Adopted August 20th, 2010

Alaska Department of Environmental Conservation

Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendices to: 

Volume II, Section II. Air Quality Control Program;

Volume II, Section III.A Statewide Carbon 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 20th, 2010
Adopted August 20th, 2010 

The State of Alaska’s State Air Quality Control Plan Volume III, Appendix to Volume II of this 

plan, is amended to include the following documents: 

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

regulations: 

• 18 AAC 50 Air Quality Control as amended through April 1st, 2010; and replacing them

with the following regulations: 

• 18 AAC 50 Air Quality Control as amended through August 20th, 2010. 

Volume II, Section III.B Anchorage Transportation Control Program, adopted into the State Air 

Quality Control Plan August 20th, 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 deletes 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 National 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 Ordinance No. 2010-35(S), adopted by the Anchorage 

Assembly May 11, 2010; an ordinance repealing Anchorage Municipal Code 

Chapters 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 ProjectLevel Conformity Analyses, no date; and 

1
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 20th, 2010 

6
7
8
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 20th, 2010 

9
Adopted August 20th, 2010

 

Anchorage 2007 Carbon 

Monoxide Emission Inventory and 

2007 – 2023 Emission Projections 

Municipality of Anchorage

Air Quality Program

Environmental Services Division

Department of Health and Human Services

March 2010

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Adopted August 20th, 2010

 

Preface

This document discusses the methodology used to prepare the base year 2007 CO emission 

inventory and emission projections for the 2007 – 2023 period covered by the Anchorage 

maintenance plan. 

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Table of Contents

INTRODUCTION.................................................................................................................................. 1

OVERVIEW HYBRID EMISSION ESTIMATION METHODOLOGY................................................. 1 

INVENTORY BOUNDARY.................................................................................................................. 1

ANCHORAGE TRANSPORTATION MODEL AND INVENTORY GRID SYSTEM......................... 3

TIME-OF-DAY ESTIMATES OF CO EMISSIONS............................................................................. 5

MOTOR VEHICLE EMISSIONS ......................................................................................................... 6

Estimation of Warm-up Idle Emissions .......................................................................................................................7

Estimation of On-Road Travel Emissions .................................................................................................................13

AIRCRAFT OPERATION EMISSIONS ............................................................................................ 18

RESIDENTIAL WOOD BURNING EMISSIONS .............................................................................. 20

EMISSIONS FROM NATURAL GAS COMBUSTION FOR 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

2007 Base year Area-wide CO Inventory29

Projected Area-wide CO Emissions (2007-2023) ..................... .........................................................29 

COMPILATION OF MICRO-AREA INVENTORY FOR TURNAGAIN MONITORING STATION . 31

REFERENCES....................................................................................................................................... 

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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

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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 critical 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 11

Table 7 Block heater plug-in rates (%) by time-of-day, trip origin and trip purpose after media 

campaign promoting block heater use 12

Table 8 Estimated warm-up idle emissions by time-of-day 

Anchorage inventory area 13

Table 9 Speed Adjustment Factors 15

Table 10 Soak distributions used in MOBILE6 modeling with comparable operating mode fractions 

used in MOBILE5b/Cold CO modeling 16

Table 11 On-road travel emissions by time-of-day 18

Table 12 24-hour CO emissions estimates from aircraft at ANC and Merrill Field in 2002 19

Table 13 Projected aircraft operations and CO emissions at ANC 19

Table 14 Projected Aircraft Operations and CO Emissions at Merrill Field Airport 20

Table 15 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) 22

Table 19 CO Emissions from natural gas combustion (excludes power generation) 23

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 26

Table 23 Estimated CO emissions from the Port of Anchorage 27

Table 24 Point Source CO Emissions Summary 28

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 micro-inventory area 34

 

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1

Introduction 

This document provides 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 

2007 was compiled. Historically, violations of the CO NAAQS have occurred 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 2007. 

In April 2007 an air quality conformity analysis was prepared when the Anchorage Long Range 

Transportation Plan was amended 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 2009, 2011, 2013, 2015, 2017, 2019, 

2021 and 2023. 

The methodology employed to develop the 2007 base year emission inventory and projections through 

2023 was very similar to that employed to develop previous emission inventories 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 to 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 boundary of the maintenance area is shown along with the expanded inventory area in 

Figure 1. The inventory area encompasses approximately 200 square kilometers of the Anchorage 

Bowl.

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Figure 1. 

Anchorage Maintenance Area Boundary with Expanded Inventory Area 

 

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 3

Anchorage Transportation Model and Inventory Grid System 

The CO inventory was 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 

evaluate 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.

Transportation 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) could be assigned to a particular grid. If a roadway link crossed the boundary between two 

or more grids, its attributes were assigned to the appropriate grid in relation to the proportion 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 onekilometer grids, the demographic characteristics of a particular grid had to be estimated from lower 

resolution 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 in 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.

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 4

Figure 2 

Anchorage Inventory Grid System 

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 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 

sources 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 vehicles in Anchorage and Fairbanks during the winters 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 using

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 

emission 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 testing 1

The MOBILE6 model was run with supplemental speed (SFTP) correction factors disabled. The 

purpose of the SFTP speed correction factors 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 estimates 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-ofday 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. Fireplace and wood stove usage is more likely to occur in the evening after 6 

p.m. For this reason, 90% 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.) 

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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% 90.0%

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 

Point 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 reflected 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-idle, “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 analysis years. 

As discussed earlier, a hybrid approach utilizing locally-generated cold temperature idle emission data in 

combination with the MOBILE6 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 warmed 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 points in the trip 

are described in Table 2.

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 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 How 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)

Duration 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 vehicle 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 MOBILE6 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 

“soaking” 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 categories (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) HBSCH = 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 2007, 2009, 2011, 2013,

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 8

2015, 2017, 2019, 2021 and 2023 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 Periods

9 a.m. – 3 p.m. 

6 p.m. – 7 a.m.

Home-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 homebased, 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 

studied. 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 actually 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 Sierra 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 average of idle times for 

garage and outside-parked vehicles.

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Adopted August 20th, 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 school

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 

winters of 1998-99 and 2000-2001 were used to construct a lookup table that provided an estimate of 

the warm-up idle emissions (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 lookup table were revised slightly from those 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 duty vehicles (LDVs) that make up the 

majority of the Anchorage vehicle population. This assumption is roughly consistent with MOBILE6 

model estimates for HDDV versus LDV emission factors. 

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Adopted August 20th, 2010

 10

Table 5. 

Idle emission look up table for calendar year 2000 (with ethanol-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 from 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 ethanolblended gasoline program, in place at the time that Sierra Research collected this 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 2011. As a consequence the idle emission rate will 

increase slightly.

*

The effect of all these changes on idle emissions can be modeled using MOBILE6. Conformity analysis 

guidance recommends using MOBILE6 emission factors at 2.5 mph to estimate idle emissions. Thus, 

predicted 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 the effect of CO controls such as the 

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

emissions. The hybrid model utilizes a look-up table derived from MOBILE6 model runs that contains 

adjustment factors that 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 time > 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 0.594 to yield the idle emission rate. 

Thus, idle emissions 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 2009) 

 = 94.8 grams x 0.594 = 56.3 grams

 

* Extending 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%.

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Adopted August 20th, 2010

 11

Table 6.

Idle CO adjustment factors 

Estimation 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 reflect 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 Usage 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 1998-1999 and 2000-2001. This research showed that in the year 

2000, engine block heaters reduced 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, the 

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 turnover, and from changes in ethanol-blended gasoline and I/M 

requirements that have or are slated to occur, discount factors were used to adjust the 86 gram per start 

CO reduction estimated from block heater usage in 2000-2001. These 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. 

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Adopted August 20th, 2010

 12

Compute block heater reduction in 2013:

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

Year 2013 cold start idle discount factor (assume no I/M with no oxy gasoline) = 0.61

Year 2013 block heater reduction = 86 g x 0.61 = 52.4 grams per cold start

Between 1999 and 2008, the municipality hired a public opinion 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 MOA 

and ADEC began a television, radio and print media campaign aimed at increasing plug-in rates among 

Anchorage drivers. For morning trips that begin at home initial survey data suggested that plug-in rates 

increased from about 10% in October 1999 to about 20% after the campaign. Since the initial survey, 

the MOA and ADEC have had on-going public awareness and incentives programs 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 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 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 for 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 start idle emission estimates (Table 5), idle emission adjustment factors (Table 6) and block heater 

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Adopted August 20th, 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 homebased trips because plug-ins rates are the highest for those trips.

Results of the spreadsheet calculation of warm-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 

Anchorage 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

(tons)

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 5.71 726,391 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 VMT and speed by road facility category* and trip purpose). The post processor also 

provided 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 MOBILE6 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 the road before 

arriving in the grid of interest. Warm engines emit less CO than cold ones.

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Adopted August 20th, 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 agreed 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 2007, 

2017, and 2027. VMT for intervening years (2009, 2011, 2013, 2015, 2019, 2021, and 2023) was

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 that 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 the 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 estimates 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 important 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 be identical to the adjustment factors determined for major 

arterials. The speed adjustment factors incorporated into the emission inventory spreadsheet are shown 

in Table 9. 

 

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Adopted August 20th, 2010

 15

Table 9. 

Speed Adjustment Factors

Facility Category

Time

Period

Observed Average 

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 prior 

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 these 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. 

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Adopted August 20th, 2010

 16

Table 10 compares 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 in 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 distributions for MOBILE6 with comparable 

operating mode fractions 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 emission factor as a function of speed and thermal state

2007 Anchorage emission inventory

0

5

10

15

20

25

30

35

10 20 30 40 50 60

Vehicle Speed

CO 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 noncatalyst 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%.

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Adopted August 20th, 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 10 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 third 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 that 

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 this 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. 

MOBILE6 Model

The MOBILE6 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 MOBILE6 has the secondary effect of reducing

Vehicle registration 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 MOBILE6 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. When the CO reduction 

provided by I/M in analysis years 2007 and 2009 was modeled with MOBILE6, an I/M 

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Adopted August 20th, 2010

 18

program effectiveness of 85% and 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 2007, 2009, 2011, 2013, 2015, 2017, 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 to 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 = ( () .....()......... ) 1 22 21 36

36

1

VMT1 EF VMT EF VMT EF

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 11. 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 11. 

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) VMT

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 They compiled air pollutant emission estimates for airports across Alaska 

including Ted Stevens Anchorage International Airport (ANC) and Merrill Field Airport in Anchorage. 

Both 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 Administration’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 includes both ground support 

equipment (GSE) and on-board auxiliary power units (APUs) that are used to provide power to aircraft 

32
Adopted August 20th, 2010

 19

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

Table 12. 

Table 12. 

24-hour CO emissions estimates from aircraft at ANC and Merrill 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 operations in 2002 when Sierra 

prepared their emissions estimates were used along with the growth projections 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 13. 

Table 13 

Projected aircraft operations and CO emissions at ANC

Calendar Year

Estimated or 

Projected 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

2021 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 

270,800 in 2020. Assuming linear growth, CO emissions can be projected for the period 2007-2023. 

These projections are shown in Table 14. 

33
Adopted August 20th, 2010

 20

Table 14 

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 estimate 

wood burning were based on data from a telephone survey11

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 previous 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 factors 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 15. 

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Adopted August 20th, 2010

 21

Table 15. 

Estimation of residential wood burning CO emission factors for Anchorage

Device

Average use 

per weekday

(hours per 

household per 

day)

Average dry 

weight of wood 

consumed 

(lbs per hour)*

Average 

amount of 

wood burned 

per household

(dry lbs / day) 

Estimated wood 

burning CO 

emissions 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 + woodstoves 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, 5th 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 15 

suggests 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 2007 - 2023 are shown in Table 16. 

Table 16. 

Estimated Anchorage-wide 24-hour CO emissions from residential wood burning

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

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Adopted August 20th, 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 199012

Table 17. 

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 shown 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 19 indicates that 

about 88% of all homes in Anchorage are heated with natural gas. A small fraction of homes are heated 

by wood 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/ 106 ft3)) 

and small boilers (84 lbs CO/ 106 ft3)

Table 18 

Peak winter season natural gas consumption rates and

CO emission rates in Anchorage (1990)

) were used to characterize residential and commercial space 

heating emission. Calculated peak natural gas consumption and emission rates are shown in Table 18. 

Consumption 

Rate

per Day

AP-42

Emission Factor

(lbs. per 106 ft3)

CO

Emission Rate

(lbs per day)

Residential

658 ft3 

per household 40

0.0263 

per household

Commercial/ Industrial 

434 ft3 

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. 

36
Adopted August 20th, 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 2007 – 2023. Emissions resulting from the 

combustion of natural gas for power generation are excluded. These emissions are accounted for 

separately in the point source inventory.

Table 19

CO Emissions from natural gas combustion (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 Blowers, 

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 Anchorage. 

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 businesses 

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-Anchorage 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 levels for non-road sources would be expected to be reduced on 

37
Adopted August 20th, 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 substantially 

reduced in midwinter. Pressure washer activity, for example, was assumed to be 10% of that estimated 

by NONROAD. Other sources were also adjusted significantly from the NONROAD model’s default 

outputs. These local adjustment factors are shown in Table 20. 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 tons 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 50 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 was 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 20 

Estimation of NONROAD CO emissions in 2007

Number 

of Units

EPA NONROAD 

Model Estimate of 

CO emissions

(unadjusted)

Activity

Adjustment

Factor

Revised CO

Inventory 

Estimate 

(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

38
Adopted August 20th, 2010

 25

In order to estimate future year emissions (2009 through 2023) the sources listed in Table 20 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 21 

CO emissions from NONROAD sources (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 of 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 

was estimated to be 131,600 gallons. Twenty-four hour consumption rates were calculated by dividing 

annual totals by 365.

EPA guidance15

Railroad fuel consumption and emissions are summarized in Table 22. 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. 

39
Adopted August 20th, 2010

 26

Table 22 

Alaska Railroad emission estimates 2007-2023

Consumption

(gal/year)

Consumption

(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, 

between 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 fuel consumption. For the 

purpose of this analysis, CO emissions from the ARR were assumed to remain the same through 2023. 

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.

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Adopted August 20th, 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 emissions estimates for the year 2005 served as the basis for the 2007 base year point 

source emission inventory prepared for this maintenance plan and projections through 2023. Point 

source emissions were expected to grow in relation to the number of households. Thus the emission 

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 emissions 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 generating facilities. 

Other sources include a sewage sludge incinerator, and two bulk fuel storage facilities.

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Adopted August 20th, 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 Elmendorf Air Force Base and Fort Richardson. These 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 the 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 west 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. The 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 factors 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 Point Source Emissions 

The estimates of actual emissions for a typical winter day (in tons per day) at each point source for the 

year 2005 and the projections for 2007 through 2023 are provided in Table 24. 

Table 24 

Point Source CO Emissions Summary (tons per day)

Projected Daily CO Emissions 

based 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

Anchorage 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 & Power, Hank 

Nikkels Plant One 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

42
Adopted August 20th, 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 100.7 tons per day. 

Motor vehicles account for an estimated 65.1% of these area-wide emissions.

Table 25 

Sources of Anchorage CO emissions in 2007 base year in Anchorage inventory area

Source Category

CO Emitted

(tons per day) % of total*

Motor vehicles 67.4 66.7%

Aircraft – Ted Stevens Anchorage International and Merrill 

Field Airport Operations 13.1 12.9%

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 (power generation, sewage sludge 

incineration) 1.3 1.3%

TOTAL 101.0 100.0%

 

Projected Area-Wide CO Emissions (2007-2023) 

As described in the previous sections, CO emissions for the Anchorage inventory area were projected 

for each of the source categories for a 24-hour day in 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021

and 2023. Results are tabulated in Table 26. 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). 

43
Adopted August 20th, 2010

 30

Table 26 

Total CO emitted during typical 24-hour winter day in the 

Anchorage bowl inventory area (tons per day) 

motor vehicles aircraft

year

idle 

mode

travel 

mode

Stevens 

Int'l

Airport

Merril

Field

wood

burning

space

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 2023

CO Emissions (tons per day)

other (nonroad, rail, marine)

point sources

space heating

fireplaces and woodstoves

aircraft

motor vehicles

44
Adopted August 20th, 2010

 31

Compilation of Micro-Area Inventory for Turnagain Monitoring Station 

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 “area-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. This 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 demonstration. In order to perform this 

demonstration, CO emissions in the area immediately surrounding the 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 shows that precise location of the Turnagain 

monitoring station in relation to the area selected for the micro-inventory.

45
Adopted August 20th, 2010

 32

Figure 5 

CO emissions distribution in Anchorage

(Turnagain micro-inventory area boundary noted with red border) 

46
Adopted August 20th, 2010

 33

Figure 6 

Aerial photo of Turnagain micro-inventory area boundary

2007 Base Year CO Micro-Inventory for Turnagain Site 

Results of the 2007 base year micro-inventory for the nine-kilometer2 area surrounding the Turnagain 

station are shown in Table 26. 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 emissions in the area. Note that 

there is no contribution from aircraft operations or point sources in the area.

47
Adopted August 20th, 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, welding, 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 27. 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 28 

Total CO emitted during typical 24-hour winter day when CO is elevated in

Turnagain micro-inventory area (tons per day)

Motor Vehicles Area Sources

idle mode travel mode wood burning space heating other

TOTAL

CO

EMISSIONS

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

 

48
Adopted August 20th, 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 2021

CO 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 microinventory 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. 

49
Adopted August 20th, 2010

 36

Figure 8

CO emission rate by time-of-day in Turnagain CO micro-inventory area (2007)

0

200

400

600

800

1,000

1,200

AM Peak PM Peak Off Peak

CO emission rate (lbs/hr)

other

fireplace and wood stoves

on-road travel

warm-up idle

50
Adopted August 20th, 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 Driving 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

 Fairbanks 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 Inventory, 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 Analysis of the Probability of Exceeding the CO Standard between 2007 and 2023, Municipality of 

Anchorage Department of Health and Human Services, April 2009.

51
Alaska Department of Environmental Conservation

Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III.B.6 

“Analysis 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 20th, 2010

52
Adopted August 20th, 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).

*

Prior 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 standard (NAAQS) in 

each year during the 2007-2023 lifetime of the Maintenance Plan. 

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

demonstration. This 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 2007 

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 program, CO reduction benefits were 

ignored because it is no longer a committed primary control measure in the SIP.

53
Adopted August 20th, 2010 

2 

yp = yh + t(α; n-2) . s{pred}

can be computed using the prediction interval. The prediction interval is defined 

mathematically as follows:

Equation 1

 where 

In 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 corresponding to the upper 90th 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 

base year 2007 design value (2007 DV90%).

‡

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, located in a residential area in west Anchorage, has the 

highest CO concentrations of the four monitors in the current network. (See analysis in the 

Attachment 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 (1999-2008) 

Highest 8-hour average CO 

Concentration (ppm)

2nd 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 industrial 

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.

54
Adopted August 20th, 2010 

3 

An Excel spreadsheet was used to compute the upper 90th percentile prediction interval from 

the second maximum concentrations at Turnagain using Equation 1. The results of this

computation are plotted in Figure 1. Figure 1 shows that there 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

90th Percentile Prediction Interval Computed from Turnagain 2nd Maximum

The precise probability of complying with the 9 ppm NAAQS in 2007 was also estimated with 

the spreadsheet. The probability associated with a second maximum of less than or equal to 

9.0 ppm can be estimated through iteration. 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. Thus, 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 2008

2nd max CO (ppm)

2007 DV90% = 7.23 ppm

55
Adopted August 20th, 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 2nd 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% 10.88

 

Estimating the Probability of Complying with the NAAQS between 2007 - 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 in 

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 future 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 kilometer2 area surrounding the Turnagain CO 

monitoring station for base year 2007 using EPA-prescribed models 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 90th percentile concentration or 2007 DV90%

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 DV2007) 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. 

 

** MOBILE6 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 

Anchorage CO Emission Inventory and Emission Projections 2007-2023, included as Appendix A of 

the Anchorage SIP submittal.

56
Adopted August 20th, 2010 

5 

Table 3

CO Emission Rates Associated with Varying Probabilities of Compliance 

with the NAAQS at the Turnagain Station 

Probability that 2nd 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 kilometer2 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 necessary 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.

57
Adopted August 20th, 2010 

6 

Table 4

Projected CO Emissions and Probabilities for Compliance with the NAAQS (2007-2023)

CO Emissions from Various Sources in the 9 km2 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%

2010 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.

Sensitivity 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 2023?

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.

58
Adopted August 20th, 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 kilometer2 area surrounding the Turnagain station 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 costs 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 two 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 

2007-2023 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 Assumptions used in 

Maintenance Demonstration 

and Probability Computations

Revised “Worst Case” 

Assumptions 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

59
Adopted August 20th, 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%

60
Adopted August 20th, 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 Highway monitors 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 

non-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 

compared 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-Pair 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 the 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.

61
Adopted August 20th, 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-Overlapping 8-hour Average CO Concentrations 

Measured 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 50th

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

concentrations. 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

Rank

Maximum 8-hour CO Concentration (ppm)

Turnagain

Garden

62
Adopted August 20th, 2010 

A-3 

Comparison of Turnagain and Seward Highway Station CO Concentrations 

October 1998 through December 2004 

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 Concentrations 

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 therefore 

appropriate to use this 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

Rank

Maximum 8-hour CO Concentration (ppm)

Turnagain

Seward Hwy

63
Adopted August 20th, 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%

64
Alaska Department of Environmental Conservation

Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III.B.9 

“Municipality of Anchorage Ordinance 2010-35(S)” 

Adopted 

August 20th, 2010

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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 20th, 2010 

122
Appendix 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 Resolution (AR) adopting the revised CO Maintenance 

Plan and an affidavit of an oral hearing to be held by the State of Alaska. The AR and oral 

hearing are expected to occur in spring or early summer 2009. 

Estimation of Background CO Concentration for Anchorage Project-Level 

Conformity Analyses 

Most project-level conformity analyses involve modeling expected CO concentrations from projects 

related to major intersections with high traffic volumes. CAL3QHC modeling assumes that CO 

concentrations predicted at roadway receptors are 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 background 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, which 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 Seward & Dimond, or Lake Otis & Tudor. CO 

concentrations along these major arterials were lower even though their traffic volumes were an order 

of magnitude higher than the neighborhood sites.‡‡ 

Thus, using CO values obtained from residential sites like the Garden or Turnagain site yields a 

background concentration estimate that is unrealistically high for modeling major roadway projects in 

Anchorage. Because most project level analyses 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 Highway 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 conducted 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 and higher CO concentrations 

result. 

§§ The intersection of Seward Highway and Benson Boulevard is the highest volume intersection in Anchorage. 

The 1997-98 CO Saturation Monitoring Study showed that concentrations at this intersection were the highest of 

all intersections monitored. Other monitored intersections included Lake Otis & Tudor, Northern Lights & 

Boniface, Old Seward & Dimond, and Spenard & Minnesota. 

123
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 located 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 between paired hourly concentrations measured at 

these two locations. (Hourly values below 3 ppm were disregarded.) 

Benson Mid-block Monitor Seward Highway Monitor 

124
Appendix to Sections III.B.10 

Figure 2 

Relationship between hourly CO concentrations measured at the Seward Highway Station and a midblock 

location 80 meters west (1997-98 data) 

y = 0.8123x

R2

 = 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 unduly 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” background 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 value for 2008 is described below. A statistical 

approach, relying on the 90th 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 ways to the probabilistic approach used in the 

Anchorage maintenance demonstration. 

125
Appendix to Sections III.B.10 

1. Use the 90th percentile prediction interval to compute the 90th percentile value of the 2nd 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 2004

2nd max CO (ppm)

2004 CO = 5.95 ppm

90th percentile prediction interval

2. Compute the corresponding 90th percentile 8-hour concentration at Benson Midblock in 2004 using 

the slope of the regression relationship shown in Figure 2. 

 Benson Midblock 2004 (90th 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. 

126
g 

g

g

p

nvironmental

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 ofthe Division’s “Quality Assurance Project Plan”.

DATE:

Anchorage, Alaska

Cywffiia Heil, Acting ycogram Manager

SUBSCRIBED AND SWORN TO before me this

[NOTARY SEAL]

i nan.

day of___________

flea

Not ry Public in and for the

State of Alaska

My commission expires: