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, 2010April 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 Mari ne Vessel Activity And Emissions Appendix C: Development Of Co mmercial 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 Es timates 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 Tilli ng 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 ProjectionsApril 2010

J:\Hawaii_EI\Reporting\Final\TOC.doc ii TABLES

Table 1-1. State of Hawaii emission inventory technical documentation.................................2 Table 3-1. Hawaii annual emissi ons, 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 emissi ons by source category, 2002, 2005, and 2018 (sea spray associated PM10 not included).............................................................10 Figure 3-1e. Annual PM2.5 emissi ons 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 ......................12April 2010

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 1 1. INTRODUCTION

This report provides a compendium of all of the emission invent ory 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 (PM 2.5 )

• Particulate matter smaller than 10 μ diameter (PM 10 )

• Sulfur dioxide (SO 2 )

• Nitrogen oxides (NO x )

• Volatile organic compounds (VOCs)

• Carbon monoxide (CO)

• Lead (Pb)

The emissions estimates also include d data collection for ammonia (NH 3 ), as a precursor of sulfate and nitrate PM 2.5 formation, as well as for the toxi c air pollutant, hydrogen sulfide (H 2 S).

Finally emission estimates included data for th e 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 faci lities and activities include such sources as gasoline dispensing facilities, construction dust, archit ectural surface coatings, industrial surface coatings, degreasing, and consumer solvents. Estimates associated with area source ammonia emissions, windblown dust, road dust, agricultural tilling dus t, 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 e quipment types that either move under their own power or are capable of bei ng 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

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 2 • Dust sources include windblown dust, road dus t, and agricultural tillage.

• Fires include emissions from agricultur al, prescribed, and wildland fires.

• Biogenic and geogenic emission sources are those occurring naturally from biological and physical processes of the earth. Biogeni c/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 count y and island level detail.

Temporal Resolution : The base years for the emission i nventory work and ai r quality modeling are 2002 and 2005. The emission inventory proj ection year is 2018. For all sources and calendar years, emissions were estimated as annu al 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 w ith 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 re ports 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 Emissi on 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

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 3 2. METHODOLOGY

This section briefly describes the methods that were used to estimate em issions for each of the major source categories. Further details on emissions estimation methods, data used, assumptions, and modeling techniques may be fo und in each of the emissions project reports listed in Table 1-1 and pr ovided 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 we re estimated by combining EPA emission factors from EPA’s MOBILE6 model, expressed in grams pe r 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 va riety of equipment types that either move under their own power or are capable of being mo ved from site to site. More specifically, these sources, which are not li censed or certified as highway ve hicles, are defined as those that move or are moved within a 12-month pe riod 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 a nd used to develop the inventory. US EPA’s NONROAD2005 model was used to estimate emi ssions 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 30n m away from the Hawaiian coastline), and includes emissions from large ocean goi ng vessels as well as harbor craft.

The near-port emissions are based on activity da ta (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 Prepar ing Port Emission Inventories" that was prepared for the US Environmental Protection Agency (ICF, 2006). The in transit emissions are based on emissions that were estim ated 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-t akeoff cycles (LTOs) available from the Federal Aviation Administration (FAA).April 2010

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 4 Locomotive Locomotive emissions in Hawaii are limited to two railroads; both are tour ist excursions and so are of little consequence in te rms of emissions. Locomotive fuel consumption was obtained for both railroad operations. Emissions were estimate d for the diesel fueled locomotives according to emission factors provided in EPA (1997) a nd for steam locomotives emission factors were based on ICF (2006).

AREA SOURCES

Area sources are smaller and widely dispersed st ationary 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” categ ory were estimated as the produc t 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 We stern Regional Air Partnership (WRAP). The development of the model, including data sour ces and estimation methodology, is documented in Chitjian and Mansell (2004).

Fires Fire emission sources include w ildland fires and agricultural land associated fires. To estimate emissions from fires, techniques developed by the Western Regional Ai r 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 methodol ogy was used to estimat e agricultu ral 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 inventor y for the State of Hawaii was developed using the estimation methodology developed for th e Western Regional Air Partnership (WRAP) by a team of contractors led by ENVIRON (ENV IRON, 2004). For more details, see Appendix K.April 2010

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 5 BIOGENIC/GEOGENIC SOURCES

Biogenic Biogenic emissions include emissions from na tural plant and soil sources. The Biogenic Emission Inventory System (BEIS), the Global Bi osphere Emissions and Interactions System (GloBEIS) developed by the National Center for Atmospheric Research and ENVIRON was used to estimate the State of Ha waii biogenic emissions inventory.

Sea spray Sea spray emissions include particulate matter emi ssions associated with sea salt aerosols. Sea spray emission inventories were estimated base d upon the sea spray particle generation function of de Leeuw et al. (2000).

Volcanic The Pu`u `O`o-Kupaianaha eruption of Kilauea be gan in 1983, and continues to be a significant source of SO 2 emissions. Estimates of volcanic emissi ons were obtained from USGS data for 2002 to 2005 (Elias, 2006) with esti mates of emissions for the summ it caldera and east rift zone.

POINT SOURCES HDOH provided point source emissions estim ates 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 Plan ning (IRP) process. For 2018 non-EGU sources, HDOH provided “new” permits and/or applicat ions submitted to the HDOH (HDOH, 2008) from which necessary information was obtained to populate the “new” (post 2005) sources in 2018.

FUTURE YEAR 2018, EMI SSION INVENTORY

2018 future year emissions were typically es timated by projecting 2005 emissions to 2018 based on appropriate growth factors, as described in each of the source ca tegory 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

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 6 3. EMISSION INVENTORY RESULTS

In this section the emission inventory results ar e 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 tech nical 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 portray ed in Figures 3-1a ( VOC), 3-1b (CO), 3-1c (NOx), 3-1d (PM10), 3-1e (PM2.5), 3-1f (SO 2 ), and 3-1g (NH 3 ). 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).

• SO 2 emission increase from 2002 to 2005, then decrease from 2005 to 2018; these trends are from trends in geogenic emissions.

• NH 3 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 c ontributor to overall NOx and PM2.5 emissions, and are a very small fraction of emissions fo r other pollutants. Point source PM2.5 and NOx emissions are estimated to increase from 2002 to 2018.

• Area source emissions are a significant cont ributor to VOC emissions and the dominant source of NH 3 emissions. Area source VOC emissions are projected to increase from 2002 to 2018, as they are primarily associated with population increase s and most of the area sources are uncontrolled. NH 3 emissions remain relativ ely 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 NH 3 inventories, but their contri bution is decreasing over time (on both an absolute and relative basis) despite increases in activity as older vehicles are re tired and replaced by newer vehicles meeting much tight er federal emissions standards.

• Off-road equipment sources are a significant contributor to CO a nd NOx inventories. Their contribution to the NOx i nventory 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 equipmen t standards that phase in over time.

• Commercial Marine Vessel (C MV) 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 ac ross all years, and the combination of emissions increases to 2018 for CMV and d ecreases in NOx emissions for on-road and off-road equipment make CMV a larger contributor to NOx emissions in 2018.April 2010

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 7 • 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 significan tly 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 th e dominant source of SO 2

.

• Dust emissions consist of the following sour ces: 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 as sociated emissions and are projected to increase from 2002 to 2005.

• Fire emissions showed considerable increa ses 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

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 8 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 14,627April 2010

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 9 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000

2002 2005 2018

YearEmissions (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

YearEmissions (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

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 10 0 10000 20000 30000 40000 50000 60000 70000

2002 2005 2018

YearEmissions (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

YearEmissions (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

YearEmissions (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

YearEmissions (tons per year)

Point
Locomotive Geogenic
CMV

Figure 3-1f. Annual SO 2 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

YearEmissions (tons per year)

OnRoad

Fires
Area

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

J:\Hawaii_EI\Reporting\ Final\Report_12Apr2010.doc 13 4. REFERENCES

Chitjian, M. and G. Mansell. 2003. “An Improv ed Ammonia Inventory for the WRAP Domain – Technical Description of the Modeling Syst em.” Prepared for the WRAP Emissions Forum. November, 2003.

de Leeuw, G, F.P. Neele, M. Hill, M.H. Sm ith, 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. Pers onal Email from Tamar Elias. United States Geological Survey. Hawaii. November.

ENVIRON. 2004. “Determining Fugitive Dust Em issions from Wind Erosion.” Final Report. Prepared for Western Governors’ Associat ion by ENVIRON Interna tional Corporation, Novato, California; ERG, Inc., Sacramento, California; Desert Research Institute, Reno, Nevada, MACTEC Engineering & Consulting, Gainsville, Florida and University of California Riverside, Rivers ide, California. March 12.

EPA. 1997. “Locomotive Emission Standards.” Regulatory Support Docume nt, 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 Em ission Factors (AP-42) – Volume 13: Miscellaneous Sources, Section 2.1 Paved Ro ads, 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 Em ission Factors. (AP-42) – Volume 13: Miscellaneous Sources, Section 2.2 Unpaved Ro ads, 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 Em issions Inventory Reports. Hawaii State Department of Health, Clean Air Branch, Honolulu, Hawaii. December.

HDOH. 2007. 2005 Hawaii Emissions I nventory 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 Departme nt of Health, Clean Air Branch, Honolulu, Hawaii. January.

ICF Consulting. 2006. “Current Methodologies and Be st 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 characte ristics from the Wate rborne Transportation Lines of the United States.”APPENDICESAPPENDIX A

ON-ROAD EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDSGolden 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. Em issions from these vehicles were estimated by combining EPA emission factors from the MOBI LE6 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 proce dures for estimating 2002, 2005, and 2018 annual, summer weekday, winter weekday, summer aver age day, and winter average day emissions.

ACTIVITY DATA

Annual average daily HPMS VMT data by is land 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 acco rding to the percent of the population of Molokai Island in Kalawa o County and percent of population of Molokai Island in Maui County based on the 2000 popula tion estimates (Hawaii DBEDT, 2006). 2018 VMT estimates were made based on 2005 VM T 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 is land are presented in Figure 1. From 2002 to 2005 the average annual VMT increase was 5.1% while the aver age 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 conducte d by ENVIRON for the Wester n 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, tra ffic count data availabl e from HDOT could be analyzed in future work such that local activity is reflected in monthly an d 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 16,000,000,000VMT (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 cl ass. 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 factor s (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 th e latest in a series of MOBILE models for estimating vehicular exhaust NOx, CO, and exha ust and evaporative VOC. Version 6.2, which is the latest publicly re leased version (February 2004) and available at http://www.epa.gov/otaq/m6.htm , was used in this work and in cludes emission factor estimates for SO 2 , PM (at various size cutoffs), and NH 3 . This version contains updated CO emissionJanuary 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) standard s for light-duty vehicles, beginning with model year 2001;

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

• Heavy-duty vehicle standards, begi nning 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-mi x 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 0.0057January 2007

C:\MARY\ALISON 2007\HI_onroad_052507.doc 4 Average speeds by roadway type were EPA defa ults 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 h eavy duty vehicles (HDV). To estimate speeds by roadway type across all vehicle types, VMT-mi x data from HDOT for 2002 were applied to WRAP speed estimates by vehicle type. 2005 VMT-mi x 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 determ ine whether Hawaii state or county specific vehicle registration data by model year were available. The Honolulu County DMV maintains registration information for the st ate of Hawaii. However, registration data by model year as required to generate a MOBILE6 registration di stribution was not readily available for this project. In future revisions to this invent ory, 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 ma ximum temperatures for 2002 and 2005 were obtained from a station on each island, availabl e in the National Center for Atmospheric Research’s (NCAR) ds472 data set, which contains meteorological da ta for airports for historical calendar years. 2018 monthly average minimu m and maximum temperatures by season and island were obtained for these sa me stations from period of re cord data available from the Western Regional Climate Center website ( http://www.wrcc.dri.edu/COMPARATIVE.html ).

EPA has developed a database for national m obile 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 hasJanuary 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 conv erted 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 th e 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 reflecte d in MOBILE6 inputs. In 2 018, E10 (a 10% ethanol gasoline blend) fuel was assumed to be used, consiste nt 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, NO x , PM 10 , PM 2.5 , SO 2 , and NH 3 were obtained for each county, month, roadwa y type, area type (urban/rural) and eight MOBILE5 vehicle classes. These were then mu ltiplied with the corresponding VMT to estimate emissions by county and island. The annual to tal emissions were obtained by summing the monthly emissions. To obtain seasonal aver age day and weekday emissions, the monthly and weekday adjustments were used to estimate em issions 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, respectiv ely. 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 in creases (see Table 3) from 2002 to 2005 and VMT growth dominating fleet turnover effects.

On-road emissions are portrayed graphicallyby 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, PM 10 , PM 2.5 , and SOx emissions in 2002 and 2005. However, in all years, emissions forJanuary 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 11January 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 12,57 8 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 13,00 4 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 Analys is: Control of Hazardous Air Pollutants from Mobile Sources: Chapter 2. U.S. Envir onmental Protection Agency. February, 2006.

Hawaii DBEDT. 2006. 2005 State of Hawaii Da ta Book. Table 1.05-- Resi dent 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 Yi bo 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 Da tasets 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 Da tasets 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 Inventorie s 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 .pdfAPPENDIX B

HAWAII COMMERCIAL MARINE VESSEL ACTIVITY AND EMISSIONS773 San Marin Drive, Suite 2115 $ Novato, California 94998 USA Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com
J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 

### 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, 2 nd revision October 30, 2008, 3 rd 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 mode s 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 aglobal 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 in clude 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 draf t 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 (SO 2 ) in Hawaii.Page 2

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 interna tional 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 draft 1 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 ocea n going vessels and local commercial vessels is sometimes drawn at sma ller 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 traffi c. Furthermore, for some ports all vess els equal and below 18 feet draft are summed together in the USACE data set (USACE, 2004).Page 3

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 bulkcarriers, 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.Page 4

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 nauticalmiles 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 receive s 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 Li hue 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.Page 5

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 Auxiliary Engines Average 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 al so 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.Page 6

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 Auxiliary Engines Average 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 dur ing loading and unloading operations.

Figure 4. Car carrier ship example - Liberty Ace (www.shipspotting.com).Page 7

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc Table 4. Average technical data for car carriers based on CARB survey (ICF, 2006) Average Auxiliary Engines Average 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 tr ansport 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 Auxiliary Engines Average 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.Page 8

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc Table 6. Average technical data for liquid bulk carriers based on CARB survey (ICF, 2006) Average Auxiliary Engines Average 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 Auxiliary Engines Average Propulsion (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 th ey 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.Page 9

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc Honolulu is the only harbor in the state of Ha waii 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 serv ing 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 ).Page 10

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 SO 2 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 (Kau ai). 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 Stat es 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 car go 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 distributi on 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.Page 11

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 destin ed 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 indi cate a continuation of sharp growth into the future.

Figure 8. Growth of Hawaii cruise passenger arrivals. Source: First Hawaiian Bank (2006).Page 12

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 year s 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 Tran sportation-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.Page 13

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc ENVIRON estimated hazardous air pollutant emissi ons (HAPs) for CMV, including deep draft, tug, barge and local passenger traffic vessels, base d on HAP fractions as specified in the U.S. EPA 2002 National Emission Inventory (NEI) documentation (EPA, 2005 and EPA, 2008 2 ). Diesel-powered HAP fractions used to estimat e 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 fact ors 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.Page 14

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 th e 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 th at 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, wh ereas 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 fromPage 15

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 factor s 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 har bor, 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 a nd tankers may make them to a significant source of in-port emissions. Cruise ships have an enor mous 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.Page 16

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 t ype 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 2007Page 17

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 NO x SO 2 CO HC PM10 PM2.5 CO 2 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 EP A 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 NO x SO 2 CO HC PM10 PM2.5 CO 2

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, 2005Page 18

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc Table 13. Emission factors for boilers (kg/tonne of fuel consumed).

Engine type NO x SO 2 CO HC PM10 PM2.5 CO 2 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 SO 2 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 SO 2 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.Page 19

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 NO x CO HC PM 10 PM 2.5 SO 2 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 em issions differs from pollutant to pollutant. Moreover, the share of cruise traffic emi ssions 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 NO x CO HC PM 10 PM 2.5 SO 2 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 anPage 20

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 tric ky and particular difficult task as many different factors may influence its growth. Factors influe ncing 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 vesse ls 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).Page 21

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000

20

00 20 02 20 04 2 0 06 20 0 8 20 10 20 12 2 0 14 20 1 6 20 18Number 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 cr uise 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.Page 22

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc -10%

0% 10% 20% 30% 40% 50% 60% 70% 80%

2001 2002 2003 2004 2005 2006 2007 2 008 200

9

2010 2 011 20

12

201

3

2014 2015 201

6

2017 2018Annulal 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 NO x CO HC PM 10 PM 2.5 SO 2 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 NO x CO HC PM 10 PM 2.5 SO 2 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 49.6 575.6Page 23

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc Table 20. Deep draft vessel emissions at Hawaii ports for 2018 according scenario C (tons).

Port and specific location NO x CO HC PM 10 PM 2.5 SO 2 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 signifi cantly 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 pr ovided 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 betw een 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 12Page 24

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc – 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 specifi c 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 domes tic 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 Prac tice 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 NO x CO HC PM 10 SO 2Page 25

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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 pot entially business took place in those years. While this trend might have continued in 2005, it cannot c ontinue 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 NO x CO HC PM 10 SO 2 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 6.06Page 26

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 NO x CO HC PM 10 SO 2 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 NO x CO HC PM 10 SO 2 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 se lf-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 andPage 27

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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 f actors 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 emi ssion 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 NO x CO HC PM 10 SO 2 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 NO x CO HC PM 10 SO 2

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.Page 28

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc Table 28. 2018 local passenger vessel emissions at Hawaii islands (tons).

Island location based on registration NO x CO HC PM 10 SO 2 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

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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, Laue r, A. 2005: "Emissions from international shipping: 1. The last 50 years. Journal of Geophysical Research, Vol 110. DI7, 305-317

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ENTEC. 2002. “Quantification of Emissions fr om 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. “L ADCO Nonroad Emission Inventory Project for Locomotive, Commercial Marine and Recreational Marine Emission Sources.” Prepared for Lake Michigan Air Director Consortium.Page 29

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J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc EPA. 2005. “Documentation for Aircraft, Commercial Marine Vessel, Locomotive, and Other Nonroad Components of the National Emissi ons 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

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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

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Starcrest Consulting Group LLC 2007: Final Dr aft: The Port of San Diego 2006 Emission Inventory. July 2007.Page 30

773 San Marin Drive, Suite 2115 $ Novato, California 94998 USA Tel: (415) 899-0700 $ Fax: (415) 899-0707 $ www.environcorp.com
J:\Hawaii EI\Reporting\Tech memos\HI_commercial_marine_123009.doc 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, 1 st 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 Co ast, 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.htmlAPPENDIX C

DEVELOPMENT OF COMMERCIAL MARI NE VESSEL EMISSION INVENTORY FOR VESSELS IN TRANSIT 30NM FROM THE HAWAIIAN SHORELINEENVIRON 773 San Marin Drive, Suite 2115 ● Novato, California 94998 USA

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 

### 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: NO x , SO 2 , PM 10 , PM 2.5 , 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 inventory 1 . 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 inventory 1

.Page 2

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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 region 2 . Table 1 shows the compounded growth factors.

Table 1. Growth Factors to scale emissions fr om 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 work 1 , 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 inventorie s. 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 reductionPage 3

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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 0.916 3

. 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.Page 4

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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.Page 5

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc Transforming the emissions data from the S TEEM projection to the HI LCC projection was accomplished with the use of Geographical Inform ation 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).Page 6

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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.Page 7

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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.Page 8

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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 ma rine 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.Page 9

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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 Im pact Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel E ngines,” 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 em issions 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 c ountry afterwards, an inspection of the USACE Foreign Traffic Vessel Entrances and Clearances 6 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 domes tic. 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”.Page 10

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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/NorthAmeri canSTEEM/ARBCEC_SECA_task3-4Report.pdf

4. EPA 2005. "Conversion Factors for Hydr ocarbon 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/dat aclen.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. ; 2007Page 11

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J:\Hawaii EI\Reporting\Tech memos\HI_Gridded_TransitCMV.110309.doc 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

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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, andauxiliary 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 - 2022APPENDIX D

OFF-ROAD EQUIPMENT EMI SSIONS ESTIMATES FOR THE HAWAIIAN ISLANDSGolden 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 th e emissions results. Off-road mobile sources encompass a wide variety of equipmen t 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 vehi cles, are defined as those that move or are moved within a 12-month period and are covere d under the EPA's emissions regulations as nonroad mobile sources. Where feasible and ap propriate, local activity data for specific source categories were gathered a nd used to develop the inventory.

US EPA’s NONROAD2005 m odel 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 eq uipment, such as leaf and snow blowers;

• logging equipment, such as sh redders and large chain saws;

• recreational equipment, such as off-road motorbikes; and

• recreational marine vessels , such as power boats.

For all of these categories, a nnual average emissions have been estimated in tons per year, and winter and summer season weekday emi ssions are estimated in tons per day.

NONROAD MODEL EQUIPMENT EMISSIONS

The EPA NONROAD2005 model was used to estimat e 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, NO X , CO, CO 2 , SO 2 , and PM (both PM10 and PM2.5). The model al so 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 thePage 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 activit y indicators, such as employees, tilled acreage, and construction valuation. For this effort, st andard NONROAD county data were modified to treat Hawaii’s eight major islands as countie s 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 effect s 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 sp ecific types of nonroad equipment, and further stratifies equipment types by horse power rating and fuel type.

The basic equation for estimating emissions in the NONROAD model is as follows:

Emissions = (P op)*(Power)*(LF)*(A)*(EF)

where Pop = Engine Population Power = Average Power (hp) LF = Load Factor (frac tion of available power) A = Activity (hrs/yr) EF = Emission Factor (g/hp-hr)

For national or state level emissions estim ation, 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 fact ors 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 geogr aphically 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 geograp hic allocation is performed is as follows:

(County Population) i /(State Population) i = (County Indicator) j /(State Indicator ) j

where i is an equipment application li ke construction or agriculture. j is an indicator type associated with equipment i

Activity is temporally allocated with an anal ogous equation, but using monthly and day of week fractions of yearly activity.Page 3

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 av ailable. 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 seve ral 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 va por pressures (RVP) and su lfur 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 mari ne vessel population and allocation was updated using population data available from th e 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 ty pe, 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 repla ced the defaults in the NONROAD inboard and outboard pleasure craft geographi cal allocation files producing a llocation factors as shown in Table 3.Page 4

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc 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 estimate d by allocating the state level estimates using econometric or other activity indicators, such as employees, tilled acr eage, and construction valuation. For this project, standard NONRO AD county data were modified to estimate emissions for Maui County by island. It wa s assumed that for Kauai County, which includes Kauai Island and Niihau Island, all Kaua i County activity occu rred on Kauai Island. Modifications were made to al location files by distributing Maui County allocations to Maui islands according to island specific data as de scribed in Table 3. 2002 allocations, which are consistent with 2005 allocations pr esented 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 C ON F RM COM MF G RVP GC POP WI B WOB

NONROAD IndicatorMaui 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 r un 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 SO 2 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 di stributions which allocat e yearly activity to seasons. These default distributions for the st ate of Hawaii are based primarily on California data. In order to more accurately reflect activit y in Hawaii, which has significantly less seasonal variation than the mainland, temporal distributio ns 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 pollu tant emissions consistent with EPAs 2002 NEI (EPA, 2005).

Ammonia Emissions

The NONROAD model does not estimate ammonia em issions. 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 ar e 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 emissi ons 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 year s. CO emissions incr ease 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, SO 2 , and benzene, respectively. From 2005 to 2018, statewide emissions increase by 27% for NH 3 and decrease by 25%, 4%, 35%, 39%, 39%, 99%, and 29% for VOC, CO, NOx, PM10, PM2.5, SO 2 , and benzene, respectively. Changes in VOC, CO, NOx, PM10, and PM2.5, and benzene emissions over time are primarily re flective of fleet turnover from older to newer cleaner engines over time. Changes in SO 2 emissions are reflective of changes in fuel sulfur content and fuel cons umption over time. NH 3 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 y ears by lawn and garden equipment, with 2002 contributions of 63%, 64%, and 62%, respectiv ely. 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 NH 3 emissions are construction equipment and lawn and garden equipment with 2002 contributi ons 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 8 6 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 1 32

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. BenzenePage 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 BenzenePage 9

G:\Hawaii EI\Reporting\Tech memos\NONROAD_062807.doc References

EPA, 2005. Documentation for Aircraft, Commerc ial Marine Vessel, Locomotive, and other NONROAD Components of the National Em issions Inventory: Volume 1 – Methodology. U.S. Environmental Protection Agency. Research Triangle Park, North Carolina. September, 2005.

FAA 2006. Terminal Area Forecast 2002, Federa l Aviation Administrati on. 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 Developm ent, 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 Developm ent, and Tourism. Accessed March 2007. Internet address: http://www.hawaii.gov/dbedt /info/economic/databook/

HDBEDT 2007c. 2002 State of Hawaii Databook, Section 1: Populatio n, Table 01.06. Hawaii State Department of Business, Economic De velopment, and Tourism. Accessed March 2007. Internet address: http://www.hawaii.gov/dbed t/info/economic/databook/

HDBEDT 2007d. 2002 State of Hawaii Databook, Section 7: Recreatio n 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: Recreatio n 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 Depa rtment of Labor & Industrial Relations' Research & Statistics Offi ce. Internet Address: http://www.hiwi.org/article.asp?PAG EID=&SUBID=&ARTICLEID=515&SEGMENTI D=2APPENDIX E

AIRCRAFT EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDSGolden 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 Estimate s 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 (NEI 2002). Activity data for aircraft emissions are takeoff cycles (LTOs), and emission f actors primarily from the Federal Aviation Administration (FAA) Emissions and Disper sion Modeling System (EDMS). The 2002 emissions were projected to future years us ing forecast LTOs available from the FAA.

The FAA Emissions Dispersion and Modeling Syst em (EDMS) combines aircraft and activity levels with emission factors to es timate annual inventories for a speci fic airport. Aircraft activity levels in EDMS are expressed in terms of landi ng and take-off cycles (LTOs), which consist of the four aircraft opera ting 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 (e xcept 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 de fault 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 pi ston-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 th e 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 Esti mates 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 criteri a air pollutant and h azardous air pollutant emissions data was downloaded fr om the EPA NEI2002 website at http://www.epa.gov/ttn/chie f/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 direc tly. It was assumed that no NH 3 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 NH 3 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 a mmonia. 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 wei ghted by the typical time spent in each mode, average hours of operation estimated from FA A 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 ac tivity 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 NO x and CO emission standards for commercial aircraft, exempting general aviation and military engines fr om the rule (ICAO, 1998), and the majority of engines are already meeting this standard.

The historic and projected LTO data by airport are available on line 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 a nd 2018, and historic data for 2002 were used to develop future year growth fact ors for all aircraft types. Ta ble 2 summarizes LTOs by island and aircraft type for calendar years 2002, 2005 a nd 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 569,913 596,414 694,818June 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 isla nd and aircraft type in each futu re year to the sum of LTOs by island and aircraft type in 2002. These future year growth factors we re 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 monthl y 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 annua l air traffic statistics for airports with FAAoperated or FAA-contracted tr affic 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 season al distributions were then app lied 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 em issions estimates by island. Statewide criteria air pollutant emissions increase betw een 4% and 6% from 2002 to 2005 while increases are between 16% and 25% from 2005 to 2018. In creases in emissions are due to statewide increases in air carrier, ai r taxi, and general aircraft LTOs from 2002 to 2005 and 2005 to 2018 which dominate military aircraft LTO decreas es from 2002 to 2005 (military aircraft LTOs remain constant from 2005 to 2018). Molokai an d Lanai emissions decrease significantly from 2002 to 2005 due to air carrier LTOs which d ecrease from 997 LTOs and 1002 LTOs in 2002 for Molokai and Lanai, respec tively, to zero air carrier LTOs for both islands in 2005.

Figures 2, 3 and 4 show the CAP emission contri butions by aircraft type for calendar years 2002, 2005 and 2018. For all years, CO emissions ar e dominated by air-taxi emissions with 2002 contributions of 40%, while NOx, PM10, PM2.5, SO 2 and VOC are dominate d in all years by air carrier emissions with contribu tions 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 NH 3 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 0.084June 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

PollutantsFraction 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

PollutantsFraction 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

PollutantsFraction 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 Emi ssions Inventory: Volume I – Methodology

. U.S. Environmental Protection Agency. Re search Triangle Park, North Carolina.APPENDIX F

HAWAII LOCOMOTIVE EMISSIONSGolden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com
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 deve loped 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 SOx 1 NH3 2 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 SO 2 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.Page 2

Golden Gate Plaza $ 101 Rowland Way $ Novato, California 94945-5010 USA Tel: (415) 899-0700 $ Fax: (415) 899-0707 $ www.environcorp.com
H:\Hawaii EI\Reporting\Tech memos\HI_locomotive_071007.doc 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,” Envi ronmental 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 Be st Practices in Preparing Port Emission Inventories,” Final Report April 4, 2006, Prep ared 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 ISLANDSGolden 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 

### 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 calculati ons, 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 contributio ns 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 a pplied to the estimated 2002 and 2005 Hawaii population by island. By-island population estima tes 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 de rived 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 (Tab le 2) to the VOC emissions. Speciate profile 2570 from EPA’s SP ECIATE database (Graphic Arts – Composite of Lithography, Rotogravure, Letter press and Flexography) was used.2

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 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 graphi c arts shops show no appreciable seasonal variation, typically 75% of em issions activity occurs on week days (EIIP, 1996a). Average summer and winter weekday emissions were ca lculated 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 refini shing 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 fo r 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 wh ere employment data were give n 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 frac tions, population fractions based on populations shown in Table 1 were used. Employment by isla nd was sought to allocate by island activity for this category from the Hawaii State Department of Labor & Industrial Relations' Research & Statistics Office; however employme nt data to the level of detail needed was not available. 2018 emission estimates were projected based on 2005 em issions 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 EP A 2002c). Two profiles are present in SPECIATE that apply to this ca tegory, profiles 2402 and 1194. Th e profiles were combined to obtain the most comprehensive list of HAPs. Wh ere 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 appl ied to reflect the promulgation of national VOC rules. This is the estimated total reduc tion of VOCs emanating fr om auto body refinishing3

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 refini shing 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 dividi ng 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 ope rations were estimated using a per employee emission factor. The emission factor of 1800 lb s/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 employm ent 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 isla nd was sought to allocate by island activity for this category from the Hawaii State Department of Labor & Industrial Relations' Research & Statistics Office, however employ ment data to the level of deta il needed was not available. 2018 emission estimates were projected based on 2005 em issions 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 so lvents in question are perchloroethylene (also PERC or tetrachloroethene), 1,1,1-trichloroethan e (also TCA or methyl chloroform) and CFC113. To extract the emission factors for the re levant pollutants, PERC and TCA, from this combined factor, survey data of the national c onsumption 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 f actor was assumed to apply to PERC and 6.9% to TCA. The emission factors resulting fr om this process are shown in Table 3.4

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 PE RC 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 dr y cleaners cannot be included in VOC calculations (EIIP, 1996b). Emissions resulting from activity in NAI CS 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 prom ulgation of the National Emission Standards for Hazardous Air Pollutants (NESHAP) rule, “Per chloroethylene Dry Cleaning Facilities” (40 CFR, Parts 963) were incorporated into emi ssion 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 cleani ng facilities and a 43 percent reduction (40 CFR, Parts 963) in 1996 from all new dry clean ing facilities were estimated nationwide as a result of rule implementation. Additionally, dr y cleaning EIIP documentation states that, "Coinop dry cleaning units are exempt from all but th e initial reporting NESHAP requirements." (EIIP 1996b) Therefore, a reduction of 43% (conserva tively 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 repor ted 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 emissi ons were calculated by dividing the annual emissions by 260 workdays/year.

INDUSTRIAL SURFACE COATING

Annual Emissions

VOC emissions originating from industrial surf ace coating operations were estimated using either per employee emission factors or per capi ta emission factors. There are actually ten distinct surface coating operations with dist inct per employee emission factors and three operations with per capita emissi on factors, for a total of 13 cate gories. These operations and the corresponding emission factors are listed in Table 4.5

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 Ma intenance 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 fa ctor, 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 we re identified for use (EIIP, 1997a and U.S. Census Bureau, 1998). A comple te listing of the NAICS cate gories 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 employme nt data were unpublished when this study was prepared, employment was estimated by linea rly projecting 2002, 2003, and 2004 employment to 2005. In cases where a year’s employment was give n 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 isla nd was sought to allocate by island activity for this category from the Hawaii State Department of Labor & Industrial Relations' Research & Statistics Office, however employme nt data to the level of detail needed was not available. For the three source categories with per capita emissi on factors, those factor s 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 su rface 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 3399146

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 3335147

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 solven t-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 t hose profiles into a single profile that could be applied to our estimates of total VOC emi ssions (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 coati ngs was estimated based on a national per-capita use factor. This factor wa s developed by dividing the 2002 a nd 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 po pulation (U.S. Census Bureau, 2006a). This allowed for the generation of the usage f actors listed in Table 7.8

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Table 7. Per-capita architectural surface coating use factors. Coating Type 2002 2005
Solvent-based coating 0.443 gal/person-y r 0.499 Water-based coating 2.046 gal/person-y r 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-bas ed coatings. Emissions for each coating type were calculated as the product of usage and the E IIP 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 promul gated after the development of the emission factors, for which the estimated impact on emi ssions 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 spec iation profile for VOC emissions from waterbased coatings and one for emissions from so lvent-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 0.0529

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Seasonal Emissions

Surface coating is not practicab le at temperatures below 50 degrees (EIIP, 1995). Monthly average temperatures in Hawaii are generally higher than 50 de grees 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 charac terization 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 activit y. Both methods used were developed by the EIIP (EIIP, 1997b) and use employment as activity and per-employee emission factors to determine emissions, howev er they differ significantly enough so as to warrant separate discussions. Table 10 s hows 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, pe r 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 ar e 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 avai lable from County Busine ss Patterns and that data is categorized by NAICS. NAICS categor ies 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 isla nd was sought to allocate by is land activity for this category from the Hawaii State Department of Labor & Indus trial Relations' Research & Statistics Office, however employment data to the level of de tail needed was not available. 2018 emission estimates were projected based on 2005 emissions estimates and growth factors derived from EGAS 5.0.10

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Table 11. EIIP per employee emission fact or for solvent cleaning equipment.

SCC Description Corresponding SIC lbs VOC/ employee-yr 1

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 em issions, 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 equi pment 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 Am endments, the production of Methyl Chloroform (TCA) for use in the United States was pha sed out in 1995. Presumably, significant use continued after that phase out as remaini ng stocks were consumed (EIIP, 1997b). ARB estimated that TCA use would continue in dimi nishing quantities for between two and ten years after the phase out (ARB, 1996). For this inventor y it is assumed that seve n years after the phase out, use of TCA is negligible a nd therefore emissions have not b een calculated for that HAP.

EIIP Method: Solvent Cleanup

The EIIP Method for estimating emissions from solvent cleanup activitie s was developed from information collected for the Industrial Clean ing Solvents Alternat ive 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 vo lume-use estimates are provided. Emissions were estimated for 8 of 9 industries for whic h 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 availa ble, 100% volatilization was conservatively11

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc assumed for all VOC used in solvent cleanup acti vities. 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 factor s. Of the five industries dropped from consideration for emissions from solvent cleanup , packaging and was dropped because emissions from the packaging industry are most commonl y associated with point sources; lithographic printing, retrograve printing, and autobody refini shing were included in other area source categories; and there was a lack of informa tion 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

Automotive-Trucks and Buses 3713 0.394 2415035000

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 th e 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 Bu siness Patterns, categor ized by NAICS. NAICS categories were identified as corresponding to the SIC categor ies 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 wa s 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 Ha waii State Department of Labor & Industrial Relations' Research & Statistics O ffice, 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, ha zardous air pollutant emissions for solvent cleanup activities were calculated based on a generic HAP speciation profile for degreasing solvents from in EPA’s SPECIA TE database (see Table 12).12

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc CUTBACK ASPHALT

Annual Emissions

According to the state represen tative 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 em issions associated with cutback asphalt in 2005 and 2018 have been assumed to be insignificant.

According to asphalt paving company represen tatives, 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 (Tab le 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 s easonal variation in the application of cutback asphalt. The EIIP document states that due to the nature of cutb ack asphalt emissions, they should be assumed to occur seven days per week. Thus the average day summer and wint er 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 products13

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc For each of these subcategories there exists a per-capita emissi on 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 colle cted prior to the 1998 national regulation restricting the VOC content of these products, th ey were adjusted to reflect the new control. EPA estimated that the tota l reduction in VOC emissions from consumer solvent use achieved by the new regulation woul d be 20% (Federal Re gister, 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 factor s 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 follo wed the guidance of the EIIP document which suggests that consumer solvent use shows little s easonal variation and that use of these products should be assumed to occur seve n days per week (EIIP, 1996a).

Table 16. Emission factors for consumer solvent use (units are lb/person-yr).

Personal Care Products Household Products Automotive A ftermarket 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-0 5 2.22E-08 1.83E-07 5.94E-06 1,4-Dioxane 8.72E-06 Ethyl benzene 2.05E-06 6.01E-0 5 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-0414

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Personal Care Products Household Products Automotive A ftermarket 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-0 5 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-0 6 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-0 4 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 divide d into multiple segments: bulk plants, bulk terminals, Stage I, Stage II, and storage tank brea thing. 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 loss es and roof seals for floating r oof 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 fu el 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 St age II emissions. Annual gasoline distribution for 2002 and 2005 by county was taken from (H awaii Department of Taxation, 2007) and distributed to the island level based on by is land 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. St age II 2018 emissions were estimated by applying 2018 VMT activity to MOBILE6 es timated 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 subm erged underground tank filling was used per Hawaii15

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 wa s adjusted as suggested by the EIIP document to correct fo r gasoline that is transported mo re than once. The adjustment used was to multiply throughput by a factor of 1.25 (EIIP, 2001f).

Stage II criteria and hazardous air pollutant em ission factors were deri ved from EPA’s MOBILE 6.2 on-road vehicles emission factor model.

Emissions of hazardous air pollutants from St age 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” fo r calendar year 2018. St age II hazardous air pollutant emissions were based on EP A 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 wa s necessary for the area source bulk plant category.

The Hawaii Clean Air Branch provided estima tes of bulk terminal emissions for 2002 and 2005 by island (HCAB, 2009). For bulk terminals, as a re sult of the 10% ethanol gasoline requirement, fuel volatility changes and volumetric change s of gasoline through bulk terminals required16

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc incorporation of emissions ad justments (in addition to EGAS projection factors) for 2018 emissions. Per Hawaii Clean Air Branch, 10% et hanol gasoline will not be used on the islands of Lanai and Molokai, therefor e 2018 emissions adjustments were not applied for these islands.

Ethanol is not blended at the refi neries; it is blended at the fina l truck loading rack just before shipping to retail outlets (HCA B, 2009). Changes in emissions due to volumetric changes in gasoline throughput were accounted for by assumi ng 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 20 18 emissions, it was assumed that adding 10% ethanol to gasoline w ould result in an increase in gasoline Reid Vapor Pressure (RVP) of approximately 1 psi. Therefore gasolin e must have lower RVP before blending to meet the Hawaii 11.5 RVP requirement and gasoline thr ough bulk terminals would be less volatile in 2018. In order to account for changes in emi ssions due to gasoline volatility changes through bulk terminals in 2018, it was assumed that gaso line 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 lo aded (pounds per square inch absolute) M = molecular weight of vapors (lb per lb/mole) T = temperature of liquid loaded

Changes in the saturation factor, molecula r weight, and temperature from 2005 to 2018 are assumed to be negligible and therefore loadi ng loss emissions changes are a function of the change in true vapor pressure. True vapor pressure was estima ted 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 estimat ed by taking the average of the estimated monthly vapor pressure weight ed by Hawaii Department of Taxation (2007) 2005 gasoline throughput.17

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 curv e at 10 percent evaporated in degrees Fahrenheit per percent (assumed that S = 3.0 for gasoline per EPA, 2006).

Emissions adjustments for bulk terminal emission s due to changes in vo lumetric 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 vehi cle fires. Emissions from vehicle fires were estimated based on the number of vehicle fires as obtained from local fi re departments (Table 19), EIIP reported emission factors (Table 20) , and the average amount of components burned per vehicle fire (500 lb/vehicle, EIIP, 2000b). S easonal fractions were estimated according to the fraction of activity that occurred in each se ason as shown in Table 19. For Hawaii County fires, the Hawaii Fire Department was able to pr ovide data regarding the number of fires in 2005, but not in 2002, so 2002 structural fires were se t equal to 2005 vehicle fires. For Honolulu County and Kauai County, information regardin g seasonal distribution was unavailable and therefore emissions were assume d to be equal in each season. For Maui County, data received18

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc for 2005 was incomplete, only including activit y 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 populati on as presented in Table 1. Ac tivity was assumed to occur 7 days per week. 2018 emission estimates were projected based on 2005 emissions estimates and growth factors deri ved from EGAS 5.0.

Table 19. Number of Hawaii vehicle fires by coun ty with assumed seasonal distribution. Year Hawaii 1 Honolulu 2 Maui 3 Kauai 4

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 sour ces to estimate HAP emissions. Therefore, HAP emissions were not calculated for vehicle fires.

STRUCTURAL FIRES

Annual Emissions

This category covers emissions from accidental st ructural fires that o ccur in residential or commercial structures. Emissions from structural fires were es timated 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 fracti on 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 July19

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 2005. Therefore 2005 August to December activity wa s set equal to activity in the same period in 2004. Fires were distributed to the island level based on populat ion as presented in Table 1. Activity was assumed to occur 7 days per wee k. 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 Hawaii 1 Honolulu 2 Maui 3 Kauai 4

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 emis sions generating processes. Procedures to estimate emissions from each process are detail ed 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 substa ntial information about the char acteristics of the wastewater processed. Using these methods for estimating em issions 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 fact ors (Tables 24 and 25) to obtain20

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 Ma ui, 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 Hawaii 1 Oahu 2 Maui 3 Molokai 3 Lanai 3 Kauai 4

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 P ublic Works and Wa ste Management, 2007

2 Rossio, 2007c

3 Couty of Maui Department of P ublic Works and Waste Management, 2007

4 County of Kauai Department of P ublic 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-0221

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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)22

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc BAKERIES

Annual Emissions

This category covers emissions from yeast l eavening 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 amou nt 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 em ission 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 do cument which suggests that bakery production is relatively uniform annu ally 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 consum ption for 2002 was taken directly from EIA reports while fuel consumption for 2005 was esti mated by using a linear regression. The fuel consumption data provided by the EIA is divided into five source catego ries and a number of fuels. The source categories and fuel types util ized in the area source inventory are shown in Table 26.23

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 resident ial 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 multip lied 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 HW F 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 ra tio used for apportioning was island level employment by NAICS to state le vel employment by NAICS. These figures were collected from Job Count by Industry made available by the State of Hawa ii 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 c odes 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 fact ors from AP-42 to the activity data. These emission fact ors are detailed in Table 28.24

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 SO 2 0.6 PM 10 7.6 Residential Natural Gas (lb/10^6 scf)

PM 2.5 7.6 VOC 0.8 CO 7.5 NOx 13 SO 2 0.02 PM 10 0.7 Residential Liquid Petroleum Gas (lb/10^3 gal)

PM 2.5 0.7 VOC 0.713 CO 5 NOx 18 SO 2 42.6 PM 10 2.38 Residential Distillate Oil (lb/10^3 gal)

PM 2.5 2.13 VOC 0.676 CO 4.83 NOx 17.4 SO 2 41.19 PM 10 2.29 Residential Kerosene (lb/10^3 gal)

PM 2.5 2.05 VOC 5.5 CO 84 NOx 100 SO 2 0.6 PM 10 7.6 Commercial/Instituti onal Natural Gas (lb/10^6 scf)

PM 2.5 7.6 VOC 0.8 CO 7.5 NOx 13 SO 2 0.02 PM 10 0.7 Commercial/Instituti onal Liquid Petroleum Gas (lb/10^3 gal)

PM 2.5 0.7 VOC 0.340 CO 5 NOx 20 SO 2 42.6 PM 10 2.38 Commercial/Instituti onal Distillate Oil (lb/10^3 gal)

PM 2.5 2.13 VOC 1.130 CO 5 NOx 55 SO 2 314 PM 10 16.44 Commercial/Instituti onal Residual Oil 1 (lb/10^3 gal)

PM 2.5 7.05 VOC 0.327 CO 4.82 NOx 19.29 SO 2 42.6 Commercial/Instituti onal Kerosene (lb/10^3 gal)

PM 10 2.2925

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Source Fuel EF Units Pollutant Emission Factor
PM 2.5 2.05 VOC 5.5 CO 84 NOx 280 SO 2 0.6 PM 10 7.6 Industrial Natural Gas (lb/10^6 scf)

PM 2.5 7.6 VOC 0.8 CO 7.5 NOx 13 SO 2 0.02 PM10 0.7 Industrial Liquid Petroleum Gas (lb/10^3 gal)

PM 2.5 0.7 VOC 0.200 CO 5 NOx 20 SO 2 42.6 PM 10 2.3 Industrial Distillate Oil (lb/10^3 gal)

PM 2.5 1.55 VOC 0.280 CO 5 NOx 55 SO 2 314 PM 10 22.2 Industrial Residual Oil 1 (lb/10^3 gal)

PM 2.5 15.0 VOC 0.192 CO 4.82 NOx 19.29 SO 2 42.6 PM 10 2.22 Industrial Kerosene (lb/10^3 gal)

PM 2.5 1.49

1 Fuel sulfur content of 2% assumed per HCAB requirement

2 Kerosene emission factors based on distillate oil emi ssions factors, multiplied by 130/140 to account for the heating value difference between kerosene and di stillate 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 fact ors were drawn from AP-42 and from the documentation for the 1999 NEI of HAPs (U.S. EP A, 1995 and U.S. EPA, 2003b). No emission factors have been developed for propane LP G. Based on a recommendation in the AP-42 background documentation, emissions were generate d 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 nece ssary to use one set of emission factors to estimate emissions for all fuels from both inst itutional/commercial and residential combustion. The equipment used in these two different cate gories probably results in different emissions; however, until more emission factors are deve loped, this was judged the best available alternative. Similarly, emission factors for the industrial combustion of di stillate oil were not available, so the emission factors for commercial combustion were used.26

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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. Theref ore, 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 esti mates, 2004 point source fuel consumption was used instead. To determine the extent of doubl e counting with the point source inventory, that inventory was queried to extract the point sour ce 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 emissi ons 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), faci lities 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 occu rs seven days per week. As differences in temperatures vary little by s eason, summer and winter emissions were assumed to be annual emissions divided by 365 days per year.27

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc The allocation for commercial/institutional and industrial combustion was based on the assumption of constant emissions in each se ason and the use of st andard EPA temporal allocation profiles, which estimate 73.5% of act ivity 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. Wee kday emissions were estimated by dividing the average day emissions by the fraction of activity occurring on weekdays.

PESTICIDES

Pesticide emissions are those resulting from th e 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 re garding pesticide usage in the St ate 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 quant ities and types of pesticides used, assumptions regarding pesticide activity were required to esti mate 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 s ource of data available amenable to emissions calculations. In this methodology, a mass balan ce approach was used to estimate nationwide emissions associated with nationwide pesticid e consumption estimates. Nationwide emissions were allocated to the county le vel based equally on two ratios : 1) county to nationwide employment data related to crop producti on (NAICS 111), and 2) county to nationwide population. For this inventory, comme rcial related pesticides as cal culated in the NEI have been set to zero to avoid double count ing with FIFRA related consumer solvent emissions estimates. Commercial agricultural related emissions were a llocated 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 pollu tant emission estimates were based on a speciation profile, which indicated benzene as th e only hazardous air pollu tant at 26.15 percent of total VOC emissions.

As described in the agricultural tilling techni cal memo, activity for most Hawaii crops is distributed uniformly throughout the year, theref ore pesticide emissions were assumed to be constant year round.

Though compiling the necessary pesticide activity da ta 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 VO C and/or hazardous air pollutant emissions. 2018 emission estimates were projected based on 2005 emissions estimates and growth factors derived from EGAS 5.0.28

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc LANDFILLS

Emissions from landfills were cal culated 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 stoppe d accepting waste

• Annual waste acceptance (Mg/year)

Information was obtained from the HCAB regardi ng 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 144,69 4 176,836 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 gene ration rate, methane generation potential and temperature as no local information was availabl e. It was then possi ble to use the AP-42 formulas to calculate methane, NMOC and ai r toxic emissions. Not knowing which landfills were used for co-disposal, the AP-42 default conc entrations 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).29

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc COMMERCIAL COOKING

Commercial cooking emission estimates were de veloped 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 modificati ons were identified. The methodology used in estimating 2002 commercial cooking emissions was to combine activity, pounds of food cooked, with emission factors as descri bed 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 establishmen t type in 2002 from D un & Bradstreet industry classifications as shown in Table 31 (EPA, 2006) . The number of restau rants by island in 2002 was obtained by allocating restaurant establishmen ts by island level food service employment. The pounds of meat cooked in each SCC was obta ined by multiplying the number of restaurants of each type by the percent of each restaurant wi th 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) para meters used in these estimates are shown in Table 32.

Consistent with EPA (2006), to estimate activity associated with shoe string potato frying, the total tons of nationwide potatoe s cooked by “Fast Food” establis hments and “Other” restaurant types were allocated to the county level base d 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 18030

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 cal culated 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 esti mated 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 assu med 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 6.431

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc

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 residentia l wood combustion activity for th e state of Hawaii. Due to extremely mild temperatures that prevail through out 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 re lative to other sources included in this emissions inventory. Local s ources of residential wood com bustion activity were sought; however, the lack of cen tralized distribution of wood for re sidential home heating use prevented further ascertainment of whether local data would support the exclusi on of residential wood combustion emissions from this emissions inventory.

TRAFFIC MARKINGS

Annual Emissions

To estimate emissions from traffic markings, co ating usage data was queried from a list of contractors supplied by HCAB w ho would apply such coatings. Each company provided the total amount of traffic marking applied in 2002 and 2005 for water-based coatings, solvent-based32

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 em ission factors for volume-use estimates of water-based and solvent-based tra ffic markings. Thinner product sp ecification 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 ap propriate 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 St ate of Hawaii emissions, however, no alternative profile offering greater a ccuracy 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).33

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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 dur ation and the fraction of PM10 emissions that are PM2.5 are shown in Table 36 ; all of these data we re 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 PM 2.5 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 assu med 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 singl e-family, new duplex, and new apartment units authorized for constr uction as summarized by county in the Hawaii databook (HDBEDT, 2009a). The average acreage pe r new single family or 1-unit structure (1/7 acre) was taken from the WRAP Fugitive Du st Handbook (Countess Environmental, 2006) for heavily populated counties. Average acreages pe r new duplex or 2-unit st ructure (1/3 acre) and per new apartment (1/2 acre) we re taken from the EPA (2006).34

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Per Hawaii Clean Air Branch input (HCAB, 200 9) 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 construc tion acreage was estimated using a co mbination of local data for dollar valuation of construction as available in HDBEB T (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 co nstruction valuation for Kauai was estimated by applying the ratio of dollar valua tion for all other counties to popul ation in all other counties to Kauai County population.

Allocation of activity from the county to isla nd level for Maui County was estimated according to island population. It was assumed that all new construction in Kaua i 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 intersta te and $1.9 million/mile for all other roadway types), and estimates of acres disturbed per m ile of road construction. Per EPA (2006), the following estimates were used for acres di sturbed per mile of road construction:

• Interstate, urban and rural; Othe r arterial, urba n - 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 acreag e was allocated to counties ba sed 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 constr uction in Kauai County oc curred on the island of Kauai.

Seasonal Emissions

It was assumed that construction would be equa lly distributed throughout the year. Based on the daily allocation used by EPA’s NONROAD mode l, 83.3 percent of weekly activity will be assumed to occur Monday through Friday.35

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc RESULTS

ENVIRON has provided linked ar ea 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 S CC, 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 cons umer products (26%), gasoline distribution (24%), industrial su rface coating (17%), architectu ral surface coating (11%), dry cleaning (7%), and graphic ar ts (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 constructio n (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 Distributio n 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.36

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Fuel Combustion 100%
Figure 2. Hawaii statewide 2002 area source NOx emissions by source category.

Fuel Combustio n 60% Commercial Cooking 34%

Structural Fires 5%

Vehicle Fires 2%

Figure 3. Hawaii statewide 2002 area source CO emissions by source category.37

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc 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.38

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc Commercial Cookin g 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.39

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc VOC emissions by category for 2002, 2005, and 2018 ar e presented in Figure 8. Changes in emissions between 2002 and 2005 are generally refl ective of changes in ac tivity data. Changes in emissions from 2005 to 2018 are reflective of E GAS 5.0 growth factors, with the exception of gasoline distribution, which incl udes 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 ye ar HDOT VMT estimates and MOBILE6 emission factors. The EGAS model genera tes growth factors at the SCC level, so for categories that include greater than one SCC, the change in emissions would be ba sed 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. Ga soline distribution emi ssions decrease from 2002 to 2005 and from 2005 to 2018 as a result of the pha se-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

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D

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Fue

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Gas ol

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a stewater Treatment Consumer Products Commercial CookingVOC Emissions (tons/year)

2002
2005
2018

Figure 8 . VOC emissions by year and area source category.40

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J:\Hawaii EI\Reporting\Tech memos\HI_area_sources_091409.doc References

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.

EIIP. 1996b. Dry Cleaning, Volume III : Chapter 4. Emission Inventory Improvement Program, Area Sources Committee. May. Internet address: http://www.epa.gov/ttn/chief/ei ip/techreport/volume03/iii04fn2.pdf

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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/ei ip/techreport/volume03/iii08.pdf

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EIIP. 2000a. Auto Body Refinishing , Volume III: Chapter 13. External Review Draft. Emission Inventory Improvement Program, Ar ea Sources Committee. May . Internet address: http://www.epa.gov/ttn/chief/e iip/techreport/volume 03/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: Populatio n, Table 01.06. Hawaii State Department of Business, Economic De velopment, and Tourism. Accessed March 2007. Internet address: http://www.hawaii.gov/dbed t/info/economic/databook/

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HDBEDT. 2007c. 2005 State of Hawaii Databook, Section 1: Populatio n, Table 01.05. Hawaii State Department of Business, Economic De velopment, and Tourism. Accessed March 2007. Internet address: http://www.hawaii.gov/dbed t/info/economic/databook/

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Rossio. 2007a. Personal communication regardi ng on-site incineration and open burning in Hawaii. Hawaii State Department of Hea lth, Clean Air Branch, Honolulu, Hawaii. February.

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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 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

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EPA. 2003b. Documentation for the Final 1999 N onpoint Area Source National Emission Inventory for Hazardous Ai r 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/ne t/1999inventory.html#final3haps

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U.S. Census Bureau. 2003. Current Industrial Reports – Paint and Allied Products: 2002

. MA325F(02)-1. U.S. Census Bureau, Wash ington D.C. Internet address: http://www.census.gov/cir/www/325/ma325f.html

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Countess Environmental. 2006. Wrap Fugitive Dust Handbook. September.APPENDIX H

AMMONIA EMISSION ESTIMATES FOR THE STATE OF HAWAIIGolden 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_nh3_rev122109.doc 

### 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 We stern 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 devel oped using county-level head counts and emission factors based on a literature review performed by Chinkin, et al. (2003). The development ofPage 2

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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 2Page 3

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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 deve loped 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 we re developed using county-level fertilizer sales data as described below. As with the livestock emissions, temporal allocation of fertilizer application ammonia emissions is typically ba sed 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 recommende d 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 400Page 4

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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 countiesPage 5

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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 andpigs 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 HorsesPage 6

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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 applicati ons 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 estimat es 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)Page 7

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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)Page 8

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Figure 1. Hawaii LULC from the NOAA Coastal Service Database

Domestic SourcesPage 9

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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 NH 3

. 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)Page 10

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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 34.3Page 11

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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. Ma nsell 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 Burea u, Washington, D.C. Internet address http://www.census.gov/popest/estimates.phpPage 12

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_nh3_rev122109.doc 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.xlsAPPENDIX I

FUGITIVE WINDBLOWN DUST EMI SSION ESTIMATES FOR THE STATE OF HAWAIIGolden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com
H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 

### 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 st udies. 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 u z = wind velocity at height z (m s -1 )

u * = friction velocity (m s -1 )

κ = von Karman's constant (0.4)

z o = aerodynamic roughness height (m)Page 2

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc Threshold Friction Velocities

The methodology relies on the determination of threshold surface friction velocities, u *t , as a function of aerodynamic surface roughness length, z 0 . 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 z 0 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 lo wer the threshold between ~90% to ~20% of the undisturbed value. Surface Roughness Lengths u *t = 0.31e 7.44 x (Zo)

R 2 = 0.60

u *t = 0.30e 7.22 x (Zo)

0 0.5

1 1.5

2 2.5

3

0.00001 0.0001 0.001 0.01 0.1 1

z o (cm)u*t(m s-1)

wind tunnel data Marticorena et al. 1997 Expon. (wind tunnel data) Expon. (Marticorena et al. 1997)Page 3

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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 discu ssion 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 a nd 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 de sert 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 fourPage 4

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc different emission factors, depending on the geom etric 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 importa nce 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 geometri c 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 = 2.45 x 10 -6 (u * ) 3.97

FS

F = 9.33 x 10 -7 (u * ) 2.44

MS

F = 1.243 x 10 -7 (u * ) 2.64

CS

F = 1.24 x 10 -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-2s-1)
FSS
FS
MS
CS Power (FSS) Power (FS) Power (MS) Power (CS)Page 5

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc • 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 adjustme nt factors for USDA’s Wind Erosion Equation (WEQ) (CARB, 1997). These correction factors we re developed for specific soil textures, crop types, and geographic locations and then applie d to the wind erosion estimates developed from the wind tunnel studies. Correction factors are de veloped 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, pa sture, 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 NOAALULC database is presented in Table 2 below. These assignments are based on a general reclassification of landuse types to those recogni zed 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.Page 6

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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/APage 7

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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 em ission 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 deve loped 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 Chaten et, 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.Page 8

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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.Page 9

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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 Me soscale 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 vari ables (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 l east 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 wasPage 10

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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 PM 10 emissions for 2002 are provided. Table 5 presents the corresponding results for calendar year 2005. The corresponding results for PM 2.5 are presented in Tables 6 and 7 for calendar years 2002 and 2005, respectively. It is assumed that
PM 2.5 is equal to 0.1*PM 10 , 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 Wi ndblown 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.Page 11

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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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 minera l 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 Boar d, 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 Specifi c Emission Factors.” Draft Technical memorandum. Prepared for Western Governor s’ Association by ENVIRON International Corporation, Novato, California; ERG, Inc., S acramento, 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, Meteor ological and Land Use Data." Technical memorandum. Prepared for Western Governor s’ 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 Emi ssions from Wind Erosion.” Final Report. Prepared for Western Governors’ Associa tion 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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H:\Hawaii EI\Reporting\Tech memos\HI_WB_Dust_emis.10-21-08.doc 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 PM 10 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 fi ne-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 ISLANDSGolden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com
H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc 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 a ssumptions 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 si ze range and units of interest (g/VMT) s = road surface silt loading (g/m 2 ) 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 sh own 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 of ten not available, as the measurements are expensive, and are typically not done for areas where the PM air qual ity standards are attain ed. As we were unable to find local silt loading estimates for Hawaii, ro ad surface silt loading estimates were taken from2

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H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc 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 va lues by average daily traffic vol ume as shown in Table 2. For roadway types where average daily traffic vol ume 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 averag e daily traffic volumes were not available (rural minor collector, rural local, and ur ban local) from (FHWA, 2007), 2002 and 2005 silt loading were assumed to be equivalent to those available in NEI 2002 documentation.

Precipitation correction factors we re estimated based on three diffe rent 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 wh ere meteorological data were pr ovided by Lakes Environmental in the form of output data files from the PSU Mesoscale Meteorological Model version 5 (MM5) at a horizontal spatial resoluti on of 4-km, precipitation correcti on factors were estimated based on MM5 measurable days of precipitation and surr ogate roadway length from U.S. Census Tiger data. Precipitation correction f actors were calculated by grid ce ll and a weighted, island average precipitation factor was estimated based on aver age precipitation by gr id cell and surrogate roadway length by grid cell. Surrogate roadwa y length for paved roads was estimated by grid cell based on the sum of the length of all US Cens us Tiger roadway types w ithin that grid cell. 2018 precipitation correction factors where MM5 data was available were assumed equivalent to 2005 estimates (2004 in the case of Ma ui) 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/m 2 )
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 of3

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H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc precipitation by month, or National Center for Atmospheric Research’s (NCAR) ds472 data set, which contains meteorological data for airports fo r 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 period4

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H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc Mean vehicle speed by roadway types were the same as used to estimate Hawaii on-road vehicle emissions. Statewide road surface material si lt content estimates of 3.8% (EPA, 2006c, NEI Appendix C) were applied to all islands. Empi rical constants, brake and tire wear emission factor estimates, and road surface moisture conten t defaults were used per AP-42 guidance, as shown in Table 4.

Table 4 . Unpaved emissions estimation parameters

.

Parameter AP-42 Default 1 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 es timated 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 tota l across all road way types used for paved roads) to calculate weighted average precipitation correction f actors 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)5

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H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc

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 emissi ons were derived from VMT used to estimate Hawaii on-road vehicle emissions. Per HDOT st aff, these VMT data include both paved and unpaved VMT. Local data regarding the frac tion 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 e ach 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 estim ates and unpaved roadway length by island are shown in Table 6. It was assumed that there wa s no variation in activity by season and weekday and weekend activity vari ation were assumed equal to frac tions 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 Length 1
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 pres ented graphically in Figure 1 and Figure 2 are based on paved and unpaved estimated emission f actors and VMT by island. Emissions increase from 2002 to 2005 and from 2005 to 2018 based on VM T 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 Ma ui Islands at between 30% and 40% of total emissions for 2002, 2005, and 2018, consistent w ith unpaved roadway VMT estimates which6

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H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc show the highest unpaved VMT on these islands.

T able 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 8257

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H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc 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

YearPM10 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

YearPM2.5 Emissions (tons/year)

Lanai
Molokai
Maui
Kauai
Oahu
Hawaii

Figure 2 . Annual road dust PM2.5 emissions.8

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H:\Hawaii EI\Reporting\Tech memos\HI_roaddust_102108.doc References

EPA, 2006a. Compilation of Air Pollution Emissi on 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. Janua ry. Internet address: http://www.epa.gov/ttn/chief/ap42

.
EPA, 2006b. Compilation of Air Pollution Emissi on 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. Janua ry. 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 (F ebruary 2006 Version). Prepared by E.H. Pechan & Associates, Inc. EPA Contra ct No. 68-D-02-063. Internet address: http://www.epa.gov/ttn/chie f/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, Econom ic Development, and Tourism. Accessed March 2007. Internet address: http://www.hawaii.gov/dbedt /info/economic/databook/APPENDIX K

AGRICULTURAL TILLING EMISSI ONS ESTIMATES FOR THE HAWAIIAN ISLANDSGolden 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_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 * s 0.6 * 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.Page 2

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J:\Hawaii EI\Reporting\Tech memos\HI_agtilling_042009rev.doc In determining the seasonality of agricultural tilling emissions, University of Hawaii Agricultural Extension staff indicated that harvest and plan ting 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%Page 3

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J:\Hawaii EI\Reporting\Tech memos\HI_agtilling_042009rev.doc 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.Page 4

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J:\Hawaii EI\Reporting\Tech memos\HI_agtilling_042009rev.doc Table 4. Number of passes per crop for Hawaii crops.

Category Passes per Harvest Sugarcane 3 Pineapple 3 Snap Beans 3 1 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 3 1
Watercress 3 1 Ginger 3 Seed Crop 6 2 Dry Onions 5 Green Onions 3 Sweet Corn 5 Cucumbers 3 Daikon 5 Eggplant 4 Bitter melon 3 1
Broccoli 3 1

1 EPA, 2001 default value

2 Rossio, 2007Page 5

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J:\Hawaii EI\Reporting\Tech memos\HI_agtilling_042009rev.doc Table 5. Hawaii acres of crops tilled.

Category 2002 2005 Sugarcane 21300 21700 Pineapple 1 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 Crop 1 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 ofPage 6

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J:\Hawaii EI\Reporting\Tech memos\HI_agtilling_042009rev.doc 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 0.39Page 7

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J:\Hawaii EI\Reporting\Tech memos\HI_agtilling_042009rev.doc 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 Qu ality 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 Nationa l 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 Maria nne 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.htmAPPENDIX L

FIRE EMISSION INVENTORY773 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 memo s\Wildland_Ag_Fire_Emissions_032309.doc 

### 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 inve ntory 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 invent ory 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:Page 2

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(Tel): 415-899-0700 z (Fax): 415-899-0707 z www.environcorp.com J:\Hawaii EI\Reporting\Tech memo s\Wildland_Ag_Fire_Emissions_032309.doc • 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 Commer ce (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.Page 3

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(Tel): 415-899-0700 z (Fax): 415-899-0707 z www.environcorp.com J:\Hawaii EI\Reporting\Tech memo s\Wildland_Ag_Fire_Emissions_032309.doc 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.

) / ( 0005

. 0 ) ( lb tons tons burned acre burned acre fuel

tons fuel tons

emissions lbs 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 PM 10 PM 25 EC OC VOC CH 4 NH 3 NO x CO SO 2 PM C (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 WR AP Fire Phase – II EI Report. For pineapple, emission factors were not available in the WR AP fire report and hence, AP-42 open burning section emission factors for pineapple were use d. For analysis, we have considered NMTOC to be equivalent to VOC for pineapple. The fuel lo ading of pineapple is not specified in AP-42 andPage 4

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(Tel): 415-899-0700 z (Fax): 415-899-0707 z www.environcorp.com J:\Hawaii EI\Reporting\Tech memo s\Wildland_Ag_Fire_Emissions_032309.doc hence, for estimation, the fuel loading of an “unspecified crop” has been used. For pineapple crop, AP-42 does not provide any guidance on br eaking 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 PM 10 PM 25 EC OC VOC CH 4 NH 3 NO x CO SO 2 PM C 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 ForestPage 5

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(Tel): 415-899-0700 z (Fax): 415-899-0707 z www.environcorp.com J:\Hawaii EI\Reporting\Tech memo s\Wildland_Ag_Fire_Emissions_032309.doc 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.Page 6773 San Marin Drive, Suite 2115●Novato, California 94998 USA(Tel): 415-899-0700z(Fax): 415-899-0707zwww.environcorp.comJ:\Hawaii EI\Reporting\Tech memos\Wildland_Ag_Fire_Emissions_032309.docTable 6.Wildland fire emissions by island.IslandTSPPM10PM25ECOCVOCCH4NH3NOxCOSO2PMC2002 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 9.35Kauai 0.58 0.48 0.41 0.03 0.20 0.23 0.23 0.02 0.10 4.89 0.03 0.07Maui 97.02 79.95 68.57 4.27 33.00 38.70 38.70 3.70 17.64 822.28 4.84 11.38Oahu 8.81 7.26 6.23 0.39 3.00 3.51 3.51 0.34 1.60 74.65 0.44 1.032005 Annual Emissions (tons/yr)Hawaii 9416 7760 6655 414 3203 3756 3756 359 1712 79805 469 1105Kauai 34 28 24 2 12 14 14 1 6 291 2 4Maui 284 234 201 12 97 113 113 11 52 2408 14 33Oahu 2123 1749 1500 93 722 847 847 81 386 17992 106 2492002 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 0.025Kauai 0.002 0.001 0.001 0.000 0.001 0.001 0.001 0.000 0.000 0.013 0.000 0.000Maui 0.264 0.217 0.186 0.012 0.090 0.105 0.105 0.010 0.048 2.234 0.013 0.031Oahu 0.024 0.020 0.017 0.001 0.008 0.010 0.010 0.001 0.004 0.203 0.001 0.0032005 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 3.002Kauai 0.093 0.077 0.066 0.004 0.032 0.037 0.037 0.004 0.017 0.790 0.005 0.011Maui 0.772 0.636 0.546 0.034 0.263 0.308 0.308 0.029 0.140 6.544 0.038 0.091Oahu 5.769 4.754 4.077 0.254 1.962 2.301 2.301 0.220 1.049 48.892 0.288 0.6772002 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 0.026Kauai 0.002 0.001 0.001 0.000 0.001 0.001 0.001 0.000 0.000 0.014 0.000 0.000Maui 0.270 0.222 0.190 0.012 0.092 0.107 0.107 0.010 0.049 2.284 0.013 0.032Oahu 0.024 0.020 0.017 0.001 0.008 0.010 0.010 0.001 0.004 0.207 0.001 0.0032005 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 3.068Kauai 0.095 0.078 0.067 0.004 0.032 0.038 0.038 0.004 0.017 0.807 0.005 0.011Maui 0.789 0.650 0.558 0.035 0.268 0.315 0.315 0.030 0.143 6.689 0.039 0.093Oahu 5.897 4.860 4.168 0.259 2.006 2.352 2.352 0.225 1.072 49.979 0.294 0.692Page 7773 San Marin Drive, Suite 2115●Novato, California 94998 USA(Tel): 415-899-0700z(Fax): 415-899-0707zwww.environcorp.comJ:\Hawaii EI\Reporting\Tech memos\Wildland_Ag_Fire_Emissions_032309.docTable 7.Agricultural burning by island.IslandTSPPM10PM25ECOCVOCCH4NH3NOxCOSO2PMC2002 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 0.19Kauai 395 383 352 57 142 130 29 36 99 1,799 44 31Maui 1,230 1,192 1,096 179 441 404 90 112 307 5,595 136 97Oahu 8.26 1.83 8.16 0.27 0.64 6.14 0.51 0.23 0.54 101 0.06 0.072005 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 0.21Kauai 421 408 375 61 151 140 32 38 105 1,923 46 33Maui 1,190 1,154 1,060 173 427 391 87 108 297 5,414 132 93Oahu 2 2 2 0.27 0.64 1.34 0.51 0.23 0.54 11 0.06 0.072002 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 0.001Kauai 1.415 1.372 1.261 0.206 0.508 0.467 0.105 0.129 0.354 6.452 0.156 0.111Maui 4.411 4.277 3.930 0.642 1.583 1.449 0.323 0.402 1.103 20.068 0.488 0.347Oahu 0.030 0.007 0.006 0.001 0.020 0.011 0.002 0.001 0.002 0.362 0.000 0.0002005 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 0.001Kauai 1.509 1.464 1.346 0.220 0.542 0.503 0.115 0.138 0.378 6.897 0.166 0.118Maui 4.268 4.139 3.803 0.621 1.532 1.402 0.313 0.389 1.067 19.421 0.473 0.335Oahu 0.007 0.007 0.006 0.001 0.002 0.005 0.002 0.001 0.002 0.041 0.000 0.0002002 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 0.000Kauai 0.579 0.561 0.516 0.084 0.208 0.191 0.043 0.053 0.145 2.638 0.064 0.045Maui 1.803 1.749 1.607 0.262 0.647 0.593 0.132 0.164 0.451 8.206 0.200 0.142Oahu 0.012 0.003 0.003 0.000 0.008 0.004 0.001 0.000 0.001 0.148 0.000 0.0002005 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 0.000Kauai 0.617 0.598 0.550 0.090 0.221 0.206 0.047 0.056 0.155 2.820 0.068 0.048Maui 1.745 1.692 1.555 0.254 0.626 0.573 0.128 0.159 0.436 7.941 0.193 0.137Oahu 0.003 0.003 0.003 0.000 0.001 0.002 0.001 0.000 0.001 0.017 0.000 0.000Page 8

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(Tel): 415-899-0700 z (Fax): 415-899-0707 z www.environcorp.com J:\Hawaii EI\Reporting\Tech memo s\Wildland_Ag_Fire_Emissions_032309.doc REFERENCES

AP -42 Section 2.5. 2007. Open Burning. Emissi on 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. De velopment 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 .pdfAPPENDIX M

BIOGENIC EMISSIONS ESTIMATES FOR THE HAWAIIAN ISLANDSGolden Gate Plaza ● 101 Rowland Way ● Novato, California 94945-5010 USA Tel: (415) 899-0700 ● Fax: (415) 899-0707 ● www.environcorp.com
H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 

### 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 orga nic compounds (VOCs), nitrogen oxides (NO x ) and carbon monoxide (CO) from plants and soils (biogenic emissions) are precursors to the formation of tropospheric ozone (Fehsenfeld et al., 1992). Bi ogenic 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 Cent er for Atmospheric Research and ENVIRON, and the Model of Emissions and Gases and Aero sols from Nature (MEGAN), a state-of-thescience biogenic emissions model which incorpor ates 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 GloBEI S 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 require ments. 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. Th erefore, 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

.Page 2

Golden Gate Plaza $ 101 Rowland Way $ Novato, California 94945-5010 USA Tel: (415) 899-0700 $ Fax: (415) 899-0707 $ www.environcorp.com
H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 ThematicMapper 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/dat es: 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 separa ting 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 pr ocedures 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.Page 3

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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-l eaved 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.Page 4

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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)Page 5

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc Figure 1. Hawaii LULC

Figure 2 . Kauai LULCPage 6

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc Figure 3 . Lanai LULC

Figure 4 . Maui LULCPage 7

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc Figure 5 . Molokai LULC

Figure 6 . Niihau LULCPage 8

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 la rger 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 e volution, the evaluation of possible global impacts of deforestation and more generally on the need for information related to political or social orientations and decisions.Page 9

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc • 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 areasPage 10

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 16,703Page 11

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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.Page 12

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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, andPage 13

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 treesPage 14

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc • 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.Page 15

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc Figure 10. Sample Hawaii Vegetation database display.

Meteorological Data

Meteorological inputs required for the GloB EIS model includes, at minimum, surface temperature and photosynthetically active radiati on (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 (Gue nther 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 obser vations of commonly measured variables (including temperature) from airports throughout the U.S. and includes stations on Kauai, Oahu, Molokai, Lanai, and Hawaii. Sufficient ds472 da ta 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 significantlyPage 16

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 temperatur es 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 aver age 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 th e 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 1 0 1 2 1 4 1 6 18 20 22

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 thePage 17

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 th e 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. Therefor e, an assignment of the classes in the current data with the existing classes, necessary for calcu lation of emission factors, used in GloBEIS had to be made. This mapping of landcover classifi cation 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 incl uded 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 10,366.3 1,853.9 106.2 350.4 649.7 1,494.0 1,402.0 161.8 16,384.2 Total Land Area by Island and Landcover (km^2) Vegetation CodesPage 18

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 eachday 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)Page 19

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 w ould 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 isPage 20

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H:\Hawaii EI\Reporting\Tech memos\HI_biogenic_emis.06-27-07.doc 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 Dr ought and Prolonged High Temperatures and Developing GloBEIS3”. Prepared fo r 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 Centra l 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 ISLANDSGolden 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\T ech memos\HI_volcano_emis_042009.doc 

### 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 Ha wai`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 SO 2 emissions. The United States Geological Su rvey (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 H 2 S are also emitted from Kilauea, these emissions are much less significant than those of SO 2 and are not included in the USGS observation program nor reported here.

Table 1. Kilauea annual emissions from the summit and rift zones.
SO 2 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, 2006Page 2

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J:\Hawaii EI\Reporting\T ech memos\HI_volcano_emis_042009.doc Emissions Estimates for AIRShed

Kilauea SO 2 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 Period 1 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 SO 2 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/Page 3

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J:\Hawaii EI\Reporting\T ech memos\HI_volcano_emis_042009.doc 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.pdfAPPENDIX 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 particle s. They found that at distances of 25 km downwind of the surf zone, sea salt particle concen trations 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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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 10 7 exp{0.23 x U 10 } x D –1.65 . (1) dD

Note that the factor of 10 7 was inadvertently omitted from the original de Leeuw et al . (2000) paper. F is the sea spray droplet flux in (droplets μ m -1 m -2 s -1 ), U 10 is the 10-meter wind speed in

m s -1 , and D is the droplet diameter in microns ( μ m). This source function is valid for droplets with diameters 1.6-20 μ m 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 μ m 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),

r wet = α r dry β , (2)

where α and β are constants that depend on the relative humidity.

α = 1.35 (1.2) exp (0.066S/( Ф -S)) (3)Page 3

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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 μ m 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 μ m diameter at formation. The integrated sea salt annual mass for Kauai changed from 1.57x10 8 kg (coarser bin structure) to 1.53 x10 8 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 20.00Page 4

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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 mete orological 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, th e 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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\\Novato2k3\projects2\Hawaii EI\Reporting\Tech memos\HI_seaspray_emis_112007.doc 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 e xperimental 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 Kahoolaw e 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 Kahoolaw e 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 Kahoolaw e 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 Kahoolaw e 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 Kahoolaw e 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 Kahoolaw e 6.8E+07 2.0E+05 1.7E+05 Hawaii 4.9E+08 1.3E+06 1.3E+06

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\\Novato2k3\projects2\Hawaii EI\Reporting\Tech memos\HI_seaspray_emis_112007.doc 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

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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

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Hess, M., P. Koepke, and I. Schult, 1998: Optical properties of aerosols and clouds: The software package OPAC. Bull. Amer. Meteor. Soc

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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

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\\Novato2k3\projects2\Hawaii EI\Reporting\Tech memos\HI_seaspray_emis_112007.doc 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 PROJECTIONSTechnical 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 us ed 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 is lands are also disconnected and so each island must also be entirely self-sufficient. Because of the isolat ed nature of the Hawaii EGUs, most facilities use distillate and residual fuel oil. Most of the EGU facilities ar e 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) – s ubsidiary of HECO (includes separate divisions for the islands of Ma ui, Molokai, and Lanai); and

• Kauai Island Utility Cooperative – independent of HECO (includes the island of Kauai).

In addition to these utility-owned EGUs, th ere are a number of ot her 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 provi ded 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, nameplat e capacity, generation type (i.e., firm or not firm), and whether the facility was included in the 2005 Ha waii 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 so urces 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 modeli ng for the Western Regional Air Partnership (WRAP) (ERG, 2006; ERG, 2007). The basic met hodology for developing these growth factors for that effort was to examine base year unit-le vel 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 a dditional 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 sc hedules, as well as main tain 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 Hawaiia n utilities. However, they were also unable to provide alternative maximum threshold cap acities (Hashiro, 2007). Also, Hawaii EGUs appear to be exempt from the mandatory repor ting 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 uni t-level data used the WRAP methodology were unavailable for Hawaii. Therefore, an alternative methodology was used.

IRP-BASED GROWTH F ACTOR 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 IR P process includes both resources that supply electricity and resources that re duce or manage the demand of el ectricity. The IRP process is mandated by the Hawaii Public Utilities Comm ission’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 electri c 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 pr ovided long-term sales forecasts in gigawatthours (GWh) and long-term peak forecasts in me gawatts (MW); the IRP documents for Molokai, Lanai, and Kauai only provided long-term peak for ecasts. These forecasts are presented in TableTechnical 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., vari ation 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 sa les and peak forecasts for the island of Maui indicate a growth rate of 33.1-33.5% between 2005 and 2018; it wa s 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 estimat ed new capacity needed for each island are presented in Table 8.

The IRP documents were also examined to dete rmine 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 ha ve been developed in the last two years, while the IRP document for KIUC was simply an a nnual 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 th roughout the 20-year IRP planning horizon (e.g., demand side management, combined heat and powe r, distributed generation, etc.) was ratioed to represent the 13-year projecti on 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:

• 3 rd phase of Maalaea dual-train combin ed cycle No. 2 (Maalaea 18) – 18 MW expected to be instal led in October 2006;

• Eight 1 MW distributed gene ration (DG) units to be in stalled 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 Tabl e 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 be en 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 ha s not yet been identified in future yearTechnical 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 fact ors of 1.0000 because the identified new capacity meets or exceeds the needed new capacity. On e exception to these growth factors is the Pepeekeo Power Plant (Hilo Coast Power) locate d 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 facili ties that have been identified in the IRP process. Based upon the existing facilit ies that are in the 2005 NIF database, it is expected that the following new future year facil ities 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 3 rd phase of dual-train combined cycle No.2 (Maalaea 18 (Maui, 2006);

• 21.2 MW simple-cycle combustion tu rbine (Waena 1) (Maui, 2011);

• 21.2 MW simple-cycle combustion tu rbine (Waena 2) (Maui, 2013);

• 25 MW biomass unit (Maui, 2018);

• 2.0 MW diesel generator (Pal aau-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 a ppropriate 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/vcmc ontent/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 Compa ny, Inc., Honolulu, Hawaii. May. Internet address: http://www.helcohi.com/vcm content/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 Cooperati ve, Lihue, Hawaii. February. Internet address: http://www.kiuc.coop/ anne/IRP_public_site/Tariff/ 2006%20IRP%20Update%20(Fi nal)%20(2-16-07).pdf

MECO, 2007. Maui Electric Company, Ltd. Dra ft Integrated Resource Plan 2007-2026 . Docket No. 04-0077. Maui Electric Company, Ltd., Honolulu, Hawaii. February 6. Internet address: http://www.mauielectric.com/vcmcon tent/MECO/RenewableEnergy/IRP/ MECOIRP3DraftReportwithExecSummary.pdf

Schaefers, 2004. “Hilo Coast Power to Close”, Honolulu Star-Bulletin . Newspaper article by Allison Schaefers. November. Internet addres s: http://starbulletin.com/2004/11/05/business/ story3.html

U.S. EPA, 2007. Emissions & Generation Resource In tegrated Database (eGRID2006), Technical Support Document, Version 2.1 . U.S. Environmental Prot ection Agency, Office of Atmospheric Programs. April.Technical MemorandumNovember 9, 2007Page 6Table 1.EGU Summary – OahuFacility NameStateIDUnit IDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?HECO - Honolulu Generating Station 00500 8 Distillate 1954 50.0 Firm YesHECO - Honolulu Generating Station 00500 9 Distillate 1957 54.0 Firm YesHECO - Waiau Power Plant 00502 W3 Residual 1947 50.0 Firm YesHECO - Waiau Power Plant 00502 W4 Residual 1950 50.0 Firm YesHECO - Waiau Power Plant 00502 W5 Residual 1959 54.4 Firm YesHECO - Waiau Power Plant 00502 W6 Residual 1961 54.4 Firm YesHECO - Waiau Power Plant 00502 W7 Residual 1966 81.6 Firm YesHECO - Waiau Power Plant 00502 W8 Residual 1968 81.6 Firm YesHECO - Waiau Power Plant 00502 W9 Distillate 1973 51.3 Firm YesHECO - Waiau Power Plant 00502 W10 Distillate 1973 51.3 Firm YesHECO - Kahe Power Plant 00501 K1 Residual 1963 81.6 Firm YesHECO - Kahe Power Plant 00501 K2 Residual 1964 81.6 Firm YesHECO - Kahe Power Plant 00501 K3 Residual 1970 85.8 Firm YesHECO - Kahe Power Plant 00501 K4 Residual 1972 90.9 Firm YesHECO - Kahe Power Plant 00501 K5 Residual 1974 134.9 Firm YesHECO - Kahe Power Plant 00501 K6 Residual 1981 134.9 Firm YesAES Hawaii, Inc. 00101 GEN1 Coal 1992 203.0 Firm YesHPOWER 00082 GEN1 MSW 1987 63.7 Firm YesKalaeloa Cogeneration Plant 00100 CT1 Residual 1989 119.2 Firm YesKalaeloa Cogeneration Plant 00100 CT2 Residual 1991 119.2 Firm YesKalaeloa Cogeneration Plant 00100 ST1 Distillate 1991 61.0 Firm YesTesoro Hawaii GEN1 Waste Oil 1982 20.0 Not Firm NoChevron Hawaii Cogen GEN1 Other Gas 1990 3.0 Not Firm NoChevron Hawaii Cogen GEN2 Other Gas 1990 3.0 Not Firm NoChevron Hawaii Cogen GEN3 Other Gas 1990 3.0 Not Firm NoTechnical MemorandumNovember 9, 2007Page 7Table 2. EGU Summary – HawaiiFacility NameState IDUnit IDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?HELCO - W.H. Hill (Kanoelehua) GeneratingStation00014 5 Residual 1965 14.1 Firm YesHELCO - W.H. Hill (Kanoelehua) GeneratingStation 00014 6 Residual 1974 23.0 Firm YesHELCO - Kanoelehua Generating Station 00014 11 Distillate 1962 2.0 Firm YesHELCO - Kanoelehua Generating Station 00014 15 Distillate 1972 2.5 Firm YesHELCO - Kanoelehua Generating Station 00014 16 Distillate 1972 2.5 Firm YesHELCO - Kanoelehua Generating Station 00014 17 Distillate 1973 2.5 Firm YesHELCO - Kanoelehua Generating Station00014 CT1 Distillate 1962 11.5 Firm YesHELCO - Keahole Power Plant 00013 CT2 Distillate 1989 17.6 Firm YesHELCO - Keahole Power Plant 00013 CT4 Distillate 2004 23.0 Firm YesHELCO - Keahole Power Plant 00013 CT5 Distillate 2004 23.0 Firm YesHELCO - Keahole Power Plant 00013 21 Distillate 1984 2.5 Firm YesHELCO - Keahole Power Plant 00013 22 Distillate 1984 2.5 Firm YesHELCO - Keahole Power Plant 00013 23 Distillate 1988 2.5 Firm YesHELCO - Puna Power Plant 00021 1 Residual 1970 15.5 Firm YesHELCO - Puna Power Plant 00021 CT3 Distillate 1992 23.6 Firm YesHELCO - Shipman Power Plant 00023 3 Residual 1955 7.5 Firm YesHELCO - Shipman Power Plant 00023 4 Residual 1958 7.5 Firm YesHELCO - Waimea Power Plant 00519 12 Distillate 1970 2.5 Firm YesHELCO - Waimea Power Plant 00519 13 Distillate 1972 2.5 Firm YesHELCO - Waimea Power Plant 00519 14 Distillate 1972 2.5 Firm YesPepeekeo Power Plant 00022 Bituminous 1974 23.8 Retired NoHELCO – Paneawa D24 Distillate 1997 1.0 Firm NoHELCO – Ouli D25 Distillate 1997 1.0 Firm NoHELCO – Punaluu D26 Distillate 1997 1.0 Firm NoHELCO – Kapua D27 Distillate 1997 1.0 Firm NoHELCO - Lalamilo Wind Farm Wind 1986 2.2 Not Firm NoHELCO – Puueo 1 Hydro 1918 0.7 Not Firm NoHELCO – Puueo 2 Hydro 2005 2.3 Not Firm NoHELCO – Waiau 1 Hydro 1921 0.7 Not Firm NoHELCO – Waiau 2 Hydro 1928 0.3 Not Firm NoPuna Geothermal Venture (PGV) I OEC11 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC12 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC13 Geothermal 1992 3.5 Firm NoTechnical MemorandumNovember 9, 2007Page 8Table 2. ContinuedFacility NameState IDUnit IDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?Puna Geothermal Venture (PGV) I OEC14 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC15 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC21 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC22 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC23 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC24 Geothermal 1992 3.5 Firm NoPuna Geothermal Venture (PGV) I OEC25 Geothermal 1992 3.5 Firm NoHamakua Energy Partners L.P. (HEP) CT1 Naphtha 2000 23.0 Firm NoHamakua Energy Partners L.P. (HEP) CT2 Naphtha 2000 23.0 Firm NoHamakua Energy Partners L.P. (HEP) ST1 Naphtha 2000 20.0 Firm NoWailuku River Hydro 8101 Hydro 1993 5.2 Not Firm NoWailuku River Hydro 8102 Hydro 1993 5.2 Not Firm NoKamaoa Wind Farm Wind 1987 9.2 Not Firm NoHRD Wind Farm Wind 2006 10.6 Not Firm NoPakini Nui Wind Farm Wind 2007 20.5 Not Firm NoOther IPP Hydro Hydro varies 0.3 Not Firm NoTechnical MemorandumNovember 9, 2007Page 9Table 3.EGU Summary – MauiFacility NameStateIDUnitIDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?MECO - Kahului Power Plant 00006 1 Residual 1948 5.0 Firm YesMECO - Kahului Power Plant 00006 2 Residual 1949 5.0 Firm YesMECO - Kahului Power Plant 00006 3 Residual 1954 11.5 Firm YesMECO - Kahului Power Plant 00006 4 Residual 1966 12.5 Firm YesMECO - Maalaea Generating Station00500 1 Distillate 1971 2.6 Firm YesMECO - Maalaea Generating Station00500 2 Distillate 1972 2.6 Firm YesMECO - Maalaea Generating Station00500 3 Distillate 1972 2.6 Firm YesMECO - Maalaea Generating Station00500 4 Distillate 1973 5.6 Firm YesMECO - Maalaea Generating Station00500 5 Distillate 1973 5.6 Firm YesMECO - Maalaea Generating Station00500 6 Distillate 1975 5.6 Firm YesMECO - Maalaea Generating Station00500 7 Distillate 1975 5.6 Firm YesMECO - Maalaea Generating Station00500 8 Distillate 1977 5.6 Firm YesMECO - Maalaea Generating Station00500 9 Distillate 1978 5.6 Firm YesMECO - Maalaea Generating Station00500 10 Distillate 1979 12.5 Firm YesMECO - Maalaea Generating Station00500 11 Distillate 1980 12.5 Firm YesMECO - Maalaea Generating Station00500 12 Distillate 1988 12.5 Firm YesMECO - Maalaea Generating Station00500 13 Distillate 1989 12.5 Firm YesMECO - Maalaea Generating Station00500 14 Distillate 1992 23.6 Firm YesMECO - Maalaea Generating Station00500 15 Distillate 1993 18.0 Firm YesMECO - Maalaea Generating Station00500 16 Distillate 1993 23.6 Firm YesMECO - Maalaea Generating Station00500 17 Distillate 1998 25.0 Firm YesMECO - Maalaea Generating Station00500 18 Distillate 2006 18.0 Firm NoMECO - Maalaea Generating Station00500 19 Distillate 2000 25.0 Firm YesMECO - Maalaea Generating Station 00500 X1 Distillate 1987 2.6 Firm YesMECO - Maalaea Generating Station 00500 X2 Distillate 1987 2.6 Firm YesMECO - Hana Substation MH1 Distillate 2001 1.0 Not Firm NoMECO - Hana Substation MH2 Distillate 2001 1.0 Not Firm NoHawaiian Comm & Sugar - Puunene Mill 00001 PUU3 Biomass 1972 10.0 Firm Yes (non-EGU)Hawaiian Comm & Sugar - Puunene Mill 00001 PUU4 Biomass 1982 20.0 Firm Yes (non-EGU)Hawaiian Comm & Sugar - Puunene Mill 00001 PUU5 Biomass 2000 16.2 Firm Yes (non-EGU)Hawaiian Comm & Sugar - Kaheka Hydro KAH1 Hydro 1925 1.5 Not Firm NoHawaiian Comm & Sugar - Kaheka Hydro KAH2 Hydro 1925 1.5 Not Firm NoHawaiian Comm & Sugar - Kaheka Hydro KAH3 Hydro 1925 1.5 Not Firm NoHawaiian Comm & Sugar - Paia Hydro PHYD Hydro 1912 0.9 Not Firm NoKaheawa Wind Partners (KWP) Wind 2006 30.0 Not Firm NoTechnical MemorandumNovember 9, 2007Page 10Facility NameStateIDUnitIDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?Makila Hydro Hydro 2007 0.5 Not Firm NoTable 4.EGU Summary – MolokaiFacility NameStateIDUnitIDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?MECO - Palaau Power Plant 00015 CAT1 Distillate 1985 1.2 Firm YesMECO - Palaau Power Plant 00015 CAT2 Distillate 1985 1.2 Firm YesMECO - Palaau Power Plant 00015 CUM3 Distillate 1985 0.9 Firm YesMECO - Palaau Power Plant 00015 CUM4 Distillate 1985 0.9 Firm YesMECO - Palaau Power Plant 00015 CUM5 Distillate 1985 0.9 Firm YesMECO - Palaau Power Plant 00015 CUM6 Distillate 1991 0.9 Firm YesMECO - Palaau Power Plant 00015 15 Distillate 1982 2.5 Firm YesMECO - Palaau Power Plant 00015 7 Distillate 1996 2.2 Firm YesMECO - Palaau Power Plant 00015 8 Distillate 1996 2.2 Firm YesMECO - Palaau Power Plant 00015 9 Distillate 1996 2.2 Firm YesTable 5.EGU Summary – LanaiFacility NameStateIDUnitIDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?MECO - Miki Basin Power Plant 00035 LL1 Distillate 1990 1.0 Firm YesMECO - Miki Basin Power Plant 00035 LL2 Distillate 1990 1.0 Firm YesMECO - Miki Basin Power Plant 00035 LL3 Distillate 1990 1.0 Firm YesMECO - Miki Basin Power Plant 00035 LL4 Distillate 1990 1.0 Firm YesMECO - Miki Basin Power Plant 00035 LL5 Distillate 1990 1.0 Firm YesMECO - Miki Basin Power Plant 00035 LL6 Distillate 1990 1.0 Firm YesMECO - Miki Basin Power Plant 00035 LL7 Distillate 1996 2.2 Firm YesMECO - Miki Basin Power Plant 00035 LL8 Distillate 1996 2.2 Firm YesTechnical MemorandumNovember 9, 2007Page 11Table 6.EGU Summary – KauaiFacility NameStateIDUnitIDFuel TypeInitialOperationDateNameplateCapacity(MW)GenerationHawaii NIF?KIUC - Port Allen Generating Station 00012 D6 Distillate 1990 8.6 Firm YesKIUC - Port Allen Generating Station 00012 D7 Distillate 1990 8.6 Firm YesKIUC - Port Allen Generating Station 00012 GT1 Distillate 1973 19.2 Firm YesKIUC - Port Allen Generating Station 00012 GT2 Distillate 1977 23.8 Firm YesKIUC - Port Allen Generating Station 00012 IC1 Distillate 1964 2.0 Firm YesKIUC - Port Allen Generating Station 00012 IC2 Distillate 1964 2.0 Firm YesKIUC - Port Allen Generating Station 00012 ST1 Distillate 1969 10.0 Firm YesKIUC - Port Allen Generating Station 00012 3 Distillate 1968 2.7 Firm YesKIUC - Port Allen Generating Station 00012 4 Distillate 1968 2.7 Firm YesKIUC - Port Allen Generating Station 00012 5 Distillate 1968 2.7 Firm YesKIUC - Port Allen Generating Station 00012 8 Distillate 1991 8.6 Firm YesKIUC - Port Allen Generating Station 00012 9 Distillate 1991 8.6 Firm YesKIUC - Kapaia Power Station CT1 Jet Fuel 2002 39.1 Firm NoMcBryde Sugar Co. Ltd. - Kalaheo Hydro KAL Hydro 1928 1.1 Not Firm NoMcBryde Sugar Co. Ltd. - Wainiha Hydro WAIA Hydro 1906 1.8 Not Firm NoMcBryde Sugar Co. Ltd. - Wainiha Hydro WAIB Hydro 1910 1.8 Not Firm NoGay Robinson DSL1 Distillate 1972 0.4 Firm NoGay Robinson DSL4 Distillate 1972 0.4 Firm NoGay Robinson HYD2 Hydro 1982 1.2 Not Firm NoGay Robinson ST4 Biomass 1985 4.0 Firm NoTechnical 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 Δ 2005-2018 (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 Δ 2005-2018 (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 4 th 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 combusti on 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 • 3 rd 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 • 3 rd party biomass plant (8.5 MW)

• 3 rd party biomass plant (4.2 MW)

• 3 rd party waste-to-energy facility (5.3 MW) 32.9