# Alaska Department of Environmental Conservation



# Amendments to:

State Air Qua lity 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 13 th 

# , 20 11

### Adopted July 13 th 

### , 2011



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

, Appendix to Volume II of this

p lan , is amended to include the following documents:

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

• 18 AAC 50 Air Quality Control as amended through April 1 3, 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 Re solution 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 N ational 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.

# DEPARTMENT OF

ENVIRONMENTAL CONSERVATION

18 AAC 50

Air Quality Control

Adopt ed

July 13 th 

## , 2011

### Register _ __, ____2011 ENVIRONMENTAL CONSERVATION

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

18 AAC 50.030. State A ir 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18 AAC 50.030(7) is repealed:

(7) repealed ___/___/_____.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

# 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 13 th 

# , 2011

# 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 13 th 

# , 20 11

### Adopted July 13 th 

### , 2011



# Appendix to Section III.B.3



# Anchorage 2007 Carbon Monoxide Emission Inventory and

2007

- 2023 Emission Projections

Municipality o f Anchorage Department of Health and Human Services

Air Quality Program

April 2011

### Adopted July 13 th 

### , 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 N on-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

### Adopted July 13 th 

### , 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 E missions in 20 07 B ase Y ear in Anchorage

I nventory A rea

................................................................................................... 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 E stimates of Annual VMT by MOVES Vehicle Type

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

T able 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

### Adopted July 13 th 

### , 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 99 th 

### 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

### Adopted July 13 th 

### , 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 revision s 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 comprehen sive 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 e stimate s of motor vehicle CO emissions during the 2007-2023 maintenance planning period. No cha nges 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 y ear 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.

### Adopted July 13 th 

### , 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

t he 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

### Adopted July 13 th 

### , 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, post

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

processor 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 ce nsus 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.

### Adopted July 13 th 

### , 2011

4 Figure 3-1 Anchorage Area Inventory Grid System

### Adopted July 13 th 

### , 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

- pe ak 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 assum ed 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

- hou r 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 S ource A ctivity by T ime P eriod

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

### Adopted July 13 th 

### , 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 bu dget 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 emissi on 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 e missions 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 time

of

- 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 partic ular 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 time s.

†† 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.

### Adopted July 13 th 

### , 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 em issions 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 distr ibution (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

- km 2



### 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).

### Adopted July 13 th 

### , 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 t hese 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 run ning 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 spreadsh eet 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 e xtended 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

em issions 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 Anc horage.

§§ 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.

### Adopted July 13 th 

### , 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 i n 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 independe nt 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 spreadshee t 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

††† MOV ES 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. T hus, it is likely that a portion of some start emissions occur in grid cells other than the one assigned by

the model.

### Adopted July 13 th 

### , 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 appr opriate 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 Municipal ity 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.

### Adopted July 13 th 

### , 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 2023avg 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 2023grams per mile without I/M

with I/M

### Adopted July 13 th 

### , 2011

12 Table 5 -4 Summary of E stimated A rea

- wide -M otor V ehicle CO E missions 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

### Adopted July 13 th 

### , 2011

13 

## 6 Aircraft Operation Emissions

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

Table 6 -1

24

- hour CO E missions 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 air craft 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 AN C 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 A ircraft O perations and CO E missions 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.

### Adopted July 13 th 

### , 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 (200 0) 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 s hown 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

### Adopted July 13 th 

### , 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 household s 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.

Residentia l wood burning assumptions are detailed in Table 7

- 1. performed by ASK Marketing and Research in 1990.

Table 7-1

R esidential W ood B urning CO E mission F actors for Anchorage



### Appliance Average use per weekday

(hours per

household

per day) Average dry weight of

wood

consumed

(lbs per hour)* Average

amou nt 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.

### Adopted July 13 th 

### , 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 Emiss ions from R esidential W ood B urning



### Calendar Year Number of

Households in Inventory Area 24-Hour Emissions (tons)

2007 84,936 6.24



### 2009 86,582 6.36



### 2011 88,229 6.48



### 2013 89,875 6.60



### 2015 91,522 6.72



### 2017 93,168 6.84



### 2019 94,045 6.91



### 2021 94,923 6.97



### 2023 95,800 7.04

### Adopted July 13 th 

### , 2011

17 

## 8 Space Heating Emissions

A telephone survey conducted by ASK Marketing and Research in 1990 indicated 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 a nd per employee consumption for

commercial/industrial customers. The most recent AP

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

### ft 3) 

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

### ft 3)

§§§§ P ersonal 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.

### Adopted July 13 th 

### , 2011

18 Table 8-2

Peak N atural G as C onsumption and CO E mission R ates in Anchorage (1990)

Consumption

Rate per Day AP

- 42

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

Emission Rate

(lbs per day)

Residential 658 ft 3

per household 40 0.0263 per household

Commercial/ Industrial 434 ft 3

per employee 84 0.0364 per employee



### 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 N atural G as C ombustion



### 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.

### Adopted July 13 th 

### , 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 startin g 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 n on

- road equipment populations and usage (i.e., hours per year) were reasonable. The NONROAD model uses a top

down approach in which state

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

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

- Anchorage customers who come to Anchorage to purchase a generator

for use in ar eas 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 as sumed 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, clea rly 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

outsi de 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 com mercial 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.

### Adopted July 13 th 

### , 2011

20 Ta ble 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 Non

road 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

### Adopted July 13 th 

### , 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

facto rs, expressed on a gram per gallon basis, were applied to ARR fuel consumption estimates to compute emissions.

Table 1 0-1

Alaska Railroad E mission E stimat es 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

em issions 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.

### Adopted July 13 th 

### , 2011

22 

## 11 Marine Vessel Emissions

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

iro n, 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 Assoc iates 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 t he 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 emissio ns 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 1 1-1

Estimated CO E missions 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

§§§§§ Commer cial 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.

### Adopted July 13 th 

### , 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 operatin g 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
great er 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 ye ar (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 -4 2 (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 allowa ble emissions.

Based on ADEC

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

attainment area. Estimated annual emissions from each source for 2005 and projected daily

emissions for the 2007

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

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

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 northwe st 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 furth est 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

### Adopted July 13 th 

### , 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 AP

42 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 1 2-1

.

Table 1 2-1

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

### Adopted July 13 th 

### , 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

### Adopted July 13 th 

### , 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 t hat 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)

### Adopted July 13 th 

### , 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 pr ojected through 2023.

2



### Results of the 2007 base year micro -inventory for the nine- kilometer 2 

### 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 cond itions total CO emissions in the micro

inventory 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 operat ions 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.

### Adopted July 13 th 

### , 2011

28 growth in population and vehicle travel are projected in the Turnagain area than the Anchorage bowl as a whole.

### Adopted July 13 th 

### , 2011

29 Table 14 -2

Estimated Total 24

- hour CO Emissions CO on Design Day in Turnagain M icro

- inventory A rea (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

### Adopted July 13 th 

### , 2011

30 

## 15 MOVES R un S pecifications and C ounty Data M anager I nputs

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 ≥ 99 th 

### 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 de veloped 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 99 th 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 Impl ementation 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 Dayambient temperature (deg F)

### Adopted July 13 th 

### , 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

0 a 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 M otorcycle activity in Anchorage during the winter months was assumed to be z ero.

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 assumption s.



### 15.3 Age Distribution

V ehicles 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 sou rce type was determined, the age distribution of vehicles in that source type was

### Adopted July 13 th 

### , 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. T he 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 )

### Adopted July 13 th 

### , 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.

W inter 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 e stimates 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 %.)

Ave rage 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 vehic le type (in 10 3 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 F raction

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.

### Adopted July 13 th 

### , 2011

34 Table 15 -3 January VMT Counts as a Percenta ge 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 ADO T&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

### Adopted July 13 th 

### , 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 o f 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

rat e 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 t he

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.

### Adopted July 13 th 

### , 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

Explanation of MOVES Codes 2007 - 2009 2011 2013

- 2023

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%

-----

### Adopted July 13 th 

### , 2011

37 15.10 Alternative Vehicle Fuels and Technology (AVFT) Inputs

Sierra Research observed differences in gas oline /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%

# Alaska Department of Environmental Conservation



# Amendments to:

State Air Quality Control Plan

Vol. III: Appendices

Appendix III. B. 6

“An alysis of Probability of Complying with the Nation al Ambient Air Quality Standard for

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



# Adopted

July 13 th



# , 20 11

### Adopted July 13 th 

### , 2011

A-1 

## y

p = 

## y h + 

## 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 201 1

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

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 stipulatio n 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 Conservati on (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 th is demonstration

must show a 90% or greater probability of meeting the national ambient a ir quality s tandard in each year during the 2007

- 2023 lifetime of the Maintenance Plan.

The MOA is using th e same methodology used in the 2004 Plan in th is revised maintenance demonstration . T his

methodology relies on conventional statistical methods to estimate the probability of complying with the NAAQS in the year

2007

, the base year for the analysis

. The “roll forward” technique, used in the previous maintenance demonstration, is used

to estimate probabil ity 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. T his revised document substitutes CO emission
estimates generated by the new EPA emissions model MOVES for previous estimates generated by AK MOBILE6.

Although the compute d probabilities of continued maintenance cha nge 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 200 7

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

- hour average not to be exceeded more than once per year. Because the NAAQS effectively disregards the highest 8

- hour average in determining compliance, the measure of whether a community meets the standard is determined by the magnitude of the second highest 8

- hour average, or second

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

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

2007

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

### Adopted July 13 th 

### , 2011

A-2 where

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

Over the past 30 years, CO monitoring has been conducted at ten permanent CO stations . In this case we want to compute the value corresponding to the upper 90 th percentile interval in base year 2007. If 2007 could be “repeated” numerous times, with the “normal” variety of meteorological conditions and other variables that effect CO
concentrations, the second maximum concentration would fall at or below this value 90% of the time. This value is the base year 2007 design value (2007 DV 90% ).

‡

First and second maximum 8

- hour CO concentrations measured at Turnagain are shown in Table 1. and at numerous additional temporary stations throughout Anchorage and Eagle River. Data suggest that the Turnagain monitor,

loca ted in a residential area in west Anchorage, has the highest CO concentrations of the four monitors in the current network. (See analysis in the A ttachment at the end of this report.) Although it is difficult to compare recent data from Turnagain with data collected from other sites a decade or more earlier, studies suggest that the CO concentrations at Turnagain are likely representative of the highest ambient CO conc entrations encountered in Anchorage. For this reason, Turnagain was selected as the site for the maintenance demonstration.

§

Table 1

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

- 200 8)

Highest 8

- hour average CO

Concentration (ppm) 2 nd Highest 8

- hour average CO

Concentration (ppm)

1999 10.1 7.6

2000 7.2 5.5

2001 9.8 7.7

2002 6.5 5.9

2003 8.3 6.7

2004 8.1 7.9

2005 5.7 4.6

2006 6.5 6.1

2007 5.5 5.3

2008 6.3 5.4

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

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

. Figure 1

shows that the re was a 90% probability that the base year 2007 value would be less than or equal to 7. 23 ppm. This computed concentration will serve as the base year 2007 design value for the roll forward analysis discussed later in

this report.

Figure 1

90 th Percentile Prediction Interval Computed from Turnagain 2 nd Maximum

† 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 monit ors 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 th e Municipality.

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

collected at this site.

### Adopted July 13 th 

### , 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 20082nd max CO (ppm) 

### 2007 DV 90% 

### = 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 i teration

. The one sided t

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

. In this case the d esired 9.0 ppm value falls very nearly at the 99.0% interval. Th us, the probability of complying with the NAAQS in 2007 was estimated to be approximately 99%. The chance of violating the NAAQS in 2007 was about 1

- in

- 100.

Table 2

Second Maximum CO Concentration Associated with Various Upper Bound Prediction Intervals

Probability that 2007 CO Concentration

will be less than Computed 2 nd Max

Concentration Computed Second Maximum CO

Concentration (ppm)

80.0% 6.64

90.0% 7.23

95.0% 7.78

97.5% 8.30

**99.0% 8.99** 

99.9% 1 0.88

Estimating the Probability of Complying with the NAAQS between 200 7 - 2023

### Adopted July 13 th 

### , 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 i n that year relative to base year emissions in 2007. In other words, if CO emissions in a future year are projected to decrease by 10% relative to base year 2007, the expected CO concentration in that fut ure year will also decrease by 10%. If this occurs, there will be concurrent increase in the probability of complying with the NAAQS in that year.

CO emissions were estimated for the 9 kilometer 2 area surrounding the Turnagain CO monitoring station for base year 2007 using EPA- prescribed model s such as the MO VES

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

estimate CO emissions. **

CO emissions in 2007 were estimated to be 10.2 0 tons per day (tpd) in the “micro- inventory area ” surrounding Turnagain. The computed 90 th percentile concentration or 2007 DV 90% was 7. 23 ppm. If one assumes that CO concentrations increase in direct proportion to emissions, the amount of CO that could be emitted in the Turnagain area and retain a 90% probability of complying with the standard can be computed as follows :

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

= (9.0 ppm/7. 23 ppm) x 10.2 0 tpd = **12.7 0**  **tpd**

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

10.2 0 tpd to 12.7 0 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 prob abilities 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 sp readsheet 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 2 nd Max CO

Concentration will be

less than 9.0 ppm Corresponding

CO Emission Rate (tpd)

99.9% 8.4 4

99.5% 9.1 5

99.0% 10.2 2

98.0% 10.7 7

97.0% 11.2 1

96.0% 11.5 0

95.0% 11.8 1

94.0% 11.98

93.0% 12.1 5

92.0% 12.3 3

91.0% 12.5 1

90.0% 12.7 0

** The MO VES 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.

### Adopted July 13 th 

### , 2011

A-5 In addition to estimating base year 2007 CO emissions in the 9 kilometer 2 area surrounding Turnagain, emissions were projected through the year 2023. Projections were prepared using the aforementioned MO VES , 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 MO VES 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 km 2 Area Surrounding the Turnagain Station (all emissions in tons per day)

Year Motor

Vehicles Fireplace or

Woodstove Space Heating Other TOTAL

CO EMISSIONS Probability of Compliance

2007 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 larg e part on ISER projections in the development of travel forecasts.

### Adopted July 13 th 

### , 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 kilometer 2 area surrounding the Turnagain stat i on will grow by about than 4% from 2007 levels. Although this appears to be a sensible assumption because the Turnagain area is an older area with little opportunity for significant growth in population, in this sensitivity analysis we will assume that the growth in VMT will be three times that projected by the Anchorage Transportation Model. In other words, we will assume that VMT and vehicle starts and idling will grow by 12% between 2007 and 2023 and determine how this affects the probability of compliance

.

Future Growth in Wood Stoves and Fireplace Use

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

- inventory area. Dur ing the 2007

- 2023 inventory period, wood heating emissions were projected increase by about 11%. Although recent telephone data suggest that Anchorage households do not plan to change

their habits with regard to wood burning, there is a possibility that wood burning rates could increase in the next

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

Results of Sensitivity Analysis

The t wo revised assumptions used in this sensitivity analysis are summarized in Table 5. The combined impact of these revised assumptions on CO emissions in the Turnagain micro

- inventory area and the con sequent 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 Assumption s used in

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

Growth in VMT and

Vehicle Starts and Idling 4% increase between 2007 and

2023 12 % incre ase between 2007 and 2023

Fireplace and Woodstove

Use No change in wood burning rates per
household between 2007

- 2023 2% growth in wood heating per year

### Adopted July 13 th 

### , 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%

### Adopted July 13 th 

### , 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

relat ively 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

m onitor s were compared in rank

- order to determine which site has the highest CO concentrations and the greatest potential for exceeding the national ambient air quality standar d (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.

### Adopted July 13 th 

### , 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 -O verlapping 8

- hour Average CO Concentrations M easured 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

RankMaximum 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

50 th highest value at Turnagain is 13% higher than the corresponding 50 th highest value at Garden. On a rank

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

### Adopted July 13 th 

### , 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

RankMaximum 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 th is site for analysis of long

- term prospects for continued compliance with the NAAQS.

### Adopted July 13 th 

### , 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%

# 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 13 th 

# , 20 11STATE

OF ALASKA

)ss. THIRD JUDICIAL DISTRICT

) AFFIDAVIT

OF ORAL HEARING I, Thomas Turner, Environmental

Program Manager

for the Department

of Environmental Conservation,

being sworn,

state

the following: On June29th 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

by reference

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

,D

t [NOTARY

SEALI

Notary Public

in and

for the State

of Alaska________________________

My commission

expires: &ate ofAloska NOTARY PUBLIC Bin

D. Dreyer