773 San Marin Drive, Suite 2115, Novato, CA 94998, Phone: 415.899.0700 06-16495 

 ENVIRON International Corporation 

FINAL EMISSION INVENTORY REPORT: 

DATA POPULATON OF AIR SYSTEM 

FOR HAWAII’S EMISSIONS DATA (AirSHED) 

Prepared for 

Hawaii Department of Health 

Clean Air Branch 

919 Ala Moana Blvd., Suite 203 

Honolulu, Hawaii 96814 

Prepared by 

ENVIRON International Corporation 

773 San Marin Drive, Suite 2115 

Novato, CA 94998

April 12, 2010 
April 2010 

J:\Hawaii_EI\Reporting\Final\TOC.doc i 

TABLE OF CONTENTS 

Page 

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

Emission Inventory Scope .........................................................................................................1 

Emission Inventory Documentation ..........................................................................................2 

2. METHODOLOGY ..................................................................................................................3 

Mobile Sources ..........................................................................................................................3 

Area Sources ..............................................................................................................................4 

Biogenic/Geogenic Sources.......................................................................................................5 

Point Sources .............................................................................................................................5 

Future Year 2018, Emission Inventory......................................................................................5 

3. EMISSON INVENTORY RESULTS ....................................................................................6 

5. REFERENCES.......................................................................................................................13 

APPENDICES 

Appendix A: On-Road Emissions Estimates For The Hawaiian Islands 

Appendix B: Hawaii Commercial Marine Vessel Activity And Emissions 

Appendix C: Development Of Commercial Marine Vessel Emission Inventory For Vessels In 

Transit 30NM From The Hawaiian Shoreline 

Appendix D: Off-Road Equipment Emissions Estimates For The Hawaiian Islands 

Appendix E: Aircraft Emissions Estimates For The Hawaiian Islands 

Appendix F: Hawaii Locomotive Emissions 

Appendix G: Area Source Emissions Estimates For The Hawaiian Islands 

Appendix H: Ammonia Emission Estimates For The State Of Hawaii 

Appendix I: Fugitive Windblown Dust Emission Estimates For The 

State Of Hawaii 

Appendix J: Road Dust Emissions Estimates For The Hawaiian Islands 

Appendix K: Agricultural Tilling Emissions Estimates For The 

Hawaiian Islands 

Appendix L: Fire Emission Inventory 

Appendix M: Biogenic Emissions Estimates For The Hawaiian Islands 

Appendix N: Volcano Emissions Estimates For The Hawaiian Islands 

Appendix O: Sea Spray Emissions Estimates For The Hawaiian Islands 

Appendix P: Hawaii EGU Projections 
April 2010 

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TABLES 

Table 1-1. State of Hawaii emission inventory technical documentation.................................2 

Table 3-1. Hawaii annual emissions, 2002, 2005, and 2018 (tons per year) ............................8 

FIGURES 

Figure 3-1a. Annual VOC emissions by source category, 2002, 2005, and 2018........................9 

Figure 3-1b. Annual CO emissions by source category, 2002, 2005, and 2018 ..........................9 

Figure 3-1c. Annual NOx emissions by source category, 2002, 2005, and 2018 ......................10 

Figure 3-1d. Annual PM10 emissions by source category, 2002, 2005, and 2018 

(sea spray associated PM10 not included).............................................................10 

Figure 3-1e. Annual PM2.5 emissions by source category, 2002, 2005, and 

2018 (sea spray associated PM2.5 not included)...................................................11 

Figure 3-1f. Annual SO2 emissions by source category, 2002, 2005, and 2018.......................11 

Figure 3-1g. Annual NH3 emissions by source category, 2002, 2005, and 2018 ......................12 
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1. INTRODUCTION 

This report provides a compendium of all of the emission inventory development work prepared 

for implementation in the Air System for Hawaii’s Emissions Data (AirSHED). This report 

provides an overview of the methods used to estimate the various emissions sources, and a 

summary of the overall emissions for base and future years. Detailed methodology documents 

for all source categories are included in the appendices to this report. 

EMISSION INVENTORY SCOPE 

The scope of the overall emission inventory effort was as follows: 

Pollutants: Emissions estimates for this inventory included the following criteria pollutants: 

• Particulate matter smaller than 2.5μ diameter (PM2.5) 

• Particulate matter smaller than 10 μ diameter (PM10) 

• Sulfur dioxide (SO2) 

• Nitrogen oxides (NOx) 

• Volatile organic compounds (VOCs) 

• Carbon monoxide (CO) 

• Lead (Pb) 

The emissions estimates also included data collection for ammonia (NH3), as a precursor of 

sulfate and nitrate PM2.5 formation, as well as for the toxic air pollutant, hydrogen sulfide (H2S). 

Finally emission estimates included data for the 188 hazardous air pollutants listed in the 1990 

Clean Air Act Amendments, under Title III. 

Source Categories: The comprehensive emission inventory consists of seven major source 

categories: 

• Point sources are stationary sources, inclusive of facilities such as refineries, power 

plants, and other types of production facilities. 

• Area sources are defined as all stationary sources that are not included in the point source 

inventory. These numerous facilities and activities include such sources as gasoline 

dispensing facilities, construction dust, architectural surface coatings, industrial surface 

coatings, degreasing, and consumer solvents. Estimates associated with area source 

ammonia emissions, windblown dust, road dust, agricultural tilling dust, and fires, are 

also included. 

• On-road mobile sources include emissions from vehicles certified for highway use – cars, 

trucks, and motorcycles. 

• Off-road mobile sources encompass a wide variety of equipment types that either move 

under their own power or are capable of being moved from site to site, and are not 

certified for highway use. Off-road mobile source categories include commercial marine, 

locomotive, aircraft, and other off-road equipment. 
April 2010 

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• Dust sources include windblown dust, road dust, and agricultural tillage. 

• Fires include emissions from agricultural, prescribed, and wildland fires. 

• Biogenic and geogenic emission sources are those occurring naturally from biological 

and physical processes of the earth. Biogenic/geogenic emissions sources included are 

vegetation, volcanoes, and sea spray. 

Geographical Domain: The emission inventories described in this report are for the entire State 

of Hawaii and include county and island level detail. 

Temporal Resolution: The base years for the emission inventory work and air quality modeling 

are 2002 and 2005. The emission inventory projection year is 2018. For all sources and 

calendar years, emissions were estimated as annual totals as well as average day and weekday 

emissions for the summer and winter seasons. 

EMISSION INVENTORY DOCUMENTATION

Table 1-1 lists the documentation associated with each major source category. Each of the 

reports listed in the table includes a detailed discussion of the emission inventory methods and 

activity data used, and assumptions made. These reports also include more detailed discussion of 

the results than is included in Section 3 of this report. All of these documents are included as 

appendices to this report. 

Table 1-1. State of Hawaii emission inventory technical documentation. 

Source Category Technical Documentation 

On-road On-road Emissions Estimates for the Hawaiian Islands (Appendix A) 

Commercial Marine 

Hawaii Commercial Marine Vessel Activity and Emissions (Appendix B), 

Development of Commercial Marine Vessel Emission Inventory for Vessels In 

Transit 30nm From The Hawaiian Shoreline (Appendix C) 

Off-road Equipment Off-road Equipment Emissions Estimates for the Hawaiian Islands (Appendix D) 

Aircraft Aircraft Emissions Estimates for the Hawaiian Islands (Appendix E) 

Locomotive Hawaii Locomotive Emissions (Appendix F) 

Area (General) Revised Area Source Emissions Estimates for the Hawaiian Islands (Appendix G) 

Area Ammonia Ammonia Emission Estimates for the State of Hawaii (Appendix H) 

Windblown Dust Fugitive Windblown Dust Emission Estimates for the State of Hawaii (Appendix I) 

Road Dust Road Dust Emissions Estimates for the Hawaiian Islands (Appendix J) 

Agricultural Tillage Agricultural Tilling Emissions Estimates for the Hawaiian Islands (Appendix K) 

Fires Fire Emission Inventory (Appendix L) 

Biogenic Biogenic Emissions Estimates for the Hawaiian Islands (Appendix M) 

Volcanoes Volcano Emissions Estimates for the Hawaiian Islands (Appendix N) 

Sea Spray Sea Spray Emissions Estimates for the Hawaiian Islands (Appendix O) 

Point 

EGU Point Source Projection Factors for the Hawaiian Islands (Appendix P). 

Emission estimates provided by HDOH: 

 Calendar Year (CY) 2002: HDOH (2006) 

 CY 2005: HDOH (2007) 

 CY 2018: HDOH (2008) 
April 2010 

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2. METHODOLOGY 

This section briefly describes the methods that were used to estimate emissions for each of the 

major source categories. Further details on emissions estimation methods, data used, 

assumptions, and modeling techniques may be found in each of the emissions project reports 

listed in Table 1-1 and provided in the appendices. 

MOBILE SOURCES 

On-road

On-road emissions include emissions from vehicles certified for highway use – cars, trucks, and 

motorcycles. Emissions from these vehicles were estimated by combining EPA emission factors 

from EPA’s MOBILE6 model, expressed in grams per mile (g/mile), with vehicle miles traveled 

(VMT) activity data. Activity data used in this analysis were Federal Highways Administration 

(FHWA) Highway Performance Monitoring System (HPMS) VMT data by county and island. 

Off-road Equipment

Off-road mobile sources encompass a wide variety of equipment types that either move 

under their own power or are capable of being moved from site to site. More specifically, 

these sources, which are not licensed or certified as highway vehicles, are defined as those 

that move or are moved within a 12-month period and are covered under the EPA's emissions 

regulations as nonroad mobile sources. Where feasible and appropriate, local activity data 

for specific source categories were gathered and used to develop the inventory. US EPA’s 

NONROAD2005 model was used to estimate emissions for off-road equipment sources 

except commercial marine, locomotives, and aircraft. 

Commercial Marine

The commercial marine vessel emission inventory includes two components: near port, and in 

transit voyage (large vessels in transit at least 30nm away from the Hawaiian coastline), and 

includes emissions from large ocean going vessels as well as harbor craft. 

The near-port emissions are based on activity data (port call data, specific speed and time in 

mode) for each of the Hawaii ports. Additionally, information on other commercial vessels and 

their activity patterns is based on freight links data and vessel trip data from the US Army Corps 

of Engineers (USACE, 2005). The modeling of ocean going vessel emissions for this study 

follows the "Current Methodologies and Best Practices in Preparing Port Emission Inventories" 

that was prepared for the US Environmental Protection Agency (ICF, 2006). The in transit 

emissions are based on emissions that were estimated spatially by the Waterway Network Ship 

Traffic, Energy and Environment Model (STEEM), and are consistent with recent emission 

inventories developed by the EPA. 

Aircraft

Aircraft emissions were estimated based upon emissions available from EPA’s 2002 National 

Emissions Inventory (NEI). The 2002 emissions were disaggregated to the island level and 

projected to future years using historical and forecast landing-takeoff cycles (LTOs) available 

from the Federal Aviation Administration (FAA). 
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Locomotive

Locomotive emissions in Hawaii are limited to two railroads; both are tourist excursions and so 

are of little consequence in terms of emissions. Locomotive fuel consumption was obtained for 

both railroad operations. Emissions were estimated for the diesel fueled locomotives according 

to emission factors provided in EPA (1997) and for steam locomotives emission factors were 

based on ICF (2006). 

AREA SOURCES 

Area sources are smaller and widely dispersed stationary sources that are not included in the 

point source inventory. 

General

General area sources include all area source emissions except emissions of ammonia, and 

emissions from windblown dust, road dust, dust from agricultural tilling, and fires. Area source 

emissions under the “general” category were estimated as the product of an emission factor and 

activity data. The methods and emission factors were typically from EPA’s Emission Inventory 

Improvement Program (EIIP) guidance documents, available at 

http://www.epa.gov/ttn/chief/eiip/techreport/, or EPA’s AP-42 emission factors, available at 

http://www.epa.gov/ttn/chief/ap42/index.html. 

Ammonia 

Ammonia emissions for the State of Hawaii were estimated using a GIS-based ammonia 

emissions modeling system developed for the Western Regional Air Partnership (WRAP). The 

development of the model, including data sources and estimation methodology, is documented in 

Chitjian and Mansell (2004). 

Fires 

Fire emission sources include wildland fires and agricultural land associated fires. To estimate 

emissions from fires, techniques developed by the Western Regional Air Partnership (WRAP) 

used in other regional fire emissions inventory assessments were adopted. 

Agricultural Tilling 

Agricultural tilling emissions are those resulting from the preparation of lands for planting and 

post-harvest activities. EPA’s 2002 NEI methodology was used to estimate agricultural tilling 

emissions. 

Road dust

Fugitive dust from paved and unpaved roads is a significant source of particulate matter (PM) 

emissions. Paved and unpaved road dust emissions were estimated based on AP-42 

methodology contained in EPA (2006a) and EPA (2006b), respectively. 

Windblown Dust

The windblown fugitive dust PM emission inventory for the State of Hawaii was developed 

using the estimation methodology developed for the Western Regional Air Partnership (WRAP) 

by a team of contractors led by ENVIRON (ENVIRON, 2004). For more details, see Appendix 

K. 
April 2010 

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BIOGENIC/GEOGENIC SOURCES 

Biogenic

Biogenic emissions include emissions from natural plant and soil sources. The Biogenic 

Emission Inventory System (BEIS), the Global Biosphere Emissions and Interactions System 

(GloBEIS) developed by the National Center for Atmospheric Research and ENVIRON was 

used to estimate the State of Hawaii biogenic emissions inventory. 

Sea spray 

Sea spray emissions include particulate matter emissions associated with sea salt aerosols. Sea 

spray emission inventories were estimated based upon the sea spray particle generation function 

of de Leeuw et al. (2000). 

Volcanic

The Pu`u `O`o-Kupaianaha eruption of Kilauea began in 1983, and continues to be a significant 

source of SO2 emissions. Estimates of volcanic emissions were obtained from USGS data for 

2002 to 2005 (Elias, 2006) with estimates of emissions for the summit caldera and east rift zone. 

POINT SOURCES 

HDOH provided point source emissions estimates for 2002 (HDOH, 2006) and 2005 (HDOH, 

2007). EGU Projections to 2018 were based on an analysis of documents developed by each 

utility as part of the Integrated Resource Planning (IRP) process. For 2018 non-EGU sources, 

HDOH provided “new” permits and/or applications submitted to the HDOH (HDOH, 2008) from 

which necessary information was obtained to populate the “new” (post 2005) sources in 2018. 

FUTURE YEAR 2018, EMISSION INVENTORY 

2018 future year emissions were typically estimated by projecting 2005 emissions to 2018 based 

on appropriate growth factors, as described in each of the source category reports in the 

appendices. In cases where no growth factor was available, 2018 emissions were typically 

assumed to be equivalent to 2018 emissions. 
April 2010 

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3. EMISSION INVENTORY RESULTS 

In this section the emission inventory results are presented by major source category in tables 

and graphs. More details on the emission inventory results for the base and future years for each 

major source category may be found in the technical documentation listed for each source 

category listed in Table 1-1. 

Table 3-1 shows the annual emissions by major source category for each of the base and future 

years. These emissions are graphically portrayed in Figures 3-1a (VOC), 3-1b (CO), 3-1c 

(NOx), 3-1d (PM10), 3-1e (PM2.5), 3-1f (SO2), and 3-1g (NH3). The tables and figures show 

the following trends with respect to calendar year: 

• VOC emissions remain relatively constant from 2002 to 2018. 

• CO emission increase from 2002 to 2005 (primarily due to increases of fire emissions), 

then decrease from 2005 to 2018 (with decreases in on-road emissions). 

• NOx emissions increase slightly from 2002 to 2018, with large increases in emissions 

from commercial marine vessels. 

• PM10 emissions increase from 2002 to 2018, with increases in fire and dust emissions. 

• PM2.5 emissions increase substantially from 2002 to 2005 (primarily due to increases in 

fire emissions) and also increase from 2005 to 2018 (with increases in dust emissions). 

• SO2 emission increase from 2002 to 2005, then decrease from 2005 to 2018; these trends 

are from trends in geogenic emissions. 

• NH3 emissions increase from 2002 to 2005 and also from 2005 to 2018, with small 

increases in all contributing source categories. 

Emissions trends by major source category vary: 

• Point source emissions are a significant contributor to overall NOx and PM2.5 emissions, 

and are a very small fraction of emissions for other pollutants. Point source PM2.5 and 

NOx emissions are estimated to increase from 2002 to 2018. 

• Area source emissions are a significant contributor to VOC emissions and the dominant 

source of NH3 emissions. Area source VOC emissions are projected to increase from 

2002 to 2018, as they are primarily associated with population increases and most of the 

area sources are uncontrolled. NH3 emissions remain relatively constant from 2002 to 

2005, and then are projected to increase from 2005 to 2018. 

• On-road mobile sources are a significant contributor to VOC, CO, NOx and NH3

inventories, but their contribution is decreasing over time (on both an absolute and 

relative basis) despite increases in activity as older vehicles are retired and replaced by 

newer vehicles meeting much tighter federal emissions standards. 

• Off-road equipment sources are a significant contributor to CO and NOx inventories. 

Their contribution to the NOx inventory is also decreasing over time on both an absolute 

and relative basis. Activity will be increasing, but most nonroad sources are now covered 

under federal nonroad engine and equipment standards that phase in over time. 

• Commercial Marine Vessel (CMV) emissions are estimated to increase slightly from 

2002 to 2005 and then more considerably from 2005 to 2018. CMV emissions make up a 

significant fraction of the NOx inventory across all years, and the combination of 

emissions increases to 2018 for CMV and decreases in NOx emissions for on-road and 

off-road equipment make CMV a larger contributor to NOx emissions in 2018. 
April 2010 

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• Aircraft emissions are a very small fraction of overall emissions across all pollutants. 

Aircraft emissions are projected to increase over time. 

• Biogenic emissions are estimated to be the largest source of VOC emissions and also 

contribute to the CO and NOx inventories. It was assumed that these emissions would 

remain constant in the future year modeling. 

• Locomotive emissions are very minor and do not contribute significantly to the overall 

inventory. 

• Geogenic emissions consist of emissions from the Kilauea volcano and sea spray 

associated particulate matter. Sea salt is a dominant source of PM10 and PM2.5 while 

the Kilauea volcano is the dominant source of SO2. 

• Dust emissions consist of the following sources: windblown dust, fugitive road dust, 

agricultural associated dust, and construction dust. Dust emissions are the largest source 

of PM10 and PM2.5 emissions after sea spray associated emissions and are projected to 

increase from 2002 to 2005. 

• Fire emissions showed considerable increases from 2002 to 2005 and were held constant 

from 2005 to 2018. Fire emissions contribute to the PM10, PM2.5 and CO inventories. 
April 2010 

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Table 3-1. Hawaii annual emissions, 2002, 2005, and 2018 (tons per year).

Major Source 

Category VOC CO NOX PM10-PRI PM25-PRI SO2 NH3 

2002 

Point 4,326 9,714 24,774 3,060 1,867 26,069 6

Area 15,232 581 1,193 14,818 2,135 0 11,139

Biogenic 128,835 21,847 4,573 0 0 0 0

Geogenic 0 0 0 312,000,000 8,640,000 598,050 0

Dust 0 0 0 52,185 4,980 0 0

Fires 623 9,143 444 1,749 1,599 189 156

OnRoad 17,878 182,664 15,862 377 229 165 929

Off-road Equipment 6,743 89,176 5,389 524 501 541 5

CMV 209 517 5,573 385 226 3,213 0

Aircraft 368 5,215 1,477 157 129 130 0

Locomotive 0 2 5 0 0 2 0

Total 174,214 318,858 59,290 312,073,254 8,651,666 628,359 12,235

2005 

Point 3,325 13,993 26,002 4,431 3,634 32,185 12

Area 15,923 584 1,037 20,604 768 0 11,136

Biogenic 130,153 22,118 4,617 0 0 0 0

Geogenic 0 0 0 348,000,000 9,590,000 961,366 0

Dust 0 0 0 52,156 4,974 0 0

Fires 5,274 107,922 2,563 11,354 9,836 769 600

OnRoad 17,471 176,332 15,697 404 240 344 1,085

Off-road Equipment 6,121 95,731 4,750 484 462 535 5

CMV 210 523 5,625 399 263 3,620 0

Aircraft 389 5,495 1,541 164 135 135 0

Locomotive 0 2 5 0 0 2 0

Total 178,867 422,700 61,837 348,090,870 9,612,372 998,957 12,838

2018 

Point 4,157 18,257 28,594 5,052 4,123 36,212 13

Area 19,519 755 1,268 28,099 3,650 0 12,530

Biogenic 130,153 22,118 4,617 0 0 0 0

Geogenic 0 0 0 382,000,000 10,640,000 683,746 0

Dust 0 0 0 54,784 5,142 0 0

Fires 5,276 107,932 2,563 11,357 9,839 769 600

OnRoad 8,793 112,015 6,737 395 186 122 1,478

Off-road Equipment 4,579 92,004 3,094 297 281 7 7

CMV 642 1,580 17,349 1,299 1,048 13,483 0

Aircraft 466 6,362 1,920 194 161 167 0

Locomotive 0 2 5 0 0 2 0

Total 173,585 361,023 66,147 382,101,478 10,664,430 734,508 14,627
April 2010 

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0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

2002 2005 2018

Year

Emissions (tons per year)

Point

OnRoad

Off-road Equipment

Fires

Biogenic

Area

Figure 3-1a. Annual VOC emissions by source category, 2002, 2005, and 2018. 

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

2002 2005 2018

Year

Emissions (tons per year)

Point

OnRoad

Off-road Equipment

Fires

Biogenic

Area

Aircraft

Figure 3-1b. Annual CO emissions by source category, 2002, 2005, and 2018. 
April 2010 

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0

10000

20000

30000

40000

50000

60000

70000

2002 2005 2018

Year

Emissions (tons per year)

Point

OnRoad

Off-road Equipment

Fires

CMV

Biogenic

Area

Aircraft

Figure 3-1c. Annual NOx emissions by source category, 2002, 2005, and 2018. 

0

20000

40000

60000

80000

100000

120000

2002 2005 2018

Year

Emissions (tons per year)

Point

Fires

Dust

CMV

Area

Figure 3-1d. Annual PM10 emissions by source category, 2002, 2005, and 2018 (sea spray 

associated PM10 not included). 
April 2010 

J:\Hawaii_EI\Reporting\Final\Report_12Apr2010.doc 11

0

5000

10000

15000

20000

25000

30000

2002 2005 2018

Year

Emissions (tons per year)

Point

OnRoad

Off-road Equipment

Fires

Dust

CMV

Area

Aircraft

Figure 3-1e. Annual PM2.5 emissions by source category, 2002, 2005, and 2018 (sea spray 

associated PM2.5 not included). 

0

200000

400000

600000

800000

1000000

1200000

2002 2005 2018

Year

Emissions (tons per year)

Point

Locomotive

Geogenic

CMV

Figure 3-1f. Annual SO2 emissions by source category, 2002, 2005, and 2018. 
April 2010 

J:\Hawaii_EI\Reporting\Final\Report_12Apr2010.doc 12

0

2000

4000

6000

8000

10000

12000

14000

16000

2002 2005 2018

Year

Emissions (tons per year)

OnRoad

Fires

Area

Figure 3-1g. Annual NH3 emissions by source category, 2002, 2005, and 2018. 
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4. REFERENCES 

Chitjian, M. and G. Mansell. 2003. “An Improved Ammonia Inventory for the WRAP Domain 

– Technical Description of the Modeling System.” Prepared for the WRAP Emissions 

Forum. November, 2003. 

de Leeuw, G, F.P. Neele, M. Hill, M.H. Smith, and E. Vignati. 2000. “Production of sea spray 

aerosol in the surf zone.” J. Geophys. Res. V105, pp. 29,397-29,409. 

Elias, 2006. Kilauea SO2 data for 2002-2005. Personal Email from Tamar Elias. United States 

Geological Survey. Hawaii. November. 

ENVIRON. 2004. “Determining Fugitive Dust Emissions from Wind Erosion.” Final Report. 

Prepared for Western Governors’ Association by ENVIRON International Corporation, 

Novato, California; ERG, Inc., Sacramento, California; Desert Research Institute, Reno, 

Nevada, MACTEC Engineering & Consulting, Gainsville, Florida and University of 

California Riverside, Riverside, California. March 12. 

EPA. 1997. “Locomotive Emission Standards.” Regulatory Support Document, United States 

Environmental Protection Agency, Office of Mobile Sources, April. And EPA 1997, 

“Emission Factors for Locomotives,” Environmental Protection Agency, EPA420-F-97-

051, December. 

EPA, 2006a. Compilation of Air Pollution Emission Factors (AP-42) – Volume 13: 

Miscellaneous Sources, Section 2.1 Paved Roads, Fifth Edition. U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. January. Internet address: http://www.epa.gov/ttn/chief/ap42. 

EPA, 2006b. Compilation of Air Pollution Emission Factors. (AP-42) – Volume 13: 

Miscellaneous Sources, Section 2.2 Unpaved Roads, Fifth Edition. U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. January. Internet address: http://www.epa.gov/ttn/chief/ap42 

HDOH. 2006. 2002, 2003, and 2004. Hawaii Emissions Inventory Reports. 

Hawaii State Department of Health, Clean Air Branch, Honolulu, Hawaii. 

December. 

HDOH. 2007. 2005 Hawaii Emissions Inventory Reports. Hawaii State 

Department of Health, Clean Air Branch, Honolulu, Hawaii. October. 

HDOH. 2008. New (post 2005) facility permits and applications submitted to the 

Clean Air Branch. Hawaii State Department of Health, Clean Air Branch, 

Honolulu, Hawaii. January. 

ICF Consulting. 2006. “Current Methodologies and Best Practices in Preparing Port Emission 

Inventories,” Final Report April 4, 2006, Prepared for U.S. Environmental Protection 

Agency 

US Army Corps of Engineers (USACE). 2005. U.S. Waterway Data – Vessel Characteristics; 

T.S. Vessel File / Volume 3. “Vessel characteristics from the Waterborne Transportation 

Lines of the United States.” 
APPENDICES 
APPENDIX A 

ON-ROAD EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
Golden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA 

 Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com

C:\MARY\ALISON 2007\HI_onroad_052507.doc 

TECHNICAL 

MEMORANDUM 

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: John Grant, Alison Pollack and Rajashi Parikh 

Date: May 25, 2007 

Subject: On-road Emissions Estimates for the Hawaiian Islands 

The category of on-road mobile source emissions includes emissions from vehicles certified for 

highway use – cars, trucks, and motorcycles. Emissions from these vehicles were estimated by 

combining EPA emission factors from the MOBILE6 model, expressed in grams per mile 

(g/mile), with vehicle miles traveled (VMT) activity data. Highway Performance Monitoring 

System (HPMS) VMT data by county and island were used in this analysis. This memo 

describes details of the modeling procedures for estimating 2002, 2005, and 2018 annual, 

summer weekday, winter weekday, summer average day, and winter average day emissions. 

ACTIVITY DATA 

Annual average daily HPMS VMT data by island for 2002 and 2005 were provided by the 

Hawaii Department of Transportation (HDOT). Every Hawaiian island but Molokai lies entirely 

within one county. The island of Molokai wholly contains Kalawao County and contains part of 

Maui County. Molokai Island VMT was distributed to the county level according to the percent 

of the population of Molokai Island in Kalawao County and percent of population of Molokai 

Island in Maui County based on the 2000 population estimates (Hawaii DBEDT, 2006). 2018 

VMT estimates were made based on 2005 VMT provided by HDOT and 2005 to 2018 growth 

estimates based on EPA Mobile Source Air Toxics Rule VMT Estimates (EPA, 2006). Annual 

VMT estimates for all years by island are presented in Figure 1. From 2002 to 2005 the average 

annual VMT increase was 5.1% while the average annual VMT increase from 2005 to 2018 was 

2.7%. 

Per input from HDOT and HCAB staff input, monthly activity were estimated to be equivalent 

across all months for all years. An average weekday correction factor of 1.05 was estimated 

based on previous work conducted by ENVIRON for the Western Regional Air Partnership 

(WRAP) (Pollack et al., 2006). Hawaii VMT-mix data were applied to estimate the average 

weekday correction of 1.05 across all vehicle types for all years. 
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 2 

While outside the scope of this project, traffic count data available from HDOT could be 

analyzed in future work such that local activity is reflected in monthly and weekday adjustments. 

HDOT has detailed traffic count data, but no analysis has been done (Hsu, 2006). 

0

2,000,000,000

4,000,000,000

6,000,000,000

8,000,000,000

10,000,000,000

12,000,000,000

14,000,000,000

16,000,000,000

VMT (miles/year)

Oahu

Molokai

Maui

Lanai

Kauai

Hawaii

Oahu 5,513,631,965 6,252,196,690 8,409,244,125

Molokai 67,809,335 101,465,985 137,235,255

Maui 1,151,721,000 1,287,100,595 1,740,828,445

Lanai 23,477,530 36,889,090 49,893,310

Kauai 640,496,890 790,237,045 1,072,132,020

Hawaii 1,375,592,290 1,650,493,865 2,237,933,625

2002 2005 2018

Sum of VMT

Year

Island

Figure 1. Hawaii VMT estimates by county for 2002, 2005, and 2018.

MOBILE6 MODELING 

General Approach 

For each island, MOBILE6 emission factors were used in combination with VMT data to 

estimate emissions by roadway type and vehicle class. Monthly emissions were first estimated 

from which annual total, summer weekday, and winter weekday emissions were derived. 

Overview of THE MOBILE6 Model

The EPA MOBILE6 model estimates emission factors (g/mile) by vehicle class, which are then 

multiplied by appropriate VMT estimates to estimate on-road vehicular emissions. The 

MOBILE6 model, released in January 2002, is the latest in a series of MOBILE models for 

estimating vehicular exhaust NOx, CO, and exhaust and evaporative VOC. Version 6.2, which 

is the latest publicly released version (February 2004) and available at 

http://www.epa.gov/otaq/m6.htm, was used in this work and includes emission factor estimates 

for SO2, PM (at various size cutoffs), and NH3. This version contains updated CO emission 
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 3 

factors for light-duty vehicles certified to the National Low Emission Vehicle (NLEV) and Tier 2 

standards. 

The MOBILE6 model includes the effects of all currently promulgated Federal motor vehicle 

control programs: 

• Tier 1 light-duty vehicle standards, beginning with the 1994 model year; 

• National Low Emission Vehicle (NLEV) standards for light-duty vehicles, beginning with 

model year 2001; 

• Tier 2 light-duty vehicle standards, beginning with model year 2004; 

• Heavy-duty vehicle standards, beginning with model year 2004; and 

• Heavy-duty vehicle standards (with low sulfur diesel), beginning with model year 2007. 

MOBILE6 Inputs

HDOT provided VMT-mix data for 2002 and 2005 by island by HPMS vehicle type and 

functional class. HPMS vehicle type VMT-mix estimates were mapped from HPMS vehicle 

type to MOBILE6 vehicle type according to methodology presented in (Lindhjem and Shepard, 

2006a). As 2018 VMT-mix data were not available, 2018 VMT-mix was assumed to be 

equivalent to 2005 VMT-mix. VMT-mix by HPMS functional class and MOBILE6 vehicle type 

is presented in Table 1. 

Table 1. Hawaii VMT-mix by HPMS functional class and MOBILE6 vehicle class. 

Year 

Vehicle 

Class 

Rural 

Interstate 

Rural 

Arterial 

Rural 

Collector 

& Local 

Urban 

Interstate 

and 

Freeway 

Urban 

Arterial 

Urban 

Collector 

& Local 

LDGV 0.5193 0.5175 0.5145 0.5117 0.5150 0.5205

LDGT12 0.3127 0.3193 0.2894 0.3322 0.3243 0.3358

LDGT34 0.1132 0.1126 0.1129 0.1107 0.1118 0.1127

HDGV 0.0167 0.0148 0.0229 0.0119 0.0132 0.0108

HDDV 0.0355 0.0274 0.0435 0.0206 0.0271 0.0145

LDDV 0.0008 0.0008 0.0008 0.0008 0.0008 0.0008

LDDT 0.0018 0.0018 0.0018 0.0018 0.0018 0.0018

2002 

MC 0.0000 0.0057 0.0141 0.0102 0.0060 0.0031

LDGV 0.5193 0.5186 0.5138 0.5134 0.5157 0.5181

LDGT12 0.3127 0.3169 0.2774 0.3323 0.3235 0.3272

LDGT34 0.1132 0.1129 0.1132 0.1111 0.1120 0.1124

HDGV 0.0167 0.0150 0.0256 0.0110 0.0145 0.0136

HDDV 0.0355 0.0276 0.0516 0.0179 0.0262 0.0204

LDDV 0.0008 0.0008 0.0008 0.0008 0.0008 0.0008

LDDT 0.0018 0.0018 0.0018 0.0018 0.0018 0.0018

2005 

& 

2018 

MC 0.0000 0.0064 0.0157 0.0116 0.0055 0.0057
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 4 

Average speeds by roadway type were EPA defaults as used in the WRAP mobile sources 

emissions inventory (Pollack et al., 2006). The WRAP speeds are by roadway type for light duty 

vehicles (LDV), light duty trucks (LDT), and heavy duty vehicles (HDV). To estimate speeds by 

roadway type across all vehicle types, VMT-mix data from HDOT for 2002 were applied to 

WRAP speed estimates by vehicle type. 2005 VMT-mix data were similarly applied to estimate 

speeds by vehicle class; however, differences were found to be less than 1% for all speeds, and 

therefore speeds generated based on 2002 data were used across all years. Table 2 lists average 

speed by roadway type. 

Table 2. Average speeds by roadway type. 

Roadway Type 

MOBILE6 

Roadway Type 

VMT-weighted 

Average Speed 

(mph) 

Interstate Freeway 44.7 

Freeways&Expways Freeway 44.7 

Principal Arterial Arterial 19.8 

Minor Arterial Arterial 19.8 

Collector Arterial 19.9 

Urban 

Local Local 12.9* 

Interstate Freeway 56.8 

Principal Arterial Arterial 44.6 

Minor Arterial Arterial 39.6 

Major collector Arterial 34.3 

Minor Collector Arterial 29.7 

Rural 

Local Local 12.9* 

*MOBILE6 Local Average Speed 

Fleet Characterization

HDOT and County DMVs were contacted to determine whether Hawaii state or county specific 

vehicle registration data by model year were available. The Honolulu County DMV maintains 

registration information for the state of Hawaii. However, registration data by model year as 

required to generate a MOBILE6 registration distribution was not readily available for this 

project. In future revisions to this inventory, the utilization of a Hawaii specific vehicle 

registration distribution would allow for more accurate characterization of on-road emissions. 

Temperature and Humidity

By island, monthly average minimum and maximum temperatures for 2002 and 2005 were 

obtained from a station on each island, available in the National Center for Atmospheric 

Research’s (NCAR) ds472 data set, which contains meteorological data for airports for historical 

calendar years. 2018 monthly average minimum and maximum temperatures by season and 

island were obtained for these same stations from period of record data available from the 

Western Regional Climate Center website (http://www.wrcc.dri.edu/COMPARATIVE.html). 

EPA has developed a database for national mobile source emissions estimates for National 

emission inventories, and this database, and the software used to run the county-level MOBILE6 

and NONROAD runs, is referred to as the National Mobile Inventory Model (NMIM). EPA has 
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 5 

posted the NMIM county level database at http://www.epa.gov/otaq/nmim.htm. Monthly 

average relative humidity data were obtained from NMIM for each county. For use in 

MOBILE6, the relative humidity values were converted to absolute (grains/pound dry air) values 

using EPA’s tool available at http://www.epa.gov/otaq/m6.htm#m60. 

Fuel Inputs

Default fuel specifications were taken from the EPA NMIM database. HCAB staff, based on 

communication with gasoline refinery contacts, determined that no oxygenates were used in 

2002 and 2005, and this is reflected in MOBILE6 inputs. In 2018, E10 (a 10% ethanol gasoline 

blend) fuel was assumed to be used, consistent with Hawaii State regulations requiring the 

phase-in of such fuel. The assumed RVP and fuel sulfur content values are shown in Table 3. 

Inspection and Maintenance

Hawaii does not have an I/M or anti-tampering program. 

Application of MOBILE6 Emission Factors

Gram-per-mile emission factors for VOC (total minus refueling), CO, NOx, PM10, PM2.5, SO2, 

and NH3 were obtained for each county, month, roadway type, area type (urban/rural) and eight 

MOBILE5 vehicle classes. These were then multiplied with the corresponding VMT to estimate 

emissions by county and island. The annual total emissions were obtained by summing the 

monthly emissions. To obtain seasonal average day and weekday emissions, the monthly and 

weekday adjustments were used to estimate emissions for the season’s months. The summer 

period was assumed to be June, July, and August, while winter was assumed to consist of the 

months of December, January, and February. 

RESULTS 

Table 4 shows the 2002 annual emissions by county for all of the criteria and visibility-related 

pollutants, and also the hazardous air pollutant benzene. As expected, Honolulu, which has a 

majority of VMT in all years, produces a majority of emissions for all pollutants in all years. 

VOC, CO, and NOx emissions decrease by 2%, 3%, and 0% from 2002 to 2005, respectively, 

and by 50%, 37%, 57% from 2005 to 2018, respectively. Significantly greater emission 

decreases from 2005 to 2018 than decreases from 2002 to 2005 are the result of fleet turnover 

compounded by the significantly lower yearly VMT growth rate (2.7%) from 2005 to 2018 

compared to the 5.1% yearly VMT growth rate from 2002 to 2005. PM10, PM2.5 and SOx 

emissions increase by 9%, 8% and 110% from 2002 to 2005, respectively and decrease by 4%, 

24% and 65% from 2005 to 2018. Emissions increases from 2002 to 2005 in PM10, PM2.5, and 

SOx are due to gasoline sulfur content increases (see Table 3) from 2002 to 2005 and VMT 

growth dominating fleet turnover effects. 

On-road emissions are portrayed graphically by vehicle type for 2002, 2005, and 2018 in Figure 

2, Figure 3, and Figure 4, respectively. Heavy-duty diesel vehicles account for a large portion of 

NOx, PM10, PM2.5, and SOx emissions in 2002 and 2005. However, in all years, emissions for 
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 6 

the pollutants presented are dominated by light duty vehicles and light duty trucks for all 

pollutants, with particular dominance in 2018. 

Table 3. NMIM county database Hawaii State fuel properties. 

Year Month 

RVP 

(psi) 

Gas 

Sulfur 

Content 

(ppm) 

Diesel 

Sulfur 

Content 

(ppm) 

Jan 11.5 24 324

Feb 11.5 24 324

Mar 11.5 24 326

Apr 11.5 24 326

May 7.1 43.3 326

Jun 7.1 43.3 329

Jul 7.1 43.3 329

Aug 7.1 43.3 329

Sep 7.1 43.3 326

Oct 7.1 43.3 326

Nov 11.5 24 326

2002 

Dec 11.5 24 324

Jan 11.5 90 324

Feb 11.5 90 324

Mar 11.5 90 326

Apr 11.5 90 326

May 7.1 90 326

Jun 7.1 90 329

Jul 7.1 90 329

Aug 7.1 90 329

Sep 7.1 90 326

Oct 7.1 90 326

Nov 11.5 90 326

2005 

Dec 11.5 90 324

Jan 11.5 30 11

Feb 11.5 30 11

Mar 11.5 30 11

Apr 11.5 30 11

May 7.1 30 11

Jun 7.1 30 11

Jul 7.1 30 11

Aug 7.1 30 11

Sep 7.1 30 11

Oct 7.1 30 11

Nov 11.5 30 11

2018* 

Dec 11.5 30 11
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 7 

Table 4. Emissions by County, Island and Pollutant in tons per year and VMT in miles per year. 

Year FIPS Island VOC NOX CO PM10 PM2.5 SOx NH3 Benzene VMT 

15101 Hawaii 2,628 2,460 27,987 61 38 29 145 62 1,375,592,290 

15301 Oahu 11,337 9,921 116,598 229 137 105 586 260 5,513,631,965 

15902 Molokai 154 135 1,298 4 2 2 7 3 67,809,335 

15701 Kauai 1,330 1,182 12,578 30 19 14 67 30 640,496,890 

15903 Lanai 57 47 449 1 1 1 2 1 23,477,530 

2002 

15901 Maui 2,371 2,117 23,753 51 32 24 122 54 1,151,721,000 

15101 Hawaii 2,792 2,609 28,353 70 43 63 176 65 1,650,493,865 

15301 Oahu 10,744 9,680 111,219 244 144 222 671 248 6,252,196,690 

15902 Molokai 198 182 1,614 5 3 5 10 4 101,465,985 

15701 Kauai 1,376 1,289 13,004 35 22 31 83 31 790,237,045 

15903 Lanai 76 66 592 2 1 2 4 2 36,889,090 

2005 

15901 Maui 2,301 2,081 21,668 55 33 49 137 52 1,287,100,595 

15101 Hawaii 1,415 1,105 18,076 66 31 21 241 31 2,237,933,625 

15301 Oahu 5,409 4,174 69,830 243 114 79 911 119 8,409,244,125 

15902 Molokai 104 74 1,081 4 2 1 14 2 137,235,255 

15701 Kauai 709 541 8,392 32 15 10 114 15 1,072,132,020 

15903 Lanai 40 27 407 2 1 0 5 1 49,893,310 

2018 

15901 Maui 1,150 879 14,299 51 24 17 187 25 1,740,828,445 

2002 Statewide 17,878 15,862 182,664 377 229 176 929 409 8,772,729,010 

2005 Statewide 17,486 15,907 176,450 412 247 371 1,081 402 10,118,383,270 

2018 Statewide 8,827 6,800 112,085 397 187 129 1,473 194 13,647,266,780 

0%

20%

40%

60%

80%

100%

VOC NOX CO PM10-PRI PM25-PRI SOx NH3 Benzene

HDDV

MC

LDGV

LDGT34

LDGT12

LDDV

LDDT

HDGV

Sum of DBLEMISSIONNUMERICVALUE

Vehicle Class

Figure 2. 2002 Emissions contributions by vehicle class. 
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 8 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

VOC NOX CO PM10-PRI PM25-PRI SOx NH3 Benzene

HDDV

MC

LDGV

LDGT34

LDGT12

LDDV

LDDT

HDGV

Sum of DBLEMISSIONNUMERICVALUE

Vehicle Class

Figure3. 2005 Emissions contributions by vehicle class. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

VOC NOX CO PM10-PRI PM25-PRI SOx NH3 Benzene

HDDV

MC

LDGV

LDGT34

LDGT12

LDDV

LDDT

HDGV

Sum of DBLEMISSIONNUMERICVALUE

Vehicle Class

Figure 4. 2018 Emissions contributions by vehicle class. 
January 2007 

C:\MARY\ALISON 2007\HI_onroad_052507.doc 9 

REFERENCES 

EPA. 2006. Draft Regulatory Impact Analysis: Control of Hazardous Air Pollutants from 

Mobile Sources: Chapter 2. U.S. Environmental Protection Agency. February, 2006. 

Hawaii DBEDT. 2006. 2005 State of Hawaii Data Book. Table 1.05-- Resident Population of 

Islands: 1950 to 2000. Hawaii Department of Business, Economic Development and 

Tourism. http://www.hawaii.gov/dbedt/info/economic/databook/

Hsu. 2006. Personal communication with Yibo Hsu of the Hawaii Department of 

Transportation. December 1, 2006. 

Lindhjem, C, S. Shepard. 2006a. Development Work for Improved Heavy-Duty Vehicle 

Modeling Capability Data Mining – FHWA Datasets Phase IV: Final Report, Appendix 

B. September, 2006. 

Lindhjem, C, S. Shepard. 2006b. Development Work for Improved Heavy-Duty Vehicle 

Modeling Capability Data Mining – FHWA Datasets Phase IV: Final Report, Appendix 

E. September, 2006. 

Pollack, A, L. Chan, P. Chandraker, J. Grant, C. Lindhjem, S. Rao, J. Russell, C. Tran. 2006. 

WRAP Mobile Source Emission Inventories Update. Prepared for the Western 

Governors’ Association. May 2006. Available at 

http://www.wrapair.org/forums/ef/UMSI/0606_WRAP_Mobile_Source_EI_Final_Report

.pdf
APPENDIX B 

HAWAII COMMERCIAL MARINE VESSEL ACTIVITY AND EMISSIONS 
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MEMORANDUM 

To: Marrianne Rossio, Hawaii CAB and Inna Yankova, Lakes Environmental 

From: Stefan Seum and Chris Lindhjem 

Date: July 18, 2007, revised January 30, 2008, 2nd revision October 30, 2008, 3rd

revision December 30, 2009 

Subject: Hawaii Commercial Marine Vessel Activity and Emissions 

INTRODUCTION 

Ocean going vessels are types of mobile sources that directly impact the air quality of coastal 

areas as well as areas further away through long-range atmospheric transport of air pollutants. In 

regards to the coastal air quality, ships contribute to ozone formation and acidic deposition 

mainly through emissions of nitrogen oxides and sulfur oxides. Ships particulate matter 

emissions may have a significant health-relevant impact on local air quality. Ships emissions of 

nitrogen oxides and sulfur oxides are of particular importance for long-range impact as they can 

impact sensitive areas further away. Moreover, ocean going vessels contribute to about 2 – 3% 

to the global carbon dioxide emissions that are responsible to a large extent for the global 

warming effect (IPCC, 2007a). 

Several studies have investigated in recent years the extent to which ocean vessels contribute to 

global air pollution (Corbett & Koehler, 2003; Enderson et.al., 2003; Eyring et.al., 2005). 

Important findings include that ocean vessels have in particular pronounced particulate and 

sulfur oxides emissions compared to other modes of transport. The pronounced significance of 

sulfur emissions as well as particulate emissions is a result of the high sulfur levels in marine 

fuels. The global average sulfur level in marine bunker fuels is assumed to be 2.7%, while a 

global cap of 4.5% sulfur in marine bunker fuels is in place (IMO, 2000). This compares to onroad sulfur levels in the United States which have reached 0.0015% (15 ppm) in 2006. 

Ocean going vessels are less studied for emission inventories in comparison with other mobile 

sources. The currently existing models for mobile sources (EPA's NONROAD model and 

CARB's OFFROAD model for example) do not include emissions from ocean going vessels. 

EPA plans to include all emission factors in its future release of the Motor Vehicle Emission 

Simulator model (MOVES). However, to date modeling near port emissions from ocean going 

vessels is largely based on inventories conducted at US and European ports. In 2005, the 

California Air Resources Board published a draft emission inventory methodology for discussion 

at the Emission Inventory Working Group that includes data from a vessel survey (CARB, 

2005). Concurrently, EPA has compiled the knowledge on marine emission inventories in a 

final report (ICF, 2006). Commercial marine vessels are likely a significant source of the criteria 

pollutants nitrogen oxides (NOx), particulate matter (PM) and sulfur dioxide (SO2) in Hawaii. 
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The share of emissions from ocean going vessels compared to other sources is also likely to 

increase in the future due to the ongoing introduction of emission controls in other modes and 

sectors and the growing transport needs of a globalized economy. California estimates that the 

emission share of nitrogen oxides from ocean going vessels will increase from 9% in 2001 to 

36% in 2020 and emission of particulate matter from 14% in 2001 to 65% in 2020 (CARB, 

2006). At US ports, contributions from international ships to port-wide emissions of nitrogen 

oxides range between 9% (Los Angeles, CA) to 28% (Miami, FL) and for particulate matter 10% 

(Corpus Christi, TX) to 29% (Miami, FL) (ICCT, 2007). 

Most relevant for Hawaii ports are the emissions from ocean going freight vessels and cruise 

ships. In addition to those two ocean going vessel (OGV) categories, there are a suite of other 

commercial vessels including excursion boats, commercial fishing vessels, tugs and tow boats 

for cargo barge services, supply vessels, research vessels, harbor tugs, dredges and others that 

call at Hawaii ports. A regular inter-island ferry service with two vessels is scheduled to start in 

2007. However, the types of commercial vessels are likely to be a much smaller source for air 

pollutants than OGVs. In the following inventory, deep draft OGVs will be separated out from 

other commercial vessels due to their large share of overall emissions. 

This document first sets some background information on the islands of Hawaii and the OGVs 

that call at Hawaii ports. The data input into the modeling exercise and the methods used for 

modeling the emissions are then described. Finally, the results are presented. The smaller 

commercial vessels are discussed in a more condensed format, following the OGVs. 

PORTS IN HAWAII 

There are ten ports in Hawaii with commercial activities as shown in Table 1. However, two 

ports are Navy ports with commercial traffic supplying the Navy, and information on those Navy 

ports is not accessible. Thus those ports are excluded from our survey. Furthermore, the 

Kaunakakai Harbor is a smaller port on Molokai with negligible deep draft1

 vessel traffic. 

Although the port is potentially viable to receive deep draft ships, it is nearly exclusively visited 

by barges.

 

1 Deep draft refers in general to vessels with a draft of less than 14 feet. However, in this study the distinction between ocean going 

vessels and local commercial vessels is sometimes drawn at smaller 18 feet. The data provided by the USACE on trips in Hawaii 

indicate a distinction above and below 18 feet draft between foreign and domestic traffic. Furthermore, for some ports all vessels equal 

and below 18 feet draft are summed together in the USACE data set (USACE, 2004). 
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Table 1. Commercial ports in the State of Hawaii. 

Name Island City Location 

Honolulu Harbor Oahu Honolulu Kapalama Basin, S Oahu 

Kewalo Basin Oahu Honolulu S Oahu 

Barbers Point Harbor Oahu Kapolei Barbers Point, SW Oahu 

Hilo Harbor Hawaii Hilo Kuhio Bay, E Hawaii 

Kawaihae Hawaii Kawaihae NNW Hawaii 

Kahului Harbor Maui Kahului N Maui 

Kaunakakai Harbor Molokai 

Kaunakaka

i S Kaunakakai 

Nawiliwili Kauai Lihue Nawiliwili Bay, SE Kauai 

Pearl Harbor Oahu Honolulu S Oahu 

Port Allen Kauai Hanapepe Hanapepe Bay, S Kauai 

The major port in Hawaii is Honolulu Harbor on Oahu, shown in Figure 1. This natural deep 

water port serves as a hub for delivering goods to other ports of the Hawaiian Islands. Honolulu 

Harbor receives all sorts of goods and ships, including container vessels, liquid and dry bulk 

carriers, break bulk carriers as well as passenger ships. Most incoming goods are being offloaded in Honolulu and then delivered to other islands by tug and barges. 

Figure 1. Honolulu Harbor schematic map. 
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In direct vicinity to the Honolulu Harbor is the Kewalo Basin, which serves mostly excursion 

ships. It is a man-made structure that sometimes experiences high surge conditions at its 

entrance. 

Another important commercial port on Oahu is Barbers Point Harbor on the Southwest edge of 

the island. It serves commercial traffic and was started when a small peer for barges was built to 

serve a nearby industrial park – the Campbell Industrial Park. However, the expansion of liquid 

and dry bulk facilities at Barbers Point Harbor has made it the second busiest port in the state of 

Hawaii. In addition Barbers Point houses ship repair and maintenance facilities. 

Hilo Harbor is the main harbor on the Island of Hawaii. Hilo Harbor is located 194 nautical 

miles from Honolulu and services Hilo city and most of the Hawaii Island. It receives innerisland traffic as well as domestic and international calls, including cruise and passenger vessel 

calls. Kawaihae is a second deep draft port on Hawaii that currently plays a minor role, but it 

provides room for future expansion and is located closer to the Kona international airport. 

Kahului is the only deep draft commercial harbor for Maui Island. Kahului is located 89 nautical 

miles southeast of Honolulu Harbor. It receives a wide range of cargo and serves as export 

facility for agricultural goods. Kahului harbor is also a major destination for passenger and 

cruise ships. 

The island of Molokai is served by Kaunakakai harbor on the south side of the island. Its facility 

encompasses inter-island barge traffic and passenger vessels. Kaunakakai has relatively small 

facilities compared with other Hawaiian ports. 

Kauai’s main commercial port is Nawiliwili harbor. It is near the city of Lihue and about 96 

nautical miles northwest of Honolulu harbor. It services both international and inter-island cargo 

traffic. It is also located in vicinity to the Lihue International Airport. A second Port at Kauai is 

Port Allen, which houses a liquid bulk facility. Port Allen is about 106 nautical miles from 

Honolulu Harbor and serves mostly to supply the Navy. 

TYPES OF DEEP DRAFT SHIPS THAT CALL IN HAWAII PORTS 

Passenger Vessels and Cruise Ships 

Hawaii is a well know destination for cruise ships such as those shown in Figure 2. Cruise ships 

are reported with an average propulsion power of 39,600 kW based on a survey by the California 

Air Resource Board, summarized in Table 2 (ICF, 2006). For instance, cruise ships that called at 

Hilo harbor in 2005 averaged at 43,600 kW. 

Often cruise ships run several medium speed diesel generator sets in a diesel electric setting. 

Furthermore, cruise ships have a continuously high load of their auxiliary engines due to the high 

demand for electricity for its amenities on board. The auxiliary engine demand while in port 

(hoteling) is also relatively high. 
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Figure 2. Hawaii cruise ship example - Pride of Aloha 

(Norwegian Cruise Line – Photo Library http://164.109.173.139/pal.htm) 

Table 2. Average technical data for cruise ships based on CARB survey (ICF, 2006). 

Average Average Auxiliary Engines 

Propulsion 

(kW) 

Number Individual Power 

(kW) 

Total Power 

(kW) 

Average Cruise 

Speed 

(kn) 

39,600 4.7 2,340 11,000 18.06

Container Vessels 

Container shipping is the increasingly common methodology to ship consumer goods and 

general cargo, certain bulk cargo and project cargo that may fit into standard container boxes 

such as that shown loaded in Figure 3. Cars are also shipped in containers, in particular from the 

US West Coast to Hawaii. Container shipping started in the late 1950’s and has seen the largest 

growth rates of all types of cargo shipping over the past years. Freight is packed in boxes of 20, 

40, 45, 48 or 53 feet length. The boxes are stacked in the ship and on deck. Container ships 

require either a container crane for loading and unloading on the dock or may be self discharging 

with cranes installed on board. 

The size of container ships varies considerably, from a few hundred 20 foot equivalent container 

units (TEU) to over 8,000 TEUs. Ships with more than 10,000 TEU capacity are on order today. 

Container ships are usually propelled by a single propeller, driven by a slow speed diesel engine. 

However, some ships, in particular on the California – Hawaii schedule, are propelled by boilers 

and steam engines. Average technical data for container ships from the CARB survey are shown 

in Table 3. 
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Figure 3. Container vessel example - Matson Manukai. Photo source: Yamanouchi (2003). 

Table 3. Average technical data for container vessels based on CARB survey (ICF, 2006) 

Average Average Auxiliary Engines 

Propulsion 

(kW) 

Number Individual Power 

(kW) 

Total Power 

(kW) 

Average Cruise 

Speed 

(kn) 

30,900 3.6 1,889 6,800 21.26

Car Carriers 

Car carriers, an example of which is shown in Figure 4, are specifically designed vessels for 

carrying automotive and product cargo, such as dozers, machinery etc. Car carriers are loaded 

through ramps and usually with the self propelled (automotive) cargo. The characteristic of car 

carriers is a high free board and closed ceiling with several layers of decks underneath. The pure 

car carriers that called at Honolulu harbor in 2005 had an average of 11,700 kW. Auxiliary 

engines power large fresh air vents during loading and unloading operations. 

Figure 4. Car carrier ship example - Liberty Ace (www.shipspotting.com). 
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Table 4. Average technical data for car carriers based on CARB survey (ICF, 2006) 

Average Average Auxiliary Engines 

Propulsion 

(kW) 

Number Individual Power 

(kW) 

Total Power 

(kW) 

Average Cruise 

Speed 

(kn) 

10,700 2.9 983 2,850 13.8

Many car carriers transport new cars on scheduled trade lanes. The vessels run in a form of liner 

service, often serving specific automotive manufacturer. However, in particular for Hawaii, the 

transport of used cars for residents plays a significant role. Those cars may be transported with 

car carriers, Ro-Ro vessels and to some degree in containers on container vessels. 

Dry Bulk Vessels 

Dry bulk vessels carry any sort of dry bulk cargo not suitable for containerized transport. Dry 

bulk cargo includes cement, coal, steel etc. Dry cargo vessels may be self-discharging with 

cranes installed on board. Specialized dry cargo vessels carry refrigerated goods in their 

refrigerated bulk compartments. Those vessels transport fruits from tropical countries, including 

Hawaii, usually to destinations in the north. Refrigerated vessels have larger auxiliary engines 

for the powering of the refrigeration units while in port. 

Table 5. Average technical data for dry bulk carriers based on CARB survey (ICF, 2006) 

Average Average Auxiliary Engines 

Propulsion 

(kW) 

Number Individual Power 

(kW) 

Total Power 

(kW) 

Average Cruise 

Speed 

(kn) 

8,000 2.9 612 1,776 17.58

Liquid Bulk Carriers 

Liquid bulk carriers comprise crude oil tankers, 

petroleum product carriers, liquefied natural gas 

carriers and chemical carriers such as that shown 

in Figure 5. Crude oil and petroleum products 

have been traditionally carried by ships. The 

heavy weight, long-term planning and storage 

economy enables the sailing with relatively slow 

speeds and low power. Oil tankers that called at 

Honolulu Harbor in 2005 average at 11,800 kW. 

Figure 5. Example liquid bulk carrier - Seabulk artic. 
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Table 6. Average technical data for liquid bulk carriers based on CARB survey (ICF, 2006) 

Average Average Auxiliary Engines 

Propulsion 

(kW) 

Number Individual Power 

(kW) 

Total Power 

(kW) 

Average Cruise 

Speed 

(kn) 

9,400 2.7 735 1,985 13.6

General Cargo Vessels 

General cargo vessels, an example of which is shown in Figure 6, are a category of vessels not 

built for a specific type of cargo or transport unit. General cargo vessels are often of small to 

mid-size and carry a variety of cargo. The vessel may have some spaces for containers and other 

areas more flexible to take on other types of cargo. Some general cargo vessels have their own 

loading and unloading cranes, making them more flexible. 

Figure 6. Example general cargo vessel - BBC Asia. 

Table 7. Average technical data for general cargo vessels based on CARB survey (ICF, 2006) 

Average Propulsion Average Auxiliary Engines 

(kW) Number Individual 

Power 

(kW) 

Total 

Power 

(kW) 

Average Cruise Speed

(kn) 

9,300 2.9 612 1,776 14.69

Assist Tugs and Tow Boats 

Assist tugs are dedicated to help larger ships to maneuver into the harbor and dock at the berth. 

The main reason for assist tugs is safety, and they can also provide additional flexible power in 

cases of unforeseen needs and emergencies. Most tug boats are twin screwed vessels with 500 – 

4,000 kW of installed power. However, the harbor tugs have likely up to 3,000 kW power, and 

larger tugs are used for ocean towing jobs. 
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Honolulu is the only harbor in the state of Hawaii with tug assist requirements. The other 

harbors have verbal agreements with the agents as well as liability regulations in place in case of 

potential damages to harbor infrastructure. However, the number of available tugs in all other 

island ports than Honolulu is very limited. Matson Navigation operates two tugs primarily 

serving Hilo and Kawaihae, and one tug serving Kahului and Maui. The Hawaiian Tug and 

Barge Corporation has one tug serving mostly ports on Kauai (HOST, unknown year). 

Otherwise, tugs must be dispatched from Oahu or used when their towing schedule makes them 

available at other ports. 

Tow boats are more like boats than assist tugs. However, they often are more powerful (up to 

5,000 kW) and are primarily utilized to pull or push cargo barges, or to salvage wrecked ships. 

The Hawaii barge traffic plays an important role in inter-island commerce. For mostly economic 

reasons, most deep draft vessels up- and unload their cargo in Honolulu or Barbers Point, from 

where the cargo is distributed by barges to the other islands. 

The number of 4960 tug and tow boat movements were considered to include assist functions, 

and are included in the tables of tug and barge traffic. No distinction could be made between 

vessel assist and barge movements. Thus, vessel assist tugs are not specifically calculated. 

Vessel assist functions of tugs is a small fraction of the tug activity, but is considered to be 

represented in the number of movements of all tugs. 

Supply and Other Miscellaneous 

There are some other vessels stationed in the ports of Hawaii, although their numbers are rather 

small. Those vessels include for example pilot boats. Pilot boats are fast, flexible boats that 

bring the harbor pilots onboard the approaching vessels and vice versa; an example is shown in 

Figure 7. 

Another common type of vessel in Hawaii is small passenger vessels for dinner cruises. 

Passenger vessels for those day and evening cruises and excursions have passenger capacities 

between 20 and 1,500, with an average of 240. Other vessels may include coast guard vessels, 

working boats, fishing boats etc. 

Figure 7. Example pilot boat. Source: Hawaii Pilots Association 

(http://www.hawaiipilots.net/Oahu.asp). 
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VESSEL ACTIVITY AND OTHER DATA 

The types of commercial marine vessels in use and the source of activity data are listed in Table 

8. Deep draft vessels are usually responsible for the largest portion of the commercial marine 

PM and SO2 emissions because they typically use a high sulfur fuel in their engines. Deep draft 

vessels call at five ports in the Hawaiian Islands: Honolulu (Oahu), Barbers Point (Oahu), 

Kahului (Maui), Hilo (Hawaii), and Nawiliwili (Kauai). The next most important vessel type 

would be the tow and tug boats that pull or push barges for freight transfer between ports. 

The data for deep draft vessel calls were derived from detailed vessel calls at Hawaii ports for 

2005. The Hawaii state started in 2002 to record all vessel calls in an electronic data base. 

Vessel calls for 2002 are not completely available, since the electronic storing of data was not 

started prior to 2002. However, a back casting for 2002 is possible on the basis of trip and 

tonnage numbers from data by the United States Army Corps of Engineers (USACE, 2004). 

Table 8. Commercial vessel types and activity data sources. 

Source 

Definition Purpose Geographic Area Activity Data Source 

Deep draft Ocean-going large vessels; 

freight and passenger Near port 

This work; Port authority of Hawaii; State 

of Hawaii; EPA best practice guide (ICF, 

2006) 

Tow Boats Barge Freight Between Island 

Traffic 

Near port links, USACE trade data and 

vessel list (USACE, 2005) 

Tugs Vessel assist and support 

functions Near port USACE (2005) 

Local 

passenger 

boats 

Passenger transport for 

leisure or ferry services Regular routes USACE (2005) 

Cargo Traffic 

The islands of Hawaii have certain distinct ship traffic patterns. Foremost, most of the cargo 

arrives or leaves the islands via the Barber Point and Honolulu harbors. From there cargo is 

primarily distributed to the other islands via barges, pushed or pulled by tow and tug boats. 

Other ports that receive some deep draft vessel calls are Hilo, Kahului and Nawiliwili. Honolulu 

Harbor received 65% and 68% of inbound cargo tonnage in 2002 and 2004 respectively. 

Barbers Point received 35% and 31% respectively. While the tonnage for Honolulu Harbor is 

also reflected in the percentage distribution of vessel calls (62% and 60% in 2002 and 2004), 

Barbers Point handles the tonnage inbound cargo with a much lower share of vessel calls (7% 

and 9% in 2002 and 2004). The low share of vessel calls can be contributed to the fact that 

Barbers Point receives mostly heavy liquid and dry bulk cargo with fewer ships. The patterns 

for outbound cargo are comparable. However, the amount of outbound cargo, by far, is lower 

than that of inbound cargo and therefore does not determine vessel traffic. 
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The entrance to the Barbers Point and Honolulu harbors divide between a western and a southern 

path. Ships arriving from or departing to for example the West Coast of the United States are 

likely to take the southern path and then traverse between Oahu and Molokai. Furthermore, 

ships to and from all Hawaii islands, except for Kauai, are using this southern path. Ships to and 

from Asia as well as Kauai will use the western entry and exit paths. All other ports on Hawaii 

islands have only one dominant entry and exit path. 

Cruise Traffic 

The cruise traffic also has followed a distinct trend. The predominant cruise voyages are interisland cruises. For example only roughly 6% to 9% (outbound and inbound) of the cruise ships 

calling at Honolulu Harbor originate or are destined outside of the islands of Hawaii. Similar 

patterns exist for Nawiliwili and Hilo Harbor with Hilo receiving the largest proportion of first 

port of Hawaii cruise ship calls (16% of all Hilo cruise ship calls). About 75% of the Hawaii 

cruise passengers arrive in Hawaii via air planes and board the cruise ships in Honolulu (State of 

Hawaii, 2006). 

Due to the dominant inter-island cruise traffic, the portions of emissions emitted on voyage 

between the islands contribute to island air quality as well. This might be in particular relevant 

for Maui because vessels traveling from Oahu to Hawaii pass Maui on its East and South shores. 

The cruise industry has seen a steep rise in passengers over the last couple of years. While the 

industry was virtually stagnant in most of the 90s, a sharp increase occurred in 2001, 2002 and 

2005. Figure 8 shows the development of cruise ship passengers from 1990 – 2005 with 

projected figures for 2006 and 2007. Forecasts indicate a continuation of sharp growth into the 

future. 

Figure 8. Growth of Hawaii cruise passenger arrivals. Source: First Hawaiian Bank (2006). 
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MODELING APPROACH 

The emissions inventory for deep draft commercial marine vessels focuses on the near-port 

sections. On voyage sections can be added by referring to work conducted for EPA in the 

context of maritime policy planning (Corbett, 2006a). The near-port section is based on activity 

data (port call data, specific speed and time in mode) for each of the Hawaii ports. Additionally, 

information on other commercial vessels and their activity patterns is based on freight links data 

and vessel trip data from the US Army Corps of Engineers database (USACE, 2005). 

The modeling of ocean going vessel emissions for this study follows the "Current Methodologies 

and Best Practices in Preparing Port Emission Inventories" that was prepared for the US 

Environmental Protection Agency (ICF, 2006). The study compiles current methodologies and 

best practices that were learned in past years when conducting port-related emissions inventories 

at various places in the United States and elsewhere. The following paragraphs outline the 

general methodology. Further below, the specific modeling approach for the Hawaiian ports is 

described. For more details on current methodologies and best practices please see ICF (2006). 

For this report a mix of detailed, mid-tier and streamlined inventory methodology was used. 

While the ocean going deep draft vessels are modeled using some ship specific data, in particular 

the emissions from other vessels were modeled utilizing averages. The usage of ship specific 

port of call data allows the emission calculations to be based on real ship activity. Furthermore, 

knowledge about the vessels that called at Hawaiian ports allows the use of detailed technical 

information on the installed engines. 

The activity-based emission calculation combines engine data with ship activity data and specific 

emission factors as follows: 

 E = P x LF x A x EF 

where 

 E = Emissions (g) 

 P = Maximum continuous rating engine power (MCR in kW) 

 LF = Load factor of the engine (percent of MCR) 

 A = Activity (hr) 

 EF = Pollutant and vessel-specific emission factor (g/kW-hr) 

The basis for all activities was the vessel port of call list for the Hawaiian ports. Information on 

vessel calls is stored electronically since mid-2002. Vessel information includes the arrival and 

departure times as well as the first and last port of call. The vessel calls were obtained from the 

State of Hawaii, Department of Transportation-Harbors Division (DOT-H). 

The emission factors were based on ship engine data, available through a large database 

provided by Lloyds Register-Fairplay ship register. The Lloyds Register-Fairplay ship register is 

the most comprehensive database for ocean going vessels. It contains information about the type 

of vessel, its size, the type of main and auxiliary engines, including the total power installed and 

others. 
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ENVIRON estimated hazardous air pollutant emissions (HAPs) for CMV, including deep draft, 

tug, barge and local passenger traffic vessels, based on HAP fractions as specified in the U.S. 

EPA 2002 National Emission Inventory (NEI) documentation (EPA, 2005 and EPA, 20082

). 

Diesel-powered HAP fractions used to estimate HAP emissions from Hawaii CMV were taken 

from Table B-3 of EPA (2005) for VOC and for PM less than 10 microns, and Table B-4 of EPA 

(2005) for PM less than 2.5 microns. HAP emissions from CMV were estimated based on vessel 

fuel consumption and emission factors from EPA (2005) and EPA (2008)2 for lead and mercury, 

respectively. Additionally, arsenic emissions were estimated based on emissions of PM less than 

2.5 microns and HAP fractions as specified in EPA (2008) 2. 

Specific Hawaiian Modeling Approach 

As with many other sources, modeling marine vessel emission requires assumptions to 

approximate the real world. The listing of vessel calls for 2005 provided the basis for modeling 

the deep draft vessel emissions. Vessel calls include the date and time of arrival and departure. 

The resulting time in port was used to determine the length of the hoteling use of auxiliary 

engines. The origin and destination of the vessel determined the traveling pattern near the ports. 

All islands but Oahu have a single lane approach. The Oahu vessel approach splits into vessels 

from North-West, West and South-West (mostly Asia) and all other directions between South 

and North. The latter approach and leave Oahu by cutting between Oahu and Molokai towards 

the West Coast of the North American Continent, or traverse South-East along the Hawaii 

islands Molokai, Lanai, Maui and Hawaii for destinations on the islands or South America. 

For modeling purposes the emission calculations were split into several links, representing a 

distinct section of the vessels approaching or leaving the islands. The links covered all vessel 

traffic within 30 nautical miles of the port. The emissions within the 30 nautical miles include 

cruise zone, reduced speed zone, pilot boarding, maneuvering and hoteling modes that 

correspond to the common traffic patterns in Hawaii.3 

The cruise zone is the zone where the vessels continue or have reached their regular traveling 

speeds. The cruise zones extend from mile 30 to mile 5 for this inventory, having a length of 25 

nautical miles. The cruise speed of the vessels is determined by the 83% of the maximum 

continues rating (MCR) of the engine (ICF, 2006) and taken out of the Lloyds vessel data. The 

emissions in the cruise zones are allocated to specific links that correspond with the waterways 

identified by the US Army Corps of Engineers and used in other studies (Corbett, 2006a). The 

allocation to the individual links is based on the last and next port of call of each individual 

vessel. However, all emissions in the cruise zones per port were added for the purpose of the 

Hawaiian emission inventory. 

The reduced speed zone (RSZ) is the zone where ships slow down prior to taking on the bar 

pilots and reaching the harbor area. Since the pilot boarding area is between 1 and 2 nautical 

miles from port entrances, we assume a 4 nautical miles RSZ approaching the pilot boarding area 

 

2

 EPA, 2008. Documentation Updates for 2002 Version 4 - November 2008. 

http://www.epa.gov/ttn/chief/net/2002inventory.html 

3 Personal communications with harbor pilot Al Dorflinger, March 14, 2007. 
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with 70% of maximum speed or 34% of MCR (the engine load is theoretically proportional to 

the cube of relative speed) as the RSZ. Both the cruise speed zone and the RSZ zone comprise 

the 'at sea' emissions for the Hawaii harbors. 

Pilots board the vessels at speeds of 5 knots and between 1 and 2 nautical miles from the harbor 

entrances. According to harbor pilots, the total time of boarding, riding into harbor and 

maneuvering takes on average 60 minutes at all ports in Hawaii. Therefore a maneuvering mode 

of one hour with 4 knots or 2% - 3% of MCR was assumed as recommended by EPA guidance 

(ICF, 2006). Finally, the time in port was taken from detailed port of call lists for estimating the 

hoteling emissions. The maneuvering mode and the time hoteling in port are added together to 

the 'in port' emissions at Hawaiian ports. 

The calculation of main engine emissions is based on kilo-Watt required for traveling in each 

distinct zone, together with the time a ship spent in each of the zone. This lead to the kilo-Watt 

hour demand for each ship in each zone. In order to estimate the speed of each vessel, the 

corresponding speed at each of the load factors (83%, 34% and 2%-3%) was calculated based on 

technical data from Lloyds Register-Fairplay register of ships. The Lloyds register is the largest 

database of vessels and contains information including cruise speed, main engine make, type, 

power and auxiliary engine power. Once the speed was established, the time spent in each zone 

could be calculated. 

The following step was to combine the vessel's engine power (P) with the load factor (LF) and 

the activity (A) in each zone, which equals the time spent in that zone. The vessels’ kilo-Watt 

power and other technical information, for example the type of engine, were also taken from data 

provided by Lloyd's Register-Fairplay. Therefore, for each individual vessel a specific speed 

and time in zones was calculated and multiplied with the main engine kilo-Watt and 

corresponding load factors. If ships could not be found in Lloyds database, detailed data were 

obtained from sister ships if possible. Vessels that could not be identified at all were allocated 

generic figures (ICF, 2006). In some cases the type of vessel, for example tanker, car carrier etc, 

could be identified through web-searches and the corresponding generic figures were used. In 

other cases no information could be found on the type of vessel and it was assumed to be a 

general cargo vessel. 

The final calculation of emissions was made ship- and pollutant-specific. Each ship was 

categorized as either equipped with steam engines or diesel engines. Diesel engines could be 

either two or four stroke. Each of those engine types has specific emission factors. Two stroke 

engines are considered slow speed engines, whereas four stroke engines usually are medium 

speed engines and thus have different emissions. The emissions were then calculated for each 

port and each pollutant separately and based on each individual ship. Only then were the 

emission figures added to provide sums per port and distinct zones. 

Some additional steps were needed to calculate the emissions from auxiliary engines. Auxiliary 

engines contribute to some degree to emissions in each zone, but are most relevant for emissions 

while the ship is in port, the so called hoteling emissions. However, even in other modes 

auxiliary emissions can be relevant and in particular for the maneuvering mode it can become the 

dominant source of emissions as the main engines come to a near halt. 

The calculation of ship specific emissions from auxiliary engines is based on technical data from 
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Lloyds register of ships and auxiliary engine load factor assumption from the "Best Practices" 

guide. (ICF, 2006). However, here as well some vessels could not be clearly identified in the 

Lloyds data set. If possible detailed data were obtained from sister ships if indication of such 

existed. In other cases web-research gave some indication of the type of ship. For those vessels, 

default average data for the specific vessel category were used based on CARB surveys (ICF, 

2006). If no information could be obtained on a vessel, it was assumed to be a general cargo 

ship. 

Past studies have identified typical load factors for each type of vessel in different operating 

modes, reflecting averages from observations mostly from CARB but also EPA and other 

sources (ICF, 2006). Those load factors were used to determine the auxiliary power in each of 

the modes for each individual ship. The calculation of resulting kilowatt hours was conducted 

based on the vessel look up database to avoid repetition of similar calculations. Table 9 lists all 

load factor assumptions. The kilowatt hour were then pre-recorded as cruise auxiliary kW, RSZ 

auxiliary kW, maneuvering auxiliary kW and hoteling auxiliary kW in the look-up table. The 

times for cruise and RSZ zones were derived vessel specific (see above) and the maneuvering 

mode was assumed to be one hour, based on communications with harbor pilots referring to one 

hour as average time from pilot boarding to berthing. 

Table 9. Auxiliary engine load factor assumptions. 

Ship-Type Cruise RSZ Maneuver Hoteling 

Auto Carrier 0.13 0.30 0.67 0.24 

Bulk Carrier 0.17 0.27 0.45 0.22 

Container Ship 0.13 0.25 0.50 0.17 

Cruise Ship 0.80 0.80 0.80 0.64 

General Cargo 0.17 0.27 0.45 0.22 

Miscellaneous 0.17 0.27 0.45 0.22 

OG Tug 0.17 0.27 0.45 0.22 

RORO 0.15 0.30 0.45 0.30 

Reefer 0.20 0.34 0.67 0.34 

Tanker 0.13 0.27 0.45 0.67 

The emissions from auxiliary engines in-port were calculated similarly on the basis of time and 

engine power. The time was taken out of the vessel call database. For Honolulu Harbor for 

example, the average stay in port for all vessels is 12.5 hours and that of Hilo Harbor close to 10 

hours. The average stay at Barbers Point harbor, which serves predominately bulk freight, is 

more than 85 hours. The load factors for auxiliary engines were taken from the ICF (2006) 

compilation, reflecting averages from observations mostly from CARB but also EPA and other 

sources. 

The high hoteling load factors for cruise ships and tankers may make them to a significant source 

of in-port emissions. Cruise ships have an enormous power demand to provide the electricity for 

the services onboard the ship. Usually those services comprise of sanitary, air conditioning, food 

preparation, recreational use and others for several thousands of people. Tanker ships use their 

auxiliary engines to load and unload cargo. The engines power large pumps onboard the ship for 

this purpose. All other vessels have relatively low load factors of their auxiliary engines while in 

port, providing the basic power needed for the crew, navigation, communication etc. 
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The emission calculations from boilers was based on the mid-tier approach of the “Best 

Practices” guide. (ICF 2006) Boilers are used during maneuvering operations and while in port 

to pre-heat the bunker fuels, and to provide steam and hot water. For this study, ENVIRON 

assumes that the boilers are switched off and propulsion heat is used, once the vessel is 

underway. It is further assumed that boilers are run on heavy fuel oil. 

Depending on the vessel type, the demand for steam and hot water differs largely. Tankers use 

steam driven pumps to assist the offloading of their cargo. Cruise ships have a comparatively 

large hot water demand during port stays to service their guests. All other vessel types have 

comparatively small demands for heat and steam. 

The calculation of emissions from boilers combines time of operation, fuel consumed and fuel 

specific emission factors. The time in port was taken out of the vessel call database. The fuel 

consumed for boilers is derived using vessel type specific power demands and a specific fuel 

consumption of 305 g fuel per kW-hour. (Table 10) Emission factors were taken from the “Best 

Practices” guide. (ICF 2006, Table 13) The uncertainty with those figures is rather large because 

the power demand is based on a limited number of ship visits. It is likely that not all tankers, the 

largest source for boiler-based emissions, use boilers to off-load their cargo. Furthermore, it is 

unlikely that even if they do, boilers may not be in operation for the entire port stays. The “Best 

Practices” guide recommends to collect boiler fuel consumption data for any detailed emissions 

inventory. (ICF 2006) 

Table 10. Boiler Fuel Consumption during Maneuvering and Hoteling. [t/hr] 

Ship-Type Maneuvering Hoteling 

Auto Carrier 0.113 0.113 

Bulk Carrier 0.033 0.033 

Container Ship 0.154 0.154 

Cruise Ship 0.305 0.305 

General Cargo 0.032 0.032 

Miscellaneous 0.113 0.113 

RORO 0.033 0.033 

Reefer 0.142 0.142 

Tanker 0.113 0.915 

Source: Starcrest Consulting Group 2007 
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EMISSION FACTORS FOR DEEP DRAFT VESSELS 

Emission factors for the main engines of deep draft ocean going vessels have been developed in 

past studies based on a limited number of data sets (Acurex 1996, Entec 2002, Starcrest 2005, 

Environ 2002 and 2004). Main engines that operate in ocean going vessels fall into EPA’s 

category 3 off-road engines (displacement >30 l/cylinder). However it is appropriate to 

differentiate those engines based on the engine speed and types of fuel used further. 

Most main engines on ocean going vessels are slow speed engines that use heavy fuel oil (HFO), 

a residual fuel fraction of the distillation process that can be mixed with a minor amount (about 

5-10%) of lighter fuel to produce an intermediate fuel oil (IFO). An engine is considered a slow 

speed engine at return per minute below 250 rpm. Other options are medium speed engines 

(>250 rpm < 1,400 rpm) sometimes found in cruise ships, and steam turbines. Table 11 lists the 

emission factors for marine main engines. Furthermore, distillate fuels, i.e. marine diesel oil 

(MDO) and marine gas oil (MGO) may be used in particular in auxiliary engines typically with 

much lower sulfur levels than HFO. The emission factors used in this study are based on the 

recommendation for preparing port inventories for EPA (ICF, 2006) and work conducted in 

preparation of the sulfur emission control areas in the Pacific (Corbett, 2006 a&b). 

Table 11. Emission factors for main engines on voyage (g/kWh). 

Engine type Fuel type NOx SO2 CO HC PM10 PM2.5 CO2 

Slow speed – two stroke HFO 18.1 10.3 1.40 0.60 1.05 0.99 620

Medium speed – four 

stroke 

HFO 14.0 11.1 1.10 0.50 1.11 1.02 677

Steam turbines HFO 2.1 16.1 0.20 0.10 1.50 1.38 ~983

Source: ICF, 2006; HC was converted to VOC using the EPA NONROAD conversion of 1.053 VOC to HC for diesel engines 

Auxiliary engines may be medium speed category 3 engines or category 2 engines. The category 

2 engines have an engine displacement > 5 and < 30 liter per cylinder and a typical power range 

between 1,000 and 3,000 kW. A combined emission factor for auxiliary engines, assuming 92% 

use of heavy fuel oil (HFO) and 8% use of marine diesel oil (MDO) for cruise ships and 71% 

HFO and 29% MDO for all other vessels was assumed for this study (CARB, 2005). Table 12 

list the emission factors used for modeling the auxiliary engine emissions. 

Table 12. Emission factors for auxiliary engines (g/kWh). 

Engine type Fuel 

type 

NOx SO2 CO HC PM10 PM2.5 CO2 

HFO 14.70 11.1 1.10 0.40 1.11 1.02 722

MDO 13.90 6.16 1.10 0.40 0.71 0.66 690

Medium speed 

MGO 13.90 2.05 1.10 0.40 0.38 0.35 690

Composite 

factor 

cruise ships 

HFO/MD

O 

92%/8% 

14.636 10.704

8

1.10 0.40 1.078 0.9912 719

Composite 

factor 

all other ships 

HFO/MD

O 

71%/29% 

14.468 9.6674 1.10 0.40 0.994 0.9156 713

Source: ICF, 2006; CARB, 2005 
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Table 13. Emission factors for boilers (kg/tonne of fuel consumed). 

Engine type NOx SO2 CO HC PM10 PM2.5 CO2 

On-board auxiliary boilers 12.30 54.0 4.60 0.38 1.30 1.04 3,114

Source: ICF, 2006; CO2 emission factor from IMO 2005 

EMISSIONS RESULTS FOR THE YEARS 2005 AND 2002 

Table 14 sums the emissions from deep draft vessels per island. The emissions are separated into 

all emissions occurring within one nautical mile of the harbor (in port) and outside one nautical 

mile of the harbor (at sea). The emissions within one nautical mile of the harbor are comprised 

of the maneuvering, hoteling emissions as well as emissions from boilers. 

Table 14. Deep draft vessel emissions at Hawaii ports for 2005 (tons). 

Port and specific location NOx CO HC PM 10 PM 2.5 SO2 

Kauai, Nawiliwili – in port 354.41 31.66 12.04 26.97 24.63 301.73

Kauai, Nawiliwili – at sea 280.13 21.83 9.60 20.58 19.01 204.91

Oahu, Barbers Point – in port 67.53 8.91 2.04 5.12 4.56 90.88

Oahu, Barbers Point – at sea 27.92 2.17 0.94 1.81 1.69 17.88

Oahu, Honolulu – in port 1,040.9 129.66 34.80 80.84 72.39 1,300.6

Oahu, Honolulu – at sea 746.18 58.45 27.07 68.75 63.83 698.18

Maui, Kahului – in port 380.81 33.44 12.02 28.99 26.49 324.30

Maui, Kahului – at sea 194.81 15.30 6.80 18.33 16.88 185.79

Hawaii, Hilo – in port 185.55 17.30 7.52 14.50 13.15 156.60

Hawaii, Hilo – at sea 293.29 23.81 10.08 23.87 20.75 239.70

Boiler emissions carry a relatively large portion of uncertainty because of the limited data sets 

that they are build on. Today only limited knowledge exist about the use activity of boilers as 

well as regarding the use of heat recovery systems while vessels are on voyage. While in some 

categories the share of boiler emissions is small, in other ports and for some pollutants this share 

might be more significant. Table 15 shows the share of boiler emissions on the in-port emissions 

of the Hawaii ports for 2005. Boiler emissions represent a particular large share in those ports 

where tanker traffic is dominant (Barbers Point) and where stays in port are rather long 

(Honolulu). 

Table 15. Share of boiler emissions in Hawaii ports for 2005 (percent). 

Port and specific location NOx CO HC PM 10 PM 2.5 SO2 

Kauai, Nawiliwili – in port 3.7% 15.8% 3.4% 5.3% 4.6% 19.5% 

Oahu, Barbers Point – in port 18.3% 52.1% 18.6% 25.6% 23.0% 59.8% 

Oahu, Honolulu – in port 15.6% 46.7% 14.4% 21.2% 18.9% 54.6% 

Maui, Kahului – in port 3.6% 15.4% 3.6% 5.0% 4.4% 18.7% 

Hawaii, Hilo – in port 4.1% 16.5% 3.2% 5.6% 4.9% 21.4% 

Emissions for the year 2002 were back-casted from 2005 based on trade and vessel call data, 

because a complete list of vessel calls was not available for 2002. Vessel activity data from the 

US Army Corps of Engineers indicates a flat number of trips between 2002 and 2005, despite an 

increase in freight movement. For the cruise sector, 2002 was already significantly up from the 

year 2000; it then stayed stagnant until 2004 and increased by over 30% towards 2005. 
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Therefore, the 2002 emissions are based on the same emissions from freight transport and a 75% 

level of cruise ship activity. The estimated 2002 emissions are listed in Table 16. Boiler 

emissions are included in “in-port” emissions. 

Table 16. Deep draft vessel emissions at Hawaii ports for 2002 (tons). 

Port and specific location NOx CO HC PM 10 PM 2.5 SO2 

Kauai, Nawiliwili – in port 267.6 23.90 9.09 20.36 18.60 227.8

Kauai, Nawiliwili – at sea 211.5 16.48 7.25 15.54 14.35 154.7

Oahu, Barbers Point – in port 67.53 8.91 2.04 5.12 4.56 90.88

Oahu, Barbers Point – at sea 27.92 2.17 0.94 1.81 1.69 17.88

Oahu, Honolulu – in port 980.9 124.0 32.58 76.20 68.17 1,246.7

Oahu, Honolulu – at sea 672.7 52.72 24.17 63.18 58.70 642.7

Maui, Kahului – in port 290.0 25.53 9.19 22.13 20.21 247.3

Maui, Kahului – at sea 152.8 12.02 5.34 15.03 13.84 152.8

Hawaii, Hilo – in port 142.2 13.29 5.77 11.14 10.08 120.3

Hawaii, Hilo – at sea 225.3 18.53 7.76 18.85 16.12 189.7

For both fore- and back-casting, it is important to note that cruise traffic contributes heavily to 

the emissions, though cruise traffic share of emissions differs from pollutant to pollutant. 

Moreover, the share of cruise traffic emissions can differ significantly for each emission 

category, such as in cruise mode, in the reduced speed zone, maneuvering and hoteling. This is 

because tanker emissions from boilers can reach a large portion within the port. Table 6-3 

details the average share of cruise ships on vessel calls and the emissions for 2005. 

Table 17. Average share of cruise traffic on vessel calls and emissions in Hawaii ports, 2005. 

Port location Vessel calls NOx CO HC PM 10 PM 2.5 SO2 

Kauai, Nawiliwili 89% 98% 98% 98% 98% 98% 98% 

Oahu, Barbers 

Point 

0% 0% 0% 0% 0% 0% 0% 

Oahu, Honolulu 20% 30% 24% 33% 27% 27% 22% 

Maui, Kahului 51% 92% 92% 91% 86% 86% 86% 

Hawaii, Hilo 69% 93% 90% 94% 87% 91% 87% 

2018 GROWTH FORECAST 

The ports in Hawaii have seen a significant increase of cargo in the past years with at the same 

time stagnating numbers of deep draft vessel calls. While between 2002 and 2004 the cargo 

tonnage grew by 21% inbound, the vessel calls declined by 4%. This leads to the assumption 

that shipping companies were able to serve the Hawaiian market with the existing vessel 

capacity in those years, potentially using some larger vessels. We assume that this trend 

continued in 2005 but that as an outlook into the future, an annual 5.86% growth in freight vessel 

calls is anticipated (Corbett and Wang, 2006). The estimated emissions from deep draft freight 

vessels are included in Table 18, Table 19 and for three growth scenarios as explained below. 

The cruise industry has seen a dramatic growth in Hawaii since 2000. While the 90’s remained 

fairly flat around 75,000 cruise guests, with some increase in the late nineties to 100,000 guests, 

the cruise ship guests suddenly exploded up to over 300,000 in 2005. After a high in 2002, the 

year 2003 saw a small decline that was recouped in 2004 to 2002 numbers, followed by an 
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increase of more than 30% in 2005 compared to the year before. According to the Hawaiian 

Bank forecast, visitors will reach 400,000 in 2007 and pre-bookings indicate over 500,000 for 

2008 (First Hawaiian Bank, 2006). 

However, forecasting cruise ship traffic is a tricky and particular difficult task as many different 

factors may influence its growth. Factors influencing the cruise ship industry include general 

wealth, economic outlook of in particular the mainland United States, cruise line capacities, 

land-side infrastructure capacities, aging of vessels and last but not least fashion. The following 

findings will be used to provide a basis for cruise industry forecasts: 

• Cruise terminal capacity may limit cruise ship calls (e.g., Hilo, HTA, 2004) 

• Long-term GDP growth in the United States is expected to be at 3.1% 

• However, GDP growth slowed in 2006 to a 2.1% growth from an expected 3.4% (Makin, 

2007), thus the expected GDP growth in the US might be below 3.1% 

• The cruise bed capacity is further increasing in the future; growth forecast for North 

America in the order of 4.25% can be found (Ocean Consultants, 2005) 

For the emissions estimates for 2018, we have developed three scenarios: 

• In scenario A: it is assumed that the same number of additional passengers will use 

Hawaii cruise ships year after year. The basis will be the anticipation to add 100,000 

passengers in 2007 and each year thereafter 

• In scenario B: it is assumed that the increase starts strongly but cools off somewhat to 

reach a 4% growth by 2011, 2% growth by 2014 and drops to 1% growth by 2016. The 

assumption is that 80% of additional passengers based on the fore-years delta are added 

annually. 

• In scenario C: a steady increase of 4.25% in cruise ship passengers annually is assumed. 

For all scenarios shown in Figure 9, it is assumed that the increase in cruise ship passengers will 

result in an one-to-one increase in ship activity and vessel emissions. Figure 10 shows the 

annual growth rates for each of the scenarios shown in Figure 9. The exponential growth 

scenario, with the recent past years’ 29% annual increase in passengers as basis, is only given as 

a reference because it seems a too strong growth that would quickly exceed the Hawaiian 

infrastructure capacities. While the Hawaii Bank forecast and some of the cruise liners 

published the potentials to add 100,000 passengers in 2006 and 2007, representing some 29% 

growth, an annual growth of that percentage seems unrealistic in the long run. With an annual 

growth of 29% the cruise ship passengers in 2018 would be more then 25 times of today's 

numbers. This degree of growth seems unlikely in particular because signs that the United States 

economy, in particular that the housing market and that the US dollar are weakening are 

currently strong. The first may lead to a real income decline and a reduction in purchasing 

power. The latter will increase inflation and in particular contribute to higher fuel costs. Since 

cruise vacations are rather luxurious types of travel, the weakening of the economy is likely to 

have a cooling off effect on the industry. Furthermore, land-side infrastructure may limit the 

cruise ship calls at the ports in Hawaii (HTA, 2004). 
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0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

Number of Visitors per cruise ships

Exponential Growth

A adding 100,000 yearly

B adding 80% of pre-year

delta

C plus 4.25% annually

Figure 9. Hawaii cruise industry growth scenario – number of visitors (Data source 2000 - 

2005: Marad; US DOT. Others: own calculation; own depiction). 

The three chosen scenarios will result in significant increases of emissions, as shown in Table 

18, Table 19 and Table 19. Considering that the cruise ship industry was basically stagnant until 

the year 2000, the certainty of any of the above described scenarios is weak. Cruise vacations 

certainly fit into a specific life style, age group, income bracket etc. One can think that cruise 

vacations may fall out of favor again or that the wealthy and retiring baby-boomer generation 

will fuel the customer base for an exploding development. A potential incident in the cruise ship 

industry may have unexpected consequences or due to the increased security concerns with 

international travel, Americans may feel most safely on a domestic cruise trip to Hawaii. Thus, 

the industry may retreat or explode. The high degree of variabilities seems in particular true for 

a specific US location such as Hawaii. Figure 10 shows the growth rates and indicates that all of 

the scenarios bear some grounds. 
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-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Annulal growth in percent

Exponential Growth

A adding 100,000 yearly

B adding 80% of pre-year

delta

C plus 4.25% annually

Figure 10. Hawaii cruise industry growth scenario – growth rates. 

Table 18. Deep draft vessel emissions at Hawaii ports for 2018 according scenario A (tons). 

Recommended scenario for policy planning purposes. 

Port and specific location NOx CO HC PM 10 PM 2.5 SO2 

Kauai, Nawiliwili – in port 1,783 159.2 60.6 135.6 123.9 1,518 

Kauai, Nawiliwili – at sea 1,408 109.8 48.3 103.5 95.6 1,031 

Oahu, Barbers Point – in port 172.9 22.8 5.23 13.1 11.7 232.7 

Oahu, Barbers Point – at sea 71.5 5.55 2.40 4.63 4.33 45.77 

Oahu, Honolulu – in port 3,269 389.4 111.5 253.7 227.9 3,873 

Oahu, Honolulu – at sea 2,651 207.4 98.6 232.1 215.2 2,347 

Maui, Kahului – in port 1,890 165.3 59.3 143.4 131.0 1,606 

Maui, Kahului – at sea 922.0 72.3 32.1 80.2 73.8 808.2 

Hawaii, Hilo – in port 912.5 84.7 36.9 70.9 64.6 767.2 

Hawaii, Hilo – at sea 1,436 114.2 49.2 111.7 99.8 1,117 

Table 19. Deep draft vessel emissions at Hawaii ports for 2018 according scenario B (tons). 

Port and specific location NOx CO HC PM 10 PM 2.5 SO2 

Kauai, Nawiliwili – in port 841.3 75.2 28.6 64.0 58.5 716.3 

Kauai, Nawiliwili – at sea 665.0 51.8 22.8 48.9 45.1 486.4 

Oahu, Barbers Point – in port 172.9 22.8 5.2 13.1 11.7 232.7 

Oahu, Barbers Point – at sea 71.5 5.55 2.40 4.63 4.33 45.77 

Oahu, Honolulu – in port 2,619 327.6 87.4 203.4 182.1 3,289 

Oahu, Honolulu – at sea 1,854 145.3 67.1 171.8 159.5 1,745 

Maui, Kahului – in port 905.9 79.6 28.6 69.0 63.0 771.7 

Maui, Kahului – at sea 466.8 36.7 16.3 44.4 40.9 450.6 

Hawaii, Hilo – in port 442.0 41.2 17.9 34.6 31.3 373.3 

Hawaii, Hilo – at sea 699.2 56.9 24.0 57.3 49.6 575.6 
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Table 20. Deep draft vessel emissions at Hawaii ports for 2018 according scenario C (tons). 

Port and specific location NOx CO HC PM 10 PM 2.5 SO2 

Kauai, Nawiliwili – in port 757.9 67.7 25.8 57.7 52.7 645.3 

Kauai, Nawiliwili – at sea 599.1 46.7 20.5 44.0 40.7 438.2 

Oahu, Barbers Point – in port 172.9 22.8 5.2 13.1 11.7 232.7 

Oahu, Barbers Point – at sea 71.5 5.6 2.4 4.6 4.3 45.8 

Oahu, Honolulu – in port 2,561 322.1 85.3 199.0 178.1 3,237 

Oahu, Honolulu – at sea 1,784 139.8 64.3 166.4 154.6 1,692 

Maui, Kahului – in port 818.7 72.0 25.9 62.4 57.0 697.8 

Maui, Kahului – at sea 426.5 33.5 14.9 41.3 38.0 418.9 

Hawaii, Hilo – in port 400.4 37.4 16.2 31.6 28.4 338.4 

Hawaii, Hilo – at sea 633.9 51.9 21.8 52.5 45.2 527.6 

The different power demand and activities between freight and cruise ships, as well as the 

different growth perspectives, result in significantly different emission growth for the Hawaii 

ports. Table 21 summarizes the percent increases for near port emissions (within one nautical 

mile of the port) according to the different scenario. 

Table 21. Growth of deep draft vessel emissions between 2002 and 2018 (percent). Emissions 

of 2005 at Hawaii ports = 100%. Range of percentage due to differences between 

pollutants. 

Islands and Ports 2002 2018 Scenario A 2018 Scenario B 2018 Scenario C 

Kauai, Nawiliwili 76 % 503 % 237 % 214 % 

Oahu, Barbers Point 100 % 256 % 256 % 256 % 

Oahu, Honolulu 92-95 % 311-339 % 250-252 % 242-247 % 

Maui, Kahului 77-79 % 472-489 % 238-240 % 216-219 % 

Hawaii, Hilo 77-78 % 476-490 % 238-239 % 216-219 % 

The emission growth scenario A should be considered for Hawaiian policy planning, reflecting a 

conservative but realistic growth trend in the cruise ship industry. Although adding 100,000 

passengers each year might stress the existing capacities both of cruise ship beds and of shore 

side infrastructure, the recent figures indicate that the demand for domestic cruise trips remains 

strong. It can be expected that the industry will creatively react to a continued strong demand 

and thus enable such a growth. However, it is recommended to revisit the issue after a few years. 

The rather explosive developments of the years 1999 to 2005 certainly deliver figures with high 

variations and a few more years of observed data will add to the certainty of the forecast. 

INTER-ISLAND BARGE SERVICES 

Much of the inter-island freight services are provided by barges, tug and tow boats. The ports at 

Nawiliwili and Kawaihae did not receive any cargo import from foreign traveling vessels in 

2004, and Hilo and Kahului received very little. In comparison, most cargo arrived at those 

ports via barge traffic (US ACOE, 2004). 

The barge traffic activity was estimated using the trips data from the US Army Corps of 

Engineers (2004). The data differentiate between several cargo categories (liquid bulk, dry bulk 

etc.), self propelled and non-self propelled and the draft of each vessel. The listing indicates that 

the vast majority of domestic cargo traffic is conducted with tugs and barges of draft less than 12 
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– 18 feet. The building of the activity pattern thus focuses on those domestic calls of barges, tug 

and towboats listed in the US ACOE data set. For foreign vessel calls, barges do not play any 

role and thus those are assumed to be covered in the deep draft section. 

Parallel to the deep draft section, the emissions are split into an in-port (within 1 nm) and at sea 

(between 1 and 30 nm of shore) section. Furthermore, for reasons of completeness, barges and 

tugs were modeled for the entire distances between the islands. Figures for the inter-island 

emissions from barges and tugs are available in the Excel worksheets. The inter-island portion 

for deep draft vessels might be added at a later time based on EPA data. However, EPA has not 

covered the inter-island barge and tug traffic. For estimating the emissions a list of inter-island 

routes was established. Each route has a specific length to it, in part derived from State of 

Hawaii public information (Port of Hawaii, unknown year), calculated as fraction from known 

distances or measured using GPS tools. The domestic traffic activity was then distributed to the 

different routes and inter-island port pairs. 

The data indicate that most cargo ships enter and leave Hawaii via the ports Barbers Point and 

Honolulu Harbor. The sum of all trips of all other ports shows smaller trip numbers than the 

trips of domestic barges, tug and towboats leaving or arriving in Barbers Point or Honolulu 

harbor. Therefore, a convention was made that all trips to other ports originate and end either in 

Barbers Point or Honolulu harbor. The division between the two ports was based on the 

percentage share of their respective number of trips. All remaining trips were then trips between 

Barbers Point and Honolulu. With this method a trip matrix was derived. The trip sums between 

the islands do not entirely zero out, which can be explained that likely not all trips are reported. 

For example empty return trips might be ignored in the US ACOE data set. Each route then 

splits in a 1 nautical mile near port section and in the inter-island distance. 

Tug and tow boats list a maximum speed of between 9 and 13 knots (various internet sources). 

Assuming a load factor in cruise mode of 83%, an average cruise speed of tugs and barges of 9.4 

knots was anticipated. This also corresponds with observations at other ports (ICF, 2006). This 

rather low speed was picked as a conservative estimate of tug speeds. With the known distances 

and assumed average speed, the hours of operation were derived. 

The average power of the tug and tow boats were taken from the vessel list of the USACOE. 

The list provides all boats registered in the State of Hawaii for several categories, including tugs 

and tows. The tug and tow boats here show an average of 2,119 kW. As recommended by ICF 

(2006), an 83% average load was assumed for the cruising mode. 

Those kW hours provided the activity data that then was combined with the pollutant specific 

emission factors. According to the Best Practice guide, approximately 80% of the ocean going 

tug and tow boats are equipped with EPA category 2 engines, which have between 5 and smaller 

30 liters displacement and between 1,000 and 3,000 kW. Their auxiliary engines have a power 

of 82 kW. The emission factors for EPA category 2 engines are listed in Table 22. At this point, 

no emission factor for PM 2.5 is established. 

Table 22. Emission factors (g/kW-hr) for EPA category 2 marine engines (ICF, 2006). 

Engine type NOx CO HC PM10 SO2 
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EPA category 2 13.2 1.10 0.50 0.72 0.63

PM2.5 estimates were made using the EPA NONROAD model estimate that PM2.5 is 97% of PM10 for diesel 

engines. 

Table 23 summarizes the estimated in-port emissions from tug and tow boats employed in interisland barge traffic. In-port is hereby defined as within one nautical mile of the port. For this 

study it was assumed that the number of trips and the emissions in 2005 were equal to the 

emissions in 2004. Between 2002 the trips of inter-island barges had declined by 25% while at 

the same time the amount of cargo transported had increased by approximately 10%. This 

indicates that a consolidation of freight and potentially business took place in those years. While 

this trend might have continued in 2005, it cannot continue indefinitely. Barge traffic must be 

anticipated to level off and then increase again with increased cargo volumes. It was assumed 

that barge traffic will increase by 2.5% annually, which represents the GDP growth forecast for 

Hawaii (State of Hawaii, 2007). With increased demand on the islands, more long distance 

cargo vessels will call directly at island ports and thus barge traffic is likely to grow more slowly 

than deep draft traffic. Table 24 shows the estimated 2002 emissions from inter-island barges, 

representing 133% of the 2005 levels. 

Table 23. 2005 tug and barge emissions within 30 nm (25 nm for Honolulu and Barbers Point 

harbors) of Hawaii ports (tons). 

Port and specific location NOx CO HC PM 10 SO2 

Kauai, Nawiliwili – barges in port 3.14 0.26 0.12 0.17 0.15

Kauai, Nawiliwili – barges at sea 87.79 7.32 3.33 4.79 4.19

Oahu, Barbers Point – barges in 

port 

12.28 1.02 0.47 0.67 0.59

Oahu, Barbers Point – barges at sea 343.9 28.66 13.03 18.76 16.42

Oahu, Honolulu – barges in port 22.90 1.91 0.87 1.25 1.09

Oahu, Honolulu – barges at sea 641.2 53.43 24.29 34.97 30.60

Maui, Kahului – barges in port 6.72 0.56 0.25 0.37 0.32

Maui, Kahului – barges at sea 188.2 15.68 7.13 10.26 8.98

Hawaii, Hilo – barges in port 4.37 0.36 0.17 0.24 0.21

Hawaii, Hilo – barges at sea 122.3 10.19 4.63 6.67 5.84

Hawaii, Kawaihae – barges in port 4.53 0.38 0.17 0.25 0.22

Hawaii, Kawaihae – barges at sea 127.0 10.58 4.81 6.93 6.06
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Table 24. 2002 tug and barge emissions within 30 nm (25 nm for Honolulu and Barbers Point 

harbors) of Hawaii ports (tons). 

Port and specific location NOx CO HC PM 10 SO2 

Kauai, Nawiliwili – barges in port 4.17 0.35 0.16 0.23 0.20 

Kauai, Nawiliwili – barges at sea 116.7

7

9.73 4.42 6.37 5.57 

Oahu, Barbers Point – barges in 

port 

16.34 1.36 0.62 0.89 0.78 

Oahu, Barbers Point – barges at sea 457.4 38.12 17.33 24.95 21.83 

Oahu, Honolulu – barges in port 30.46 2.54 1.15 1.66 1.45 

Oahu, Honolulu – barges at sea 852.7 71.06 32.30 46.51 40.70 

Maui, Kahului – barges in port 8.94 0.74 0.34 0.49 0.43 

Maui, Kahului – barges at sea 250.3 20.85 9.48 13.65 11.94 

Hawaii, Hilo – barges in port 5.81 0.48 0.22 0.32 0.28 

Hawaii, Hilo – barges at sea 162.7 13.56 6.16 8.87 7.76 

Hawaii, Kawaihae – barges in port 6.03 0.50 0.23 0.33 0.29 

Hawaii, Kawaihae – barges at sea 168.9 14.07 6.40 9.21 8.06 

Table 25 summarizes the amount of emissions from tug and barges in 2018. With the assumed 

2.5% annual growth, the traffic would have reached 138% of the 2005 levels in 2018. 

Table 25. 2018 tug and barge emissions within 30 nm (25 nm for Honolulu and Barbers Point 

harbors) of Hawaii ports (tons). 

Port and specific location NOx CO HC PM 10 SO2 

Kauai, Nawiliwili – barges in port 4.33 0.36 0.16 0.24 0.21 

Kauai, Nawiliwili – barges at sea 121.16 10.10 4.59 6.61 5.78 

Oahu, Barbers Point – barges in 

port 

16.95 1.41 0.64 0.92 0.81 

Oahu, Barbers Point – barges at sea 474.63 39.55 17.98 25.89 22.65 

Oahu, Honolulu – barges in port 31.60 2.63 1.20 1.72 1.51 

Oahu, Honolulu – barges at sea 884.80 73.73 33.52 48.26 42.23 

Maui, Kahului – barges in port 9.27 0.77 0.35 0.51 0.44 

Maui, Kahului – barges at sea 259.67 21.64 9.84 14.16 12.39 

Hawaii, Hilo – barges in port 6.03 0.50 0.23 0.33 0.29 

Hawaii, Hilo – barges at sea 168.80 14.07 6.39 9.21 8.06 

Hawaii, Kawaihae – barges in port 6.26 0.52 0.24 0.34 0.30 

Hawaii, Kawaihae – barges at sea 175.21 14.60 6.64 9.56 8.36 

FERRIES, EXCURSION CRAFT, AND OTHER VESSELS 

Excursion boats and other vessels were taken from a list of vessels provided by the Coast Guard 

to the USACE (2005). The category of other self-propelled craft includes a wide variety of 

vessels registered in the U.S. including passenger vessel of all types and a very few support 

vessels. 

The predominant Hawaii registered passenger vessels are local cruise boats and excursion 

vessels. Those vessels provide services for the local tourist industry such as dinner cruises and 
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excursions to remote parts of the islands. Furthermore, between the island of Lanai and Maui a 

ferry service exist and a new high speed ferry service will be launched between the islands of 

Oahu and Maui and Oahu and Kauai in July 2007. 

The emissions from excursion boats and local cruise boats were based on an assumed hours of 

operation per year. The Best Practices document recommends an average use of 1,971 hours per 

year, which translates approximately to 5 hours per day for those vessels. Since no detailed 

information on the load factors for those vessels is available, the 43% load that originates from 

EPA's NONROAD model will be used and emission factors for category 1 and 2 engines are also 

available from ICF (2006). The vessel activity is distributed to the islands of their registration, 

anticipating that their main service occurs around those islands. The resulting kW activities per 

year are for Kauai 98,800, for Oahu 28,172,171, for Maui 5,454,187 and for Hawaii 2,063,490. 

For calculating the specific emissions, EPA's emission factors for category one and two marine 

engines were used. Those emission factors cover all marine engines between 37 and 3,000 kW 

and a displacement smaller than 30 liters. Only three passenger vessels had more than 3,000 kW 

engine power. However, since they likely use regular non-road diesel rather than marine diesel 

oil the category 2 emission factors were applied for those vessels as well. 

Table 26 summarizes the emissions from local passenger vessels allocated to the different 

islands. 

Table 26. 2005 local passenger vessel emissions at Hawaii islands (tons). 

Island location based on registration NOx CO HC PM 10 SO2 

Kauai 1.04 0.18 0.03 0.04 0.07

Oahu 390.6 37.97 13.93 19.47 19.56

Maui 75.08 8.53 2.49 3.39 3.79

Hawaii 22.75 3.41 0.61 0.68 1.43

For the local passenger vessels a steady 2.5% growth factor was assumed for past years. This 

2.5% growth factor is in line with the GDP growth forecast for the state of Hawaii. Table 28

summarizes the plotted 2002 emissions from local passenger vessels. 

Table 27. Local passenger vessel emissions at Hawaii islands for 2002 (in tons). 

Island location based on 

registration 

NOx CO HC PM 10 SO2 

Kauai 0.97 0.16 0.03 0.04 0.06 

Oahu 362.7 35.26 12.94 18.08 18.17 

Maui 69.72 7.92 2.31 3.15 3.52 

Hawaii 21.12 3.17 0.57 0.63 1.33 

For the local passenger vessel emission forecast the 2.5% growth factor, similar to that for barge 

traffic was assumed. This 2.5% growth factor is in line with the GDP growth forecast for the 

state of Hawaii. Table 28 summarizes the expected emissions from local passenger vessels in the 

year 2018. 
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Table 28. 2018 local passenger vessel emissions at Hawaii islands (tons). 

Island location based on registration NOx CO HC PM 10 SO2 

Kauai 1.44 0.24 0.04 0.06 0.09 

Oahu 538.50 52.34 19.20 26.84 26.97 

Maui 103.50 11.76 3.43 4.67 5.22 

Hawaii 31.36 4.70 0.85 0.94 1.98 

REFERENCES 

Acurex Environmental Corporation 1996. Marine Vessel Emissions Inventory and Control 

Strategy. December. 

CARB 2005: DRAFT “Emission Estimation Methodology for Ocean Going Vessels.” Submitted 

for working group discussions for 10/05/2005. 

Corbett, J. J. and H. Koehler 2003: Updated Emissions from Ocean Shipping, Journal of 

Geophysical Research - Atmospheres, 28 (D20):4650-4666. 

Corbett, J.J.; Wang, C.; Firestone, J. 2006a. “Estimation, Validation, and Forecasts of Regional 

Commercial Marine Vessel Inventories – Task 1 and 2: Baseline Inventory and Ports 

Comparison.” Prepared for California Air Resources Board, California EPA and the 

Commission for Environmental Cooperation in North America. 

Corbett, J.J.; Wang, C.; Firestone, J. 2006b. “Estimation, Validation, and Forecasts of Regional 

Commercial Marine Vessel Inventories; Task 3 and 4: Forecast Inventories for 2010 and 

2020.” Prepared for California Air Resources Board, the California Environmental 

Protection Agency and the Commission for Environmental Cooperation, North America. 

Endresen, Ø., Sørgård, E., et.al. 2003: "Emission from international sea transportation and 

environmental impact. Journal of Geophysical Research Vol. 108, No. D17, 4560-4582. 

Eyring, V., Köhler, H.W., Aardenne, J.van, Lauer, A. 2005: "Emissions from international 

shipping: 1. The last 50 years. Journal of Geophysical Research, Vol 110. DI7, 305-317. 

ENTEC. 2002. “Quantification of Emissions from Ships Associated with Ship Movements 

between Ports in the European Community.” Report prepared for the European 

Commission. 

ENVIRON International Corporation. 2002. “Commercial Marine Emission Inventory 

Development.” Prepared for US EPA. 

ENVIRON International Corporation. 2004. “LADCO Nonroad Emission Inventory Project for 

Locomotive, Commercial Marine and Recreational Marine Emission Sources.” Prepared 

for Lake Michigan Air Director Consortium. 
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EPA. 2005. “Documentation for Aircraft, Commercial Marine Vessel, Locomotive, and Other 

Nonroad Components of the National Emissions Inventory: Volume I – Methodology”. 

U.S. Environmental Protection Agency. September. 

First Hawaiian Bank. 2006. “Economic Forecast, 2006 – 2007 Edition.” 

http://www.fhb.com/pdf/EconForecastHawaii06.pdf

HOST – Hawaiian Ocean Safety Team (unknown year): Tugboat assistance in Oahu's 

neighboring island ports. http://hosthawaii.org/sop0397.html 

HTA. 2004. “Hawaii Tourism Authority: Hawaii Tourism Strategic Plan 2005 – 2015.” 

ICF. 2006. “Current Methodologies and Best Practices in Preparing Port Emission Inventories.” 

Final report April 4, 2006 prepared for US EPA. (Please note: later version of the 

document posted at EPA's web-site) 

IMO 2000: "Study of Greenhouse Gas Emissions from Ships" Final Report to the International 

Maritime Administration. Prepared by Carnegie Mellon, DNV, Marintek and Econ. 

IMO 2005: “Interim guidelines for voluntary ship CO2 emission indexing for use in trials”. 

MEPC/Circ.471, 29 July 2005, International Maritime Organization. 

IPCC 2007a. "Climate Change 2007: The Physical Science Basis. Summary for Policymakers" 

Working Group I contribution to the Intergovernmental Panel on Climate Change Fourth 

Assessment Report. February 5, 2007. 

Makin, John. 2007. “Recession in 2007? Economic Outlook.” Published by the American 

Enterprise Institute for Public Policy Research, March 21, 2007. 

http://www.aei.org/publications/filter.all,pubID.25823/pub_detail.asp 

Ocean Shipping Consultants. 2005. “Summary of the study: The world cruise shipping industry 

to 2020 – a detailed appraisal of prospects.” 

http://www.osclimited.com/releases/world_cruise_shipping_to_2020pr.pdf 

Port of Hawaii: Commercial Harbors Systems Handbook. Available online at: 

http://www.state.hi.us/dot/harbors/port%20hawaii.htm

Starcrest Consulting Group LLC 2005: Port of Los Angeles Basin Air Emissions Inventory – 

2001. Prepared for Port of Los Angeles. 

Starcrest Consulting Group LLC 2007: Final Draft: The Port of San Diego 2006 Emission 

Inventory. July 2007. 
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State of Hawaii. 2006. “Department of Business, Economic Development and Tourism: 

Monthly Visitor Statistics.” Available online at: 

http://www.hawaii.gov/dbedt/info/economic/visitor-stats/tourism 

State of Hawaii. 2007. “Department of Business, Economic Development and Tourism: 

Outlook of the Economy, 1st quarter 2007.” Available online at 

http://www.hawaii.gov/dbedt/info/economic/data_reports/qser/outlook-economy 

US Army Corps of Engineers (USACE). 2004. “Waterborne Commerce of the United States, 

Part 4 – Waterways and Harbors Pacific Coast, Alaska and Hawaii.” Calendar year 2004. 

US Army Corps of Engineers (USACE). 2005. U.S. Waterway Data – Vessel Characteristics; 

T.S. Vessel File / Volume 3. Vessel characteristics from the Waterborne Transportation 

Lines of the United States 

Yamanouchi, K. 2003. New Matson ship 'Built for Hawaii'. The Honolulu Advertiser online, 

http://the.honoluluadvertiser.com/article/2003/Oct/07/bz/bz02a.html 
APPENDIX C 

DEVELOPMENT OF COMMERCIAL MARINE VESSEL EMISSION INVENTORY 

FOR VESSELS IN TRANSIT 30NM FROM THE HAWAIIAN SHORELINE 
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TECHNICAL 

MEMORANDUM 

To: Clean Air Branch 

Hawaii Department of Health 

From: Lynsey Parker, Christian Lindhjem, and Alison Pollack 

Date: November 4, 2009 

Subject: Development of Commercial Marine Vessel Emission Inventory for Vessels In 

Transit 30nm From The Hawaiian Shoreline 

INTRODUCTION 

A commercial marine vessel (CMV) emission inventory (EI) for large vessels in transit at least 

30nm away from the Hawaiian coastline that is spatially allocated onto three nested domains 

with 4/12/36km grid cell resolutions was generated. Annual average emissions of the pollutants: 

NOx, SO2, PM10, PM2.5, CO, Hydrocarbons (HC), and volatile organic carbons (VOC) for the 

years: 2002, 2005, 2018 were produced. The EI is based on emissions that were estimated 

spatially by the Waterway Network Ship Traffic, Energy and Environment Model (STEEM) that 

is described in the next section, and is consistent with the CMV EI used by the EPA in its recent 

emission inventory1

. This technical memorandum details the inputs and methodology used to 

generate the Hawaiian CMV in transit EI, summarizes the total emissions, and describes the 

uncertainties associated with the final EI. 

INPUTS 

The CMV emissions that form the basis for the Hawaiian CMV in transit EI were generated by 

researchers at the University of Delaware, College of Earth, Ocean, and Environment and are 

made publicly available through their website 

(http://coast.cms.udel.edu/NorthAmericanSTEEM/). The emissions are spatially resolved by the 

STEEM model into a grid format that covers most of the Pacific Ocean and extends eastward 

over part of the Atlantic; Figure 1 shows the geographic extent of the STEEM emissions. 

STEEM emissions estimates are available for 2002, 2010 and 2020. Using the STEEM CMV 

emission dataset for deep draft vessels in transit for Hawaii is consistent with the approach used 

by the EPA in its recent CMV emission inventory1. 
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Figure 1. Geographic extent of STEEM emissions. 

Growth Factors 

STEEM emissions estimates are only available for the years 2002, 2010, and 2020. In order to 

generate CMV EI for 2002, 2005 and 2018 the STEEM emissions were scaled by a growth factor 

of 5% compounded annually, which is the average annualized growth rate determined by EPA 

for the Hawaiian region2

. Table 1 shows the compounded growth factors. 

Table 1. Growth Factors to scale emissions from STEEM EI year to Hawaii EI year. 

Hawaiian EI Year STEEM EI Year Growth Factor 

2002 2002 1 

2005 2002 1.157625 

2018 2020 0.90703 

Adjustment Factors 

Following the same methodology as in recent EPA work1

, the PM emissions were scaled by 

0.933 and the SO2 emissions were scaled by 0.975 to incorporate the U.S. Government’s recent 

review of available engine test data and fuel sulfur levels that decrease PM and SO2 emissions 

(for all years). 

Control Factors 

The MARPOL Annex VI; 2000 Tier 1 NOx standard has been in effect for a number of years 

and was incorporated in recent EPA inventories. EPA used a NOx reduction factor of 0.902 for 

the year 2020 (EPA-HQ-OAR-2007-0121-0063-1). ENVIRON assumed linear change in NOX 

reduction factor from 1 in 2002 to 0.902 in 2020, to calculate the 2005 and 2018 NOx reduction 

factors, listed in Table 2. Note that the 2002 STEEM emissions incorporated the NOx reduction 
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factor for 2002 and therefore do not need modifying. Also, the 2020 STEEM emissions 

accounted for the IMO NOx reductions, by scaling the 2002 NOx emissions by 0.9163

. 

Therefore to avoid double counting the IMO NOx factors, the STEEM 2020 NOx emissions 

were be multiplied by (0.916)^-1 prior to multiplying by 0.914. 

Table 2. MARPOL Annex VI; 2000 Tier 1 NOx reduction factors. 

Year NOx reduction factor 

2005 0.989 

2018 0.914 

Pollutant Mapping Factors 

A mapping from the STEEM pollutants to HI pollutants is defined in Table 3. Some of the 

STEEM pollutants are identical to the HI pollutants; for these pollutants the mapping factor is 

one. The other HI pollutants are derived from the STEEM pollutants as shown in Table 3. The 

total hydrocarbon (THC) were converted to VOC using a factor of 1.053 following the EPA 

(2005)4 estimate of the ratio of VOC to THC for nonroad equipment. 

Table 3. Pollutant mapping factors. 

Hawaii Pollutant STEEM Pollutant Mapping Factor 

NOx NOx 1 

SO2 SOx 1 

PM10 PM 1 

PM2.5 PM 0.92 

CO CO 1 

HC HC 1 

VOC HC 1.053 

PROCESSING METHODOLOGY

The processing methodology can be described as three main steps: 

1) Transform emissions in STEEM projection to emissions in HI Projection 

2) Create list of grid cells within 30 nm of HI coast to be zeroed out 

3) Apply adjustments to STEEM to incorporate growth/adjustments/controls/species 

mapping

Transform emissions in STEEM projection to emissions in Hawaii Projection 

The STEEM grid domain is defined with respect to a cylindrical projection, which differs from 

the Lambert Conic Conformal projection (LCC) of the Hawaiian modeling domain; therefore the 

STEEM emissions were re-projected to the Hawaiian LCC. The STEEM and Hawaiian modeling 

domain parameters are described in Tables 4 and 5. See Figure 2 for an illustration of the nested 

Hawaii modeling domains. 

Table 4. Comparison of STEEM and Hawaii projection and domain specifications. 
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STEEM Domain Specifications Hawaii Domain Specifications 

Projection: Equidistant_Cylindrical 

Parameters: 

False_Easting: 0.0 – default ESRI parameter 

False_Northing: 0.0 – default ESRI parameter 

Central_Meridian: 180.0 degrees – UD defined 

Standard_Parallel_1: 0.0 – default ESRI 

parameter 

Linear Unit: User_Defined_Unit (1000 m) – UD 

defined 

domain: 

left -1000 km, right 18000 km, top 8000 km, 

bottom 0 km. 

Projection: LCC 

Parameters: 

• reference latitude = 23 degrees 

• reference longitude = -158 degrees 

• True latitude 1 = 30 degrees 

• True latitude 2 = 60 degrees 

Datum: 

• Spherical, WGS-G 

Table 5. Hawaii modeling grid definitions. 

Grid Resolution # Cells X, # Cells Y Reference coordinates of 

lower left corner (km) 

4 km 208,154 (-360, -588) 

12 km 226,226 (-1404,-1764) 

36 km 165,129 (-2952,-2304) 

Figure 2. Hawaii nested modeling domains. 
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Transforming the emissions data from the STEEM projection to the HI LCC projection was 

accomplished with the use of Geographical Information System (GIS) software. Using GIS tools 

the following steps were performed: 

• Convert STEEM GIS RASTER format data to polygon shapefile data format. 

• Create HI fishnet and extract STEEM emissions at each Hawaiian grid cell. See Figure 3 

for illustration of grid cells and STEEM emissions, and Figure 4 for windowed out 

Hawaiian emissions on 36km grid. 

• Create ASCII text file for each grid, pollutant and the years 2002, 2020, with emissions 

values on each grid cell. 

Figure 3. Hawaiian domain grid cell center points (black dots) superimposed with STEEM 

shipping lane emissions (green). 
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Figure 4. Example showing how the STEEM emissions were windowed out and aggregated to 

the 36km Hawaiian grid. 

Create List of Grid Cells within 30nm of Hawaiian Coastline 

This step was accomplished using the GIS buffer of the Hawaiian islands, and intersecting the 

buffer with the HI fishnet, to create a list of grid cells to be zeroed out 

Incorporate All Scaling Adjustments 

A PERL program was written to modify the emissions to incorporate: 

• Aggregating emissions to larger grid cells 

• Scaling to incorporate growth/control/adjustment 

• Zeroing out within 30nm 

• Summarizing final emissions to create totals 

See Figures 5 and 6 for examples of spatially allocated Hawaiian CMV EI Emissions. 
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Figure 5. Example of Hawaiian CMV emissions for three nested grids. 

Figure 6. Hawaiian CMV emission on the 4km grid and part of the 12km grid, illustrating the 

zeroed out emissions within 30nm of land. 
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SUMMARY TABLES

The emissions were summed over each of the three modeling domains for each year and 

pollutant. The results are shown in Table 6. 

Table 6. Domain-wide total emissions. 

Domain Wide Total [tonnes/year] 

Grid

 

Year NOx SO2 PM10 PM2.5 CO HC VOC 

 2002 9,463 5,482 735 677 733 310 326

4km 2005 10,834 6,346 851 783 849 359 378

 2018 21,670 13,765 1,851 1,703 1,849 782 823

 

 2002 80,844 46,822 6,276 5,774 6,250 2,638 2,778

12km 2005 92,555 54,201 7,265 6,684 7,235 3,054 3,215

 2018 187,163 118,889 15,996 14,716 15,960 6,755 7,113

 

 2002 405,462 234,912 31,957 29,401 31,422 13,442 14,154

36km 2005 464,200 271,934 36,994 34,034 36,375 15,560 16,385

 2018 933,665 593,203 79,824 73,438 79,744 33,674 35,459

DISCUSSION OF UNCERTAINTIES

There are two areas of uncertainty regarding the CMV transit emissions estimates generated: (a) 

impacts of regulatory activity and (b) included/omitted vessels in STEEM inventory. 

Impact of Regulatory Activity on Emissions Estimates for Future Years 

A list of CMV regulatory activity in provided in Appendix 1 including international, national, 

and California specific elements. It was beyond the scope of this work to provide an estimate of 

the expected effect of ongoing regulations on the emissions, beyond the MARPOL Annex VI; 

2000 Tier 1 NOx standard. For example, the marine compression ignition diesel engine, Feb 

2003 rule, is applied to U.S. ships only; thus the percentage of U.S. and foreign ships would need 

to be estimated across the entire 36km domain, on all the hundreds of shipping lane grid cells 

where shipping lanes often cross one another, implying different emission reduction factors on 

different shipping lanes. Also, some of the regulations are imposed on auxiliary engines only, so 

a breakdown of emissions by auxiliary and main engine would be needed, and this information is 

not available. In addition, some regulations are ARB regulations, which are applicable to certain 

ships, and/or within California waters only. To estimate the impact of these regulations, a very 

detailed study would have to be undertaken to determine the magnitude and regional reach of the 

emissions reductions. The global sulfur cap, while adopted by IMO, has yet to be ratified by the 

member states, so it is not yet final under international treaties. Likewise the adoption of an 

Emission Control Area (ECA) for the US waters has not yet been approved and the specific 

geographical applicability of the US ECA has not been finalized. The emissions developed for 

this work incorporate the same regulations for the 2018 EI as the baseline future EPA EI. 
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Included/Omitted Vessels in the STEEM Inventory 

STEEM includes only emissions that are associated with ships engaged in foreign commerce; 

i.e., U.S. flagged vessels carrying domestic cargo are not included. This omission is discussed in 

the EPA technical document, “Draft Regulatory Impact Analysis: Control of Emissions of Air 

Pollution from Category 3 Marine Diesel Engines,” EPA-420-D-09-002, June 2009 (Chapter 3)5. 

 After extensive review and analysis EPA concluded that it was too difficult to determine a 

defensible adjustment for each grid cell to incorporate domestic traffic emissions and estimated 

that the overall adjustment would be small. Both STEEM and EPA conducted analyses to 

estimate the percentage of domestic traffic emissions omitted, and concluded the omitted vessel 

emissions would be a small percentage of the overall emissions. It is possible (and probable) that 

the percentage of omitted emissions in the Hawaiian domains could be larger than the overall 

U.S. percentage due to vessel traffic between the mainland U.S. and Hawaii, but it was beyond 

the scope of this work to quantify the omitted emissions. ENVIRON did, however, include 

emissions from all ship calls engaged in both foreign and domestic commerce in the near port 

Hawaiian inventories. Therefore the near port emissions inventories include all US and foreign 

flagged vessels within 30 nautical miles of each port, so no underestimate occurs near shore. 

In an effort to determine if the STEEM emissions include vessels in transit from the mainland to 

Hawaii, if they are continuing to a foreign country afterwards, an inspection of the USACE 

Foreign Traffic Vessel Entrances and Clearances6

 datasets was made. Typically, each record in 

the dataset describes either one entrance to a port, or one clearance from a port, and details the 

previous port of call for entering ships or the next port of call for departing ships. All foreign 

ships entering or leaving a port should be included in the dataset, regardless of whether the 

previous or next port of call is foreign of domestic. U.S. flagged ships should be included in the 

dataset, if it is engaged in foreign commerce, and the majority of the U.S. flagged ships are 

traveling to or from a foreign port. It appears that there are approximately 50 calls to Hawaii 

from the mainland by U.S. flagged ships. It is not known whether this comprises a significant 

amount of vessels or not, or why these particular (domestic) trips are recorded in the USACE 

database; these ships do appear to be continuing on to foreign ports after stopping in Hawaii. The 

description of the database is as follows: 

“These files contain the following data for each major port or waterway: the date a vessel made 

entry into (entrance record) or cleared (clearance record) the U.S. Customs port; the vessel's full 

name; the type of vessel by one digit rig type or ICST (International Classification of Ships by 

Type) code; the vessel's flag of registry; the vessel's last (entrance record) or next (clearance 

record) port of call, whether foreign or domestic; the vessel's Net and Gross Registered Tonnage; 

and each vessel's draft in feet”. 
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REFERENCES

1. Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 

Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Assessment 

and Standards Division Office of Transportation and Air Quality U.S. Environmental 

Protection Agency (EPA-420-D-09-002 June 2009). 

1a. related Reference: EPA-HQ-OAR-2007-0121-0063 

2. http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007-chap2.pdf. 

3. http://coast.cms.udel.edu/NorthAmericanSTEEM/ARBCEC_SECA_task3-4Report.pdf

4. EPA 2005. "Conversion Factors for Hydrocarbon Emission Components," EPA420-R-05-

015 December 2005 NR-002c, 

http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05015.pdf

5. “Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 

Marine Diesel Engines,” EPA-420-D-09-002, June 2009 (chapter 3) 

6. http://www.iwr.usace.army.mil/ndc/data/dataclen.htm#TOP 

http://www.iwr.usace.army.mil/ndc/data/dataclen.htm#Foreign%20Traffic%20Vessel%20En

trances%20and%20Clearances 

7. http://coast.cms.udel.edu/NorthAmericanSTEEM/

8. Wang,C., J.J. Corbett, and J. Firestone, Modeling Energy Use and Emissions from North 

American Shipping: Application of the Ship Traffic, Energy, and Environment Model, 

Environ. Sci. Technol.; 2007
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APPENDIX 1

Table A-1. Summary of the air pollution emission regulations for commercial marine engines. 

Rule Agency Description Enforcement 

Entity 

Compliance 

Dates Status 

MARPOL Annex VI; 2000 

Tier I NOx standard 

International 

Maritime 

Organization 

(US Coast 

Guard lead) 

Any engine > 

130kW installed 

on a vessel 

constructed on or 

after 1/1/2000 

and any engine 

that undergoes a 

major conversion 

on or after 

1/1/2000. 

Vessels 

constructed 

between 1990 

and 2000 would 

be retrofit to this 

standard. 

US Coast 

Guard 

May 2005, 

(Voluntary in 

2000) 

Ship builders generally 

complied with this standard 

back to 2000. 

MARPOL Annex VI 

Global Sulfur Caps 

International 

Maritime 

Organization 

(US Coast 

Guard lead) 

The global sulfur 

cap reduced 

initially to 3.5% 

(from the current 

4.5%) effective 

from 1 January 

2012; then 

progressively to 

0.5%, effective 

from 1 January 

2020 

US Coast 

Guard 

January 2020 

for final sulfur 

standards 

Adopted on 10 October 2008 

MARPOL Annex VI SOx 

Emissions Control Area 

(SECA) for North America 

US 

Designated 

(EPA/ARB 

lead) 

US application for 

a SECA. Sulfur 

levels capped at 

1.5%, 1.0% after 

July 1 2010, and 

0.1% on or after 

2015. 

US Coast 

Guard 

2000 1.5% S 

2010 1.0% S 

2015 for 

0.1% S 

US preparing justification and 

other background materials 

but may apply potentially out 

200 nm from shore as defined 

by Exclusive Economic Area 

(EEA) 

MARPOL Annex VI Tier II 

and Tier III exhaust 

emission standards 

International 

Maritime 

Organization 

(US Coast 

Guard lead) 

Tier II standards 

for vessels 2011 

and later; Tier III 

for vessels 2016 

and later while 

operating in an 

ECA. 

US Coast 

Guard 

2011 for all 

new vessels 

2016 for all 

new vessels 

when 

operating in 

certain areas 

Adopted on 10 October 2008 

Ongoing negotiations from a 

US delegation (including 

EPA) for amendments to 

MARPOL, Annex VI) 

Marine compressionignition (diesel) engine 

rule 

EPA Harbor Craft 

engines below 30 

liters per cylinder 

National exhaust 

emission 

standards for new 

engines at or 

EPA Tier 1, 2, 3, 4 

emission 

standards 

below 30 

liters per 

cylinder. 

Comply with 

Feb 28, 2003 (Tier 1 and 2) 

March 2008 (Tier 3 and 4) 
 Page 12 

ENVIRON 

773 San Marin Drive, Suite 2115 ● Novato, California 94998 USA 

 (Tel): 415-899-0700 z (Fax): 415-899-0707 z www.environcorp.com

J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc

Rule Agency Description Enforcement 

Entity 

Compliance 

Dates Status 

above 30 liters 

per cylinder 

(“category 3” 

marine diesel 

engines) 

international 

rules for 

above 30 

l/cylinder 

Auxiliary engine low 

sulfur fuel rule 

ARB Requires low 

sulfur fuel for use 

with auxiliary 

engines. Effective 

2007 within 24 

nm of coast; 

marine fuel must 

be Marine Gas 

Oil or Marine 

Diesel Oil 

containing less 

than 0.5% sulfur 

(must be Marine 

Gas Oil 

containing less 

than 0.1% sulfur 

starting in 2010) 

ARB January 1, 

2007 

In place – and currently 

enforced –under litigation 

2007 and 2010 phase-in 

period. 

The proposed regulation 

would apply to diesel main 

and auxiliary engines, and 

auxiliary boilers on oceangoing vessels. As discussed 

below, this regulation is 

expected to become legally 

effective in 2009 

Main engine and boiler 

low sulfur fuel rule 

ARB Requires low 

sulfur fuel use in 

main engines and 

boilers similar to 

auxiliary engine 

requirements. 

ARB July 24, 2008 

Board 

Approval 

Early 2009 

At-Berth Ocean-Going 

Vessels Regulation 

ARB Control hoteling 

emissions via one 

of several 

possible methods

ARB January 2, 

2009 

In place. Phase in 2010-2020 

Vessel Speed Reduction 

(VSR) 

ARB Evaluating need 

for VSR measure 

at major ports 

and along 

coastline. 

ARB TBD Under evaluation for mid 

2008 

Clean Ship program ARB Evaluating 

measure or 

incentive program 

to require cleaner 

or retrofitted 

vessels in 

CA ports 

ARB TBD Under development for late 

2008. Likely phase in from 

2010-2020 

Commercial Harbor Craft ARB Accelerated 

turnover 

(scrappage) of 

older engines and 

vessels 

ARB January 1, 

2009 

In place with phase-in from 

2009 - 2022 
APPENDIX D 

OFF-ROAD EQUIPMENT EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
Golden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA 

 Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com 

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

TECHNICAL 

MEMORANDUM

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: John Grant, Rajashi Parikh, and Alison Pollack 

Date: July 2, 2007 

Subject: Off-road Equipment Emissions Estimates for the Hawaiian Islands 

This technical memo describes the methods used to estimate off-road equipment emissions 

for calendar years 2002, 2005, and 2018, and provide the emissions results. Off-road mobile 

sources encompass a wide variety of equipment types that either move under their own 

power or are capable of being moved from site to site. More specifically, these sources, 

which are not licensed or certified as highway vehicles, are defined as those that move or are 

moved within a 12-month period and are covered under the EPA's emissions regulations as 

nonroad mobile sources. Where feasible and appropriate, local activity data for specific 

source categories were gathered and used to develop the inventory. 

US EPA’s NONROAD2005 model was used to estimate emissions for most off-road sources. 

The NONROAD model estimates emissions from off-road equipment in the following 

categories: 

• agricultural equipment, such as tractors, combines, and balers; 

• airport ground support, such as terminal tractors; 

• construction equipment, such as graders and back hoes; 

• industrial and commercial equipment, such as fork lifts and sweepers; 

• residential and commercial lawn and garden equipment, such as leaf and snow blowers; 

• logging equipment, such as shredders and large chain saws; 

• recreational equipment, such as off-road motorbikes; and 

• recreational marine vessels, such as power boats. 

For all of these categories, annual average emissions have been estimated in tons per year, 

and winter and summer season weekday emissions are estimated in tons per day.

NONROAD MODEL EQUIPMENT EMISSIONS 

The EPA NONROAD2005 model was used to estimate emissions for all off-road mobile source 

categories except locomotive, commercial marine and aircraft. The final version of the model is 

available on the NONROAD model web site at http://www.epa.gov/otaq/nonrdmdl.htm. 

The NONROAD model estimates emissions for six exhaust pollutants: VOC, NOX, CO, CO2, 

SO2, and PM (both PM10 and PM2.5). The model also estimates emissions of non-exhaust HC 

for six modes — crankcase, hot soaks, diurnal, vapor, displacement, refueling, spillage, running 

loss, tank permeation, and hose permeation emissions. It provides emission estimates at the 
 Page 2

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

national, state, and county level. County-level emissions are determined by allocating the state 

level estimates using econometric or other activity indicators, such as employees, tilled acreage, 

and construction valuation. For this effort, standard NONROAD county data were modified to 

treat Hawaii’s eight major islands as counties as described below. The NONROAD model can 

be run to estimate seasonal, monthly or annual emission estimates on a period total or typical day 

basis. The latter can also take into account weekday/weekend differences. 

The NONROAD 2005 model incorporates the effects of all federally promulgated emission 

certification standards applicable to: 

• Diesel engines 

• Small gasoline engines (handheld and non-handheld equipment <25 hp) 

• Recreational marine gasoline engines 

• Recreational and commercial marine diesel engines 

The model includes more than 80 basic and 260 specific types of nonroad equipment, and further 

stratifies equipment types by horsepower rating and fuel type. 

The basic equation for estimating emissions in the NONROAD model is as follows: 

 Emissions = (Pop)*(Power)*(LF)*(A)*(EF) 

where 

Pop = Engine Population 

Power = Average Power (hp) 

LF = Load Factor (fraction of available power) 

A = Activity (hrs/yr) 

EF = Emission Factor (g/hp-hr) 

For national or state level emissions estimation, the corresponding engine population is 

determined and then multiplied by the average power, activity, and emission factors. National 

average engine power, load factor (the relative fraction of maximum available power that engine 

uses on average), annual activity, and emission factors can be directly used to calculate the 

national annual total emissions. For county level estimates, equipment population by county 

must first be estimated in the model by geographically allocating the state engine population 

through the use of econometric or physical indicators, such as construction valuation or water 

surface area. The manner in which the geographic allocation is performed is as follows: 

(County Population)i /(State Population)i = (County Indicator)j /(State Indicator) j 

where 

 i is an equipment application like construction or agriculture.

 j is an indicator type associated with equipment i 

Activity is temporally allocated with an analogous equation, but using monthly and day of week 

fractions of yearly activity. 
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G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

The NONROAD model has default estimates for most variables and factors used in the 

calculations. All of these estimates are in model input files, and can be changed by the user if 

data more appropriate to the local area are available. The following sub-sections describe 

modifications to NONROAD model inputs that were made for this work. 

Model Inputs and Application 

The NONROAD model requires specification of several inputs. As discussed in the on-road 

documentation, ENVIRON attempted to obtain data from local refiners but did not receive 

useable information. As a result, fuel Reid vapor pressures (RVP) and sulfur content from the 

EPA’s NMIM database were used, as agreed upon by HCAB. Seasonal averages were derived 

from monthly data by taking the arithmetic average of the NMIM data for the months included in 

each season. The resulting values of gasoline RVP and gasoline and non-road diesel sulfur 

content used in the modeling are shown in Table 1. 

Table 1. Seasonal fuel RVP and sulfur content assumed in NONROAD modeling. 

Year 1 

Season RVP (psi) 

Oxygen 

Weight (%) 

Gas Sulfur 

(ppm) 

Diesel Sulfur 

(ppm) 

Marine Diesel 

Sulfur (ppm) 

2002 

Autumn 8.6 0.000 37 2381 2421

Winter 11.5 0.000 24 2381 2421

Spring 10.0 0.000 30 2381 2421

Summer 7.1 0.000 43 2381 2421

2005 

Autumn 8.6 0.000 90 2381 2421

Winter 11.5 0.000 90 2381 2421

Spring 10.0 0.000 90 2381 2421

Summer 7.1 0.000 90 2381 2421

2018 

Autumn 8.6 3.448 30 11 11

Winter 11.5 3.448 30 11 11

Spring 10.0 3.448 30 11 11

Summer 7.1 3.448 30 11 11

Monthly average daily minimum/maximum/average temperature data were derived as described 

in the on-road documentation. 

Pleasure Craft 

The default NONROAD state recreational marine vessel population and allocation was updated 

using population data available from the Hawaii Data Book (HDBEDT 2007a, HDBEDT 

2007b). These data represent the total number of vessels by type (personal watercraft (PWC), 

outboard, inboard and Sterndrive) in 2002 and 2005 and registered vessels by island in 2002 and 

2005. Vessel populations by type were separately apportioned by fuel type, engine technology, 

and horsepower range using the existing NONROAD distributions. The statewide population by 

vessel type replaced the defaults in the NONROAD population input file as shown in Table 2. 

The total vessel populations by island replaced the defaults in the NONROAD inboard and 

outboard pleasure craft geographical allocation files producing allocation factors as shown in 

Table 3. 
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Table 2. Comparison of Hawaii default and local population estimates for recreational marine 

vessels. 

Vessel Type 

NONROAD Default 

2002 Population 

Revised 2002 

Population 

Revised 2005 

Population 

Outboard 26,533 4,306 7,488

Personal Water Craft 3,583 1,415 1954

Inboard 866 6,525 3,100

Inboard/Sterndrive 5,542 2,174 2,031

Total 36,524 14,420 14,573

Table 3. Comparison of default and local allocation estimates for recreational marine vessels. 

County 

NONROAD default 

Allocation 

2002 Revised 

Allocation 

2005 Revised 

Allocation 

 Hawaii County 24% 16% 17%

 Oahu County 34% 58% 52%

 Kalawao County 0% 0% 0%

 Kauai County 14% 11% 10%

 Maui County 28% 14% 14%

 Statewide 100% 100% 100%

Geographical Allocation 

County-level emissions in NONROAD are estimated by allocating the state level estimates using 

econometric or other activity indicators, such as employees, tilled acreage, and construction 

valuation. For this project, standard NONROAD county data were modified to estimate 

emissions for Maui County by island. It was assumed that for Kauai County, which includes 

Kauai Island and Niihau Island, all Kauai County activity occurred on Kauai Island. 

Modifications were made to allocation files by distributing Maui County allocations to Maui 

islands according to island specific data as described in Table 3. 2002 allocations, which are 

consistent with 2005 allocations presented graphically in Figure 1. 
 Page 5

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

Table 3. NONROAD county allocation file modification for island level allocations. 

NONROAD 

Indicator 

Code NONROAD Allocation Indicator 

Island Allocation 

Metric 

Island Allocation 

Metric Reference 

HOU 

Number of single and double 

(duplex) family housing Human Population HDBEDT 2007c 

LSC 

Employees in Landscape 

Horiticulture Services Human Population HDBEDT 2007c 

CON Dollars Spent in Construction 

Employees in Nat. 

Resources, Mining, 

Constr. HDLIR, 2007 

FRM 

Harvested Cropland in Farming 

Industry 

Acres of Agricultural 

Land 

NOAA Coastal 

Service Landcover 

COM 

Number of Wholesale 

Establishments Human Population HDBEDT 2007c 

RVP 

Number of Camps & Recreational 

Vehicle Park Establishments Human Population HDBEDT 2007c 

GC Number of Public Golf Courses 

Public and Private 

Golf Courses 

HDBEDT 2007d, 

HDBEDT 2007e 

WIB 

Rec marine state fuel 

consumption, county water surface 

area Inboard 

Pleasure Craft 

Population 

HDBEDT 2007a, 

HDBEDT 2007b 

WOB 

Rec marine state fuel 

consumption, county water surface 

area Outboard 

Pleasure Craft 

Population 

HDBEDT 2007a, 

HDBEDT 2007b 

AIR Air Carrier and Air Taxi activity FAA, 2006 

MFG Employees in Manufacturing Industry HDLIR, 2007 

POP Human Population HDBEDT 2007c 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

AIR

HOU

LSC

CON

FRM

COM

MFG

RVP

GC 

POP

WIB

WOB

NONROAD Indicator

Maui Island Fraction

Kahoolawe

Lanai

Molokai

Maui

Figure 1. 2002 Maui County to island allocation fractions. 
 Page 6

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

Temporal Distribution 

Both the annual and the seasonal modeling were done for each island independently. For annual 

emissions estimation, the NONROAD model was run for each season to obtain seasonal total 

emissions, and the annual emissions in tons per year were obtained by taking the sum of these 

seasonal VOC, NOx, CO, PM10, and SO2 emissions. Seasonal NONROAD runs were also 

estimated using the summer and winter fuel RVPs and temperatures and the weekday option to 

obtain summer and winter seasonal weekday emissions. 

The NONROAD model contains default temporal distributions which allocate yearly activity to 

seasons. These default distributions for the state of Hawaii are based primarily on California 

data. In order to more accurately reflect activity in Hawaii, which has significantly less seasonal 

variation than the mainland, temporal distributions in Hawaii were set such that equivalent 

activity was assumed for all seasons across all equipment types. 

Hazardous Air Pollutant Emissions 

Hazardous Air Pollutant (HAP) emissions were estimated based on SCC and fuel dependent 

ratios to criteria air pollutant emissions consistent with EPAs 2002 NEI (EPA, 2005). 

Ammonia Emissions 

The NONROAD model does not estimate ammonia emissions. However, gasoline (non-catalyst) 

and diesel ammonia emission factors based on fuel consumption are available. These are being 

used in EPA’s emission inventory estimates for 2002 using EPA’s new NMIM model 

(information regarding NMIM available at http://www.epa.gov/otaq/nmim.htm). 

For this effort, gasoline and diesel equipment are assumed to emit ammonia at a rate of 116 mg 

and 83.3 mg per gallon of fuel consumption, respectively. 

ESTIMATED EMISSIONS 

NONROAD model off-road equipment emissions estimates are presented for 2002, 2005, and 

2018 by island in Table 4. Consistent with on-road emissions, and population estimates, Oahu 

accounts for a majority of emissions in all years. CO emissions increase by 7% from 2002 to 

2005 while NH3 emissions are relatively constant during the same time period. From 2002 to 

2005, statewide emissions decrease by 9%, 12%, 8%, 8%, 1%, and 6% for VOC, NOx, PM10, 

PM2.5, SO2, and benzene, respectively. From 2005 to 2018, statewide emissions increase by 

27% for NH3 and decrease by 25%, 4%, 35%, 39%, 39%, 99%, and 29% for VOC, CO, NOx, 

PM10, PM2.5, SO2, and benzene, respectively. Changes in VOC, CO, NOx, PM10, and PM2.5, 

and benzene emissions over time are primarily reflective of fleet turnover from older to newer 

cleaner engines over time. Changes in SO2 emissions are reflective of changes in fuel sulfur 

content and fuel consumption over time. NH3 emissions are reflective of changes in fuel 

consumption over time. 

Figure 2, Figure 3, and Figure 4 show emissions contributions by equipment type. VOC, CO, 

and benzene emissions are dominated in all years by lawn and garden equipment, with 2002 

contributions of 63%, 64%, and 62%, respectively. NOx, PM10, PM2.5, and SO2 are dominated 

in all years by construction equipment with 2002 contributions of 47%, 47%, 48%, and 55%, 

respectively. Major contributors to NH3 emissions are construction equipment and lawn and 

garden equipment with 2002 contributions of 33% and 29%, respectively. 
 Page 7

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

Table 4. Hawaii Off-road equipment emissions by island, pollutant and calendar year. 

Island VOC CO NOX PM10-PRI PM25-PRI SO2 NH3 Benzene 

2002 

Hawaii 825 10,120 967 93 89 102.9 0.8 24 

Oahu 4,370 58,708 3,270 315 301 320.9 3.2 129 

Kauai 582 7,304 472 47 45 49.6 0.5 17 

Maui 880 11,623 618 63 60 61.3 0.6 25 

Molokai 58 789 37 3 3 3.6 0.0 2 

Lanai 27 416 20 2 2 2.0 0.0 1 

Total 6,743 88,961 5,384 524 500 540 5.2 197

2005 

Hawaii 782 10,841 865 85 81 103.3 0.8 23 

Oahu 3,948 63,101 2,880 292 278 317.5 3.2 120 

Kauai 523 7,789 405 44 41 47.5 0.5 15 

Maui 789 12,467 550 59 56 61.3 0.6 24 

Molokai 55 846 28 3 3 3.0 0.0 2 

Lanai 24 442 15 2 1 1.7 0.0 1 

Total 6,121 95,486 4,745 484 462 534 5.2 184

2018 

Hawaii 566 10,196 529 45 43 0.9 1.1 16 

Oahu 2,987 61,091 1,882 183 173 4.2 4.1 86 

Kauai 375 7,351 271 27 26 0.5 0.6 11 

Maui 593 11,885 373 38 36 0.8 0.8 17 

Molokai 40 802 24 2 2 0.0 0.0 1 

Lanai 18 411 11 1 1 0.0 0.0 1 

Total 4,579 91,737 3,090 297 281 7 6.6 132 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

VOC CO NOX PM10-PRI PM25-PRI SO2 NH3 71432

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Logging Equipment

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Agricultural Equipment

Equipment Type

Figure 2. 2002 statewide emissions contributions by equipment type. 

Benzene 
 Page 8

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

VOC CO NOX PM10-PRI PM25-PRI SO2 NH3 71432

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Logging Equipment

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Agricultural Equipment

Equipment Type

Figure 3. 2005 statewide emissions contributions by equipment type. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

VOC CO NOX PM10-PRI PM25-PRI SO2 NH3 71432

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Logging Equipment

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Agricultural Equipment

Sum of SumOfDBLEMISSIONNUMERICVALUE

Equipment Type

Figure 4. 2018 statewide emissions contributions by equipment type. 

Benzene 

Benzene 
 Page 9

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 

References 

EPA, 2005. Documentation for Aircraft, Commercial Marine Vessel, Locomotive, and other 

NONROAD Components of the National Emissions Inventory: Volume 1 – 

Methodology. U.S. Environmental Protection Agency. Research Triangle Park, North 

Carolina. September, 2005. 

FAA 2006. Terminal Area Forecast 2002, Federal Aviation Administration. Washington, D.C. 

December. Internet address: http://www.apo.data.faa.gov/main/taf.asp

HDBEDT 2007a. 2002 State of Hawaii Databook, Section 18:, Table 18.49. Hawaii State 

Department of Business, Economic Development, and Tourism. Accessed March 2007. 

Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT 2007b. 2005 State of Hawaii Databook, Section 18:, Table 18.49. Hawaii State 

Department of Business, Economic Development, and Tourism. Accessed March 2007. 

Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT 2007c. 2002 State of Hawaii Databook, Section 1: Population, Table 01.06. Hawaii 

State Department of Business, Economic Development, and Tourism. Accessed March 

2007. Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT 2007d. 2002 State of Hawaii Databook, Section 7: Recreation and Travel, Table 

07.52. Hawaii State Department of Business, Economic Development, and Tourism. 

Accessed March 2007. Internet address: 

http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT 2007e. 2005 State of Hawaii Databook, Section 7: Recreation and Travel, Table 

07.51. Hawaii State Department of Business, Economic Development, and Tourism. 

Accessed March 2007. Internet address: 

http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDLIR 2007. Job Count by Industry. State Department of Labor & Industrial Relations' 

Research & Statistics Office. Internet Address: 

http://www.hiwi.org/article.asp?PAGEID=&SUBID=&ARTICLEID=515&SEGMENTI

D=2
APPENDIX E 

AIRCRAFT EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
Golden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA 

 Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com 

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

TECHNICAL 

MEMORANDUM

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: Rajashi Parikh, John Grant, and Alison Pollack 

Date: July 11, 2007 

Subject: Aircraft Emissions Estimates for the Hawaiian Islands 

AIRCRAFT EMISSIONS METHODOLOGY 

County-level 2002 aircraft emissions for the State of Hawaii were obtained from work performed 

for EPA’s 2002 National Emissions Inventory (NEI2002). Activity data for aircraft emissions 

are takeoff cycles (LTOs), and emission factors primarily from the Federal Aviation 

Administration (FAA) Emissions and Dispersion Modeling System (EDMS). The 2002 

emissions were projected to future years using forecast LTOs available from the FAA. 

The FAA Emissions Dispersion and Modeling System (EDMS) combines aircraft and activity 

levels with emission factors to estimate annual inventories for a specific airport. Aircraft activity 

levels in EDMS are expressed in terms of landing and take-off cycles (LTOs), which consist of 

the four aircraft operating modes: taxi and idle, take-off, climb-out, and approach. Default 

values for the amount of time a specific aircraft spends in each mode, or the time-in-modes 

(TIMs), are included in EDMS (except for taxi/idle), but may be updated with airport-specific 

values where available. A TIM value of 26 minutes was used for taxing and idling per EPA 

State Implementation Plan (SIP) guidance on estimating aircraft emissions (EPA, 2005).

In addition, the model also includes updateable default settings for the mixing height and aircraft 

engine assignments. 

Aircraft emissions are estimated for four aircraft categories: 

• Air carriers, which are larger turbine-powered commercial aircraft with at least 60 seats or 

18,000 lbs payload capacity; 

• Air taxis, which are commercial turbine or piston-powered aircraft with less than 60 seats 

or 18,000 lbs payload capacity; 

• General aviation aircraft, which are small piston-powered, non-commercial aircraft; and 

• Military aircraft. 
June 2006 Page 2

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

ANNUAL AIRCRAFT EMISSIONS 

Aircraft emissions were estimated for the calendar year 2002, 2005 and 2018. County-level 2002 

annual aircraft emissions were obtained from NEI2002 database and used to project 2005 and 

2018 aircraft emissions. Table 1 summarizes Hawaii airports included in the analysis. 

Table 1. Hawaii Airport Included in Aircraft emissions estimation. 

Airport Island Airport Name 

HDH Oahu Dillingham airport 

HI01 Kauai Princeville airport 

HNL Oahu Honolulu International 

HNM Maui Hana Airport 

ITO Hawaii Hilo international airport 

JHM Maui Kapalua airport 

JRF Oahu Kalaeloa airport 

KOA Hawaii Kona International airport 

LIH Kauai Lihue airport 

LNY Lanai Lanai airport 

LUP Molokai Kalaupapa airport 

MKK Molokai Molokai airport 

MUE Hawaii Waimea- Kohola airport 

OGG Maui Kahului airport 

PAK Kauai Port Allen 

UPP Hawaii Upolu airport 

NEI2002 Aircraft Annual Emissions Estimates and Additional Pollutants 

For the 2002 aircraft emissions, annual emission files prepared for the NEI2002 formed the basis 

of the work. The 2002 by county and SCC criteria air pollutant and hazardous air pollutant 

emissions data was downloaded from the EPA NEI2002 website at 

http://www.epa.gov/ttn/chief/net/2002inventory.html. For the 2002 NEI, the EDMS model was 

run for about 1100 towered airports across the U.S. using detailed 2002 aircraft/LTO activity 

data. Additional calculations were performed to estimate the additional pollutants. Key 

elements of those calculations are described by aircraft type below. 

Air Carriers – The NEI2002 inventory data for criteria air pollutants and hazardous air pollutants 

for Air Carriers were used directly. It was assumed that no NH3 is emitted from air carrier 

turbine engines, which normally run lean. 

Air Taxi, General Aviation and Military Aircraft – The NEI2002 inventory data for criteria air 

pollutants and hazardous air pollutants for these Aircraft types were used directly. Additional 

calculations were made to estimate the emissions of the NH3 in the inventory. For ammonia, air 

taxi and military aircraft were assumed to be dominated by turbine-powered aircraft running 

lean, thus producing a negligible amount of ammonia. For general aviation, ammonia was 

estimated using a fleet-average fuel consumption rate from the EDMS data for piston engines, 

operational mode-specific fuel flow rates weighted by the typical time spent in each mode, 

average hours of operation estimated from FAA data, and a g/gallon emission factor for noncatalyst light-duty gasoline engines. 
June 2006 Page 3

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

2005 and 2018 AIRCRAFT EMISSIONS 

2005 and 2018 annual aircraft emissions were projected from the NEI 2002 emissions, by island 

and aircraft type, using LTO forecasts as the activity data. The fraction of the LTOs by aircraft 

type and island were used to allocate the NEI 2002 county level emissions to the island level. 

Emission factors were assumed to be unchanged over time. The International Civil Aviation 

Organization (ICAO) has promulgated NOx and CO emission standards for commercial aircraft, 

exempting general aviation and military engines from the rule (ICAO, 1998), and the majority of 

engines are already meeting this standard. 

The historic and projected LTO data by airport are available online from the Federal Aviation 

Administration (FAA) Terminal Area Forecast (TAF) database, at 

http://www.apo.data.faa.gov/main/taf.asp, for all aircraft categories for which emissions were 

estimated. Projected LTO data for years 2005 and 2018, and historic data for 2002 were used to 

develop future year growth factors for all aircraft types. Table 2 summarizes LTOs by island and 

aircraft type for calendar years 2002, 2005 and 2018 and Figure 1 presents these results 

graphically. 

Table 2. 2002, 2005 and 2018 LTOs by island and by aircraft type. 

Island Aircraft Type 2002 2005 2018 

Hawaii AC 20,445 19,298 22,561 

 AT 30,411 42,559 44,656 

 GA 45,311 60,523 83,332 

 MIL 12,150 13,117 13,117 

Hawaii Totals 108,317 135,497 163,665 

Oahu AC 87,472 93,175 120,681 

 AT 24,821 33,116 40,459 

 GA 147,087 132,608 137,221 

 MIL 24,468 17,970 17,970 

Oahu Totals 283,848 276,869 316,331 

Kauai AC 14,094 13,374 14,260 

 AT 32,801 31,149 35,560 

 GA 6,608 10,962 13,379 

 MIL 2,488 2,691 2,691 

Kauai Totals 55,991 58,175 65,889 

Maui AC 25,625 28,710 34,841 

 AT 46,768 50,087 60,565 

 GA 19,952 20,926 23,673 

 MIL 1,921 1,944 1,944 

Maui Totals 94,265 101,666 121,022 

Molokai AC 499 - - 

 AT 14,671 13,768 16,261 

 GA 6,880 6,097 7,164 

 MIL 2,038 1,448 1,448 

Molokai Totals 24,087 21,313 24,873 

Lanai AC 511 - - 

 AT 2,723 2,722 2,866 

 GA 123 123 123 

 MIL 50 50 50 

Lanai Totals 3,407 2,894 3,038 

Statewide Totals 569,913 596,414 694,818 
June 2006 Page 4

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

Growth factors to project emissions from 2002 to 2005 and 2002 to 2018 were calculated as the 

ratio of the sum of LTOs by island and aircraft type in each future year to the sum of LTOs by 

island and aircraft type in 2002. These future year growth factors were then applied to 2002 

emission estimates by island and aircraft type to develop future year emission inventories. 

0

50000

100000

150000

200000

250000

300000

350000

Sum

of

2002

Sum

of

2005

Sum

of

2018

Sum

of

2002

Sum

of

2005

Sum

of

2018

Sum

of

2002

Sum

of

2005

Sum

of

2018

Sum

of

2002

Sum

of

2005

Sum

of

2018

Sum

of

2002

Sum

of

2005

Sum

of

2018

Sum

of

2002

Sum

of

2005

Sum

of

2018

Hawaii Oahu Kauai Maui Molokai Lanai

MIL

GA

AT

AC

Aircraft Type

Figure 1. Hawaii aircraft LTOs by-island and year. 

Seasonal Emissions Estimates 

To estimate seasonal island-level emission inventories, the monthly distribution of activity for 

airports in Hawaii for 2002 and 2005 were obtained from the FAA’s Air Traffic Activity Data 

System (ATADS), available at http://www.apo.data.faa.gov/main/atads.asp. The ATADS is the 

official source for historical monthly or annual air traffic statistics for airports with FAAoperated or FAA-contracted traffic control towers. 2002 and 2005 winter and summer activity 

fractions were estimated using ATADS monthly activity data by aircraft type. 2018 summer and 

winter allocation factors were estimated to be the average of 2002 and 2005 seasonal activity 

fractions. These state-level seasonal distributions were then applied to the annual island-level 

emissions to derive the seasonal emissions. Seasonal allocation factors for 2002, 2005 and 2018 

by aircraft type are presented in Table 5. 
June 2006 Page 5

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

Table 5. 2002, 2005 and 2018 Seasonal allocation factors by aircraft type. 

Season Aircraft Type 2002 2005 2018 

AC 0.268 0.267 0.267

AT 0.273 0.277 0.275

GA 0.261 0.264 0.263

Summer 

MIL 0.247 0.279 0.263

AC 0.237 0.243 0.240 

AT 0.228 0.240 0.234 

GA 0.236 0.229 0.232 

Winter 

MIL 0.246 0.226 0.236 

RESULTS 

Table 6 summarizes 2002, 2005 and 2018 annual emissions estimates by island. Statewide 

criteria air pollutant emissions increase between 4% and 6% from 2002 to 2005 while increases 

are between 16% and 25% from 2005 to 2018. Increases in emissions are due to statewide 

increases in air carrier, air taxi, and general aircraft LTOs from 2002 to 2005 and 2005 to 2018 

which dominate military aircraft LTO decreases from 2002 to 2005 (military aircraft LTOs 

remain constant from 2005 to 2018). Molokai and Lanai emissions decrease significantly from 

2002 to 2005 due to air carrier LTOs which decrease from 997 LTOs and 1002 LTOs in 2002 for 

Molokai and Lanai, respectively, to zero air carrier LTOs for both islands in 2005. 

Figures 2, 3 and 4 show the CAP emission contributions by aircraft type for calendar years 2002, 

2005 and 2018. For all years, CO emissions are dominated by air-taxi emissions with 2002 

contributions of 40%, while NOx, PM10, PM2.5, SO2 and VOC are dominated in all years by air 

carrier emissions with contributions in 2002 at 98%, 47%, 98%, and 52%, respectively. 

Table 6. 2002, 2005 and 2018 Annual CAP Emissions (tons/yr) by Island. 

Island Year VOC NOX CO 

PM10-

PRI 

PM25-

PRI SO2 NH3

2002 54 185 1,022 30 24 19 0.014

2005 66 177 1,290 35 27 18 0.019

Hawaii 

2018 75 207 1,480 40 31 21 0.026

2002 191 881 2,242 69 59 73 0.046

2005 197 938 2,213 70 60 78 0.042

Oahu 

2018 242 1,213 2,554 85 73 100 0.043

2002 34 121 635 19 16 13 0.002

2005 34 115 635 19 15 12 0.003

Kauai 

2018 38 123 718 21 17 13 0.004

2002 73 277 987 31 26 24 0.006

2005 80 310 1,065 34 28 27 0.007

Maui 

2018 96 376 1,274 41 34 33 0.007

2002 14 7 287 6 4 1 0.002

2005 12 1 255 5 4 0 0.002

Molokai 

2018 14 2 297 6 4 0 0.002

2002 3 6 42 1 1 0 0.000

2005 2 0 37 1 1 0 0.000

Lanai 

2018 2 0 39 1 1 0 0.000

2002 368 1,477 5,215 157 129 130 0.071

2005 389 1,541 5,495 164 135 135 0.073

Statewide 

Totals 

2018 466 1,920 6,362 194 161 167 0.084
June 2006 Page 6

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CO NOX PM10-PRI PM25-PRI SO2 VOC

Pollutants

Fraction of Ttoal Emission(tons/yr)

MIL

GA

AT

AC

Figure 2. Hawaii 2002 annual aircraft emissions distribution by pollutant and aircraft type. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CO NOX PM10-PRI PM25-PRI SO2 VOC

Pollutants

Fraction of Ttoal Emission(tons/yr)

MIL

GA

AT

AC

Figure 3. Hawaii 2005 annual aircraft emissions distribution by pollutant and aircraft type.
June 2006 Page 7

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CO NOX PM10-PRI PM25-PRI SO2 VOC

Pollutants

Fraction of Ttoal Emission(tons/yr)

MIL

GA

AT

AC

Figure 4. Hawaii 2018 annual aircraft emissions distribution by pollutant and aircraft type. 
June 2006 Page 8

G:\Hawaii EI\Reporting\Tech memos\HI_Aircraft_071107.doc 

References 

EPA, 2005. Documentation for Aircraft, Commercial Marine Vessel, Locomotive, and other 

Nonroad Components of the National Emissions Inventory: Volume I – Methodology. 

U.S. Environmental Protection Agency. Research Triangle Park, North Carolina. 
APPENDIX F 

HAWAII LOCOMOTIVE EMISSIONS 
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H:\Hawaii EI\Reporting\Tech memos\HI_locomotive_071007.doc 

MEMORANDUM

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: Chris Lindhjem 

Date: July 10, 2007 

Subject: Hawaii Locomotive Emissions 

There are two railroads operating in Hawaii; both are tourist excursions and so are of little 

consequence in terms of emissions. The Hawaiian Railroad (http://www.hawaiianrailway.com/) 

operates usually on weekends or other holidays on Oahu near Honolulu, and uses small older 

model diesel locomotive engines designed for switch engines and built in the 1940’s. The Sugar 

Cane Train (http://www.sugarcanetrain.com/) operates daily on Maui making regular runs, and is 

unique in that it is a steam engine not currenlty used in railroads across the U.S; it also has a 

lightly used switching engine. 

Locomotive activity data is fuel consumption, and the two railroads were asked what their fuel 

consumption rates were. The Hawaiian Railroad averages 40 gallons a week for 2080 gallons 

per year. The Sugar Cane Train burns 84,000 gallons of a low sulfur fuel oil in the steam engine, 

and about 1000 gallons of diesel fuel in the switching engine. 

Emission factors for locomotives have been developed for diesel engines, but not for steam 

engines. The emission factors for diesel engines prior to any emissions controls are shown in 

Table 1 (EPA, 1997). The emission factors for steam engines are not known for locomotives 

but do appear for commercial marine engines, so these marine steam engine emission factors are 

used for this work (ICF, 2006). The switching engine cycle has much lower loads and more idle 

time than the line-haul cycle, so it will be used to describe the diesel engine emissions in Hawaii. 

Table 1. Emission factor estimates used in this work (in grams per gallon or g/kg).

Engine Type HC CO NOx PM SOx1 NH32

Precontrolled Line-haul (g/gallon) 10 26.6 270 6.7 16.7 0.116 

Precontrolled Switching (g/gallon) 21 38.1 362 9.2 16.7 0.116 

Steam Boiler (g/kg) 0.38 4.6 12.3 1.3 5.2 0.036 

1

 Reported as SO2 and derived from an average sulfur level of 2600 ppm (EPA, 2004a). 2

 EPA (2004b). 

The emissions are estimated by applying the emission rates to the fuel consumption; the resulting 

emissions are shown in Table 2. 
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Table 2. Hawaii railroad emissions estimates (tons per year).

Engine Type HC CO NOx PM SOx NH3 

Oahu Switching Engine 0.05 0.09 0.83 0.02 0.04 0.000

Maui Steam 0.11 1.37 3.67 0.39 1.55 0.011

Maui Switching Engine 0.02 0.04 0.40 0.01 0.02 0.000

References 

EPA. 1997. “Locomotive Emission Standards.” Regulatory Support Document, United States 

Environmental Protection Agency, Office of Mobile Sources, April. And EPA 1997, 

Emission Factors for Locomotives,” Environmental Protection Agency, EPA420-F-97-

051, December. 

 

EPA. 2004a. Personal communication with Lester Wyborny, March 11. 

EPA. 2004b. Personal communication with Craig Harvey, March 10. 

Hawaiian Railroad, communication with Theresa Chordas (http://www.hawaiianrailway.com/) 

ICF Consulting. 2006. “Current Methodologies and Best Practices in Preparing Port Emission 

Inventories,” Final Report April 4, 2006, Prepared for U.S. Environmental Protection 

Agency 

Sugar Cane Train (http://www.sugarcanetrain.com/), personal communication with Ray Catugal 

808-661-0087 (808-264-9008) 
APPENDIX G 

AREA SOURCE EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
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TECHNICAL 

MEMORANDUM

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: John Grant, Rajashi Parikh, and Alison Pollack 

Date: September 11, 2009 

Subject: Revised Area Source Emissions Estimates for the Hawaiian Islands 

This technical memorandum describes in detail all of the calculations, source data, and 

assumptions used to estimate all area source emissions except ammonia sources, which have 

been provided in a separate technical memorandum. At the end of the section, summary graphs 

are provided to show source category contributions to the area source emissions inventory and 

changes in area source emissions over time. 

GRAPHIC ARTS 

Annual Emissions 

To estimate annual VOC emissions from graphic arts, a national per-capita emission factor of 1.3 

lbs VOC/person-year (EIIP, 1996a) was applied to the estimated 2002 and 2005 Hawaii 

population by island. By-island population estimates shown in Table 1 were derived from 2002 

and 2005 by-county estimates from HDBEDT (2007a and HDBEDT 2007b) allocated to the 

island level based on by-island fractions derived from HDBEDT (2007c). 2018 emission 

estimates were projected based on 2005 emissions estimates and growth factors derived from 

EGAS 5.0. 

Table 1. Hawaii State Population by Island. 

Island 2002 2005 

Hawaii 154,794 167,293 

Oahu 896,019 905,266 

Maui 123,055 128,427 

Molokai 7,745 8,083 

Lanai 3,340 3,486 

Kahoolawe 0 0 

Kauai 59,782 62,469 

Niihau 164 171 

State Total 1,244,898 1,275,194 

HAP emissions were determined by applying a speciation profile (Table 2) to the VOC 

emissions. Speciate profile 2570 from EPA’s SPECIATE database (Graphic Arts – Composite 

of Lithography, Rotogravure, Letterpress and Flexography) was used.
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Table 2. HAP Speciation profile for graphic arts. 

HAPS 

% of 

Total VOC

Benzene 1.94

Ethyl benzene 0.27

Methyl ethyl ketone 7.97

methyl Isobutyl Ketone 2.17

Toluene 11.28

M-Xylene and P-Xylene 0.81

O-Xylene 0.45

Isopropyl benzene 0.30

N-hexane 0.60

(U.S. EPA, 2002a) 

Seasonal Emissions 

EIIP documentation suggests that while graphic arts shops show no appreciable seasonal 

variation, typically 75% of emissions activity occurs on weekdays (EIIP, 1996a). Average 

summer and winter weekday emissions were calculated by dividing annual emissions by the 

product of 365 days per year and 75%. 

AUTO BODY REFINISHING 

Annual Emissions 

VOC emissions originating in auto body refinishing operations were estimated using a per 

employee emission factor. The emission factor of 759.6 lbs/employee-year (EIIP, 2000a) was 

applied to by island employment estimates in NAICS 811121 (Automotive body paint & interior 

R&M). By county employment for 2002, 2003, and 2004 was obtained from County Business 

Patterns (U.S. Census Bureau, 2005). As 2005 employment data were unpublished when this 

study was prepared, employment was estimated by linearly projecting 2002, 2003, and 2004 

employment to 2005. In cases where employment data were given as a range, employment was 

estimated to be the average of the two extremes (e.g. given range = 0 – 19, value used = 9.5). To 

allocate by county employment to island fractions, population fractions based on populations 

shown in Table 1 were used. Employment by island was sought to allocate by island activity for 

this category from the Hawaii State Department of Labor & Industrial Relations' Research & 

Statistics Office; however employment data to the level of detail needed was not available. 2018 

emission estimates were projected based on 2005 emissions estimates and growth factors derived 

from EGAS 5.0. 

Annual emissions of HAPs were then estimated using the speciation profiles from EPA’s 

SPECIATE database (U.S. EPA 2002b, U.S EPA 2002c). Two profiles are present in 

SPECIATE that apply to this category, profiles 2402 and 1194. The profiles were combined to 

obtain the most comprehensive list of HAPs. Where a pollutant was listed in both profiles, the 

value given in profile 2402 was used due to its derivation from more recent studies. 

For VOC emissions, a 33 percent reduction was applied to reflect the promulgation of national 

VOC rules. This is the estimated total reduction of VOCs emanating from auto body refinishing 
 3 

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to be achieved by the national VOC rule (Federal Register, 1998a). Given that HAP emissions 

are based on speciation of VOCs, they too were diminished by 33 percent. 

Seasonal Emissions 

EIIP documentation suggests that auto body refinishing shops typically operate five days per 

week and that emissions are not seasonally variable (EIIP, 2000a). Average summer and winter 

weekday emissions were calculated by dividing the annual emissions by 260 workdays/year. 

ON-SITE INCINERATION 

Per HCAB staff (Rossio, 2007a), there are not significant emissions resulting from activity 

related to on-site incineration. 

OPEN BURNING 

Per HCAB staff (Rossio, 2007a), there are not significant emissions resulting from activity 

related to open burning. 

DRY CLEANING 

Annual Emissions 

VOC emissions originating in dry cleaning operations were estimated using a per employee 

emission factor. The emission factor of 1800 lbs/employee-year (EIIP, 1996b) was applied to 

county level employment in NAICS 812320 (Dry cleaning and laundry services, except coinoperated). By county employment for 2002, 2003, and 2004 was obtained from County Business 

Patterns (U.S. Census Bureau, 2005). As 2005 employment data were unpublished when this 

study was prepared, employment was estimated by linearly projecting 2002, 2003, and 2004 

employment to 2005. In cases where the employment was given as a range, employment was 

estimated to be the average of the two extremes (e.g. given range = 0 – 19, value used = 9.5). To 

allocate by county employment to the island level, population fractions based on populations 

shown in Table 1 were used. Employment by island was sought to allocate by island activity for 

this category from the Hawaii State Department of Labor & Industrial Relations' Research & 

Statistics Office, however employment data to the level of detail needed was not available. 2018 

emission estimates were projected based on 2005 emissions estimates and growth factors derived 

from EGAS 5.0. 

Annual emissions of HAPs were estimated using an employment-based emission factor for total 

halogenated solvents (THS). The halogenated solvents in question are perchloroethylene (also 

PERC or tetrachloroethene), 1,1,1-trichloroethane (also TCA or methyl chloroform) and CFC113. To extract the emission factors for the relevant pollutants, PERC and TCA, from this 

combined factor, survey data of the national consumption of each solvent was used. Based on a 

national survey, 39% of solvents used are PERC, 3% are TCA and 1% are CFC-113 (EIIP, 

1996b). Therefore, 90.7% of the THS emission factor was assumed to apply to PERC and 6.9% 

to TCA. The emission factors resulting from this process are shown in Table 3. 
 4 

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Table 3. HAP Emission factors for dry cleaning. 

Solvent 

THS Emission 

Factor 

(EIIP, 1996b)

% of Total Use 

(EIIP, 1996b)

% of 

THS 

Pollutant-Specific 

Emission Factor 

mineral spirits 57 

PERC 39 90.7 889 

TCA 3 6.98 68.4 

CFC-113 

980 lb/employee-yr 

1 2.33 

These employment based emission factors for PERC and TCA were applied to state of Hawaii 

employment for both NAICS 812320 and NAICS 812310. NAICS 812310 was included in the 

calculation for HAPs and not for VOCs because coin-operated dry cleaners use PERC only. 

PERC is not considered photochemically reactive and is therefore not included in ozone VOC 

inventories, so coin-operated dry cleaners cannot be included in VOC calculations (EIIP, 1996b). 

Emissions resulting from activity in NAICS 812310 are reported under SCC 2420020000 and 

those resulting from activity in NAICS 812320 are reported under SCC 2420010000. 

Emission reductions resulting from the 1993 promulgation of the National Emission Standards 

for Hazardous Air Pollutants (NESHAP) rule, “Perchloroethylene Dry Cleaning Facilities” (40 

CFR, Parts 963) were incorporated into emission estimates. 40 CFR, Part 963 requires the 

control of dry cleaning PERC emissions to the level of maximum achievable control technology 

(MACT). A PERC emission reduction that corresponds to approximately 44 percent of the total 

1996 PERC emissions from all existing dry cleaning facilities and a 43 percent reduction (40 

CFR, Parts 963) in 1996 from all new dry cleaning facilities were estimated nationwide as a 

result of rule implementation. Additionally, dry cleaning EIIP documentation states that, "Coinop dry cleaning units are exempt from all but the initial reporting NESHAP requirements." (EIIP 

1996b) Therefore, a reduction of 43% (conservatively estimated to be the lesser of 43% and 

44%) was applied to all PERC HAP emissions; with the emissions reduction reflected only in the 

non-coin-op dry cleaners reported PERC emissions (SCC 2420010000). 

Seasonal Emissions 

There is no seasonal variation of dry cleaning emissions, and a 5 day/week operation schedule 

can be assumed (EIIP, 1996b). Average summer and winter weekday emissions were calculated 

by dividing the annual emissions by 260 workdays/year. 

INDUSTRIAL SURFACE COATING 

Annual Emissions 

VOC emissions originating from industrial surface coating operations were estimated using 

either per employee emission factors or per capita emission factors. There are actually ten 

distinct surface coating operations with distinct per employee emission factors and three 

operations with per capita emission factors, for a total of 13 categories. These operations and the 

corresponding emission factors are listed in Table 4. 
 5 

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Table 4. Industrial surface coating SCCs and emission factors. 

SCC Description 

Type of 

Emission Factor 

Emission 

Factor 

2401015000 Factory Finished Wood lb/employee 131 

2401020000 Furniture lb/employee 944 

2401040000 Metal Cans lb/employee 6,029 

2401050000 Misc. Finished Metals lb/employee 2,877 

2401055000 Machinery and Equipment lb/employee 77 

2401060000 Appliances lb/employee 463 

2401065000 Electronic/Electrical lb/employee 290 

2401070000 Motor Vehicles lb/employee 794 

2401080000 Marine lb/employee 308 

2401085000 Railroad/Other lb/employee 35 

2401090000 Misc. Manufacturing lb/person 0.6 

2401100000 High Performance Industrial Maintenance Coatings lb/person 0.8 

2401200000 Other Special Purpose Coatings lb/person 0.8 

(EIIP, 1997a) 

For each type of operation with a per employee factor, the emission factor was applied to island 

level employment in numerous NAICS categories. The EIIP document gives source categories 

with corresponding SIC codes, but given that the most recent County Business Patterns use 

NAICS codes, the corresponding NAICS codes were identified for use (EIIP, 1997a and U.S. 

Census Bureau, 1998). A complete listing of the NAICS categories by associated SCC is 

presented in Table 5. 

By county employment for 2002, 2003, and 2004 was obtained from County Business Patterns 

(U.S. Census Bureau, 2005). As 2005 employment data were unpublished when this study was 

prepared, employment was estimated by linearly projecting 2002, 2003, and 2004 employment to 

2005. In cases where a year’s employment was given as a range, employment for that year was 

estimated to be the average of the two extremes (e.g. given range = 0 – 19, value used = 9.5). To 

allocate by county employment to the island level, population fractions based on populations 

shown in Table 1 were used. Employment by island was sought to allocate by island activity for 

this category from the Hawaii State Department of Labor & Industrial Relations' Research & 

Statistics Office, however employment data to the level of detail needed was not available. For 

the three source categories with per capita emission factors, those factors were applied to the 

2002 and 2005 by island population estimates. 2018 emission estimates were projected based on 

2005 emissions estimates and growth factors derived from EGAS 5.0. 

Table 5. NAICS categories from which industrial surface coating employment activity were 

drawn. 

Factory Finished Wood Automobiles 

SIC NAICS SIC NAICS SIC NAICS SIC NAICS 

2426-2429 32192 243 to 245 321213 3711 33612 3711 336211 

2426-2429 321113 243 to 245 321214 3711 336111 3711 336992 

2426-2429 321912 243 to 245 321911 3711 336112 

2426-2429 321918 243 to 245 321918 Sheet/Strip/Coil 

2426-2429 321999 243 to 245 321991 SIC NAICS SIC NAICS 

2426-2429 337215 243 to 245 321992 3479 332812 3479 339912 

243 to 245 33711 2493 321219 3479 339911 3479 339914 
 6 

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Factory-Finished Wood Metal Containers

SIC NAICS SIC NAICS SIC NAICS SIC NAICS

243 to 245 321211 2499 333414 341 332431 

243 to 245 321212 2499 339999 341 332439 

Furniture Appliances

SIC NAICS SIC NAICS SIC NAICS SIC NAICS 

25 33636 25 337129 363 333298 363 335222 

25 33791 25 337211 363 333414 363 335224 

25 33792 25 337212 363 335211 363 335228 

25 337121 25 337214 363 335212 363 339999 

25 337122 25 337215 363 335221 

25 337124 25 339111 Other Transportation

25 337125 25 339942 SIC NAICS SIC NAICS 

25 337127 37 33633 37 336322 

Machinery/Equipment 37 33634 37 336399 

SIC NAICS SIC NAICS 37 33635 37 336411 

35 33241 35 333515 37 33651 37 336412 

35 33271 35 333516 Machinery/Equipment (concl.)

35 33312 35 333518 SIC NAICS SIC NAICS 

Machinery/Equipment (cont.) 37 54171 37 336413 

SIC NAICS SIC NAICS 37 332912 37 336414 

35 33321 35 333611 37 333911 37 336415 

35 33322 35 333612 37 333924 37 336419 

35 33651 35 333613 37 336212 37 336991 

35 314999 35 333618 37 336213 37 336992 

35 332212 35 333911 37 336214 37 336999 

35 332323 35 333912 37 336312 

35 332439 35 333913 Marine

35 332991 35 333921 SIC NAICS SIC NAICS 

35 332997 35 333922 373 48839 373 336611 

35 332999 35 333923 373 81149 373 336612 

35 333111 35 333924 Electrical Insulation

35 333112 35 333991 SIC NAICS SIC NAICS 

35 333131 35 333992 3357 331319 3357 335921 

35 333132 35 333993 3357 331422 3357 335929 

35 333291 35 333994 3357 331491 3612 335311 

35 333292 35 333995 

35 333293 35 333996 

35 333294 35 333997 

35 333295 35 333999 

35 333298 35 334111 

35 333311 35 334112 

35 333312 35 334113 

35 333313 35 334119 

35 333319 35 334418 

35 333411 35 334518 

35 333412 35 334613 

35 333414 35 335311 

35 333415 35 336311 

35 333511 35 336391 

35 333512 35 336399 

35 333513 35 339942 

35 333514 
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Annual emissions of HAPs were then estimated by applying a speciation profile to the annual 

VOC emissions of each source category. The available speciation data were divided into two 

speciation profiles, one for water-borne coatings and another for solvent-borne coatings. The 

national percentage of sales of each coating type (9% water-borne coatings & 91% solvent borne 

coatings [U.S. EPA, 2003b]) was used to combine those profiles into a single profile that could 

be applied to our estimates of total VOC emissions (see calculations below). The resulting 

speciation profile is shown in Table 6. 

Table 6. Industrial surface coating HAP speciation profile. 

Average VOC Weight Fraction 

Pollutant 

In waterborne 

coatings 

In solventborne 

coating Combined 

Acetophenone - 0.0006 0.0005 

Cumene - 0.0012 0.0011 

Dibutyl Phthalate 0.0031 - 0.0003 

Ethyl Benzene - 0.0062 0.0056 

Ethylene Glycol 0.1271 0.0048 0.0158 

Glycol ethers 0.1434 0.0334 0.0433 

Isophorone - 0.0053 0.0048 

Methanol 0.0429 0.0151 0.0176 

methyl ethyl ketone - 0.0065 0.0059 

methyl isobutyl ketone - 0.0162 0.0147 

Naphthalene - 0.0022 0.0020 

Toluene - 0.0118 0.0107 

Xylenes (Mixture of o, m, and p Isomers) 0.0348 0.0317 

(U.S. EPA, 2003b)

Seasonal Emissions 

There is no seasonal variation of industrial surface coating emissions, and a 5 day/week 

operation schedule can be assumed (EIIP, 1997a). Average summer and winter weekday 

emissions were calculated by dividing the annual emissions by 260 workdays/year. 

ARCHITECTURAL SURFACE COATING 

Annual Emissions 

By county usage of architectural surface coatings was estimated based on a national per-capita 

use factor. This factor was developed by dividing the 2002 and 2005 national usage of surface 

coatings (U.S. Census Bureau, 2003 and U.S. Census Bureau, 2006, respectively) by the 

estimated 2002 and 2005 national population (U.S. Census Bureau, 2006a). This allowed for the 

generation of the usage factors listed in Table 7. 
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Table 7. Per-capita architectural surface coating use factors.

Coating Type 2002 2005 

Solvent-based coating 0.443 gal/person-yr 0.499

Water-based coating 2.046 gal/person-yr 2.224

Multiplying these usage factors by the estimated 2002 and 2005 by island populations gave the 

total by island usage of solvent-based and water-based coatings. Emissions for each coating type 

were calculated as the product of usage and the EIIP emission factors listed in Table 8. The 

resulting emissions were then decreased by 20% to obtain the final VOC emissions. This 

reduction accounts for a national VOC rule promulgated after the development of the emission 

factors, for which the estimated impact on emissions was a reduction of 20% (Federal Register, 

1998b). 

Table 8. Architectural surface coating VOC emission factors. 

VOC Emission Factors lb VOC/gal

Solvent-based coating 3.87

Water-based coating 0.74

(EIIP, 1995) 

Hazardous air pollutant emissions were then calculated based on the speciation of the VOC 

emissions. The EIIP document provides one speciation profile for VOC emissions from waterbased coatings and one for emissions from solvent-based coatings shown in Table 9 (EIIP, 

1995). 

Table 9. Architectural surface coating HAP speciation profile. 

Weight 

Fraction of VOC

Water-Based Coatings

112345 Diethylene Glycol Monobutyl Ether 0.007

71432 Benzene 0.003

75003 Ethyl chloride 0.006

107211 Ethylene glycol 0.005

74873 Methyl chloride 0.005

75092 Methylene chloride 0.055

Solvent-Based Coatings 

68122 N,N-Dimethylformamide 0.005

100414 Ethyl Benzene 0.043

107211 Ethylene glycol 0.006

1330207 Xylenes (Mixture of o, m, and p Isomers) 0.026

67561 methanol 0.039

78933 Methyl ethyl ketone 0.056

108101 Methyl isobutyl ketone 0.006

108883 Toluene 0.052
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Seasonal Emissions 

Surface coating is not practicable at temperatures below 50 degrees (EIIP, 1995). Monthly 

average temperatures in Hawaii are generally higher than 50 degrees year-round. Therefore, it 

was assumed that emissions will occur uniformly year-round in Hawaii. Activity occurs seven 

days per week according to EIIP documentation (EIIP, 1995). 

DEGREASING 

Annual Emissions 

In order to achieve the most detailed characterization that is possible with the resources 

available, emissions for solvent degreasing were estimated using two different approaches. Each 

of these approaches covered a different type of solvent utilization activity. Both methods used 

were developed by the EIIP (EIIP, 1997b) and use employment as activity and per-employee 

emission factors to determine emissions, however they differ significantly enough so as to 

warrant separate discussions. Table 10 shows which SCCs were covered by each method. 

Table 10. Degreasing SCCs listed by the methodology used. 

Solvent Cleaning 

Equipment 

Solvent Cleanup 

Activities 

2415360000 2415035000 

2415345000 2415020000 

2415230000 2415005000 

2415245000 2415030000 

 2415025000 

 2415040000 

 2415045000 

For all categories of degreasing emissions, per EIIP Guidance (EIIP, 1997b) emissions were 

assumed to occur uniformly year round, six days per week. 

EIIP Method: Solvent Cleaning Equipment

Solvent cleaning equipment emission factors are shown in Table 11. The activity used to 

calculate degreasing emissions from solvent cleaning equipment was 2002 and 2005 state of 

Hawaii employment. Employment data are available from County Business Patterns and that 

data is categorized by NAICS. NAICS categories were identified as corresponding to the SIC 

categories in question (U.S. Census Bureau, 1998). To allocate by the available county 

employment data to the island level, population fractions based on populations shown in Table 1 

were used. Employment by island was sought to allocate by island activity for this category 

from the Hawaii State Department of Labor & Industrial Relations' Research & Statistics Office, 

however employment data to the level of detail needed was not available. 2018 emission 

estimates were projected based on 2005 emissions estimates and growth factors derived from 

EGAS 5.0. 
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Table 11. EIIP per employee emission factor for solvent cleaning equipment. 

SCC Description Corresponding SIC 

lbs VOC/ 

employee-yr1

2415360000 

Cold Cleaning - 

Automobile Repair 

417, 423, 551,552, 554-

556,753 264 

2415345000 

Cold Cleaning - 

Manufacturing 25, 33-39 23 

2415230000 

Vapor and In-Line 

Cleaning - Electronics 

and Electrical 36 28 

2415245000 

Vapor and In-Line 

Cleaning - Other 

25, 33-39, 417,423, 551, 

552,554-556, 753 10 1 Emission factors were adjusted downward to account for PERC emissions, not to be included in an ozone VOC inventory. 

(EIIP, 1997b) 

Hazardous air pollutant emissions were calculated based on a generic HAP speciation profile for 

degreasing solvents in EPA’s SPECIATE database (see Table 12). This profile was applied to 

the solvent cleaning equipment VOC emissions. 

Table 12. Hazardous air pollutant speciation profile for degreasing solvents from EPA’s 

SPECIATE database. 

Code Pollutant Percent of VOC

110805 Cellosolve Solvent 0.41%

71556 Methyl Chloroform (TCA) 23.67%

75092 Methylene Chloride 4.28%

127184 Tetrachloroethylene 2.31%

79016 Trichloroethylene 8.28%

(U.S. EPA, 2002e) 

As part of Title VI of the Clean Air Act Amendments, the production of Methyl Chloroform 

(TCA) for use in the United States was phased out in 1995. Presumably, significant use 

continued after that phase out as remaining stocks were consumed (EIIP, 1997b). ARB 

estimated that TCA use would continue in diminishing quantities for between two and ten years 

after the phase out (ARB, 1996). For this inventory it is assumed that seven years after the phase 

out, use of TCA is negligible and therefore emissions have not been calculated for that HAP. 

EIIP Method: Solvent Cleanup

The EIIP Method for estimating emissions from solvent cleanup activities was developed from 

information collected for the Industrial Cleaning Solvents Alternative Control Techniques 

Document (ACT) (EIIP, 1997a). The Industrial Cleaning Solvents ACT provides estimates of 

solvent amounts used at the national level for cleanup activities for 15 industries. These 

estimates were drawn from references that were prepared as early as 1979 and as recently as 

1993. For 9 of the 15 industries, the ACT provides estimates of national VOC emissions from 

cleanup and for the other 6 industries solvent volume-use estimates are provided. Emissions 

were estimated for 8 of 9 industries for which ACT provided national VOC emissions estimates 

and 2 of 6 for which ACT provided national VOC usage emissions. For the two industries for 

which VOC emission estimates were not available, 100% volatilization was conservatively 
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assumed for all VOC used in solvent cleanup activities. Emission factors were calculated for 

each industry by taking the midpoint of the range of the year VOC emissions or solvent use and 

dividing this number by the 1990 U.S. employment (U.S. Census, 1996) for the industry. Table 

13 shows industries for which emissions in the state of Hawaii were estimated, SIC codes, 

corresponding SCC codes, and emission factors. Of the five industries dropped from 

consideration for emissions from solvent cleanup, packaging and was dropped because emissions 

from the packaging industry are most commonly associated with point sources; lithographic 

printing, retrograve printing, and autobody refinishing were included in other area source 

categories; and there was a lack of information regarding employment for fiber-reinforced 

plastic (FRP) boats. 

Table 13. Industries for which solvent cleanup activities were estimated. 

SCC Industry SIC 

Emission Factors 

(tons/year/employee) 

Automotive-Manufacturing 3711 0.139 

2415035000 Automotive-Trucks and Buses 3713 0.394 

Automotive-Parts/Accessories 3714 5.57x10-3 

2415020000 Automotive-Stamping 3465 2.89x10-3 

2415005000 Furniture 2500 8.99x10-2 

2415030000 Electrical Equipment 3600 1.51x10-3 

2415025000 Magnetic Tape 3577 1.37x10-2 

2415040000 Photographic Supplies 3681 5.55x10-3 

2415045000 Adhesives 2891 8.33x10-3 

2415045000 Plastics 3000 8.74x10-5 

Emission estimates were made by multiplying the per-employee emission factor by the number 

of employees estimated for each island employed in that industry in 2002 and 2005. 

Employment data are available from County Business Patterns, categorized by NAICS. NAICS 

categories were identified as corresponding to the SIC categories in question (U.S. Census 

Bureau, 1998). By county employment for 2002, 2003, and 2004 was obtained from County 

Business Patterns (U.S. Census Bureau, 2005). As 2005 employment data were unpublished 

when this study was prepared, employment was estimated by linearly projecting 2002, 2003, and 

2004 employment to 2005. County employment data for 2002 and estimated 2005 employment 

were collected for these NAICS categories. Employment by island was sought to allocate by 

island activity for this category from the Hawaii State Department of Labor & Industrial 

Relations' Research & Statistics Office, however employment data to the level of detail needed 

was not available. 2018 emission estimates were projected based on 2005 emissions estimates 

and growth factors derived from EGAS 5.0. 

As in solvent cleaning equipment emissions, hazardous air pollutant emissions for solvent 

cleanup activities were calculated based on a generic HAP speciation profile for degreasing 

solvents from in EPA’s SPECIATE database (see Table 12). 
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CUTBACK ASPHALT 

Annual Emissions 

According to the state representative of the Hawaii Asphalt Paving Institute, significant 

quantities of cutback asphalt were not used in 2005 nor will cutback asphalt be used in the future 

(Romanowski, 2007). Therefore, activity and emissions associated with cutback asphalt in 2005 

and 2018 have been assumed to be insignificant. 

According to asphalt paving company representatives, in 2002 there was approximately 12,000 

gallons of medium cure cutback asphalt applied (Romanowski, 2007; Matsumoto, 2007). 

Cutback asphalt use was allocated is the islands based on the fraction of VMT occurring on each 

island in 2002 provided by HDOT for on-road emissions estimates. 

With no data available on the diluent content, a midpoint value for medium cure asphalt was 

used (35%), as recommended by EIIP. The EIIP document also provides an emission factor of 

20% VOC by weight of cutback asphalt and a HAP speciation profile (Table 14) to apply to 

VOC emissions (EIIP, 2001c). 

Table 14. Percent weight of HAPs in VOC emissions from cutback asphalt. 

HAP Percent Weight of VOC 

100414 Ethyl Benzene 2.3%

108883 Toluene 6.4%

1330207 Xylenes (Mixture of o, m, and p Isomers) 12.2%

(EIIP, 2001c) 

Seasonal Emissions 

It was assumed that there is no seasonal variation in the application of cutback asphalt. The EIIP 

document states that due to the nature of cutback asphalt emissions, they should be assumed to 

occur seven days per week. Thus the average day summer and winter emissions were calculated 

as the annual emissions divided by 365 days. 

CONSUMER SOLVENT USE 

Annual Emissions 

Consumer solvent use was divided into the seven subcategories in Table 15. 

Table 15. Consumer solvent use SCCs. 

2460100000 Personal Care Products 

2460200000 Household products 

2460400000 Automotive aftermarket products 

2460600000 Adhesives and sealants 

2460800000 FIFRA-regulated products 

2460500000 Coatings and related products 

2460900000 Miscellaneous products 
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For each of these subcategories there exists a per-capita emission factor both for VOC and for 

HAPs in the Emission Inventory Improvement Program’s chapter on Consumer Solvent Use 

(EIIP, 1996a). Given that these emission factors were based on data collected prior to the 1998 

national regulation restricting the VOC content of these products, they were adjusted to reflect 

the new control. EPA estimated that the total reduction in VOC emissions from consumer 

solvent use achieved by the new regulation would be 20% (Federal Register, 1998). Following 

that estimate, we have decreased the per-capita emission factors in EIIP by 20%. The resulting 

emission factors are listed in Table 16. 2002 and 2005 annual emissions estimates were 

computed as the product of these emission factors and the annual estimated county populations, 

listed in Table 1. 2018 emission estimates were projected based on 2005 emissions estimates 

and growth factors derived from EGAS 5.0. 

Seasonal Emissions 

Average summer and winter weekday emissions were calculated by dividing the annual 

emissions by 365 days/year. Here we have followed the guidance of the EIIP document which 

suggests that consumer solvent use shows little seasonal variation and that use of these products 

should be assumed to occur seven days per week (EIIP, 1996a). 

Table 16. Emission factors for consumer solvent use (units are lb/person-yr).

Personal 

Care 

Products 

Household 

Products 

Automotive 

Aftermarket 

Products 

Adhesives 

& Sealants 

FIFRARegulated 

Products 

Coatings & 

Related 

Products Misc. 

VOC 1.856 0.632 1.088 0.456 1.424 0.76 0.056

Acetamide 1.10E-07

Acetophenone 6.82E-06

Acrylic acid 3.15E-09

Benzene 3.78E-06

Carbon tetrachloride 3.28E-10

Chlorobenzene 5.73E-02 1.21E-05

Chloroform 2.88E-05 7.64E-04

Dibenzofurans 6.46E-06

1,4-Dichlorobenzene 3.83E-02 2.82E-02

1,2-Dichloroethane 3.70E-06 2.82E-08

1,3-Dichloropropene 1.28E-01

Dimethyl formamide 2.17E-05 2.22E-08 1.83E-07 5.94E-06

1,4-Dioxane 8.72E-06

Ethyl benzene 2.05E-06 6.01E-05 1.09E-05 1.04E-03 5.49E-04

Ethylene oxide 1.21E-02

Formaldehyde 5.39E-06 2.01E-05 3.05E-04 6.84E-04

Gylcol ethers 1.22E-05 4.25E-03 2.15E-02 1.02E-04 4.52E-03 1.79E-03 1.94E-04

Hexane 1.67E-03 2.82E-03 6.26E-02 1.91E-03

Hydrochloric acid 1.40E-06

Hydrogen fluoride 7.00E-08 1.13E-05

Isophorone 7.58E-04
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Personal 

Care 

Products 

Household 

Products 

Automotive 

Aftermarket 

Products 

Adhesives 

& Sealants 

FIFRARegulated 

Products 

Coatings & 

Related 

Products Misc. 

Methanol 4.54E-07 5.33E-04 5.29E-01 5.46E-04 7.58E-04 1.28E-02 1.47E-02

Methyl bromide 1.78E-01

Methyl ethyl ketone 1.40E-05 3.59E-04 2.43E-03 3.13E-02 1.61E-05 6.35E-03 8.08E-06

Methyl isobutyl ketone 8.64E-05 6.98E-04 9.92E-04 7.21E-05 4.21E-03

Methyl-tert-butyl ether 1.89E-05

Methylene chloride 1.91E-03 3.86E-03 7.02E-03 5.45E-04 1.58E-02 1.90E-05

Naphthalene 4.42E-07 1.81E-06 8.56E-05 3.68E-02 4.60E-06

2-Nitropropane 1.70E-06

Perchloroethylene 2.37E-03 1.88E-02 5.40E-04 1.54E-04 1.18E-04 6.02E-04

Toluene 2.73E-03 4.66E-04 1.99E-02 6.74E-02 2.53E-01 1.97E-06

1,1,1-Trichloroethane 5.96E-04 2.28E-02 6.10E-02 1.71E-01 4.79E-02 6.15E-03 1.97E-04

Trichloroethylene 3.47E-05 2.14E-04 3.10E-05 1.10E-04

Triethylamine 2.50E-04 4.21E-04

Vinyl acetate 3.95E-08

Xylenes 2.62E-03 9.60E-03 7.81E-03 1.10E-01 3.24E-02 3.45E-04

GASOLINE DISTRIBUTION 

Annual Emissions 

Emissions from gasoline distribution are divided into multiple segments: bulk plants, bulk 

terminals, Stage I, Stage II, and storage tank breathing. Bulk plant and bulk terminal emissions 

are associated with loading of gasoline into a storage tank or tank truck, working losses from 

fixed roof tanks, and working losses and roof seals for floating roof tanks. Stage I emissions are 

those associated with the delivery of gasoline to gas stations (i.e., from the tanker truck into the 

underground storage tank) and underground storage tank “breathing”. Stage II emissions are 

those that occur at the pump when fuel is transferred to vehicles. 

Stage I, Stage II, and working and breathing loss emissions are estimated as the product of 

emission factors and activity level. Activity for this category is gasoline throughput for Stage I 

emissions and vehicle miles traveled (VMT) for Stage II emissions. Annual gasoline distribution 

for 2002 and 2005 by county was taken from (Hawaii Department of Taxation, 2007) and 

distributed to the island level based on by island annual VMT estimates. 2018 Stage I, bulk 

plant, and bulk terminal emission estimates were projected based on 2005 emissions estimates 

and growth factors derived from EGAS 5.0. Vehicle miles traveled for 2002, 2005, and 2018 as 

obtained from HDOT as described in the On-road emissions technical documentation, were used 

as activity estimates for Stage II emissions. Stage II 2018 emissions were estimated by applying 

2018 VMT activity to MOBILE6 estimated 2018 emission factors. 

For each segment there is a distinct emission factor, as presented in Table 17. The EIIP 

document presents several emission factors for underground tank filling based on the filling 

practices in the state. The factor for submerged underground tank filling was used per Hawaii 
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Administrative Rules §11-60.1-39 which require such filling for all gasoline tanks with 250 

gallon or greater capacity. 

Table 17. Gasoline distribution emission factors. 

Emission Factors 

lb VOC/1000 gal 

gasoline 

throughput Source 

Bulk Plants 8.62 EPA, 2006 

Weighted Uncontrolled 

Submerged Fill 8.9303

Weighted Controlled Fill 0.9298

Weighted Uncontrolled 

Splash Fill 22.3151

Weighted Uncontrolled 

Submerged Fill 8.0916

Weighted Controlled Fill 0.7651

Bulk Terminals 

Weighted Uncontrolled 

Splash Fill 22.3151

HCAB, 2009 

Empty truck in transit 0.055 EIIP, 2001f 

Full truck in transit 0.005 EIIP, 2001f 

Submerged filling 7.3 EIIP, 2001f 

Stage I 

Underground tank breathing and 

emptying 1.0 EIIP, 2001f 

Stage II 

Refueling: Spillage and 

Displacement losses Varies

EPA 

MOBILE6.2 

In the case of trucks in transit, the activity of total gasoline throughput was adjusted as suggested 

by the EIIP document to correct for gasoline that is transported more than once. The adjustment 

used was to multiply throughput by a factor of 1.25 (EIIP, 2001f). 

Stage II criteria and hazardous air pollutant emission factors were derived from EPA’s MOBILE 

6.2 on-road vehicles emission factor model. 

 

Emissions of hazardous air pollutants from Stage I emissions were estimated based on the 

“baseline fuel” speciation profile provided in the EIIP document for calendar years 2002 and 

2005 and for “reformulated ethanol gasoline” for calendar year 2018. Stage II hazardous air 

pollutant emissions were based on EPA MOBILE6.2 derived emission factors. 

Per the Gasoline Distribution Industry (Stage I) Background Information document (EPA, 1994), 

bulk plant throughput was estimated as 3% of total estimated Hawaii Department of Taxation 

(2007) annual throughput No bulk plant point sources were in the AirSHED point sources 

emission inventory and therefore no point source reconciliation was necessary for the area source 

bulk plant category. 

The Hawaii Clean Air Branch provided estimates of bulk terminal emissions for 2002 and 2005 

by island (HCAB, 2009). For bulk terminals, as a result of the 10% ethanol gasoline requirement, 

fuel volatility changes and volumetric changes of gasoline through bulk terminals required 
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incorporation of emissions adjustments (in addition to EGAS projection factors) for 2018 

emissions. Per Hawaii Clean Air Branch, 10% ethanol gasoline will not be used on the islands 

of Lanai and Molokai, therefore 2018 emissions adjustments were not applied for these islands. 

Ethanol is not blended at the refineries; it is blended at the final truck loading rack just before 

shipping to retail outlets (HCAB, 2009). Changes in emissions due to volumetric changes in 

gasoline throughput were accounted for by assuming 10% less gasoline throughput per estimated 

unit volume of 2018 fuel consumption for bulk terminals. 

In order to estimate the effect of fuel volatility changes on 2018 emissions, it was assumed that 

adding 10% ethanol to gasoline would result in an increase in gasoline Reid Vapor Pressure 

(RVP) of approximately 1 psi. Therefore gasoline must have lower RVP before blending to meet 

the Hawaii 11.5 RVP requirement and gasoline through bulk terminals would be less volatile in 

2018. In order to account for changes in emissions due to gasoline volatility changes through 

bulk terminals in 2018, it was assumed that gasoline through bulk terminals for Hawaii, Oahu, 

Maui, and Kauai would have an average 2018 RVP 0.85 psi (1 psi decrease for 85% of gasoline) 

less than the 2005 RVP. It was assumed that changes in bulk terminal emissions could be 

represented similarly to changes in emissions resulting from Stage I gasoline service station 

loading (EPA, 2006). 

L = (12.46 * S * P * M) / T 

where: L = uncontrolled loading loss of liquid loaded (in lb/1,000 gallons) 

 S = saturation factor 

 P = true vapor pressure of liquid loaded (pounds per square inch absolute) 

 M = molecular weight of vapors (lb per lb/mole) 

 T = temperature of liquid loaded 

Changes in the saturation factor, molecular weight, and temperature from 2005 to 2018 are 

assumed to be negligible and therefore loading loss emissions changes are a function of the 

change in true vapor pressure. True vapor pressure was estimated by month and island per the 

following equation (EPA, 2006), with monthly temperatures taken from the ds472 data set for 

each island. 2005 RVP was assumed to be 11.5 psi and 2018 RVP was assumed to be 10.65 psi. 

Annual average true vapor pressure was estimated by taking the average of the estimated 

monthly vapor pressure weighted by Hawaii Department of Taxation (2007) 2005 gasoline 

throughput. 
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where: P = stock true vapor pressure in pounds per square inch absolute 

 T = stock temperature in degrees Fahrenheit 

 RVP = Reid vapor pressure in pounds per square inch 

S = slope of the ASTM distillation curve at 10 percent evaporated in degrees 

Fahrenheit per percent (assumed that S = 3.0 for gasoline per EPA, 2006). 

Emissions adjustments for bulk terminal emissions due to changes in volumetric throughput and 

and volatility decreases were estimated as 13% decreases for each affected island. 

Seasonal Emissions 

Emissions were assumed to be constant year round based on the assumption of equivalent onroad VMT activity in all months as described in the on-road technical documentation. Weekly 

allocation factors are presented in Table 18. 

Table 18. Weekly activity for fuel distribution. 

Process Days per 

Week 

Trucks in Transit 6*

Fuel Delivery to Outlets 6*

Vehicle Refueling 7*

Storage Tank Breathing 7*

Bulk Plants 7**

Bulk Terminals 7**

*(EIIP, 2001f) 

**CAIR Platform Temporal Allocation (http://www.epa.gov/ttnchie1/emch/temporal/index.html) 

VEHICLE FIRES 

Annual Emissions 

This category covers emissions from accidental vehicle fires. Emissions from vehicle fires were 

estimated based on the number of vehicle fires as obtained from local fire departments (Table 

19), EIIP reported emission factors (Table 20), and the average amount of components burned 

per vehicle fire (500 lb/vehicle, EIIP, 2000b). Seasonal fractions were estimated according to 

the fraction of activity that occurred in each season as shown in Table 19. For Hawaii County 

fires, the Hawaii Fire Department was able to provide data regarding the number of fires in 2005, 

but not in 2002, so 2002 structural fires were set equal to 2005 vehicle fires. For Honolulu 

County and Kauai County, information regarding seasonal distribution was unavailable and 

therefore emissions were assumed to be equal in each season. For Maui County, data received 
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for 2005 was incomplete, only including activity to July 2005. Therefore 2005 August to 

December activity was set equal to activity in the same period in 2004. Fires were distributed to 

the island level based on population as presented in Table 1. Activity was assumed to occur 7 

days per week. 2018 emission estimates were projected based on 2005 emissions estimates and 

growth factors derived from EGAS 5.0. 

Table 19. Number of Hawaii vehicle fires by county with assumed seasonal distribution. 

Year Hawaii1 Honolulu2 Maui3 Kauai4 

2002 

Number of Vehicle Fires 144 396 115 113 

Fraction in summer 0.32 0.25 0.27 0.25 

Fraction in winter 0.33 0.25 0.30 0.25 

2005 

Number of Vehicle Fires 144 300 140 99 

Fraction in summer 0.32 0.25 0.24 0.25 

Fraction in winter 0.33 0.25 0.29 0.25 

1 Hawaii County Fire Department, 2007 

2 Honolulu County Fire Department, 2007 

3 Rossio, 2007b 

3 Kauai County Fire Department, 2007 

Table 20. Vehicle fire emission factors. 

Pollutant 

Emission Factor 

(lbs/ton burned) 

PM 100 

CO 125 

VOC 32 

NOx 4 

(EIIP, 2000b) 

Although HAPs are undoubtedly emitted during vehicle fires, there was insufficient information 

pertaining to HAP emission factors in either AP-42 or EIIP sources to estimate HAP emissions. 

Therefore, HAP emissions were not calculated for vehicle fires. 

STRUCTURAL FIRES 

Annual Emissions 

This category covers emissions from accidental structural fires that occur in residential or 

commercial structures. Emissions from structural fires were estimated based on the number of 

structural fires in 2002 and 2005 in Hawaii (Table 21), EIIP reported emission factors (Table 

22), and the average fuel loading per structural fire (1.15 tons/fire, EIIP, 2001g). Seasonal 

fractions were estimated according to the fraction of activity that occurred in each season as 

shown in Table 21. For Hawaii County fires, the Hawaii Fire Department was able to provide 

data regarding the number of fires in 2005, but not in 2002, so 2002 structural fires were set 

equal to 2005 structural fires. For Honolulu County and Kauai County, information regarding 

seasonal distribution was unavailable and therefore emissions were assumed to be equal in each 

season. For Maui County, data received for 2005 was incomplete, only including activity to July 
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2005. Therefore 2005 August to December activity was set equal to activity in the same period 

in 2004. Fires were distributed to the island level based on population as presented in Table 1. 

Activity was assumed to occur 7 days per week. 2018 emission estimates were projected based 

on 2005 emissions estimates and growth factors derived from EGAS 5.0. 

Table 21. Number of Hawaii structural fires by county with assumed seasonal distribution. 

Year Hawaii1 Honolulu2 Maui3 Kauai4 

2002 

Number of Structural Fires 46 651 127 142 

Fraction in summer 0.24 0.25 0.25 0.25 

Fraction in winter 0.26 0.25 0.24 0.25 

2005 

Number of Structural Fires 46 728 171 160 

Fraction in summer 0.24 0.25 0.25 0.25 

Fraction in winter 0.26 0.25 0.29 0.25 

1 Hawaii County Fire Department, 2007 

2 Honolulu County Fire Department, 2007 

3 Rossio, 2007b 

3 Kauai County Fire Department, 2007 

Table 22. Structural Fire emission factors. 

Pollutant 

Emission 

Factor 

PM 10.8

VOC 11

NOx 1.4

Criteria 

CO 60

Hydrogen cyanide 35.49

Formaldehyde 1.02

Acrolein 4.41

HAP 

Hydrochloric acid 15.11

(EIIP, 2001g) 

WASTEWATER 

Annual Emissions 

The treatment of wastewater involves many emissions generating processes. Procedures to 

estimate emissions from each process are detailed in EIIP Volume II, Chapter 5 and AP-42 

Section 4.3 (EPA, 1995). The AP-42 methods require the parameters of the equipment at the 

wastewater facility and substantial information about the characteristics of the wastewater 

processed. Using these methods for estimating emissions from wastewater was beyond the scope 

of this report. 

After a review of potential methodologies for estimating wastewater treatment emissions, the 

2002 NEI methodology was chosen as it is well documented and served to estimate average 

emissions typical of wastewater treatment processes. This methodology requires wastewater 

treatment plant flow rates (Table 23) be applied to emission factors (Tables 24 and 25) to obtain 
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emissions estimates. 2018 emission estimates were projected based on 2005 emissions estimates 

and growth factors derived from EGAS 5.0. 

Seasonal activity fractions were used to distribute emissions to each season. Information 

regarding seasonal flows was not available for Maui, Molokai, and Lanai island treatment plants 

and therefore was assumed to be constant year round. Emissions were assumed to occur seven 

days per week. 

Table 23. Hawaii by island wastewater treatment plant annual flow rates and seasonal activity 

fractions. 

Year Hawaii1 Oahu2 Maui3 Molokai3 Lanai3 Kauai4

2002 

Total Annual Flow (MMG) 1,027 40,107 5,318 84 111 881

Summer Fraction 0.24 0.25 0.25 0.25 0.25 0.26

Winter Fraction 0.26 0.24 0.25 0.25 0.25 0.24

2005 

Total Annual Flow (MMG) 1,157 40,488 5,318 82 104 819

Summer Fraction 0.25 0.24 0.25 0.25 0.25 0.26 

Winter Fraction 0.25 0.25 0.25 0.25 0.25 0.25 

1

 County of Hawaii Department of Public Works and Waste Management, 2007 2

 Rossio, 2007c 

3

 Couty of Maui Department of Public Works and Waste Management, 2007 4

 County of Kauai Department of Public Works and Waste Management, 2007. 

Table 24. Wastewater treatment VOC emission factor. 

Pollutant

Emission Factor 

(lbs/MMG) Source 

VOC 8.9 U.S. EPA, 2004a 

NH3 0.169 U.S. EPA, 2006 

Table 25. Wastewater treatment HAP emission factors. 

Pollutant 

Emission Factor 

(lbs/MMG) 

1,1,2,2-Tetrachloroethane 2.04E-05

1,1,2-Trichloroethane 1.36E-05

1,2,4-Trichlorobenzene 1.01E-03

1,3-Butadiene 2.92E-04

1,4-Dichlorobenzene 2.51E-03

1-Chloro-2,3-Epoxypropane 5.26E-05

2,4-Dinitrotoluene 5.60E-04

2-Nitropropane 3.40E-06

Acetaldehyde 3.61E-03

Acetonitrile 4.02E-03

Acrolein 4.47E-03

Acrylonitrile 4.50E-03

Allyl Chloride 2.26E-04

Benzene 7.84E-02
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Pollutant 

Emission Factor 

(lbs/MMG) 

Benzyl Chloride 9.51E-05

Biphenyl 8.76E-04

Carbon Disulfide 5.03E-02

Carbon Tetrachloride 1.31E-02

Chlorobenzene 5.63E-03

Chloroform 7.50E-02

Chloroprene 2.77E-04

Cresols (includes o,m,p) 1.87E-05

Dimethyl Sulfate 1.53E-05

Ethyl Acrylate 2.04E-05

Ethyl benzene 8.92E-02

Ethylene Oxide 2.58E-03

Formaldehyde 2.29E-04

Glycol Ethers 1.34E-01

Hexachlorobutadiene 8.49E-06

Hexachlorocyclopentadiene 6.79E-06

Methanol 1.33E-01

Methyl Chloroform (1,1,1-Trichloroethane) 6.56E-03

Methyl Ethyl Ketone (2-Butanone) 3.31E-02

Methyl Isobutyl Ketone (Hexone) 3.13E-02

Methyl (Methacrylate) 3.62E-03

Methyl tert-Butyl Ether 7.42E-04

Methylene Chloride 1.06E-01

N,N-Dimethylaniline 3.75E-03

Naphthalene 1.53E-02

Nitrobenzene 7.64E-05

o-Toluidine 2.04E-05

P-Dioxane 2.09E-04

Propionaldehyde 4.08E-05

Propylene Dichloride 1.34E-04

Propylene Oxide 8.53E-03

Styrene 3.18E-02

Tetrachloroethylene 4.97E-02

Toluene 1.43E-01

Trichloroethylene 3.56E-03

Vinyl Acetate 8.92E-04

Vinyl Chloride 7.81E-05

Vinylidene Chloride 4.93E-03

Xylenes (includes o, m, and p) 6.96E-01

(U.S. EPA, 2006) 
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BAKERIES 

Annual Emissions 

This category covers emissions from yeast leavening of baked goods at commercial and retail 

bakeries. There are two types of yeast dough mixing processes commonly used in bakeries: 

sponge-dough and straight dough. The sponge dough process, most commonly used at 

commercial bakeries, produces the largest amount of VOC emissions. The straight dough 

process is primarily used by retail bakeries and has much lower VOC emissions. 

To estimate annual VOC emissions from bakeries, per-capita consumption was estimated using a 

per capita consumption factor of 70 lb/person (EIIP, 1999). This emission factor was applied to 

the by island 2002 and 2005 population estimates as shown in Table 1 to estimate total 2002 and 

2005 bread consumption. It was conservatively assumed that all bread consumed was from 

sponge dough processes. An emission factor of 5 lbs VOC / 1000 lbs baked (EIIP, 1999) was 

used to relate bread production to emissions. 

2018 emission estimates were projected based on 2005 emissions estimates and growth factors 

derived from EGAS 5.0. 

Seasonal Emissions 

Average summer and winter weekday emissions were calculated by dividing total annual 

emissions by 365 in accordance with the EIIP document which suggests that bakery production 

is relatively uniform annually and daily (EIIP, 1999). 

FUEL COMBUSTION 

Annual Emissions 

State energy use data were collected from the Energy Information Administration (EIA) for 

years 1999 through 2003 (EIA, 2005a). Fuel consumption for 2002 was taken directly from EIA 

reports while fuel consumption for 2005 was estimated by using a linear regression. The fuel 

consumption data provided by the EIA is divided into five source categories and a number of 

fuels. The source categories and fuel types utilized in the area source inventory are shown in 

Table 26. 
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Table 26. Fuel combustion SCCs. 

Source Categories Fuels SCC 

Residential Coal 2104002000

Residential Natural Gas 2104006000

Residential Liquid Petroleum Gas 2104007000

Residential Distillate Oil 2104004000

Residential Kerosene 2104011000

Commercial/Institutional Coal 2103002000

Commercial/Institutional Natural Gas 2103006000

Commercial/Institutional Liquid Petroleum Gas 2103007000

Commercial/Institutional Distillate Oil 2103004000

Commercial/Institutional Residual Oil 2103005000

Commercial/Institutional Kerosene 2103011000

Industrial Coal 2102002000

Industrial Natural Gas 2102006000

Industrial Liquid Petroleum Gas 2102007000

Industrial Distillate Oil 2102004000

Industrial Residual Oil 2102005000

Industrial Kerosene 2102011000

To apportion state level residential consumption to the island level, by county 2000 Census data 

on the number of homes heating with each fuel type was used. The number of homes on an 

island heating with that fuel (HWF) was multiplied by the ratio of population on that island in 

the estimation year (2002 or 2005) to the population in 2000 (U.S. Census, 2006b and HDBEDT, 

2007a). Maui and Kauai County housing were split into island fractions according to 

populations shown in Table 1. The resulting HWF were summed for a state total HWF. The 

fraction of fuel use to be apportioned to each island was the HWF for the county divided by the 

total HWF for the state. Multiplying that ratio by state level residential consumption of that fuel 

gives by island activity. 

For industrial and commercial activity, the ratio used for apportioning was island level 

employment by NAICS to state level employment by NAICS. These figures were collected from 

Job Count by Industry made available by the State of Hawaii Department of Industrial and Labor 

Relations (HDLIR, 2007) and County Business Patterns offered by the US Census Bureau (U.S. 

Census Bureau, 2005). The NAICS codes used are shown in Table 27. 

Table 27. NAICS used to spatially allocate fuel consumption.

Commercial Industrial 

42,44,51,52,53,54,55,56,61,62,71,72,81,95,99 22,31 

Emissions of criteria pollutants were estimated by applying emission factors from AP-42 to the 

activity data. These emission factors are detailed in Table 28. 
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Table 28. AP-42 fuel consumption emission factors for criteria pollutants.

Source Fuel EF Units Pollutant Emission Factor 

VOC 5.5

CO 40 

NOx 94 

SO2 0.6

PM10 7.6

Residential Natural Gas (lb/10^6 scf) 

PM2.5 7.6

VOC 0.8

CO 7.5

NOx 13 

SO2 0.02 

PM10 0.7

Residential Liquid Petroleum 

Gas 

(lb/10^3 gal) 

PM2.5 0.7

VOC 0.713 

CO 5

NOx 18 

SO2 42.6 

PM10 2.38 

Residential Distillate Oil (lb/10^3 gal) 

PM2.5 2.13 

VOC 0.676 

CO 4.83 

NOx 17.4 

SO2 41.19 

PM10 2.29 

Residential Kerosene (lb/10^3 gal) 

PM2.5 2.05 

VOC 5.5

CO 84 

NOx 100 

SO2 0.6

PM10 7.6

Commercial/Instituti

onal 

Natural Gas (lb/10^6 scf) 

PM2.5 7.6

VOC 0.8

CO 7.5

NOx 13 

SO2 0.02 

PM10 0.7

Commercial/Instituti

onal 

Liquid Petroleum 

Gas 

(lb/10^3 gal) 

PM2.5 0.7

VOC 0.340 

CO 5

NOx 20 

SO2 42.6 

PM10 2.38 

Commercial/Instituti

onal 

Distillate Oil (lb/10^3 gal) 

PM2.5 2.13 

VOC 1.130 

CO 5

NOx 55 

SO2 314 

PM10 16.44 

Commercial/Instituti

onal 

Residual Oil1 (lb/10^3 gal) 

PM2.5 7.05 

VOC 0.327 

CO 4.82 

NOx 19.29 

SO2 42.6 

Commercial/Instituti

onal 

Kerosene (lb/10^3 gal) 

PM10 2.29 
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Source Fuel EF Units Pollutant Emission Factor 

PM2.5 2.05 

VOC 5.5

CO 84 

NOx 280 

SO2 0.6

PM10 7.6

Industrial Natural Gas (lb/10^6 scf) 

PM2.5 7.6

VOC 0.8

CO 7.5

NOx 13 

SO2 0.02 

PM10 0.7

Industrial Liquid Petroleum 

Gas 

(lb/10^3 gal) 

PM2.5 0.7

VOC 0.200 

CO 5

NOx 20 

SO2 42.6 

PM10 2.3

Industrial Distillate Oil (lb/10^3 gal) 

PM2.5 1.55 

VOC 0.280 

CO 5

NOx 55 

SO2 314 

PM10 22.2 

Industrial Residual Oil1 (lb/10^3 gal) 

PM2.5 15.0 

VOC 0.192 

CO 4.82 

NOx 19.29 

SO2 42.6 

PM10 2.22 

Industrial Kerosene (lb/10^3 gal) 

PM2.5 1.49 

1

 Fuel sulfur content of 2% assumed per HCAB requirement 2

 Kerosene emission factors based on distillate oil emissions factors, multiplied by 130/140 to account for 

the heating value difference between kerosene and distillate oil fuel for VOC, CO, NOx, PM10, and 

PM2.5. 

Annual emissions of hazardous air pollutants were calculated using the same activity data as 

used for criteria pollutants. Emission factors were drawn from AP-42 and from the 

documentation for the 1999 NEI of HAPs (U.S. EPA, 1995 and U.S. EPA, 2003b). No emission 

factors have been developed for propane LPG. Based on a recommendation in the AP-42 

background documentation, emissions were generated using emission factors for natural gas to 

obtain an order of magnitude estimate (U.S. EPA, 1995). 

In addition to the above assumptions, it was necessary to use one set of emission factors to 

estimate emissions for all fuels from both institutional/commercial and residential combustion. 

The equipment used in these two different categories probably results in different emissions; 

however, until more emission factors are developed, this was judged the best available 

alternative. Similarly, emission factors for the industrial combustion of distillate oil were not 

available, so the emission factors for commercial combustion were used. 
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2018 emission estimates were projected based on 2005 emissions estimates and growth factors 

derived from EGAS 5.0. 

Point Source Reconciliation 

The area source fuel combustion emissions estimates utilized the estimated total fuel 

consumption in Hawaii as its fundamental measure of activity. In fact, much of that fuel was 

consumed by industrial facilities and to a lesser extent, commercial facilities that are represented 

in the point source emission inventory. Therefore, to eliminate double counting of emissions 

from fuel combustion, it was necessary to subtract fuel consumed by these facilities from the 

area source emissions calculation. 

NIF files that contain 2002 and 2004 point source emissions and activity were received from 

LAKES software (Yankova, 2007). As the 2005 point source NIF file was not available at the 

time of the preparation of these emission estimates, 2004 point source fuel consumption was 

used instead. To determine the extent of double counting with the point source inventory, that 

inventory was queried to extract the point source facilities with combustion processes. The 

resulting list of facilities was further reduced by eliminating electric generation facilities and 

facilities that combusted fuel(s) not included in area source activity. These steps were taken to 

account for the fact that the area source emissions calculations did not include fuel used by 

electric generation facilities or such process byproducts. For the facilities that remained, fuel 

consumed by those point sources was extracted from the area source fuel combustion emissions 

calculation. Per EIA staff (Taylor, 2007), facilities with NAICS codes of 11, 21, 23, and 31-33 

were included in statewide fuel consumption as industrial sources, while non-EGU facilities with 

other NAICS codes were included as commercial fuel consumption. The facilities for which fuel 

consumption was determined and reconciled are listed in Table 29 below. 

Table 29. Point sources reconciled with area source fuel combustion. 

Sector County Facility ID Facility Name 

2002

Industrial 15009 00001 HC & S - Puunene Mill 

 15003 00015 Ameron Kapaa Quarry 

 15009 00013 Maui Pineapple Co. 

Commercial 15003 00516 Tesoro - Barber's Pt. Harbor Loading Facility 

 15003 00082 HPOWER 

2005

Industrial 15009 00001 HC & S - Puunene Sugar Mill 

 15009 00010 Ameron Hawaii Camp 10 Quarry 

 15003 00015 Ameron Hawaii Kapaa Quarry 

 15009 00013 Maui Pineapple Co. 

Commercial 15003 00082 HPOWER 

Seasonal Emissions 

Calculation of the summer/winter average day and weekday emissions were performed in two 

ways. For residential consumption, activity occurs seven days per week. As differences in 

temperatures vary little by season, summer and winter emissions were assumed to be annual 

emissions divided by 365 days per year. 
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The allocation for commercial/institutional and industrial combustion was based on the 

assumption of constant emissions in each season and the use of standard EPA temporal 

allocation profiles, which estimate 73.5% of activity occurs on weekdays. Average summer and 

winter weekday emissions were calculated by dividing annual emissions by the product of 365 

days per year and 73.5%. Summer and winter average day emissions were assumed to be annual 

emissions divided by 365 days per year. Weekday emissions were estimated by dividing the 

average day emissions by the fraction of activity occurring on weekdays. 

PESTICIDES 

Pesticide emissions are those resulting from the application of pesticides by individual 

consumers or by commercial agricultural users. EIIP methodology to estimate emissions for 

pesticide usage requires information related to the type and quantities of pesticide used to 

determine activity levels and emission factor estimates. Regarding pesticide activity in Hawaii, 

the Hawaii Department of Agriculture and the University of Hawaii Agriculture Extension 

Office both indicated that data regarding pesticide usage in the State of Hawaii was very limited. 

No source of Hawaii specific data were available for estimating pesticide usage activity. In 

absence of suitable local data regarding the quantities and types of pesticides used, assumptions 

regarding pesticide activity were required to estimate emissions associated with pesticides. 

After a review methodologies for estimating pesticide emissions, the 2002 NEI methodology 

(EPA, 2006) was chosen as it was the most recent source of data available amenable to emissions 

calculations. In this methodology, a mass balance approach was used to estimate nationwide 

emissions associated with nationwide pesticide consumption estimates. Nationwide emissions 

were allocated to the county level based equally on two ratios: 1) county to nationwide 

employment data related to crop production (NAICS 111), and 2) county to nationwide 

population. For this inventory, commercial related pesticides as calculated in the NEI have been 

set to zero to avoid double counting with FIFRA related consumer solvent emissions estimates. 

Commercial agricultural related emissions were allocated to the island level and also projected 

from 2002 to 2005 for commercial agricultural use by island level agricultural employment 

(HDLIR, 2007). 

Consistent with EPA, 2006, hazardous air pollutant emission estimates were based on a 

speciation profile, which indicated benzene as the only hazardous air pollutant at 26.15 percent 

of total VOC emissions. 

As described in the agricultural tilling technical memo, activity for most Hawaii crops is 

distributed uniformly throughout the year, therefore pesticide emissions were assumed to be 

constant year round. 

Though compiling the necessary pesticide activity data were beyond the scope of this project, 

estimating pesticide emissions using standard methodology (EIIP, 2001d) with local usage data 

would enhance future emission inventory efforts as pesticides associated with agricultural 

production could be an important source of VOC and/or hazardous air pollutant emissions. 

2018 emission estimates were projected based on 2005 emissions estimates and growth factors 

derived from EGAS 5.0. 
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LANDFILLS 

Emissions from landfills were calculated using the equations from AP-42 (U.S. EPA, 1995). The 

minimum information required to use those equations is: 

• Year landfill first accepted waste 

• Year landfill stopped accepting waste 

• Annual waste acceptance (Mg/year) 

Information was obtained from the HCAB regarding needed characteristics to implement AP-42 

equations for all landfills in Hawaii as shown in Table 30. 

Table 30. Hawaii Landfill Characteristics. 

Island Facility Type Open 

Date 

Waste 

Accepted

2002 

(tons)1

Waste 

Accepted

2005 

(tons)1

Waste 

in Place 

(CY) 

Waste 

in 

Place 

(million 

CY) 

Design 

Capacity 

(Mg) 

Kauai Kekaha MSW 1993 76,718 89,161 1,769,000 1.77 2,202,740

Oahu Waimanalo Gulch* MSW 1989 328,899 387,756 5,532,300 5.53 8,412,596

Oahu Kaneohe MCBH MSW 1974 10,000 10,000 660,000 0.66 742,705

Maui Central Maui MSW 1987 144,694 176,836 3,230,770 3.23 2,470,247

Maui Hana Landfill MSW 1969 1,460 1,900 164,250 0.16 250,000

Molokai Molokai MSW 1992 7,300 6,260 173,600 0.17 170,585

Molokai Kalaupapa MSW 1980 26 26 No data No data No data 

Lanai Kanai MSW 1969 2,556 4,220 194,180 0.19 115,870

Hawaii West Hawaii MSW 1994 99,798 135,279 2,181,818 2.18 5,858,494

Hawaii South Hilo MSW 1960 73,946 83,522 2,450,000 2.45 1,111,075

*Per HCAB, flaring controls have been incorporated beginning in August, 2005. 

In addition to the aforementioned parameters, it was also necessary to adopt the EPA’s 

recommended default values for methane generation rate, methane generation potential and 

temperature as no local information was available. It was then possible to use the AP-42 

formulas to calculate methane, NMOC and air toxic emissions. Not knowing which landfills 

were used for co-disposal, the AP-42 default concentrations of benzene, NMOC and toluene for 

“No or Unknown co-disposal” were used. 

2018 emission estimates were projected based on 2005 emissions estimates and growth factors 

derived from EGAS 5.0. 

Emissions from landfills are assumed to be constant year-round, without any day-of-week 

variation (EIIP, 2001d). 
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COMMERCIAL COOKING 

Commercial cooking emission estimates were developed based on methodology implemented in 

EPA’s 2002 NEI. Local information was sought to replace or revise EPA methodology, but no 

sources of data suitable for such modifications were identified. The methodology used in 

estimating 2002 commercial cooking emissions was to combine activity, pounds of food cooked, 

with emission factors as described in detail in EPA, 2006. 

Activity associated with meat cooking, in pounds of meat cooked, was estimated based on the 

number of restaurants by county and establishment type in 2002 from Dun & Bradstreet industry 

classifications as shown in Table 31 (EPA, 2006). The number of restaurants by island in 2002 

was obtained by allocating restaurant establishments by island level food service employment. 

The pounds of meat cooked in each SCC was obtained by multiplying the number of restaurants 

of each type by the percent of each restaurant with each equipment type, the average number of 

pieces of equipment by type at each restaurant, and the average pounds of meat cooked by 

equipment type and meat type. EPA (2006) parameters used in these estimates are shown in 

Table 32. 

Consistent with EPA (2006), to estimate activity associated with shoestring potato frying, the 

total tons of nationwide potatoes cooked by “Fast Food” establishments and “Other” restaurant 

types were allocated to the county level based on the fraction of nationwide “Fast food” 

establishments and nationwide “Other” restaurants establishments, respectively. 

Table 31. Number of restaurants in 2002 by category and county (EPA, 2006). 

Restaurant Category Hawaii Honolulu Kauai Maui 

Ethnic 78 448 37 84

Family 11 44 5 11

Fast Food 103 391 42 70

Seafood 3 16 3 10

Steak & Barbecue 1 43 5 10

Totals 196 942 92 185

Table 32. Parameters used to estimate meat cooking activity (EPA, 2006). 

Equipment 

Type

ChainDriven 

Charbroilers 

Clamshell 

Griddles 

Deep-Fat 

Fryers 

Flat 

Griddles 

Under-Fired 

Charbroilers

SCC 2302002100 2302003200 2302003000 2302003100 2302002200 

Average pounds of meat cooked per piece of equipment per week by meat type 

Hamburger 798 1314 274 362 270

Other 0 0 274 57.5 41.5

Pork 57.6 118 58.6 112 148

Poultry, 

skinless 266 108 208 111 179

Poultry, with 

skin 147 113 365 88 144

Seafood 119 632 159 92.1 143

Steak 236 94 181 166 180

 
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Equipment 

Type

ChainDriven 

Charbroilers 

Clamshell 

Griddles 

Deep-Fat 

Fryers 

Flat 

Griddles 

Under-Fired 

Charbroilers

SCC 2302002100 2302003200 2302003000 2302003100 2302002200 

Percent of restaurants with equipment by restaurant type 

Ethnic 3.5 4 81.9 62.7 47.5

Family 10.1 1.4 91.4 82.9 60.9

Fast Food 18.6 14.7 96.8 51.9 30.8

Seafood 0 10.5 100 36.8 52.6

Steak & 

Barbecue 6.9 0 82.8 89.7 55.2

Average number of pieces of equipment for restaurants with equipment by restaurant type 

Ethnic 1.62 1.8 1.63 1.88 1.54

Family 1.71 0 2.34 2.03 1.29

Fast Food 1.07 2.09 3.1 1.43 1.58

Seafood 0 1.5 2.47 1.11 1.1

Steak & 

Barbecue 0 0 2.42 1.35 1.63

Cooking activity was projected from 2002 to 2005 based on by island employment in “Food 

Services and Drinking Places” in HDLIR (2007) employment data. For Molokai and Lanai, 

where such growth factors were unable to be calculated due to the way employment was reported 

for these island in HDLIR (2007), statewide growth estimates were used. 2018 emission 

estimates were projected based on 2005 emissions estimates and growth factors derived from 

EGAS 5.0. 

Emissions for commercial cooking were estimated by multiplying emission factors from EPA 

(2006) shown in Table 33 to the estimated pounds of food cooked per island by food type and 

SCC. 

Emissions from commercial cooking were assumed to be constant year-round, without any dayof-week variation (EIIP, 2001d). 

Table 33. Criteria Pollutant Emission Factors (pounds pollutant per ton food cooked). 

Equipment 

Type (SCC) Food Type VOC CO 

PM10-

PRI 

PM25-

PRI 

Hamburger 4.54 16.58 14.8 14.6 

Pork 3.64 9.68 21.0 19.8 

Poultry, skinless 3.64 9.68 21.0 19.8 

Poultry, with skin 3.64 9.68 21.0 19.8 

Seafood 0.76 - 6.6 6.4 

Chain-Driven 

Charbroilers 

(230200210) Steak 4.54 16.58 14.8 14.6 

Hamburger 7.88 27.44 65.4 63.8 

Other 1.72 9.94 34.4 33.6 

Pork 3.64 9.68 21.0 19.8 

Poultry, skinless 3.64 9.68 21.0 19.8 

Poultry, with skin 3.64 9.68 21.0 19.8 

Clamshell 

Griddles 

(230200220) 

 

 

 

 Seafood 0.76 - 6.6 6.4 
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Equipment 

Type (SCC) Food Type VOC CO 

PM10-

PRI 

PM25-

PRI 

 

 Steak 1.72 9.94 34.4 33.6 

Pork 0.24 - - - 

Poultry, skinless 0.24 - - - 

Poultry, with skin 0.24 - - - 

Seafood 0.28 - - - 

Deep-Fat 

Fryers 

(230200300) 

 

 

 

 Shoestring potatoes 0.42 - - - 

Hamburger 0.14 0.76 10.0 7.6 

Other 0.14 0.76 10.0 7.6 

Pork 0.80 0.90 - - 

Poultry, skinless 0.80 0.90 - - 

Poultry, with skin 0.80 0.90 - - 

Seafood 0.22 - - - 

Flat Griddles 

(230200310) 

 

 

 

 

 

 Steak 0.14 0.76 10.0 7.6 

Hamburger 0.02 - 1.7 1.4 

Pork 0.11 - - - 

Poultry, skinless 0.11 - - - 

Poultry, with skin 0.11 - - - 

Seafood 0.03 - - - 

Under-Fire 

Charbroilers 

(230200320) 

 

 

 

 

 Steak 0.02 - 1.7 1.4 

RESIDENTIAL WOOD CUMBUSTION 

The contribution of emissions from residential wood combustion was assumed to be negligible. 

EIA (2005a) showed no residential wood combustion activity for the state of Hawaii. Due to 

extremely mild temperatures that prevail throughout most of Hawaii, there is not much use of 

wood for home heating: according to the 2000 U.S. Census (2006b), only 1,957 housing units, 

or 0.5% of all homes in Hawaii are heated by wood. Considering the small number of homes 

heating with wood and the mild temperatures in Hawaii, it is unlikely that residential wood 

combustion would produce significant emissions relative to other sources included in this 

emissions inventory. Local sources of residential wood combustion activity were sought; 

however, the lack of centralized distribution of wood for residential home heating use prevented 

further ascertainment of whether local data would support the exclusion of residential wood 

combustion emissions from this emissions inventory. 

TRAFFIC MARKINGS 

Annual Emissions 

To estimate emissions from traffic markings, coating usage data was queried from a list of 

contractors supplied by HCAB who would apply such coatings. Each company provided the 

total amount of traffic marking applied in 2002 and 2005 for water-based coatings, solvent-based 
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coatings, and thinner. Usage was summed over all companies and allocated to the island level 

based on the fraction of population on each island as shown in Table 3. 

The EIIP document (EIIP, 1997c) provides VOC emission factors for volume-use estimates of 

water-based and solvent-based traffic markings. Thinner product specification data was used to 

estimate a VOC emission factor for the small quantity of this substance used. The activity data 

for each type of product was multiplied by the appropriate emission factor to determine VOC 

emissions resulting from traffic marking for each island. 

Table 34. Traffic marking VOC Emission Factors. 

Date Type Water-based Solvent Based Thinner Units 

Volume-use 0.72 3.64 1.25 lb/gal 

HAP emissions were determined using the same basic process. Consumption estimates were 

multiplied by the EIIP reported volume percent and density of each HAP in the average coating 

(shown in Table 35) to estimate the emission of that HAP. The HAP speciation profile is based 

on a sales-weighted average traffic paint from a 1991 survey. The use of this profile may result 

in some inaccuracy in the representation of State of Hawaii emissions, however, no alternative 

profile offering greater accuracy was identified. 

Table 35. HAP speciation profile for traffic markings. 

HAP Volume % Density (lb/gal)

Carbon tetrachloride 0.009 12.19

Cumene 0.002 7.19

Ethylbenzene 0.009 7.24

Ethylene glycol 0.086 9.31

Glycol ethers 0.04 7.01

Methyl ethyl ketone 1.514 6.89

Methyl isobutyl ketone 0.002 6.71

Methyl methacrylate 0.044 7.84

Naphthalene 0.002 9.55

Propylene oxide 0.115 6.93

Styrene 0.277 7.55

Toluene 6.914 7.23

Xylenes (mixed isomers) 0.499 7.18

(EIIP, 1997c)

2018 emission estimates were projected based on 2005 emissions estimates and growth factors 

derived from EGAS 5.0. 

Seasonal Emissions 

Emissions from traffic marking are assumed to be constant year-round. Traffic marking is 

assumed to occur five days per week as indicated in the EIIP document (EIIP, 1997c). 
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Construction Dust 

Annual Emissions 

Construction dust emissions were estimated using the equation below from EPA (2006): 

E = EF * A * D * 24/PE * S/9% 

where: E = construction PM10 emissions 

 EF = emission factor (mass per area per duration) 

 A = disturbed area 

 D = duration of construction 

 PE = precipitation-evaporation constant 

 S= percent dry silt content 

Construction emission factors by type along with construction duration and the fraction of PM10 

emissions that are PM2.5 are shown in Table 36; all of these data were derived from EPA 

(2006). 

Table 36. Construction durations and emission factors. 

Construction Activity Duration 

(months) 

Emission Factor 

(tons PM10/ 

acre/month) 

Percent of 

Emissions 

as PM2.5

Residential: 1-Unit Structures 6 months 0.032 10 

Residential: 2-Unit Structures 6 months 0.032 10 

Residential: Apartments 12 months 0.11 10 

Non Residential Building 11 months 0.19 10 

Road Construction 12 months 0.42 10 

The precipitation evaporation constant was assumed to be 57.9 per EPA (2006) and the percent 

dry silt content was estimated by island based on the USGS LULC Database silt content data as 

described in previously submitted technical memoranda for agricultural tilling dust emissions 

(Grant et. al. 2009). 

2018 emission estimates were projected based on 2005 emissions estimates and growth factors 

derived from EGAS 5.0. 

Residential 

Residential construction acreage was estimated using data on new single-family, new duplex, 

and new apartment units authorized for construction as summarized by county in the Hawaii 

databook (HDBEDT, 2009a). The average acreage per new single family or 1-unit structure (1/7 

acre) was taken from the WRAP Fugitive Dust Handbook (Countess Environmental, 2006) for 

heavily populated counties. Average acreages per new duplex or 2-unit structure (1/3 acre) and 

per new apartment (1/2 acre) were taken from the EPA (2006). 
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Per Hawaii Clean Air Branch input (HCAB, 2009) it was assumed that the fraction of new 

houses with basements is negligible. 

Allocation of activity from the county to island level for Maui was made according to island 

population. It was also assumed that all new construction in Kauai County occurred on the 

island of Kauai. 

Non-residential 

Non-residential construction acreage was estimated using a combination of local data for dollar 

valuation of construction as available in HDBEBT (2009b) and dollar valuation to acreage factor 

(1.55 acres/million dollars) from the EPA (2006). The county of Kauai non-residential 

construction valuation was not reported. The construction valuation for Kauai was estimated by 

applying the ratio of dollar valuation for all other counties to population in all other counties to 

Kauai County population. 

Allocation of activity from the county to island level for Maui County was estimated according 

to island population. It was assumed that all new construction in Kauai County occurred on the 

island of Kauai. 

Road Construction 

Road construction acreage was estimated using capital outlay of road construction reported by 

the Federal Highway Administration in the 2002 and 2005 Highway Statistics publications, 

combined with an estimate of dollars spent per mile of road construction as estimated in EPA 

(2006) ($4 million/mile for freeways and interstate and $1.9 million/mile for all other roadway 

types), and estimates of acres disturbed per mile of road construction. Per EPA (2006), the 

following estimates were used for acres disturbed per mile of road construction: 

• Interstate, urban and rural; Other arterial, urban - 15.2 acres/mile 

• Other arterial, rural - 12.7 acres/mile 

• Collectors, urban - 9.8 acres/mile 

• Collectors, rural - 7.9 acres/mile 

As in EPA (2006), total state acreage was allocated to counties based on the percentage of total 

residential building starts in each county (HDBEDT, 2009a). 

Allocation of activity from the county to island level for Maui was made according to island 

population. It was assumed that all new construction in Kauai County occurred on the island of 

Kauai. 

Seasonal Emissions 

It was assumed that construction would be equally distributed throughout the year. Based on the 

daily allocation used by EPA’s NONROAD model, 83.3 percent of weekly activity will be 

assumed to occur Monday through Friday. 
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RESULTS 

ENVIRON has provided linked area source emissions summary spreadsheets that provide 

annual, summer weekday, winter weekday, summer average day, and winter average day for 

2002, 2005 and 2018 emissions for all area sources by SCC, for all pollutants. Figures 1 through 

6 graphically portray the 2002 annual emissions by source category for each pollutant. 

Significant contributors to area source VOC emissions are consumer products (26%), gasoline 

distribution (24%), industrial surface coating (17%), architectural surface coating (11%), dry 

cleaning (7%), and graphic arts (5%). Area source NOx emissions are only from fuel 

combustion. CO emissions are dominated by fuel combustion (60%) and commercial cooking 

(34%). Area source SOx emissions are virtually all associated with fuel combustion. PM10 area 

source emissions are dominated by construction (94%). PM2.5 area source emissions are 

dominated by construction (65%) and commercial cooking (25%). 

Degreasing

3% Dry Cleaning

7%

Graphic Arts

5%

Industrial Surface Coating

17%

Pesticides

1%

Bakeries

1%

Consumer Products

26%

Gasoline Distribution

24%

Wastewater Treatment

1%

Architechtural Coatings

11%

Landfills

0%

Commercial Cooking

1%

Auto Body Refinishing

2%

Figure 1. Hawaii statewide 2002 area source VOC emissions by source category. 
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Fuel Combustion

100%

Figure 2. Hawaii statewide 2002 area source NOx emissions by source category. 

Fuel Combustion

60%

Commercial Cooking

34%

Structural Fires

5%

Vehicle Fires

2%

Figure 3. Hawaii statewide 2002 area source CO emissions by source category. 
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Fuel Combustion

100%

Figure 4. Hawaii statewide 2002 area source SOx emissions by source category. 

Commercial Cooking

4%

Construction Dust

94%

Fuel Combustion

2%

Figure 5. Hawaii statewide 2002 area source PM10 emissions by source category. 
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Commercial Cooking

25%

Fuel Combustion

9%

Construction Dust

65%

Figure 6. Hawaii statewide 2002 area source PM2.5 emissions by source category. 

Gasoline Distribution

21%

Graphic Arts

14%

Pesticides

50%

Wastewater Treatment

2%

Architechtural Coatings

2%

Commercial Cooking

8%

Auto Body Refinishing

4%

Figure 7. Hawaii statewide 2002 area source emissions by source category. 
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VOC emissions by category for 2002, 2005, and 2018 are presented in Figure 8. Changes in 

emissions between 2002 and 2005 are generally reflective of changes in activity data. Changes 

in emissions from 2005 to 2018 are reflective of EGAS 5.0 growth factors, with the exception of 

gasoline distribution, which includes Stage II refueling emissions as well as bulk terminal 

emission changes due to the usage of gasoline with 10% ethanol. 2018 Stage II refueling 

emissions were estimated based on future year HDOT VMT estimates and MOBILE6 emission 

factors. The EGAS model generates growth factors at the SCC level, so for categories that 

include greater than one SCC, the change in emissions would be based on the sum of SCC 

growth factors applied to 2005 by SCC emissions. Percent changes in other pollutants were 

consistent with changes in VOC emissions. Gasoline distribution emissions decrease from 2002 

to 2005 and from 2005 to 2018 as a result of the phase-in of Stage II on-board vapor recovery 

systems which began for gasoline fueled vehicles in model year 1998. 

0

1000

2000

3000

4000

5000

6000

Auto Body Refinishing

Degreasing

Dry Cleaning

Fuel Combustion

Gasoline Distribution

Graphic Arts

Industrial Surface Coating

Landfills

Traffic Marking

Architechtural Coatings

Cutback Asphalt

Pesticides

Bakeries

Vehicle Fires

Structural Fires

Wastewater Treatment

Consumer Products

Commercial Cooking

VOC Emissions (tons/year)

2002

2005

2018

Figure 8. VOC emissions by year and area source category. 
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References

EIIP. 1996a. Graphic Arts, Volume III: Chapter 7. Emission Inventory Improvement Program, 

Areas Sources Committee. November. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii07.pdf. 

EIIP. 1996b. Dry Cleaning, Volume III: Chapter 4. Emission Inventory Improvement Program, 

Area Sources Committee. May. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii04fn2.pdf. 

EIIP. 1997a. Industrial Surface Coating, Volume III: Chapter 8. Emission Inventory 

Improvement Program, Area Sources Committee. September. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii08.pdf. 

EIIP. 2000a. Auto Body Refinishing, Volume III: Chapter 13. External Review Draft. 

Emission Inventory Improvement Program, Area Sources Committee. May . Internet 

address: http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii13_march2005.pdf. 

Federal Register. 1998a. National Volatile Organic Compound Emission Standards for 

Automobile Refinish Coatings. Final rule. 63 FR 48806-48819. 

HDBEDT. 2007a. 2002 State of Hawaii Databook, Section 1: Population, Table 01.06. Hawaii 

State Department of Business, Economic Development, and Tourism. Accessed March 

2007. Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT. 2007b. 2005 State of Hawaii Databook, Section 1: Population, Table 01.06. Hawaii 

State Department of Business, Economic Development, and Tourism. Accessed March 

2007. Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT. 2007c. 2005 State of Hawaii Databook, Section 1: Population, Table 01.05. Hawaii 

State Department of Business, Economic Development, and Tourism. Accessed March 

2007. Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT. 2009a. 2007 State of Hawaii Databook, Section 21: Population, Table 21.07. Hawaii 

State Department of Business, Economic Development, and Tourism. Accessed March 

2007. Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/ 

HDBEDT. 2009b. 2005 State of Hawaii Databook, Section 21: Population, Table 21.02. Hawaii 

State Department of Business, Economic Development, and Tourism. Accessed March 

2007. Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/

Rossio. 2007a. Personal communication regarding on-site incineration and open burning in 

Hawaii. Hawaii State Department of Health, Clean Air Branch, Honolulu, Hawaii. 

February. 

U.S. Census Bureau, 2005. County Business Patterns. U.S. Census Bureau, Washington D.C. 

Internet address: http://www.census.gov/epcd/cbp/view/cbpview.html. 
 41 

Golden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA 

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc

U.S. Census Bureau. 1998. 1997 NAICS and 1987 SIC Correspondence Tables. U.S. Census 

Bureau, Washington D.C. Internet address: 

http://www.census.gov/epcd/www/naicstab.htm. 

EPA. 2003b. Documentation for the Final 1999 Nonpoint Area Source National Emission 

Inventory for Hazardous Air Pollutants (Version 3). U.S. Environmental Protection 

Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North 

Carolina. August 26. Internet address: 

http://www.epa.gov/ttn/chief/net/1999inventory.html#final3haps. 

U.S. Census Bureau. 2003. Current Industrial Reports – Paint and Allied Products: 2002. 

MA325F(02)-1. U.S. Census Bureau, Washington D.C. Internet address: 

http://www.census.gov/cir/www/325/ma325f.html. 

U.S. Census Bureau. 2006. Current Industrial Reports – Paint and Allied Products: 2005. 

MA325F(02)-1. U.S. Census Bureau, Washington D.C. Internet address: 

http://www.census.gov/cir/www/325/ma325f.html. 

U.S. Census Bureau, 2006a. Table 1: Annual Estimates of the Population for the United States, 

Regions, and States and for Puerto Rico: April 1, 2000 to July 1, 2006. Population 

Division, U.S. Census Bureau, Washington, D.C. Internet address: 

http://www.census.gov/popest/states/NST-ann-est.html.

U.S. Census Bureau, 2006b. DP-4. Profile of Selected Housing Characteristics: 2000. U.S. 

Census Bureau, Washington, D.C. Internet address: 

http://quickfacts.census.gov/qfd/states/15000lk.html.

Federal Register. 1998b. National Volatile Organic Compound Emission Standards for 

Architectural Coatings. Final rule. 63 FR 48848-48887. September 11, 1998. 

EIIP. 1995. Architectural Surface coating, Volume III: Chapter 3. Emission Inventory 

Improvement Program, Area Sources Committee. November. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/archsfc.pdf.

EIIP. 1997b. Solvent Cleaning, Volume III: Chapter 6. Emission Inventory Improvement 

Program, Area Sources Committee. September. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii06fin.pdf. 

ARB. 1996. Solvent Cleaning/Degreasing Source Category Emission Inventory. California Air 

Resources Board, Research Division, Sacramento, California. August. Internet address: 

ftp://ftp.arb.ca.gov/carbis/research/apr/past/93-341a.pdf. 

Romanowski. 2007. Personal communication regarding cutback asphalt use in Hawaii. Hawaii 

Asphalt Paving Institute, State Representative. Hawaii State Department of Health, Clean 

Air Branch, Honolulu, Hawaii. March. 
 42 

Golden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA 

 Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com 

J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc

Matsumoto. 2007. Personal communication regarding cutback asphalt use in Hawaii. Grace 

Pacific Corp., Honolulu, Hawaii. March. 

EIIP. 1996a. Graphic Arts, Volume III: Chapter 7. Emission Inventory Improvement Program, 

Areas Sources Committee. November. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii07.pdf. 

Federal Register. 1998. National Volatile Organic Compound Emission Standards for 

Consumer Products. Final rule. 63 FR 48819-48847. September 11, 1998. 

EIIP. 2001c. Asphalt Paving. Volume III: Chapter 17. Emission Inventory Improvement 

Program, Area Sources Committee. January. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii17_apr2001.pdf. 

Grant, J., A. Pollack, and R. Parikh. 2007. Onroad Emission Estimates for the Hawaiian 

Islands. Technical Memorandum prepared for Lakes Environmental Software and 

Hawaii Clean Air Branch. January. 

EIIP. 2001f. Gasoline Marketing (Stage I and Stage II). Volume III. Chapter 11. Emission 

Inventory Improvement Program, Area Sources Committee. January. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii11_apr2001.pdf. 

EIIP. 2000b. Area Sources Category Method Abstract – Vehicle Fires. Emission inventory 

Improvement Program, Area Sources Committee. May. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/vehclf13.pdf. 

EIIP. 2001g. Area Sources Category Method Abstract – Structural Fires. Emission inventory 

Improvement Program, Area Sources Committee. May. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/vehclf13.pdf. 

EPA. 1995. Compilation of Air Pollution Emission Factors (AP-42) – Volume I: Stationary 

Point and Area Sources, Fifth Edition. U.S. Environmental Protection Agency, Office of 

Air Quality Planning and Standards, Research Triangle Park, North Carolina. January. 

Internet address: http://www.epa.gov/ttn/chief/ap42.

EPA. 2004a. Factor Information Retrieval (FIRE) program. Version 6.25. SCC 50100701. 

EPA. 2006. Documentation for the Draft 2002 Nonpoint Source National Emission Inventory 

for Criteria and Hazardous Air Pollutants (February 2006 Version). Prepared by E.H. 

Pechan & Associates, Inc. EPA Contract No. 68-D-02-063. Internet address: 

http://www.epa.gov/ttn/chief/net/2002inventory.html. 

EIIP. 1999. Area Source Category Method Abstract – Bakeries, Volume III: Chapter 3. 

Emission inventory Improvement Program, Area Sources Committee. July. Internet 

address: http://www.epa.gov/ttn/chief/eiip/techreport/volume03/bakeries.pdf. 

HDLIR, 2007. Job Count by Industry. State Department of Labor & Industrial Relations' 

Research & Statistics Office. Internet Address: 
 43 

Golden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA 

 Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com 

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http://www.hiwi.org/article.asp?PAGEID=&SUBID=&ARTICLEID=515&SEGMENTI

D=2 

EPA. 2003b. Documentation for the Final 1999 Nonpoint Area Source National Emission 

Inventory for Hazardous Air Pollutants (Version 3). U.S. Environmental Protection 

Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North 

Carolina. August 26. Internet address: 

http://www.epa.gov/ttn/chief/net/1999inventory.html#final3haps. 

EPA. 1995. Compilation of Air Pollution Emission Factors (AP-42) – Volume I: Stationary 

Point and Area Sources, Fifth Edition. U.S. Environmental Protection Agency, Office of 

Air Quality Planning and Standards, Research Triangle Park, North Carolina. January. 

Internet address: http://www.epa.gov/ttn/chief/ap42.

EPA. 2002d. Source Category Code / Temporal Allocation Cross Reference File. U.S. 

Environmental Protection, Office of Air Quality Planning and Standards, Research 

Triangle Park, North Carolina. April. Internet address: 

http://www.epa.gov/ttn/chief/emch/temporal/index.html. 

Hawaii State Department of Taxation. 2007. Monthly Tax Collection Reports: Liquid Fuel Tax 

Base and Collections. Honolulu, Hawaii. Internet address: 

http://www.state.hi.us/tax/a5_3txcolrptarchive.htm. 

Kauai County Fire Department, 2007. Personal Communication Regarding the Number of 

Structural and Vehicle Fires in 2002 and 2005. Kauai, Hawaii. 

Hawaii County Fire Department, 2007. Personal Communication Regarding the Number of 

Structural and Vehicle Fires in 2005. Hilo, Hawaii. 

Rossio, 2007b. Personal Communication Regarding Maui County Fires from Jan 2002 to July 

2005. Hawaii Department of Health, Clean Air Branch. Honolulu, Hawaii. 

Honolulu County Fire Department, 2007. Personal Communication Regarding the Number of 

Structural and Vehicle Fires in 2002 and 2005. Honolulu, Hawaii. 

County of Maui Department of Public Works and Waste Management, 2007. Personal 

Communication from John Kostman. March 20, 2007. 

County of Hawaii Department of Public Works and Waste Management, 2007. Tindall, 2007. 

Personal Communication from Lewis Tindall. March 15 2007. 

Rossio. 2007c. Personal communication regarding Oahu Wastewater Treatment Quantities. 

Hawaii State Department of Health, Clean Air Branch, Honolulu, Hawaii. April 27, 

2007. 

County of Kauai Department of Public Works and Waste Management, 2007. Personal 

Communication from Valentino Reyna. April, 2007. 
 44 

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EIIP. 2001d. Pesticides – Agricultural and Nonagricultural. Volume III: Chapter 9. Emission 

Inventory Improvement Program, Area Sources Committee. June. Internet address: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii09_jun2001.pdf. 

Taylor. 2007. Personal communication with Yvonne Taylor. National Energy Information 

Center for Energy Information. Energy Infomration Administration, Washington D.C. 

June. 

Yankova. 2007. Personal communication with Inna Yankova. LAKES Environmental 

Software. March. 

EPA. 2002a. Speciate 3.2. Profile 2570. Profile added July 1, 1999.

EPA. 2002b. Speciate 3.2. Profile 2402. Profile added July 1, 1999.

EPA. 2002c. Speciate 3.2. Profile 1194. Profile added January 5, 1989. 

EPA. 1994. Gasoline Distribution Industry (Stage I) – Background Information for Proposed 

Standards. EPA-453/R-94-002a. U.S. Environmental Protection, Office of Air Quality 

Planning and Standards, Research Triangle Park, North Carolina. January.

HCAB. 2009. Personal Communication with Marianne Rossio. Hawaii Clean Air Branch. 

August. 

Grant et.al. 2009. Technical Memorandum: Agricultural Tilling Emissions Estimates for the 

Hawaiian Islands. J. Grant, A. Pollack, and R. Parikh. ENVIRON International 

Corporation. August. 

Countess Environmental. 2006. Wrap Fugitive Dust Handbook. September. 
APPENDIX H 

AMMONIA EMISSION ESTIMATES FOR THE STATE OF HAWAII 
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TECHNICAL MEMORANDUM 

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: Gerard Mansell and Rajashi Parikh 

Date: 6 June 2007 (Revised 21 December 2009) 

Subject: Ammonia Emission Estimates for the State of Hawaii 

Introduction 

Ammonia emissions for the State of Hawaii were estimated using a GIS-based ammonia 

emissions modeling system developed for the Western Regional Air Partnership (WRAP). The 

development of the model, including data sources and estimation methodology, is documented in 

Chitjian and Mansell (2004). The model treats the source categories of primary significance in 

the overall emission inventory (excluding the mobile, industrial point and fire source categories). 

 The emission source categories estimated for Hawaii include livestock, fertilizer application, 

natural soils and domestic sources. Ammonia emission estimates for wild animals were not 

estimated due to lack of activity data (wild animal populations). Where possible, the model 

considers environmental conditions (wind speed, temperature, soil moisture and pH) in 

developing the emission factors. The ammonia modeling system incorporates wind speed and 

temperature in the diurnal temporal allocation of emission estimates. However, as only annual 

and average total daily emissions are estimated for this effort, this feature of the model was not 

utilized, and therefore these meteorological data were not required. 

The ammonia emissions were estimated for all counties in the State of Hawaii for calendar years 

2002, 2005 and 2018. Results are presented for annual and average day for each year 

considered. 

Emission Estimation Methodology 

A GIS-based Ammonia Emission modeling system developed for the WRAP was used to 

estimate ammonia emissions for the State of Hawaii. The model uses county-level activity data 

and emission factors to estimate ammonia emission for the major source categories, including 

livestock, commercial fertilizer application, natural soils and domestic sources. Environmental 

parameters are used to adjust certain emission factors based on soil pH, as well as to refine the 

diurnal variation of emission estimates. Landuse/landcover data are used for spatial allocation 

purposes. The model generates gridded emission inventories in addition to county-level 

emission estimates. For the present effort, the model was run at a spatial resolution of 4-km and, 

using GIS processing, the results are allocated to each of the Hawaiian Islands. The estimation 

methodology and emission factors used or each source category considered is presented below. 

Livestock

Ammonia emissions from livestock were developed using county-level head counts and emission 

factors based on a literature review performed by Chinkin, et al. (2003). The development of 
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activity data for livestock is discussed in later sections of this memorandum. Estimates were 

developed for beef and dairy cattle, poultry, swine, sheep, goats and horses. The approach used 

in the current inventory does not treat the individual processes leading to ammonia emissions 

from various management practices, as has been the subject of recent research in the emission 

inventory development community. Rather, emission factors based on a “whole animal” 

approach are used. The emission factors used in the development of the Hawaii inventory are 

presented in Table 1. 

Table 1. Livestock emission factors (kg/animal-yr). 

Animal Species Emission Factor 

Dairy Cow 25 

Beef Cow 9 

Poultry 0.1 

Swine 7 

Horses 8.0 

Sheep 1.34 

Goats 0.58 

Temporal allocation of livestock ammonia emissions is typically based on monthly, or seasonal, 

allocation factors associated with temporal variations in animal populations. Lacking specific 

animal population variations, emissions are often allocated based on temporal variations in 

agricultural operations. In the case of Hawaii, no such variation was reported (Keyser, 2007). 

Therefore it is assumed that ammonia emissions from livestock operations are uniformly 

distributed throughout the year. 

Fertilizer Application

Ammonia emission estimates from commercial fertilizer application were developed using 

emission factors from the European Environment Agency (EEA, 2002) as recommended in the 

development and application of the WRAP NH3 model. Emission factors for fertilizer 

application are presented in Table 2. As discussed in Chitjian and Mansell (2004), fertilizer 

emission factors are adjusted as a function of the soil pH. Based on recent research conducted 

by Potter et al. (2001), the emission factors are scaled according to the following relation as a 

function of the soil pH: 

a = 0.3125*pH – 1.01 

Table 2. Fertilizer emission factors. 

Fertilizer Type %N 

Anhydrous Ammonia 4

Aqua ammonia 2.4

Nitrogen solutions 8

Urea 15

Ammonium nitrate 2
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Fertilizer Type %N 

Ammonium sulfate 10

Calcium ammonium nitrate 2

Ammonium thiosulfate 2.4

Other straight nitrogen 2.4

Ammonium phosphates 5

N-P-Ka 2

Potassium nitrate 2.4

Miscellaneous 2.4

The SSURGO1 soils geographic database developed by USDA Natural Resource Conservation 

Service was used as the primary soils database for this study for determining soil pH. . The 

SSURGO1 database was obtained from http://soildatamart.nrcs.usda.gov. No SSURGO soils 

data were available for the islands of Kahoolawe and Niihua. Therefore, a uniform soil pH 

across these islands was assumed. 

Ammonia emissions from fertilizer application were developed using county-level fertilizer sales 

data as described below. As with the livestock emissions, temporal allocation of fertilizer 

application ammonia emissions is typically based on monthly, or seasonal, allocation factors 

associated with temporal variations in fertilizer application rates. In the case of Hawaii, no such 

variation was reported ((Keyser, 2007). Therefore it is assumed that ammonia emissions from 

commercial fertilizer application are uniformly distributed throughout the year 

Natural Soils

Natural soils can be both a source and a sink of ammonia emissions depending on the ambient 

NH3 concentrations, climatic conditions and the conditions of the soils. While there are a 

number of researchers considering this issue, ammonia emissions from natural soils remain 

highly uncertain. For the current inventory, ammonia emission from natural soils were estimated 

based on emission factors developed or recommended by Battye et al., (2003) and Chinkin et al., 

(2003). The emission factors are presented in Table 3. Activity data for natural soil ammonia 

emissions are based on landuse/landcover data, as described below. Emissions from agricultural 

lands are assumed to be included in the fertilizer application estimates. Natural soil ammonia 

emissions are assumed uniformly distributed throughout the year. 

Table 3. Emission factors for natural soils. 

Land Type 

Emission Factor 

(kg/km^2-yr) 

Urban 10 

Barren/Desert land 10 

Deciduous Forest 174 

Evergreen Forest 54 

Mixed Forest 114 

Shrubland 400 

Grasslands 400 

Fallow 205 

Urban/Recreational Grasses 400 
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Land Type 

Emission Factor 

(kg/km^2-yr) 

Wetlands 400 

Domestic Sources

Ammonia emissions from domestic source considered in the current inventory include human 

respiration and perspiration, disposable and cloth diapers and domestic pets (cats and dogs). The 

emission factors are presented in Table 4, as recommended by Chitjian and Mansell (2004). 

Table 4. Emission factors for domestic ammonia sources. 

Source 

Emission 

Factor Unit 

Cats 0.348 lb N/cat/yr 

Dogs 2.17 lb N/dog/yr 

Human Perspiration 0.55 lb NH3/person/yr 

Human Respiration 0.0035 lb NH3/person/yr 

Cloth Diapers 6.9 lb NH3/infant/yr 

Disposable Diapers 0.36 lb NH3/infant/yr 

Activity data for domestic sources are based on county-level populations and pet ratios as 

implemented in Chitjian, et al. (2000). Ammonia emissions from domestic sources are assumed 

uniformly distributed throughout the year. 

Activity Data 

The development of the necessary activity data for estimation ammonia emissions for the State 

of Hawaii is described below. 

Livestock

Estimates were developed for beef and dairy cattle, poultry, sheep and lambs, horses and goats. 

Animal headcounts for 2002 and 2005 were based on the National Agricultural Statistics Service 

(NASS) county livestock files (NASS, 2007). Note that animal headcounts for 2018 were held 

constant at 2005 levels. Spatial allocation of livestock activity data were based on the percentage 

of agricultural land available for each county. The estimation of the percent land coverage was 

obtained from the landuse classification of the NOAA Coastal Service Database as described in 

Mansell and Kemball-Cook, 2006 and used as to spatially allocate livestock state-level data to 

the county-level. 

2002 Livestock Data Compilation 

For the year 2002, the activity data for beef and dairy cattle, goats, hogs and pigs and horses 

were obtained at the county level. The acquired poultry data were available for Honolulu County 

and D98 combined counties. D98 combined counties include Hawaii, Kauai and Maui Counties 

(Nails, Hawaii Static Service). As shown in Table 5, for allocating the D98 combined counties 
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data to Hawaii, Kauai and Maui counties, the percentage of agricultural land available for each 

county was used. NAAS provided the annual number of sheep and lambs for Hawaii State, 

Honolulu and Maui counties. To distribute the sheep and lambs data to Hawaii and Kauai 

counties, it was assumed that the fraction of Hawaii State to Hawaii and to Kauai counties 

respectively would be same as for the 1997 distribution. Mark Henderson (Hawaii Static 

Services) has provided annual state Equine numbers. County estimates for equine were obtained 

using the distribution fractions used for horses. Equine was combined with Horses and 

documented as total horse numbers. 

Table 5. Percentage of agricultural land available for each county in 2002 

County % Agricultural 

Land 

Hawaii 23.05% 

Kauai 25.07% 

Maui 51.88% 

2005 Livestock Data Compilation 

For the year 2005, annual numbers for beef and dairy cattle by county, poultry and, hogs and 

pigs by state were obtainable. For estimating the poultry and, hogs and pigs data to the countylevel, the percentage of agricultural land available in 2005 for each county was used to allocate 

the state data. Table 6 shows the distribution used. As the data for the total number of horses and 

goats for 2005 was unpublished when this study was prepared, the data were estimated by 

linearly projecting 1992, 1997, and 2002 state annual numbers to 2005. For allocating goats and 

horses to the county-level, it was assumed that the distribution between state to counties would 

be same as for 2002. Table 7 shows the spatial allocation factors for goats and horses. 2002 and 

2005 animal headcounts for the State of Hawaii are presented in Table 8. 

Table 6. Percentage of agricultural land available for each county in 2005 

County % Agricultural 

Land 

Hawaii 18.61% 

Honolulu 19.23% 

Kauai 20.25% 

Maui 41.91% 

Table 7. 2005 Spatial allocation factors for Horses and Goats 

County % Horses % Goats 

Hawaii 53.96% 30.76%

Honolulu 8.11% 11.00%

Kauai 16.23% 29.49%

Maui 21.70% 28.75%

Table 8. 2002 and 2005 Livestock annual headcounts for Hawaii 

County Beef 

Cattle 

Dairy 

Cattle Poultry Hogs & Pigs 

Lambs 

& 

Sheep 

Goats Horses 
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2002 Livestock 

Hawaii 61,600 52,000 29,960 2,329 3658 1,650 2,748 

Honolulu 1,200 7,200 495,000 13,337 358 590 413 

Kauai 4,500 5,200 32,590 1,882 15818 1,582 826 

Maui 11,000 9,300 67,449 5,816 75 1,542 1,105 

2005 Livestock 

Hawaii 61,400 54,600 101,823 3,537 3658 1,503 2,723 

Honolulu 2,400 5,600 105,185 3,654 358 537 409 

Kauai 6,300 5,700 110,760 3,847 15818 1,441 819 

Maui 11,200 7,800 229,231 7,962 75 1,405 1,095 

Livestock ammonia emission estimates for 2018 were grown from 2005 estimates based on 

EGAS growth factors by SCC (= 1.198). 

Fertilizer Application

Ammonia emissions from fertilizer applications for 2002 and 2005 were developed using the 

state fertilizer sales data, allocated to county-level, obtained from the National Agricultural 

Static Services (NAAS, 2007). Emission estimates for 2018 were grown from 2005 estimates 

based on EGAS growth factors by SCC (= 1.198). An assumption was made by the association 

of American Plant and Food Control Officials and the Fertilizer institute, that 2001 and 2002 

Fertilizer Consumption for Hawaii State would be the same as for 2000. For estimating 2005 

emissions from fertilizers it was assumed that the 2005 fertilizer consumption would be same as 

2002. The percentage of crop harvested acreage data by county was used as a spatial allocation 

factor for 2002 and 2005 (NAAS, 2007). Table 9 shows the percentage of crop harvested acreage 

by county. Table 10 summarizes the fertilizer consumption in 2002 and 2005 by county. 

Table 9. 2002 and 2005 percentage of crop harvested acreage by county

County 2002 % Crop 

Harvested

2005 % Crop 

Harvested

Hawaii 14.78% 15.44%

Honolulu 12.98% 12.41%

Kalawao 0.00% 0.00%

Kauai 19.99% 20.59%

Maui 52.25% 51.55%

Table 10. 2002 and 2005 Fertilizer consumption by county (Short tons)
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County Nitrogen 

solutions Urea Ammonium sulfate

N-P-K Miscellaneous

2002 Commercial Consumption

Hawaii 2,216 301 1,857 5,471 3,456

Honolulu 1,945 264 1,631 4,804 3,035

Kalawao 0 0 0 0 0

Kauai 2,997 407 2,512 7,400 4,675

Maui 7,833 1,065 6,566 19,344 12,220

2005 Commercial Consumption

Hawaii 2,315 315 1,940 5,716 3,611

Honolulu 1,861 253 1,560 4,595 2,903

Kalawao 0 0 0 0 0

Kauai 3,087 420 2,587 7,622 4,815

Maui 7,728 1,051 6,478 19,085 12,056

Natural Soils 

Activity data for the estimation of natural soil ammonia emissions are based on 

landuse/landcover (LULC) data. For the present inventory, the NOAA Coastal Service 

Database for the Hawaiian Islands was used. These data were derived from Landsat Enhanced 

Thematic Mapper imagery as described in Dobson, et al., 1995, and were summarized in Mansell 

and Kemball-Cook, 2006. The same landuse/landcover data were used for all inventory years. 

These data are summarized for the Hawaiian Islands in Table 11 and displayed for the island of 

Hawaii in Figure1. 

Table 11. NOAA Coastal Service Landcover summary for each of the Hawaiian Islands (km^2)
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LUCODE Description Hawaii Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua Total

1 Unclassified 110 0 0 0 6 0 5 0 121

2 High Intensity Urban 16 8 0 0 1 73 4 0 102

3 Low Intensity Urban 76 37 0 5 12 137 26 0 293

4 Cultivated Lands 96 215 0 0 13 99 104 0 527

5 Grasslands 2,476 427 44 102 119 162 133 14 3,477

6 Deciduous Forest 0 0 0 0 0 0 0 0 0

7 Evergreen Forest 3,122 535 0 26 95 331 293 1 4,404

8 Mixed Forest 0 0 0 0 0 0 0 0 0

9 Scrub/Shrub 1,890 371 10 170 318 600 724 99 4,182

10 Palustrine Forested Wetlands 0 0 0 0 0 0 21 0 22

11 Palustrine Scrub/Shrub Wetlands 0 0 0 0 0 1 12 1 14

12 Palustrine Emergent Wetlands 0 1 0 0 0 3 4 1 8

13 Estuarine Forested Wetlands 0 0 0 0 0 1 0 0 2

14 Estuarine Scrub/Shrub Wetlands 0 0 0 0 0 0 0 0 0

15 Estuarine Emergent Wetlands 0 0 0 0 0 0 0 0 0

16 Unconsolidated Shore 2 2 0 4 1 3 1 1 15

17 Bare Land 2,414 153 29 16 13 20 16 11 2,673

18 Water 69 36 6 6 17 75 35 19 262

19 Palustrine Aquatic Bed 0 0 0 0 0 0 0 0 0

20 Estuarine Aquatic Bed 0 0 0 0 0 0 0 0 0

21 Tundra 0 0 0 0 0 0 0 0 0

22 Snow/Ice 0 0 0 0 0 0 0 0 0

Total Total 10,272 1,785 90 330 595 1,505 1,378 146 16,101

NOAA Coastal Service Landuse/Landcover Summary for Hawaii (km^2)

Figure 1. Hawaii LULC from the NOAA Coastal Service Database 

Domestic Sources
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The 2002 and 2005 population data by age and by county are based on US Census Bureau data 

(U.S Bureau, 2007). For 2018 the total projected data, obtained for the state, were by single age 

group and were allocated to the county-level using 2005 state to county distribution fractions 

(U.S Census Bureau, 2007a). Table 12 summarizes total and infant (0-4years) population for 

2002, 2005 and 2018. 

Table 12. Total and Infant Population for 2002, 2005 and 2018 

2002 Population 

Hawaii Honolulu Kalawao Kauai Maui 

Total Population 154,794 896,019 132 59,946 134,007

Infant 10,090 60,421 0 3,713 9,283

2005 Population 

Total Population 167,293 905,266 111 62,640 139,884

Infant 11,242 65,465 0 4,194 9,455

2018 Population 

Total Population 183,982 995,572 122 68,889 153,838

Infant 12,406 72,242 0 4,628 10,434

Ammonia Emission Summaries 

The estimated annual ammonia emissions by island are presented in Tables 13 through 15 for 

calendar years 2002, 2005 and 2018, respectively. The corresponding average day emission 

estimates are presented in Tables 16 through 18. As noted previously, no temporal variation 

was assumed for these emission estimates. Ammonia emissions for each island were obtained 

from the county-level estimates using GIS processing to apportion the county estimates to each 

island. Assuming the emission results are uniformly distributed across each 4-km grid cell, these 

data are re-cast as an emission density, allocated to each island using GIS overlays, then 

recalculated based on the total area of each island. All estimates are presented in tons of NH3. 

Note that although non-zero, domestic ammonia emissions are displayed in the tables below as 

zero, due to numerical rounding. 

Table 13. Annual ammonia emissions for 2002 (tons/year) 

2002 Annual NH3 Emissions (tons/year) 

Source Category 

Hawai

i Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total 

Domestic Sources 102 69 0 2 5 596 32 0 807

Fertilizer Application 549 1,939 0 2 125 454 578 0 3,647

Livestock 2,038 238 23 56 65 333 194 21 2,968

Natural Soils 2,139 386 24 122 199 361 412 50 3,693

Total 4,827 2,632 47 182 394 1,745 1,217 71 11,114

Table 14. Annual ammonia emissions for 2005 (tons/year) 
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2005 Annual NH3 Emissions (tons/year) 

Source Category 

Hawai

i Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total 

Domestic Sources 111 71 0 2 5 619 35 0 843

Fertilizer Application 573 1,913 0 2 123 434 595 0 3,641

Livestock 2,122 235 23 55 64 199 240 26 2,963

Natural Soils 2,139 386 24 122 199 361 412 50 3,693

Total 4,946 2,605 47 181 391 1,613 1,282 76 11,141

Table 15. Annual ammonia emissions for 2018 (tons/year)

2018 Annual NH3 Emissions (tons/year) 

Source Category 

Hawai

i Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total 

Domestic Sources 122 78 0 2 6 681 38 0 928

Fertilizer Application 687 2,292 0 3 147 520 713 0 4,362

Livestock 2,542 281 27 66 77 239 287 31 3,550

Natural Soils 2,139 386 24 122 199 361 412 50 3,693

Total 5,491 3,038 52 193 429 1,801 1,451 81 12,534

Table 16. Average daily ammonia emissions for 2002 (tons/day) 

2002 Average Daily NH3 Emissions (tons/day) 

Source Category 

Hawai

i Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total 

Domestic Sources 0.28 0.19 0.00 0.01 0.01 1.63 0.09 0.00 2.21

Fertilizer Application 1.50 5.31 0.00 0.01 0.34 1.24 1.58 0.00 9.99

Livestock 5.58 0.65 0.06 0.15 0.18 0.91 0.53 0.06 8.13

Natural Soils 5.86 1.06 0.07 0.33 0.55 0.99 1.13 0.14 10.12

Total 13.23 7.21 0.13 0.50 1.08 4.78 3.33 0.19 30.45

Table 17. Average daily ammonia emissions for 2005 (tons/day) 

2005 Average Daily NH3 Emissions (tons/day) 

Source Category 

Hawai

i Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total 

Domestic Sources 0.30 0.20 0.00 0.01 0.01 1.69 0.09 0.00 2.31

Fertilizer Application 1.57 5.24 0.00 0.01 0.34 1.19 1.63 0.00 9.98

Livestock 5.81 0.64 0.06 0.15 0.18 0.55 0.66 0.07 8.12

Natural Soils 5.86 1.06 0.07 0.33 0.55 0.99 1.13 0.14 10.12

Total 13.55 7.14 0.13 0.50 1.07 4.42 3.51 0.21 30.52

Table 18. Average daily ammonia emissions for 2018 (tons/day)

2018 Average Daily NH3 Emissions (tons/day) 

Source Category 

Hawai

i Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total 

Domestic Sources 0.3 0.2 0.0 0.0 0.0 1.9 0.1 0.0 2.5

Fertilizer Application 1.9 6.3 0.0 0.0 0.4 1.4 2.0 0.0 12.0

Livestock 7.0 0.8 0.1 0.2 0.2 0.7 0.8 0.1 9.7

Natural Soils 5.9 1.1 0.1 0.3 0.5 1.0 1.1 0.1 10.1

Total 15.0 8.3 0.1 0.5 1.2 4.9 4.0 0.2 34.3
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References 

Chinkin, L.R., P.A. Ryan, and D.L. Coe. 2003. “Recommended Improvements to the CMU 

Ammonia Emission Inventory Model for Use by LADCO”. Prepared Lake Michigan Air 

Directors Consortium. 

Chitjian, M. and G. Mansell. 2003. “An Improved Ammonia Inventory for the WRAP Domain 

– Technical Description of the Modeling System.” Prepared for the WRAP Emissions 

Forum. November, 2003. 

Chitjian, M., J. Koizumi, C. Botsford, G. Mansell and E. Winegar. 2000. Final 1997 Gridded 

Ammonia Emissions Inventory Update for the South Coast Air Basin. Prepared for the 

South Coast Air Quality Management District. August, 2000. 

Dobson, J. et al., NOAA Coastal Change Analysis Program(C-CAP): Guidance for Regional 

Implementation, NOAA Technical Report NMFS 123, U.S. Department of Commerce, 

April 1995. 

European Environment Agency. 2002. Joint EMEP/CORINAIR Atmospheric Emission 

Inventory Guidebook, Third Edition. Copenhagen: European Environment Agency. 

Henderson, M. 2007. Hawaii Static Service. Personal communication. March. 

Keyser, H. 2007. Phone conversation with Harold Keyser, County Administrator. University of 

Hawaii Kahului Cooperative Extension Office. February 28, 2007. 

Mansell, G.E. and S. Kemball-Cook. 2006. Biogenic Emission Estimates for the Hawaiian 

Islands. Technical Memorandum prepared for Lakes Environmental Software and 

Hawaii Clean Air Branch. December. 

Nails. 2007. Hawaii Static Service. Personal communication. March. 

NASS. 2007. Hawaii Agricultural Statistics Data Base. National Agricultural Statistics 

Service, Washington D.C March. Internet address: 

http://www.nass.usda.gov/Statistics_by_State/Hawaii/index.asp#.html

Potter, C., C. Krauter, and S. Klooster. 2001. “Statewide Inventory Estimates of Ammonia 

Emissions from Native Soils and Chemical Fertilizers in California.” Prepared for the 

California Air Resources Board. June 2001. 

U.S. Census Bureau, 2007. 2002 and 2005 Annual Estimates of the Population for the United 

States, Regions, and States and for Puerto Rico: April 1, 2000 to July 1, 2005. 

Population Division, U.S. Census Bureau, Washington, D.C. Internet address 

http://www.census.gov/popest/estimates.php
 Page 12 

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U.S. Census Bureau, 2007b Interim State Projections of Population by Single Year of Age: July 

1, 2004 to 2030. Population Division, Interim State Population Projections, 2005, U.S. 

Census Bureau, Washington, D.C. Internet address 

 http://www.census.gov/population/projections/DownldFile3.xls
APPENDIX I 

FUGITIVE WINDBLOWN DUST EMISSION ESTIMATES FOR THE 

STATE OF HAWAII 
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TECHNICAL MEMORANDUM 

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: Gerard Mansell 

Date: 27 June 2007 (revised 21 October 2008) 

Subject: Fugitive Windblown Dust Emission Estimates for the State of Hawaii 

Introduction 

This Technical Memorandum describes the development of the windblown fugitive dust 

particulate matter (PM) emission inventories for 2002 and 2005 for the State of Hawaii. The 

necessary input data, including land use/land cover, soil characteristics, agricultural information 

and meteorology, are identified and described. The emission estimation methodology, 

developed by the Western Regional Air Partnership (WRAP), and the specific implementation 

for the Hawaiian Islands is described. Results for calendar years 2002 and 2005 are presented 

and discussed. Various limitations for the current application and recommendations for 

improvements are presented. 

Emission Estimation Methodology 

The WRAP Regional Modeling Center (RMC) developed a windblown fugitive dust PM 

emission estimation methodology as a multi-phase project over the past several years. The 

current methodology is based on the WRAP RMC Phase II development effort and builds upon a 

review of recent literature and windblown dust studies. A summary of the literature review can 

be found in Mansell, et al., 2004. Based on a review of wind tunnel studies it was noted that the 

two important components to characterize the dust emission process from an erodible surface are 

the threshold friction velocity that defines the inception of the emission process as a function of 

the wind speed and as influenced by the surface characteristics, and the strength of the emissions 

that follow the commencement of particle movement. The two critical factors affecting emission 

strength are the wind speed (surface wind friction velocity) that drives the saltation system, and 

the soil characteristics. 

Surface Friction Velocities

Surface friction velocities are determined from the aerodynamic surface roughness lengths and 

the 10-meter wind speeds. Friction velocity u*, is related to the slope of the velocity versus the 

natural logarithm of height through the relationship: 

 

* o

z

z

z ln 1

u

u

κ = 

where uz = wind velocity at height z (m s-1) 

 u* = friction velocity (m s-1) 

κ = von Karman's constant (0.4) 

 zo = aerodynamic roughness height (m) 
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Threshold Friction Velocities

The methodology relies on the determination of threshold surface friction velocities, u*t, as a 

function of aerodynamic surface roughness length, z0. In addition to aerodynamic roughness, the 

degree of disturbance of the surface also plays a key role in the estimation of threshold friction 

velocities. Based on the work of Marticorena et al. (1997), relationships between u*t and z0

where identified and compared with wind tunnel data from Gillette et al. (1980, 1982), Gillette 

(1988) and Nickling and Gillies (1989). This comparison is presented in Figure 1. 

Figure 1. Comparison between the Marticorena et al. (1997) modeled relationship of threshold 

friction velocity and aerodynamic roughness length and wind tunnel data from Gillette et al. 

(1980, 1982), Gillette (1988) and Nickling and Gillies (1989).

Several general relationships can be described for threshold friction velocity data. Two major 

factors have the greatest influence on the threshold of wind erodible soils: the degree of 

disturbance and the aerodynamic roughness. For loose or disturbed soils the most important 

factor that controls the threshold friction velocity is aerodynamic roughness. The effect of 

surface disturbance on threshold friction velocity has been considered by numerous researchers 

(Gillette et al., 1980; 1982; Gillette, 1988; Nickling and Gillies, 1989). Accordingly, for a given 

surface type, the effect of disturbance is to lower the threshold between ~90% to ~20% of the 

undisturbed value. 

Surface Roughness Lengths

u*t = 0.31e7.44x(Zo)

R2 = 0.60

u*t = 0.30e7.22x(Zo)

0

0.5

1

1.5

2

2.5

3

0.00001 0.0001 0.001 0.01 0.1 1

zo (cm)

u*t (m s-1)

wind tunnel data Marticorena et al. 1997

Expon. (wind tunnel data) Expon. (Marticorena et al. 1997)
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Surface friction velocities, including the threshold friction velocity, are a function of the 

aerodynamic surface roughness lengths. The surface roughness lengths are in turn dependent on 

surface characteristics, particularly land use/land cover. While these values can vary 

considerable for a given land type, published data are available which provide a range of surface 

roughness lengths for various land use types and vegetation cover. 

Application of the relationship shown in Figure 1 to assign a threshold friction velocity to a 

surface requires information on a surface’s aerodynamic roughness length. This type of 

information is not generally available in land use databases, because they were not specifically 

developed to quantify aerodynamic properties of surfaces. Based on the designation of land use 

type, the aerodynamic roughness can be assigned based on previously reported values for similar 

surfaces. In the current implementation of the windblown dust emission model, surface 

roughness lengths are assigned based on the land cover type. These assignments for the current 

application to Hawaii are presented below. 

A degree of uncertainty exists upon assigning an aerodynamic roughness length to a surface, as it 

will be complicated by the individual condition of the surface, which can change through time on 

several scales. For agricultural fields, aerodynamic roughness will change as a function of plant 

height and cover through a growing season and the tillage practices. These affects are considered 

for agricultural lands within the model, as described below. For natural surfaces, the 

aerodynamics can change through the season as well as annually through several years affecting 

dust production cycles. This is linked to plant growth in response to annual and long term 

climate variability, which will affect plant cover. 

Emission Fluxes

Field and wind tunnel experiments suggest that dust emissions are proportional to wind friction 

speed and approximate theoretical model predictions, but the considerable scatter in the available 

data make it impossible to clearly define this dependence (Nickling and Gillies, 1993). Different 

surfaces appear to have different constants of proportionality for the flux versus wind friction 

velocity relationship, implying that the flux is predictable, but surface and soil properties affect 

the magnitude of the flux. A detailed discussion of wind tunnel studies, including various 

limitations and measured data, can be found in ENVIRON, 2003a; 2003b. 

Alfaro, et al. (2003) re-analyzed the Nickling and Gillies (1989) data and found that the tendency 

of a surface to emit dust depends not primarily on its textural qualities, but on the size 

distribution of the loose soil aggregates available for saltation, and the aerodynamic roughness 

length that conditions the emission threshold. The re-analysis was based in part on the work of 

Chatenet, et al. (1996) in which they found that desert soils could be broadly divided into four 

populations based upon their soil aggregate populations. The differences between the four 

groups are based upon the estimated geometric mean diameter of the soil particles. The four size 

classes are 125 mm, 210 mm, 520 mm, and 690 mm, which are labeled FFS, FS, MS, and CS by 

Chatenet, et al. (1996). 

Using the Alfaro, et al. (2003) approach, emissions of dust for soils can be confined to four 
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different emission factors, depending on the geometric mean grain size, as determined by the 

methods of Chatenet, et al. (1996). The model predictions were tested against the wind tunnel 

data set of Nickling and Gillies (1989) and found to fit the measured data satisfactorily. The 

relationships between emission fluxes and surface friction velocities for each of the four soil 

classes are displayed in Figure 2. Of key importance is that Chatenet, et al. (1996) established 

relationships between the 12 soil types that are defined in the classical soil texture triangle and 

their four dry soil types (silt [FSS], sandy silt [FS], silty sand [MS], and sand [CS]). The soil 

texture categorization and the relationships among texture assignments and soil groupings are 

discussed below. 

Figure 2. The emission flux as a function of friction velocity predicted by the Alfaro and Gomes 

(2001) model constrained by the four soil geometric mean diameter classes of Alfaro et al. 

(2003). 

Agricultural Land Adjustments

Unlike other types of vacant land, windblown dust emissions from agricultural land are subject 

to a number of non-climatic influences, including irrigation and seasonal crop growth. As a 

result, several non-climatic correction or adjustment factors were developed for applicability to 

the agricultural wind erosion emissions. These factors included: 

• Long-term effects of irrigation (i.e., soil “clodiness”); 

• Crop canopy cover; 

FFS

F = 2.45x10-6 (u*)

3.97

FS

F = 9.33x10-7 (u*)

2.44

MS

F = 1.243x10-7(u*)

2.64

CS

F = 1.24x10-7 (u*)

3.44

0.000000001

0.00000001

0.0000001

0.000001

0.00001

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Friction Velocity (m s-1)

Emission Flux (F, g cm-2 s-1)

FSS

FS

MS

CS

Power (FSS)

Power (FS)

Power (MS)

Power (CS)
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• Post-harvest vegetative cover (i.e., residue); 

• Bare soil (i.e., barren areas within an agriculture field that do not develop crop 

canopy for various reasons, etc.); and 

• Field borders (i.e., bare areas surrounding and adjacent to agricultural fields). 

The methodology used to develop individual non-climatic correction factors were based upon 

previous similar work performed by the California Air Resources Board (CARB) in their 

development of California-specific adjustment factors for USDA’s Wind Erosion Equation 

(WEQ) (CARB, 1997). These correction factors were developed for specific soil textures, crop 

types, and geographic locations and then applied to the wind erosion estimates developed from 

the wind tunnel studies. Correction factors are developed only for the 17 field crops specifically 

identified in the BELD3.1 data set used by the WRAP during model development efforts (i.e., 

alfalfa, barley, corn, cotton, grass, hay, oats, pasture, peanuts, potatoes, rice, rye, sorghum, 

soybeans, tobacco, wheat, and miscellaneous crops). Because BELD crops data are not available 

for Hawaii, and the spatial distribution of crops by crop type is not available in the Hawaii 

LULC data (described below), all crop lands were considered to be miscellaneous crops for 

which agricultural adjustment factors are not applicable. 

Input Data 

Input data required for the implementation of the WRAP windblown dust emission model, 

specifically for application to the State of Hawaii, are described below. These include 

landcover, soil characteristics and meteorology. 

Landcover Data 

As noted above, estimation of wind blown fugitive dust emissions is dependant on 

landuse/landcover (LULC) data. For the present model application, the NOAA Coastal Service 

Database for the Hawaiian Islands was used. These data were derived from Landsat Enhanced 

Thematic Mapper imagery as described in Dobson, et al., 1995, and were summarized in Mansell 

and Kemball-Cook, 2006. These data are summarized for the Hawaiian Islands in Table 1 and 

displayed for the island of Hawaii in Figure 3. 

The assumed surface roughness lengths as a function of the landcover type using the NOAA 

LULC database is presented in Table 2 below. These assignments are based on a general reclassification of landuse types to those recognized by the model; urban, wetlands/water, forest, 

grasslands, shrublands, agricultural and barren lands. Note that the assumed surface roughness 

lengths for urban and forest lands are such that the threshold friction velocities are of a 

magnitude high enough to preclude the generation of emissions from wind erosion. No dust is 

estimated from wetlands or water bodies. 
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Table 1. NOAA Coastal Service Landcover summary by category for each of the Hawaiian 

Islands (km^2)

LUCODE Description Hawaii Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua Total

1 Unclassified 110 0 0 0 6 0 5 0 121

2 High Intensity Urban 16 8 0 0 1 73 4 0 102

3 Low Intensity Urban 76 37 0 5 12 137 26 0 293

4 Cultivated Lands 96 215 0 0 13 99 104 0 527

5 Grasslands 2,476 427 44 102 119 162 133 14 3,477

6 Deciduous Forest 0 0 0 0 0 0 0 0 0

7 Evergreen Forest 3,122 535 0 26 95 331 293 1 4,404

8 Mixed Forest 0 0 0 0 0 0 0 0 0

9 Scrub/Shrub 1,890 371 10 170 318 600 724 99 4,182

10 Palustrine Forested Wetlands 0 0 0 0 0 0 21 0 22

11 Palustrine Scrub/Shrub Wetlands 0 0 0 0 0 1 12 1 14

12 Palustrine Emergent Wetlands 0 1 0 0 0 3 4 1 8

13 Estuarine Forested Wetlands 0 0 0 0 0 1 0 0 2

14 Estuarine Scrub/Shrub Wetlands 0 0 0 0 0 0 0 0 0

15 Estuarine Emergent Wetlands 0 0 0 0 0 0 0 0 0

16 Unconsolidated Shore 2 2 0 4 1 3 1 1 15

17 Bare Land 2,414 153 29 16 13 20 16 11 2,673

18 Water 69 36 6 6 17 75 35 19 262

19 Palustrine Aquatic Bed 0 0 0 0 0 0 0 0 0

20 Estuarine Aquatic Bed 0 0 0 0 0 0 0 0 0

21 Tundra 0 0 0 0 0 0 0 0 0

22 Snow/Ice 0 0 0 0 0 0 0 0 0

Total Total 10,272 1,785 90 330 595 1,505 1,378 146 16,101

NOAA Coastal Service Landuse/Landcover Summary for Hawaii (km^2)

Table 2. Surface roughness lengths by LULC category. 

LUCODE Description Dust Category

Surface 

Roughness (cm) 

1 Unclassified N/A N/A

2 High Intensity Urban Urban Land 50.000

3 Low Intensity Urban Urban Land 50.000

4 Cultivated Lands Agricultural Land 0.031

5 Grasslands Grasslands 0.500

6 Deciduous Forest Forest 100.000

7 Evergreen Forest Forest 100.000

8 Mixed Forest Forest 100.000

9 Scrub/Shrub Shrublands 0.150

10 Palustrine Forested Wetlands N/A N/A

11 Palustrine Scrub/Shrub Wetlands N/A N/A

12 Palustrine Emergent Wetlands N/A N/A

13 Estuarine Forested Wetlands N/A N/A

14 Estuarine Scrub/Shrub Wetlands N/A N/A

15 Estuarine Emergent Wetlands N/A N/A

16 Unconsolidated Shore Barren Land 0.002

17 Bare Land Barren Land 0.002

18 Water N/A N/A

19 Palustrine Aquatic Bed N/A N/A

20 Estuarine Aquatic Bed N/A N/A

21 Tundra N/A N/A

22 Snow/Ice N/A N/A
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Figure 3. Hawaii LULC from the NOAA Coastal Service Database 

Soil Characteristics Data

Soils characteristics data (soil texture) are used in the model to determine dust emissions rates as 

a function of wind speeds. Application of the emission factor relations described above requires 

the characterization of soil texture in terms of the 4 soil groups considered by the model. The 

characteristics, or type, of soil is one of the parameters of primary importance for the application 

of the emission estimation relations derived from wind tunnel study results. 

The SSURGO1 soils geographic database developed by USDA Natural Resource Conservation 

Service was used as the primary soils database for this study. The SSURGO1 database was 

obtained from http://soildatamart.nrcs.usda.gov. No SSURGO soils data were available for the 

islands of Kahoolawe and Niihua. Therefore, a uniform soil texture across these islands was 

assumed. 

 

The classification of soil textures and soil group codes is based on the standard soil triangle that 

classifies soil texture in terms of percent sand, silt and clay. Combining the soil groups defined 

by the work of Alfaro, et al. (2003) and Chatenet, et al. (1996) and the standard soil triangle 

provides the mapping of the 12 soil textures to the 4 soil groups considered in their study. The 

soil texture mappings are summarized in Table 3 while Figure 4 displays the soil texture data 

used for the project. 
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Table 3. Soil texture and soil group codes. 

Soil Texture 

Soil Texture 

Code Soil Group 

Soil Group 

Code 

No Data 0 N/A 0 

Sand 1 CS 4 

Loamy Sand 2 CS 4 

Sandy Loam 3 MS 3 

Silt Loam 4 FS 1 

Silt 5 FSS 2 

Loam 6 MS 3 

Sandy Clay Loam 7 MS 3 

Silty Clay Loam 8 FSS 1 

Clay Loam 9 MS 3 

Sandy Clay 10 MS 3 

Silty Clay 11 FSS 1 

Clay 12 FS 2 

Figure 4. Soil texture for the Island of Hawaii from the SSURGO1 database. 
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Meteorological Data 

To estimate dust emissions from wind erosion, in general, hourly, gridded wind speeds are 

required of the model. In the present case, estimates are to be made by island for an average 

annual, winter and summer day. Meteorological data were provided by Lakes Environmental in 

the form of output data files from the PSU Mesoscale Meteorological Model version 5 (MM5) at 

a horizontal spatial resolution of 4-km. Model output data were available for all islands except 

Lanai and Molokai. The required surface wind speeds and precipitation rates were extracted 

from the MM5 model results and re-formatted for input to the windblown dust emission model. 

For the islands of Lanai and Molokai, meteorological data were obtained and processed as 

described below. 

For Lanai and Molokai, wind speed data were obtained from the National Center for 

Atmospheric Research’s (NCAR) ds472 data set for 2002 and 2005. NCAR’s ds472 contains 

hourly observations of commonly measured variables (including wind speed) from airports 

throughout the U.S. and includes stations on Kauai, Oahu, Molokai, Lanai, and Hawaii. 

Sufficient ds472 data were available for at least one station for each county for both 2002 and 

2005. Given the lack of hourly gridded data, observational data from a single meteorological 

station for these two islands was assumed representative for the entire island. Precipitation data 

from the ds472 data set were not utilized. Instead, in order to incorporate the effects of rainfall 

on the estimated windblown dust emissions, the results obtained without precipitation were 

scaled based on the results of model runs for the island of Maui. Model simulations were 

conducted for Maui with and without the MM5 precipitation fields and the ratio of the results by 

season were used to scale the results for Lanai and Molokai. 

Model Application and Results 

The application of the WRAP RMC windblown dust emission model was performed by defining 

a gridded domain encompassing all of the Hawaiian Islands. Because the model was developed 

to estimate gridded emissions appropriate for air quality modeling, defining a gridded modeling 

domain for the present application eliminated the need to reconfigure or revise the existing 

modeling system. The resulting gridded emissions outputs were then summed to obtain a total 

annual emissions estimate for each island. The required input data sets used were described 

above. The model considers precipitation and the effect on the subsequent wind erosion of soils. 

 The overall effect is to reduce the windblown dust emissions during and after periods of 

significant precipitation. 

Results for 2002 and 2005

Windblown fugitive PM dust emissions for the State of Hawaii were developed using the WRAP 

RMC windblown dust emission model as described above. The model was run for every hour 

for calendar years 2002 and 2005. Representative average day emissions for summer and winter 

for 2002 and 2005 were developed by summing the hourly data for each month and then dividing 

by the total number of hours in each season. An average annual day emission estimate was 
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obtained by summing all hours for each year and dividing by 365. Note that as discussed in the 

Project Work Plan, windblown dust emissions are not projected for future years. The results for 

calendar year 2005 should be used for any future year modeling. Results were aggregated 

spatially for each island. 

Table 4 presents the results of the windblown dust emission modeling for Hawaii for 2002. 

Results are presented for each island separately and totaled for the state. Average winter, 

summer and annual day fugitive dust PM10 emissions for 2002 are provided. Table 5 presents 

the corresponding results for calendar year 2005. The corresponding results for PM2.5 are 

presented in Tables 6 and 7 for calendar years 2002 and 2005, respectively. It is assumed that 

PM2.5 is equal to 0.1*PM10, as discussed in Mansell, et al., 2004. 

Table 4. 2002 Windblown fugitive PM10 emissions for Hawaii (tpd). 

2002 Average Daily Windblown PM10 Dust Emissions (tons)

Hawaii Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total

Winter Day 11.0 58.7 6.9 4.1 7.8 7.6 11.8 1.3 109.2

Summer Day 12.7 92.7 9.7 5.5 25.2 6.8 10.9 1.0 164.6

Annual Day 11.1 67.4 8.9 4.5 13.3 5.7 9.8 1.0 121.6

Table 5. 2005 Windblown fugitive PM10 emissions for Hawaii (tpd). 

Hawaii Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total

Winter Day 5.5 35.6 7.8 4.6 10.8 3.6 7.2 0.7 75.8

Summer Day 10.8 78.3 9.6 15.6 51.2 7.1 9.6 0.9 183.3

Annual Day 8.8 53.2 8.4 9.8 31.7 6.1 9.3 0.9 128.2

2005 Average Daily Windblown PM10 Dust Emissions (tons)

Table 6. 2002 Windblown fugitive PM2.5 emissions for Hawaii (tpd). 

Hawaii Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total

Winter Day 1.1 5.9 0.7 0.4 0.8 0.8 1.2 0.1 10.9

Summer Day 1.3 9.3 1.0 0.6 2.5 0.7 1.1 0.1 16.5

Annual Day 1.1 6.7 0.9 0.4 1.3 0.6 1.0 0.1 12.2

2002 Average Daily Windblown PM2.5 Dust Emissions (tons)

Table 7. 2005 Windblown fugitive PM2.5 emissions for Hawaii (tpd). 

Hawaii Maui Kahoolawe Lanai Molokai Oahu Kauai Niihua State Total

Winter Day 0.5 3.6 0.8 0.5 1.1 0.4 0.7 0.1 7.6

Summer Day 1.1 7.8 1.0 1.6 5.1 0.7 1.0 0.1 18.3

Annual Day 0.9 5.3 0.8 1.0 3.2 0.6 0.9 0.1 12.8

2005 Average Daily Windblown PM2.5 Dust Emissions (tons)

These estimated windblown dust emissions are very small, relative to windblown dust in other 

areas that ENVIRON has investigated. The reason that the emissions are so small is that there is 

not much barren land in Hawaii that is dusty, the tropical climate supports much more plant 

growth than in other areas, and the frequent precipitation reduces the small amount of dust 

emissions that might be present. 
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References 

Dobson, J. et al., NOAA Coastal Change Analysis Program(C-CAP): Guidance for Regional 

Implementation, NOAA Technical Report NMFS 123, U.S. Department of Commerce, 

April 1995. 

Alfaro, S.C., Rajot, J.L., and Nickling, W.G. Estimation of PM20 emissions by wind erosion: 

main sources of uncertainties. Geomorphology (in press). 2003. 

Alfaro, S.C. and Gomes, L. Modeling mineral aerosol production by wind erosion: emission 

intensities and aerosol size distributions in source areas. J. Geophys. Res. 106 (16): 

18075-18084. 2001. 

ARB. 1997 “Section 7.11 – Supplemental Documentation for Windblown Dust – Agricultural 

Lands.” California Air Resources Board, Emission Inventory Analysis Section, 

Sacramento, California. April. 

Chatenet, B., Marticorena, B., Gomes, L., and Bergametti, G. 1996. Assessing the microped 

size distributions of desert soils erodible by wind. Sedimentology 43: 901-911.. 

ENVIRON. 2003a. “Determining Fugitive Dust Emissions from Wind Erosion – Task 2: 

Development of Emission Inventory Specific Emission Factors.” Draft Technical 

memorandum. Prepared for Western Governors’ Association by ENVIRON International 

Corporation, Novato, California; ERG, Inc., Sacramento, California; and Desert Research 

Institute, Reno, Nevada. March 17. 

ENVIRON. 2003b. “Determining Fugitive Dust Emissions from Wind Erosion – Task 1: 

Analysis of Wind Tunnel Study Results, Meteorological and Land Use Data." Technical 

memorandum. Prepared for Western Governors’ Association by ENVIRON International 

Corporation, Novato, California; ERG, Inc., Sacramento, California; Desert Research 

Institute, Reno, Nevada, MACTEC Engineering & Consulting, Gainsville, Florida and 

University of California Riverside, Riverside, California. January 15. 

ENVIRON. 2004. “Determining Fugitive Dust Emissions from Wind Erosion.” Final Report. 

Prepared for Western Governors’ Association by ENVIRON International Corporation, 

Novato, California; ERG, Inc., Sacramento, California; Desert Research Institute, Reno, 

Nevada, MACTEC Engineering & Consulting, Gainsville, Florida and University of 

California Riverside, Riverside, California. March 12. 

Gillette, D.A., 1988. Threshold friction velocities for dust production for agricultural soils. 

Journal of Geophysical Research, 93(D10): 12645-12662. 

Gillette, D.A., Adams, J., Endo, E. and Smith, D., 1980. Threshold velocities for input of soil 

particles into the air by desert soils. Journal of Geophysical Research, 85(C10): 5621-

5630. 
 Page 12 

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Gillette, D.A., Adams, J., Muhs, D. and Kihl, R., 1982. Threshold friction velocities and rupture 

moduli for crusted desert soils for the input of soil particles into the air. Journal of

Geophysical Research, 87(C10): 9003-9015 

Gillette, D.A., Fryrear, W. D., Gill, T. E., Ley, T., Cahill, T. A., and Gearhart, E. A. 1997. 

Relation of vertical flux of PM10 to total Aeolian horizontal mass flux at Owens Lake. J. 

Geophysical. Research. 102:26009-26015. 

Mansell, G.E. and S. Kemball-Cook. 2006. Biogenic Emission Estimates for the Hawaiian 

Islands. Technical Memorandum prepared for Lakes Environmental Software and 

Hawaii Clean Air Branch. December. 

Mansell, G.E., R. Morris and M. Omary. 2004. Recommendations and model performance 

evaluation for the Phase II Windblown Fugitive Dust Emission Project. Technical 

Memorandum prepared for the WRAP Dust Emission Joint Forum. July, 

Mansell, G.E., 2003a. Revised windblown fugitive dust emission estimation methodology 

Technical Memorandum prepared for Michael Uhl, Department of Air Quality 

Management, Clark County, NV. October 6. 

Mansell, G.E., 2003b. Summary of WRAP fugitive dust methodology assumptions model 

sensitivity. Technical Memorandum prepared for Michael Uhl, Department of Air 

Quality Management, Clark County, NV. November 14. 

Marticorena, B., and Bergametti, G., 1995. Modeling the atmospheric dust cycle,1. Design of an 

a soil-derived dust emissions scheme. J. Geophysics. Research. 100: 16415-16430. 

Marticorena, B., Bergametti, G., Gillette, D., and Belnap, J. 1997. Factors controlling threshold 

friction velocity in semiarid and arid areas of the United States. J. Geophysics Research

102 (D19): 23277-23287. 

Nickling, W.G. and Gillies, J.A. 1989. Emission of fine-grained particulate from desert soils. In: 

M. Leinen and M. Sarnthein (Editors), Paleoclimatology and Paleometeorology: Modern 

and Past Patterns of Global Atmospheric Transport. Kluwer Academic Publishers, pp. 

133-165. 

Nickling, W.G. and Gillies, J.A. 1993. Dust emission and transport in Mali, West Africa. 

Sedimentology 40: 859-868. 
APPENDIX J 

ROAD DUST EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
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TECHNICAL 

MEMORANDUM 

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: John Grant, Alison Pollack and Rajashi Parikh 

Date: October 29, 2008 

Subject: Road Dust Emissions Estimates for the Hawaiian Islands 

Fugitive dust from paved and unpaved roads is a significant source of particulate matter (PM) 

emissions. In general the processes that affect paved road dust emissions include the weight of 

the vehicles that drive on the roadway surface, the particles on the roadway surface available for 

entrainment, and precipitation on the roadway which decreases road dust emissions. Common 

among road dust inventories are a number of assumptions that must be made to estimate 

emissions, but for which detailed local data are typically not available: unpaved roadway VMT, 

unpaved roadway silt content, unpaved road surface moisture content, and paved roadway silt 

content. In the following text, the procedure that has been used to estimate road dust emissions 

and the data used to generate these emissions are described. Estimated PM10 and PM2.5 road 

dust emissions for 2002, 2005, and 2018 are then provided. 

General Approach 

Paved Roads 

Paved road dust emissions were estimated by island based on recently revised AP-42 

methodology (EPA, 2006a): 

E = ( k * (s/2)0.65 * (W/3)1.5 – C ) * (1 – P/(4*N)) 

where: E = particulate emission factor (g/VMT) 

k = particle size multiplier for particle size range and units of interest (g/VMT) 

s = road surface silt loading (g/m2

) 

W = mean vehicle weight (tons) 

C = emission factor for 1980's vehicle fleet exhaust, brake wear and tire wear. 

P = number of days with at least 0.01 inch of precipitation 

N = number of days in the averaging period

AP-42 default input parameters were used as shown in Table 1. A mean vehicle weight of 3.18 

tons was estimated based on EPA EIIP documentation 

(http://www.epa.gov/ttn/chief/eiip/techreport/volume09/index.html).

Local silt loading estimates are often not available, as the measurements are expensive, and are 

typically not done for areas where the PM air quality standards are attained. As we were unable 

to find local silt loading estimates for Hawaii, road surface silt loading estimates were taken from
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NEI 2002 documentation, in which silt loadings were assigned by roadway type based on 2001 

average annual traffic volume data from FHWA average daily traffic volume estimates and 

(EPA, 2006) default silt loading values by average daily traffic volume as shown in Table 2. For 

roadway types where average daily traffic volume data were available from (FHWA, 2007), 

2002 and 2005 average annual traffic volume estimates showed silt loadings consistent with NEI 

2002 estimates. For roadway types for which average daily traffic volumes were not available 

(rural minor collector, rural local, and urban local) from (FHWA, 2007), 2002 and 2005 silt 

loading were assumed to be equivalent to those available in NEI 2002 documentation.

Precipitation correction factors were estimated based on three different sources of precipitation 

data in order to incorporate the best available data into emissions estimates. Days of 

precipitation by island, as shown in Table 3, were used to estimate precipitation correction 

factors by island. For islands where meteorological data were provided by Lakes Environmental 

in the form of output data files from the PSU Mesoscale Meteorological Model version 5 (MM5) 

at a horizontal spatial resolution of 4-km, precipitation correction factors were estimated based 

on MM5 measurable days of precipitation and surrogate roadway length from U.S. Census Tiger 

data. Precipitation correction factors were calculated by grid cell and a weighted, island average 

precipitation factor was estimated based on average precipitation by grid cell and surrogate 

roadway length by grid cell. Surrogate roadway length for paved roads was estimated by grid 

cell based on the sum of the length of all US Census Tiger roadway types within that grid cell. 

2018 precipitation correction factors where MM5 data was available were assumed equivalent to 

2005 estimates (2004 in the case of Maui) in order to retain accuracy associated with the by grid 

cell estimates. 

Table 1. Paved roads parameters (EPA, 2006a). 

Parameter PM-10 PM-2.5 

k (g/VMT) 7.3 1.1 

C (g/VMT) 0.2119 0.1617 

Table 2. Road surface silt loading estimates.

Roadway Type 

Silt loading 

(g/m2) 

Interstate 0.015 

Principle arterial 0.03

Minor arterial 0.06

Major collector 0.2

Minor collector 0.2

Rural 

Local 0.2 

Interstate 0.015 

Freeways & 

Expressways 0.015

Principle Arterial 0.03

Minor arterial 0.03

Collector 0.06

Urban 

Local 0.2 

For the islands of Lanai and Molokai, where MM5 data were not available, meteorological data 

drawn from Hawaii State Climate Office period of record data 

(http://lumahai.soest.hawaii.edu/Hsco/ppt.htm), which contains period of record days of 
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precipitation by month, or National Center for Atmospheric Research’s (NCAR) ds472 data set, 

which contains meteorological data for airports for historical calendar years data, were used to 

estimate precipitation correction factors. 

Table 3. Paved road days with at least 0.01 inches of rain by island. 

Cal. 

Year Island FIPS 

Annual 

(n of 365 

days)

Summer 

(n of 92 

days)

Winter (n 

of 92 

days) Source (Period if different for Cal. Year)

2002 Hawaii 15101 167 42 42 MM5 Data 

 Oahu 15301 147 33 38 MM5 Data 

 Maui 15901 97 18 32 MM5 Data 

 Molokai 15902 137 42 37 ds472, Molokai Airport 

 Lanai 15903 114 26 32 HSOC, Lanai City (Period of Record)

 Kahoolawe 15904 No VMT estimated for this island by HDOT 

 Kauai 15701 191 45 45 MM5 Data 

 Niihau 15702 No VMT estimated for this island by HDOT

2005 Hawaii 15101 173 50 33 MM5 Data 

 Oahu 15301 149 35 38 MM5 Data 

 Maui 15901 123 26 34 MM5 Data (CY 2004)

 Molokai 15902 178 36 67 ds472, Molokai Airport 

 Lanai 15903 114 26 32 HSOC, Lanai CIty (Period of Record)

 Kahoolawe 15904 No VMT estimated for this island by HDOT 

 Kauai 15701 183 45 45 MM5 Data 

 Niihau 15702 No VMT estimated for this island by HDOT 

2018 Hawaii 15101 173 50 33 MM5 Data (CY 2005)

 Oahu 15301 149 35 38 MM5 Data (CY 2005)

 Maui 15901 123 26 34 MM5 Data (CY 2004)

 Molokai 15902 105 21 33 HSOC, Molokai Airport (Period of Record)

 Lanai 15903 114 26 32 HSOC, Lanai CIty (Period of Record)

 Kahoolawe 15904 No VMT estimated for this island by HDOT 

 Kauai 15701 183 45 45 MM5 Data (CY 2005)

 Niihau 15702 No VMT estimated for this island by HDOT 

Unpaved Roads 

Unpaved road dust emissions were estimated by island based on recently revised AP-42 

methodology (EPA, 2006b): 

E = [( k * (s/12)a * (S/30)d) / (M/0.5)c] - C * (N – P/N) 

where: E = particulate emission factor (lb/VMT) 

k, a, b, c = empirical constants 

s = road surface silt content (%) 

M = road surface moisture content (%) 

S = mean vehicle speed (mph) 

C = emission factor for 1980's vehicle fleet exhaust, brake wear and tire wear. 

P = number of days with at least 0.01 inch of precipitation 

N = number of days in the averaging period 
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Mean vehicle speed by roadway types were the same as used to estimate Hawaii on-road vehicle 

emissions. Statewide road surface material silt content estimates of 3.8% (EPA, 2006c, NEI 

Appendix C) were applied to all islands. Empirical constants, brake and tire wear emission 

factor estimates, and road surface moisture content defaults were used per AP-42 guidance, as 

shown in Table 4.

Table 4. Unpaved emissions estimation parameters. 

Parameter 

AP-42 

Default1 

k (lb/VMT) 1.8 

a 1 

d 0.5 

c 0.2 

M 50% 

C - PM10 (g/vmt) 0.00047 

C - PM2.5 (g/vmt) 0.00036 

1

 EPA, 2006b (AP-42 text) 

Days of measurable precipitation data used to estimate unpaved road precipitation correction 

factors are shown in Table 4. These were estimated as described above for paved roads, 

although the rural secondary roads from US Census Tiger data were used as surrogate for 

unpaved roads (rather than the total across all road way types used for paved roads) to calculate 

weighted average precipitation correction factors where MM5 data were available. 

Table 4. Unpaved road days with at least 0.01 inches of rain by island. 

Cal. 

Year Island FIPS 

Annual 

(n of 365 

days)

Summer 

(n of 92 

days)

Winter 

(n of 92 

days) Source (Period if different for Cal. Year)

2002 Hawaii 15101 163 40 40 MM5 Data 

 Oahu 15301 148 34 37 MM5 Data 

 Maui 15901 113 23 36 MM5 Data 

 Molokai 15902 137 42 37 ds472, Molokai Airport 

 Lanai 15903 114 26 32 HSOC, Lanai City (Period of Record)

 Kahoolawe 15904 -- -- -- -- 

 Kauai 15701 187 44 44 MM5 Data 

 Niihau 15702 -- -- -- -- 

2005 Hawaii 15101 169 48 33 MM5 Data 

 Oahu 15301 151 35 40 MM5 Data 

 Maui 15901 138 30 36 MM5 Data (CY 2004)

 Molokai 15902 178 36 67 ds472, Molokai Airport 

 Lanai 15903 114 26 32 HSOC, Lanai CIty (Period of Record)

 Kahoolawe 15904 -- -- -- -- 

 Kauai 15701 181 44 45 MM5 Data 

 Niihau 15702 -- -- -- -- 

2018 Hawaii 15101 169 48 33 MM5 Data (CY 2005)

 Oahu 15301 151 35 40 MM5 Data (CY 2005)

 Maui 15901 138 30 36 MM5 Data (CY 2004)
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Cal. 

Year Island FIPS 

Annual 

(n of 365 

days)

Summer 

(n of 92 

days)

Winter 

(n of 92 

days) Source (Period if different for Cal. Year)

 Molokai 15902 105 21 33

HSOC, Molokai Airport (Period of 

Record)

 Lanai 15903 114 26 32 HSOC, Lanai CIty (Period of Record)

 Kahoolawe 15904 -- -- -- -- 

 Kauai 15701 181 44 45 MM5 Data (CY 2005)

 Niihau 15702 -- -- -- -- 

VMT Estimates 

VMT estimates applied to estimate road dust emissions were derived from VMT used to estimate 

Hawaii on-road vehicle emissions. Per HDOT staff, these VMT data include both paved and 

unpaved VMT. Local data regarding the fraction of VMT on unpaved roads was sought from 

HDOT, but was not available. The fraction of statewide VMT by roadway type was taken from 

NEI, which estimated that 3.9%, 2.1% and 0.004% of VMT on rural minor collector, rural local 

and urban local roadways were from unpaved VMT, respectively (EPA, 2006c, Appendix B). 

The total unpaved VMT was distributed to each island according to the Hawaii Data Book 

fraction of unpaved roads on each island (HDEBDT, 2007). According to the State of Hawaii 

Data Book, statewide unpaved roadway length did not change from 2002 to 2005, and was 

assumed to remain constant in 2018. VMT estimates and unpaved roadway length by island are 

shown in Table 6. It was assumed that there was no variation in activity by season and weekday 

and weekend activity variation were assumed equal to fractions used in on-road emission 

estimates. 

Table 6. VMT estimates and unpaved roadway length. 

Paved VMT (1000 miles/day) 

Unpaved VMT (1000 

miles/day) 

Island 2002 2005 2018 2002 2005 2018 

Unpaved 

Roadway 

Length1 

Hawaii 3,758 4,504 6,107 11.0 17.7 23.9 59.3 

Kauai 1,751 2,158 2,928 4.2 6.7 9.1 22.49 

Lanai 62 97 131 2.6 4.1 5.6 14 

Maui 3,145 3,509 4,747 10.5 16.9 22.8 56.7 

Molokai 184 274 371 2.2 3.6 4.8 12 

Oahu 15,104 17,127 23,036 1.5 2.4 3.2 7.92 

Totals 24,003 27,670 37,320 31.8 51.3 69.4 172.41 

1

 Source: HDBEDT, 2007. 

Results 

Emissions estimates shown in Table 7 and presented graphically in Figure 1 and Figure 2 are 

based on paved and unpaved estimated emission factors and VMT by island. Emissions increase 

from 2002 to 2005 and from 2005 to 2018 based on VMT growth. Over 50% of paved road dust 

emissions are from Oahu Island for 2002, 2005, and 2018, consistent with paved VMT estimates 

which show a majority of paved VMT on Oahu Island. The most significant contributions to 

unpaved road dust emissions are from Hawaii and Maui Islands at between 30% and 40% of total 

emissions for 2002, 2005, and 2018, consistent with unpaved roadway VMT estimates which
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show the highest unpaved VMT on these islands.

Table 7. Paved and unpaved road dust emissions by island (tons/year).

Paved Unpaved Total 

Year FIPS Island PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 

2002 15101 Hawaii 901 28 625 62 1,527 90

 15301 Oahu 2,693 111 90 9 2,783 120

 15701 Kauai 579 28 209 21 787 49

 15901 Maui 832 32 747 74 1,579 106

 15902 Molokai 105 7 142 14 247 21

 15903 Lanai 36 2 183 18 219 21

Total 5,147 209 1,995 198 7,142 408

2005 15101 Hawaii 1,161 42 980 97 2,142 140

 15301 Oahu 3,548 165 143 14 3,691 179

 15701 Kauai 746 38 348 35 1,094 72

 15901 Maui 973 42 1,082 108 2,055 150

 15902 Molokai 152 10 188 19 340 29

 15903 Lanai 57 4 294 29 351 33

Total 6,638 301 3,035 302 9,673 603

2018 15101 Hawaii 1,575 57 1,327 132 2,901 189

 15301 Oahu 4,772 221 193 19 4,966 241

 15701 Kauai 1,012 51 470 47 1,483 98

 15901 Maui 1,316 57 1,464 146 2,780 203

 15902 Molokai 218 15 354 35 572 50

 15903 Lanai 76 5 399 40 475 45

Total 8,970 407 4,207 418 13,177 825
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0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2002

Paved

2005

Paved

2018

Paved

2002

Unpaved

2005

Unpaved

2018

Unpaved

Year

PM10 Emissions (tons/year)

Lanai

Molokai

Maui

Kauai

Oahu

Hawaii

Figure 1. Annual road dust PM10 emissions. 

0

50

100

150

200

250

300

350

400

450

2002

Paved

2005

Paved

2018

Paved

2002

Unpaved

2005

Unpaved

2018

Unpaved

Year

PM2.5 Emissions (tons/year)

Lanai

Molokai

Maui

Kauai

Oahu

Hawaii

Figure 2. Annual road dust PM2.5 emissions. 
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References 

EPA, 2006a. Compilation of Air Pollution Emission Factors (AP-42) – Volume 13: 

Miscellaneous Sources, Section 2.1 Paved Roads, Fifth Edition. U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. January. Internet address: http://www.epa.gov/ttn/chief/ap42. 

EPA, 2006b. Compilation of Air Pollution Emission Factors (AP-42) – Volume 13: 

Miscellaneous Sources, Section 2.2 Unpaved Roads, Fifth Edition. U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. January. Internet address: http://www.epa.gov/ttn/chief/ap42. 

EPA. 2006c. Documentation for the Draft 2002 Nonpoint Source National Emission Inventory 

for Criteria and Hazardous Air Pollutants (February 2006 Version). Prepared by E.H. 

Pechan & Associates, Inc. EPA Contract No. 68-D-02-063. Internet address: 

http://www.epa.gov/ttn/chief/net/2002inventory.html. 

EPA, 1993. Background Document -Emission Factor Documentation for AP-42, Section 13.2.1 

Paved Roads - March 1993. Internet address: 

http://www.epa.gov/ttn/chief/ap42/ch13/bgdocs/b13s02-1.pdf 

HDBEDT 2007. 2002 State of Hawaii Databook, Section 18: Transportation, Table 18.062 

Hawaii State Department of Business, Economic Development, and Tourism. Accessed 

March 2007. Internet address: http://www.hawaii.gov/dbedt/info/economic/databook/
APPENDIX K 

AGRICULTURAL TILLING EMISSIONS ESTIMATES FOR THE 

HAWAIIAN ISLANDS 
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J:\Hawaii EI\Reporting\Tech memos\HI_agtilling_042009rev.doc 

TECHNICAL 

MEMORANDUM

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: John Grant, Alison Pollack and Rajashi Parikh 

Date: August 31, 2009 

Subject: Agricultural Tilling Emissions Estimates for the Hawaiian Islands 

AGRICULTURAL TILLING 

Agricultural tilling emissions are those resulting from the preparation of lands for planting and 

post-harvest activities. Particulate matter is emitted as a result of the disturbance of the soil by 

the machinery employed in this activity. The crops considered to be important in this calculation 

for the state of Hawaii were those row crops with acreage data on the National Agricultural 

Statistics Service website (NASS, 2007a). The most important crops in Hawaii related to 

agricultural tilling emissions are sugarcane, pineapple, and seed-crops. 

General Approach 

The approach used to estimate emissions from agricultural tilling is based on methodology 

implemented in EPA, 2001: 

E = c * k * s0.6 * p * a 

where: E = PM emissions 

c = constant 4.8 lbs/acre-pass 

k = dimensionless particle size multiplier (PM10=0.21; PM2.5=0.042) 

s = silt content of surface soil, defined as the mass fraction of particles smaller 

than 75 microns diameter found in soil to a depth of 10 cm (%) 

p = number of passes or tillings in a year 

a = acres of land tilled 

The soil preparation activities considered occur both post harvest and prior to planting. For most 

of the crops considered, tilling activity is most closely linked to pre-harvest preparations; 

therefore, the tilling activity in a given year is more directly linked to acres planted. As acres 

planted data were not available, we have assumed that the acreage harvested in a given year 

provides an acceptable activity estimate for all tilling activities, including those relating to 

planting. 
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In determining the seasonality of agricultural tilling emissions, University of Hawaii Agricultural 

Extension staff indicated that harvest and planting operations occur year round (Keyser, 2007). 

Emissions from agricultural tilling have therefore been assumed to be uniform, year-round, with 

the exception of tilling emissions associated with seed-crop. A representative of the Monsanto 

Company familiar with seed-crop activity stated that 60% to 65% of planting and harvesting 

occurs from October to February (Rossio, 2007). Therefore, seed-crop activity was assumed to 

occur as shown in Table 1. 

Table 1. Seed-crop activity profile. 

 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 

Activity 

Fractions 

(Rossio, 2007) 62.5% 37.5% 

Monthly Fraction 12.5% 12.5% 12.5% 12.5% 12.5% 5.4% 5.4% 5.4% 5.4% 5.4% 5.4% 5.4%

No tilling emissions have been estimated for coffee, macadamia nuts, bananas, avocados, 

guavas, and tropical specialty fruits, as these crops are frequently planted without the use of 

tilling operations (Keyser, 2007; Bittenbender, 2007) and the duration of time between planting 

cycles is much longer than for other crops (Bittenbender, 2007; USDA, 2006). Ninety percent 

of papayas grown on the island of Hawaii are planted without the use of tilling operations and no 

tilling emissions have been estimated for a 90% fraction of papayas (Ferreira, 2007). 

Silt Content

A weighted average percent silt content by island was estimated based on the land coverage by 

USDA soil type classification from USGS LULC Database (Mansell and Cook, 2006) and 

percent silt content by USDA soil classification as shown in Table 2. Percent silt content by soil 

type was taken from EPA, 2001 when available, and from the USDA soil classification triangle 

when not available as shown in Table 3. 

Table 2. Weighted average percent silt content by island. 

Island Silt Content 

Hawaii 44% 

Maui 46% 

Lanai 51% 

Molokai 52% 

Oahu 48% 

Kauai 51% 
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Table 3. Percent silt content by USDA soil classification. 

Soil Type Silt Content Reference 

Sand 0.12

Loamy Sand 0.12

Sandy Loam 0.33

Silt Loam 0.52

Clay Loam 0.29

Clay 0.29

Organic Materials 0.46

EPA, 2001 

Silt 0.90

Loam 0.40

Sandy Clay Loam 0.15

Silty Clay Loam 0.55

Sandy Clay 0.10

Silty Clay 0.50

USDA Soil 

Classificatio

n Triangle 

Number of Passes

The number of passes or tillings per acre of crop harvested was taken from the RUSLE2 model 

management scenarios (USDA, 2007) as shown in Table 4. If information was not available for 

the number of passes, the EPA, 2001 default value of 3 passes/year was assumed. 

Acres of Land Tilled

Table 5 shows the total acres of crops harvested for the state of Hawaii for 2002 and 2005 

(NASS, 2007a). 

 

With the exception of seed crop land area, acres harvested for Maui County were distributed 

according to the acres of agricultural land area on each Maui County island based on the NOAA 

Coastal Service Landcover (Mansell and Cook, 2006). Note that the NOAA Coastal Landcover 

indicated no agricultural area on Kahoolawe or Niihau Island, so it was assumed that there are no 

land tillage emissions associated with these islands. Per Rossio (2007), seed-crop are grown on 

Kauai, Oahu, Maui, and Molokai. Due to confidentiality considerations, information was 

unavailable regarding the exact distribution of production by island. Per the advice of a 

representative of Monsanto Company familiar with seed-crop activity (Rossio, 2007), equivalent 

activity on each island was assumed. 
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Table 4. Number of passes per crop for Hawaii crops. 

Category 

Passes 

per Harvest 

Sugarcane 3 

Pineapple 3

Snap Beans 31

Chinese Cabbage 5 

Head Cabbage 5 

Celery 5 

Lettuces 5 

Romaine 5 

Mustard 3 

Tomatoes 5 

Radish 5 

Sweet Potatoes 3 

Italian Squash 2 

Watermelon 5 

Pumpkin 3 

Squash Oriental 3 

Taro 3 

Green Peppers 31

Watercress 31

Ginger 3 

Seed Crop 62

Dry Onions 5 

Green Onions 3 

Sweet Corn 5 

Cucumbers 3 

Daikon 5 

Eggplant 4 

Bitter melon 31

Broccoli 31

1

 EPA, 2001 default value 

2

 Rossio, 2007 
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Table 5. Hawaii acres of crops tilled. 

Category 2002 2005

Sugarcane 21300 21700

Pineapple1 6367 4667

Snap Beans 180 130

Chinese Cabbage 340 260

Head Cabbage 540 430

Celery 50 25

Lettuces 120 110

Romaine 150 140

Mustard 130 110

Tomatoes 580 660

Radish 10 10

Sweet Potatoes 300 410

Italian Squash 175 200

Watermelon 550 450

Pumpkin 80 35

Squash Oriental 10 40

Taro 430 360

Green Peppers 215 230

Watercress 35 35

Ginger 160 100

Seed Crop1 3100 3870

Dry Onions 200 220

Green Onions 125 155

Sweet Corn 610 410

Cucumbers 390 440

Daikon 250 250

Eggplant 60 80

Bitter melon 20 30

Broccoli 150 125

Papayas 167 148

1 Koehler, 2007 

2018 Future Year Emissions

Any estimate of crop tilling activity or emissions in 2018 is speculative, and there is no clear 

growth or decline factor available. 2018 emissions are therefore set to the same levels as 2005. 

Results 

By island emissions and activity estimates, presented in Table 6, reveal that over 50% of crop 

acres tilled and over 50% of agricultural tilling emissions occur on Maui Island. Of all crops, 

sugar cane makes up the highest percentage of total statewide acres tilled at greater than 57% 

and 60% of all tilled acres in 2002 and 2005, respectively. Greater than 70% of the acres of 
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sugar cane harvested statewide in 2002 and 2005 are estimated to be harvested on Maui Island, 

contributing to the significantly higher agricultural tilling emissions on Maui Island relative to 

other islands. Emissions decrease slightly from 2002 to 2005. 

Tilling activity is assumed to occur seven days per week (EPA, 2002). Seasonal emissions 

presented are summer and winter average day and average weekday. All particulate matter 

emissions from agricultural tilling are filterable. 

Table 6. Agricultural tilling emissions by island. 

Annual Emissions 

(tpy) 

Summer Day 

Emissions (tpd) 

Winter Day 

Emissions (tpd) 

Island 

Annual 

Activity 

(acres tilled) PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 

2002 Emissions Summary 

Hawaii 2,532 50.682 10.136 0.139 0.028 0.139 0.028 

Maui 21,619 358.823 71.765 0.960 0.192 1.014 0.203 

Kahoolawe 0 0.000 0.000 0.000 0.000 0.000 0.000 

Lanai 4 0.096 0.019 0.000 0.000 0.000 0.000 

Molokai 1,033 30.623 6.125 0.059 0.012 0.117 0.023 

Oahu 3,703 84.905 16.981 0.209 0.042 0.265 0.053 

Kauai 7,902 149.561 29.912 0.385 0.077 0.443 0.089 

Nihau 0 0.000 0.000 0.000 0.000 0.000 0.000 

Totals 36,794 674.69 134.94 1.75 0.35 1.98 0.40 

2005 Emissions Summary 

Hawaii 2,121 42.700 8.540 0.117 0.023 0.117 0.023 

Maui 20,341 340.145 68.029 0.903 0.181 0.971 0.194 

Kahoolawe 0 0.000 0.000 0.000 0.000 0.000 0.000 

Lanai 4 0.083 0.017 0.000 0.000 0.000 0.000 

Molokai 1,188 36.096 7.219 0.068 0.014 0.141 0.028 

Oahu 3,211 73.583 14.717 0.172 0.034 0.242 0.048 

Kauai 8,019 152.761 30.552 0.388 0.078 0.460 0.092 

Nihau 0 0.000 0.000 0.000 0.000 0.000 0.000 

Totals 34,884 645.37 129.07 1.65 0.33 1.93 0.39 
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REFERENCES 

Bittenbender, H.C. 2007. Phone conversation with H.C. Bittenbender, Extension Specialist for 

Coffee and Kava. University of Hawaii at Manoa, Department of Tropical Plant and Soil 

Sciences. March 7, 2007. 

EPA. 2002. Source Category Code / Temporal Allocation Cross Reference File. U.S. 

Environmental Protection, Office of Air Quality Planning and Standards, Research 

Triangle Park, North Carolina. April. Internet address: 

http://www.epa.gov/ttn/chief/emch/temporal/index.html. 

EPA. 2001. Procedures Document for National Emission Inventory, Criteria Air Pollutants 

1985-1999. Washington D.C. March 2001. EPA-454/R-01-006. 

Ferreira, S. 2007. Phone conversation with S. Ferreira, Extension Specialist for Papaya. 

University of Hawaii at Manoa, Department of Tropical Plant and Soil Sciences. March 

8, 2007. 

Koehler. 2007. Phone conversation with Paul Koehler, Monsanto Company. May 15, 2007. 

Mansell, G.E. and S. Kemball-Cook. 2006. Biogenic Emission Estimates for the Hawaiian 

Islands. Technical Memorandum prepared for Lakes Environmental Software and 

Hawaii Clean Air Branch. December. 

Keyser, H. 2007. Phone conversation with Harold Keyser, County Administrator. University of 

Hawaii Kahului Cooperative Extension Office. February 28, 2007. 

NASS. 2007a. Agricultural Statistics Data Base. National Agricultural Statistics Service, 

Washington D.C January. Internet address: 

http://www.nass.usda.gov/hi/stats/t_of_c.htm

NASS. 2007b. Phone conversation with Steven Gunn, Deputy Director. National Agricultural 

Statistics Service Hawaii Field Office. February 28, 2007. 

Rossio. 2007. Email communication from Marianne Rossio, Hawaii Department of Health, 

Clean Air Branch. May 8, 2007. 

USDA. 2006. RUSLE2 Model version 1.26.6.4. 

http://fargo.nserl.purdue.edu/rusle2_dataweb/RUSLE2_Index.htm
APPENDIX L 

FIRE EMISSION INVENTORY 
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TECHNICAL MEMORANDUM

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: Amnon Bar-Ilan, Rajashi Parikh, Gerry Mansell, Alison Pollack 

Date: March 24, 2009 

Subject: Fire Emission Inventory 

INTRODUCTION 

This Technical Memorandum describes the development of an emissions inventory for the State 

of Hawaii for wildfire and agricultural burning. The emission estimates are reported as per the 

requirements of the Lakes Environmental modeling software for which they will be used. The 

2002 and 2005 Hawaii emission inventory contains both activity information and emission 

estimates for wildfire and agricultural burning which are delivered as two distinct products. 

Activity Data 

Fire activity data for the 2002 and 2005 Hawaii emission inventory were gathered from personal 

communications with the Hawaii Department of Health (DOH) Clean Air Branch, from personal 

communication with Hawaii Protection Foresters for each county in the state, from personal 

communication with major agricultural burning facilities, and from databases of information 

maintained by Hawaii DOH. These data varied in resolution, completeness and accuracy. The 

methods described in this report involved transforming raw data from the various sources into 

the most consistent and useful emissions inventory for Hawaii. Emissions for agricultural 

burning and wildfire burning were calculated by island. The various methodologies employed 

for estimating the fire emissions inventory are documented in this report. 

Emission Calculation Overview 

The 2002 and 2005 Hawaii fire emissions inventory adopted techniques developed by the 

Western Regional Air Partnership (WRAP) used in other regional fire emissions inventory 

assessments (WRAP, 2007). The general fire emission calculation used the following equation: 

emission mass (ton pollutant) = fire size (acre) * fuel loading (ton/acre) 

 * emission factor (lb/ton) * 0.0005 (ton/lb) 

where:
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• emission mass was calculated per year per pollutant for wildland fire and agricultural 

burning 

• fire size was an island-specific input 

• in the case of wildfires, fuel loading was selected from the Fire and Environmental 

Application Research Team website, and in the case of agricultural burning it was 

acquired from EPA, AP-42, open burning section 

• the wildfires emission factors were selected from the WRAP Fire Ph3-4 EI Report and 

for the agricultural burning emissions factors for sugarcane were obtained from the 

applicable AP- 42 open burning section using the midpoint if a range of values was 

presented

FIRE ACTIVITY DATABASE COMPILATION 

Data Sources 

The goal of the data collection was to obtain a complete description of the fire activity in 2002 

and 2005 as the available data allowed. The available site-specific sources of wildfire and 

agricultural burning activity data collected for the 2002 and 2005 Hawaii inventory are shown in 

Table 1. 

Table 1. Wildfire and agricultural burning activity data sources. 

Data Source 

Source 

Type Description 

Division of Wildlife and Forestry 

of each island Wildfire 

Information on acres burned and fuel loading for 2002 

and 2005 calendar year (Hatayama, 2007; Pawn, 2007; 

DeSilva, 2007; and Smith, 2007) 

WRAP Fire Ph3-4 EI Report Wildfire 

Emission factors for non-piled prescribed fire (WRAP, 

2007) 

Department of Health, Hawaii Agricultural

Information on acres burned, fuel loading and emission 

factors for 2002 calendar year (HDH, 2007) 

HC&S , Gay & Robinson Inc. Agricultural

Information on acreage burned data for each individual 

facility (Gay & Robinson, 2007 and HC&S, 2007a) 

EIIP, AP -42, Open Burning Agricultural

Emission factors for sugarcane and fuel loading of 

pineapple (AP-42, Feb. 2007) 

WRAP Fire Ph- II EI Report Agricultural Emission factors for sugarcane and fuel loading 

Wildfire Activity Data Compilation 

The wildfire emissions were calculated by island and by type of fire (e.g., grassland, shrubland, 

and forest) for 2002 and 2005. The total acreage burned due to wildfire was obtained from the 

Division of Forestry and Wildlife’s Protection Foresters for each island. For Hawaii and Maui 

islands the total acreage burned was distributed among the types of fires by applying the fraction 

of land usage for that type of wildland. For Oahu and Kauai Island the total acreage data were 

provided as well as the type of land that burned. The land usage data by type of wildland was 

obtained from the U.S Department of Commerce (1995). Non-piled prescribed fire emission 

factors were used from WRAP Fire Ph 3-4 EI report. The fuel loading data was obtained from 

the Fire and Environmental Application Research Team website (Feb, 2007). This website 

provided the fuel loading information for each type of wildland in the state of Hawaii. 
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Depending on the type of wildland, the average fuel loading data were used for the estimation. 

Table 2 and Table 3 summarize the fuel loading and emission factors used in the analysis. 

Average emissions per day for summer and winter were calculated by applying temporal 

allocation factors. To estimate seasonal emissions, the activity data was assumed to be uniformly 

distributed for all seasons (i.e. 25% each for winter, spring, summer and fall). 

The final emission calculations are shown below. 

acre burned (tons ) 0.0005 (tons / lb)

acre burned

tons fuel

tons fuel

lbs emissions emissions = × × ×

Table 2. Wildland fire fuel loadings. 

Type of Wild 

Land 

Default Fuel 

Loading 

(ton/acres) 

Fuel Loading 

from Oahu 

(ton/acres) 

Grass Land 5.74 4.34 

Shrub Land 10.65 9.42 

Forest 39 12.75 

Table 3. Wildfire emission factors. 

TSP PM10 PM25 EC OC VOC CH4 NH3 NOx CO SO2 PMC

(lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton)

Grassland 34.1 28.1 24.1 1.5 11.6 13.6 13.6 1.3 6.2 289 1.7 4

Shrubland 34.1 28.1 24.1 1.5 11.6 13.6 13.6 1.3 6.2 289 1.7 4

Forest 34.1 28.1 24.1 1.5 11.6 13.6 13.6 1.3 6.2 289 1.7 4

Agricultural Burning Activity Data Compilation: 

Burning of sugarcane fields is extensive in the state of Hawaii compared to burning of other 

agricultural crops. Hawaiian Commercial & Sugar Company (HC&S) is Hawaii's largest 

producer of raw sugar on Maui Island, accounting for over 60% of all of the state's sugar 

(HC&S, 2007b). Gay and Robinson is also a large producer of raw sugar on Kauai Island. HC&S 

and Gay and Robinson are considered to be the major facilities contributing to agricultural 

burning activity in the state of Hawaii. For calculating emissions of 2002 and 2005 agricultural 

burning, activity data for total acreage burned by HC&S in 2002 were obtained from the Hawaii 

DOH. The data for Gay and Robinson were obtained from Gay and Robinson, and HC&S 

provided the HC&S 2005 acreage burned data. 

In addition to these two large sources, there are a number of medium-sized and small-sized 

agricultural businesses in Hawaii. The medium-sized businesses, which cultivate crops like 

pineapple and macadamia nuts, were considered in this analysis. The acreage burned from the 

medium size facilities in 2002 and 2005 were provided by the Hawaii DOH. The emission 

factors and fuel loading were obtained from WRAP Fire Phase – II EI Report. For pineapple, 

emission factors were not available in the WRAP fire report and hence, AP-42 open burning 

section emission factors for pineapple were used. For analysis, we have considered NMTOC to 

be equivalent to VOC for pineapple. The fuel loading of pineapple is not specified in AP-42 and 
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hence, for estimation, the fuel loading of an “unspecified crop” has been used. For pineapple 

crop, AP-42 does not provide any guidance on breaking down PM Ef to PM10 and PM2.5 Efs. 

To make a conservative assumption ENVIRON recommended assuming pineapple PM Ef equal 

to PM2.5 Ef and PM10 Ef equal to zero. Table 4 and Table 5 summarize the fuel loading and 

emission factors used in the analysis. Average emissions per day for summer and winter were 

calculated by applying temporal allocation factors. These factors were estimated from the day to 

day HC&S acreage burned data provided for 2002 from the Hawaii DOH. 

Table 4. Agricultural fire fuel loadings. 

Type of Wild Land 

Fuel Loading 

(ton/acres) 

Unspecified Crop 2.6 

Macadamia Nuts 2.5 

Sugar Cane 14 

Unspecified weeds 1 

Pineapple 2 

Corn 4.2 

Table 5. Agricultural burning emission factors. 

PM PM10 PM25 EC OC VOC CH4 NH3 NOx CO SO2 PMC CROP (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton) (lb/ton)

Unspecified 

Crop 9.01 8.86 8.47 2.22 3.81 5.96 4.5 1.27 2.51 63.55 0.33 0.39

Macadamia 

Nuts 11.16 10.96 10.25 2.74 4.71 8.85 6.68 1.85 7.3 92.7 0.14 0.7

Sugar Cane 11.2 10.86 9.98 1.63 4.02 3.68 0.82 1.02 2.8 50.96 1.24 0.88

Unspecified 

weeds 18.02 17.73 16.95 4.43 7.62 11.93 9.01 2.54 5.02 127.09 0.67 0.78

Pineapple 8 0 8 N/A N/A 6 N/A N/A N/A 112 N/A N/A 

Corn 12.62 12.42 11.96 1.86 4.35 9.12 3.5 1.55 3.64 77.56 0.4 0.46

N/A – Not available

ASSUMPTIONS AND UNCERTAINTY 

The following assumptions or uncertain data should be noted: 

• The wildfire and agricultural emissions were not estimated for the 2018 calendar year. 

Due to the unpredictable nature of wildfires it is not possible to project 2018 wildfire 

emissions based on either 2002 or 2005 wildfire emissions. Agricultural burning occurs 

at a relatively constant level in Hawaii where a significant increase in cultivated acres is 

not likely by 2018. 

• Forested land fuel loading factors were obtained from the Fire and Environmental 

Application Research Team website for each island in the state and then averaged. This 

was compared with data for 2005 forested land fuel loading provided directly by the 

Oahu Forestry Department. It was found that the factor provided by Oahu Forest 
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Department did not agree well with the average factor, and thus the average factor was 

used. 

• For the agricultural burning two major facilities as well as the medium size facilities were 

considered. The Hawaii DOH has made some assumptions to estimate the actual acreage 

burned from medium size facilities in 2002. They were unable to obtain the actual 

acreage burned data from some of the medium size facilities for 2002 and made an 

estimation assuming that 2005 and 2002 acreage burned would be same for those 

facilities. The emissions from small facilities were not included in the analysis, as the 

data for these facilities were not available and they were determined to represent only a 

small fraction of the total agricultural acres burned in the state. 

• To estimate the summer and winter average daily emissions, temporal fractions used for 

agricultural burning were based on HC&S 2002 daily data provided by the Hawaii DOH. 

The fraction was assumed to be constant for all the facilities as we do not have any 

information on how much acreage was burned in winter and summer for different 

facilities. For wildfires, the summer and winter average daily emissions were estimated 

with the assumption of uniform distribution of activity data for all seasons. 

SUMMARY OF RESULTS 

Table 6 summarizes the wildfire emissions for 2002 and 2005 by island. Table 7 summarizes 

emissions from the agricultural burning in 2002 and 2005. 
 

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J:\Hawaii EI\Reporting\Tech memos\Wildland_Ag_Fire_Emissions_032309.doc Table 6. Wildland fire emissions by island.

Island TSP PM10 PM25 EC OC VOC CH4 NH3 NOx CO SO2 

2002 Annual Emissions (tons/yr)

Hawaii 79.72 65.70 56.34 3.51 27.12 31.80 31.80 3.04 14.50 675.67 3.97 Kauai 0.58 0.48 0.41 0.03 0.20 0.23 0.23 0.02 0.10 4.89 0.03 Maui 97.02 79.95 68.57 4.27 33.00 38.70 38.70 3.70 17.64 822.28 4.84 Oahu 8.81 7.26 6.23 0.39 3.00 3.51 3.51 0.34 1.60 74.65 0.44 

2005 Annual Emissions (tons/yr)

Hawaii 9416 7760 6655 414 3203 3756 3756 359 1712 79805 469 Kauai 34 28 24 2 12 14 14 1 6 291 2 Maui 284 234 201 12 97 113 113 11 52 2408 14 Oahu 2123 1749 1500 93 722 847 847 81 386 17992 106 

2002 Summer Average Day Emissions (tons/day)

Hawaii 0.217 0.179 0.153 0.010 0.074 0.086 0.086 0.008 0.039 1.836 0.011 Kauai 0.002 0.001 0.001 0.000 0.001 0.001 0.001 0.000 0.000 0.013 0.000 Maui 0.264 0.217 0.186 0.012 0.090 0.105 0.105 0.010 0.048 2.234 0.013 Oahu 0.024 0.020 0.017 0.001 0.008 0.010 0.010 0.001 0.004 0.203 0.001 

2005 Summer Average Day Emissions (tons/day)

Hawaii 25.588 21.086 18.084 1.126 8.704 10.205 10.205 0.975 4.652 216.861 1.276 Kauai 0.093 0.077 0.066 0.004 0.032 0.037 0.037 0.004 0.017 0.790 0.005 Maui 0.772 0.636 0.546 0.034 0.263 0.308 0.308 0.029 0.140 6.544 0.038 Oahu 5.769 4.754 4.077 0.254 1.962 2.301 2.301 0.220 1.049 48.892 0.288 

2002 Winter Average Day Emissions (tons/day)

Hawaii 0.221 0.182 0.157 0.010 0.075 0.088 0.088 0.008 0.040 1.877 0.011 Kauai 0.002 0.001 0.001 0.000 0.001 0.001 0.001 0.000 0.000 0.014 0.000 Maui 0.270 0.222 0.190 0.012 0.092 0.107 0.107 0.010 0.049 2.284 0.013 Oahu 0.024 0.020 0.017 0.001 0.008 0.010 0.010 0.001 0.004 0.207 0.001 

2005 Winter Average Day Emissions (tons/day) 

Hawaii 26.157 21.554 18.486 1.151 8.898 10.432 10.432 0.997 4.756 221.680 1.304 Kauai 0.095 0.078 0.067 0.004 0.032 0.038 0.038 0.004 0.017 0.807 0.005 Maui 0.789 0.650 0.558 0.035 0.268 0.315 0.315 0.030 0.143 6.689 0.039 Oahu 5.897 4.860 4.168 0.259 2.006 2.352 2.352 0.225 1.072 49.979 0.294 
 

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Island TSP PM10 PM25 EC OC VOC CH4 NH3 NOx CO SO2 

2002 Annual Emissions (tons/yr)

Hawaii 3.08 3.03 2.83 0.76 1.30 2.44 1.85 0.51 2.02 25.61 0.04 Kauai 395 383 352 57 142 130 29 36 99 1,799 44 Maui 1,230 1,192 1,096 179 441 404 90 112 307 5,595 136 Oahu 8.26 1.83 8.16 0.27 0.64 6.14 0.51 0.23 0.54 101 0.06 

2005 Annual Emissions (tons/yr)

Hawaii 3.55 3.49 3.27 0.87 1.50 2.75 2.08 0.58 2.15 29 0.06 Kauai 421 408 375 61 151 140 32 38 105 1,923 46 Maui 1,190 1,154 1,060 173 427 391 87 108 297 5,414 132 Oahu 2 2 2 0.27 0.64 1.34 0.51 0.23 0.54 11 0.06 

2002 Summer Average Day Emissions (tons/day)

Hawaii 0.011 0.011 0.010 0.003 0.005 0.009 0.007 0.002 0.007 0.092 0.000 Kauai 1.415 1.372 1.261 0.206 0.508 0.467 0.105 0.129 0.354 6.452 0.156 Maui 4.411 4.277 3.930 0.642 1.583 1.449 0.323 0.402 1.103 20.068 0.488 Oahu 0.030 0.007 0.006 0.001 0.020 0.011 0.002 0.001 0.002 0.362 0.000 

2005 Summer Average Day Emissions (tons/day)

Hawaii 0.013 0.013 0.012 0.003 0.005 0.010 0.007 0.002 0.008 0.104 0.000 Kauai 1.509 1.464 1.346 0.220 0.542 0.503 0.115 0.138 0.378 6.897 0.166 Maui 4.268 4.139 3.803 0.621 1.532 1.402 0.313 0.389 1.067 19.421 0.473 Oahu 0.007 0.007 0.006 0.001 0.002 0.005 0.002 0.001 0.002 0.041 0.000 

2002 Winter Average Day Emissions (tons/day)

Hawaii 0.005 0.004 0.004 0.001 0.002 0.004 0.003 0.001 0.003 0.038 0.000 Kauai 0.579 0.561 0.516 0.084 0.208 0.191 0.043 0.053 0.145 2.638 0.064 Maui 1.803 1.749 1.607 0.262 0.647 0.593 0.132 0.164 0.451 8.206 0.200 Oahu 0.012 0.003 0.003 0.000 0.008 0.004 0.001 0.000 0.001 0.148 0.000 

2005 Winter Average Day Emissions (tons/day) 

Hawaii 0.005 0.005 0.005 0.001 0.002 0.004 0.003 0.001 0.003 0.042 0.000 Kauai 0.617 0.598 0.550 0.090 0.221 0.206 0.047 0.056 0.155 2.820 0.068 Maui 1.745 1.692 1.555 0.254 0.626 0.573 0.128 0.159 0.436 7.941 0.193 Oahu 0.003 0.003 0.003 0.000 0.001 0.002 0.001 0.000 0.001 0.017 0.000 
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REFERENCES 

AP -42 Section 2.5. 2007. Open Burning. Emission Factors and Fuel Loading factors for open 

Burning of Agricultural Material (Sugarcane). February. 

http://www.epa.gov/ttn/chief/ap42/ch02/final/c02s05.pdf

De Silva, Lance. 2007. Division of Forestry and Wildlife, Maui Island. Personal 

communication. January. 

Fire and Environmental Application Research Team. 2007. January. 

http://depts.washington.edu/nwfire/dps/ 

Gay and Robinson, Inc. 2007. Personal communication with Howard Greene. February 12. 

Hatayama, J. 2007. Division of Forestry and Wildlife, Hawaii Island. Personal communication. 

January. 

Hawaii Department of Health (HDH). 2007. Clean Air Branch. Personal Communication with 

Marianne Rossio, January. 

HC&S. 2007a. Personal Communication with Derek Heafey. February 9. 

HC&S. 2007b. http://www.hcsugar.com/index.html. February. 

Pawn, Earl. 2007. Division of Forestry and Wildlife, Oahu Island. Personal communication. 

January. 

Smith, Dan K. 2007. Division of Forestry and Wildlife, Kauai Island. Personal 

communication. January. 

U.S. Department of Commerce. 1995. Documentation on Guidance for Regional 

Implementation, NOA Technical Report NMFS 123, Dobson, J. et al. April. 

Western Regional Air Partnership. 2007. Development of 2000-04 Baseline Period and 2018 

year Emission Inventories. http://www.airsci.com/wrap/phase3-

4/Workplan_Ph3and4_Final.pdf January. 

Western Regional Air Partnership. 2007. 2002 Fire Emission Inventory for WRAP region 

Phase-II.

http://www.wrapair.org/forums/fejf/documents/WRAP_2002_PhII_EI_Report_20050722

.pdf 
APPENDIX M 

BIOGENIC EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
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TECHNICAL MEMORANDUM 

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: Gerard Mansell and Sue Kemball-Cook 

Date: 29 March 2007 

Subject: Biogenic Emissions Estimates for the Hawaiian Islands 

Introduction 

Emissions from natural sources are important to the photochemistry of the lower atmosphere. In 

particular, emissions of volatile organic compounds (VOCs), nitrogen oxides (NOx) and carbon 

monoxide (CO) from plants and soils (biogenic emissions) are precursors to the formation of 

tropospheric ozone (Fehsenfeld et al., 1992). Biogenic emissions of ozone precursors depend 

upon many factors including plant species type, plant biomass density, soil type, ambient 

temperature and solar radiation. These relationships introduce spatial and temporal variations 

into the rates of biogenic emissions such that detailed environmental input data and complex 

computer models are required to accurately estimate biogenic emissions. 

A number of biogenic emission estimation models are currently available. These include EPA’s 

Biogenic Emission Inventory System (BEIS), the Global Biosphere Emissions and Interactions 

System (GloBEIS) developed by the National Center for Atmospheric Research and ENVIRON, 

and the Model of Emissions and Gases and Aerosols from Nature (MEGAN), a state-of-thescience biogenic emissions model which incorporates updated emission factors for various plant 

and tree species, recently developed by EPA/NSF/NCAR. For this project, the GloBEIS model 

was chosen to estimate biogenic emissions for the Hawaiian Islands for calendar years 2002 and 

2005. While the MEGAN biogenic emissions model is available for use, it is more 

sophisticated than either the BEIS or GloBEIS models, and incorporates more detailed input 

information. However, given the limited data availability for Hawaii, equivalent results can be 

obtained using GloBEIS with lesser resource requirements. Biogenic emissions are typically not 

estimated for future years as their development is based on specific meteorology and land use 

data, which are not known for future years. Therefore, future year 2018 biogenic emissions are 

held constant at base year 2005 levels. 

The GloBEIS model has the capability to generate biogenic emissions for arbitrary regions, or 

objects. These may be grid cells in the case of dispersion modeling, counties or any general user 

defined region. GloBEIS treats emissions from vegetation (VOC) and soil microbial activity 

(NOx and CO). GloBEIS requires land cover data for the region of interest and temperatures and 

cloud cover, or solar radiation, data for the region and time period of interest. Optional data 

include the Palmer Drought Index and the Leaf Area Index when the model is run with drought 

effects taken into consideration. The most recent version of the model is version 3.1, which was 

released in 2002 (Guenther et al., 2002). A detailed discussion of the development and 

application of the GloBEIS model as well as the model itself may be obtained from 

www.globeis.com. 
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This Technical Memorandum describes the development of the biogenic emission inventories for 

2002 and 2005 for Hawaii, including the necessary landcover, vegetation and meteorological 

input data. 

Landcover and Vegetation Data 

The estimation of biogenic emissions require the specification of the landcover and vegetation 

data. A number of potential landcover and vegetation databases for use in the development of 

biogenic emission for the Hawaiian Islands were obtained and reviewed. These data are 

described and summarized below. 

NOAA Coastal Service Database 

The NOAA Coastal Service Database was developed to improve the understanding of coastal 

uplands and wetlands, and their linkages with the distribution, abundance, and health of living 

marine resources. The data were derived from portions of two Landsat Enhanced Thematic 

Mapper scenes which were analyzed according to the Coastal Change Analysis Program (CCAP) protocol to determine land cover. The data were field validated to produce a land cover 

inventory for Hawaii. 

The land cover data was processed using an iterative classification approach. Landsat Enhanced 

Thematic Mapper data for path/row/dates: 664/46 1-27-00, 64/45 7-3-99, 64/45 2-28-00, 64/45 

2-12-00 were analyzed to create a land cover for the island of Oahu. Each scene was 

individually classified, focusing first on separating major categories (e.g. water, forest, marsh, 

herbaceous upland, and developed) using standard supervised classification techniques. Through 

field work in December 2000 numerous individual areas were chosen as training sites for the 

land cover classification. Subsequent field work and the use of collateral data such as multiple 

source DOQs, TIGER road data, wetlands from HI DLNR, and 10 Meter DEMs from USGS led 

to further refinements in the image classification. In small areas where landcover class confusion 

could not be separated spectrally, human pattern recognition was used to recode the data. A 

detailed description of the data classification procedures can be found in Dobson, J. et al., 1995. 

The resulting classification scheme is provided below. These data are summarized for the 

Hawaiian Islands in Table 1 and displayed in Figure 1 through Figure 7. 

NOAA Coastal Service Classification Scheme 

1 Unclassified: This class contains no data due to cloud conditions or data voids. 

2 High Intensity Developed: Contains little or no vegetation. This subclass includes heavily 

built-up urban centers as well as large constructed surfaces in suburban and rural areas. Large 

buildings (such as multiple family housing, hangars, and large barns), interstate highways, and 

runways typically fall into this subclass. 
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3 Low Intensity Developed: Contains substantial amounts of constructed surface mixed with 

substantial amounts of vegetated surface. Small buildings (such as single family housing, farm 

outbuildings, and sheds), streets, roads, and cemeteries with associated grasses and trees 

typically fall into this subclass. 

4 Cultivated Land: Includes herbaceous (cropland) and woody (e.g., orchards, nurseries, and 

vineyards) cultivated lands. 

5 Grassland: Dominated by grasses and non-grasses (forbs). 

6 Deciduous Forest: Includes areas dominated by single stemmed, woody vegetation unbranched 

0.6 to 1 meter (2 to 3 feet) above the ground and having a height greater than 6 meters (20 feet). 

7 Evergreen Forest: Includes areas in which more than 67 percent of the trees remain green 

throughout the year. Both coniferous and broad-leaved evergreens are included in this category. 

8 Mixed Forest: Contains all forested areas in which both evergreen and deciduous trees are 

growing and neither predominate. 

9 Scrub/Shrub: Areas dominated by woody vegetation less than 6 meters in height. This class 

includes true shrubs, young trees, and trees or shrubs that are small or stunted because of 

environmental conditions. 

10 Palustrine Forested Wetland: Includes all nontidal wetlands dominated by woody vegetation 

greater than or equal to 6 meters in height, and all such wetlands that occur in tidal areas in 

which salinity due to ocean-derived salts is below 0.5 parts per thousand (ppt). 

11 Palustrine Scrub/Shrub Wetland: Includes all nontidal wetlands dominated by woody 

vegetation less than or equal to 6 meters in height, and all such wetlands that occur in tidal areas 

in which salinity due to ocean-derived salts is below 0.5 ppt. 

12 Palustrine Emergent Wetland: Includes all nontidal wetlands dominated by trees, shrubs, 

persistent emergents, emergent mosses, or lichens, and all such wetlands that occur in tidal areas 

in which salinity due to ocean-derived salts is below 0.5 ppt. 

13 Estuarine Forest Wetland: Includes all tidal wetlands dominated by woody vegetation greater 

than or equal to 6 meters in height, and all such wetlands that occur in tidal areas in which 

salinity due to ocean-derived salts is above 0.5 parts per thousand (ppt). 

14 Estuarine Scrub/Shrub Wetland: Includes all tidal wetlands dominated by woody vegetation 

less than or equal to 6 meters in height, and all such wetlands that occur in tidal areas in which 

salinity due to ocean-derived salts is above 0.5 ppt. 

15 Estuarine Emergent: Characterized by erect, rooted, herbaceous hydrophytes (excluding 

mosses and lichens) that are present for most of the growing season in most years. Perennial 

plants usually dominate these wetlands. All water regimes are included except those that are 

subtidal and irregularly exposed. 
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16 Unconsolidated Shore: Characterized by substrates lacking vegetation except for pioneering 

plants that become established during brief periods when growing conditions are favorable. 

Erosion and deposition by waves and currents produce a number of landforms, such as beaches, 

bars, and flats, all of which are included in this class. 

17 Bare Land: Composed of bare soil, rock, sand, silt, gravel, or other earthen material with little 

or no vegetation. 

18 Water: Includes all areas of open water with less than 30 percent cover of trees, shrubs, 

persistent emergent plants, emergent mosses, or lichens. 

19 Palustrine Aquatic Bed: Includes wetlands and deepwater habitats dominated by plants that 

grow principally on or below the surface of the water for most of the growing season in most 

years. 

20 Estuarine Aquatic Bed: Includes widespread and diverse Algal Beds in the Marine and 

Estuarine Systems, where they occupy substrates characterized by a wide range of sediment 

depths and textures. They occur in both the Subtidal and Intertidal Subsystems and may grow to 

depths of 30 m (98 feet). 

21 Tundra: Includes treeless cover beyond the latitudinal limit of the boreal forest in poleward 

regions and above the elevation range of the boreal forest in high mountains. 

22 Snow/Ice: Includes persistent snow and ice persist for greater portions of the year. 

Table 1. NOAA Coastal Service Landcover summary by category for each of the Hawaiian 

Islands. 

Description Hawaii Kauai Lanai

Maui & 

Kahoolawe Niihau Oahu Molokai Total

Unclassified 119 5 0 0 0 0 6 129

High Intensity Developed 18 6 0 10 0 102 1 138

Low Intensity Developed 83 33 6 48 0 159 17 346

Cultivated Land 97 120 0 224 0 99 13 554

Grassland 2,536 143 118 540 22 183 135 3,678

Deciduous Forest 0 0 0 0 0 0 0 0

Evergreen Forest 3,147 306 27 553 2 337 104 4,475

Mixed Forest 0 0 0 0 0 0 0 0

Scrub/Shrub 1,942 758 196 411 137 632 376 4,451

Palustrine Forested Wetland 0 22 0 0 0 0 0 22

Palustrine Scrub/Shrub Wetland 0 12 0 0 1 1 0 15

Palustrine Emergent Wetland 0 4 0 1 1 3 0 9

Estuarine Forested Wetland 0 0 0 0 0 2 4 7

Estuarine Scrub/Shrub Wetland 0 0 0 0 0 0 0 0

Estuarine Emergent Wetland 0 0 0 0 0 0 0 0

Unconsolidated Shore 4 7 9 7 4 10 4 46

Bare Land 2,522 22 17 210 22 31 19 2,843

Palustrine Aquatic Bed 0 0 0 0 0 0 0 0

Estuarine Aquatic Bed 0 0 0 0 0 0 0 0

Tundra 0 0 0 0 0 0 0 0

Snow/Ice 0 0 0 0 0 0 0 0

Total Land Area 10,469 1,439 374 2,004 189 1,559 678 16,712

Area (km^2)
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Figure 1. Hawaii LULC 

Figure 2. Kauai LULC 
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Figure 3. Lanai LULC

Figure 4. Maui LULC 
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Figure 5. Molokai LULC 

Figure 6. Niihau LULC 
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Figure 7. Oahu LULC 

NALC 2000

The North America Land Cover (NALC) for the year 2000 (Latifovic, et al. 2002) was 

developed jointly by the Natural Resources Canada - Canada Centre for Remote Sensing and the 

USGS EROS Data Center as part of the larger Global Land Cover 2000 project implemented by 

the Global Vegetation Monitoring Unit, Joint Research Center (JRC) of the European 

Commission. The North American database was compiled using SPOT VEGETATION data 

from VGT sensors during the 2000 growing season at a spatial resolution of 1-km. Considerable 

effort was devoted to processing the data to ensure data consistency for quantitative land surface 

analysis. The project included three primary objectives, taking into account the need for 

measurements of surface characteristics: 

• Surface parameter mapping: This is the basic requirement, especially for climate and 

meteorological studies where boundary conditions need to be prescribed as in the case of 

Global Circulation Models. Factors such as albedo, surface roughness, and resistances 

to heat are important variables for these types of models and they can be determined 

directly from the measurements or inferred from identification of land cover. 

• Agricultural, pastoral and forest production: The focus here is on the management of 

crop production for agricultural exporting countries, the monitoring of pastoral resources 

and their dependence from meteorological evolution, the evaluation of possible global 

impacts of deforestation and more generally on the need for information related to 

political or social orientations and decisions. 
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• Terrestrial biosphere monitoring and modeling: The contribution of the continental 

biosphere to the biogeochemical cycles (exchanges of carbon and other trace gases) and 

to water and energy exchanges is one of the objectives of global models. Interaction with 

human activities is also of interest to the project. 

The data are available as GIS raster datasets for each continent, in a geodetic coordinate system 

and can be obtained from the project website at 

http://www.gvm.sai.jrc.it/glc2000/ProductGLC2000.htm. The land use classification scheme is 

presented in Table 2. Figure 8 displays these data for the State of Hawaii. A summary of the 

land area by land cover category for Hawaii is provided in Table 3. 

Table 2. Global Land Cover 2000 Classification Scheme. 

GLC Global Class (according to LCCS terminology) 

1 Tree Cover, broadleaved, evergreen LCCS >15% tree cover, tree height >3m 

(Examples of sub-classes at regional level : closed > 40% tree cove; open 15-40% tree cover)

2 Tree Cover, broadleaved, deciduous, closed 

3 Tree Cover, broadleaved, deciduous, open (open 15-40% tree cover) 

4 Tree Cover, needle-leaved, evergreen 

5 Tree Cover, needle-leaved, deciduous 

6 Tree Cover, mixed leaf type 

7 Tree Cover, regularly flooded, fresh water (& brackish) 

8 Tree Cover, regularly flooded, saline water (aily variation of water level) 

9 Mosaic: Tree cover / Other natural vegetation 

10 Tree Cover, burnt 

11 Shrub Cover, closed-open, evergreen (Examples of sub-classes at reg. level: (i) sparse tree layer) 

12 Shrub Cover, closed-open, deciduous (Examples of sub-classes at reg. level: (i) sparse tree layer) 

13 Herbaceous Cover, closed-open (Examples of sub-classes at regional level: (i) natural, (ii) pasture, 

(iii) sparse trees or shrubs) 

14 Sparse Herbaceous or sparse Shrub Cover 

15 Regularly flooded Shrub and/or Herbaceous Cover 

16 Cultivated and managed areas (Examples of sub-classes at reg. level: (i) terrestrial; (ii) aquatic 

(=flooded during cultivation), and under terrestrial: (iii) tree crop & shrubs (perennial), (iv) 

herbaceous crops (annual), non-irrigated, (v) herbaceous crops (annual), irrigated) 

17 Mosaic: Cropland / Tree Cover / Other natural vegetation 

18 Mosaic: Cropland / Shrub or Grass Cover 

19 Bare Areas 

20 Water Bodies (natural & artificial) 

21 Snow and Ice (natural & artificial) 

22 Artificial surfaces and associated areas 
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Figure 8. 2000 Global Land Cover database for Hawaii 

Table 3. 2000 Global Land Cover summary for Hawaii.

LULC Category 

Code Description Area (km^2)

1 Tree Cover; Broadleaved evergreen forest 5,035

2 Tree Cover; Broadleaved deciduous forest, closed canopy 0

3 Tree Cover; Broadleaved deciduous forest, open canopy 0

4 Tree Cover; Needle-leaved evergreen forest 0

5 Tree Cover; Needle-leaved deciduous forest 0

6 Tree Cover; Mixed leaf type 0

7 Tree Cover; regularly flooded, fresh water (& brackish) 0

8 Tree Cover; regularly flooded, saline water 0

9 Mosaic; Forest / shrub cover 1,097

10 Tree Cover; burnt 0

11 Shrub Cover; closed-open, evergreen 0

12 Shrub Cover; closed-open, deciduous 0

13 Herbaceous cover 2,314

14 Sparse herbaceous / Sparse shrub cover 2,011

15 Regularly flooded shrub and/or herbaceous cover 2,009

16 Agriculture; cultivated and managed areas 555

17 Mosaic; Cropland / Tree Cover / Other natural vegetation 25

18 Mosaic; Croplnand / Shrub or Grass cover 0

19 Bare areas 2,418

20 Water Bodies 0

21 Snow and Ice 0

22 Artificial surfaces 296

23 No Data 944

Total Total 16,703
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USGS LULC Database 

The USGS 1:100,000 scale Land Use/Land Cover (LULC) database was developed as part of the 

U.S. Geological Survey National Mapping Program and provides land cover characteristics for 

most of the contiguous U.S. and Hawaii. The data describe the vegetation, water, natural surface 

and cultural features of the land surface. The data are available in a number of formats, all of 

which are easily imported into a GIS, and were developed from aerial photographs acquired from 

NASA high-altitude missions as well as other ancillary data sources. Manual interpretation of 

the aerial photography, with additional input from field surveys and other land cover data, was 

used to digitize and classify the maps and land cover data using an Anderson Level 2 

classification scheme. The dates of data acquisition range from the mid 1907’s through the 

1980’s with the final datasets corresponding to a nominal 1990 base year. 

Figure 9 displays these data for the Hawaiian Islands. A summary of the LUILC data for Hawaii 

by square kilometer for each of the 36 LULC categories is presented in Table 4. 

Figure 9. 1990 USGS LULC database. 
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Table 4. 1990 USGS Landcover summary for Hawaii.

LULC Category 

Code Description Area (km^2)

11 Residential 284

12 Commercial and Services 173

13 Industrial 17

14 Transportation/Communications/Utilities 32

15 Industrial and Commercial Complexes 1

16 Mixed Urban or Built-up Land 10

17 Other Urban or Built-up Land 56

21 Cropland and Pasture 2,715

22 Orchards, Grvoes, Vineyards, Nurseries 74

23 Confined Feeding Operations 0

24 Other Agricultural Land 9

31 Herbaceous Rangeland 221

32 Shrub and Brush Rangeland 3,765

33 Mixed Rangeland 1,295

41 Deciduous Forest Land 0

42 Evergreen Forest Land 5,657

43 Mixed Forest Land 0

51 Streams and Canals 1

52 Lakes 6

53 Reservoirs 8

54 Bays and Estuaries 41

61 Forested Wetland 50

62 Nonforested Wetland 14

71 Dry Salt Flats 0

72 Beaches 8

73 Sandy Areas Other than Beaches 2

74 Bare Exposed Rock 2,244

75 Strip Mines, Quarries, Gravle Pits 4

76 Transitional Areas 37

77 Mixed Barren Land 5

81 Shrub and Brush Tundra 0

82 Herbaceous Tundra 0

83 Bare Ground 0

84 Wet Tundra 0

85 Mixed Tundra 0

91 Perrenial Snowfields or Ice 0

92 Glaciers 0

Total Total 16,729

Hawaii Vegetation Data

The State of Hawaii maintains vegetation databases as GIS coverages which include varying 

degrees of detail for the islands of Molokai, Lanai, Maui and Hawaii. The data describe the 

upland plant communities on four of the major Hawaiian Islands and was prepared as part of a 

survey conducted by the U.S. Fish and Wildlife Service in 1976-1981 to determine the current 

status of native forest birds and their associated habitats. As part of the survey project, map 

sheets were produced at the scale of 1:24,000, overlaying the U.S. Geological Survey 

topographic quad maps for selected portions of the islands of Hawai'i, Lana'i, Maui, and 
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Moloka'i. Map units were differentiated on the basis of tree canopy cover, tree height, and 

dominant species composition of the tree and understory vegetation layers. A hierarchical 

classification system was developed that allows for presentation and discussion of the vegetation 

units at three levels of detail. A complete description of the development of the database can be 

found in Jacobi, 1989. The GIS data layers can be downloaded from 

http://www.hawaii.gov/dbedt/gis/veg.htm. An example display of these data is provided below in 

Figure 10. 

The vegetation classification scheme provides detail regarding the vegetation species, canopy 

characteristics, understory and overstory composition and other features of the landscapes. The 

data are coded to indicate which of the various vegetation species and other features are 

dominant within each mapped unit. The classification schemes include the following information 

and characteristics: 

• Species Association Type 

o Dry habitat species 

o Mesic (moist) habitat species 

o Wet habitat species 

• Tree Canopy Crown Cover 

o Closed canopy, most crowns interlocking; > 60% cover 

o Open canopy, some or no interlocking crowns; >25-60% cover 

o Scattered trees; 5-25% cover 

o Very scattered trees; <5% cover 

• Tree Canopy Height 

o Low scrub trees, monopodial; 2-5 m tall 

o Scrub trees, moderate stature; >5-10 m tall 

o Tall stature trees; >10 m tall 

• Tree species composition of overstory 

o Acacia koa (koa) 

o Aleurites moluccana (kukui) 

o Cheirodendron trigynum (olapa) 

o Diospyros ferrea (lama) 

o Euphorbia sp. ('akoko) 

o Metrosideros polymorpha ('ohia) 

o Myrica faya (faya tree) 

o Myoporum sandwicensis (naio) 

o Native trees 

o Psidium calleianum (strawberry guava, waiawi) 

o Sapindus saponaria (manele, soapberry) 

o Sophora chrysophylla (mamane) 

o Introduced trees 
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• Tree species composition of understory 

o Structured bog 

o Matted ferns: Dicranopteris spp.,Hicriopteris sp.,Sticherus sp. 

o Mixed native-introduced grasses, sedges, or rushes 

o Native grasses 

o Native shrubs 

o Passiflora mollissima (banana poka - introduced) 

o Sphagnum sp. 

o Native treeferns, Cibotium spp. (hapu'u) 

o Introduced grasses, sedges, or rushes 

o Introduced herbaceous species 

o Introduced shrubs 

o Bare ground (at least 25% of the area without vegetation) 

• Modifier for other information 

o Recently burned 

o Recently cleared or logged 

o Volcanic fume defolieation 

o Miscellaneous unit - mix of native and introduced species in low elevation areas 

o Pioneer vegetation, seral stage on recent lava flow 

o Many standing dead or defoliated trees 

• Degree of Disturbance 

o Communities totally dominated by native species of plants. 

o Communities that have the dominant vegetation layer occupied by native species 

and the subdominant layer primarily occupied by exotic species. 

o Communities dominated by introduced species but contain remnant populations 

of native species; no native community structure remaining. 

o Communities that are totally dominated by introduced plants; virtually no native 

species remaining. 

o Non-vegetated areas or disturbance not determined. 

Although these data describe in fair detail the various plant and tree species present and their 

characteristics, the structure of the database does not allow the specification of the percentages 

of each of the various types for a given map unit. These data are necessary for the development 

of biogenic emissions estimates. In addition, the data covers only the upland communities of the 

Islands, and only a subset of the Island chain is included with the database. 
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Figure 10. Sample Hawaii Vegetation database display. 

Meteorological Data 

Meteorological inputs required for the GloBEIS model includes, at minimum, surface 

temperature and photosynthetically active radiation (PAR) data. Lacking sufficient PAR data, 

cloud cover fields may be used instead. For the present application, due to the lack of sufficient 

detail and resolution of available PAR and cloud cover data, biogenic emissions estimates were 

made assuming a 20% cloud cover over each of the Islands. A description of the development 

of the surface temperature fields is provided in the following section. 

Diurnal Temperature Profile Data for GloBEIS

The GloBEIS biogenic emissions model (Guenther et al., 2002; Yarwood et al., 1999a; 1999b) 

requires as input diurnal temperature profiles. Ideally, the temperature field over the Hawaiian 

Islands would be supplied in the form of gridded high–resolution (~1-3 km) data. This type of 

data was not readily available, so temperatures were derived from accessible meteorological 

data. The National Center for Atmospheric Research’s (NCAR) ds472 data set was used for 

2002 and 2005. NCAR’s ds472 contains hourly observations of commonly measured variables 

(including temperature) from airports throughout the U.S. and includes stations on Kauai, Oahu, 

Molokai, Lanai, and Hawaii. Sufficient ds472 data were available for at least one station for 

each county for both 2002 and 2005. NCAR ds472 has data for all twelve months for 2002, but 

is missing data for the entire month of December 2005. Stations with elevations significantly 
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above sea level were excluded from the calculation. For example, Bradshaw Army Airfield on 

the Island of Hawaii is situated at an elevation of 1887 meters, and temperatures measured there 

are unlikely to be representative of the temperatures of most of the vegetation on the rest of the 

island. Data from several other stations (Lanai City, Barking Sands) were also excluded because 

of the lack of sufficient data to form an average for all hours of the diurnal cycle. Because 

meteorological data was not available for each individual island (e.g. neither Niihau nor 

Kahoolawe has a ds472 station), calculation of the mean diurnal temperature profiles was done 

at the county level. 

We used the ds472 data for all stations in each county to form an average diurnal temperature 

cycle for each county for the entire year, for summer (June, July, and August) and winter 

(December, January, and February) of each year. For the year 2005, there is no ds472 data for 

December, and the winter average is formed using data from January and February only. These 

county-average diurnal temperature profiles were then applied to each island in the county. The 

annual mean diurnal temperature profile and profiles for a typical (i.e. average) summer and 

winter day are shown in Figure 11. Note that the range of variability in the temperatures during 

the year is small (5o or less), reflecting the temperate climate of the Hawaiian Islands. 

2005 Kauai Diurnal Temperature Profiles

292

293

294

295

296

297

298

299

300

301

302

0

2

4

6

8

10

12

14

16

18

2022

Hour (HST)

Temperature (K)

Annual Winter Summer

Figure 11. Diurnal temperature profiles for Kauai for 2005 for typical summer and winter 

days, and for the annual average. 

GloBEIS Application 

Version 3.1 of the GloBEIS model was applied to estimate emissions for the State of Hawaii. 

Based on the review of available databases describing the landcover and vegetation within the 
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state it was determined that the most appropriate data available for this purpose is the USGS 

LULC database. Due to the lack of detail of the various datasets reviewed, as well as the lack 

of specific emission factor information for the unique and exotic plant and tree species within the 

State of Hawaii use, the USGS LULC data provided the moist complete data for which emission 

factors were available. However, these data were developed primarily for use in the North 

American mainland, and are not likely to be completely representative of the vegetation found in 

Hawaii. Nevertheless, it was felt that the results obtained using these data would provide a 

reasonable estimate of the biogenic emissions for the state, given the various resource constraints 

of the current project (Guenther, 2006) 

While the GloBEIS model has been developed for use with data sets derived from the USGS 

LULC databases, the data available for Hawaii included a slightly modified version of the 

classification of landcover and vegetation. Therefore, an assignment of the classes in the current 

data with the existing classes, necessary for calculation of emission factors, used in GloBEIS had 

to be made. This mapping of landcover classification schemes is provided in Table 5. Table 5 

also provides a summary of the landcover and vegetation for each of the Hawaiian Islands, as 

used in the current project. 

Other inputs for the application of GloBEIS included the temperature data discussed previously, 

as well as an assumed constant cloud cover fraction of 20% across the Islands. While version 

3.1 of the GloBEIS model accepts a number of other inputs associated with various options, the 

lack of supporting data precluded their use for this project. 

Table 5. Vegetation summary for Hawaii. 

USGS 

Code

BELD3 

Code

GloBEIS 

Code Hawaii Maui Kahoolawe Lanai Molokai Oahu Kauai Niihau Total

11 3 99101 58.1 25.7 0.0 0.9 1.5 144.1 27.6 0.0 258.0

12 3 99101 11.2 7.9 0.0 0.3 1.1 110.7 9.4 0.0 140.7

13 3 99101 3.7 2.0 0.0 0.0 0.1 4.8 0.2 0.0 10.7

14 3 99101 6.3 2.6 0.0 0.4 1.4 11.4 1.3 0.0 23.3

15 3 99101 0.3 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.8

16 3 99101 5.3 0.3 0.0 0.0 0.0 3.4 0.0 0.0 9.1

17 3 99101 8.4 5.2 0.0 0.6 0.8 26.7 3.9 0.0 45.6

21 5 99110 1,408.1 472.1 0.0 69.6 90.0 285.2 360.2 0.0 2,685.3

22 5 99110 64.1 2.4 0.0 0.0 0.4 3.8 1.2 1.5 73.4

23 5 99110 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1

24 5 99110 2.9 0.8 0.0 0.3 1.3 3.4 0.0 0.0 8.8

31 8 99116 46.3 48.2 2.1 22.2 91.6 3.1 5.9 0.0 219.6

32 10 99116 2,447.9 439.4 69.9 154.0 111.8 212.0 83.4 144.2 3,662.6

33 9 99116 984.3 99.8 0.0 47.6 35.7 71.6 29.7 0.1 1,268.7

42 13 99104 3,172.3 677.7 0.7 54.5 304.7 584.5 823.7 0.0 5,618.0

51 16 99121 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.8

52 16 99121 0.1 0.7 0.0 0.0 0.0 0.0 0.1 4.5 5.5

53 16 99121 0.3 1.9 0.0 0.0 0.4 2.0 3.3 0.0 7.9

54 16 99121 0.5 1.4 1.5 0.0 0.7 19.3 0.0 0.0 23.3

61 17 99026 1.5 0.3 0.0 0.0 0.2 0.2 46.4 0.0 48.5

62 16 99121 1.3 1.8 0.0 0.0 1.2 3.7 1.9 0.0 9.9

72 18 99143 0.0 0.0 0.0 0.0 1.8 0.0 0.0 4.0 5.9

73 18 99143 0.0 0.7 0.0 0.0 0.9 0.0 0.3 0.0 1.9

74 18 99143 2,116.8 54.1 32.0 0.0 2.9 0.0 2.3 2.3 2,210.5

75 18 99143 1.9 0.9 0.0 0.0 0.6 0.7 0.2 0.0 4.3

76 18 99143 24.8 7.3 0.0 0.0 0.5 3.5 0.0 0.0 36.1

77 18 99143 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.1 5.1

Total 10,366.3 1,853.9 106.2 350.4 649.7 1,494.0 1,402.0 161.8 16,384.2

Vegetation Codes Land Area by Island and Landcover (km^2)
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Results 

Biogenic emissions for the State of Hawaii were developed by treating each island as an object 

within GloBEIS3.1. The model was applied for a representative average day for summer and 

winter for 2002 and 2005 using the temperature data discussed previously. An average annual 

day was also modeled for 2002 and 2005. Note that as discussed in the Project Work Plan, 

biogenic emissions are not projected for future years. The results for calendar year 2005 should 

be used for any future year modeling. each month of the calendar year 2002. Hourly emission 

estimates were then summed to provide an average daily county-level emission estimate. 

The results of the GloBEIS modeling for Hawaii for calendar years 2002 and 2005 are presented 

in Table 6 and table 7, respectively. Results are presented for each island separately and totaled 

for the state. Average winter, summer and annual day biogenic VOC, NOx and CO emissions 

for 2002 and 2005 are provided. 

It should be noted that these emission emissions estimates are not appropriate for grid-based air 

quality modeling efforts since average temperature profiles were used. Biogenic emissions are 

dependent on the specific meteorological conditions, varying for each hour of the days and each 

day of the year. For air quality modeling, biogenic emissions must be estimated on a dayspecific basis. Other limitations of these emission estimates are discussed below. Where 

appropriate, recommendations associated with these limitations are provided. 

Table 6. 2002 Biogenic emissions for Hawaii (tpd). 

Island VOC NOx CO VOC NOx CO VOC NOx CO

Hawaii 197.3 7.8 33.1 167.9 7.1 28.9 223.6 8.3 36.8

Maui 41.1 1.6 7.0 33.9 1.4 5.9 48.0 1.7 8.0

Kahoolawe 0.3 0.1 0.0 0.2 0.1 0.0 0.3 0.1 0.1

Lanai 4.0 0.5 0.7 3.3 0.4 0.6 4.6 0.5 0.8

Molokai 18.3 0.5 3.1 15.1 0.5 2.6 21.4 0.6 3.6

Oahu 42.9 1.1 7.4 35.4 1.0 6.3 50.0 1.2 8.5

Kauai 48.6 0.7 8.5 40.3 0.6 7.2 58.3 0.8 9.9

Niihau 0.5 0.3 0.1 0.4 0.3 0.1 0.6 0.3 0.1

T otal 353.0 12.5 59.9 296.5 11.4 51.6 407.0 13.5 67.7

Annual Winter Summer

2002 Average Daily Emissions (tons/day)
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Table 7. 2005 Biogenic emissions for Hawaii (tpd). 

Island VOC NOx CO VOC NOx CO VOC NOx CO

Hawaii 199.8 7.8 33.5 168.4 7.1 29.0 223.6 8.3 36.7

Maui 41.9 1.6 7.2 32.4 1.4 5.7 49.7 1.8 8.3

Kahoolawe 0.3 0.1 0.0 0.2 0.1 0.0 0.3 0.1 0.1

Lanai 4.0 0.5 0.7 3.1 0.4 0.5 4.8 0.5 0.8

Molokai 18.7 0.6 3.2 14.4 0.5 2.6 22.1 0.6 3.7

Oahu 41.6 1.1 7.2 32.6 0.9 5.8 49.9 1.2 8.4

Kauai 49.8 0.7 8.7 38.5 0.6 6.9 59.1 0.8 10.1

Niihau 0.5 0.3 0.1 0.4 0.3 0.1 0.6 0.3 0.1

T otal 356.6 12.6 60.6 290.0 11.3 50.6 410.2 13.6 68.2

2005 Average Daily Emissions (tons/day)

Annual Winter Summer

As noted above, a number of limitations are associated with these biogenic emission estimates. 

These include: 

• Representativeness of the landcover and vegetation – The data used to characterize the 

vegetation species and landcover are likely not representative of the unique species in 

Hawaii. The data set used in the current application was developed primarily for use on 

the US Mainland and may not be appropriate for Hawaii. In addition, the classification 

scheme used, although fairly consistent with the related emission factor calculations in 

GloBEIS, requires some assumptions with respect to mapping with those available in the 

model. Landcover and vegetation data may be improved upon through a more exhaustive 

data search and collection effort, given the increased resources required for such an 

undertaking. Extensive field studies could also provide more detailed and comprehensive 

landcover and vegetation databases. 

• Lack of emission factors – For the unique and exotic species of trees and vegetation in 

Hawaii, very few emission factors are available. While a limited number of these unique 

species have associated emissions factors, the majority have not been research 

extensively at this time. As with the development and classification of detailed 

vegetation and landcover data, field studies would provide the additional emissions factor 

information, provided the necessary resources. 

• Meteorological data – As discussed above, the available meteorological data necessary 

for estimating biogenic emissions is somewhat limited for the current application. In 

addition, the large variation in elevation across the Hawaiian Islands, and the resulting 

changes in temperature, are not represented in the data used for these biogenic emission 

estimates. Meteorological data required for estimating biogenic emissions are typically 

provided through the application of mesoscale meteorological models, such as MM5. 

These models use standard weather station data, as well as data from other synoptic scale 

analyses to generate hourly, gridded data suitable for air quality and emissions modeling, 

particularly biogenic emissions. When full-scale, grid-based air quality modeling is 
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performed for the State of Hawaii, these meteorological data should be used to update the 

biogenic emissions inventories described herein. 

References 

Dobson, J. et al., NOAA Coastal Change Analysis Program(C-CAP): Guidance for Regional 

Implementation, NOAA Technical Report NMFS 123, U.S. Department of Commerce, 

April 1995. 

Fehsenfeld, F., Calvert, J., Fall, R., Goldan, P., Guenther, A., Hewitt, N., Lamb, B., Liu, S., 

Trainer, M., Westberg, H., Zimmerman, P. (1992) Emissions of volatile organic 

compounds from vegetation and the implication for atmospheric chemistry. Global 

Biogeochemical Cycles. 6(4), pp. 389-430. 

Guenther, A., C. Wiedinmyer, B. Baugh, S. Shepard, U. Ganesh, and G. Yarwood. 2002. 

“Biogenic VOC Emission Estimates for the TexAQS 2000 Emission Inventory: 

Estimating Emissions During Periods of Drought and Prolonged High Temperatures and 

Developing GloBEIS3”. Prepared for Texas Natural Resource Conservation 

Commission, Austin, TX. April. 

Guenther, A. 2006. Personnel communication (via e-mail). December, 15. 

Jacobi, James D., 1989. Technical Report #68, "Vegetation Maps of the Upland Plant 

Communities on the Islands of Hawai'i, Maui, Moloka'i, and Lana'i." June. 

Wiedinmyer, C.; Strange, W.; Estes, M.; Yarwood, G.; Allen, D.T. Biogenic Hydrocarbon 

Emissions Estimates for North Central Texas; Atmos. Environ. 2000, 34, 3419-3435. 

Yarwood, G., G. Wilson, S. Shepard, and A. Guenther. 1999a. User’s Guide to the Global 

Biosphere Emissions and Interactions System – Version 2.0. Available from 

http://www.globeis.com. 

Yarwood, G., G. Wilson, C. Emery, and A. Guenther 1999b Development of GLOBEIS – A 

State of the Science Biogenic Emissions Modeling System. Final Report to the Texas 

Natural Resource Conservation Commission, 12100 Park 35 Circle, Austin, Texas 

78753. Available from http://www.globeis.com. 
APPENDIX N 

VOLCANO EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
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TECHNICAL 

MEMORANDUM 

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: John Grant and Alison Pollack 

Date: April 20, 2009 

Subject: Volcano Emissions Estimates for the Hawaiian Islands 

Introduction 

Kilauea, on the Big Island of Hawai`i is Hawai`i’s youngest volcano, and one of the world’s 

most active. The Pu`u `O`o-Kupaianaha eruption of Kilauea began in 1983, and continues to the 

present time (Heliker and Brantley, 2004). This eruption is a significant source of SO2

emissions. The United States Geological Survey (USGS) Hawaiian Volcano Observatory 

continuously monitors these emissions at two distinct sources, and has published data collected 

from 1979 through 2001 (Elias, et al., 1998; Elias and Sutton, 2002). Published annual 

emissions totals are available for the years 1998 to 2001 for the two distinct sources monitored 

by USGS: the summit caldera and east rift zone (Elias and Sutton, 2002). 

ENVIRON obtained preliminary data for 2002 to 2005 from the USGS (Elias, 2006) with 

preliminary estimates of annual emissions totals for the summit caldera and east rift zone and 

observed daily emissions for the same time period. Table 1 summarizes annual emissions from 

1998 to 2005 for the eastern rift zone and summit locations. Although CO and H2S are also 

emitted from Kilauea, these emissions are much less significant than those of SO2 and are not 

included in the USGS observation program nor reported here. 

Table 1. Kilauea annual emissions from the summit and rift zones. 

SO2 Emissions (short tons/year) 

Year Summit Rift Total Reference 

1998 4.76E+04 9.17E+05 9.65E+05 

1999 4.08E+04 5.49E+05 5.90E+05 

2000 4.29E+04 5.40E+05 5.83E+05 

2001 4.30E+04 5.79E+05 6.22E+05 

Elias and Sutton, 2002 

2002 4.37E+04 5.54E+05 5.98E+05 

2003 4.53E+04 5.72E+05 6.17E+05 

2004 3.93E+04 4.95E+05 5.34E+05 

2005 6.03E+04 9.01E+05 9.61E+05 

Elias, 2006 
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Emissions Estimates for AIRShed 

Kilauea SO2 emissions estimates to be included in AIRShed are summarized in Table 2. 2002 

and 2005 annual emissions estimates were taken directly from data provided to ENVIRON 

(Elias, 2006). 2002 and 2005 annual emissions were estimated by the USGS by processing 2002 

and 2005 daily observational emissions data using a non-parametric digital filter and then 

summing the daily emission rates for the given year (Elias, 2006). Summer and winter day 

emissions shown in Table 2 were estimated for each zone by taking the average of the daily 

emissions data within the winter or summer time period in a given year and adjusting this simple 

average value by the ratio of average annual daily emissions based on USGS calculated annual 

emissions to the simple average annual daily emissions calculated based on USGS daily 

observational emissions. 

Table 2. Kilauea emissions to be included in AIRShed. 

Average Daily Emissions SO2 (tons/day) 

Year Time Period1 Summit 

East Rift 

Zone Total 

2002 Annual 1.20E+02 1.52E+03 1.64E+03 

2002 Summer Day 1.29E+02 1.90E+03 2.03E+03 

2002 Winter Day 1.25E+02 1.52E+03 1.65E+03 

2005 Annual 1.65E+02 2.47E+03 2.63E+03 

2005 Summer Day 2.05E+02 2.31E+03 2.52E+03 

2005 Winter Day 1.05E+02 3.81E+03 3.91E+03 

2018 Annual 1.24E+02 1.75E+03 1.87E+03 

2018 Average Day 1.24E+02 1.75E+03 1.87E+03 1

 2002 and 2005 summer and winter day emissions apply to both weekday and average day emissions. 2018 average 

day emissions apply to both summer and winter average day and weekday emissions. 

2018 SO2 emissions estimates from Kilauea are obviously highly uncertain. Any type of 

projection into the future would be an over prediction if the volcano were to cease erupting, or 

the volcanic eruptions could become much more sever in the future than in recent years. For this 

analysis, an average emission rate based on 1998-2005 historical data was used to estimate the 

2018 annual emissions. 2018 winter and summer day estimates were estimated as 2018 

emissions divided by 365 days per year. 

References 

Elias, 2006. Kilauea SO2 data for 2002-2005. Personal Email from Tamar Elias. United States 

Geological Survey. Hawaii. November 2006. 

 

Elias, et al. 1998. SO2 emission rates from Kilauea Volcano, Hawai'i, 1979-1997. USGS Open 

File Report 98-462. 

http://hvo.wr.usgs.gov/products/OF98462/
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Elias and Sutton. 2002. SO2 Emission Rates from Kilauea Volcano, Hawai'i, an Update: 1998-

2001. USGS Open File Report 02-460. 

http://geopubs.wr.usgs.gov/open-file/of02-460/of02-460.html

Heliker, C., and Brantley, S.R. 2004. The ongoing Pu`u `O`o-Kupaianaha eruption of Kilauea 

Volcano, Hawai`i: U.S. Geological Survey Fact Sheet 2004-3085. 

http://pubs.usgs.gov/fs/2004/3085/fs2004-3085.pdf
APPENDIX O 

SEA SPRAY EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDS 
TECHNICAL 

MEMORANDUM 

To: Lakes Environmental Software and Hawaii Clean Air Branch 

From: Susan Kemball-Cook 

Date: November 20, 2007 

Subject: Sea Spray Emissions Estimates for the Hawaiian Islands 

Introduction 

In the marine boundary layer and in coastal regions, sea salt aerosols affect the atmospheric 

cycling of sulfate, nitrate, and ozone, among other species (Liao et al. 2004). Sea salt aerosols 

scatter solar radiation, serve as sites for heterogeneous reactions, and affect cloud properties 

through their role as cloud condensation nuclei (Guelle et al. 2001). Sea salt aerosols must 

therefore be accounted for in near-coastal visibility and ozone modeling (Knipping and Dabdub 

2003). Vignati et al. (2001) showed that sea spray particles produced through the action of 

breaking waves in the surf zone play an important part in processes involving sea salt aerosols in 

the coastal boundary layer. De Leeuw et al. (2000) found that concentrations of sea salt particles 

over the coastal surf zone are enhanced by 1-2 orders of magnitude relative to those of the 

oceanic background. They used a simple model to estimate the concentrations of aerosols 

generated by the surf in regions downwind of the surf zone. The model accounted for both 

dispersion and deposition of the aerosol particles. They found that at distances of 25 km 

downwind of the surf zone, sea salt particle concentrations remain at about 10% of their original 

concentration over the surf zone. The island of Hawaii is 150 km across at its widest point, and 

some of the Hawaiian Islands (e.g. Kahoolawe, Lanai) lie entirely within 25 km of their 

coastline. Therefore, surf-generated aerosols may constitute a significant source of particulate 

matter contributing to both PM2.5 and PM10 over the Hawaiian Islands. In this section, we 

describe the calculation of the sea salt emission inventory for surf-generated sea spray particles 

for 2002, 2005, and the future year 2018 using the sea spray particle generation function of de 

Leeuw et al. (2000). 

Note that sea spray generated over the open ocean away from the coastline may contribute to the 

sea spray aerosol burden over the Hawaiian Islands (Gong et al., 2002). This aerosol source is 

not accounted for in the de Leeuw et al. (2000) source function, but must be taken into account 

in any future visibility modeling of the Hawaiian Islands. 

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Parameterization of Sea Spray Fluxes from the Surf Zone 

Sea spray emissions from the surf zone for each county for the years 2002, 2005, and 2018 were 

calculated using the source function of de Leeuw et al. (2000), 

dF = 1.1 x 107 exp{0.23 x U10} x D –1.65 . (1)

dD 

Note that the factor of 107 was inadvertently omitted from the original de Leeuw et al. (2000) 

paper. F is the sea spray droplet flux in (droplets μm

-1

 m

-2

 s

-1), U10 is the 10-meter wind speed in 

m s-1, and D is the droplet diameter in microns (μm). This source function is valid for droplets 

with diameters 1.6-20 μm at formation, and applies at wind speeds of up to 9 m s-1. This source 

function was derived from measurements of sea spray produced by swell-induced surf with very 

long fetches on the Pacific Ocean (de Leeuw et al. 2000). The authors note that they had 

expected the sea spray aerosol flux to depend on the significant wave height and the wave 

period, but they found no clear correlation between these parameters and the production of sea 

spray aerosols. They found that the sea spray flux depended only on the 10-meter wind speed 

and the droplet size. 

In order to calculate the mass of sea spray aerosol droplets emitted off the coast of the Hawaiian 

Islands, we divide the droplet size range 1.6 to 20 μm into a set of size bins and calculate the sea 

spray droplet flux in each bin. Then, we integrate over all of the size bins to get a total sea spray 

droplet flux. The flux can then be integrated around the perimeter of each county’s coastline and 

the width of the surf zone to obtain the total sea spray mass for each county for summer, winter, 

and the full year for 2002, 2005, and 2018. The surf zone was defined to be 100 meters across 

(de Leeuw et al. 2000; Knipping and Dabdub 2003). 

Because salt is hygroscopic, sea salt aerosols change size and density with variations in the 

relative humidity of the environment (Lewis and Schwartz 2004). As sea salt aerosols move 

inland from the surf zone where they are generated, the water content of the aerosol changes. 

Sea salt mass fluxes derived from experiments are typically reported as dry sea salt mass fluxes 

(de Leeuw et al. 2000; Porter et al. 2003). In previous modeling studies which have treated sea 

salt aerosols, sea salt aerosol emissions were reported as the mass of dry sea salt emitted from 

the ocean surface (e.g. Grini et al. 2002; Guelle et al. 2001). Atmospheric models typically 

advect only the dry sea salt mass and then calculate for each model grid cell a wet sea salt 

diameter according to the relative humidity in that grid cell. This wet diameter is then used in 

the calculation of deposition. For the sea salt aerosols generated using (1), we calculate a dry 

radius assuming a relative humidity of 98.3% at the point of formation (de Leeuw et al. 2000). 

The dry radius is calculated using the method of Fitzgerald (1975), 

rwet = α rdryβ

 , (2) 

where α and β are constants that depend on the relative humidity. 

α = 1.35 (1.2) exp (0.066S/(Ф-S)) (3) 
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β = exp((0.00077S/(1.009-S)) (4) 

and S is the relative humidity reported as a fraction. 

We can then calculate a dry volume for each particle, assuming spherical particles composed 

entirely of NaCl. The density of dry salt particles was assumed to be 2200 kg m-3 (Hess et al.

1998). From this, we calculate the dry mass flux of sea salt emitted from the ocean surface 

based on the sea salt droplet flux calculated with the source function (1). Next, we describe the 

inputs to the surf zone sea spray source function. 

Bin Structure 

The bin structure for the sea spray droplet flux was based on that of Grini et al. (2002) from their 

global chemistry-transport model study of sea salt aerosols. We modified their bin structure to 

fit the size range of applicability of the de Leeuw source function and to have bin edges that 

simplify the tabulation of the flux of sea salt in the 1.6-2.5 and 2.5-10 μm size ranges. The bin 

structure is shown in Table 1. 

We performed a test in order to examine the sensitivity of the sea salt mass fluxes to the bin 

structure. We replaced the modified Grini et al. (2002) bin structure, with a finer bin structure 

using 37 bins at 0.5 micron intervals from ranging from 1.6 to 20 μm diameter at formation. The 

integrated sea salt annual mass for Kauai changed from 1.57x108 kg (coarser bin structure) to 

1.53 x108 kg (finer bin structure) which corresponds to a reduction of about 2.5%. This should 

be representative for the other islands as well. Given the uncertainties in the source function and 

the specification of the surf zone width and island perimeters, the use of the coarser bin structure 

is appropriate and simplifies the calculation. 

Table 1. Bin structure used for sea salt aerosol calculation. Bin center and edges are in 

microns. 

Lower Bin 

Edge 

Bin Center Upper Bin 

Edge 

1.60 1.80 2.00 

2.00 2.25 2.50 

2.50 2.80 3.10 

3.10 3.90 4.70 

4.70 5.90 7.10 

7.10 8.55 10.00 

10.00 13.20 16.40 

16.40 18.20 20.00 
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Meteorological Data 

Ideally, the 10 meter wind speed field over the Hawaiian Islands would be supplied to the source 

function in the form of gridded high–resolution (~1-3 km) meteorological data interpolated to a 

height of 10 meters above sea level. This type of data was not readily available, so near-surface 

wind speeds were derived from available meteorological data. The National Center for 

Atmospheric Research’s (NCAR) ds472 data set was used for 2002 and 2005. NCAR’s ds472 

contains hourly observations of commonly measured variables from airports throughout the U.S. 

and includes stations on Kauai, Oahu, Molokai, Lanai, and Hawaii. Dataset variables include 

near-surface wind speed and direction. Sufficient ds472 data were available for at least one 

station for each county for 2002 and 2005. In 2005, Kauai has only one station with sufficient 

data because the Barking Sands meteorological station has very little data for 2005. NCAR 

ds472 has data for all twelve months for 2002, but is missing data for the entire month of 

December 2005. Stations with elevations significantly higher than 10 meters were excluded 

from the calculation. For example, Bradshaw Army Airfield on the Island of Hawaii is situated 

at an elevation of 1887 meters, and wind speeds measured there are unlikely to be representative 

of the coastal 10 meter wind speed. Data from Lanai City were also excluded because of the 

elevation of the station. Because meteorological data were not available for each island (e.g. 

neither Niihau nor Kahoolawe has a ds472 station), calculation of the 10 meter wind speed was 

done at the county level. 

Within a given county, there can be a great variety in measured wind speeds and direction as a 

result of local influences near the measurement site. For example, the topography of Maui 

diverts the oncoming trade wind flow around the mountains and has a marked effect on both 

wind speed and direction. During a given day, the wind speed and direction at a measurement 

site may fluctuate as a result of the sea breeze cycle and its interaction with the trade wind flow. 

Given the level of approximation inherent in the sea spray source function and because we have 

only one or two data points for each county, we form an average over the year to simplify the 

calculation. We use the ds472 data for all stations in each county to form an average wind speed 

for each county for the entire year, for summer (June, July, and August) and winter (December, 

January, and February) of each year. For the year 2005, there is no ds472 data for December, 

and the winter average is formed using data from January and February only. These average 

wind speeds were then used to approximate the 10 meter wind speed in the sea spray source 

function, and calculate the corresponding average sea spray flux for that time period. 

For the future year, 2018, we used monthly long-term (1968-1996) mean winds from the NCEP 

Reanalysis climatology (http://www.cdc.noaa.gov/ncep_reanalysis/) to estimate sea spray 

emissions. Annual average surface wind speed maps for the region encompassing the Hawaiian 

Islands were downloaded, and averages for each county were calculated by inspection of the 

maps. There was good correspondence between wind speed averages computed with ds472 and 

with the NCEP Reanalysis. The mean surface wind speed maps for the annual average, summer 

average, and winter average are shown in Figure 1. 
 Page 5 

Figure 1a. Annual long term mean surface wind speed from the NCEP Reanalysis. 

Figure 1b. Winter (DJF) long term mean surface wind speed from the NCEP 

Reanalysis. 

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Figure 1c. Summer (JJA) long term mean surface wind speed from the NCEP 

Reanalysis. 

The average wind speeds for annual, summer, and winter periods for 2002, 2005, and 2018 for 

each country are shown in Tables 2 through 4. 

Table 2. Summary of ds472 Wind speed data for 2002. 

County Mean Annual Summer Winter 

 Wind Speed (m/s) Wind Speed (m/s) Wind Speed (m/s) 

Kauai 4.6 4.8 4.5 

Honolulu 4.1 4.5 4.0 

Maui 6.2 7.2 6.0 

Hawaii 3.5 3.4 3.6 

Table 3. Summary of ds472 Wind speed data for 2005. 

County Mean Annual Summer Winter 

 Wind Speed (m/s) Wind Speed (m/s) Wind Speed (m/s) 

Kauai 6.1 6.5 5.3 

Honolulu 4.3 4.8 4.1 

Maui 6.5 7.3 5.5 

Hawaii 3.4 3.4 3.5 

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Table 4. Summary of NCEP reanalysis wind speed data for 2018. 

County Annual Average Summer Average Winter Average 

 Wind Speed (m/s) Wind Speed (m/s) Wind Speed (m/s) 

Kauai 6.5 7.2 6.1 

Honolulu 6.3 6.8 5.8 

Maui 5.7 6.0 5.3 

Hawaii 5.3 5.3 5.1 

 

Results 

The estimated average sea salt fluxes for typical summer and winter days as well as the full year 

for each island for 2002, 2005, and 2018 are shown in Tables 5 through 7. The sea salt fluxes 

derived for the Hawaiian Islands as described above range from to 193-477 μg m-2

 s

-1 and can be 

compared with fluxes derived from observations. 

De Leeuw et al. (2000) reported observed salt fluxes between 297 and 1034 μg m-2 s

-1 for two 

coastal sites in California. Porter et al. (2003) reported a flux of 63 μg m-2 s

-1 for a site at 

Bellows Beach, Oahu. Porter et al. (2003) note that their salt fluxes are lower than those of de 

Leeuw et al. (2000) and attribute this to the inconsistent nature of the surf breaking on the outer 

reef upwind of their detector, and the fact that their detector was unable to measure the salt flux 

from waves breaking directly on the beach at the measurement site. Petelski and Chomka (1996) 

observed sea salt mass fluxes between 3 and 384 μg m-2 s

-1 at a site located on the Baltic coast. 

Their measured lower limit of 3 μg m-2 s

-1 may be attributed to the lower intensity of Baltic surf 

compared to that of the Pacific and the lower salinity of the water (10% salinity in the Baltic 

compared to 33% off the coast of California), and the different bubble structure found in the 

Pacific and the Baltic (de Leeuw et al. 2000; Porter et al. 2003). The sea salt mass flux at a 

given site will be highly dependent on local winds, swell, ocean bottom contours, and the 

topography of the surrounding land area. All of the observed fluxes reported in the studies 

discussed above were measured during short experimental campaigns, and are likely to differ 

from the values we compute, which are averaged over a longer time period. We conclude that 

the averaged sea salt fluxes derived here are reasonable in magnitude when compared to the 

limited available observations. 

Sea salt mass in the PM2.5 (Tables 8 through 10) and PM10 (Tables 11 through 13) size ranges 

are reported for each county for 2002, 2005, and 2018. Total sea salt mass (in kg) integrated 

over all size bins is reported for each island in Tables 14 through 16. 
 Page 8 

Table 5. 2002 Sea salt mass flux. 

Island Annual Average Sea Salt Summer Average Sea Salt Winter Average Sea Salt

Mass Flux (ug m-2 s-1) Mass Flux (ug m-2 s-1) Mass Flux (ug m-2 s-1)

Kauai 259 267 251

Niihau 259 267 251

Honolulu 228 251 227

Maui 369 470 359

Molokai 369 470 359

Lanai 369 470 359

Kahoolawe 369 470 359

Hawaii 201 198 207

Table 6. 2005 Sea salt mass flux. 

Island Annual Average Sea Salt Summer Average Sea Salt Winter Average Sea Salt

Mass Flux (ug m-2 s-1) Mass Flux (ug m-2 s-1) Mass Flux (ug m-2 s-1)

Kauai 362 398 303

Niihau 362 398 303

Honolulu 242 271 228

Maui 402 477 320

Molokai 402 477 320

Lanai 402 477 320

Kahoolawe 402 477 320

Hawaii 195 193 199

Table 7. 2018 Sea salt mass flux. 

Island Annual Average Sea Salt Summer Average Sea Salt Winter Average Sea Salt

Mass Flux (ug m-2 s-1) Mass Flux (ug m-2 s-1) Mass Flux (ug m-2 s-1)

Kauai 399 463 364

Niihau 399 463 364

Honolulu 381 428 336

Maui 332 356 299

Molokai 332 356 299

Lanai 332 356 299

Kahoolawe 332 356 299

Hawaii 299 299 289

Table 8. 2002 PM2.5 Sea salt mass. 

Island Annual Typical Summer Day Typical Winter Day

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 8.9E+05 2.5E+03 2.4E+03

Niihau 5.4E+05 1.5E+03 1.4E+03

Honolulu 1.0E+06 3.0E+03 2.7E+03

Maui 1.8E+06 6.3E+03 4.8E+03

Molokai 1.3E+06 4.5E+03 3.4E+03

Lanai 7.8E+05 2.7E+03 2.1E+03

Kahoolawe 4.3E+05 1.5E+03 1.1E+03

Hawaii 1.9E+06 5.0E+03 5.3E+03

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Table 9. 2005 PM2.5 Sea salt mass. 

Island Annual Typical Summer Day Typical Winter Day

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 1.2E+06 3.7E+03 2.9E+03

Niihau 7.6E+05 2.3E+03 1.7E+03

Honolulu 1.1E+06 3.3E+03 2.7E+03

Maui 2.0E+06 6.4E+03 4.3E+03

Molokai 1.4E+06 4.5E+03 3.1E+03

Lanai 8.6E+05 2.8E+03 1.9E+03

Kahoolawe 4.7E+05 1.5E+03 1.0E+03

Hawaii 1.8E+06 4.9E+03 5.1E+03

Table 10. 2018 PM2.5 Sea salt mass. 

Island Annual Typical Summer Day Typical Winter Day

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 1.4E+06 4.4E+03 3.4E+03

Niihau 8.4E+05 2.7E+03 2.1E+03

Honolulu 1.7E+06 5.2E+03 4.1E+03

Maui 1.6E+06 4.8E+03 4.0E+03

Molokai 1.2E+06 3.4E+03 2.9E+03

Lanai 7.1E+05 2.1E+03 1.7E+03

Kahoolawe 3.9E+05 1.1E+03 9.6E+02

Hawaii 2.8E+06 7.6E+03 7.4E+03

Table 11. 2002 PM10 Sea salt mass. 

Island Annual Typical Summer Day Typical Winter Day

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 3.2E+07 9.1E+04 8.5E+04

Niihau 2.0E+07 5.5E+04 5.2E+04

Honolulu 3.6E+07 1.1E+05 9.9E+04

Maui 6.6E+07 2.3E+05 1.7E+05

Molokai 4.6E+07 1.6E+05 1.2E+05

Lanai 2.8E+07 9.9E+04 7.6E+04

Kahoolawe 1.6E+07 5.4E+04 4.1E+04

Hawaii 6.8E+07 1.8E+05 1.9E+05

Table 12. 2005 PM10 Sea salt mass. 

Island Annual Typical Summer Day Typical Winter Day

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 4.5E+07 1.4E+05 1.0E+05

Niihau 2.7E+07 8.3E+04 6.3E+04

Honolulu 3.9E+07 1.2E+05 1.0E+05

Maui 7.2E+07 2.3E+05 1.6E+05

Molokai 5.1E+07 1.6E+05 1.1E+05

Lanai 3.1E+07 1.0E+05 6.8E+04

Kahoolawe 1.7E+07 5.5E+04 3.7E+04

Hawaii 6.6E+07 1.8E+05 1.8E+05

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Table 13. 2018 PM10 Sea salt mass. 

Island Annual Typical Summer Day Typical Winter Day

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 5.0E+07 1.6E+05 1.2E+05

Niihau 3.0E+07 9.6E+04 7.6E+04

Honolulu 6.1E+07 1.9E+05 1.5E+05

Maui 5.9E+07 1.7E+05 1.5E+05

Molokai 4.2E+07 1.2E+05 1.0E+05

Lanai 2.6E+07 7.5E+04 6.3E+04

Kahoolawe 1.4E+07 4.1E+04 3.5E+04

Hawaii 1.0E+08 2.8E+05 2.7E+05

Table 14. 2002 Total Sea salt mass. 

Island Annual Summer Winter

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 1.6E+08 4.4E+05 4.2E+05

Niihau 9.6E+07 2.7E+05 2.5E+05

Honolulu 1.8E+08 5.3E+05 4.8E+05

Maui 3.2E+08 1.1E+06 8.5E+05

Molokai 2.3E+08 7.9E+05 6.0E+05

Lanai 1.4E+08 4.8E+05 3.7E+05

Kahoolawe 7.6E+07 2.6E+05 2.0E+05

Hawaii 3.3E+08 8.9E+05 9.3E+05

Table 15. 2005 Total Sea salt mass. 

Island Annual Summer Winter

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 2.2E+08 6.6E+05 5.0E+05

Niihau 1.3E+08 4.0E+05 3.1E+05

Honolulu 1.9E+08 5.8E+05 4.9E+05

Maui 3.5E+08 1.1E+06 7.6E+05

Molokai 2.5E+08 8.0E+05 5.4E+05

Lanai 1.5E+08 4.9E+05 3.3E+05

Kahoolawe 8.3E+07 2.7E+05 1.8E+05

Hawaii 3.2E+08 8.7E+05 9.0E+05

Table 16. 2018 Total Sea salt mass. 

Island Annual Summer Winter

Sea Salt Mass (kg) Sea Salt Mass (kg) Sea Salt Mass (kg)

Kauai 2.4E+08 7.7E+05 6.0E+05

Niihau 1.5E+08 4.7E+05 3.7E+05

Honolulu 3.0E+08 9.1E+05 7.2E+05

Maui 2.9E+08 8.5E+05 7.1E+05

Molokai 2.0E+08 6.0E+05 5.0E+05

Lanai 1.2E+08 3.7E+05 3.1E+05

Kahoolawe 6.8E+07 2.0E+05 1.7E+05

Hawaii 4.9E+08 1.3E+06 1.3E+06

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References 

de Leeuw, G, F.P. Neele, M. Hill, M.H. Smith, and E. Vignati. 2000. Production of sea spray 

aerosol in the surf zone. J. Geophys. Res. V105, pp. 29,397-29,409. 

Fitzgerald, J. W. 1975. "Approximation formula for the equilibrium size of an aerosol particle 

as a function of its dry size and composition and the ambient relative humidity." Journal 

of Applied Meteorology 14: 1044-1049. 

Gong, S.L., L.A. Barrie, and M. Lazare. 2002: Canadian Aerosol Module (CAM): A sizesegregated simulation of atmospheric aerosol processes for climate and air-quality 

models 2. Global sea-sea aerosol and its budgets. J. Geophys. Res. V108, pp. AAC 13-1-

13-14. 

Grini, A. G. Myhre, J.K. Sundet, and I.S.A. Isaksen. 2002: Modeling the annual cycle of sea 

salt in the global 3D model Oslo CTM2: concentrations, fluxes, and radiative impact. J. 

Climate V15, pp. 1717-1730. 

Guelle, W., M. Schulz, Y. Balkanski and F. Dentener. 2001. "Influence of the source 

formulation on modeling the atmospheric global distribution of sea salt aerosol." J. 

Geophys. Res. 106(D2421): 27509-27524. 

Hess, M., P. Koepke, and I. Schult, 1998: Optical properties of aerosols and clouds: The 

software package OPAC. Bull. Amer. Meteor. Soc., 79, 831-844. 

Knipping and Dabdub. 2003: Impact of chlorine emissions from sea-salt aerosol on coastal 

urban ozone. Environ. Sci. Technol. V37, pp. 257-284. 

Lewis. E.R. and S.E. Schwartz. 2004. “Sea Salt Aerosol Production”. American Geophysical 

Union. Washington, D.C. 

Liao, H., J. H. Seinfeld, P. J. Adams and L. J. Mickley. 2004. "Global radiative forcing of 

coupled tropospheric ozone and aerosols in a unified general circulation model." Journal 

of Geophysical Research, D109: 16207-16240. 

Petelski, T. and M. Chomka. 1996. “Marine aerosol fluxes in the coastal zone, BAEX 

experimental data”. Oceanologia V38, pp. 469-484. 

Porter, J.N., B.R. Lienert, S.K. Sharma, E. Lau, and K. Horton. 2003. “Vertical and horizontal 

aerosol scattering fields over Bellows Beach, Oahu, during the SEAS experiment”. 

Journal of Oceanic and Atmospheric Technology. V20: 1375-1387. 

Takemura, T. H., H. Okamoto, Y. Maruyama, A. Numaguti, A. Higurashi and T. Nakajima 

(2000). "Global three-dimensional simulation of aerosol optical thickness distribution of 

various origins." J. Geophys. Res. 105: 17853-17873. 
 Page 12 

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Vignati, E., G. de Leeuw, and R. Berkowicz. 2001. “Modeling coastal aerosol transport and 

effects of surf-produced aerosols on processes in the marine atmospheric boundary 

layer.” J. Geophys. Res. 106: 20225-20238. 
APPENDIX P 

HAWAII EGU PROJECTIONS 
Technical Memorandum 

November 9, 2007 

Page 1 

Technical Memorandum 

To: Alison Pollack, ENVIRON 

From: Marty Wolf 

Subject: Hawaii Projections 

Date: November 9, 2007

INTRODUCTION 

This technical memorandum documents ERG’s development of 2018 projected emissions for 

point sources in Hawaii. In particular, ERG developed 2005 base year projection factors for 

EGU point sources, which allow the estimation of projected 2018 emissions. In addition, ERG 

has also identified new post-2005 point sources that will be coming on-line prior to 2018. The 

results in this technical memorandum can be used by ENVIRON and Lakes in their development 

of the comprehensive Hawaii emissions inventory for 2002, 2005, and 2018 and the associated 

emissions inventory database management system. 

HAWAII EGUS 

The overall Hawaii point source inventory is predominantly consists of EGU facilities. Hawaii 

is not connected to the continental power grid and must be entirely self-sufficient for power 

generation. Also, the individual islands are also disconnected and so each island must also be 

entirely self-sufficient. Because of the isolated nature of the Hawaii EGUs, most facilities use 

distillate and residual fuel oil. Most of the EGU facilities are owned and operated by the four 

main utilities: 

• Hawaiian Electric Company, Inc. (HECO) – largest utility (includes the island of 

Oahu); 

• Hawaii Electric Light Company, Inc. (HELCO) – subsidiary of HECO (includes the 

island of Hawaii); 

• Maui Electric Company, Inc. (MECO) – subsidiary of HECO (includes separate 

divisions for the islands of Maui, Molokai, and Lanai); and 

• Kauai Island Utility Cooperative – independent of HECO (includes the island of 

Kauai). 

In addition to these utility-owned EGUs, there are a number of other independent power 

producers (IPPs) that supply power to the Hawaii electricity grid. 
Technical Memorandum 

November 9, 2007 

Page 2 

An overall summary of the Hawaii EGUs is provided in Tables 1 through 6 for the islands of 

Oahu, Hawaii, Maui, Molokai, Lanai, and Kauai. Each table lists facility name, state ID (if 

applicable), unit ID, fuel type, initial operation date, nameplate capacity, generation type (i.e., 

firm or not firm), and whether the facility was included in the 2005 Hawaii NIF files. The 

generator information used to develop these tables was obtained from the 2006 Emissions & 

Generation Resource Integrated Database (eGRID2006), Version 2.1, which contains data for 

2004 (U.S. EPA, 2007). Firm sources refer to those sources that continually supply electricity to 

the grid, while “not firm” source refer to those sources that intermittently or sporadically supply 

electricity (i.e., hydro facilities, wind farms, and “as available” excess generation from industrial 

sources). 

WRAP GROWTH FACTOR METHODOLOGY 

ERG previously developed EGU growth factors for 2018 for facilities located in the Western 

United States in support of regional haze modeling for the Western Regional Air Partnership 

(WRAP) (ERG, 2006; ERG, 2007). The basic methodology for developing these growth factors 

for that effort was to examine base year unit-level electricity generation levels and then grow 

each unit up to a maximum threshold capacity (e.g., 0.85 for coal-fired boilers, 0.25 for simplecycle natural-gas fired turbines, etc.). Any further additional capacity needed to meet projected 

generation was then attributed to future power plants. 

Unfortunately, this methodology was not appropriate for Hawaii. The primary reason is that the 

operation of Hawaii EGUs is fundamentally different from those located in the Western states. 

Because each island’s power supply must be self-supporting, Hawaiian utilities tend to operate 

their EGUs with more frequent maintenance schedules, as well as maintain a higher level of 

reserve capacity. As a result, utility staff agreed that the maximum threshold capacities used in 

the Western states were not appropriate for Hawaiian utilities. However, they were also unable 

to provide alternative maximum threshold capacities (Hashiro, 2007). Also, Hawaii EGUs 

appear to be exempt from the mandatory reporting of some types of emissions statistics and 

operating data that are required from EGUs in the continental U.S. (e.g., Acid Rain Program 

reporting, etc.). Therefore, some of the unit-level data used the WRAP methodology were 

unavailable for Hawaii. Therefore, an alternative methodology was used. 

IRP-BASED GROWTH FACTOR METHODOLOGY 

The alternative methodology that was used is based upon documents developed by each utility as 

part of the Integrated Resource Planning (IRP) process. Each of the utilities in Hawaii is 

required to periodically conduct an IRP process, which requires the development of long-range 

plans to meet future energy needs. The IRP process includes both resources that supply 

electricity and resources that reduce or manage the demand of electricity. The IRP process is 

mandated by the Hawaii Public Utilities Commission’s IRP Framework, Decision and Order No. 

11630 in Docket No. 6617, as revised on May 22, 1992. 

The most recent IRP documents from the four electric utilities in Hawaii were used to identify 

projected electricity needs in 2018 (HECO, 2005; HELCO, 2007; MECO, 2007; KIUC, 2007). 

The IRP documents for Oahu, Hawaii, and Maui provided long-term sales forecasts in gigawatthours (GWh) and long-term peak forecasts in megawatts (MW); the IRP documents for Molokai, 

Lanai, and Kauai only provided long-term peak forecasts. These forecasts are presented in Table 
Technical Memorandum 

November 9, 2007 

Page 3 

7. In general, the sales and peak forecasts for Oahu, Hawaii, and Maui corresponded very 

closely with each other (i.e., variation within a few tenths of a percent). Based on this close 

correspondence, it was assumed that sales would also be similar to the identified peak forecasts 

for Molokai, Lanai, and Kauai. 

It was assumed that the utilities would maintain similar operating behavior (e.g., reserve capacity 

levels, etc.) in 2018 as in 2005. Therefore, projected growth in demand was assumed to 

correspond with similar growth levels in firm generating capacity (i.e., excluding hydro, wind, 

and as-available sources). For instance, the sales and peak forecasts for the island of Maui 

indicate a growth rate of 33.1-33.5% between 2005 and 2018; it was then assumed that firm 

nameplate capacity would need to grow by 96.7 MW (i.e., existing firm capacity 292.0 MW × 

33.1%). The existing firm capacity and estimated new capacity needed for each island are 

presented in Table 8. 

The IRP documents were also examined to determine what future year projects/policies (i.e., 

between 2005 and 2018) had been identified for implementation. These future year 

projects/policies are also presented in Table 8. It should be noted that the IRP documents for 

HECO, HELCO, and MECO were new IRPs that have been developed in the last two years, 

while the IRP document for KIUC was simply an annual update to its 1997 IRP. As a result, the 

future year projects/policies identified in the HECO, HELCO, and MECO IRPs are quite 

specific, while the future year projects/polices in the KIUC IRP documents are not well-defined. 

Identified capacity that was implemented throughout the 20-year IRP planning horizon (e.g., 

demand side management, combined heat and power, distributed generation, etc.) was ratioed to 

represent the 13-year projection period between 2005 and 2018. As an example, the following 

projects/policies representing a total of 93.4 MW of new capacity were identified from the IRP 

documents for the island of Maui: 

• 3rd phase of Maalaea dual-train combined cycle No. 2 (Maalaea 18) – 18 MW 

expected to be installed in October 2006; 

• Eight 1 MW distributed generation (DG) units to be installed in 2009 (1), 2010 (2), 

2012 (4), and 2017 (1); 

• Two 21.2 MW simple-cycle combustion turbines (Waena 1 and 2) to be installed in 

2011 and 2013; and 

• One 25 MW biomass unit to be installed in 2018. 

The identified new capacity was then subtracted from the estimated new capacity needed for 

each island; these results are also shown in Table 8. For the island of Maui, subtracting the 93.4 

MW of identified new capacity from the 96.7 MW of needed new capacity results in 3.3 MW of 

new capacity that has not yet been identified. For Molokai and Lanai, the identified new 

capacity meets or exceeds the needed new capacity. 

The overall island-specific growth factors for existing EGU sources in the 2005 NIF file were 

estimated by dividing the new capacity that has not yet been identified in future year 
Technical Memorandum 

November 9, 2007 

Page 4 

projects/policies by the total existing firm capacity. Based on this methodology, the following 

EGU growth factors were estimated: 

• Oahu – 1.0230; 

• Hawaii – 1.1566; 

• Maui – 1.0112; 

• Molokai – 1.0000; 

• Lanai – 1.0000; and 

• Kauai – 1.2297. 

Molokai and Lanai were assigned growth factors of 1.0000 because the identified new capacity 

meets or exceeds the needed new capacity. One exception to these growth factors is the 

Pepeekeo Power Plant (Hilo Coast Power) located on the island of Hawaii (Facility ID 00022). 

This facility was shut down at the end of 2004 (Schaefers, 2004). Although records exist for the 

Pepeekeo Power Plant in the 2005 NIF database, all records are zero, which indicates no 

operation. Therefore, a growth factor of 0.0000 was assigned to the Pepeekeo Power Plant. 

NEW FACILITIES 

As identified in Table 8, there are a number of new future year facilities that have been identified 

in the IRP process. Based upon the existing facilities that are in the 2005 NIF database, it is 

expected that the following new future year facilities will also need to be included in the 2018 

projected emissions inventory: 

• 100 MW simple-cycle combustion turbine (Oahu – 2009); 

• 16 MW steam turbine generator (Keahole ST-7) (Hawaii, 2009); 

• 18 MW 3rd phase of dual-train combined cycle No.2 (Maalaea 18 (Maui, 2006); 

• 21.2 MW simple-cycle combustion turbine (Waena 1) (Maui, 2011); 

• 21.2 MW simple-cycle combustion turbine (Waena 2) (Maui, 2013); 

• 25 MW biomass unit (Maui, 2018); 

• 2.0 MW diesel generator (Palaau-10) (Molokai, 2014); and 

• 2.2 MW diesel generator (Miki Basin LL-9) (Lanai, 2017). 

At the present time, it is not clear what the appropriate emission levels for these future year 

facilities should be. 
Technical Memorandum 

November 9, 2007 

Page 5 

REFERENCES 

Hashiro, 2007. Personal communication between Gary Hashiro (Hawaiian Electric Company, 

Inc.) and Marty Wolf (ERG). October 25. 

HECO, 2005. Hawaiian Electric Company, Inc. Integrated Resource Plan 2006-2025. Docket 

No. 03-0253. Hawaiian Electric Company, Inc., Honolulu, Hawaii. October 28. Internet 

address: http://www.heco.com/vcmcontent/IntegratedResource/IRP/PDF/ 

HECO_IRP3_Eval_Rpt_May2007.pdf 

HELCO, 2007. Hawaii Electric Light Company, Inc. Integrated Resource Plan 2007-2026. 

Docket No. 04-0046. Hawaii Electric Light Company, Inc., Honolulu, Hawaii. May. Internet 

address: http://www.helcohi.com/vcmcontent/HELCO/RenewableEnergy/IRP/ 

HELCO_IRP3_Report_Final.pdf 

KIUC, 2007. 2006 Integrated Resource Plan Update of the 1997 Integrated Resource Plan. 

Docket No. 96-0266. Kauai Island Utility Cooperative, Lihue, Hawaii. February. Internet 

address: http://www.kiuc.coop/anne/IRP_public_site/Tariff/ 

2006%20IRP%20Update%20(Final)%20(2-16-07).pdf 

MECO, 2007. Maui Electric Company, Ltd. Draft Integrated Resource Plan 2007-2026. Docket 

No. 04-0077. Maui Electric Company, Ltd., Honolulu, Hawaii. February 6. Internet address: 

http://www.mauielectric.com/vcmcontent/MECO/RenewableEnergy/IRP/ 

MECOIRP3DraftReportwithExecSummary.pdf 

Schaefers, 2004. “Hilo Coast Power to Close”, Honolulu Star-Bulletin. Newspaper article by 

Allison Schaefers. November. Internet address: http://starbulletin.com/2004/11/05/business/ 

story3.html 

U.S. EPA, 2007. Emissions & Generation Resource Integrated Database (eGRID2006), 

Technical Support Document, Version 2.1. U.S. Environmental Protection Agency, Office of 

Atmospheric Programs. April. 
Technical Memorandum 

November 9, 2007 Page 6 Table 1. EGU Summary – Oahu Facility Name 

State 

ID Unit ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

HECO - Honolulu Generating Station 00500 8 Distillate 1954 50.0 Firm HECO - Honolulu Generating Station 00500 9 Distillate 1957 54.0 Firm HECO - Waiau Power Plant 00502 W3 Residual 1947 50.0 Firm HECO - Waiau Power Plant 00502 W4 Residual 1950 50.0 Firm HECO - Waiau Power Plant 00502 W5 Residual 1959 54.4 Firm HECO - Waiau Power Plant 00502 W6 Residual 1961 54.4 Firm HECO - Waiau Power Plant 00502 W7 Residual 1966 81.6 Firm HECO - Waiau Power Plant 00502 W8 Residual 1968 81.6 Firm HECO - Waiau Power Plant 00502 W9 Distillate 1973 51.3 Firm HECO - Waiau Power Plant 00502 W10 Distillate 1973 51.3 Firm HECO - Kahe Power Plant 00501 K1 Residual 1963 81.6 Firm HECO - Kahe Power Plant 00501 K2 Residual 1964 81.6 Firm HECO - Kahe Power Plant 00501 K3 Residual 1970 85.8 Firm HECO - Kahe Power Plant 00501 K4 Residual 1972 90.9 Firm HECO - Kahe Power Plant 00501 K5 Residual 1974 134.9 Firm HECO - Kahe Power Plant 00501 K6 Residual 1981 134.9 Firm AES Hawaii, Inc. 00101 GEN1 Coal 1992 203.0 Firm HPOWER 00082 GEN1 MSW 1987 63.7 Firm Kalaeloa Cogeneration Plant 00100 CT1 Residual 1989 119.2 Firm Kalaeloa Cogeneration Plant 00100 CT2 Residual 1991 119.2 Firm Kalaeloa Cogeneration Plant 00100 ST1 Distillate 1991 61.0 Firm Tesoro Hawaii GEN1 Waste Oil 1982 20.0 Not Firm Chevron Hawaii Cogen GEN1 Other Gas 1990 3.0 Not Firm Chevron Hawaii Cogen GEN2 Other Gas 1990 3.0 Not Firm Chevron Hawaii Cogen GEN3 Other Gas 1990 3.0 Not Firm 
Technical Memorandum 

November 9, 2007 Page 7 Table 2. EGU Summary – Hawaii Facility Name State ID Unit ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

HELCO - W.H. Hill (Kanoelehua) Generating Station 00014 5 Residual 1965 14.1 Firm HELCO - W.H. Hill (Kanoelehua) Generating Station 00014 6 Residual 1974 23.0 Firm HELCO - Kanoelehua Generating Station 00014 11 Distillate 1962 2.0 Firm HELCO - Kanoelehua Generating Station 00014 15 Distillate 1972 2.5 Firm HELCO - Kanoelehua Generating Station 00014 16 Distillate 1972 2.5 Firm HELCO - Kanoelehua Generating Station 00014 17 Distillate 1973 2.5 Firm HELCO - Kanoelehua Generating Station 00014 CT1 Distillate 1962 11.5 Firm HELCO - Keahole Power Plant 00013 CT2 Distillate 1989 17.6 Firm HELCO - Keahole Power Plant 00013 CT4 Distillate 2004 23.0 Firm HELCO - Keahole Power Plant 00013 CT5 Distillate 2004 23.0 Firm HELCO - Keahole Power Plant 00013 21 Distillate 1984 2.5 Firm HELCO - Keahole Power Plant 00013 22 Distillate 1984 2.5 Firm HELCO - Keahole Power Plant 00013 23 Distillate 1988 2.5 Firm HELCO - Puna Power Plant 00021 1 Residual 1970 15.5 Firm HELCO - Puna Power Plant 00021 CT3 Distillate 1992 23.6 Firm HELCO - Shipman Power Plant 00023 3 Residual 1955 7.5 Firm HELCO - Shipman Power Plant 00023 4 Residual 1958 7.5 Firm HELCO - Waimea Power Plant 00519 12 Distillate 1970 2.5 Firm HELCO - Waimea Power Plant 00519 13 Distillate 1972 2.5 Firm HELCO - Waimea Power Plant 00519 14 Distillate 1972 2.5 Firm Pepeekeo Power Plant 00022 Bituminous 1974 23.8 Retired HELCO – Paneawa D24 Distillate 1997 1.0 Firm HELCO – Ouli D25 Distillate 1997 1.0 Firm HELCO – Punaluu D26 Distillate 1997 1.0 Firm HELCO – Kapua D27 Distillate 1997 1.0 Firm HELCO - Lalamilo Wind Farm Wind 1986 2.2 Not Firm HELCO – Puueo 1 Hydro 1918 0.7 Not Firm HELCO – Puueo 2 Hydro 2005 2.3 Not Firm HELCO – Waiau 1 Hydro 1921 0.7 Not Firm HELCO – Waiau 2 Hydro 1928 0.3 Not Firm Puna Geothermal Venture (PGV) I OEC11 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC12 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC13 Geothermal 1992 3.5 Firm 
Technical Memorandum 

November 9, 2007 Page 8 Table 2. ContinuedFacility Name State ID Unit ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

Puna Geothermal Venture (PGV) I OEC14 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC15 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC21 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC22 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC23 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC24 Geothermal 1992 3.5 Firm Puna Geothermal Venture (PGV) I OEC25 Geothermal 1992 3.5 Firm Hamakua Energy Partners L.P. (HEP) CT1 Naphtha 2000 23.0 Firm Hamakua Energy Partners L.P. (HEP) CT2 Naphtha 2000 23.0 Firm Hamakua Energy Partners L.P. (HEP) ST1 Naphtha 2000 20.0 Firm Wailuku River Hydro 8101 Hydro 1993 5.2 Not Firm Wailuku River Hydro 8102 Hydro 1993 5.2 Not Firm Kamaoa Wind Farm Wind 1987 9.2 Not Firm HRD Wind Farm Wind 2006 10.6 Not Firm Pakini Nui Wind Farm Wind 2007 20.5 Not Firm Other IPP Hydro Hydro varies 0.3 Not Firm 
Technical Memorandum 

November 9, 2007 Page 9 Table 3. EGU Summary – Maui Facility Name 

State 

ID 

Unit 

ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

MECO - Kahului Power Plant 00006 1 Residual 1948 5.0 Firm MECO - Kahului Power Plant 00006 2 Residual 1949 5.0 Firm MECO - Kahului Power Plant 00006 3 Residual 1954 11.5 Firm MECO - Kahului Power Plant 00006 4 Residual 1966 12.5 Firm MECO - Maalaea Generating Station 00500 1 Distillate 1971 2.6 Firm MECO - Maalaea Generating Station 00500 2 Distillate 1972 2.6 Firm MECO - Maalaea Generating Station 00500 3 Distillate 1972 2.6 Firm MECO - Maalaea Generating Station 00500 4 Distillate 1973 5.6 Firm MECO - Maalaea Generating Station 00500 5 Distillate 1973 5.6 Firm MECO - Maalaea Generating Station 00500 6 Distillate 1975 5.6 Firm MECO - Maalaea Generating Station 00500 7 Distillate 1975 5.6 Firm MECO - Maalaea Generating Station 00500 8 Distillate 1977 5.6 Firm MECO - Maalaea Generating Station 00500 9 Distillate 1978 5.6 Firm MECO - Maalaea Generating Station 00500 10 Distillate 1979 12.5 Firm MECO - Maalaea Generating Station 00500 11 Distillate 1980 12.5 Firm MECO - Maalaea Generating Station 00500 12 Distillate 1988 12.5 Firm MECO - Maalaea Generating Station 00500 13 Distillate 1989 12.5 Firm MECO - Maalaea Generating Station 00500 14 Distillate 1992 23.6 Firm MECO - Maalaea Generating Station 00500 15 Distillate 1993 18.0 Firm MECO - Maalaea Generating Station 00500 16 Distillate 1993 23.6 Firm MECO - Maalaea Generating Station 00500 17 Distillate 1998 25.0 Firm MECO - Maalaea Generating Station 00500 18 Distillate 2006 18.0 Firm MECO - Maalaea Generating Station 00500 19 Distillate 2000 25.0 Firm MECO - Maalaea Generating Station 00500 X1 Distillate 1987 2.6 Firm MECO - Maalaea Generating Station 00500 X2 Distillate 1987 2.6 Firm MECO - Hana Substation MH1 Distillate 2001 1.0 Not Firm MECO - Hana Substation MH2 Distillate 2001 1.0 Not Firm Hawaiian Comm & Sugar - Puunene Mill 00001 PUU3 Biomass 1972 10.0 Firm Hawaiian Comm & Sugar - Puunene Mill 00001 PUU4 Biomass 1982 20.0 Firm Hawaiian Comm & Sugar - Puunene Mill 00001 PUU5 Biomass 2000 16.2 Firm Hawaiian Comm & Sugar - Kaheka Hydro KAH1 Hydro 1925 1.5 Not Firm Hawaiian Comm & Sugar - Kaheka Hydro KAH2 Hydro 1925 1.5 Not Firm Hawaiian Comm & Sugar - Kaheka Hydro KAH3 Hydro 1925 1.5 Not Firm Hawaiian Comm & Sugar - Paia Hydro PHYD Hydro 1912 0.9 Not Firm Kaheawa Wind Partners (KWP) Wind 2006 30.0 Not Firm 
Technical Memorandum 

November 9, 2007 Page 10 Facility Name 

State 

ID 

Unit 

ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

Makila Hydro Hydro 2007 0.5 Not Firm Table 4. EGU Summary – Molokai Facility Name 

State 

ID 

Unit 

ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

MECO - Palaau Power Plant 00015 CAT1 Distillate 1985 1.2 Firm MECO - Palaau Power Plant 00015 CAT2 Distillate 1985 1.2 Firm MECO - Palaau Power Plant 00015 CUM3 Distillate 1985 0.9 Firm MECO - Palaau Power Plant 00015 CUM4 Distillate 1985 0.9 Firm MECO - Palaau Power Plant 00015 CUM5 Distillate 1985 0.9 Firm MECO - Palaau Power Plant 00015 CUM6 Distillate 1991 0.9 Firm MECO - Palaau Power Plant 00015 15 Distillate 1982 2.5 Firm MECO - Palaau Power Plant 00015 7 Distillate 1996 2.2 Firm MECO - Palaau Power Plant 00015 8 Distillate 1996 2.2 Firm MECO - Palaau Power Plant 00015 9 Distillate 1996 2.2 Firm Table 5. EGU Summary – Lanai Facility Name 

State 

ID 

Unit 

ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

MECO - Miki Basin Power Plant 00035 LL1 Distillate 1990 1.0 Firm MECO - Miki Basin Power Plant 00035 LL2 Distillate 1990 1.0 Firm MECO - Miki Basin Power Plant 00035 LL3 Distillate 1990 1.0 Firm MECO - Miki Basin Power Plant 00035 LL4 Distillate 1990 1.0 Firm MECO - Miki Basin Power Plant 00035 LL5 Distillate 1990 1.0 Firm MECO - Miki Basin Power Plant 00035 LL6 Distillate 1990 1.0 Firm MECO - Miki Basin Power Plant 00035 LL7 Distillate 1996 2.2 Firm MECO - Miki Basin Power Plant 00035 LL8 Distillate 1996 2.2 Firm 
Technical Memorandum 

November 9, 2007 Page 11 Table 6. EGU Summary – Kauai Facility Name 

State 

ID 

Unit 

ID Fuel Type 

Initial 

Operation 

Date 

Nameplate 

Capacity 

(MW) Generati

KIUC - Port Allen Generating Station 00012 D6 Distillate 1990 8.6 Firm KIUC - Port Allen Generating Station 00012 D7 Distillate 1990 8.6 Firm KIUC - Port Allen Generating Station 00012 GT1 Distillate 1973 19.2 Firm KIUC - Port Allen Generating Station 00012 GT2 Distillate 1977 23.8 Firm KIUC - Port Allen Generating Station 00012 IC1 Distillate 1964 2.0 Firm KIUC - Port Allen Generating Station 00012 IC2 Distillate 1964 2.0 Firm KIUC - Port Allen Generating Station 00012 ST1 Distillate 1969 10.0 Firm KIUC - Port Allen Generating Station 00012 3 Distillate 1968 2.7 Firm KIUC - Port Allen Generating Station 00012 4 Distillate 1968 2.7 Firm KIUC - Port Allen Generating Station 00012 5 Distillate 1968 2.7 Firm KIUC - Port Allen Generating Station 00012 8 Distillate 1991 8.6 Firm KIUC - Port Allen Generating Station 00012 9 Distillate 1991 8.6 Firm KIUC - Kapaia Power Station CT1 Jet Fuel 2002 39.1 Firm McBryde Sugar Co. Ltd. - Kalaheo Hydro KAL Hydro 1928 1.1 Not Firm McBryde Sugar Co. Ltd. - Wainiha Hydro WAIA Hydro 1906 1.8 Not Firm McBryde Sugar Co. Ltd. - Wainiha Hydro WAIB Hydro 1910 1.8 Not Firm Gay Robinson DSL1 Distillate 1972 0.4 Firm Gay Robinson DSL4 Distillate 1972 0.4 Firm Gay Robinson HYD2 Hydro 1982 1.2 Not Firm Gay Robinson ST4 Biomass 1985 4.0 Firm 
Technical Memorandum 

November 9, 2007 

Page 12 

Table 7. Electricity Projections from IRP Documents 

Oahu Hawaii Maui Molokai Lanai Kauai 

2005 (MW) 1,416.0 207.4 221.1 6.35 5.15 76.0

2018 (MW) 1,660.2 260.9 295.1 7.20 6.70 103.0

Δ2005-2018 (MW) 244.2 53.5 74.0 0.85 1.55 27.0

Long-Term 

Peak 

%Δ 17.2% 25.8% 33.5% 13.4% 30.1% 35.5%

2005 (GWh) 8,118.4 1,153.7 1,239.5 n/a n/a n/a

2018 (GWh) 9,509.5 1,451.7 1,649.8 n/a n/a n/a

Δ2005-2018 (GWh) 1,391.1 298.0 410.3 n/a n/a n/a

Long-Term 

Sales 

%Δ 17.1% 25.8% 33.1% n/a n/a n/a

Table 8. Future Projects/Policies Identified from IRP Documents

Island 

Existing 

Firm 

Capacity 

(MW) 

New 

Capacity 

Needed 

(MW) 

Future 

Identified 

Projects/ 

Policies 

(MW) Description of New Capacity 

New 

Capacity 

Not Yet 

Identified 

(MW) 

Oahu 1,754.4 300.6 260.3

• Additional capacity from Kalaeloa Cogen 

beginning 4th quarter 2005 (29.0 MW) 

• Demand Side Management (98.2 MW) 

• Combined Heat & Power, Distributed 

Generation (32.5 MW) 

• Photovoltaics (in 2007 and 2015) (0.6 MW) 

• Simple-cycle combustion turbine (in 2009) 

(100 MW) 

40.3

Hawaii 319.6 82.6 32.5

• Keahole steam turbine generator (ST-7) 

(2009) (16 MW) 

• Demand Side Management (13.2 MW) 

• Combined Heat & Power, Distributed 

Generation (3.3 MW) 

50.1

Maui 292.0 96.7 93.4

• 3rd phase of Maalaea dual-train combined 

cycle No. 2 (Maalaea 18) (October 2006) 

(18.0 MW) 

• Eight 1 MW distributed generation units 

(2009 – 1, 2010 – 2, 2012 – 4, 2017 – 1) (8 

MW) 

• Waena 1 simple-cycle combustion turbine 

(2011) (21.2 MW) 

• Waena 2 simple-cycle combustion turbine 

(2013) (21.2 MW) 

• Biomass unit (2018) (25.0 MW) 

3.3

Molokai 15.1 2.0 2.2 • Palaau-10 (2014) (2.2 MW) -0.2

Lanai 10.4 3.1 3.1 • Manele Bay Hotel CHP (2008) (0.9 MW) 

• Miki Basin LL-9 (2017) (2.2 MW) 0.0

Kauai 143.4 50.9 18.0

• 3rd party biomass plant (8.5 MW) 

• 3rd party biomass plant (4.2 MW) 

• 3rd party waste-to-energy facility (5.3 MW) 

32.9