed between the end of one trip and the beginning of the succeeding trip was presumed to be equal to the soak time for that driver’s vehicle. Estimates of average soak times derived from the Hellenthal travel behavior survey are shown in Table 3. Morning home

- based trips for work, school and shopping have the longest average soak time (12 hours) while NHB trips and home

- based trips originating at locations other than home have the shortest average soak time ( one hour).

Trip Type Trip origin AM Peak

7 a. m.

– 9 a.m. PM Peak 3 p.m.

– 6 p.m. Off

- Peak Periods

9 a.m. – 3 p.m. 6 p.m.

– 7 a. m.

Home

- based work home 12 3 3

work 5 5 5

Home

- based s chool home 12 2 2

school 0.5 0.5 0.5

Home

- based shopping home 12 2 2
shopping 1 0.5 0.5

Home

- based other home 12 2 2

other 1 1 1

Non home

- based work NA 4 5 3

Non home

- based, non

- work NA 1 1 1

Truck NA 2 2 2



### Estimation of Idle Emissions as a Function of Idle Duration and Soak Time

Emission data from the testing Sierra Research conducted in Anchorage and Fairbanks during the

winter s of 1998

- 99 and 2000

- 2001 were used to construct a lookup table that provided an estimate of

the warm

- up idle emiss ions (in grams CO per start) as a function of idle duration and soak time. CO and HC emissions were measured during the first 20 minutes following a cold start. The values in the look

up table were revised slightly from th ose used in the Year 2000 attainment plan to reflect the supplemental data collected by Sierra Research in the winter of 2000

- 2001. The revised lookup table is

shown in Table 5. The values were utilized in the emission inventory spreadsheet to compute idle

emissions.

No data were collected from commercial trucks during the idle study. These comprise a small part of the total vehicle population and are largely low

- emitting heavy

- duty diesel vehicles (HDDV). These vehicles were assumed to emit CO at 30% the rate of the average light dut y vehicles (LDVs) that make up the majority of the Anchorage vehicle population. This assumption is roughly consistent with MOBILE 6

model estimates for HDDV versus LDV emission factors.

### A dopted A ugust 20 th 

### , 2010

10 Table 5. Idle emission look up table for calendar year 2000 (with e thanol

- blended gasoline) CO emissions (in grams per start) as a function of soak time and idle duration

Pre-Soak

Time (hrs) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.00 1.6 3.2 4.8 6.4 8.0 9.5 11.1 12.7 14.3 15.9 17.4 19.0 20.6 22.2 23.8

0.17 1.9 3.5 5.1 6.7 8.3 9.9 11.4 13.0 14.6 16.2 17.8 19.4 20.9 22.5 24.1

0.25 2.4 4.0 5.6 7.2 8.7 10.3 11.9 13.5 15.1 16.7 18.2 19.8 21.4 23.0 24.6

0.50 4.8 6.4 8.0 9.6 11.1 12.7 14.3 15.9 17.5 19.1 20.6 22.2 23.8 25.4 27.0

1.00 11.1 14.3 15.9 17.5 19.1 20.7 22.3 23.8 25.4 27.0 28.6 30.2 31.7 33.3 34.9

1.50 16.4 23.8 26.1 27.7 29.3 30.8 32.4 34.0 35.6 37.2 38.8 40.3 41.9 43.5 45.1

2.00 20.8 32.6 36.7 38.5 40.1 41.7 43.3 44.9 46.4 48.0 49.6 51.2 52.8 54.4 55.9

2.50 24.5 39.9 46.6 49.1 50.7 52.3 53.9 55.5 57.1 58.7 60.2 61.8 63.4 65.0 66.6

3.00 27.5 45.9 55.3 58.9 60.6 62.2 63.8 65.4 67.0 68.6 70.1 71.7 73.3 74.9 76.5

4.00 32.0 55.0 68.8 74.8 77.5 79.1 80.7 82.3 83.8 85.4 87.0 88.6 90.2 91.8 93.3

5.00 35.1 61.1 78.0 86.3 90.0 91.9 93.5 95.1 96.6 98.2 99.8 101.4 103.0 104.6 106.1

6.00 37.2 65.3 84.3 94.4 99.1 101.2 102.8 104.4 106.0 107.6 109.2 110.7 112.3 113.9 115.5

7.00 38.6 68.2 88.6 100.0 105.3 107.8 109.5 111.0 112.6 114.2 115.8 117.4 119.0 120.5 122.1

8.00 39.6 70.1 91.5 103.8 109.7 112.5 114.1 115.7 117.3 118.9 120.4 122.0 123.6 125.2 126.8

9.00 40.3 71.4 93.5 106.4 112.7 115.6 117.3 118.9 120.5 122.1 123.7 125.3 126.8 128.4 130.0

10.00 40.7 72.3 94.8 108.2 114.7 117.8 119.6 121.2 122.7 124.3 125.9 127.5 129.1 130.6 132.2

12.00 41.2 73.4 96.4 110.3 117.0 120.4 122.1 123.7 125.3 126.9 128.5 130.1 131.6 133.2 134.8 Revised Year 2000 Idle Emissions (assumes 2.7% EtOH and Year 2000 Anchorage I/M)

Initial Idle Time (min)



### The cold temperature idle data collected by Sierra Research provides a “snapshot

- in

- time estimate” of

cold start emissions f rom the fleet in 2000

- 2001. Since this data was collected, a number of changes

have occurred that have and will continue to change fleet

- wide idle emissions factors. The ethanol

blended gasoline program , in place at the time that Sierra Research collecte d th is idle emission data, was discontinued in 2003. The fleet is being continually replaced with newer and presumably cleaner

vehicles. The net effect of this fleet turnover is a continual reduction in the idle CO emission rate over

time. The latest revision of the SIP deletes the commitment to I/M and for the purposes of this analysis

the benefits of I/M are assumed to be zero in 20 11. As a consequence the idle emission rate will

increase slightly

. *



### The effect of all these changes on idle emissions ca n be modeled using MOBILE6. Conformity analysis guidance recommends using MOBILE6 emission factors at 2.5 mph to estimate idle emissions. Thus,

p redicted reductions in the MOBILE6 emission factor at 2.5 mph were used to adjust the initial 2000

- 2001 idle data from Sierra. MOBILE6 can be used to estimate the idle CO reduction from fleet turnover

on overall idle CO emission rates over time relative to the 2000

- 2001 period when the Sierra data was collected. MOBILE6 can also be configured to help estimate t he effect of CO controls such as the

ethanol

- blended gasoline program (which was discontinued in 2003 ) and of the I/ M program on id le

emissions . The hybrid model utilizes a look

- up table derived from MOBILE6 model runs that contains adjustment factors tha t account for fleet turnover, and changes in ethanol gasoline and I/M requirements. These adjustment factors are shown in Table 6. For example, in order to determine the idle emission factor for a cold start trip (soak tim e > one hour) in the year 2009 ( assuming that the I/M

program is in place the ethanol

- blended gasoline program is not reinstituted ) , the data and Table 5 would be multiplied by an adjustment factor of 0. 5 94 to yield the idle emission rate.

Thus, idle emission s for a trip with a 3 minute idle following a 10

- hour cold soak is computed as follows :

2009 idle EF = (Yr 2000 Idle EF for 3 min idle after 10 hr cold soak) x (adj factor for 20 09 )

= 94.8 grams x 0. 5 94 = 56.3 grams

* Extendi ng the new car grace period from four to six years is expected to diminish the effectiveness of I/M in reducing CO
emissions during idling by about 15%.

### A dopted A ugust 20 th 

### , 2010

11 Table 6.

Idle CO a djustment f actors

Es timation of idle CO based on 2000

- 2001 Sierra Data

Warm Start Idle (Cold Soaks < one hour) Cold Start Idle (Cold Soaks >= one hour)

Year w IM & oxy w IM,

no oxy no IM,

no oxy Year w IM & oxy w IM,

no oxy no IM,

no oxy

2000 1.00 1.15 1.39 2000 1 .00 1.15 1.39

2007 0.64 0.70 0.82 2007 0.61 0.64 0.83

2008 0.58 0.63 0.74 2008 0.55 0.61 0.75

2009 0.55 0.59 0.71 2009 0.52 0.57 0.72

2010 0.53 0.57 0.68 2010 0.50 0.55 0.69

2011 0.51 0.54 0.65 2011 0.48 0.52 0.66

2012 0.49 0.52 0.62 2012 0.46 0.50 0.63

2013 0.47 0.50 0.60 2013 0.44 0.48 0.61

2014 0.45 0.48 0.58 2014 0.43 0.46 0.59

2015 0.44 0.47 0.57 2015 0.41 0.45 0.58

2016 0.43 0.46 0.55 2016 0.40 0.44 0.56

2017 0.42 0.45 0.54 2017 0.39 0.43 0.55

2018 0.41 0.44 0.53 2018 0.38 0.42 0.53

2019 0.40 0.43 0.52 2019 0.37 0.41 0.52

2020 0.39 0.42 0.51 2020 0.36 0.40 0.51

2021 0.39 0.41 0.50 2021 0.36 0.39 0.51

2022 0.38 0.41 0.49 2022 0.35 0.39 0.50

2023 0.38 0.41 0.49 2023 0.35 0.39 0.49

Note: Shaded cells in table above refl ect adjustment factors used to model actual or anticipated changes in implementation of ethanol

- blended gasoline and I/M programs. Ethanol was discontinued in 2003 and I/M is slated to continue indefinitely.



### Modeling the Effect of Engine Block Heater U sage on Warm

- up Idle CO Emissions

Quantifying the benefits of engine block heater use was a principal objective of emission studies conducted by Sierra Research in 199 8- 1999 and 2000

- 2001. This research showed that in the year
2000, engine block heaters r educed CO emissions by an average of 86 grams after a cold start.

For the purpose of estimating the effect of block heater use on CO emissions in this inventory, t he absolute benefit of block heater use on CO reductions was presumed to proportional to the average idle CO emission rate of the fleet. Thus the absolute reductions from block heater usage were expected to decline over time as the fleet is replaced with newer, lower emitting vehicles. To account for idle emission changes resulting from fleet t urnover, and from changes in ethanol

- blended gasoline and I/M requirements that have or are slated to occur, disco unt factors were used to adjust the 86 gram per start

CO reduction estimated from block heater usage in 2000

- 2001

. T hese discount factors are shown in

Table 6.

An example of how these discount factors are used along with the 2000

- 2001 Sierra data to compute idle emissions is shown in the example below for analysis year 2013.

### A dopted A ugust 20 th 

### , 2010

12 

### Compute block hea ter reduction in 2013:

Year 2000 block heater CO reduction = 86 grams pr cold start

Year 201 3 cold start idle discount factor (assume no I/M with no oxy gasoline ) = 0. 61

Year 201 3 block heater reduction = 86 g x 0. 61 = 52.4 grams per cold start

Between 1999 and 200 8 , t he municipality hired a public op inion research firm to perform annual telephone surveys to estimate engine block heater plug

- in rates among Anchorage drivers at ambient temperatures below 15 **°** F. 6



### In Anchorage almost all block heater usage occurs at home because electrical receptacles are not generally available at work places and other locations. For this reason, the emission inventory spreadsheet was configured to assign plug

- in benefits only to trips that begin at home during the 7 a.m.

– 9 a.m. period and for the first portion ( 9 a.m. – 3 p.m. ) of the off

- peak period. Trips beginning at work, shopping centers, and other “non

- home” locations were assumed to have a zero plug

- in rate. The survey firm estimated at

- home plug

- in rates before and after the M OA

and ADEC began a t elevision, radio and print media campaign aimed at increasing plug

- in rates among Anchorage drivers. For morning trips that begin at home i nitial s urvey data suggested that plug

- in rates increased from about 10% in October 1999 to about 20% after the camp aign. Since th e initial survey,

the MOA and ADEC have had on

- going public awareness and incentives program s to encourage block heater use. Survey data suggest that some additional increases in plug

- in rates may have occurred

, however, for the purpose of the maintenance demonstration, the plug

- in rate was assumed static at

20%.

Home

- based morning trips comprise a small fraction of all trips taken over the entire day. When this is considered, the overall plug

- in rate for all trips taken during the day is about 2%

. The plug

- in rate assumptions used to model block heater benefits in the spreadsheet are shown in Table 7

.

Table 7. Block heater plug

- in rates by time

- of

- day, trip origin and trip purpose

after media campaign promoting block heater use

Trip Type Trip orig in AM Peak

7 a. m.

– 9 a.m. PM Peak 3 p.m.

– 6 p.m. Off

- Peak Periods

9 a.m. – 3 p.m. 6 p.m.

– 7 a. m.

Home

- based work home 20% 0% 10%

work 0% 0% 0%

Home

- based school home 20% 0% 0%

school 0% 0% 0%

Home

- based shopping home 10% 0% 0%
shopping 0% 0% 0 %

Home

- based other home 20% 0% 5%

other 0% 0% 0%

Non home

- based work NA 0% 0% 0%

Non home

- based,

non

- work NA 0% 0% 0%

Truck NA 0% 0% 0%



### The transportation model post

- processor provides data on the number of trips generated within each grid cell f or a particular time period for each of the seven trip purposes . The emission inventory spreadsheet uses this data along with user

- supplied data on idle duration (Table 3 ), soak time (Table 4 ),

per s tart idle emission estimates (Table 5) , idle emission ad justment factors (Table 6 ) and block heater

### A dopted A ugust 20 th 

### , 2010

13 

### usage rates (Table 7 ) to estimate total idle emissions for each grid cell. A spreadsheet algorithm was developed that utilizes post

- processor employment and household data from each grid cell to estimate

the proportion of trips that originate at home versus work or “other” locations for each of the seven trip

purposes

. The largest plug

- in benefits were accrued in grid cells with large numbers of morning home

based trips because plug

- ins rates are the highest for those trips.

Results of the spreadsheet calculation of w arm

- up idle emission estimates are summarized in Table 8

. These estimates include estimated reductions resulting from block heater use. Idle emissions increase in 2011 because I/M Program benefits are assumed to cease after 2010 . Note that the estimated emission rate (emissions per vehicle start) are highest during the AM peak. Summary of Warm

- up Idle Emissions Estimates for 2007

- 2023

Table 8.

Estimated warm

- up idle emissions by time

- of

- day

A nchorage inventory area - (all values in tons per day)

AM Peak PM Peak Off

- Peak Periods Total

#

Vehicle

Starts Total

Emissions

(tons) #

Vehicle

Starts Total

Emissions

(tons) #

Vehicle

Starts Total

Emissions

(tons) #

Vehicle

Starts Total

Emissions (ton s)

2007 91,852 5.56 172,607 3.68 374,548 7.11 639,007 16.35

2009 92,960 4.81 175,095 3.19 379,554 6.19 663,669 14.19

2011 94,069 5.27 177,584 3.52 384,559 6.85 673,862 15.64

2013 95,177 4.97 180,072 3.32 389,564 6.46 681,460 14.76

2015 96,285 4.77 182 ,561 3.18 394,570 6.20 689,376 14.15

2017 97,393 4.59 185,049 3.06 399,575 5.97 697,378 13.62

2019 97,888 4.41 187,971 2.99 406,167 5.83 706,895 13.24

2021 98,383 4.29 190,893 2.95 412,759 5.74 716,572 12.99

2023 98,878 4.21 193,815 2.94 419,351 5.71 7 26,391 12.86



### Estimation of On

- Road Travel Emissions

On

- road travel emissions were estimated on a grid

- by

- grid basis using travel outputs (vehicle miles

traveled or V MT and speed by road facility category * and trip purpose). The post processor also

pro vided information that was used to indirectly develop grid

- by

- grid estimates of the thermal state †

* The post

- processor developed estimates of VMT and speeds for five facility categories which include (1) freeways and ramps; (2) major arterials; (3) minor arterials; (4) collectors; and (5) local roads. In addition, the post

- processor estimated “intrazonal” VMT, travel that occurs within a traffic analysis zone and not explicitly accounted for by the travel demand model. 

### . of vehicles operating on each facility type These estimates of the travel activity and characteristics were used in conjunction with emission factor estimates generated by MOBILE 6 with supplemental FTP speed correction factors disabled to better reflect winter season driving behavior in Alaska.

† The thermal state of a vehicle mode is dependent on the soak time, idle duration, and the amount of time spent traveling on t he road before arriving in the grid of interest. Warm engines emit less CO than cold ones.

### A dopted A ugust 20 th 

### , 2010

14 

### VMT Estimation

The Anchorage Transportation Model and its post

- processor were used to estimate VMT within each of the grids in the inventory area. The transportation model was validated against 2002 traffic data and

meets FHWA standards

. 7 

### Past model estimates of VMT have agree d closely with count

- based estimates from the Highway Performance Monitoring System (HPMS). 8



### For the maintenance projections prepared for this plan, transportation model runs were made for 200 7

, 2017

, and 2027 . VMT for intervening years (200 9 , 2011, 2013, 2015, 2019, 2021, and 2023) w as estimated by interpolation. Transportation model estimates and projections of VMT are shown in Table 8. No adjustments were made to transportation model estimates because of their close agreement with previous HPMS

- based VMT estimates.

Because there are 5 facility categories and 7 trip purposes, the VMT in each one

- kilometer grid was separated into 35 (5 x 7 ) different categories, each with potentially different travel activity characteristics. The number of VMT categories grows to 36 when intrazonal VMT is considered. ( Intrazonal trips are defined as trips tha t begin and end within the same transportation analysis zone in the Transportation Model. All intrazonal VMT was presumed to be on local roads.)

The travel accrued within each of these seven purposes was assigned a different operating mode depending on t he idle duration, soak time, and prior travel time associated with each

. Thus, freeway travel accrued by home

- based work trips was likely assigned a different CO emission rate than freeway travel accrued by non home

- based work trips

. Thus, the VMT within a single one

- kilometer grid could be disaggregated into 36 different operating modes (and emission rates) depending on the trip purpose and facility type.

Vehicle Speed Estimation

The Anchorage Transportation Model and its post

- processor provide estimates of vehicle speeds by facility category and time

- of

- day. Thus for each grid, the post

- processor generates an estimate of the average speed of vehicles traveling on freeways, major arterials, minor arterials, collectors and local streets. The speed esti mates for these facility categories are average speeds and include periods when vehicles are stopped at signals or in traffic. Thus speed estimates generated by the model change in relation to the amount of congestion on the network. If network capacity is not expanded in relation to growth in VMT, slower speeds result.

Because the primary purpose of the transportation model is to evaluate the capacity needs of the roadway and transit network, the speed outputs generated by the model are not considered to be as important as VMT. Unlike VMT, modeled speed estimates are usually not reconciled to observed network values. Thus modeled vehicle speed estimates can deviate substantially from observed speeds. Indeed, the vehicle speed estimates generated by the Anchorage Transportation Model were significantly higher than those measured in a recent travel time study conducted by the Municipality and the Alaska Department of Transportation in October
– November 1998. 9



### Because speed is an importan t variable in the estimation of CO emissions, the emission inventory spreadsheet was used to apply linear speed adjustment factors to the speed outputs from the model to bring them into closer agreement with speeds observed in the travel time study. In the travel time study, average vehicle speed was measured on freeways and major arterials during the AM

, PM and off peak periods. Because data were not available for minor arterials and collectors, speed adjustment factors for these facility categories were assumed to b e identical to the adjustment factors determined for major arterials. The speed adjustment factors incorporated into the emission inventory spreadsheet are shown

in Table 9.

### A dopted A ugust 20 th 

### , 2010

15 Table 9. Speed Adjustment Factors

Facility Category Time

Period Observed Av erage Speed

Oct – Nov 1998

MOA travel time study (MPH) Predicted Average Speed Anchorage Transportation Model (1996) (MPH) Speed Adjustment

Factor

Freeways AM Peak

. 56.6 49.2 1.0

Freeways Off

- peak 61.2 48.0 1.0

Freeways PM Peak

. 57.8 49.2 1 .0
Major Arterials AM Peak. 29.7 40.2 0.74

Major Arterials Off

- peak 29.4 35.1 0.84

Major Arterials PM Peak. 24.7 39.5 0.63

Minor Arterials AM Peak.

--- 38.7 0.74

Minor Arterials Off

- peak

--- 36.2 0.84

Minor Arterials PM Peak.

- -- 38.5 0.63

Collectors AM Peak.

--- 30.1 0.74

Collectors Off

- peak

--- 28.7 0.84

Collectors PM Peak.

--- 29.8 0.63



### Note that model output freeway speeds were significantly different from observed speed but they were not adjusted (i.e., adjustment factor = 1.0). The travel time study did not include ramps in the estimation of observed freeway speed. However, the transportation model included on

- ramps and off

- ramps in the model as part of the freeway category. The higher speeds observed in the travel time study were presumed to be the result of not including ramps in speed measurements. The freeway speed outputs from the model were deemed reasonable and no adjustment was applied.

A default speed of 15 miles per hour was assigned to all VMT on local roadways and 25 miles per hour

for intrazonal travel.

Estimation of Vehicle Thermal State

One of the most important variables in the estimation of vehicle CO emissions during the travel mode is

the thermal state of the engine. Cold vehicles emit significantly more CO

. The thermal state of the vehicle at any given point in a trip is a function of its soak time (the time since the engine was last running and start

- up), the amount of time it was warmed

- up prior to the trip, and the amount of pri or travel time :

Operating mode

**=** 

## ƒ (soak time, idle duration, prior travel time)

MOBILE6 allows the user to supply assumptions regarding the soak distribution of the vehicles started by time

- of

- day and emission factor estimates are very sensitive to thes e assumptions. Modeled emissions are significantly higher when a large proportion of vehicles are assumed to have had long

soak times.

Sierra Research developed a method that allowed the computed thermal state of the vehicle with a given soak, idle and travel time to be translated into the operating mode fractions used to model on

- road

emission factors for the MOBILE5b/Cold CO

- based Anchorage attainment plan. However, MOBILE6 no longer uses the operating mode fraction as a model input. Instead, Sierra identified six soak distributions that correspond to the bag fractions used in the attainment plan.

### A dopted A ugust 20 th 

### , 2010

16 

### Table 10 c ompares the bag fraction approach used in the attainment plan to the soak distribution approach used in the maintenance plan. To develop the maintenance inventory, the VMT accrued by a particular trip type (e.g. home

- based work trips beginning at home) was assumed to be characterized by one of six possible thermal states. For example, if transportation model outputs indicated that this VMT

was i n the coldest thermal state, MOBILE6 was run with a soak distribution in which 41.8% of the

vehicles were assumed have a soak time of 10 minutes and 58.2% of vehicles a soak time of 12 hours or more. If transportation model outputs indicated that the VMT was in the hottest thermal state, 94% of the VMT was accrued by vehicles with a soak time of 10 minutes and just 6% by vehicles with a soak

time of 12 hours or more. MOBILE6 emission factors for “cold VMT” were significantly higher than “hot

VMT.”

Table 10.

Soak d istributions for MOBILE6 with c omparable

o perating m ode f ractions used in MOBILE 5b/ Cold CO Model

Thermal

State Operating Mode Fraction (input for MOBILE5b/Cold CO Model)

PCCN / PCHC / PCCC * Soak Distribution

% of vehicles soaked for

10 min vs. 12 hours (input for MOBILE6 Model)

Cold

Hot 27.9 / 20.0 / 27.9 41.8% 10 min, 58.2% 12 hours

22.9 /25.0 / 22.9 52.2% 10 min, 47.8% 12 hours

17.9 / 30.0 / 17.9 62.7% 10 min, 37.3% 12 hours

12.9 / 35.0 / 12.9 73.1% 10 min, 26.9% 12 hours

7.9 / 40.0/ 7.9 83.6% 10 min, 16.4% 12 hours

2.9 / 45.0 / 2.9 94.0% 10 min, 6.0% 12 hours

Figure 3

MOBILE6 On

- road e mission f actor as a f unction of s peed and t hermal s tate

200 7 Anchorage e mission i nventory



### 0 5 10 15 20 25 30 35

10 20 30 40 50 60

Vehicle SpeedCO Emission (grams pr mile) 

### cold

hot

* PCCN = % of VMT accrued by non

- catalyst

- equipped vehicles operating in cold start mode, PCHC = % of VMT accrued by catalyst and non

catalyst vehicles operating in hot start mode; and PCCC = % of VMT accrued by catalyst

- equipped vehicles operating in cold start mode. The sum of these % do not add to 100%. The unspecified portion is the % of VMT accrued by vehicles in the hot

- stabilized mode. (If
PCCN/PCHC/PCCC = 22.9 /25.0 / 22.9, then the % VMT accrued in the hot stabilized mode would be 100
– (22.9+25.0) = 52.1%.

### A dopted A ugust 20 th 

### , 2010

17 

### The discontinuities at 15 and 35 mph in Figure 3 reflect a change in the facility type inputs to MOBILE6. All VMT accrued at speeds above 35 mph was assumed to be on freeways and all local road VMT was assigned a default speed of 15 mph. All other VMT was assumed to be accrued on arterials

.

An extensive look

- up table was then developed for the emission inventory spreadsheet that allowed one of the six soak distributions in Table 1 0 to be assigned on the basis of the various possible soak times, idle durations, and prior travel times. Soak time and idle duration were supplied as user inputs in the spreadsheet and were based on the local driver behavior studies discussed in the earlier section on estimation of idle emissions. These user inputs varied by time

- of

- day and trip purpose.

The th ird variable necessary in the estimation of operating mode was the average prior travel time of the vehicles traveling within the grid of interest. If vehicles had long prior travel times they were likely to be in a fully warm state, and hence, a large proportion of the VMT accrued in the grid would be in the hot fraction. Anchorage Transportation Model post

- processor outputs were used to estimate prior travel

time. The post

- processor provides separate estimates of the amount of VMT accrued by vehicles t hat began their trips less than 505 seconds ago and more than 505 seconds ago. A spreadsheet algorithm was then developed to estimate average prior travel time for the VMT accrued within each grid by facility type and trip purpose.

The end result of thi s work was a spreadsheet look

- up table that allowed the assignment of a particular

soak distribution or thermal state for each the 36 different categories of VMT in each grid. Separate assignments were provided by facility category and for the trip purposes within each facility category. Because the emission factor is a function of the soak distribution, different emission factors were assigned to the VMT within each grid depending on the time

- of

- day, trip purpose, and facility type.

MOBILE 6 Model

The M OBILE 6 emission factor model was used to estimate travel emissions. MOBILE6 was run with Supplemental Federal Test Procedure (SFTP) speed correction factors disabled. The SFTP speed correction factors are used to model the so called “aggressive driving component” of the drive cycle used to compute emission factors. The effects of SFTP were disabled in the model to reflect observed drive cycle behavior in Alaska. Sierra Research conducted studies in Anchorage and Fairbanks to

characterize the behavior of Alaskan drivers in the winter. As one might expect, they found a low proportion of driving in hard acceleration or hard deceleration modes when roads are often icy. They
determined that the old FTP, without the so

- called “aggressive driving supplement”, fairly approximated the winter drive cycle in Alaska. The primary effect of excluding the SFTP was to reduce emission factors computed for the on road portion of trip emissions. However, disabling the SFTP emission component in MOBILE 6 has the secondary effect of reducing

V ehicle registrati on distributions were based on data from detailed parking lot surveys conducted by ADEC during the winters of 1999 and 2000. The assumptions about the age distribution of vehicles were compared to parking lot survey data collected in 2007. There was very little difference in the age distributions determined in 1999 and 2001 and the more recent data. All these surveys indicated that the in use vehicle population is newer than suggested by vehicle registration data. the benefits of fleet turnover on future emissions. In other words, using MOBILE 6 with SFTP disabled provides a more pessimistic

maintenance forecast than the “default” version of the model with SFTP factors enabled.

Odometer measurements collected by the Anchorage I/M program allowed mileage accumulation rates of vehicles subject to I/M requirements to be estimated. Default mileage accumulation rates were used for diesels and other I/M exempt vehicles.

MOBILE6 was configured to reflect the assumption that there would be no CO reductions from I/M after

2010. I/M was assumed to be in place in analysis years 2007 and 2009. W hen the CO reduction provided by I/M in analysis years 2007 and 2009 w as modeled with MOBILE6, a n I/M

### A dopted A ugust 20 th 

### , 2010

18 

### program effectiveness of 85% an d compliance rate of 90% among non

- OBD vehicles was assumed

. The compliance rate for OBD

- equipped vehicles was assumed to be slightly higher, 93%. Copies of input files for model runs for analysis years 200 7, 200 9

, 2011, 2013 , 201 5 , 201 7

, 2019, 2021 and 2023 are available upon request.

Calculation of On Road CO Emissions

An Excel spreadsheet was developed to assemble the information necessary to calculate CO emissions from on road travel in each grid cell. As discussed earlier, the spreadsheet was used t o compute the

emission contributions of 36 possible different categories of travel, with varying speeds and operating modes. The emissions from these various categories of travel were then summed to determine on

- road emissions in each grid using the following formula:

On

- road emissions = 

### ) .....()......... () (
36 21 22 1 36

1 1 

### EF VMT EF VMT EF VMT

i 

### × **×+×** 

# ∑

**=**



### Summary of On

- road Travel Emissions Estimates for 2007

- 2023

Results of the spreadsheet calculation of travel emissions are shown by time of day in Table 1 1. Note

that emissions increase slightly between 2009 and 2011 due to the assumed termination if the I/M program and then decline slowly thereafter.

Table 1 1. On road travel emissions by time

- of

- day (all values in tons per day)

AM Peak PM Peak Off

- Peak Periods Total

VMT Emissions (tons) V MT Emissions (tons) VMT Emissions (tons) VMT Emissions (tons)

2007 527,941 8.01 886,324 14.27 1,930,047 28.76 3,344,312 51.04

2009 540,120 7.03 905,950 12.53 1,971,213 25.39 3,417,283 44.95

2011 552,298 7.82 925,576 13.83 2,012,380 28.13 3,490,253 49.79

2013 564,476 7.46 945,202 13.15 2,053,546 26.77 3,563,224 47.37

2015 576,655 7.20 964,828 12.67 2,094,713 25.81 3,636,195 45.68

2017 588,833 6.99 984,453 12.17 2,135,879 25.06 3,709,166 44.22

2019 597,788 6.86 1,003,095 12.07 2,178,132 24.68 3,779,015 43.62

2021 606,744 6.73 1,021,736 11.90 2,220,386 24.34 3,848,865 42.97

2023 615,699 6.67 1,040,377 11.85 2,262,639 24.32 3,918,715 42.85



### Aircraft Operation Emissions

In June of 2005 Sierra Research, Inc. prepared the “Alaska Aviation Inventory” for the Western Regional Partnership (WRAP). 10 

### T hey compiled air pollutant emission estimates for airports across Alaska including Ted Stevens Anchorage International Airport (ANC) and Merrill Field A irport in Anchorage.

B oth summer and winter CO emissions associated with aircraft operation for various pollutants were estimated for the year 2002. Sierra collaborated with CH2MHill to collect the specific information on aircraft operations at ANC and Merrill Field necessary for input into the Federal Aviation Ad ministration’s EDMS Model (Version 4.2). EDMS was used to generate estimates of CO emissions from aircraft and aircraft support equipment. In EDMS, aircraft support equipment include s both ground support equipment (GSE) and on

- board auxiliary power units (APUs) that are used to provide power to aircraft

### A dopted A ugust 20 th 

### , 2010

19 

### when on the ground. Winter season CO emissions estimates for ANC and Merrill are shown in

Table 1 2

.

Table 1 2.

24

- hour CO e missions e stimates from a ircraft at ANC and Merril l Field in 2002

Aircraft Support Equipment

APU and GSE (tons per day) Aircraft (tons per day) TOTAL

ANC 8.21 3.32 11.53

Merrill 0.00 0.63 0.63



### ANC is currently revising their master plan. The draft Master Plan contains an analysis of historical trends in aircraft operations and projections through 2027. The draft Plan projects an average annual growth rate of 2.4% between 2005 and 2027. Historical data on total operation s in 2002 when Sierra prepared their emissions estimates were used along with the growth projection s in the draft Master Plan to project future emissions from ANC. Emissions were presumed to grow in direct proportion to total operations. Results are shown in Table 1 3

.

Table 1 3 Projected a ircraft o perations and CO e missions at ANC

Calendar Year Estimated or

Pro jected Annual Aircraft Operations CO Emissions (tons per day)

2002

(base year of Sierra inventory ) 309,236 11.53

2007 331,708 12.37

2009 347,845 12.97

2011 363,982 13.57

2013 379,810 14.16

2015 395,327 14.74

2017 410,845 15.32

2019 435,440 16.24

2 021 460,036 17.16

2023 484,631 18.07



### Winter CO emissions from Merrill Field were computed in a similar manner. Sierra’s 2002 CO emissions estimate (0.633 tons/day) was scaled upward in proportion to the projected increase in aircraft operations at Merrill. The Merrill Field Master Plan (2000) contains growth projections for the period 1997 through 2020. Annual operations are projected to increase from 187,190 in 1997 to

2 70,800 in 2020. Assuming linear growth, CO emissions can be projected f or the period 2007

- 2023. These projections are shown in Table 1 4.

### A dopted A ugust 20 th 

### , 2010

20 Table 1 4

Projected Aircraft Operations and CO Emissions at Merrill Field Airport

Calendar Year Estimated or Projected Aircraft Operations CO Emissions (tons per day)

1997 187,190

2002

(base year of Sierra inventory ) 205,366 0.633

2007 223,542 0.689

2009 230,813 0.711

2011 238,083 0.734

2013 245,353 0.756

2015 252,624 0.779

2017 259,894 0.801

2019 267,165 0.823

2021 274,435 0.846

2023 281,706 0.868



### Residential Wood Burning Emissions

The basic assumptions used in the preparation of emission estimates from residential wood burning were not changed from those used in the Year 2000 Anchorage Attainment Plan. Assumptions regarding wood burning activity levels (i.e. the number of households engaging in wood burning on a winter season design day) were corroborated by a telephone survey conducted by Ivan Moore Research (IMR) in 2003. IMR asked approximately 600 Anchorage residents whether they had used their fireplace or woodstove during the preceding day. The survey was conducted when the preceding day had a minimum temperature between 5 and 15 degrees F. Survey results were roughly consistent with the assumptions used in the attainment plan inventory. The basic assumptions used to estima te wood burning were based on data from a telephone survey 11



### The ASK survey asked Anchorage residents how many hours per week they burned wood in their fireplace or wood stove. performed by ASK Marketing and

Research in 1990.

*

* A p revious telephone survey attempted to quantify wood consumption directly by asking residents how much wood (e.g., cords) they burned each winter. Many residents had difficult quantifying their consumption in this manner, for this reason the 1990 survey asked about hours of usage per

week. 

### Because the AP

- 42 emission fa ctors for fireplaces and wood stoves are based on consumption in terms of the amount of wood (dry weight) burned, hourly usage rates from the survey had to be converted into consumption rates. Based on discussions between MOA and several reliable

sources (OMNI Environmental Services, Virginia Polytechnic Institute, Colorado Department of Health), average burning rates (in wet weight) of 11 pounds per hour for fireplaces and 3.5 pounds per hour for wood stoves were assumed for the Anchorage area. Residential wood burning assumptions are

detailed in Table 1 5.

### A dopted A ugust 20 th 

### , 2010

21 Table 1 5.

Estimation of r esidential w ood b urning CO e mission f actor s for Anchorage

Device Av era g e u se

per w eekday

(hours per household per day) Average d ry

w eight of w ood

c onsumed (lbs per hour)* A verage
amount of

w ood burned per household (dry lbs / day) Estimated w ood

b urn ing CO

e missions per

household (lbs/day)

Fireplaces 0.156 7.15 lbs/hr 1.11 0.141

Wood Stoves 0.032 2.275 lbs/hr 0.073 0.006

TOTAL Fireplaces + wood s toves 0.188

------ 1.18 0. 147

* The moisture content of wood burned was assumed to be 35%. Thus, dry burning rates were 65% of wet rates.
** The wood stove emission factor was determined by assuming that the wood stove population in Anchorage is comprised
of equal proportions of conventional , catalyst, and non

- catalyst stoves. The emission factor above was calculated as the
weighted average of the AP

- 42 emission factors for each stove type. AP

- 42, 5 th Edition (Oct 1996)



### Survey results suggest wood burning rates are relatively low in the Anchorage area. The vast majority of wood burning is “pleasure burning;” very few residents need to burn wood for primary or supplemental heat. If the average fire in the fireplace and/or woodstove is assumed to last three hours, Table 1 5

sugg ests that about 1 in every 16 households in Anchorage burns wood on a typical winter weekday.

The Anchorage Transportation Model post

- processor provided information on the number of households in each grid. The calculated CO emission rate of 0.147 lbs of CO per day was assigned to each household in a grid. Thus wood burning emissions were highest in grids with high housing density.

Projecting future trends in wood heating in Anchorage is difficult. On one hand, anecdotal evidence suggests that fewer wood burning appliances are being installed in new homes in Anchorage

. This is consistent with trends being observed nationally. On the other hand, increases in natural gas prices could result in increases in wood heating. For the purpose of this inventory, residential wood burning was assumed to increase in direct proportion with the number of households in the Anchorage inventory

area. Area

- wide wood burning emissions for the period 200 7 - 2023 are shown in Table 1 6.

Table 1 6.

Estimated Anchorage

- wid e 24

- hour CO e missions from r esidential w ood b urning

Calendar Year Number of

Households in Inventory Area 24

- Hour Emissions (tons)

2007 84,936 6.24

2009 86,582 6.36

2011 88,229 6.48

2013 89,875 6.60

2015 91,522 6.72

2017 93,168 6.84

2019 94,045 6 .91

2021 94,923 6.97

2023 95,800 7.04

### A dopted A ugust 20 th 

### , 2010

22 

### Emissions from Natural Gas Combustion for Space Heating

The methodology used to compute natural gas space heating emissions for the maintenance

demonstration is identical to that used in the Year 2000 Anchorage CO Attainment Demonstration and the 2004 Anchorage CO Maintenance Plan. A telephone survey conducted by ASK Marketing and

Research in 1990 12

Table 1 7. Methods of Home Heating in Anchorage (ASK Marketing & Research, 1990) 

### indicated that natural gas is the fuel used for virtually all space heating in Anchorage. ASK survey results are s hown in Table 17.

Natural gas 88.2%

Electricity 9.2%

Fuel oil 0.2%

Wood / other 1.3%

Don't know 1.1%

Total 100.0%



### Enstar distributes natural gas to Kenai, Anchorage and other parts of Southcentral Alaska. According to Enstar, in 1996 approximately 80% of their gas sales were to Anchorage. 13



### A detailed report of natural gas sales to residential, commercial and industrial customers was available for calendar year 1990 Table 1 9 indicates that about 88% of all homes in Anchorage are heated with natural gas. A small fraction of homes are heated by wo od or fuel oil. Wood heating has already been quantified separately in the inventory. The consumption of fuel oil for space heating was small in 1990 and likely even smaller in 2007 . Calculated

area

- wide CO emissions from space heating with fuel oil are negligible (less than 25 pounds per day) and are not included in the inventory. Finally, the emissions associated with electrical heating occur at the generation plant. These emissions are accounted for separately in the point source inventory.

* 

### for Southcentral Alaska. 14 

### Peak winter usage rates were estimated for residential customers and for commercial/industrial customers from this report. Demographic data (i.e. number of households, number of employees) were used to estimate per household consumption rates for residential customers and per employee consumption for commercial/industrial customers. The most

recent AP

- 42 CO emission factors (July 1998) for uncontrolled residential furnaces (40 lbs CO/ 10 6 

### ft 3) 

### ) and small boilers (84 lbs CO/ 10 6 

### ft 3)

Table 1 8

Peak w inter s eason n atural g as c onsumption r ates and CO emission rates in Anchorage (1990) 

### ) were used to characterize residential and commercial space heating emission. Calculated peak natural gas consumption and emissio n rates are shown in Table 18.

Consumption

Rate per Day AP

- 42

Emission Factor (lbs. per 10 6 ft 3 ) CO

Emission Rate

(lbs per day)

Residential 658 ft 3

per household 40 0.0263 per household

Commercial/ Industrial 434 ft 3

per employee 84 0.0364 per employee

* Although data from more recent years was available, the reporting format had changed and less detailed data were available. Unlike the 1990 report, natural gas consumption was not reported separately for residential, commercial/industrial, and power generation customers.

### A dopted A ugust 20 th 

### , 2010

23 

### On an area

- wide basis, CO emissions from natural gas combustion were calculated by multiplying the CO emission rates in Table 19 by the number of households and employees in the inventory area.

Table 19 presents the results of this calculation for the period 200 7 – 2023. Emissions resulting from the combustion of natural gas for power generation are excluded. These emissions are accounted for

sepa rately in the point source inventory.

Table 19

CO Emissions from n atural g as c ombustion (excludes power generation)

Calendar

Year Number of

Households in Inventory Area Number of Employees in Inventory Area Calculated

Total Natural Gas

Consumption ( mcf) CO Emissions

from Natural Gas

Combustion

(tons/day)

2007 84,936 145,516 119,127 3.77

2009 86,582 146,755 120,749 3.82

2011 88,229 147,994 122,372 3.86

2013 89,875 149,234 123,994 3.91

2015 91,522 150,473 125,617 3.95

2017 93,168 151,712 127,238 3.99

2019 94,045 153,731 128,693 4.04

2021 94,923 155,750 130,148 4.09

2023 95,800 157,769 131,602 4.14



### CO emissions from natural gas combustion were also calculated on a grid

- by

- grid basis by multiplying the emission rate per household or per employee by the number of households or employees in each grid. Thus, grid cells with a large number of households and/or employees were assigned the greatest

emissions.



### Other Miscellaneous Sources

Use of NONROAD to Estimate Emissions from Snowmobiles, Snow B lowers,

Welders, Air Compressors and Other Miscellaneous Sources



### As a starting point for this analysis, the EPA NONROAD model (version 2005) was run for base year

2007 . The model provides estimates of non

- road equipment types and activity levels for Ancho rage. These model outputs were reviewed carefully to assess whether or not nonroad equipment populations and usage (i.e., hours per year) were reasonable. The NONROAD model uses a top

- down approach in

which state

- level equipment populations are allocated to counties on the basis of activity indicators that are specific to certain equipment types. Anchorage is the major wholesale and retail distribution center for the state. Because the NONROAD model activity indicator is based on the number of businesse s within a particular SIC code, the model has a tendency to over

- allocate the equipment to Anchorage and ignore usage that occurs outside the Anchorage area. For example, the NONROAD estimate for generator sets is likely heavily skewed by sales to non

- Anc horage customers who come to Anchorage to purchase a generator for use in areas outside of the power grid.

The default model outputs are given in terms of average monthly, year

- round use. These outputs were adjusted to reflect the fact that activity lev els for non

- road sources would be expected to be reduced on

### A dopted A ugust 20 th 

### , 2010

24 

### a typical midwinter exceedance day when ambient temperatures are near 0 **°** F. The activity levels of

all

- terrain vehicles, motorcycles, pressure washers, air compressors and pumps are likely substa ntially reduced in midwinter. Pressure washer activity, for example, was assumed to be 10% of that estimated by NONROAD

. O ther sources were also adjusted significantly from the NONROAD model’s default
outputs

. These local adjustment factors are shown in Table 2 0. It is important to note, that without adjustment, the NONROAD model’s estimate of CO emissions from the sources listed in the table is 120.8 tons per day in 2007, whereas total motor vehicle emissions (idle plus travel) are estimated to be just 67.1 tons per day. Given what is known about the CO problem in Anchorage, clearly something is amiss. After the activity adjustment factors are applied to the NONROAD model estimates, the total

contribution from the sources listed in the table is 9.1 to ns per day.

Default output emissions from commercial and residential snowblowers were also reduced. Anchorage climatological records indicate that CO exceedances are typically preceded by cold, clear weather without snow. Thus, snowblower activity is likely to be lower on elevated CO days. For this reason the NONROAD estimate of residential and commercial snowblower activity was cut by 50%.

The NONROAD model default estimate for the snowmachine population in Anchorage is 34,985

. Although there are a considerable number of snowmobiles in Anchorage, virtually all use occurs outside of the nonattainment area. Snowmobile use in Anchorage is banned on public land throughout the Anchorage nonattainment area because of safety and noise issues. Although there is some use in surrounding parklands, (i.e., Chugach State Park) these areas are located at least three miles from the emission inventory area boundary. However, there is likely to be some small amount of engine operation for maintenance purposes, etc. This was assumed to average about 0.1 hours per unit per month inside the inventory area. This usage rate is about 5 0 times lower than the NONROAD default

value.

Finally, some of the NONROAD model outputs were clearly unreasonable. For example, there is no commercial logging activity in the Anchorage bowl. For this reason, the NONROAD model’s estimate of CO emissions from logging equipment chain saws w as disregarded. The NONROAD estimate of

“other” chainsaw use was cut by 80% to reflect that little garden or home wood cutting activity is likely to take place in mid

- winter.

Table 2 0

Estimation of NONROAD CO e missions in 2007

Number

of Units EPA NONROAD

Model Estimate of

CO emissions (unadjusted) Activity Adjustment

Factor Revised CO Inventory

Estimat e (tons/day)

air compressors 251 0.83 0.50 0.42

ATVs 14,481 0.90 0.02 0.02

chainsaws 6,159 0.56 0.20 0.14

concrete saws 144 0.60 0.25 0.15

forklifts 94 0.41 1.00 0.41

generator sets 4,758 7.13 0.25 1.78
pressure washers 1,898 3.08 0.10 0.31

pumps 1,227 1.73 0.25 0.43

snowblowers commercial 864 2.26 0.50 1.13

snowblowers residential 9,517 1.02 0.50 0.51

snowmobiles 34,985 96.73 0.02 1.93

welders 419 2.10 0.50 1.05

other 91,767 3.47 varies 0.84

TOTAL NONROAD 120.83 9.12

### A dopted A ugust 20 th 

### , 2010

25 

### In order to estimate future year emissions (2009 through 2023) the sources listed in Table 2 0 were increased in proportion to growth in households or employment. If the nonroad road source was primarily related to household activities, the growth in emissions was assumed to be proportional to the projected growth in the number of households in the inventory area. These household - related sources include snowmobiles, motorcycles and generator sets. If the nonroad source was primarily related to commercial activity, growth in emissions was assumed to be tied to growth in employment. Commercial or employment

- related sources include welders, pumps and air compressors.

The emissions from the sources listed above were apportioned among the grid cells that make up the inventory area by using the number of households or employment in the grid as a surrogate for source activity. Activities that would normally primarily occur in residential areas (snowmobiles, residential and commercial snowblower use, ATVs and motorcycles) were apportioned on the basis of the number of households in each grid. Activities that would normally occur in commercial or industrial areas (welders, pumps, and air compressors), were apportioned on the basis of the amount of employment in each grid.

Table 2 1

C O e missions from NONROAD s ources (2007

- 2023)

Calendar Year CO Emissions

from NONROAD Sources (tons/day)

2007 9.12

2009 9.24

2011 9.35

2013 9.47

2015 9.59

2017 9.70

2019 9.82

2021 9.93

2023 10.04

Railroad Emissions



### Because railroad emissions are a relatively insignificant source of CO, no changes have been made to the estimates or methodology employed in the 2004 CO Maintenance Plan. The Alaska Railroad (ARR) supplied data on line haul and switchyard fuel consumption to the Alaska Department o f Environmental Conservation for calendar year 1999. Total fuel consumption in the Anchorage switchyard was estimated to be 370,000 gallons during calendar year 1999. ARR also provided data on line haul fuel consumption between milepost 64 and 146. Annual fuel consumption along this 82

- mile section of track was estimated to be 771,000 gallons. Only 14 miles of track (roughly MP 104 through MP 118) are inside the emission inventory area. The proportionate share of consumption within the inventory area

w as estimated to be 131,600 gallons. Twenty

- four hour consumption rates were calculated by dividing annual totals by 365.

EPA guidance 15



### Railroad fuel consumption and emissions are summarized in Table 2 2 . Switchyard emissions were distributed to the three grid cells that encompass the rail yard in the Ship Creek area of Anchorage. The rail route in Anchorage crosses 15 grids cells in the Anchorage inventory area. Line haul emissions were distributed equally among these 15 grid cells. provides separate emission factors for yard and line haul emissions. These factors, expressed on a gram per gallon basis, were applied to ARR fuel consumption estimates to compute

emissions.

### A dopted A ugust 20 th 

### , 2010

26 Table 2 2

Alaska Railroad e mission e stimates 2007

- 2023

Consumption

(gal/year) Con sumption (gal/day) Locomotive

Emission

Factor (grams/gal) CO emissions (tons/day)

Yard 370,000 1,014 38.1 0.04

Line Haul 131,634 361 26.6 0.01

Total 501,634 1,375 0.05



### Although railroad activity is expected to increase in future years, above the activity levels reported in
1999, the emissions increases that might be expected from this growth are likely to be offset by improvements in locomotive control technology. The Alaska Railroad recently replaced 28 of their 62 locomotives with new models that produce less pollution and are more fuel efficient. In addition,

b etween 2002 and 2007, the railroad equipped two

- thirds of their locomotives with devices that reduce the amount of time locomotives idle in the Anchorage switchyard and reduce f ue l consumption. For the purpose of this analysis, CO emissions from the ARR were assumed to remain the same through 20 2 3. Although this is a crude assumption, the significance of ARR emissions is very small. Hence, refining these future year projections would have a negligible effect on the overall inventory.

Marine Vessel Emissions



### The Port of Anchorage serves primarily as a receiving port for goods such as containerized freight, iron

, steel and wood products, and bulk concrete and petroleum. Commercial shipping lines, including Totem Ocean Trailer Express and Horizon Lines bring in four to five ships weekly into the Port. The Port is currently undergoing a significant expansion that is intended to modernize the facility and double its size. In 2005, over 5 million tons of commodities moved across the Port’s docks.

Despite the magnitude of this activity at the Port, CO emissions are relatively small. In June 2005, Pechan and Associates prepared an emission inventory for the ADEC that estimated winter and

summer season CO emissions from the Port for the year 2002. 16 

### This report provided an estimate of total emissions that occur from all four modes of commercial marine activity for the winter (defined as October through March). These four modes include cruise, reduced speed zone (RSV), maneuvering, and hotelling. However, as defined for modeling purposes, the cruise and RSV modes occur far from Port. Cruise mode activity occurs more than 25 miles form Port and the RSV mode occurs 2 miles or

more from Port. Because cruise and RSV mode CO emissions occur so far from Port and therefore have little or no influence on CO concentrations in the Anchorage CO maintenance area, these emissions were excluded from this inventory. *

* Cruise and RSV emissions account for about 56% of total winter CO emissions. Therefore only 44% of the emissions in the Pechan inventory
were included in this inventory. 

### In addition to the 2002 inventory, the Pechan inventory

also includes a forecast of winter CO emissions for 2005 and 2018. Interpolation and extrapolation was

used to estimate CO emissions from Port of Anchorage marine activity from 2007 – 2023. These

estimates are shown in Table 23.

### A dopted A ugust 20 th 

### , 2010

27 Table 23. Estimated CO emissions from the Port of Anchorage

Year Estimated CO

emissions (tons per day)

2007 0.09

2009 0.10

2011 0.11

2013 0.12

2015 0.12

2017 0.13

2019 0.13

2021 0.13

2023 0.13



### Emissions from Point Sources

Point source em issions estimates for the year 2005 served as the basis for the 200 7 base year point source emission inventory prepared for this maintenance plan and projections through 2 023

. Point source emissions were expected to grow in relation to the number of households. Thus the e mission estimates for 2005 were adjusted upward in proportion to the growth in the number of households in the inventory boundary area.

ADEC is responsible for issuing operating permits to all stationary sources that have fuel

- burning equipment with a combined rating capacity of greater than 100 million Btu per hour. The MOA also issues operating permits to all point sources in Anchorage with a combined rating capacity of greater than 35 million Btu per hour. The ADEC and MOA permit systems were used to inventory all stationary sources that are required to obtain such permits in the Anchorage non

- attainment area. In addition, point sources that produce more than 10 tons per year (TPY) of CO (minor sources) were individually quantified to achieve a more precise estimate of the minor source contribution to the overall emission inventory from stationary sources.

The identification of minor sources was accomplished by contacting fuel distributors in Anchorage. We determined whether any facilities consumed sufficient quantities of fuel to exceed the annual 10 TPY of CO threshold. Using EPA's emission factors, AP

- 42 (fifth edition), fuel quantities equivalent to 10 TPY of CO were compared to sales of fuel to large users. This identified potential 10+ TPY of CO point sources. This approach determined that only permitted sources in Anchorage emitted more than 10

TPY of CO.

The ADEC point source computations were based on annual information provided by the source. The

emission factors were from the most current version of AP

- 42. The ADEC calculated daily point source emissions for a typical wintertime day during the peak CO season by dividing the annual activity levels by the number of days per year

. Actual facility operating information was available for 2005 . Source emission estimates were based on actual fuel consumption and operations rather than permit allowable

emissions.

Based on ADEC

- issued air quality permits, there are six point sources in the Anchorage non

- attainment

area. Estimated annual emission s from each source for 2005 and projected daily emissions for the

2007

- 2023 period are listed in the table at the end of this section. Three of the six point sources identified in the Anchorage inventory were gas

- fired (primarily natural gas) electrical g enerating facilities. Other sources include a sewage sludge incinerator , and two bulk fuel storage facilities.

### A dopted A ugust 20 th 

### , 2010

28 Source Descriptions and Emission Estimation Information



### There are three point sources that are located outside the non

- attainment area. Two are located on military bases at El m endorf Air Force Base and Fort Richardson. T hese facilities were excluded from the base year inventory because the CO emissions on these two military facilities are not considered significant contributors to the Anchorage attainment problem. The third facility is Anchorage Municipal Light and Power Sullivan Power Plant. It is located approximately two kilometers east of the northwest corner boundary of the nonattainment area. Even though this source is located outside th e boundaries of both the attainment area and emission inventory area, it is included in the inventory. Emissions from the Sullivan Plant were assigned to the furthest northwest grid in the inventory area. This grid is located approximately 2 kilometers w est of the power plant.

The ADEC used facility

- reported information and AP

- 42 emission factors to estimate emissions for each

of the six point sources. Th e methodology and emission factors used to estimate actual emissions at each facility is available upon request

. The ADEC Operating Permit system results in the collection of the emission information through requirements for annual and triennial emission reports, on

- site inspections, the reporting of source test data and quarterly production levels and fuel usage, and interactions with each source. In addition, there was no CO emission control equipment identified on any of the sources included in the inventory. Therefore, 100% of the emission estimates resulting from the application of the AP

- 42 factor s identified

above was assumed for the inventories.

Based on the above information, the application of a Rule Effectiveness factor did not appear to be appropriate and was not included for any of the point sources included in this inventory.

Summary of Po int Source Emissions



### The estimates of actual emissions for a typical winter day (in tons per day) at each point source for the year 200 5 and the projections for 200 7 through 2023 are provided in Table 24

.

Table 2 4

P oint Source CO Emissions Summary (tons per day)

Projected Daily CO Emissions

b ased on growth in number of households

Owner 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023

Tesoro Alaska Petroleum Company, Anchorage

Terminals I & II 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Anchora ge Water & Wastewater Utility

, Point Woronzof, John Asplund Wastewater Treatment Facility 0.26 0.27 0.27 0.28 0.28 0.29 0.30 0.30 0.30 0.30 Chugach Electric

Association

, International Station

Power Plant 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Anchorage Municipal Light & Power , George

Sullivan Plant Two 0.93 0.95 0.97 0.99 1.00 1.02 1.04 1.05 1.06 1.07 Anchorage Municipal Light & Powe r, Hank

Nikkels Plant On e 0.07 0.07 0.07 0.07 0.07 0.07 0.08 0.08 0.08 0.08

Flint Hills Resources
Alaska, LLC 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

TOTAL POINT

SOURCE EMISSIONS 1.28 1.31 1.33 1.36 1.38 1.41 1.43 1.45 1.46 1.47

### A dopted A ugust 20 th 

### , 2010

29 

### Emissions Summary

2007 Base Year Area

- wide CO Inventory



### Based on the methodology outlined in the previous section, total CO emissions from all sources in the inventory area were calculated for a typical winter weekday in 2007, when conditions are conducive to

elevated CO concentrations. Total area

- wide CO emissions are estimated to be 10 0.7 tons per day.

Motor vehicles account for an estimated 65.1% of these area

- wide emissions.

Table 2 5 Sources of Anchorage CO e missions in 2007 b ase year in Anchorage inventory area

Source Category CO Emitted (tons per day) % of total*

Motor vehicles 67. 4 66. 7 %

Aircraft

– Ted Stevens Anch orage International and Merrill Field Airport Operations 13.1 1 2.9 %

Wood burning

– fireplaces and wood stoves 6.2 6.2%

Space heating

– natural gas 3.8 3.7% Miscellaneous (snowmobiles, snow removal, welding, rail, marine, etc.) 9.3 9.2% Point sources (p ower generation, sewage sludge incineration) 1.3 1.3%

TOTAL 10 1.0 100.0%

Projected Area

- Wide CO Emissions (2007

- 2023)



### As described in the previous sections, CO e missions for the Anchorage inventory area were projected for each of the source categories for a 24

- hour day in 200 7 , 200 9 , 20 11 , 2013, 2015, 2017, 2019, 2021

and 2023. Results are tabulated in Table 2 6. Area

- wide CO emissions for the period 2007

- 2023 are plotted in Figure 4

. CO emissions decline over time due to expected improvements in emission controls

on newer vehicles. Total area

- wide CO emissions are expected to increase slightly because of the growth of other sources such as Ted Stevens Anchorage International Airport. Nevertheless, total CO emissions projected for 2023 ( 88.3 tons per day) are approximately 12.5 % lower than emissions in base year 2007 ( 101.0 tons per day).

### A dopted A ugust 20 th 

### , 2010

30 Table 2 6

Total CO e mitted during t ypical 24

- hour winter day in the Anchorage b owl i nventory a rea (tons per day)

motor vehicles aircraft

year idle

mode t ravel

mode Stevens

Int'l Airport Merril

Field w ood burning s pace heating rail/

marine nonroad Point

Sources TOTAL

CO

EMISSIONS

2007 16.3 51.0 12.4 0.7 6.2 3.8 0.2 9.1 1.3 101.0

2008 15.3 48.0 12.7 0.7 6.3 3.8 0.2 9.2 1.3 97.4

2009 14.2 45.0 13.0 0.7 6.4 3.8 0.2 9.2 1.3 93.7

2010 13.7 43.9 13.3 0.7 6.4 3.8 0.2 9.3 1.3 92.6

2011 15.6 49.8 13.6 0.7 6.5 3.9 0.2 9.4 1.3 100.9

2012 15.2 48.6 13.8 0.7 6.5 3.9 0.2 9.4 1.3 99.7

2013 14.8 47.4 14.1 0.8 6.6 3.9 0.2 9.5 1.3 98.5

2014 14.5 46.5 14.4 0.8 6.7 3.9 0.2 9.5 1.3 97.8

2015 14.2 45.7 14.7 0.8 6.7 4.0 0.2 9.6 1.3 97.1

2016 13.9 44.9 15.0 0.8 6.8 4.0 0.2 9.6 1.3 96.6

2017 13.6 44.2 15.3 0.8 6.8 4.0 0.2 9.7 1.3 96.0

2018 13.4 43.9 15.8 0.8 6.9 4.0 0.2 9.8 1.3 96.1

2019 13.2 43.6 16.2 0.8 6.9 4.0 0.2 9. 8 1.4 96.2

2020 13.1 43.3 16.7 0.8 6.9 4.1 0.2 9.9 1.4 96.4

2021 13.0 43.0 17.2 0.8 7.0 4.1 0.2 9.9 1.4 96.5

2022 12.9 42.9 17.6 0.9 7.0 4.1 0.2 10.0 1.4 97.0

2023 12.9 42.8 18.1 0.9 7.0 4.1 0.2 10.0 1.4 97.5

Figure 4.

Projected Area-wide CO Emissions in Anchorage (2007-2023)

0.0 20.0 40.0 60.0 80.0 100.0 120.0

2007 2009 2011 2013 2015 2017 2019 2021 2023CO Emissions (tons per day)

other (nonroad, rail, marine)

point sources

space heating

fireplaces and woodstoves

aircraft

motor vehicles

### A dopted A ugust 20 th 

### , 2010

31 

### Compilation of Micro

- Area Inventor y for Turnagain M onitoring S tation

The area

- wide CO inventory discussed in the previous section will be necessary to prepare the motor vehicle emission budget for use in future region

- wide air quality conformity determinations. However,

this “are a- wide view” of emissions is not very useful in analyzing the factors leading to high CO concentrations at particular locations in Anchorage. Monitoring data, including a saturation monitoring study conducted in 1997

- 98 have demonstrated that CO concentrations vary widely throughout Anchorage and that some areas are more prone to high concentrations and have a greater potential to violate the national ambient air quality standard.

The Turnagain monitoring station, located in a Spenard

- area neighborhood

, has the highest CO concentrations of all the monitoring stations in Anchorage. Maximum 8

- hour concentrations are typically 10 to 20% higher than the next highest site called Garden in east Anchorage. During the 1997

- 98 CO Saturation Study 8

- hour CO concentrations at Turnagain were the highest among the 20 sites included in the study. 17 

### An analysis of the probability of exceeding the national ambient air quality standard has

been performed for both the Turnagain and Garden sites . T his analysis suggests that the probability of violating the standard at Turnagain at current CO emission levels is about 1 in 100 while the probability of violating at the Garden station is less than 1 in 1,000. 18



### Because the Anchorage inventory data is disaggregated into one

- kilometer For this reason, it was decided that the Turnagain site should be used for the maintenance dem onstration. In order to perform this

demonstration

, CO emissions in the area immediately surrounding th e Turnagain site must be known for base year 2007 and projected through 2023

.

2



### In 2007, this nine square kilometer area contained an estimated population of 19,776. Total estimated employment was 9, 005. This area is one of the most densely populated areas in the Anchorage bowl. grids, CO emissions can be analyzed in the area immediately surrounding the Turnagain station. A nine

- square kilometer area including and surrounding the Turnagain site was selected for analysis. The area selected is shown in Figure 5 . As can be seen in the figure, the emissions in the nine grids comprising this analysis area are among the highest in the inventory area. Figure 6 show s that precise location of the Turnagain monitoring station in relation to the area selected for the micro

- inventory.

### A dopted A ugust 20 th 

### , 2010

32 Figure 5

CO e missions d istr ibution in Anchorage ( Turnagain m icro -i nventory a rea b oundary n oted with r ed b order )

### A dopted A ugust 20 th 

### , 2010

33 Figure 6

Aerial p hoto of Turnagain m icro -i nventory a rea b oundary

2007 Base Year CO Micro

- Inventory for Turnagain Site



### Results of the 2007 base year micro

- inventory for the nine

- kilometer 2 

### area surrounding the Turnagain

station are shown in Table 2 6 . Total CO emissions in the micro

- inventory area are estimated to be 6.01 tons per day. Motor vehicles account for an estimated 73.4 % of the em issions in the area. Note that there is no contribution from aircraft operations or point sources in the area.

### A dopted A ugust 20 th 

### , 2010

34 Table 27 Sources of CO Emissions in Turnagain Micro

- inventory Area

2007 Base Year

Source Category CO Emitted (tons per day) % of

total

Motor vehicles 4.42 73.4%

Aircraft – Ted Stevens Anchorage International and Merrill Field Airport Operations

--- ---

Wood burning – fireplaces and wood stoves 0.62 10.3%

Space heating

– natural gas 0.28 4.6%

Miscellaneous (snowmobiles, snow removal, weldi ng, rail, marine, etc.) 0.70 11.7%

Point sources (power generation, sewage sludge incineration)

---

TOTAL 6.01 100.0%

Projected CO Emissions in the Turnagain Micro

- Inventory Area (2007

- 2023)

Projected emissions in the Turnagain micro

- inventory area are tabulated for the period 2007

- 2023 in

Table 2 7

. CO emissions increase slightly in 2011 due to the assumed termination of the I/M Program and decline steadily thereafter. By 2023 CO emissions in the Turnagain area are projected to decline by about 12 % from the 2007 base year.

Table 2 8

Total CO e mitted during t ypical 24

- hour winter day when CO is elevated in Turnagain micro -i nventory a rea (tons per day)

Motor Vehicles Area Sources

idle mode travel mode wood burning space heating other TOTAL

CO

EMI SSIONS

2007 1.16 3.26 0.62 0.28 0.70 6.01

2009 1.08 3.04 0.62 0.28 0.70 5.73

2011 1.10 3.08 0.64 0.28 0.71 5.82

2013 1.07 2.99 0.65 0.28 0.72 5.70

2015 1.03 2.90 0.65 0.28 0.72 5.59

2017 1.01 2.83 0.66 0.28 0.72 5.51

2019 0.98 2.77 0.66 0.29 0.73 5. 43

2021 0.96 2.71 0.67 0.29 0.73 5.36

2023 0.94 2.65 0.67 0.29 0.73 5.29

### A dopted A ugust 20 th 

### , 2010

35 Figure 7

Projected CO Emissions in Turnagain CO Micro

- Inventory Area

2007

- 2023



### 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

2007 2009 2011 2013 2015 2017 2019 2021CO Emissions (tons per day)

Other

Space Heating

Fireplace or Woodstove

Motor Vehicles

Time

- of

- Day Inventory at Turnagain



### CO sources vary by time

- of

- day. For example, idle emissions are an important source of CO during the morning commute hours but less so during other times of day. For this reason, separate estimates of CO emissions were generated for each of the 200 grid cells that comprise the Anchorage inventory area

for the AM Peak (7 AM
– 9 AM), the PM Peak (3 PM
– 6 PM) and Off Peak (6 PM
– 7 AM, 9 AM – 3 PM) periods. Results are available by request.

Figure 8 shows that CO emission rates vary considerably by time

- of

- day in the Turnagain micro

inventory area. Time

- of

- day modeling suggests that CO emission rates are highest during the AM Peak (7 AM
– 9 AM). CO concentrations at the Turnagain site are typically highest during morning hours, corresponding with this period of peak emissions.

### A dopted A ugust 20 th 

### , 2010

36 Figure 8

CO e mission r ate by t ime

- of -d ay in Turnagain CO m icro -i nventory a rea (2007)

0 200 400 600 800 1,000 1,200

AM Peak PM Peak Off PeakCO emission rate (lbs/hr) other

fireplace and wood stoves

on-road travel

warm-up idle

### A dopted A ugust 20 th 

### , 2010

37 

### References

1 

### “Analysis of Alaska Vehicle CO Emission Study Data,” prepared by Sierra Research for the Municipality of Anchorage, February 3, 2000.

2 

### Cold Temperature D riving Cycle Development and Emission Testing, prepared for the Alaska Department of Environmental Conservation by Sierra Research, 2000.

3 

### “Winter Season Warm

- up Driver Behavior in Anchorage,” Air Quality Program, Municipality of Anchorage, June 2001.

4 

### F airbanks Cold Temperature Vehicle Testing: Warm

- up Idle, Between

- Trip Idle, and Plug

- In, prepared for the Alaska Department of Environmental Conservation by sierra Research, Inc., July 2001

5 

### “Anchorage Travel Survey,” prepared by Hellenthal & Associates for the Municipality of Anchorage,

1992.

6 

### Anchorage Air Quality Telephone Survey reports, prepared annually by Ivan Moore Research for the Municipality of Anchorage, 1999 - 2007.

7 

### Anchorage Travel Model Calibration and Validation Report

, February 2005

8 

### 1997 and 1998 Vehicle Miles of Travel in the Anchorage Bowl, prepared by Alaska Department of Transportation and Public Facilities and the Municipality of Anchorage, January 2000.

9 

### Anchorage Travel Time Study, November 1998.

10 

### Alaska Aviation Emission I nventory, prepared for the Alaska Department of Environmental Consearvation, June 2005.

11 

### “Air Quality Survey of Anchorage Residents,” prepared by ASK Marketing & Research for the Municipality of Anchorage, April 1990.

12 

### Ibid.

13 

### Personal communication with Dan Dieckgraff, Enstar Natural Gas, March 22, 2001.

14 

### FERC Form No. 2 (ED 12

- 88), submitted by ENSTAR Natural Gas Company, 1991.

15 

### EPA Technical Highlights Document, EPA420 -F- 97

- 051, December 1997.

16 

### Commercial Marine Inventories for Select Alaska Ports, prepared for the Alaska Department of Environmental Conservation by E.H. Pechan and Associates, June 2005.

17 

### Winter 1997

- 98 Anchorage Carbon Monoxide Saturation Study, Municipality of Anchorage Department of Health and Human Services, September 1998.

18 

### Ana lysis of the Probability of Exceeding the CO Standard between 2007 and 2023, Municipality of Anchorage Department of Health and Human Services, April 2009.

# Alaska Department of Environmental Conservation



# Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III. B. 6

“An alysis of Probability of Complying with the National Ambient Air Quality Standards

for Carbon Monoxide in Anchorage between 2007 and 2023, dated March 2010”



# Adopted

August 20 th 

# , 20 10

### Adopted August 20 th 

### , 2010

1 

### Appendix to Section III.B.6 , Anchorage CO Maintenance Plan

Air Quality Program Municipality of Anchorage Department of Health and Human Services

March 2010



## Analysis of the Probability of Complying with the National Ambient Air Quality Standard for CO in Anchorage between 2007 and 2023

Background

In July 2008, the Anchorage Assembly directed the Municipal Department of Health and Human

Services to work with the State of Alaska to remove the I/M Program as a requirement in the State Implementation Plan for air quality with a stipulation that it be retained as a local option and not be subject to a further SIP revision if further local action results in changes to or a discontinuation of the program. As a result a new probabilistic maintenance demonstration must be prepared that analyzes the impact of terminating I/M on prospects for future compliance with the national ambient air quality standard (NAAQS)

. *



### Pr ior to the preparation of the previous Anchorage CO Maintenance Plan in 2004, the Municipality of Anchorage (MOA)

, the Alaska Department of Environmental Conservation (ADEC) and EPA Region 10 staff agreed that a probabilistic approach should be used in the Anchorage

maintenance demonstration. The MOA, ADEC and EPA agreed that this demonstration must show a 90% or greater probability of meeting the national ambient air quality s tandard (NAAQS) in each year during the 2007

- 2023 lifetime of the Maintenance Pl an.

The MOA is using th e same methodology used in the 2004 Plan in th is revised maintenance

demonstration . T his methodology relies on conventional statistical methods to estimate the probability of complying with the NAAQS in the year 2007

, the base year for the analysis

. The “roll forward” technique, used in the previous maintenance demonstration, is used to estimate probability of complying with the standard in future years. This technique relies on CO emissions projections for years 2008 through 2023 to help estimate the probability of complying with the NAAQS during this time period.



## Method

Estimating the Probability of Complying with the NAAQS in Base Year 200 7

The NAAQS for CO is set at 9 ppm for an 8

- hour average not to be exceeded more than once per year. Because the NAAQS effectively disregards the highest 8- hour average in determining compliance, the measure of whether a community meets the standard is determined by the magnitude of the second highest 8

- hour average, or second maximum

. For this reason, this analysis focuses on the probability of the second maximum being above or below the 9 ppm NAAQS.

Standard regression analysis techniques can be used to estimate the probability of complying with the CO NAAQS in 2007

. By definition, a violation occurs when the second maximum concentration is higher than 9 ppm. The probability that this will or will not occur

* Even though I/M may continue for many years as a local option pr o gram, CO reduction benefits were ignored because it is no longer a committed primary control measure in the SIP.

### Adopted August 20 th 

### , 2010

2 

## y

p = 

## y h + 

## t ( **α** ; n

- 2) 

## . s { pred } can be computed using the prediction interval. The prediction interval is defined mathematically as follows:

**Equation 1**



### where

I n this circumstance, we are interested only in the upper limit of the prediction interval †



### Over the past 30 years, CO monitoring has been conducted at ten permanent CO stations . In

this case we want to compute the value correspond ing to the upper 90 th 

### percentile interval in base year 2007. If 2007 could be “repeated” numerous times, with the “normal” variety of meteorological conditions and other variables that effect CO concentrations, the second

maximum concentration would fall at or below this value 90% of the time. This value is the

bas e year 2007 design value (2007 DV 90% 

### ).

‡



### First and second maximum 8

- hour CO concentrations measured at Turnagain are shown in

Table 1. and at numerous additional temporary stations throughout Anchorage and Eagle River. Data
suggest that the Turnagain monitor, loca ted in a residential area in west Anchorage, has the highest CO concentrations of the four monitors in the current network. (See analysis in the

A ttachment at the end of this report.) Although it is difficult to compare recent data from Turnagain with data collected from other sites a decade or more earlier, studies suggest that the CO concentrations at Turnagain are likely representative of the highest ambient CO

concentrations encountered in Anchorage. For this reason, Turnagain was selected as the

site for the maintenance demonstration.

§

Table 1

1st and 2nd Maximum CO Concentrations at Turnagain Station (1999

- 200 8)

Highest 8

- hour avera ge CO

Concentration (ppm) 2 nd Highest 8

- hour average CO

Concentration (ppm)

1999 10.1 7.6

2000 7.2 5.5

2001 9.8 7.7

2002 6.5 5.9

2003 8.3 6.7

2004 8.1 7.9

2005 5.7 4.6

2006 6.5 6.1

2007 5.5 5.3

2008 6.3 5.4

† This is known as a one

- sided prediction interval. In this case we use the one

- sided t

- statistic when using Equation 1.

‡ For the purposes of this discussion, we define a permanent monitoring station as one that has employed Federal Reference Method monitors over the course of at least one CO season. Temporary monitoring was conducted with bag samplers in the 1980’s and more recently with portable i ndustrial hygiene

- type CO monitors. Temporary monitoring has been conducted at more than 30 locations in the Municipality.

§ The Turnagain station began operation October 16, 1998; thus 1999 was the first complete year of

data collected at this site.

### Adopted August 20 th 

### , 2010

3 An Excel spreadsheet was used to compute the upper 90 th 

### percentile prediction interval from

the second maximum concentrations at Turnagain using Equation 1. The results of th is computation are plotted in Figure 1

. Figure 1 shows that the re was a 90% probability that the base year 2007 value would be less than or equal to 7.23 ppm. This computed concentration will serve as the base year 2007 design value for the roll forward analysis

discussed later in this report.

Figure 1

90 th Percentile Prediction Interval Computed from Turnagain 2 nd Maximum



### The precise probability of complying with the 9 ppm NAAQS in 2007 was also estimated with the spreadsheet . T he probability associated with a second maximum of less than or equal to

9 .0 ppm can be estimated through i teration . The one sided t

- statistic associated with various probabilities can be used in Equation 1 until the desired 9.0 ppm value is bracketed within two prediction intervals (see Table 2)

. In this case the desired 9.0 ppm value falls very nearly at the 99.0% interval. Th us, the probability of complying with the NAAQS in 2007 was estimated to be approximately 99%. The chance of violating the NAAQS in 2007 was about

1- in

- 100. 7.6

5.5 7.7

5.9 6.7 7.9

4.6 6.1

5.3 5.4 90%

90%



### 0 2 4 6 8 10 12

1999 2000 2001 2002 2003 2004 2005 2006 2007 20082nd max CO (ppm) 

### 2007 DV 90% 

### = 7.23 ppm

### Adopted August 20 th 

### , 2010

4 Table 2 Second Maximum CO Concentration Associated with Various Upper Bound Prediction Intervals

Probability that 2007 CO

Concentration will be less than Computed 2 nd Max Concentration Computed Second Maximum

CO Concentration (ppm )

80.0% 6.64

90.0% 7.23

95.0% 7.78

97.5% 8.30

**99.0% 8.99** 

99.9% 1 0.88



### Estimating the Probabi lity of Complying with the NAAQS between 200 7 - 2023

One assumption implicit in using the roll forward method is that the second maximum CO concentration in any future year will be proportional to the magnitude of the CO emissions i n that year relative to base year emissions in 2007. In other words, if CO emissions in a future year are projected to decrease by 10% relative to base year 2007, the expected CO

concentration in that fut ure year will also decrease by 10%. If this occurs, there will be concurrent increase in the probability of complying with the NAAQS in that year.

CO emissions were estimated for the 9 kilometer 2 

### area surrounding the Turnagain CO monitoring station for ba se year 2007 using EPA

- prescribed model s such as the MOBILE6, NONROAD, AP

- 42 and the FHWA model EDMS to estimate CO emissions. **



### CO emissions in 2007 were estimated to be 5.99 tons per day (tpd) in the “micro

- inventory

area ” surrounding Turnagain. The computed 90 th 

### percentile concentration or 2007 DV 90%



### was 7.23 ppm. If one assumes that CO concentrations increase in direct proportion to emissions, the amount of CO that could be emitted in the Turnagain area and retain a 90% probability of complying with the standard can be computed as follows :

Amount of CO emissions associated with a 90% probability of complying with the NAAQS = (9.0 ppm / 2007 DV 2007 ) x CO emissions in 2007

= (9.0 ppm/7. 23 ppm) x 6.01 tpd = **7. 48**  **tpd**



### This computation suggests that if CO emissions in the Turnagain area increased from 6.01 tpd to 7. 48 tpd

, the probability of complying with the NAAQS would be 90%. In the same manner as shown above, the amount of emissions corresponding with other probabilities of compliance (i.e. 90%, 95%, 99%, etc.) can be readily computed with the spreadsheet. The spreadsheet was used to create a lookup table listing probabilities along with corresponding quantity of emissions. Table 3 shows the results of these spreadsheet computations. As would be expected, the probability of complying with the NAAQS increases with lower

emission rates.

** MO BILE6 is used to estimate vehicle emissions, NONROAD us used to estimate various nonroad

sources such as snowmobiles and portable electrical generators, EDMS is used for airport operations

and AP

- 42 is used to estimate various area sources such as natural gas space heating, fireplaces and wood stoves. These models and emission inventory procedures are described more fully in the Anchorag e CO Emission Inventory and Emission Projections 2007

- 2023, included as Appendix A of the Anchorage SIP submittal.

### Adopted August 20 th 

### , 2010

5 Table 3 CO Emission Rates Associated with Varying Probabilities of Compliance with the NAAQS at the Turnagain Station

Probability that 2 nd Max CO

Concentration will be

less than 9.0 ppm Corresponding

CO Emission Rate (tpd)

99.9% 4.97

99.5% 5.39

99.3% 5.63

99.0% 6.02

98.0% 6.35

97.0% 6.60

96.0% 6.78

95.0% 6.96

94.0% 7.06

93.0% 7.16

92.0% 7.26

91.0% 7.37

90.0% 7.48



### In addition to estimating base year 2007 CO emissions in the 9 kilometer 2 

### area surrounding Turnagain, emissions were projected through the year 2023. Projections were prepared using the aforementioned MOBILE6, NONROAD, AP

- 42, and EDMS modeling procedures. Population and employment forecasts prepared by the University of Alaska Institute of

Economic and Social Research (ISER) were used to estimate key parameters n ecessary to estimate growth in vehicle travel ††



### The results of this “micro

- inventory” and forecast of CO emissions in the Turnagain area are

shown in Table 4. The probability of complying with the NAAQS at the level of emissions projected for each year was determined from the lookup table (Table 3). , space heating, fireplace and woodstove use and other

CO emission sources. The MOBILE6 model was configured to reflect that the four

- year new car exemption will be extended to six years beginning January 2010

.

†† The Anchorage Transportation Model was used to provide information on vehicle travel. It relies in large part on ISER projections in the development of travel forecasts.

### Adopted August 20 th 

### , 2010

6 Table 4 Projected CO Emissions and Probabilities for Compliance with the NAAQS (2007

- 2023)

CO Emissions from Various Sources in the 9 km 2 Area Surrounding the Turnagain Station (all emissions in tons per day)

Year Motor

Vehicles Fireplace or

Woodstove Space Heating Other TOTAL

CO EMISSIONS Probability of Compliance

2007 4.42 0.62 0.28 0.70 6.01 99.0%

2008 4.13 0.62 0.28 0.70 5.73 99.3%

2009 3.84 0.63 0.28 0.71 5.45 99.5%

20 10 3.71 0.63 0.28 0.71 5.33 99.6%

2011 4.18 0.64 0.28 0.71 5.82 99.2%

2012 4.06 0.65 0.28 0.72 5.70 99.3%

2013 3.93 0.65 0.28 0.72 5.59 99.4%

2014 3.84 0.66 0.28 0.73 5.51 99.4%

2015 3.75 0.66 0.29 0.73 5.43 99.5%

2016 3.67 0.67 0.29 0.73 5.36 99.6%

2017 3.59 0.67 0.29 0.74 5.29 99.6%

2018 3.50 0.68 0.29 0.74 5.20 99.7%

2019 3.40 0.68 0.29 0.74 5.12 99.8%

2020 3.33 0.68 0.29 0.75 5.05 99.9%

2021 3.26 0.68 0.29 0.75 4.99 99.9%

2022 3.21 0.69 0.29 0.75 4.95 >99.9%

2023 3.16 0.69 0.30 0.76 4.90 >99.9%



### Table 4 suggests that there is a very high likelihood of complying with the NAAQS at the Turnagain station. CO emissions are projected to increase slightly in 2011 if the I/M program is (assumed) terminated but the probability of compliance remains above 99%. Although not

shown here, a similar analysis was performed for the Garden station. That analysis indicated that there is an even greater likelihood of compliance at that site. The probability of compliance was greater than 99.9% each year between 2007 and 2023.



## Sensitivit y Analysis

The roll forward probability analysis presented in the last section relies on modeled projections of future emissions. What happens to the estimated probabilities if these projections underestimated the growth in CO emissions between 2007 and 2 023?

This sensitivity analysis investigates the sensitivity of the probability estimates presented in

Table 4 to assumptions regarding:

1. future growth in vehicle miles traveled (VMT), vehicle starts and idling, and ;

2. future growth of wood stove and fireplace use.

For the purpose of this analysis, we will adjust initial assumptions regarding VMT, and wood stove and fireplace use and re

- compute the estimated probability of complying with the NAAQS during the 2007

- 2023 period. The manner in which each of these assumptions was revised is described in the next section.

### Adopted August 20 th 

### , 2010

7 Revised Assumptions Used in Sensitivity Analysis :

Future Growth in VMT , Vehicle Starts and Idling

Imbedded in these emission computations is the assumption that amount of vehicle miles

traveled (VMT) on streets in the 9 kilometer 2 

### area surrounding the Turnagain stat i on will grow by about than 4% from 2007 levels. Although this appears to be a sensible assumption because the Turnagain area is an older area with little opportunity for significant growth in population, in this sensitivity analysis we will assume that the growth in VMT will be three times that projected by the Anchorage Transportation Model. In other words, we will assume that VMT and vehicle starts and idling will grow by 12% between 2007 and 2023 and

determine how this affects the probability of compliance

.

Future Growth in Wood Stoves and Fireplace Use

Woodstove and fireplace emissions were assumed to grow in proportion to the growth in the number of households in the Turnagain micro

- inventory area. During the 2007

- 2023 inventory period, wood heating emissions were projected increase by about 11%. Although recent telephone data suggest that Anchorage households do not plan to change their habits with regard to wood burning, there is a possibility that wood burning rates could increase in

the next decade if households decide to heat with wood to avoid rising cost s of heating with natural gas. For the purpose of this analysis we will assume that wood heating will grow 2% per year per household during the inventory period.

Results of Sensitivity Analysis

The t wo revised assumptions used in this sensitivity analysis are summarized in Table 5.

The combined impact of these revised assumptions on CO emissions in the Turnagain

micro

- inventory area and the consequent effect on probabilities of compliance during the

200 7- 2023 maintenance plan period is shown in Table 6.

Table 6 suggests that even when the assumptions used in the sensitivity analysis are combined to create a “worst case scenario”, the probability of compliance with NAAQS is well above 90% each year. Even with higher rates of growth in vehicle travel and wood burning, CO emissions continue to decline. The probability of compliance remains at 99% or higher even with these higher growth rates.

Table 5 Comparison of Original Assumptions used in Maintenance Demonstration with Revised Assumptions used in Sensitivity Analysis

Original Assumption s used in

Maintenance Demonstration and Probability Computations Revised “Worst Case” Assumption s Used in Sensitivity Analysis

Growth in VMT and

Vehicle Starts and Idling 4% increase between 2007 and

2023 12 % increase between 2007 and

2023

Fireplace and

Woodstove Use No change in wood burning rates per household between 2007

- 2023 2% growth in wood heating per year

### Adopted August 20 th 

### , 2010

8 Table 6 Comparison of CO Emissions and Probabilities of Compliance with the NAAQS Original Assumptions used in Maintenance Demonstration vs. Revised Assumptions used in Sensitivity Analysis

Original Assumptions Revised Assumptions in Sensitivity Analysis

Estimated Total CO

Emissions (tpd) Probability

of Compliance Estimated Total CO

Emissions (tpd) Probability

of Compliance

2007 6.01 99.0% 6.01 99.1%

2008 5.73 99.3% 5.77 99.2%

2009 5.45 99.5% 5.51 99.4%

2010 5.33 99.6% 5.43 99.5%

2011 5.82 99.2% 5.94 99.1%

2012 5.70 99.3% 5.86 99.2%

2013 5.59 99.4% 5.77 99.2%

2014 5.51 99.4% 5.72 99.3%

2015 5.43 99.5% 5.67 99.3%

2016 5.36 99.6% 5.63 99.3%

2017 5.29 99.6% 5.59 99.4%

2018 5.20 99.7% 5.53 99.4%

2019 5.12 99.8% 5.47 99.5%

2020 5.05 99.9% 5.44 99.5%

2021 4.99 99.9% 5.41 99.5%

2022 4.95 >99.9% 5.39 99.5%

2023 4.90 >99.9% 5.38 99.6%

### Adopted August 20 th 

### , 2010

A-1 

## Attachment

Rank

- Pair Order Comparison of CO Concentrations at Turnagain with Garden and Seward Highway Monitoring Stations

Permanent monitoring at Turnagain station began in October 1998 following the completion of a CO Saturation Monitoring Study during the winter of 1997

- 98. This study monitored CO concentrations at some 20 locations using temporary industrial hygiene

- type monitoring

devices. The saturation study indicated that the Turnagain site had the highest concentrations of all the sites in the study.

The permanent monitoring stations at Turnagain and Garden are located in older residential neighborhoods with relatively low traffic volumes on the roadways adjacent to the monitoring probe. The Seward Highway station (decommissioned in December 2004) was located at the intersection of two heavily traveled arterials, the Seward Highway and Benson Boulevard. In Anchorage CO monitoring is conducted at these permanent stations during the winter months defined as October through March.

Non

- overlapping 8

- hour maximum CO concentrations measured at the Turnagain, Garden and Seward Hig hway m onitor s were compared in rank

- order to determine which site has the highest CO concentrations and the greatest potential for exceeding the national ambient air quality standard (NAAQS) for CO. A rank

- order comparison involves sequentially ranking

no n- overlapping 8

- hour average concentrations at the two sites being compared in descending order. In other words, the highest concentration measured at one site is compared to the highest concentration at the other, the second highest at the one site is

co mpared to the second highest at the other, the third highest at one site is compared to the third highest at the other, and so on.

Rank

- pair comparisons of data were performed only in time periods when data were available from both sites

. In other words, in order to perform a fair comparison between two sites, the data compared was limited to periods when both sites were in operation and collecting valid data. Table 1 show the time periods when paired

- data from Turnagain was compared to the other two stations. ‡‡

Table A-1

Comparison Periods for Rank -P air Analysis

Stations Compared Comparison Period

Turnagain with Garden 10/16/98
– 12/31/07

Turnagain with Seward Hwy 10/16/98
– 12/31/05



### A spreadsheet program was constructed to identify t he highest 50 non

- overlapping 8

- hour maximum CO concentrations at each site for the comparison periods shown in Table 1.

‡‡ The Turnagain site did not begin operating until October 16, 1998 and monitoring was discontinued at the Seward Highway site on December 31, 2004. Garden has been in more

- or

- less continuous operation since late 1970’s. When data comparisons between two sites were performed the analysis was limited to time periods when both sites were collecting data.

### Adopted August 20 th 

### , 2010

A-2 **Comparison of Turnagain and Garden Station**  **CO Concentrations -**  **October 1998 through December 2007**

Results of the rank

- order comparison between the Turnagain and Garden CO stations are shown in Figure 1. (Data used to construct this plot can be found at the end of this report.)

Figure A-1

Rank

- Order Comparison of Highest Fifty Non -O verlapping 8

- hour Average CO Concentrations

M easured at the Turnagain and Garden Monitoring Stations

October 1998

– December 2007



### Figure 1 shows that the 50 highest 8

- hour average concentrations at the Turnagain station

are about 12 % to 25% higher than the corresponding rank

- pair value at Garden. The greatest differences occur among the highest ranks. For example the highest 8

- hour concentration at Turnagain is 23% higher than the highest value at Garden while the 50 th


### highest value at Turnagain is 13% higher than the corresponding 50 th 

### highest value at

Garden. On a rank

- pair basis, the values at Turnagain are significantly and consistently higher than those at Garden. This is particularly true at the extreme (i.e. highest)

concent rations. This would suggest that Turnagain has a greater potential of exceeding or violating the NAAQS than Garden. For this reason, data from the Turnagain station were used to perform the probabilistic analysis for the maintenance demonstration. 

### 0.0 2.0 4.0 6.0 8.0 10.0 12.0

1 6 11 16 21 26 31 36 41 46

RankMaximum 8-hour CO Concentration (ppm)



### Turnagain

Garden

### Adopted August 20 th 

### , 2010

A-3 **Comp arison of Turnagain and Seward Highway**  **Station CO Concentrations**  **October 1998 through December 200 4** 

A similar analysis was performed comparing data from the Turnagain station to Seward Highway. In this case the analysis was confined to the period October 16, 1998 to December 31, 2004 because the Seward Highway station was decommissioned at the end of 2004. The results of this analysis are shown in Figure 2.

Figure A-2

Rank

- Order Comparison of Highest Fifty Non

- overlapping 8

- hour Average CO Concen trations measured at the Turnagain and Seward Highway Monitoring Stations

October 1998

– December 2004



### Among the highest 50 paired 8

- hour concentrations, concentrations at Turnagain are 12% to 38% higher than Seward. The largest differences between the two sites are observed in the very highest 8

- hour concentrations where differences between rank

- pairs are typically 30% or more. This would suggest that Turnagain has a considerably greater potential of exceeding or violating the NAAQS than Seward.

**Conclusion**

This analysis demonstrates that the Turnagain site exhibits the highest CO concentrations and greatest potential for violating the NAAQS in the Anchorage network. It is ther efore appropriate to use th is site for analysis of long

- term prospects for continued compliance with
the NAAQS. 

### 0.0 2.0 4.0 6.0 8.0 10.0 12.0

1 6 11 16 21 26 31 36 41 46

RankMaximum 8-hour CO Concentration (ppm)



### Turnagain

Seward Hwy

### Adopted August 20 th 

### , 2010

A-4 Turnagain

Oct 1998

– Dec 2007 Garden

Oct 1998

– Dec 2007

rank 8- hr avg (ppm) date end

hour rank 8- hr avg (ppm) date end

hour % Diff

1 10.14 1/6/99 19 1 8.23 1/6/99 18 23.3%

2 9.78 12/16/01 20 2 7.80 12/6/99 14 25.3%

3 8.27 12/6/03 1 3 6.80 12/24/98 19 21.6%

4 8.11 1/5/04 18 4 6.78 1/13/04 21 19.5%

5 8.06 12/24/98 23 5 6.66 2/12/99 12 21.0%

6 7.88 1/4/04 20 6 6.37 2/9/99 14 23.7%

7 7.74 11/14/01 12 7 6.36 1/3/04 21 21.7%

8 7.69 12/16/98 24 8 6.33 1/5/04 20 21.5%

9 7.61 1/3/04 21 9 6.18 1/27/99 13 23.3%

10 7.61 2/23/99 12 10 6.17 1/4/04 21 23.3%

11 7.48 1/1/04 22 11 6.14 12/5/03 23 21.9%

12 7.40 12/18/01 17 12 6.10 12/16/01 22 21.3%

13 7.31 2/8/99 11 13 5.84 1/1/04 23 25.2%

14 7.24 12/6/99 14 14 5.72 1/2/04 22 26.6%

15 7.23 12/5/01 15 15 5.70 11/27/99 24 26.8%

16 7.21 1/16/00 3 16 5.69 12/20/03 19 26.7%

17 7.16 11/28/99 1 17 5.59 10/22/98 11 28.2%

18 6.53 11/29/06 16 18 5.58 12/3/01 15 17.0%

19 6.50 2/23/99 3 19 5.45 1/15/04 14 19.2%

20 6.49 2/6/02 12 20 5.43 1/5/99 13 19.6%

21 6.30 12/3/01 16 21 5.40 1/7/04 14 16.6%

22 6.28 12/8/01 1 22 5.39 1/13/00 14 16.5%

23 6.13 2/18/01 6 23 5.38 1/12/00 15 14.0%

24 6.13 11/14/01 3 24 5.25 3/18/02 23 16.7%

25 6.11 1/24/06 12 25 5.23 2/22/99 12 17.0%

26 6.09 2/11/99 9 26 5.21 12/26/98 24 16.8%

27 6.09 1/17/06 14 27 5.21 2/11/00 15 16.8%

28 5.96 2/22/99 13 28 5.18 1/15/00 24 15.2%

29 5.95 12/4/01 16 29 5.14 1/14/99 14 15.7%

30 5.93 11/10/99 12 30 5.14 2/10/00 13 15.3%

31 5.90 1/4/99 24 31 5.09 11/29/01 15 16.0%

32 5.90 12/1/01 5 32 5.08 11/14/01 13 16.3%

33 5.87 1/13/04 1 33 5.06 2/13/99 1 16.0%

34 5.86 1/25/02 12 34 5.06 1/17/06 14 15.8%

35 5.75 12/27/98 4 35 5.00 11/22/99 14 15.0%

36 5.71 12/1/01 24 36 5.00 1/23/03 14 14.3%

37 5.69 1/28/05 11 37 4.99 2/10/99 12 14.1%

38 5.68 11/15/98 24 38 4.98 1/16/00 17 14.1%

39 5.65 11/25/06 12 39 4.96 12/4/01 16 13.9%

40 5.61 2/9/99 13 40 4.94 12/14/04 20 13.6%

41 5.58 12/14/01 15 41 4.91 11/20/98 15 13.5%

42 5.56 12/12/99 3 42 4.90 1/22/03 14 13.5%

43 5.50 12/19/07 14 43 4.83 11/10/99 13 14.0%

44 5.48 11/7/98 2 44 4.81 2/8/99 12 13.8%

45 5.46 1/12/00 13 45 4.81 1/18/05 13 13.7%

46 5.44 2/1/02 13 46 4.79 1/27/05 14 13.5%

47 5.40 11/25/06 3 47 4.78 1/7/04 23 12.9%

48 5.37 1/14/04 2 48 4.74 2/9/99 2 13.3%

49 5.36 12/26/03 16 49 4.74 12/18/01 16 13.2%

50 5.35 12/27/02 15 50 4.74 2/6/02 13 12.9%

# Alaska Department of Environmental Conservation



# Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III. B. 9

“ Municipality of Anchorage Ordinance 20 10- 35 (S) ”



# Adopted

August 20 th 

# , 20 10

# Alaska Department of Environmental Conservation

Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III. B. 10

“Estimation of Background Carbon Monoxide Concentrations for Anchorage Project -Level Conformity Analyses”

&

Affidavit of Oral Hearing



# Adopted

August 20 th 

# , 20 10Appendix to Sections III.B.10 

### Appendix to III.B.10

Note: In addition to the document below, the State of Alaska will include Appendix III.B.10 containing the Anchorage Assembly Resoluti on (AR) adopting the revised CO Maintenance Plan and an affidavit of an oral hearing to be he ld by the State of Alaska. The AR and oral hearing are expected to occur in spring or early summer 2009.



## Estimation of Background CO Concen tration for Anchorage Project-Level Conformity Analyses

Most project-level conformity analyses involve m odeling expected CO concentrations from projects related to major intersections with high traffic volumes. CAL3QHC modeling assumes that CO concentrations predicted at roadway receptors ar e the sum of two sources: (1) emissions from the roadway(s) and/or intersections being modeled; or (2) “background CO” from other roadways and emissions sources not directly accounted for in the model.

Typically, background CO is estimated from ba ckground or neighborhood-scale monitors in the vicinity. For example, a background CO estimate might be taken from measurements from a nearby residential neighborhood. Although this might make sense initially, this approach to estimating background CO is not appropriate in Anchorage.

In Anchorage, CO concentrations in some residential areas are substantially higher than those near major roadways. A CO monitoring study conducted in 1997-98 showed that CO concentrations measured at the Turnagain and Garden sites, whic h are located on relatively low volume residential streets were 20% to 50% higher than concentrations measured near major roadway intersections such as the Seward Highway & Benson Boulevard, Old Se ward & Dimond, or Lake Otis & Tudor. CO concentrations along these major arterials were lowe r even though their traffic volumes were an order of magnitude higher than the neighborhood sites. ‡‡



### Thus, using CO values obtained from residential site s like the Garden or Turnagain site yields a background concentration estimate that is unrealistic ally high for modeling major roadway projects in Anchorage. Because most project level anal yses involve major roadways where mechanical turbulence is important in reducing CO concentrations , it is inappropriate to use data from residential sites to estimate the background value.

In order to better determine an appropriate background value for CAL3QHC modeling, CO data from

two monitors near the intersection of Seward Hi ghway and Benson Boulevard were examined. The
first site, known as the Seward Highway site, was located on the southwest corner of the intersection of Seward Highway & Benson Boulevard. §§ 

### (See Figure 1.) It collected data from this location between 1987 and 2004. Monitoring was also conducte d at a second site, approximately 80 meters to the west on Benson Boulevard during the winter of 1997-98. For the purposes of this discussion this

monitor will be called Benson Mid-block . Because this second monitor was setback further from the

‡‡ As noted in Section III.B.5, mechanical turbulence from vehicle traffic is believed to provide some localized atmospheric mixing and thus reduce CO levels on days when natural atmospheric mixing is very limited.
Because traffic levels are low in residential area, less mechanical mixing occurs an d higher CO concentrations

result.

§§ The intersection of Seward Highway and Benson Boulevar d is the highest volume intersection in Anchorage. The 1997-98 CO Saturation Monitoring Study showed that co ncentrations at this inters ection were the highest of

all intersections monitored. Other monitored intersec tions included Lake Otis & Tudor, Northern Lights & Boniface, Old Seward & Dimond, and Spenard & Minnesota.Appendix to Sections III.B.10 

### Seward Highway, it was less affected by the emissions from idling traffic queued up on Benson waiting for the red light at Seward Highway.

Figure 1

Aerial Photo of Intersection of Seward Highway and Blvd Seward Highway Monitor was locate d approximately 80 meters east of the Benson Mid-block Monitor



### CO concentrations were approximately 19% lower at Benson Midblock than the Seward Highway site. The scatter plot in Figure 2 shows the relationship be tween paired hourly concentrations measured at these two locations. (Hourly values below 3 ppm were disregarded.) Seward Highway Monitor Benson Mid-block MonitorAppendix to Sections III.B.10 Figure 2

Relationship between hourly CO concentrations mea sured at the Seward High way Station and a midblock location 80 meters west (1997-98 data)

y = 0.8123x

R 2 = 0.8448

0.0 2.0 4.0 6.0 8.0 10.0 12.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Seward Highway CO (ppm)Benson - Midblock CO (ppm)



### Although concentrations at the Benson Mid-block site were lower than those at the Seward Highway site, concentrations there were still probably u nduly influenced by the heavy traffic on Benson Boulevard to be considered a good background site. The probe for Benson Mid-block was located just 10 meters south of nearest traffic lane. If the probe for Benson Mid-block were to have been setback 50 or 100 meters from Benson Boulevard a more realistic background value for this busy midtown area might have been obtained. Nevertheless, concentrations at Benson Mid-block offer a more
reasonable (and lower) estimate of the “true” bac kground concentration near major arterials than values obtained from monitors in Anchorage residential areas.

The Benson Mid-block monitor therefore provides a conservative or high estimate of background CO for CAL3QHC modeling. CO monitoring at Benson Mid-block was discontinued in the late 1990’s. Nevertheless, the present-day background value can be estimated using the regression relationship

between the Seward Highway and Benson Mid-block sites.

The methodology used to estimate the background CO va lue for 2008 is described below. A statistical approach, relying on the 90 th 

### percentile prediction interval, was used to compute the background

concentration for 2008 from data collected from the Seward Highway and Benson Mid-Block monitors. This methodology is similar in many wa ys to the probabilistic approach used in the Anchorage maintenance demonstration.Appendix to Sections III.B.10 

### 1. Use the 90 th 

### percentile prediction interval to compute the 90 th 

### percentile value of the 2 nd 

### maximum 8-hour average at Seward Highway in 2004. (Monitoring was discontinued in December 2004.)

90th Percentile Prediction Interval 2nd Maximum 8-hour Average at Seward Highway

11.8 12.2
11.6

9.8 9.5 9.9 11.0

8.4 10.5

7.0 7.9

6.5

4.8 5.2 4.7 5.4 5.5

0 2 4 6 8 10 12 14

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 20042nd max CO (ppm) 

### 2004 CO = 5.95 ppm 90th percentile prediction interval



### 2. Compute the corresponding 90 th 

### percentile 8-hour concentration at Benson Midblock in 2004 using the slope of the regression rela tionship shown in Figure 2.

Benson Midblock 2004 (90 th 

### percentile) = (5.95 ppm) x 0.8123 = 4.8 ppm (This value is the computed background CO concentration for 2004.)

3. Use MOBILE6 to project the background concentration in 2008 from the 2004 level. ***

MOBILE6 emission

factor @ 2.5 mph 8-hour CO (ppm) 1-hour CO** (ppm)

2004 45.307 4.8 6.9

2005 42.525 4.5 6.5

2006 37.043 4.0 5.6

2007 35.537 3.8 5.4

2008 33.722 3.6 5.1



### ** In accordance with guidance, persistence factor of 0.7 was used to compute the 1-hr concentration from the 8-hr. i.e., 1 hr bkg CO (2008) = 3.6 ppm/0.7 = 5.1 ppm



### The computed background CO concentration is therefore:

Background 8-hour CO = 3.6 ppm

Background 1-hour CO = 5.1 ppm

*** CAL3QHC guidance suggests that the background CO concentration should be adjusted downward over time in proportion to the decline in idle emissions projected by MOBILE6. The MOBILE6 emission factor at 2.5 mph is used as a surrogate for idle emissions.STATE

OF ALASKA

) THIRD JUDICIAL DISTRICT

)ss. ) AFFIDAVIT

OF ORAL HEARENG I, Cynthia

Heil. Acting Environmental

Program Manager

for the Department

of Environmental Conservation,

being sworn,

state

the following: On August

2”, 2010,
from 12:30-1:30

pm,

in Room

A,

at 619 Ship Creek Avenue,

Suite

249,

in Anchorage,

Alaska,

I presided

over

a public hearing

held

in accordance

with

AS 44.62.210

for the purpose

of taking testimony

in connection

with

the adoption

by reference

in

18 AAC 50.030 changes

being made

to the State Implementation

Plan. These changes include

the suspension

of the Municipality

of Anchorage’s

Vehicle Inspection

and Maintenance

(JIM) Program

and

the Department’s
commuter

vehicle program;

the adoption

of the Eagle River PMIO Limited Maintenance

Plan;

and
the adoption

of the Division’s
“Quality Assurance

Project
Plan”. DATE:____________ Anchorage,

Alaska

Cywffiia

Heil, Acting ycogram Manager SUBSCRIBED

AND SWORN

TO before

me this [NOTARY

SEAL] i

n an.____

day of___________ flea Not

ry Public

in and

for the State

of Alaska My commission

expires: