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; and

Volume II, Section III.B Anchorage Transportation Control Program 

Adopted 

July 13th, 2011 

1
Adopted July 13th, 2011 

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 13, 2011; 

and replacing them with the following regulations: 

• 18 AAC 50 Air Quality Control as amended through July 13, 2011. 

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

Quality Control Plan July 13, 2011 is amended as follows:

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

· Anchorage Assembly Resolution No. 2011-133, dated May 10, 2011, a resolution of 

the Municipality of Anchorage adopting revisions to the Anchorage Carbon 

Monoxide Maintenance Plan (dated April 28, 2011).

 

• 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 April 2011. 

• 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 April 2011. 

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

· Alaska Department of Environmental Conservation’s Affidavit of Oral Hearing.

2
DEPARTMENT OF

ENVIRONMENTAL CONSERVATION

18 AAC 50

Air Quality Control

Adopted

July 13th, 2011

3
Register ___, ____2011 ENVIRONMENTAL CONSERVATION

1 

The lead in language of 18 AAC 50.030 is amended to read: 

18 AAC 50.030. State Air Quality Plan. Volumes II and III of the State Air Quality 

Control Plan for implementing and enforcing the provisions of AS 46.14 and this chapter, as 

amended through July 13, 2011 [FEBRUARY 11, 2011], are adopted by reference. The plan 

includes the following documents which are also adopted by reference: 

. . . 

18 AAC 50.030(7) is repealed: 

(7) repealed ___/___/_____. (Eff. 1/18/97, Register 141; am 6/21/98, Register 146; am 

9/4/98, Register 147; am 1/1/2000, Register 152; am 12/30/2000, Register 156; am 9/21/2001, 

Register 159; am 1/27/2002, Register 161; am 3/27/2002, Register 161; am 5/3/2002, Register 

162; am 2/20/2004, Register 169; am 6/24/2004, Register 170; am 10/1/2004, Register 171; am 

12/14/2006, Register 180; am 12/30/2007, Register 184; am 5/17/2008, Register 186; am 

7/25/2008, Register 187; am 11/9/2008, Register 188; am 5/6/2009, Register 190; am 11/4/2009, 

Register 192; am 4/1/2010, Register 193; am 10/29/2010, Register 196; am 4/13/2011, Register 

198; am__/__/____, Register ___) 

Authority: AS 46.03.020 AS 46.14.030 Sec. 30, ch. 74, SLA 1993 

AS 46.14.020 AS 46.14.140

4
Alaska Department of Environmental Conservation

Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III.B.1

Anchorage Assembly Resolution No. 2011-133 

Adopted

July 13th, 2011

5
6
7
8
9
10
Alaska Department of Environmental Conservation

Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III.B.3 

Anchorage 2007 Carbon Monoxide Emission Inventory and 2007-2023 Emission 

Projections, dated April 2011 

Adopted

July 13th, 2011

11
Adopted July 13th, 2011 

Appendix to Section III.B.3

Anchorage 2007 Carbon Monoxide 

Emission Inventory and 

2007-2023 Emission Projections

Municipality of Anchorage

Department of Health and Human Services

Air Quality Program

April 2011

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Adopted July 13th, 2011 

Table of Contents

1 Introduction .............................................................................................................. 1

2 Inventory Boundary ................................................................................................... 2

3 Anchorage Transportation Model and Inventory Grid System ......................................... 3

4 Time-of-Day Estimates of Emissions Activity............................................................... 5

5 Motor Vehicle Emissions............................................................................................ 6

6 Aircraft Operation Emissions.................................................................................... 13

7 Residential Wood Burning Emissions......................................................................... 15

8 Space Heating Emissions.......................................................................................... 17

9 Non-road Sources.................................................................................................... 19

10 Railroad Emissions.................................................................................................. 21

11 Marine Vessel Emissions.......................................................................................... 22

12 Point Source Emissions............................................................................................ 23

13 Compilation of Area-wide Emissions Summary........................................................... 25

14 Compilation of Turnagain Area Micro-inventory......................................................... 26

15 MOVES Run Specifications and County Data Manager Inputs...................................... 30

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Adopted July 13th, 2011 

List of Tables

Table 4-1 Apportionment of CO Source Activity by Time Period............................................. 5

Table 5-1 Lookup File for Start and Extended Idle CO Emission Factors .................................. 8

Table 5-2 Lookup File for Running CO Emission Factors...................................................... 10

Table 5-3 Sample Computation of Running Emissions for Grid Cell 1104............................... 11

Table 5-4 Summary of Estimated Area-wide-Motor Vehicle CO Emissions in Anchorage......... 12

Table 6-1 24-hour CO Emissions from ANC and Merrill Field in 2002 ................................... 13

Table 6-2 Projected Aircraft Operations and CO Emissions at ANC ....................................... 13

Table 6-3 Projected Aircraft Operations and CO Emissions at Merrill Field Airport ................. 14

Table 7-1 Residential Wood Burning CO Emission Factors for Anchorage.............................. 15

Table 7-2 Anchorage-wide 24-hour CO Emissions from Residential Wood Burning................. 16

Table 8-1 Methods of Home Heating in Anchorage .............................................................. 17

Table 8-2 Peak Natural Gas Consumption and CO Emission Rates in 

Anchorage (1990) .............................................................................................. 18

Table 8-3 CO Emissions from Natural Gas Combustion........................................................ 18

Table 9-1 Estimation of NONROAD CO emissions in 2007 .................................................. 20

Table 9-2 CO emissions from Non-road sources 2007-2023 .................................................. 20

Table 10-1 Alaska Railroad Emission Estimates 2007-2023 .................................................... 21

Table 11-1 Estimated CO Emissions from the Port of Anchorage............................................. 22

Table 12-1 Point Source CO Emissions Summary .................................................................. 24

Table 13-1. Sources of Anchorage CO Emissions in 2007 Base Year in Anchorage 

Inventory Area................................................................................................... 25

Table14-1 Sources of CO Emissions in Turnagain Micro-inventory Area 

2007 Base Year Design Day................................................................................ 27

Table 14-2 Estimated Total 24-hour CO Emissions CO on Design Day in Turnagain 

Micro-inventory Area......................................................................................... 29

Table 15-1 Anchorage Vehicle Populations by MOVES Source Type....................................... 31

Table 15-2 DOT Count-based Estimates of Annual VMT by MOVES Vehicle Type ................. 33

Table 15-3 January VMT Counts as a Percentage of an “Average” Month on Anchorage Roads.. 34

Table 15-4 Distribution of VMT by Hour on Anchorage Roads ............................................... 34

Table 15-5 Gasoline Fuel Formulation Assumptions Used in Anchorage 

MOVES Modeling ............................................................................................. 36

Table 15-6 I/M Assumptions Used in Anchorage MOVES Modeling ....................................... 36

Table 15-7 Comparison of Anchorage Fleet to MOVES Defaults % of Diesel-Fueled 

Passenger Cars .................................................................................................. 37

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Adopted July 13th, 2011 

List of Figures

Figure 3-1 Anchorage Area Inventory Grid System.................................................................. 4

Figure 13-1 Estimated CO Emissions by Source in 2007 .......................................................... 25

Figure 14-1 CO emissions distribution in Anchorage ............................................................... 26

Figure 15-1 Composite Hourly Temperatures on 99th Percentile CO Days in Anchorage .............. 30

Figure 15-2 Comparison of DMV and Survey-Based Vehicle Age Distributions of 

Passenger Cars and Passenger Trucks in Anchorage............................................... 32

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Adopted July 13th, 2011 

1

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). This is the latest of a succession of revisions to a document originally prepared in support 

of the Anchorage CO Maintenance Plan submitted in 2004 and last revised in March 2010. The 

March 2010 revision relied on a MOBILE6-based methodology to estimate and project CO emissions 

from mobile sources in Anchorage.*

This document includes a comprehensive inventory of the sources of CO emissions for base year 

2007. Historically, violations of the CO NAAQS have occurred most often on cold 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. 

 Since that document was prepared the EPA has mandated that all 

conformity analyses and SIP demonstrations utilize a new model called MOVES instead of MOBILE6 

beginning in 2012. As a consequence, this document has been revised to utilize a new MOVES-based 

methodology to prepare new estimates of motor vehicle CO emissions during the 2007-2023 

maintenance planning period. No changes have been made to the emissions estimates that do not rely 

on the MOVES or MOBILE6 models (e.g. point, non-road and area sources) and no substantive 

changes have been made to the narrative discussing these sources. 

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. 

 

*

Actually a modified version of MOBILE6 called AK MOBILE6 was used. AK MOBILE6 differs from the 

conventional MOBILE6 model because it includes methods to estimate CO emissions from extended idling 

of light duty motor vehicles and the increased emission rates that occur from vehicles running before they 

are fully warm. This is explained later in more detail in Section 5.1.

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Adopted July 13th, 2011 

2

2 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 2-1. The inventory area encompasses approximately 200 

square kilometers of the Anchorage Bowl.

Figure 2-1 

Anchorage Maintenance Area with Expanded Inventory Boundary

 

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Adopted July 13th, 2011 

3

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 the metropolitan planning 

organization in the Municipality of Anchorage known as AMATS† 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, postprocessing software can be used to overlay a grid system on the inventory area. The postprocessor is 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 and speed) can be assigned to a particular grid. If a roadway link crosses the boundary 

between two or more grids, its attributes are 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. 

The transportation model generates separate travel activity estimates for the AM peak (7 am – 9 

am), PM peak (3 pm – 6 pm) and off-peak hours (9 am – 3 pm, 6 pm – 6 am) and travel activity 

estimates are further disaggregated by road facility type and trip purpose. ‡, §

Demographic information (population, number of dwelling units, income, and employment 

information) is collected by census tract. Because most census tracts in Anchorage are larger in 

size than the one- kilometer grids the demographic characteristics of a particular grid must 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 3-1.

 

† AMATS stands for Anchorage Metropolitan Area Transportation Solutions. 

‡ There are five road facility types defined in the Anchorage Transportation Model: (1) freeway/expressway; (2) major 

arterial; (3) minor arterial; (4) collector; and (5) local road.

§

 The Anchorage Transportation model categorizes travel into seven purposes: (1) home-based work, (2) home-based 

school, (3) home-based shopping, (4) home-based other, (5) non home-based work, (6) non home-based, non-work; and 

(7) freight-related truck trips. Thus, for each time period, the model produces estimates of the number of trip starts and 

VMT in each grid by trip purpose.

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Adopted July 13th, 2011 

4

Figure 3-1 

Anchorage Area Inventory Grid System

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Adopted July 13th, 2011 

5

4 Time-of-Day Estimates of Emissions Activity

Separate estimates of mobile source CO emissions were prepared for the morning commute (7 

a.m. – 9 a.m.), the evening commute (3 p.m. – 6 p.m.) and combined off-peak periods (6 p.m. – 7 

a.m. and 9 a.m. – 3 p.m.). These estimates relied on time-of-day activity estimates (e.g., number 

of trip starts and VMT) generated by the Anchorage Transportation Model. A 24-hour inventory 

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

Activity estimates for non-vehicular sources were available on a 24-hour basis only, however. 

Time-of-day estimates had to be developed from these 24-hour values. For some sources (e.g. 

airport, natural gas combustion), activity was assumed to be continuous throughout the day and 

emissions were apportioned accordingly. 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 4-1 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.)

Table 4-1 

Apportionment of CO Source Activity by Time Period

Source Category

AM Peak.

7 a.m. – 9 a.m.

PM Peak.

3 p.m. – 6 p.m.

Off-Peak

9 a.m. – 3 p.m. 

6 p.m. – 7 a.m. Comments

motor vehicle start and 

running emissions

From model

(~20%)

From model

(~25%)

From model

(~55%)

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

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Adopted July 13th, 2011 

6

5 Motor Vehicle Emissions

The EPA has mandated the use of MOVES for all SIP planning and conformity determinations 

beginning March 2012. In a preliminary analysis, Sierra Research showed that MOVES-based 

mobile source CO emissions estimates for the Anchorage CO inventory area were 50% or more 

greater than those produced by the current AK MOBILE6 model. This means that any conformity 

analysis performed after March 2012 will generate substantially higher estimates of emissions. If 

the current AK MOBILE6-based Anchorage CO emissions budget in the Alaska SIP is not 

amended before then Anchorage could exceed the allowable mobile source emission budget. For 

this reason, Anchorage has decided to re-estimate mobile source emissions during the 2007-2023 

maintenance planning horizon and amend the existing budget using the new MOVES-based 

emission estimation methodology. This section describes how MOVES was utilized along with the 

Anchorage Transportation Model to develop an amended CO emission inventory and projections. 

This same methodology will be used to develop a new MOVES-based mobile source emission 

budget and for future conformity determinations.

5.1 Overview of Previously Used AK MOBILE6-based Emission Estimation Methodology

Mobile source CO emissions estimates previously relied on emission factors produced from a 

modified MOBILE6-based emissions factor model known as AK MOBILE6. These emission 

factors were applied to pertinent traffic activity outputs from the Anchorage Transportation Model 

to estimate emissions. Emission factors produced by AK MOBILE6 were applied to the activity 

estimates generated by the transportation model’s post-processor to generate CO emissions 

estimates for each grid. AK MOBILE6 differed from the standard version of MOBILE6 because it 

included an off-model computation of start emissions that was based on local cold start emissions 

data collected in Alaska by Sierra Research. For Anchorage, these off-model computations were 

made with a spreadsheet model that allowed factors such as soak time, average idle duration and 

the proportion of vehicles that are plugged-in (i.e., using a block heater prior to start-up) to be 

accounted for in the estimation of start emissions. These factors varied by time of day and trip 

purpose. For example, during the AM peak, the assumed average idle duration for a home-based 

work trip (7 minutes) was longer than a non-home based, non-work trip (1 minute). The 

spreadsheet model was used to compute start emissions in a particular grid from the 

transportation model’s estimate of the number of vehicle starts by each trip purpose in that grid 

and the assumed idle duration, soak time and block heater plug-in rates for those trips. Look-up 

tables containing start emission factors as a function of idle duration and soak time were used to 

estimate emissions for the starts in the grid.**

For running emissions, the “conventional” MOBILE6 model was used to generate spreadsheet 

lookup tables with gram per mile emission factors by speed and thermal state. MOBILE6 allowed 

the user to supply assumptions regarding the soak time distribution of the vehicles started by timeof-day and running emission factor estimates were very sensitive to these assumptions.

 

††

 

** These look-up tables were developed by Sierra Research from emissions data collected in Alaska during the winter of 

2000-2001. The spreadsheet also utilizes Sierra Research data to compute the CO reductions from block heater usage.

 

Modeled emissions were higher for those time periods when a large proportion of vehicles of a 

particular trip purpose were assumed to have had long soak times and lower when most soak 

times are short. For example, home-based work trips during the AM peak had higher CO 

emission rates because they included a large proportion of vehicles with long soak times.

†† Soak time is the amount of time that a vehicle has been parked with the engine off prior to startup. Sierra used six 

different soak time distributions to characterize the thermal state (i.e., how warmed up they were) of the vehicles 

operating on a road at a particular time. 

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Adopted July 13th, 2011 

7

Sierra Research defined six soak time distributions to characterize the thermal state of the fleet of 

vehicles operating in a particular grid during each of the three time periods. Travel model outputs 

were used to determine which of these six soak distributions was most appropriate for modeling 

the running emissions on a particular road facility type (e.g., freeway vs. collector) and trip purpose 

for each grid. The emission factors generated by MOBILE6 were then incorporated into a lookup 

table in the spreadsheet model. The spreadsheet could then identify an appropriate running 

emission factor based on the travel model’s estimate of the average speed and thermal state of 

vehicles operating on the different road facility types within each grid. These emissions factor 

were multiplied by the VMT in the grid to compute running emissions from the VMT in that grid for 

each trip purpose and road facility type. 

The spreadsheet computed CO emissions by summing the start and running emissions from all 

200 grids in the inventory area. For conformity determinations these emissions were compared to 

the emission budget in the SIP. The CO maintenance demonstration examined emissions from a 

nine-grid sub-area surrounding the Turnagain CO monitor in west Anchorage, an area believed to 

be representative of the highest CO concentrations in the Anchorage area... 

5.2 New MOVES-based Methodology

The new MOVES-based methodology used to develop the inventory and projections in this 

revised document mirrors the AK MOBILE6-based methodology in many ways. It still relies on 

the same basic grid-based travel activity outputs (e.g., vehicle starts, VMT, average speed) that 

the AK MOBILE6 method used. The MOVES methodology uses a modified version of the 

spreadsheet model previously employed, substituting MOVES-generated running and start 

emission factors for the AK MOBILE6 factors used previously.

Perhaps the biggest difference in the new MOVES vs. AK MOBILE6-based method is that the 

new spreadsheet model no longer relies on local emission test data produced by Sierra Research 

to calculate start emissions; it uses a single fleet-wide start emission factor that varies by hour of 

the day, generated by MOVES. This simplifies spreadsheet computations; the AK MOBILE6 

spreadsheet generated seven separate start emission factors corresponding to each trip purpose 

because each trip purpose had different assumptions about the soak time and idle duration that 

could affect both start and running emissions. Although MOVES considers the soak distribution 

(by hour) in the computation of start emissions, the running emission rate is independent of both 

ambient temperature and soak time. 

To model emissions, MOVES requires extensive user-supplied model inputs that reflect local 

conditions such as vehicle fleet characteristics, fuel composition, ambient temperature, I/M 

program characteristics, road type distribution. These inputs, and how they are be derived are 

discussed in the last section of this report (see MOVES Run Specifications and County Data 

Manager Inputs. 

5.2.1 MOVES-based Start Emissions Computation

As noted earlier, the Anchorage Transportation Model provides estimates of the number of vehicle 

starts occurring in each of the 200 one-km2

We have found that the simplest way to do this is to run MOVES and direct the model to output 

emissions and activity levels only for those processes relating to start and/or extended idle 

grids in the inventory area for three separate time 

periods: AM peak, PM peak and off-peak periods. In order to use this detailed information, our 

objective was to get MOVES to produce an emission factor that would provide estimate of 

average CO emissions per vehicle start (grams CO per start).

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Adopted July 13th, 2011 

8

emissions.‡‡

When MOVES is run using the inventory computation option, it generates a number of MYSQL 

output files. Two of these are of particular interest. The first details the level of process activity 

(e.g., number of vehicles starts, hours of extended idling by combination long haul trucks for each 

hour of the day and the second details the quantity of CO emitted each hour from each process 

activity.

 Specifically these processes are: (1) start exhaust; (2) crankcase start exhaust; (3) 

crankcase extended idle exhaust; and (4) extended idle exhaust. Although MOVES provides an 

emission rates computation option (in Scale on the Navigation Panel), we have found that running 

MOVES using the inventory option is an easier method of computing emission rates. 

§§

Table 5-1 

Lookup File for Start and Extended Idle CO Emission Factors

 The emission factors required for the spreadsheet model (e.g., CO emitted per vehicle 

start) can then be easily computed on an hour-by-hour basis from the two MYSQL output files. A 

lookup file for these emission factors can be created from the MYSQL files for use in the 

spreadsheet model. An example of such a lookup file is shown in Table 5-1 below.

MOVES Start & Idle EF

2007 Base Year with I/M 

Ending Hour

% of daily starts 

in hour

tailpipe + crankcase CO

(g/start)

% of daily extended 

idling from long haul 

trucks in hour

extended idle CO

g/ truck start

1 0.75% 67.36 6.28% 142.11

2 0.34% 115.21 6.11% 170.18

3 0.11% 144.20 5.77% 109.97

4 0.20% 139.88 5.26% 105.84

5 0.37% 172.69 4.58% 75.42

6 0.76% 190.40 4.07% 61.45

7 3.80% 190.41 3.57% 30.73

8 5.98% 166.07 3.06% 26.98

9 6.33% 149.02 2.89% 38.69

10 5.51% 101.19 2.55% 21.95

11 5.02% 103.63 2.38% 23.25

12 6.98% 101.78 2.21% 33.29

13 7.27% 89.94 2.21% 33.29

14 6.35% 104.91 2.21% 29.59

15 5.97% 103.30 2.38% 28.68

16 7.87% 115.08 2.55% 27.11

17 7.74% 108.91 3.23% 83.40

18 7.71% 123.00 3.74% 150.22

19 6.85% 118.64 4.58% 207.41

20 4.79% 111.59 5.26% 136.08

21 4.02% 131.62 5.94% 195.53

22 2.39% 111.08 6.28% 227.38

23 1.95% 135.95 6.45% 291.90

24 0.95% 133.77 6.45% 259.47

 

‡‡ In MOVES extended idle emissions refer only to idle emissions from combination long haul trucks. Other extended idle 

emissions such as those that occur among passenger cars and trucks during long warm up periods prior to the morning 

commute are not included. Extended idle emissions from combination long haul trucks make up a very small portion of 

total CO emissions in Anchorage. 

§§ The Anchorage Transportation Model does not provide an estimate of the hours of extended idling among long haul 

trucks. It does, however, provide an estimate of the number of freight truck starts. Thus, the extended idle emission factor

was related back to the MOVES estimate of long haul truck starts (MOVES source id = 62) rather than hours of extended 

idling. The resulting emission factor was therefore grams CO emitted per long haul truck start. 

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Adopted July 13th, 2011 

9

The spreadsheet model applies a weighted average emission factor from the lookup table above 

to the amount of start or extended idle activity estimated by the transportation model for the time 

period in question.***

The spreadsheet model assigns start and extended idle emissions to the grid cell where the 

transportation model determined the vehicle start to have occurred.

For example, during the AM peak period (7 am – 9 am), the weighted 

average tailpipe + crankcase start emission factor is 157.3 g/start. If the transportation model 

estimated that there were 800 starts in a particular grid cell, computed start emissions in that grid 

would be 125,840 grams or 277 lbs. A different start emission factor would be used for PM and 

off-peak starts. For example, the start emission factor for the PM peak (3 pm – 6 pm) is lower 

(115.7 g/start) than the AM peak because vehicles started during that period, on average, have 

shorter soak times and warmer engines than those started in the morning.

†††

Even though the spreadsheet model has the capability of estimating the benefits of engine block 

heater usage, the previous CO maintenance plan did not take credit for these benefits. For 

consistency, this “MOVES-amended” 2007 base year inventory and maintenance projections 

does not either.

5.2.2 MOVES-based Running Emissions Computation

As is the case with start emissions, MOVES requires extensive user-supplied inputs to estimate 

running emissions. These inputs and the run specification used to generate running emissions are 

discussed in detail in Attachment to this appendix. As noted earlier, the Anchorage Transportation 

Model provides grid-based estimates of VMT and vehicle speed by road facility type three 

separate time periods. This subsection will discuss how MOVES is used to generate the running 

emission factors (grams per mile) necessary to estimate running emissions in each of the model 

grids from the transportation model estimates of travel activity.

We have found that the simplest way to generate running CO emission factors is to run MOVES in 

the emission rates rather than the inventory mode used to generate start and extended idle 

emission factors. MOVES includes two processes that relate to running emission factors: (1) 

running exhaust; and (2) crankcase running exhaust. Using the emission rates mode, we select 

these two pollutant processes and MOVES will generate emission factors by speed bin and road 

type for both processes. The emissions from both of these processes are independent of ambient 

temperature and time-of-day, so the MOVES model output is fairly simple. Because we are using 

the emission rates mode, MOVES generates a MYSQL output file, called rateperdistance that

provides emission factors in grams per mile. A spreadsheet model lookup table can be derived 

from the MYSQL output file generated by the MOVES run. Because the MOVES output produces 

emission factors by speed bin, we use an interpolation process to produce emission factors in one 

mile per hour increments for use in the lookup table. 

The Anchorage Travel Model produces estimates of the VMT in each grid disaggregated into five 

facility types. MOVES emission factors for restricted access road (road type = 4) is applied to 

transportation model estimates of the VMT accrued on freeways and expressways and MOVES 

emission factors for unrestricted access roads (road type = 5) are applied to VMT accrued on 

 

*** The weighted average emission factor for each time period is determined by weighting the emission rate for each hour 

in the time by the MOVES proportion of starts that occur in those hours. Example:

 AM peak start EF = (166.07 x 5.98% +149.02 x 6.33%)/(5.98% + 6.33%) = 157.3 g/start

††† MOVES defines start emissions as “the addition to running emissions caused by the engine start.” Unless a vehicle 

spends a substantial time warming up, a large portion of these “start emissions” occur as the vehicle moves during the 

first part of its trip. Thus, it is likely that a portion of some start emissions occur in grid cells other than the one assigned by 

the model. 

24
Adopted July 13th, 2011 

10

local, collector, minor arterial and major arterial roadways. ‡‡‡ The transportation model provides 

speed estimates for each of the five facility types within each grid. These speed estimates are 

used to select the appropriate running emission factor in the spreadsheet look-up table.§§§ The 

estimated VMT on the five road facility types in each grid is multiplied by the appropriate emission 

factor to estimate running emissions. Table 3 shows an example of a spreadsheet emission 

factor lookup table (portions of the look-up table have been cut so that it can fit on one page).

Table 5-2 

Example Spreadsheet Lookup File for Running CO Emission Factors

Base Year 2007 MOVES

Interpolated Running Emission Factors (with I/M)

by Road Type

Speed

speed

bin

road type=4

urban restricted

road type=5

urban restricted

2.5 1 45.206 44.658

3.0 1 41.326 40.991

4.0 1 33.566 33.657

5.0 2 25.806 26.323

6.0 2 23.899 24.569

.

. 

.

.

. 

.

.

. 

.

.

. 

.

27.0 6 10.362 10.919

28.0 6 10.346 10.773

29.0 6 10.330 10.626

.

. 

.

.

. 

.

.

. 

.

.

. 

.

55.0 12 10.965 8.151

56.0 12 10.902 8.181

57.0 12 10.839 8.210

58.0 12 10.776 8.239

59.0 12 10.712 8.269

.

. 

.

.

. 

.

.

. 

.

.

. 

.

72.0 15 12.909 10.928

73.0 15 13.366 11.442

74.0 15 13.824 11.956

75.0 16 14.281 12.470

 

‡‡‡ MOVES actually has four road types (rural restricted access, rural unrestricted access, urban restricted access, and 

urban unrestricted access) but Anchorage only has the two urban-type roads in its CO inventory area.

§§§ Vehicle speed estimates generated by the Anchorage Transportation Model were significantly different than those 

measured in a travel time study conducted by the Municipality and the Alaska Department of Transportation in 1998. 

Empirical speed correction factors, derived from that travel time study are applied to transportation model speed 

estimates. To match travel time study estimates, transportation model estimates of speed for freeway/expressways are 

increased by 17% and speeds on collectors and minor and major arterial roadways are reduced by 17%. A “default” 

speed of 15 mph is assumed for VMT on local roads. 

25
Adopted July 13th, 2011 

11

The lookup table is used in conjunction with Anchorage Transportation Model estimates of VMT 

and speed on each facility type within a grid to estimate running emissions within the grid. Table 4 

shows a sample computation of running emissions for Grid Cell ID =1104 (an area near the 

intersection of Northern Lights Boulevard and Seward Highway) during the AM peak period in 

2007.

Table 5-3 

Sample Computation of Running Emissions for Grid Cell 1104

Facility Type

MOVES 

Road 

Type

VMT

(miles)

Speed

(mph)

Emission 

Factor

(g/mi)

CO Emissions

(lbs)

Freeway/Expressway 4 1,751 57.4 10.8 42

Major Arterial 5 5,119 31.0 10.3 116

Minor Arterial 5 3,001 29.8 10.6 70

Collector 5 44 23.6 11.9 1

Local Streets 5 474 15.0 14.8 15

TOTAL -- 10,390 244

5.3 Summary of Motor Vehicle Emissions

Table 5-4 summarizes key motor vehicle activity (vehicle starts, truck starts and VMT) along with 

the emissions resulting from this activity. The vehicle inspection and maintenance program (I/M) 

was assumed to be operating in 2007 and 2009 with a 4-year grace period for new vehicles and 

with a 6-year grace period in 2011. The program was assumed to be discontinued in 2013 and 

beyond. Start emissions make up the greatest part of all motor vehicle emissions. They make up 

about 68% of motor vehicle emissions in base year 2007. Their contribution grows to about 78% 

by 2023. 

 

** ** When start and running emission rates during the period 2007-2023 were examined, running emission rates were 

found to decrease steadily (as expected) but start emission rates generated by MOVES actually increased between 2007 

and 2015. Given the fact that the vehicle fleet is normally presumed to become newer and cleaner over time, especially 

with the phase-in of new EPA-mandated vehicle cold temperature emission standards in 2010, it suggested an anomaly 

in the model. We understand that EPA is investigating and that if a problem is found it will be remedied in a future release 

of MOVES. The impact on the overall emission trend analysis presented here and in the CO Maintenance Plan is 

relatively insignificant. 

Start EF

120.0

125.0

130.0

135.0

2007 2011 2015 2019 2023

avg grams per start

without I/M

with I/M

Running EF

5.0

7.0

9.0

11.0

13.0

2007 2011 2015 2019 2023

grams per mile

without I/M

with I/M

26
Adopted July 13th, 2011 

12

Table 5-4 

Summary of Estimated Area-wide-Motor Vehicle CO Emissions in Anchorage

Year

Start Emissions

Extended Idle 

(Truck only) Running Emissions

Total CO 

Emissions

(tons/day)

I/M 

status

Total 

Starts

(per 

day)

Start 

Emissions

(tons/day)

Truck

Starts

(per 

day)

Extended 

Idle 

Emission

(lbs)

VMT

(mi/day)

Running 

Emissions

(tons/day)

2007 yes 639,007 84.83 2,496 0.34 3,344,312 40.46 125.62

2009 yes 647,609 87.23 2,533 0.29 3,417,283 36.17 123.70

2011 yes 656,211 89.39 2,571 0.35 3,490,253 33.40 123.14

2013 no 664,813 93.80 2,608 0.36 3,563,224 34.28 128.44

2015 no 673,415 95.37 2,645 0.36 3,636,195 31.59 127.32

2017 no 682,017 96.33 2,682 0.37 3,709,166 28.84 125.55

2019 no 692,026 97.72 2,711 0.38 3,779,015 28.62 126.71

2021 no 702,035 99.14 2,739 0.39 3,848,865 27.78 127.31

2023 no 712,044 100.26 2,767 0.39 3,918,715 27.46 128.11

27
Adopted July 13th, 2011 

13

6 Aircraft Operation Emissions

In June of 2005 Sierra Research, Inc. prepared the “Alaska Aviation Inventory.”††††

Table 6-1 

24-hour CO Emissions from ANC and Merrill Field in 2002

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

shown in Table 6-1. 

Aircraft Support 

Equipment

APU and GSE

(tons per day)

Aircraft

(tons per day)

TOTAL

(tons per day)

ANC 8.21 3.32 11.53

Merrill 0.00 0.63 0.63

The ANC 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 

6-2. 

Table 6-2 

Projected Aircraft Operations and CO Emissions at ANC

Calendar Year

Estimated or 

Projected Annual 

Aircraft 

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

 

†††† Alaska Aviation Emission Inventory, prepared for the Alaska Department of Environmental Conservation, June 2005.

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Adopted July 13th, 2011 

14

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 6-3. 

Table 6-3 

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 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

29
Adopted July 13th, 2011 

15

7 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 survey‡‡‡‡

The ASK survey asked Anchorage residents how many hours per week they burned wood in their 

fireplace or wood stove. 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 7-1.

performed by ASK Marketing and Research in 1990.

Table 7-1 

Residential Wood Burning CO Emission Factors for Anchorage

Appliance 

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

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 9 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 

 

‡‡‡‡ “Air Quality Survey of Anchorage Residents,” prepared by ASK Marketing & Research for the Municipality of 

Anchorage, April 1990.

30
Adopted July 13th, 2011 

16

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 7-2.

Table 7-2 

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

31
Adopted July 13th, 2011 

17

8 Space Heating Emissions

A telephone survey conducted by ASK Marketing and Research in 1990indicated that natural gas 

is the fuel used for virtually all space heating in Anchorage. ASK survey results are shown in 

Table 8-1.The methodology used to compute natural gas space heating emissions for this 

maintenance demonstration is identical to that used in the Year 2000 Anchorage CO Attainment 

Demonstration and the 2004 Anchorage CO Maintenance Plan. 

Table 8-1 

Methods of Home Heating in Anchorage 

(ASK Marketing & Research, 1990)

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.§§§§

A detailed report of natural gas sales to residential, commercial and industrial customers was 

available for calendar year 1990***** †††††

 Table 

11 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. 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)

 

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

) were 

used to characterize residential and commercial space heating emission. Calculated peak natural 

gas consumption and emission rates are shown in Table 8-2.

***** Although data from more recent years were 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. 

††††† FERC Form No. 2 (ED 12-88), submitted by ENSTAR Natural Gas Company, 1991.

32
Adopted July 13th, 2011 

18

Table 8-2 

Peak Natural Gas Consumption and CO Emission Rates in Anchorage (1990)

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

On an area-wide basis, CO emissions from natural gas combustion were calculated by multiplying 

the CO emission rates in Table 13 by the number of households and employees in the inventory 

area. Table 8-3 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 8-3 

CO Emissions from Natural Gas Combustion

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

* excludes natural gas used by utilities for electrical power generation

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. 

33
Adopted July 13th, 2011 

19

9 Non-road Sources

Non-road sources include miscellaneous fuel burning sources such snowmobiles, chain saws, 

portable generators, snow blowers and other equipment used for snow removal. 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 non-road equipment 

populations and usage (i.e., hours per year) were reasonable. The NONROAD model uses a topdown 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 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 14. 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 nearly equal in magnitude to the 

MOVES estimate for motor vehicle emissions (125.7 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 snow blowers were also reduced. 

Anchorage climatological records indicate that CO exceedances are typically preceded by cold, 

clear weather without snow. Thus, snow blower activity is likely to be lower on elevated CO days. 

For this reason the NONROAD estimate of residential and commercial snow blower activity was 

cut by 50%. 

The NONROAD model default estimate for the snowmobile 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. Because there is no commercial logging in 

the CO maintenance area, the NONROAD estimate of CO emissions from logging equipment 

chain saws was disregarded and it was cut by 80% to reflect that little garden or home wood 

cutting activity is likely to take place in mid-winter. 

34
Adopted July 13th, 2011 

20

Table 9-1 

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

To estimate future year emissions, the sources listed in Table 9-1 were increased in proportion to 

growth in households or employment. If the non-road source was primarily related to household 

activities, the growth in emissions was assumed to mirror the projected growth in households. 

Household-related sources include snowmobiles and residential snow blowers. For sources 

primarily related to commercial activity such as welders, pumps and air compressors, growth in 

emissions was tied to growth in employment. Non-road emission projections are shown in 

Table 9-2.

Table 9-2 

CO emissions from Non-road sources 2007-2023 (tons per day)

Calendar Year

CO Emissions from Nonroad Sources 

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

35
Adopted July 13th, 2011 

21

10 Railroad Emissions

Because railroad emissions are a relatively insignificant source of CO, no changes have been 

made to the estimates or methodology originally 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 guidance‡‡‡‡‡

Railroad fuel consumption and emissions are summarized in Table 10-1. 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. 

Table 10-1 

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.

 

‡‡‡‡‡ EPA Technical Highlights Document, EPA 420-F-97-051, December 1997.

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Adopted July 13th, 2011 

22

11 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.§§§§§

******

 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. 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 11-1.

Table 11-1 

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

 

§§§§§ Commercial Marine Inventories for Select Alaska Ports, prepared for the Alaska Department of Environmental 

Conservation by E.H. Pechan and Associates, June 2005.

****** 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.

37
Adopted July 13th, 2011 

23

12 Point Source Emissions

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 nonattainment 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.

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 

38
Adopted July 13th, 2011 

24

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 AP42 factors identified above was assumed for the inventories. Thus 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.

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 12-1. 

Table 12-1 

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

39
Adopted July 13th, 2011 

25

13 Compilation of Area-wide Emissions Summary

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. Table 13-1 shows that total area-wide CO emissions 

are estimated to be 159.5 tons per day. Motor vehicles account for an estimated 78.8%. Figure 

13-1 shows that most of these motor vehicle emissions are from start emissions.

Table 13-1.

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

Source Category

CO Emitted

(tons per 

day) % of total

Motor vehicles 125.6 78.8%

Aircraft Operations

Ted Stevens Anchorage International and Merrill Field Airport 13.1 8.2%

Wood burning – fireplaces and wood stoves 6.2 3.9%

Space heating – natural gas 3.8 2.4%

Miscellaneous (snowmobiles, snow removal, welding, rail, marine, 

etc.) 9.3 5.8%

Point sources (power generation, sewage sludge incineration) 1.3 0.8%

TOTAL 159.3 * 100.0%

*

Total does not add to 100.0% because of rounding.

Figure 13-1 

Estimated CO Emissions by Source in 2007

Running

Start & Idle

Other Merrill

Field

Space Heating

Wood Heating

Point 

Sources

Stevens

 Int'l

Airport

Combined, motor 

vehicle emissions 

account for 78.8% 

of the total 2007 

CO inventory

40
Adopted July 13th, 2011 

26

14 Compilation of Turnagain Area Micro-inventory

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 (see Figure 14-1), has exhibited the 

highest CO concentrations of all the monitoring stations in Anchorage. 

Figure 14-1 

CO emissions distribution in Anchorage

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

41
Adopted July 13th, 2011 

27

During the 1997-98 CO Saturation Study 8-hour CO concentrations at Turnagain were the highest 

among the 20 sites included in the study.††††††

‡‡‡‡‡‡

 Even though the probability of violating the CO 

NAAQS at Turnagain is estimated to be just 1-in-100, analysis suggests that the probability of 

violating the standard at other monitoring stations is much lower. 

Because the Anchorage inventory data is disaggregated into one-kilometer

For this reason, the 

Turnagain site is being 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

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

station are shown in Table 14-1 for a “design day” when conditions are conducive to the highest 

ambient CO concentrations. Emissions were modeled for a cold January weekday, when hourly 

temperatures vary from 2.6 to 6.2 deg F. Under these conditions total CO emissions in the microinventory area were estimated to be 10.23 tons per day. Motor vehicles account for an estimated 

84.4% of the emissions in the area. Note, unlike the area-wide inventory, there is no contribution from 

aircraft operations or point sources in the area. Motor vehicles account for 84.4% of all CO emissions 

in the micro-inventory area. 

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 14-1. 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. 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.

Table14-1 

Sources of CO Emissions in Turnagain Micro-inventory Area 2007 Base Year

Design Day

Source Category

CO Emitted

(tons per day) % of total

Motor vehicles 8.61 84.4%

Wood burning – fireplaces and wood stoves 0.62 6.1%

Space heating – natural gas 0.28 2.7%

Miscellaneous (e.g.; snowmobiles, snow removal) 0.70 6.8%

TOTAL 10.20 * 100.0% * Total does not add to 10.20 tons per day because of rounding.

Projected emissions in the Turnagain micro-inventory area are tabulated for the period 2007-

2023 in 

Table 14-2. CO emissions increase slightly in 2013 due to the assumed termination of the I/M 

Program in 2012 but decline steadily thereafter. In contrast to the slight 6.6% increase in CO 

emissions projected area-wide between 2007 and 2023, emissions in the Turnagain area are 

expected to decline by about 5% during this same period. This is because slower rates of 

 

†††††† Winter 1997-98 Anchorage Carbon Monoxide Saturation Study, Municipality of Anchorage Department of Health 

and Human Services, September 1998.

‡‡‡‡‡‡ Analysis of the Probability of Exceeding the CO Standard between 2007 and 2023, Municipality of Anchorage 

Department of Health and Human Services, February 2011.

42
Adopted July 13th, 2011 

28

growth in population and vehicle travel are projected in the Turnagain area than the 

Anchorage bowl as a whole. 

43
Adopted July 13th, 2011 

29

Table 14-2 

Estimated Total 24-hour CO Emissions CO on Design Day in

Turnagain Micro-inventory Area (tons per day)

Projected CO Emissions by Source

motor vehicles

projected 

population

start & 

extended 

idle running

wood 

burning

space 

heating other TOTAL

2007 19,776 6.01 2.60 0.62 0.28 0.70 10.20

2009 20,090 6.14 2.28 0.63 0.28 0.71 10.03

2011 20,404 6.26 2.08 0.64 0.28 0.71 9.97

2010 20,247 6.20 2.18 0.63 0.28 0.71 10.00

2013 20,718 6.52 2.11 0.65 0.28 0.72 10.29

2015 21,032 6.59 1.92 0.66 0.29 0.73 10.18

2017 21,346 6.62 1.72 0.67 0.29 0.73 10.03

2019 21,536 6.58 1.64 0.68 0.29 0.74 9.93

2021 21,725 6.55 1.55 0.68 0.29 0.75 9.82

2023 21,915 6.49 1.48 0.69 0.30 0.76 9.71

44
Adopted July 13th, 2011 

30

15 MOVES Run Specifications and County Data Manager Inputs

The EPA provides guidance on how MOVES should be run for SIP-related emission inventories and 

for conformity determinations in a guidance document.24 It stipulates that MOVES should be run 

using the County domain scale. When MOVES is run under this domain scale it requires a series of 

user-supplied local data files, designated in the MOVES County Data Manager, that reflect the 

specific characteristics of the area being modeled. There are nine data files required. Each of these 

data files, and the origin of the data within them, is discussed. In addition, this section also discusses 

alternative vehicle fuels and technology (AVFT) inputs used to characterize the Anchorage gasoline 

vs. diesel fueled fleet in MOVES.

15.1 Meteorology Data 

Anchorage experiences its highest CO in mid-winter. We examined hourly ambient temperature data 

on those days with the very highest 8-hour average CO (criteria ≥ 99th percentile) at the Turnagain 

monitor between 1999 and 2009.25

Figure 15-1 

The 21 days with the highest 8-hour average CO concentration 

were selected from this 11 year period and a composite diurnal temperature profile was developed 

from data collected by the NWS at Ted Stevens Anchorage International Airport. Note that there is 

very little variation over the course of the day.

Composite Hourly Temperatures on 99th Percentile CO Days in Anchorage

The hourly data above has been used as temperature inputs when MOVES is used to estimate 

starting emissions. (Ambient temperature assumptions have no effect on CO running emissions.) 

Although CO emissions estimates are unaffected by relative humidity (RH) assumptions, MOVES 

requires field for RH to be filled in; 80% was used. 

 

24 Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation 

Plans and Transportation Conformity, EPA 420-B-09-046, December 2009

25 The 99.5 percentile criterion was normalized to account for the downward trend in CO that has occurred over 

the past decade. In 1999 the 99.5 percentile concentration was 7.2 ppm; in 2009 it was 5.3 ppm. 

4.8 4.5

3.3 3.3 3.4 2.9 2.7 3.6 3.4 3.3

4.6 5.1

6.2 6.0 5.6 5.3

3.9 3.6 3.2 3.9 3.2 3.8 3.0 2.7

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

ambient temperature (deg F)

45
Adopted July 13th, 2011 

31

15.2 Source Type Population

ADEC hired Sierra Research, Inc. to characterize the source type population or types of vehicles 

(e.g.; passenger car, passenger truck, combination long haul truck) in the Anchorage fleet by using a 

VIN (vehicle identification number) decoder to examine the Alaska DMV database narrowed to zip 

codes in the Anchorage CO inventory area. This effort has provided an excellent estimation of the 

number of vehicles in each of the 13 vehicle types defined by MOVES.

The result of this effort (excerpted from Sierra’s report to ADEC) is shown in Table 15-1. Although the 

VIN decoder estimate revealed a motorcycle population of 8,446 in the inventory area, the effective 

population was assumed to be zero in January when CO emissions were modeled with MOVES.

Table 15-1 

Anchorage Vehicle Populations by MOVES Source Type

Source 

Type ID Source Type Description

Vehicle 

Population

Assumed % 

Growth between 

2007 and 2023

11 Motorcycle

8,446

0a 0.0%

21 Passenger Car 62,404 27.5%

31 Passenger Truck 122,558 27.5%

32 Light Commercial Truck 12,371 20.3%

41 Intercity Bus 195 13.3%

42 Transit Bus 242 27.5%

43 School Bus 328 13.3%

51 Refuse Truck 85 13.3%

52 Single Unit Short-haul Truck 1,370 13.3%

53 Single Unit Long-haul Truck 118 13.3%

54 Motor Home 5,499 0.0%

61 Combination Short-haul Truck 941 13.3%

62 Combination Long-haul Truck 601 13.3%

Total Vehicle Fleet 215,158 a

 Motorcycle activity in Anchorage during the winter months was assumed to be zero.

Vehicle populations (and the distribution among vehicle types) had to be projected through 2023. 

Historical I/M data were examined and that data suggested that the I/M eligible vehicle population (largely

passenger cars and trucks) has been growing at an average rate of approximately 2.5% per year, about 

twice the rate of population growth in Anchorage. Absent any data on the expected growth of these 

vehicles by vehicle type, varying assumptions were made about growth in specific categories. For 

example, because of increases in the cost of fuel, the motor home population was expected remain 

constant throughout the planning period. The population in the Anchorage inventory area is projected to 

grow by 13.3% between 2007 and 2023. Table 15-3 shows the assumed growth in the vehicle 

populations during the same time period. It should be noted that MOVES emission estimates for 

composite fleet emissions are not particularly sensitive to these growth assumptions. 

15.3 Age Distribution

Vehicles age distribution for each of the thirteen MOVES source types were estimated by Sierra 

Research using DMV, parking lot survey and MOVES default data. After the population for 

each source type was determined, the age distribution of vehicles in that source type was 

46
Adopted July 13th, 2011 

32

computed. Source types 41 through 62 (buses, heavy trucks and motor homes) relied on DMV 

data for their age distribution calculations. 

Sierra observed that the apparent age distributions for passenger cars and trucks (source types 

21, 31 and 32) estimated from parking lot survey data differed from the DMV data. Roughly half 

of the parking lot-surveyed vehicles fall into a category of model year 2004 and newer (Figure 

15-2). The DMV data shows 10% fewer vehicles in the same age group. The parking lot survey 

data was relied upon to determine the age distribution for passenger cars, passenger trucks, 

light commercial trucks and motorcycles because it was believed to more accurately represent 

the active vehicle fleet in the winter CO season. DMV data were relied upon for age 

distributions of buses, short-haul, long-haul and refuse trucks, and motor homes. 

Figure 15-2 

Comparison of DMV and Survey-Based Vehicle Age Distributions of 

Passenger Cars and Passenger Trucks in Anchorage

(from Sierra Research memo to ADEC, 1/12/2011) 

47
Adopted July 13th, 2011 

33

15.4 Vehicle Type VMT

15.4.1 HPMS Vehicle Type VMT Year 

The ADOT&PF (Central Region 2007-2009) Traffic Volume Report.26 includes traffic count data that 

can be easily mapped into the six basic (10, 20, 30, 40, 50, 60) MOVES vehicle VMT categories. 

Winter traffic count data from eleven count stations in the Anchorage inventory area were used to 

estimate the percentage of VMT accrued by each MOVES vehicle type.27 The Anchorage Traffic 

Model estimates that daily weekday VMT in the inventory area is 3,344,312. An estimate of annual 

VMT can be made by scaling-up the daily weekday estimate as follows (DOT data suggests that 

Saturday traffic is about 90% of weekday and Sunday is about 75 %.)

Average daily VMT = ((3,344,312 x 5) + (3,344,312 x 0.9) + (3,344,312 x 0.75))/7 = 3,177,096

Average annual VMT = 1,159,640,186

Estimates of annual VMT used in MOVES modeling are shown below.

Table 15-2 

DOT count-based estimates of Annual VMT by MOVES vehicle type (in 103 miles)

MOVES Vehicle Type

10 20 30 40 50 60 All

% of VMT 0 405,020 677,962 5,676 33,832 98,184 1,220,674

Annual VMT 0.0% 33.2% 55.5% 0.5% 2.8% 8.0% 100%

15.4.2 Month VMT Fraction

The ADOT&PF Traffic Volume Report also includes data on traffic volumes by month... The table 

below shows January traffic count data from Anchorage roads relative to an “average” month in the 

year, where an average month is 100%. January traffic is 88.6% of average. On an average month, 

the fraction of annual of VMT accrued = 1/12 = 0.0833. The amount accrued in January is lower 

(0.866 x 0.0833 = 0.0752). This value was used in MOVES to model the January VMT fraction.

 

26 Data from:

http://www.dot.alaska.gov/stwdplng/transdata/traffic/cen_reports/07_08_09ATVR_Final_9_2_2010.pdf

27. According to DOT these data may be somewhat unreliable in distinguishing between cars and pickup trucks 

because of counter limitations. For this reason, the relative proportions of cars vs. truck may be re-adjusted 

pending the results of the ADEC VIN decoder effort discussed above.

48
Adopted July 13th, 2011 

34

Table 15-3 

January VMT Counts as a Percentage of an “Average” Month on Anchorage Roads

SANDLAKE ROAD 85.1%

OLD SEWARD HIGHWAY - NORTH OF SUNDOWN COURT 92.1%

A Street at Chester Creek 90.7%

C Street at Chester Creek 90.0%

Arctic Boulevard –South of 76th Avenue 87.0%

DeBarr Road –East of Wintergreen Street 88.4%

Dimond Boulevard –West of Arctic Boulevard 86.2%

Ingra and Gambell at Chester Creek 86.8%

International Airport Road – West of Fairbanks St 89.2%

Minnesota Drive at Chester Creek 88.1%

Minnesota Drive – NORTH OF DIMOND BOULEVARD (WIM) 92.1%

Minnesota Drive –South of Int'l Airport Road 89.9%

Northern Lights Blvd – East of LaTouche Street 89.0%

Northern Lights Blvd – West of Forest Park Drive 88.5%

O'Malley Road – East of Seward Highway 84.8%

TUDOR ROAD - WEST OF TUDOR CENTER DRIVE 89.6%

TUDOR ROAD - WEST OF PATTERSON STREET 87.9%

Average 88.6%

fraction of annual VMT occurring in January = 0.0752

15.4.3 Day VMT Fraction

Default data from MOVES was used to apportion weekend and weekday travel. These assumptions 

do not significantly affect estimates for running emissions because we are using emission factors 

along with Anchorage Transportation Model estimates of VMT to compute emissions. We will be 

estimating CO emissions for weekdays only.

15.4.4 Hour VMT Fraction

The ADOT&PF Traffic Volume Report also includes information that can be used to estimate the 

VMT fraction by hour of the day. The results of this analysis are shown below. These values were 

used as inputs in the MOVES modeling.

Table 15-4 

Distribution of VMT by Hour on Anchorage Roads

Hour Proportion of Daily VMT Hour Proportion of Daily VMT

1 0.01241 13 0.06582

2 0.00794 14 0.06594

3 0.00618 15 0.06782

4 0.00447 16 0.07529

5 0.00506 17 0.08206

6 0.01141 18 0.08153

7 0.02724 19 0.06500

8 0.05124 20 0.04953

9 0.04929 21 0.04112

10 0.04447 22 0.03571

11 0.04776 23 0.02641

12 0.05824 24 0.01835

49
Adopted July 13th, 2011 

35

15.5 Average Speed Distribution

Sierra Research examined speed estimates from the Anchorage Transportation Model and 

constructed a spreadsheet that converted these speed estimates into the speed distributions (16 

speed bins) required by MOVES for each source types and road type by hour of the day. Although 

an effort was made to accurately reflect these speed distributions in the MOVES speed distribution 

input file, this information is less critical because will be using MOVES in the emission rates mode 

rather than the inventory mode when developing the inventory, emission budget and for performing 

conformity analyses. The spreadsheet model was used to estimate the average speed of vehicles 

traveling on the two road types within each grid and select an appropriate MOVES-based emission 

rate based on that speed. 

15.6 Road Type Distribution

The Anchorage Transportation Model provides information of the amount of VMT accrued on five 

different road types and these five types can be mapped into the four road types required by 

MOVES. (The Anchorage inventory area has only two of these road types, urban unrestricted 

access and urban restricted access.) Transportation model estimates of VMT accrued on local, 

collector, minor arterial and major arterial roadways are combined to make up an estimate of the 

urban unrestricted access road type defined by MOVES. Freeway and expressway VMT estimates 

are simply re-defined as urban restricted access VMT. For 2007, the transportation model estimates 

that about 73% of travel occurs on unrestricted access roads and the remainder on restricted access.

15.7 Ramp Fraction

Absent better information, the MOVES default ramp fraction (8%) was used as the fraction for 

Anchorage.

15.8 Fuel Supply and Fuel Formulation 

The MOVES defaults for fuel supply and formulation assume that market share of gasoline blended 

with 10% ethanol in Anchorage will increases to 100% by 2012. Tesoro Alaska, the main refiner and 

gasoline supplier in Anchorage has informed us that there is an exemption to the Renewable Fuel 

Standard (RFS) in Alaska and that they have no plans to blend ethanol in the gasoline in the 

foreseeable future.28

 

28 E-mail communication from Kip Knudson, Tesoro Alaska 1/11/2011.

 For this reason, for modeling purposes, we “zeroed” out the market share of 

ethanol-blended gasoline. Tesoro also informed us that they met the ultra-low sulfur specification 

before 2007 and that they did not envision further changes in sulfur content in the coming years. 

Table 15-5 shows gasoline fuel formulation assumptions for the period 2007-2011 and for 2013-

2023. The main difference in specification is the lowered benzene content after 2012. This change 

is unlikely to have an impact on modeled CO emissions. MOVES supplied defaults were used for the 

diesel fuel spec. Although there is some minimal use of other motor fuels such as natural gas, this 

was assumed to be zero for modeling purposes.

50
Adopted July 13th, 2011 

36

Table 15-5 

Gasoline Fuel Formulation Assumptions Used in Anchorage MOVES Modeling

Time Period

2007-2011 2013-2023

Fuel Formulation ID 8077 8859

Fuel Subtype ID 10 10

RVP 15.2457 15.2457

Sulfur Level 30 30

ETOH Volume % 0 0

MTBE Volume % 0 0

ETBE Volume % 0 0

TAME Volume % 0 0

Aromatic Content % 31.7475 29.8113

Olefin Content % 0.92 0.92

Benzene Content % 3.7 0.6445

e200 54.7901 57.0202

e300 91.781 93.8415

15.9 I/M

Table 15-6 shows the Vehicle Inspection and Maintenance (I/M) Program assumptions used in the 

MOVES modeling. In 2007, the Anchorage I/M program included a 4-year testing exemption or 

grace period for new cars. In 2011 this grace period was extended to 6 years. For modeling 

purposes the I/M program was assumed to be discontinued in 2012.

Table 15-6 

I/M Assumptions Used in Anchorage MOVES Modeling

Time Period

2007 - 2009 2011 2013-2023 Explanation of MOVES Codes

I/M in operation? Yes Yes No

Grace Period 4 yrs 6 yrs -----

Source Types 21, 31, 32 21, 31, 32 -----

Passenger cars = 21, passenger trucks = 

31, light commercial trucks =32

I/M test standards

MY <1996 12 12 ----- Two-mode, 2500 RPM/Idle Test =12

I/M test standards

MY ≥ 1996 51 51 ----- Exhaust OBD Check = 51

Inspection 

frequency 2 2 ----- Biennial =2 

Compliance Factor

MY <1996 90% 90% -----

Compliance Factor

MY ≥ 1996 93% 93% ----- 

51
Adopted July 13th, 2011 

37

15.10 Alternative Vehicle Fuels and Technology (AVFT) Inputs 

Sierra Research observed differences in gasoline/diesel splits from MOVES default values 

varied from 1% to 30% for most source types and model years. Because these differences 

were relatively substantial in some instances, the MOVES AVFT option was used to input 

Anchorage-specific information on gasoline diesel splits when it was available. (Default values 

were used pre MY 1981 vehicles). Sierra found that Anchorage tended to have a higher diesel 

fraction than the default for most source types. As an example, the diesel fraction of the MOVES 

default or compared to Anchorage for passenger cars (source type 21) in Table 15-7. 

Table 15-7 

Comparison of Anchorage Fleet to MOVES Defaults

% of Diesel-Fueled Passenger Cars

Model Year

MOVES

Default Anchorage AVFT

1981 7.64% 22.43%

1982 6.09% 19.18%

1983 3.10% 8.29%

1984 1.88% 6.60%

1985 1.17% 4.92%

2001 0.38% 0.48%

2002 0.38% 0.86%

2003 0.38% 0.98%

2004 0.38% 0.74%

2005 0.38% 1.13%

2006 0.38% 1.79%

2007 0.38% 0.00%

2008 0.38% 0.06%

2009 0.38% 0.73%

2010 0.38% 0.73%

52
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 Standard for 

Carbon Monoxide in Anchorage between 2007 and 2023, dated April 2011” 

Adopted 

July 13th

, 2011 

53
Adopted July 13th, 2011 

A-1 

yp = yh + t(α; n-2) . s{pred}

Appendix to Section III.B.6, Anchorage Carbon Monoxide Maintenance Plan

Air Quality Program

Municipality of Anchorage 

Department of Health and Human Services

April 2011 

Analysis of the Probability of Complying with the National Ambient Air Quality Standard for Carbon 

Monoxide 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 has been 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 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.

This is a “technical revision” of an earlier document prepared in March 2010. This revised document substitutes CO emission 

estimates generated by the new EPA emissions model MOVES for previous estimates generated by AK MOBILE6. 

Although the computed probabilities of continued maintenance change slightly as a consequence, there is very little change 

in conclusions from the probability analysis regarding prospects for continued maintenance of the CO NAAQS. The analysis 

suggests that there is a very high probability of continued compliance with the NAAQS through 2023.

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

defined mathematically as follows:

Equation 1

 

* Even though I/M could continue as a local option program, CO reduction benefits were ignored because 

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

54
Adopted July 13th, 2011 

A-2 

 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

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

 

† 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.

55
Adopted July 13th, 2011 

A-3 

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

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.

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

56
Adopted July 13th, 2011 

A-4 

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 MOVES, NONROAD, AP-42 and the FHWA model EDMS to 

estimate CO emissions.**

CO emissions in 2007 were estimated to be 10.20 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 10.20 tpd = 12.70 tpd

This computation suggests that if CO emissions in the Turnagain area increased from 

10.20 tpd to 12.70 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. 

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% 8.44

99.5% 9.15

99.0% 10.22

98.0% 10.77

97.0% 11.21

96.0% 11.50

95.0% 11.81

94.0% 11.98

93.0% 12.15

92.0% 12.33

91.0% 12.51

90.0% 12.70

 

** The MOVES model 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.

57
Adopted July 13th, 2011 

A-5 

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 MOVES, 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 MOVES 

model was configured to reflect that the four-year new car exemption will be extended to six years beginning January 

2010 and discontinued in 2012.. 

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 8.61 0.62 0.28 0.70 10.20 99.1%

2008 8.51 0.62 0.28 0.70 10.12 99.1%

2009 8.42 0.63 0.28 0.71 10.03 99.1%

2010 8.38 0.63 0.28 0.71 10.00 99.1%

2011 8.34 0.64 0.28 0.71 9.97 99.2%

2012 8.49 0.65 0.28 0.72 10.13 99.1%

2013 8.63 0.65 0.28 0.72 10.29 98.9%

2014 8.57 0.66 0.28 0.73 10.24 99.0%

2015 8.51 0.66 0.29 0.73 10.19 99.1%

2016 8.42 0.67 0.29 0.73 10.11 99.1%

2017 8.34 0.67 0.29 0.74 10.03 99.1%

2018 8.28 0.68 0.29 0.74 9.99 99.1%

2019 8.22 0.68 0.29 0.74 9.94 99.2%

2020 8.16 0.68 0.29 0.75 9.88 99.2%

2021 8.10 0.68 0.29 0.75 9.83 99.2%

2022 8.03 0.69 0.29 0.75 9.77 99.2%

2023 7.97 0.69 0.30 0.76 9.71 99.3%

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 2013 if the I/M program is terminated as assumed, but the probability of 

compliance remains at or near 99% through the 2007-2023 period. 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:

 

†† 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.

58
Adopted July 13th, 2011 

A-6 

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.

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 98% 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 July 13th, 2011 

A-7 

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 10.20 99.1% 10.20 99.1%

2008 10.12 99.1% 10.16 99.1%

2009 10.03 99.1% 10.12 99.1%

2010 10.00 99.1% 10.14 99.1%

2011 9.97 99.2% 10.15 99.1%

2012 10.13 99.1% 10.36 98.8%

2013 10.29 98.9% 10.57 98.4%

2014 10.24 99.0% 10.57 98.4%

2015 10.19 99.1% 10.56 98.4%

2016 10.11 99.1% 10.53 98.5%

2017 10.03 99.1% 10.50 98.5%

2018 9.99 99.1% 10.49 98.5%

2019 9.94 99.2% 10.49 98.5%

2020 9.88 99.2% 10.47 98.6%

2021 9.83 99.2% 10.46 98.6%

2022 9.77 99.2% 10.45 98.6%

2023 9.71 99.3% 10.43 98.6%

60
Adopted July 13th, 2011 

A-8 

Attachment A

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 July 13th, 2011 

A-9 

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

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

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.

62
Adopted July 13th, 2011 

A-10

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

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

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.

63
Adopted July 13th, 2011 

A-11

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.10

Affidavit of Oral Hearing 

Adopted 

July 13th, 2011 

65
, 

, 

e

tal Program Manager for the Department of Environmental

Conservation, being sworn, state the following:

On June 29th 2011, 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 adopting byreference the Municipality of Anchorage’s revised Carbon Monoxide Maintenance Plan; MOA’s

revised “Anchorage 2007 Carbon Monoxide Emission Inventory and 2007-2023 Emission

Projections” and MOA’s revised “Analysis of Probability of Complying with the National

Ambient Air Quality Standards for Carbon Monoxide in Anchorage between 2007 and 2023”.

DATE:

Anchorage, Alaska

Thomas Turner. Program Manager

SUBSCRIBED AND SWORN TO before me this

day of c51-4nc ,Dt

[NOTARY SEALI Notary Public in and for the

State of AlaskaMy commission expires:

&ate ofAloska

NOTARY PUBLIC

Bin D. Dreyer