### Appendix A.

These documents are also available in the DOCKET EPA

- R09

- OAR

- 2012

- 0345: Approval and

Promulgation of Implementation Plans; Hawaii; Regional Haze Federal Implementation Plan., in Section

C.3 Documents Related to Modeling to Support the Subjec t to BART and Reasonable Progress.

C.3.d Subject

- to

- Best Available Retrofit Technology (BART) Modeling for the State of Hawaii,

Application of the CALPUFF Modeling System, Alpine Geophysics, LLC. 2010

- 03

- 03

C.3.a Subject

- to

- Best Available Retrofit Technology (BART) and Reasonable Progress (RP)

Prioritization Modeling Protocol for the State of Hawaii Application of the CALPUFF Modeling System,

Alpine Geophysics, LLC. 2009

- 11

- 30

# Subject

- to

- Best Available Retrofit Technology (BART)

Modeling for the State of Hawaii

Application of the CALPUFF Modeling System

Prepared for:



### Hawaii State Department of Health

Environmental Management Division

Clean Air Branch Prepared by:

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, Colo rado 80007



### 3 March 2010

### i 

## Table of Contents

Section No. Page No.

PURPOSE .................................

............................................................ ............ 1

1 INTRODUCTION AND STU DY OBJECTIVES

................................ ................................ ............. 1

1.1 I NTRODUCTION

................................ ................................ ................................ ................................ ............................ 1



### 2 MODELING APPROACH

................................ ................................ ................................ ................. 2

2.1 BART

- E LIGIBLE /RP

- E LIGIBLE S OURCES

................................ ................................ ................................ .................... 2

2.2 C LASS I A REAS

................................ ................................ ................................ ................................ ............................ 3

2.3 P OLLUTANTS TO C ONSIDER

................................ ................................ ................................ ................................ ......... 4

2.4 E MISSION S AND S TACK D ATA

................................ ................................ ................................ ................................ ...... 6

2.5 N ATURAL B ACKGROUND

................................ ................................ ................................ ................................ ............. 6

2.6 V ISIBILITY C ALCULATION

................................ ................................ ................................ ................................ ........... 7

2.7 M ODEL E XECUTION

................................ ................................ ................................ ................................ ..................... 8

2.7.1 Subject

- to

- BART Analysis

................................ ................................ ................................ ................................ ..... 8

2.7.2 Implementing Subject

- to

- BART/RP Prioritization Modeling An alysis

................................ ................................ . 9



### 3 VISIBILITY MODELING SYSTEM

................................ ................................ .............................. 11

3.1 CALMET

................................ ................................ ................................ ................................ ................................ .......... 11

3.1.1 Me teorological Data

................................ ................................ ................................ ................................ ............. 13

3.1.2 CALMET Control File Settings

................................ ................................ ................................ ........................... 18

3.2 CALPUFF

................................ ................................ ................................ ................................ ................................ . 19

3.2.1 Emissions

................................ ................................ ................................ ................................ ............................. 19

3.2.2 Ozone Background

................................ ................................ ................................ ................................ ............... 20

3.2.3 Ammonia Background

................................ ................................ ................................ ................................ ......... 20

3.2.4 Receptor Locations

................................ ................................ ................................ ................................ ............... 20

3.3 CALPOST AND VISIBILITY POST

- PROCESSING

................................ ................................ .............................. 20



### 4. SUBJECT

- TO

- BART MODELING RESULT S

................................ ................................ ................. 21

5. CONCLUSION

................................ ................................ ................................ ................................ ... 26

6. REFERENCES

................................ ................................ ................................ ................................ ... 27

APPENDIX A CALMET PA RAMETE R VALUES

................................ ................................ ............... 29

APPENDIX B CALPUFF P ARAMETER VALUES

................................ ................................ .............. 34

APPENDIX C CALPOST P ARAMETER VALUES

................................ ................................ ............. 43

### ii 

## List of Figures

Figure No. Page No.

Figure 1 BART

- Eligible Facility Locations in Proximity to Class I Areas.

................................ ................................ ................... 4

Figure 2. Subject

- to

- BART/ RP Prioritization Flowchart

................................ ................................ ................................ ........... 10

Figure 3: Modeled 36/12/4 km MM5 Domain.

................................ ................................ ................................ ............................ 17

Figure 4. Mode led 4km MM5 Domain.

................................ ................................ ................................ ................................ ....... 18

### iii 

## List of Tables

Table No. Page No.

Table 1. BART

- eligible Sources.

................................ ................................ ................................ ................................ .................. 3

Table 2. Federal Mandatory Class I Areas in the M odeling Domain.

................................ ................................ ............................ 3

Table 3. 20% Best Natural Conditions

– Concentrations and Rayleigh Scattering By Class I Area.

................................ ............. 7

Table 4. CALPUFF Modeling System

................................ ................................ ................................ ................................ ........ 11

Table 5. MM5 Vertical Domain Specification.

................................ ................................ ................................ ........................... 16

Table 6: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HC&S Puunene Operating on either

bagasse or coal. . Visibility impacts over 0.5 dv over 98 th percentile (T op 1

- 7) are shaded yellow. Visibility impacts

over 0.5 dv for 98 th percentile (8 th high) are shaded red.

................................ ................................ ................................ ... 23

Table 7. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Chevron Refinery. . Visibility impacts

over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility impacts over 0.5 dv for 98 th percentile (8 th

high) are shaded red

................................ ................................ ................................ ................................ ............................. 23

Table 8: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoe s for the Tesoro Refinery. Visibility impacts over

0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility impacts over 0.5 dv for 98 th percentile (8 th high) are

shaded red

................................ ................................ ................................ ................................ ................................ ............ 24

Table 9. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Hu Honua Bioenergy

– Pepeekeo facility.

Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility impacts over 0.5 dv for 98 th

percentile (8 th high) are shaded red

................................ ................................ ................................ ................................ ...... 24

Table 10: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the MECO

- Hahului facility. Visibility

impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility impacts over 0.5 dv for 9 8 th percentile

(8 th high) are shaded red

................................ ................................ ................................ ................................ ....................... 24

Table 11. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HELCO Kanoelehua Hill facility.

Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) ar e shaded yellow. Visibility impacts over 0.5 dv for 98 th

percentile (8 th high) are shaded red

................................ ................................ ................................ ................................ ...... 25

Table 12. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HECO

- Waiau facility. Visibility

impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility impacts over 0.5 dv for 98 th percentile

(8 th high) are shaded red

................................ ................................ ................................ ................................ ....................... 25

Table 13. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for t he HECO

- Kahe facility. Visibility impacts

over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility impacts over 0.5 dv for 98 th percentile (8 th

high) are shaded red

................................ ................................ ................................ ................................ ............................. 25

### 1

PURPOSE

This Report is prepared for the State of Hawaii and describes the application

methodology and results of the CALPUFF Model for de termining whether the Hawaiian

Best Available Retrofit Technology (BART)

- eligible source s are Subject

- to

- BART under

the Regional Haze (RH) Rule, 40 CFR 308.

1 INTRODUCTION AND STUDY OBJECTIVES

1.1 Introduction

Under the federal RH Rule, 40 CFR 308, Hawaii is required to develop a plan to establish

Reasonable Progress goals for incremental visibility improvements in achieving natural

background conditio ns within its two Federal Class I areas. Natural conditions exist

when a Class I area is absent of anthropogenic or man

- made air pollution emissions. In

establishing RP goals, Hawaii will need to consider emission reduction measures for

specified source groups or activities including the application of BART for existing major

stationary sources.

On 6 July 2005, the U.S. Environmental Protection Agency (EPA) published final

amendments to its 1999 RH Rule in the Federal Register, including 40 CFR Part 51,

Appendix Y, the final guidance for BART determinations ( BART Guideline; EPA, 2005;

70 FR 39104

- 39172). The rule applies to any BART

- eligible source that ―emits any air

pollutant which may reasonably be anticipated to cause or contribute to any impairment

of visibility‖ in any mandatory Class I federal area. States retain the authority to exempt

certain BART

- eligible sources based on dispersion modeling demonstrating that the

source cannot reasonably be anticipated to cause or contribute to visibility impa irment in

a Class I area. Hawaii has elected to use this modeling option for BART

- eligible sources.

According to 40 CFR Part 51, Appendix Y, a BART

- eligible source is considered to

―contribute‖ to visibility impairment in a Class I area if the modeled 98 th 

### percentile

change in deciviews is equal to or greater than the ―contribution threshold.‖ Any BART

eligible source, through modeling, determined to cause or contribute to visibility

impairment above the threshold in any Class I area is subject to BART. The EPA BART

Guidelines suggest a contribution threshold of no greater than a 0.5 change in deciview

be used. Hawaii has chosen to use the recommended 0.5 deciview threshold f or Subject

to

- BART determination

.

The U.S. EPA Region 9, U.S. National Park Ser vice (NPS), and the U.S. Fish and

Wildlife Service (FWS) were consulted during development of the Modeling Protocol

(Alpine Geophysics, 2010) and the performance of this project , which follows very

closely the BART Guideline.

### 2

1.2 Objectives

The object ives of this study are to provide:

A consistent approach in determining which BART

- eligible source is Subject

- to

BART, requiring the 5

- factor analysis pursuant to 40 CFR Part 51, Appendix Y,

Guidelines for BART Determinations under the RH Rule (BART Guide line);

A clearly delineated modeling methodology;

A common CALMET/CALPUFF/POSTUTIL/CALPOST modeling configuration;

and

A common format for reporting modeling results for each BART

- eligible source

.

2 MODELING APPROACH

This section describes the components o r information used for the CALPUFF modeling

and the overall process in determining whether a source is anticipated to cause or

contribute to visibility impairment to Hawaii’s federal Class I areas. Eligible source

criteria, Class I area identification, po llutants considered, emissions data required, natural

background conditions, visibility impairment determination, and the analysis process are

covered here.

2.1 BART

- Eligible/RP

- Eligible Sources

Sources evaluated in this assessment include BART

- eligible sourc es. BART

- eligible

sources determined to cause or contribute to visibility impairment in any Class I area will

be subject to further air pollution control analyses.

The term ―BART

- eligible emission unit‖ refers to any single emission unit that meets the

following criteria:

1) Emits greater than 250 tons/year of a visibility

- impairing pollutant;

2) Was put into place between August 7, 1962 and August 7, 1977; and

3) Falls within one of the 26 listed source categories in the guidance.

A ―BART

- eligible sou rce‖ is the total of all BART

- eligible emission units at a single

facility. If a source has several emission units, only those that meet the BART

- eligible

criteria are included in the definition of ―BART

- eligible source.‖

Hawaii identified nine BART

- elig ible facilities shown in Table 1.

### 3

Table 1 . BART

- eligible Sources.

Facilities

Chevron Refinery

Tesoro Refinery

HECO

– Kahe

HECO

– Waiau

HC&S Puunene

MECO

– Kahului

HELCO

– Kanoelehua Hill

Hu Honua Bioenergy, LLC

- Pepeek eo

(formerly Hilo Coast)

Kauai Utilities

– Port Allen

2.2 Class I Areas

The mandatory Class I federal areas in Hawaii that could be impacted by BART

- eligible

sources, are presented in Table 2. Refer to Figure 1 below showing the location of the

Class I areas in relation to BART

- eligible facilities/sources.

Table 2 . Federal Mandatory Class I Areas in the Modeling Domain.

Class I Area IMPROVE Monitor Federal Land Manager

Haleakala National Park HALE 1 National Park Service

Hawaii Volcanoes National Park HAVO 1 National Park Service



### The Haleakala National Park consists of 30,183 acres in central to eastern portions of the

island of Maui, 24,719 acres of which are designated wilde rness. Park elevations range

from 3,055 m (10,023 ft) at the summit of Haleakala to sea level at the mouth of

Kipahulu Valley on the extreme eastern boundary. The Park thus includes a wide variety

of climate zones, from high mountain elevations above the M arine Boundary Layer, to

leeward lowlands that are dry relative to windward lowlands at the same elevation.

The Haleakala National Park IMPROVE site is HALE1, located at an elevation of 1,158

m (3,799 ft) on the northward slope of Haleakala, ~5.7 km north west of the Park

Boundary.

The Hawaii Volcanoes National Park IMPROVE Site is HAVO1, located on the east to

southeastern slopes of Mauna Loa on the island of Hawaii. More specifically, it is

situated on the northeastern rim of the Kilauea crater. The si te elevation is 1

, 204 m (3

, 949

ft), some 124 m above the crater floor at 1080 m (3

, 542 ft). One of the major terrain

feature affecting meteorology and air quality at the monitoring site, other than the

erupting Kilauea volcano, is the 4

, 170 m (13,681 ft) high Mauna Loa cone, about 35 km

(22 mi) to the west of the monitoring site.

### 4

The IMPROVE data is used to represent measured pollutant impacts at the two National

Parks.

Figure 1 BART

- Eligible Facility Locations in Proximity to Cl ass I Areas.

2.3 Pollutants to Consider

The BART Guideline specifies that sulfur dioxide (SO2), oxides of nitrogen (NOx) and

direct particulate matter (PM) emissions, including both PM10 and PM2.5 should be

included for the Subject

- to

- BART modeling analyses

.

The BART Guideline also discusses the inclusion of volatile organic compound (VOC),

ammonia and ammonia compounds as visibility impairing pollutants. These pollutants

will be considered separately if it is determined that these emissions from a BART

e ligible source are reasonably anticipated to cause or contribute to visibility impairment.

### 6

2.4 Emissions and Stack Data

The BART Guideline states, ―the emission estimates used in the models are intended to

reflect steady

- state operating conditions during pe riods of high capacity utilization.‖

These emissions should not generally include start

- up, shutdown, or malfunction

emissions. The BART Guideline recommends that states use the 24

- hour average actual

emission rate from the highest emitting day of the me teorological period modeled

. Hawaii requested source information of the highest emitting day between calendar years

2003 to 2007 from BART

- eligible sources and will similarly request the same

information from identified RP

- eligible sources.

Depending on the availability of emissions data, the following emissions information

(listed in order of priority) were be used with CALPUFF for the Subject

- to

- BART

modeling:

24

- hour average actual emission rate from the highest emitting day within the

modeling perio d (preferred) based on representative CEM or stack test data; or

24

- hour average emissions calculated using emission factors specified in Title V

permits.

24

- hour average emissions calculated using AP

- 42 emission factors or other

equivalent methods.

Allowa ble emissions (maximum 24

- hour allowable).

DOH work ed with sources to develop appropriate emission rates.

If plant

- wide emissions for SO 2 

### , NO

x 

### , and PM 10 

### , from eligible sources, are less than the

significant emission rate (SER) used for Prevention of Sign ificant Deterioration review,

emissions of that pollutant are not included in the Subject

- to

- BART modeling. However,

if plant

- wide emissions exceed the SERs for these pollutants, then all emissions of that

pollutant from i ndividual emission units are eval uated even if emissions are below the

SER for an individual emission unit.

2.5 Natural Background

The BART Guideline references EPA’s ―Guidance for Estimating Natural Conditions

under the Regional Haze Rule‖ (EPA, 2003a). This guidance lists three sets of N atural

Conditions corresponding to Annual Average, Best 20% Days and Worst 20% Days.

Due to limited access to available meteorological data, the 20% best natural days are

used, in lieu of annual average natural days, to represent natural visibility backgr ound in

Class I areas. This definition of natural background is consistent with the intent of the

BART Guideline (Federal Register Vol. 70, No. 128, pf 39125). The specific natural

background values used in this study are drawn from the Federal Land Mana gers’ Air

Quality Related Values Workgroup (FLAG) Phase I Report

– Revised (FLAG, 2008).

Class I specific background extinction coefficients are presented in Table 3. The option

### 7

presented in EPA’s guidance for refining the default visibility background a re not used in

this study

.

Table 3 . 20% Best Natural Conditions

– Concentrations and Rayleigh Scattering By

Class I Area.

Background Extinction Coefficients (20% Best Days)

Class I Area (NH 4 ) 2 SO 4

μg/m3 NH 4 NO 3

Μg/m3 OM

μg/m3 EC

μg /m3 Soil

μg/m3 CM

μg/m3 Sea

Salt

μg/m3 Rayleigh

Mm

- 1

Haleakala

National Park 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawaii

Volcanoes

National Park 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10



### 2.6 Visibility Calculation

The CALPUFF modeling techniques used in this study provide ground level

concentrations of visibility impairing pollutants. The concentration estimates from

CALPUFF are used with the FLAG referenced IMPROVE equation to calculate the

extinction coefficient ( b

ext 

### ) , as shown below.

b ext 

### = 2.2 × f S 

### ( RH) × [Small Sulfate] + 4.8 × f L 

### (RH) × [Large Sulfate]

• 2.4 × f

S 

### (RH) × [Small Nitrate] + 5.1 × f L 

### (RH) × [Large Nitrate]

• 2.8 × [Small Organic Mass] + 6.1 × [Large Organic Mass]

• 10 × [Elemental Carbon]

• 1 × [Fine Soil]

• 0.6 × [Coarse Mass]

• 1.7 × f

SS 

### (RH) × [Sea Salt]

• Rayleigh Scattering (Site Specific)

• 0.33 × [NO 2 

### (ppb)] {or as: 0.1755 × [NO 2 

### (μg/m 3 

### )]}

Where:

[ ] indicates concentrations in μg/m 3



### f S 

### (RH) = Relative humidity adjustment factor for small sulfate and nitrate

f L 

### (RH) = Relati ve humidity adjustment factor for large sulfate and nitrate

f SS 

### (RH) = Relative humidity adjustment factor for sea salt

For Total Sulfate < 20 μg/m 3 

### : [Large Sulfate] = ([Total Sulfate] / 20 μg/m 3 

### ) × [Total Sulfate]

For Total Sulfate ≥ 20 μg/m 3 

### :

[Large Sulfate] = [Total Sulfate]

And:

[Small Sulfate] = [Total Sulfate]

– [Large Sulfate]



### To calculate large and small nitrate and organic mass, substitute ({Large, Small,

Total} {Nitrate, Organic Mass}) for Sulfate.

### 8

The monthly relative humidity adjustmen t factors from the above equation are found in

Appendix C.

The change in visibility for the Subject

- to

- BART analyses are compared against the 20%

best natural background conditions. The delta

- deciview, dv, value is calculated from

the source’s contribut ion to extinction, b ext (source) 

### , and background extinction, b ext(bkg) 

### , as

follows:

dv = 10 ln (b ext(bkg) 

### +b ext (source) 

### ) / b ext(bkg) 

### )

2.7 Model Execution

2.7.1 Subject

- to

- BART Analysis

The Subject

- to

- BART modeling is used to determine which eligible sources are

reasonably anticipated to cause or contribute to visibility impairment at any Class I area.

This study uses Option 1 in Section III of the BART Guideline for this analysis. This

option is the Individual Source Attribution Approach. With this approach, each BART

and RP

- eligible source is modeled separately and the impact on visibility impairment in

any Class I area is determined.

For determining the visibility threshold, the recommendations in the BART Guideline are

followed to assess whether a BART

- s ource is reasonably anticipated to cause or

contribute to any visibility impairment in a federal Class I area. According to the BART

Guideline:

“A single source that is responsible for a 1.0 deciview change or more should be

considered to “cause” visibil ity impairment; a source that causes less than a 1.0

deciview change may still contribute to visibility impairment and thus be subject to

BART

. As a general matter, any threshold that you use for determining whether a source

“contributes” to visibility im pairment should not be higher than 0.5 deciviews.

As recommended in the BART Guideline, if a single source causes a 0.5 deciview or

greater change from natural background, then that source is determined to be reasonably

anticipated to contribute to any vi sibility impairment in a Class I area and will be subject

to BART. For this single source analysis, the Subject

- to

- BART modeling will not

consider the frequency, magnitude, and duration of impairment.

Outside the work of this study , DOH has made a prelim inary determination whether an

eligible source is Subject

- to

- BART by following the screening methodology described in

Section III, Option 2 of the BART Guideline. Based on a 0.5 deciview threshold, if a

source calculated Q/D < 10, an eligible source would be exempt from further BART

analysis at least for the first RH planning period.

### 9 Q = Potential Emissions (tons/year) of SO 2 

### +NOx+PM 10 

### from all eligible units

within a facility

D = Distance (kilometers) between the eligible source and the boundary of the

n earest Class I area

Generally, if a source is a smaller emitter or is located far from a Class I area, and its Q/D

is less than 10, the source is not expected to contribute to visibility impairment at a Class

I area.

Using this approach

, the DOC determin ed that the Kauai Utilities Port Allen facility

should not be Subject

- to

- BART.

2.7.2 Implementing Subject

- to

- BART/RP Prioritization Modeling Analysis

Alpine Geophysics, LLC (Alpine) has perform ed the Subject

- to

- BART modeling in

accordance with the Modeling P ro tocol for the BART

- eligible sources. Figure 2 is a flow

chart of how individual facilities are evaluated for its eligible emission units.

Consideration for allowing a more refined Subject

- to

- BART analysis in the event a

source is found to be Subject

- to

- BART is not a part of this study’s scope.

### 10

Figure 2 . Subject

- to

- BART/ RP Prioritization Flowchart

Note: “Pre

- BART” units = identified “RP

- eligible” units.

### 11



## 3 VISIBILITY MODELING SYSTEM

In general, th e Subject

- to

- BART modeling used the CALPUFF suite of programs and

follow ed the procedures and recommendations outlined in two documents: the IWAQM

(Interagency Workgroup on Air Quality Models) and the FLAG (Federal Land Managers

Air Quality Related Values Workgroup) report s (EPA 1998, FLAG 2008). Tables listing

the modeling parameters for each CALPUFF module are located in the Appendices.

The specific CALPUFF programs and their version numbers that were used in the

Subject

- to

- BART/ modeling are presented in Table 4. The CALPUFF modeling system

is the regulatory approved versions except for the CALPOST processor. CALPOST

version 6.221 is used to enable this application to use the most up

- to

- date FLAG (6

- 29

- 2008) guidance. The CALMET model was run using the August 20, 20 09, ―EPA

- FLM

Recommended CALMET input File Values.‖

The CALMET meteorological domain, as described below, covers the entire Hawaiian

Island chain.

Table 4 . CALPUFF Modeling System

Program Version Level

CALMET 5.8 060911

CALPU FF 5.8 060911

CALPOST 6.221 080724

POSTUTIL 1.56 070627



### 3.1 CALMET

Input requirements for the CALMET model include various meteorological and

geophysical data sets, and a control input file with appropriate settings. Required

meteorological data incl ude surface, upper

- air, and precipitation observations, and

mesoscale model output data fields. Specifics of these data are provided below.

Geophysical input data include terrain elevation and land

- use data. Though CALMET

may be run with mesoscale model meteorological data, alone (i.e., no observations), the

EPA modeling guideline recommends ―blending‖ observations with the mesoscale mo del

fields. Therefore, this study include d observations in a blended approach.

The dispersion modeling use d the CALMET wind fields for the calendar year 2005.

While a minimum of three years of mesoscale meteorological model output is

recommended for conducting the initial step in the first

- level analysis, meteorological

data sets for more than 2005 were unavailable for t he modeling domain under this

contract. The CALMET wind fields for 2005 cover the entire island chain and

### 12

sufficiently encompass both Class I areas within 300km of any BART

- facility included

in this analysis.

Land Use Processing : CALMET requires both l and use/land cover and terrain height

data for the application region. These are generated using the CALMET CTGPROC,

TERREL and MAKEGEO as described below.

TERREL is the terrain pre

- processor that averages terrain features to the

modeling grid resolution ; TERREL constructs the basic properties of the gridded

domain and defines the coordinates upon which meteorological data are stored.

Key parameters include specification of grid type, location, resolution and terrain

elevation.

CTGPROC computes the frac tional land use for the modeling grid resolution.

Land use characteristics for each grid cell are assigned using CTGPROC. The

primary variable adjustment associated with CTGRPOC is selection of an

appropriate land use database. Version 2.0 of the North Am erican Land Cover

Characteristics database was used.

MAKEGEO is the final pre

- processor that combines the terrain and land use data

for input to CALMET. Generating the appropriate MAKEGEO.INP control file

requires only minimal alteration of the default assignments. Key modifications

include specifying domain attributes and ensuring input files are correctly

referenced.

Terrain : CALMET requires both terrain height and land use/land cover for the

application region. These are generated using the CALMET C TGPROC, TERREL and

MAKEGEO processors. The terrain data was created using the TERREL (version 3.684

level 070327) processor and the Shuttle Radar Topography Mission (SRTM)

- GTOPO 30

second (~1 km) resolution dataset.

Land Use: The land use data set was created using the Composite Theme Grid

CTGROC processor (version 2.682, level 070430) and the United States Geological

Survey (USGS) Global Land Cover Characterization (GLCC) version 2.0 database. The

GLCC database is available at 30 second (~1km) resolut ion.

Observed Data Processing: Observation data from both available surface and upper

- air

stations in the modeling will be used. The surface data wer taken from the NOAA

Techniques Development Lab (TDL) database and the upper

- air data from the NOAA

Fo recast Systems Lab (FSL) database.

CALMET Model Options : The options used in the CALMM5 processing will generally

follow the FLAG (FLAG, 2008) and IWAQM (EPA, 1998) guidance. Specific

CALMET input values will be updated using the August 20, 2009, ―EPA

- F LM

Recommended Input File Values.‖

### 13

3.1.1 Meteorological Data

3.1.1.1 Mesoscale Model Data

Alpine was contracted to apply the Pennsylvania State University/National Center for

Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1993; Grell et

al., 1994: UCAR, 2003a) for calendar year 2005 to provide data for the

CALMET/CALPUFF modeling as well as for additional modeling and analytical studies

conducted for the Hawaiian Islands.

A complete description of the modeling approach and a statistical evaluation of the

annual MM5 model are described in McNally and Wilkinson, 2008. A brief description

of the modeling approach follows:

Model Selection : The publicly available non

- hydrostatic version of MM5 (version

3.7.4) was used for this modeling study. Prepro cessor programs of the MM5 modeling

system including TERRAIN , REGRID, LITTLE_R, and INTERPF (UCAR, 2003b) were

used to develop model inputs.

Horizontal Domain Definition: The computational grid is presented in Figures 3 and 4.

The outer 36km domain (D01 ) has 165 x 129 grid cells, selected to provide boundary

conditions to the 12km domain. The mid

- scale 12km domain (D02) has 226 x 226 grid

cells, selected to maximize the coverage of the western region around the Hawaiian

Islands. The 4km domain (D03) ha s 208 x 154 grid cells, selected to cover the Island

chain with a greater than 100km buffer around the perimeter. The projection was in

Lambert Conformal Coordinates (LCC) with a grid projection pole of 23 o



### , - 158 o 

### with true

latitudes of 30 o 

### and 60 o



### .

Vert ical Domain Definition: The MM5 modeling was based on 34 vertical layers with

an approximately 38 meter deep surface layer. The MM5 vertical domain is presented in

both sigma and height coordinates in Table 5.

Topographic Inputs: Topographic informatio n for the MM5 was developed using the

NCAR and the United States Geological Survey (USGS) terrain databases. The grid will

be based on the 2 min (~4 km) Geophysical Data Center global data interpolated to the

model grid using a Cressman

- type objective anal ysis scheme. To avoid interpolating

elevated terrain over water bodies, after the terrain databases are interpolated onto the

MM5 grid, the NCAR graphic water body database is used to correct elevations over

water bodies.

Vegetation Type and Land Use I nputs: Vegetation type and land use information were

developed using the most recently released PSU/NCAR databases provided with the

MM5 distribution. Standard MM5 surface characteristics corresponding to each land use

category were employed.

### 14

Atmospheri c Data Inputs: Two choices for the first guess meteorological fields were

available: (1) the NCEP Global Tropospheric Final Analysis; and (2) the NCAR/NCEP

Reanalysis Project (NNRP) data (UCAR, 2007). Though the NNRP data were used in a

previous study of Hawaii (Yang and Chen, 2003; Yang et. al. 2005), the NNRP data have

relatively coarse 2.5x2.5 degree resolution whereas the NCEP Final Analysis data have

much higher 1x1 degree resolution. For the current modeling study, the NCEP Final

Analysis dataset w as used.

Available surface and upper

- air observations were used in the objective analyses. These

data were incorporated into the analyses datasets following the procedures outlined by

Stauffer and Seaman (1990) and were quality

- inspected by MM5 pre

- proce ssors using

automated gross

- error checks and ―buddy‖ checks. In addition, radiosonde soundings

were subjected to vertical consistency checks. The synoptic

- scale data used for the

initialization (and in the analysis nudging discussed later) were obtained from the

conventional National Weather Service (NWS) twice

- daily radiosondes (UCAR, 2008a)

and 3

- hr NWS surface observations (UCAR, 2008b).

Water Temperature Inputs: The water temperature data were taken from the NCEP

RTG global one

- half degree analysis (Gemmill et al., 2007; NCEP, 2008).

FDDA Data Assimilation: This simulation use d both analysis and observation based

nudging. For the 36km and 12km grids analysis nudging coefficients of 2.5x10

- 4 

### and

1.0x10

- 4 

### were used for winds and temperature, respect ively. For the 4km grid analysis

nudging to observed winds from the NCAR DS472.0 TDL dataset (UCAR, 2008c) was

employed with a coefficient of 1.0x10

- 3



### .

Physics Options : Sensitivity testing using approximately two

- week long episodes in

early February 2005 and early July 2005 were performed to help select the best

performing physics options (McNally, 2008). Although the performance was similar for

the different options, the best performing configuration was the version used by the

University of Hawaii and the National Weather Service (Yang and Chen, 2003; Yang et.

al. 2005 ) . Namely:

Warm Rain;

Grell Cumulus Parameterization;

MRF PBL;

NOAH Land Surface Model; and

Cloud Radiation.

Application Methodology : The MM5 model was executed in 5

- day blocks ini tialized at

12Z every 4 days with a 90 second time step. Model results were output every 60

minutes and output files are split at 24 hour intervals. Twelve (12) hours of spin

- up were

included in each 4

- day block before the data were used in the atmospher ic simulations

and subsequent model performance evaluation.

### 15

3.1.1.2 CALMM5 Processing

The final step in the meteorological processing is to reformat the MM5 data using the

CALMM5 processor. CALMM5 was run for the 4km MM5 nest to extract the whole

region, less a few grid cells around the perimeter that are contaminated by MM5

boundary artifacts.

### 16

Table 5 . MM5 Vertical Domain Specification.

k(MM5) sigma pressure.(mb) height(m) depth(m)

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0.250 32500 8621 900

28 0.300 37000 7720 817

27 0.350 41500 6903 750

26 0.400 46000 6153 693

25 0.450 50500 5461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.90 0 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0.950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0

### 17

Figure 3 : Modeled 36/12/4 km MM5 Domain.

### 18

Figure 4 . Modeled 4km MM5 Domain.

3.1.2 CALMET Control File Settings

These CALMET wind fields wer e used by all BART

- elegible sources wit hin the

modeling domain . The wind fields were prepared using CALMET. Details of the

parameter settings in CALMET are provided in Appendix A to this document; however,

the major assumptions are summarized below.

1) The initial

- guess fields will use the 4

- km MM5 outputs, forec ast from hours 13

–

108 of each 5 day run segment , January

– December, 2005.

2) Both the Subject

- to

- BART used the wind fields at 4km resolution.

### 19

3) There wer e 10 vertical layers with face heights of 0, 20, 40, 65, 120, 200, 400,

700, 1200, 2 200, and 4000 meters.

4) 

### CALMET was run using surface, buoy and upper air observations. Upper air data

from the LIH (WBAN 22536) site on Kauai and the ITO (WBAN 21504) site on

Hilo wer e used. Buoy data collected from the National Data Buoy Center for

sites 51001, 51002, 51003, 51004, KLIH1 and OOUHI were used and surface

data from 14 surface stations in the TDL DS472.0 dataset were used.

3.2 CALPUFF

The CALPUFF modeling used Version 5.8. The application generally follows the

recommendation of the IWAQM and FL AG guidance documents. Details of the

parameter settings in CALPUFF are provided in Appendix B to this document; however,

the major features are summarized below:

1) The calendar year 2005 CALMET input file were developed by Alpine

Geophysics, LLC and be p rovided as input

- ready to CALPUFF.

2) The Subject

- to

- BART modeling examine d the visibility impairment on Class I

areas within 300km of each single source or facility. The computational

modeling domain is sufficient to include all Class I areas within a 300km radius

of a source.

3) Pasquill

- Gifford Dispersion coefficients are used.

4) MESOPUFF

- II chemistry algorithm are used.

5) Puff splitting is not used, following the recommendations of the FLMs.

6) Source elevations are based on the same terrain files as the receptor e levations.

7) The CALUTIL is used to perform HNO3/NO3 repartitioning using the parameters

in Appendix B.

CALPUFF is applied separately for each Class I Area (Haleakala National Park and

Hawaii Volcanoes National Park) on the island of Maui and Hawaii.

3.2.1 Emis sions

Section 2.4 above presents the emissions and stack data that is required from the

facilities. This section only discusses the emissions estimates needed in CALPUFF.

Primary emission, species include available input species of PM, SO 2 

### , SO 4 

### , NO

x 

### ; a nd the

additional modeled species HNO 3 

### and NO 3 

### . SO 2 

### emissions were reviewe d to ensure

―double

- counting‖ was avoided.

The primary PM species are treated as follows:

Filterable:

Elemental Carbon (EC) (<2.5 μm)

PM Fine (PMF) (<2.5 μm)

### 20 PM Coarse (PMC) (2.5

– 10 μm)

Condensable:

Organic Carbon (SOA)

Inorganic Aerosol (SO 4 

### )

The condensable fraction is treated as primary emissions in the CALPUFF input file and

assumed to be 100% in the PM 2.5 

### fraction (see NPS Web site listed below).

Facility specific speciati on emissions data are used when provided. The following size

distributions are used in CALPUFF:

Pollutant Mean diameter Standard deviation

SO 4 

### , NO 3 

### , PMF, SOA, EC 0.48 2

PMC 2.5 5

3.2.2 Ozone Background

Background ozone will be assume d at an annual constant level of 40 ppb

.

3.2.3 Ammonia Background

Because the Hawaiian Island chain does not fit easily within the three land use values

proposed in IWAQM (10 parts per billion (ppb) for grasslands, 0.5 ppb for forest, and 1

ppb for arid lands), ammonia back ground concentrations were determined in consultation

with the participating FLMs. A review of the ammonia emissions inventory for Hawaii

in comparison with mainland agricultural areas indicate a background of 1.5 ppb to be

representative based on land us e and agricultural and livestock activity.

3.2.4 Receptor Locations

Predicted visibility impacts are computed at receptors locations for the Class I Areas

within the domain which are available for download from the National Park Service Web

site http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm

, 

### Source and receptor elevations

are based on actual elevations. The elevations for the meteorological grid points were

taken from the CALMET modeling.

3.3 CALPOST AND VISIBILITY POST

- PROCESSING

The CALPOST processing used Version 6.221 and POSTUTIL Version 1.56. Details of

the parameter settings in CALPOST and POSTUTIL are provided in Appendix C, with

some of the major features summarize d below for calculating visibility impairment:

### 21

1) For the visibility calculation, Method 8, Mode 5 (MVISBK=8, M8_MODE=5) is

employed. This method uses monthly average relative humidity and f(RH) values

for each Class I area, which are based on data from FL AG (FLAG 2008).

2) Particulate species for the visibility analysis include SO 4 

### , NO 3 

### , EC, OC, PMF,

and PMC, as reported in the CALPOST output files.

3) POSTUTIL was not be used to speciate modeled PM 10 

### concentrations, as PM 10 

### is

speciated into its components (PMF , PMC, SOA, EC, SO 4 

### ) and entered as

primary emissions in CALPUFF. However , HNO 3 

### /NO 3 

### partition option in

POSTUTIL was used

4) Natural background extinction calculations use the 20% best natural days for each

Class I area in the modeling domain. Tables provid ing the monthly f(RH) and

20% best days coefficients are provided in Appendix C, and are based on data

from FLAG (FLAG 2008).

5) The 98 th 

### percentile value is calculated for all BART

- eligible sources at each

mandatory Class I area.

6) The contribution threshold has the implied level of precision equal to the level of

precision reported by CALPOST. Therefore, the 98 th 

### percentile value will be

reported to three decimal places.



## 4. SUBJECT

- TO

- BART MODELING RESULTS

The change in visibility impairment for the Subje ct

- to

- BART/RP prioritization modeling

is based on the increase in t he haze index from a BART

- eligible source relative to natural

background, defined as the 20% best natural visibility days for each Class I area. This

definition of natural background is co nsistent with the intent of the BART Guideline

(Federal Register Vol. 70, No. 128, pf 39125). Although the use of the annual 20%

natural visibility days was subsequently allowed ( EPA 2006, Memorandum from Joseph

W. Paisie to Kay Prince dated July 19, 2006 . Subject Regional Haze Regulations and

Guidelines for BART Determinations ), the 20% best natural visibility days is being used

due to the availability of one year’s worth of meteorological data . Three years worth of

meteorological data is recommended by guidance

.

The U.S. EPA recommends using the 98 th 

### percentile value from the distribution of values

containing the highest modeled delta

- deciview ( dv) value for each day of the simulation

from all modeled receptors at a given Class I area. The 98 th 

### perce ntile dv value will be

determined with the 8 th 

### highest value for the year modeled (2005).

This 8 th 

### highest value is compared to the contribution threshold ( dv≥0.5 dv). If there

are more than seven (7) days with values greater than the contribution threshold in any

single meteorological year for any of the Class I areas, then the source is considered

Subject

- to

- BART in the first RH planning period. Tables fo r each of BART eligible

sources are presented. For each source the top 8 visibility impacts ( dv ) are presented

for both Haleakala and Hawaii Volcanoes. Values where the visibility impacts for the

top 1

- 7 impacts (higher that 98 th 

### percentile) exceed the 0.5 dv threshold are shaded

### 22

yellow. Values where the visibilities for the 8 th 

### high (the 98 th 

### percentile) exceed the

threshold, and are thus Subject

- to

- BART, are shaded red.

Table 6 presents the top 8 visibility impacts ( dv ) for the HC&S Puunene facilit y. Since

this facility can operate on both Bagasse (a fibrous residue from sugar cane processing),

or coal, impacts were examined separately for each fuel . For both fuels at both Class 1

areas none of the top 8 visibility impacts exceed the 0.5 dv thresh old. Based on this

modeling assessment, the HC&S Puunene facility is not Subject

- to

- BART.

Table 7 presents the top 8 visibility impacts ( dv ) for the Chevron Refinery. At neither of

the Class 1 areas did any of the top 8 visibility impacts exceed the 0.5 dv threshold.

Based on this modeling assessment, the Chevron Refinery is not Subject

- to

- BART.

Table 8 presents the top 8 visibility impacts ( dv ) for the Tesoro Refinery. At neither of

the Class 1 areas did any of the top 8 visibility impacts exceed th e 0.5 dv threshold.

Based on this modeling assessment, the Tesoro Refinery is not Subject

- to

- BART.

Table 9 presents the top 8 visibility impacts ( dv ) for the Ho Honua Bioenergy

– Pepeekeo facility. At the Haleakala class 1 area the impacts are all belo w the 0.5 dv

threshold. However, at the Hawaii Volcanoes class 1 area, the top 8 impact s all exceed

the 0.5 dv threshold. Based on this modeling assessment, the Ho Honua Bioenergy

– Pepeekeo facility is Subject

- to

- BART.

Table 10 presents the top 8 vi sibility impacts ( dv ) for the MECO

- Kahului facility. The

only impact that exceeds the 0.5 dv threshold was the highest impact at the Haleakala

Class 1 area. No other of the top 8 visibility impacts, including the 8 th 

### high (98 th



### percentile value)

, exceed the 0.5 dv threshold. Based on this modeling assessment, the

MECO Kahului facility is not Subject

- to

- BART.

Table 1 1 presents the top 8 visibility impacts ( dv ) for the HELCO Kanoelehua Hill

facility. For this facility the top 8 visibility impacts at b oth of the Class 1 areas exceed

the 0.5 dv threshold. Based on this modeling assessment, the HELCO Kanoelehua Hill

facility is Subject

- to

- BART.

Table 12 presents the top 8 visibility impacts ( dv ) for the HECO Waiau facility. The

only impacts that exce eds the 0.5 dv threshold were the highest two impact s at the

Haleakala Class 1 area. None other of the top 8 visibility impacts, including the 8 th 

### high

(98 th 

### percentile value), exceed the 0.5 dv threshold. Based on this modeling assessment,

the HECO Wai au facility is not Subject

- to

- BART.

Table 13 presents the top 8 visibility impacts ( dv ) for the HECO Kahe facility. The

only impacts that exceeds the 0.5 dv threshold were the highest five impact s at the

Haleakala Class 1 area. The rest of the top 8 vi sibility impacts, including the 8 th 

### high (98 th



### percentile value), did not exceed the 0.5 dv threshold. Based on this modeling

assessment, the HECO Kahe facility is not Subject

- to

- BART.

### 23

Table 6 : Top 8 visibility impacts ( dv ) a t Haleakala and Hawaii Volcanoes for the HC&S Puunene

Operating on either bagasse or coal. . Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7)

are shaded yellow. Visibility impacts over 0.5 dv for 98 th percentile (8 th high) are shaded red.

Source High Haleakala Hawaii Volcanoes

dv dv

HC&S Puunene (Bagasse) 1 0.119 0.029

HC&S Puunene (Bagasse) 2 0.102 0.024

HC&S Puunene (Bagasse) 3 0.101 0.017

HC&S Puunene (Bagasse) 4 0.090 0.011

HC&S Puunene (Bagasse) 5 0.061 0.010

HC&S Puunene (Bagasse) 6 0.060 0.010

HC&S Puunene (Bagasse) 7 0.059 0.009

HC&S Puunene (Bagasse) 8 0.059 0.008

HC&S Puunene (Coal) 1 0.236 0.094

HC&S Puunene (Coal) 2 0.224 0.092

HC&S Puunene (Coal) 3 0.184 0.090

HC&S Puunene (Coal) 4 0.181 0.089

HC&S Puunene (C oal) 5 0.170 0.073

HC&S Puunene (Coal) 6 0.150 0.073

HC&S Puunene (Coal) 7 0.140 0.053

HC&S Puunene (Coal) 8 0.133 0.039

Table 7 . Top 8 visibility impacts ( dv ) at Haleakala and Hawaii Volcanoes for the Chevron

Refinery. . V isibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow.

Visibility impacts over 0.5 dv for 98 th percentile (8 th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

Chevron Refinery 1 0.208 0.055

Chevron Refi nery 2 0.135 0.032

Chevron Refinery 3 0.083 0.025

Chevron Refinery 4 0.082 0.019

Chevron Refinery 5 0.060 0.019

Chevron Refinery 6 0.031 0.017

Chevron Refinery 7 0.029 0.017

Chevron Refinery 8 0.021 0.016

### 24

Table 8 : Top 8 visibility impacts ( dv ) at Haleakala and Hawaii Volcanoes for the Tesoro Refinery.

Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility impacts

over 0.5 dv for 98 th percentile (8 th high) are shaded red

Source Hig h Haleakala Hawaii Volcanoes

dv dv

Tesoro Refinery 1 0.237 0.062

Tesoro Refinery 2 0.173 0.038

Tesoro Refinery 3 0.099 0.033

Tesoro Refinery 4 0.092 0.027

Tesoro Refinery 5 0.079 0.023

Tesoro Refinery 6 0.040 0.019

Tesoro Refinery 7 0.027 0.01 9

Tesoro Refinery 8 0.025 0.017

Table 9 . Top 8 visibility impacts ( dv ) at Haleakala and Hawaii Volcanoes for the Hu Honua

Bioenergy

– Pepeekeo facility. Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are

shaded yellow. Visibility impacts over 0.5 dv for 98 th percentile (8 th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

Hu Honua Bioenergy

– Pepeekeo 1 0.444 1.660

Hu Honua Bioenergy

– Pepeekeo 2 0.444 1.102

Hu Honua Bioenergy

– Pepeekeo 3 0.404 1.067

Hu Honua Bioenergy

– Pepeekeo 4 0.398 0.689

Hu Honua Bioenergy

– Pepeekeo 5 0.376 0.651

Hu Honua Bioenergy

– Pepeekeo 6 0.373 0.552

Hu Honua Bioenergy

– Pepeekeo 7 0.364 0.551

Hu Honua Bioenergy

– Pepeekeo 8 0.323 0.540

Table 10 : Top 8 visibility impacts ( dv ) at Haleakala and Hawaii Volcanoes for the MECO

- Hahului facility. Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow.

Visibility impacts over 0.5 dv for 98 th percentile (8 th hi gh) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

MECO

- Hahului 1 0.566 0.250

MECO

- Hahului 2 0.467 0.250

MECO

- Hahului 3 0.447 0.240

MECO

- Hahului 4 0.363 0.238

MECO

- Hahului 5 0.345 0.183

MECO

- Hahului 6 0.298 0.163

MECO

- Hahului 7 0.289 0.163

MECO

- Hahului 8 0.232 0.108

### 25

Table 11 . Top 8 visibility impacts ( dv ) at Haleakala and Hawaii Volcanoes for the HELCO

Kanoelehua Hill facility. Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded

yellow. Visibility impacts over 0.5 dv for 98 th percentile (8 th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

HELCO Kanoelehua Hill 1 1.132 4.681

HELCO Kanoelehua Hill 2 1.023 4.088

HELCO Kanoelehua Hill 3 0. 872 2.967

HELCO Kanoelehua Hill 4 0.865 2.879

HELCO Kanoelehua Hill 5 0.856 2.759

HELCO Kanoelehua Hill 6 0.846 2.448

HELCO Kanoelehua Hill 7 0.812 2.403

HELCO Kanoelehua Hill 8 0.808 2.334

Table 12 . Top 8 visibility impacts ( dv ) at Haleakala and Hawaii Volcanoes for the HECO

- Waiau

facility. Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility

impacts over 0.5 dv for 98 th percentile (8 th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

HECO

- Waiau 1 0.573 0.211

HECO

- Waiau 2 0.512 0.131

HECO

- Waiau 3 0.251 0.117

HECO

- Waiau 4 0.248 0.111

HECO

- Waiau 5 0.211 0.092

HECO

- Waiau 6 0.193 0.071

HECO

- Waiau 7 0.103 0.039

HECO

- Waiau 8 0.083 0.038

Table 13

Table 13 . Top 8 visibility impacts ( dv ) at Haleakala and Hawaii Volcanoes for the HECO

- Kahe

facility. Visibility impacts over 0.5 dv over 98 th percentile (Top 1

- 7) are shaded yellow. Visibility

impacts over 0.5 dv f

or 98 th percentile (8 th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

HECO

- Kahe 1 1.277 0.299

HECO

- Kahe 2 1.153 0.286

HECO

- Kahe 3 0.707 0.222

HECO

- Kahe 4 0.686 0.198

HECO

- Kahe 5 0.656 0.196

HECO

- Kahe 6 0.381 0.16 2

HECO

- Kahe 7 0.240 0.142

HECO

- Kahe 8 0.221 0.132

### 26



## 5. CONCLUSION

A CALPUFF modeling analysis was performed to determine which BART

- eligible

sources in Hawaii are Subject

- to

- BART. The modeling assessment looked at the HC&S

Puunene facility, the Chev ron Refinery, the Tesoro Refinery, the Hu Honua Bioenergy

– Pepeekeo facility, the MECO

– Hahului facility, the HELCO Kanoelehua Hill facility,

the HECO

– Waiau facility, and the HECO

– Kahe facility. The only facilities that

showed a 98 th 

### percentile (8 th 

### high) 24

- hour average visibility impact over the 0.5 dv

impact threshold were the Hu Honua Bioenergy

– Pepeekeo and the HELCO

-

Kanoelehua Hill facilities. Thus

, the Hu Honua Bioenergy

– Pepeekeo and the HELCO

-

Kanoelehua Hill facilities are Subject

- to

- B ART. The HC&S Puunene facility, the

Chevron Refinery, the Tesoro Refinery, the MECO

– Hahului facility the HECO

– Waiau

facility, and the HECO

– Kahe facility are not Subject

- to

- BART.

### 27



## 6

. REFERENCES

40 CFR Part 51, Appendix W. Guidelines on Air Quality Models

Alpine Geophysics, 2010. ― Subject

- to

- Best Available Retrofit Technology (BART) and

Reasonable Progress (RP) PrioritizationModeling Protocol for the State of Hawaii :

Application of the CALPUFF Modeling System ‖ prepared for the Hawaii State

Departmen t of Health, Environmental Management Division, Clean Air Branch, prepared

by Alpine Geophysics, LLC, Arvada, CO.

Dudhia, J., 1993. ―A Non

- hydrostatic Version of the Penn State/NCAR Mesoscale

Model: Validation Tests and Simulation of an Atlantic Cyclone a nd Cold Front‖, Mon.

Wea. Rev., Vol. 121. pp. 1493

- 1513.

EPA (U.S. Environmental Protection Agency) 1998. Interagency Workgroup on Air

Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for

Modeling Long Range Transport Impacts, EPA

- 454/R

- 98

- 019, December 1998.

EPA 2003. Guidance for Estimating Natural Visibility Conditions under the Regional

Haze Rule, EPA

- 454/B

- 03

- 005, September, 2003.

Federal Register, Vol. 70, No. 128. Regional Haze Regulations and Guidelines for Best

Available Retr ofit Technology (BART) Determinations. pp. 39104

– 30172, July 6, 2005.

FLAG 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)

2000. Phase I Report. December 2000.

FLAG 2008. Federal Land Managers’ Air Quality Related Values Workg roup (FLAG)

2008. Phase I Report

— Revised. June 27, 2008 draft.

Gemmill, William, Bert Katz and Xu Li, 2007. Daily Real

- Time Global Sea Surface

Temperature

- High Resolution Analysis at NOAA/NCEP. NOAA/NWS/NCEP/MMAB

Office Note Nr. 260, 39 pp.

Grell, G. A., J. Dudhia, and D. R. Stauffer. 1994. A Description of the Fifth Generation

Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN

- 398

- STR,

138 pp.

McNally, D.E., 2008,‖MM5 Sensitivity to Alternate Configurations over the Hawaiian

Islands‖, prepared for the Hawaii State Department of Health, Environmental

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada,

CO.

McNally, D.E, and J.G. Wilkinson 2008, ‖MM5 Application for 2005 Over the

Hawaiian Islands‖, prepared for the Hawaii State Department of Health, Environmental

### 28

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada,

CO.

NCEP, 2008. Real

- time, global, sea surface temperature (RTG_SST) analysis. 10

- July.

polar.ncep.noaa.gov/sst

/oper/Welcome.html

NOAA, 2009. Mauna Loa Observatory. 29

- April, mlo.noaa.gov/livedata/livedata.html.

UCAR, 2003a. MM5 Community Model: Current Release. 09

- April.

www.mmm.ucar.edu/mm5/ove rviewCurrent.html

UCAR, 2003b. MM5 Community Model: MM5 Modeling System Overview. 10

- April.

www.mmm.ucar.edu/mm5/overviewProgram.html

UCAR, 2008a. ds353.4 Home Page. NCEP ADP Global Up per Air Observation Subsets,

January 1973

– continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds353.4.

UCAR, 2008b. ds464.0 Home Page. NCEP ADP Global Surface Observations, February

1975

– continuing. CISL Research Data Archive. dss.ucar.edu/ datasets/ds464.0.

UCAR, 2008c. ds472.0 Home Page. TDL U.S. and Canada Surface Hourly

Observations, daily 1976Dec

– cont. CISL Research Data Archive.

dss.ucar.edu.datasets.ds472.0.

### 29 

## Appendix A CALMET Parameter Values

### 30

Table A

- 1. CALMET Parameter Val ues The CALMET model will be run using the August 20, 2009, ―EPA

- FLM Recommended

CALMET input File Values.‖

Input

Group Variable Description Default Value Recommended Value

0 DIADAT Input file: preprocessed surface temperature data

(DIAG.DAT) User De fined

0 GEODAT Input file: Geophysical data (GEO.DAT) User Defined User Define

0 LCFILES Convert file name to lower case User Defined

0 METDAT Output file (CALMET.DAT) User Defined

0 METLST Output file (CALMET.LST) User Defined

0 MM4DAT I nput file: MM4 data (MM4.DAT) User Defined

0 NOWSTA Input files: Names of NOWSTA overwater stations User Defined 6

0 NUSTA Number of upper air data sites User Defined 2

0 PACDAT Output file: in Mesopuff II format (PACOUT.DAT) User Defined

0 P RCDAT Input file: Precipitation data (PRECIP.DAT) User Defined

0 PRGDAT Input file: CSUMM prognostic wind data (PROG.DAT) User Defined

0 SEADAT Input files: Names of NOWSTA overwater stations

(SEAn.DAT) User Defined

0 SRFDAT Input file: Surfac e data (SURF.DAT) User Defined

0 TSTFRD Output file (TEST.FRD) User Defined

0 TSTKIN Output file (TEST.KIN) User Defined

0 TSTOUT Output file (TEST.OUT) User Defined

0 TSTPRT Output file (TEST.PRT) User Defined

0 TSTSLP Output file (TES T.SLP) User Defined

0 UPDAT Input files: Names of NUSTA upper air data files (UPn.DAT) UPn.DAT

0 WTDAT Input file: Terrain weighting factors (WT.DAT) User Defined

1 CLDDAT Input file: Cloud data (CLOUD.DAT) User Defined Not used

1 IBDY Begin ning day User Defined

1 IBHR Beginning hour User Defined

1 IBMO Beginning month User Defined

1 IBTZ Base time zone User Defined 0

1 IBYR Beginning year User Defined

1 IRLG Number of hours to simulate User Defined User Define

1 IRTYPE Output file type to create (must be 1 for CALPUFF) 1 1

1 ITEST Flag to stop run after Setup Phase 2 2

1 LCALGRD Are w

- components and temperature needed? T T

1 MREG Text to options to conrim they conform to regulatory options User Defined 1

2 PMAP M ap Projection UTM LCC

2 FEAST False easting at the projection origin 0.0 0.0

2 FNORTH False northing at the projection origin 0.0 0.0

2 DATUM WGS

- G, NWS

- 27, NWS

- 84, ESR

- S,... WGS

- G

2 DGRIDKM Grid spacing User Defined 4

2 UTMHEM Hemisphere for UTM pro jection N N

2 IUTMZN UTM Zone User Defined User Define

2 LLCONF When using Lambert Conformal map coordinates

- rotate

winds from true north to map north? F F

2 NX Number of east

- west grid cells User Defined 199

2 NY Number of north

- south grid ce lls User Defined 145

2 NZ Number of vertical layers User Defined 10

2 RLAT0 Latitude used if LLCONF = T User Defined 23.0N

2 RLON0 Longitude used if LLCONF = T User Defined 158.0W

2 XLAT1 Latitude of 1 st standard parallel User Defined 30

2 XLAT2 Latitude of 2 nd standard parallel User Defined 60

2 XORIGKM Southwest grid cell X coordinate User Defined

- 360

### 31 Input

Group Variable Description Default Value Recommended Value

2 YORIGKM Southwest grid cell Y coordinate User Defined

- 588

2 ZFACE Vertical cell face heights (NZ+1 values) User Defined 0,20,40,80, 160,320,640,

1200,2000,3000,4000

3 IFORMO Format of unformatted file (1 for CALPUFF) 1 1

3 LSAVE Save met. data fields in an unformatted file? T T

4 ICLOUD Is cloud data to be input as gridded fields? (0 = No) 0 0

4 IFORMC Format of cloud data (2 = formatted) 2 2

4 IFORMP Format of precipitation data (2 = formatted) 2 2

4 IFORMS Format of surface data (2 = formatted) 2 2

4 NOOBS Use or non

- use of surface, overwater, upper observations User Defined 0

4 NPSTA Number of stations in PRECIP.DAT User Defined 14

4 NSSTA Number of stations in SURF.DAT file User Defined 14

5 ALPHA Empirical factor triggering kinematic effects 0.1 0.1

5 BIAS Surface/upper

- air weighting factors (NZ values) NZ*0 NZ*0

5 CRITFN Critical Froude number 1 1

5 DIVL IM Maximum acceptable divergence 5.00E

- 06 5.00E

- 06

5 FEXTR2 Multiplicative scaling factor for extrap surface obs to uppr

layrs NZ*0.0 NZ*0.0

5 ICALM Extrapolate surface calms to upper layers? (0 = No) 0 0

5 IDIOPT1 Compute temperatures from observa tions (0 = True) 0 0

5 IDIOPT2 Compute domain

- average lapse rates? (0 = True) 0 0

5 IDIOPT3 Compute internally inital guess winds? (0 = True) 0 0

5 IDIOPT4 Read surface winds from SURF.DAT? ( 0 = True) 0 0

5 IDIOPT5 Read aloft winds from UPn.DAT? ( 0 = True) 0 0

5 IEXTRP Extrapolate surface winds to upper layers? (

- 4 = use

similarity theory and ignore layer 1 of upper air station data)

- 4

- 4

5 IFRADJ Adjust winds using Froude number effects? (1 = Yes) 1 1

5 IKINE Adjust winds using kinematic ef fects? (1 = Yes) 0 0

5 IOBR Use O’Brien procedure for vertical winds? (0 = No) 0 0

5 IPROG Using prognostic or MM

- FDDA data? (0 = No) 0 14

5 ISLOPE Compute slope flows? (1 = Yes) 1 1

5 ISTEPPG Timestep (hours) of the prognostic model input data 1 1

5 ISURFT Surface station to use for surface temperature (between 1

and NSSTA) User Defined 9

5 IUPT Station for lapse rates (between 1 and NUSTA) User Defined 1

5 IUPWND Upper air station for domain winds (

- 1 = 1/r**2 interpolation

of all stations)

- 1

- 1

5 IWFCOD Generate winds by diagnostic wind module? (1 = Yes) 1 1

5 KBAR Level (1 to NZ) up to which barriers apply NZ 10

5 LLBREZE Use Lake Breeze module F F

5 LVARY Use varying radius to develop surface winds? F F

5 METBXID Station IDs in the region User Defined

5 NBAR Number of Barriers to interpolation User Defined 0

5 NBOX Number of Lake Breeze regions User Defined 0

5 NINTR2 Max number of stations for interpolations (NA values) 99 99

5 NITER Max number of passes in divergenc e minimization 50 50

5 NLB Number of stations in region User Defined 0

5 NSMTH Number of passes in smoothing (NZ values) 2, 4*(NZ

- 1) 2,4*(NZ

- 1)

5 R1 Relative weight at surface of Step 1 field and obs User Defined 50

5 R2 Relative weight aloft of Step 1 field and obs User Defined 1 00

5 RMAX1 Max surface over

- land extrapolation radius (km) User Defined 10 0

5 RMAX2 Max aloft over

- land extrapolation radius (km) User Defined 2 00

5 RMAX3 Maximum over

- water extrapolation radius (km) User Define d 2 00

5 RMIN Minimum extrapolation radius (km) 0.1 0.1

### 32 Input

Group Variable Description Default Value Recommended Value

5 RMIN2 Distance (km) around an upper air site where vertical

extrapolation is excluded (Set to

- 1 if IEXTRP = ±4) 4

- 1

5 RPROG Weighting factor for CSUMM prognostic wind data User Defined 0

5 T ERRAD Radius of influence of terrain features (km) User Defined 1 5

5 XBBAR X coordinate of Beginning of each barrier User Defined 0

5 XBCST X Point defining the coastline (staight line) User Defined 0

5 XEBAR X coordinate of Ending of each barrier User Defined 0

5 XECST X Point User Defined 0

5 XG1 X Grid line 1 defining region of interest User Defined 0

5 XG2 X Grid line 2 User Defined 0

5 YBBAR Y coordinate of Beginning of each barrier User Defined 0

5 YBCST Y Point User Defined 0

5 YEBAR Y coordinate of Ending of each barrier User Defined 0

5 YECST Y Point User Defined 0

5 YG1 Y Grid line 1 User Defined 0

5 YG2 Y Grid Line 2 User Defined 0

5 ZUPT Depth of domain

- average lapse rate (m) 200 200

5 ZUPWND Bottom and top of la yer for 1 st guess winds (m) 1, 1000 1.,1000.

6 CONSTB Neutral mixing height B constant 1.41 1.41

6 CONSTE Convective mixing height E constant 0.15 0.15

6 CONSTN Stable mixing height N constant 2400 2400

6 CONSTW Over

- water mixing height W constant 0.16 0.16

6 CUTP Minimum cut off precip rate (mm/hr) 0.01 0.01

6 DPTMIN Minimum capping potential temperature lapse rate 0.001 0.001

6 DSHELF Coastal/shallow water length scale 0 0

6 DZZI Depth for computing capping lapse rate (m) 200 200

6 FCORIO L Absolute value of Coriolis parameter 1.00E

- 04 1.00E

- 04

6 HAFANG Half

- angle for looking upwind (degrees) 30 30

6 IAVET Conduct spatial averaging of temperature? (1 = True) 1 1

6 IAVEZI Spatial averaging of mixing heights? (1 = True) 1 1

6 ICOARE O verwater surface fluxes method and parameters 10 0

6 ICOOL COARE cool skin layer computation 0 0

6 ILEVZI Layer to use in upwind averaging (between 1 and NZ) 1 1

6 ILUOC3D Land use category ocean in 3D.DAT datasets 16 16

6 IMIXH Method to compute the convective mixing height

- 1

- 1

6 IRAD Form of temperature interpolation (1 = 1/r) 1 1

6 IRHPROG 3D relative humidity from observations or from prognostic

data 0 1

6 ITPROG 3D temps from obs or from prognostic data? 0 2

6 ITWPROG Option for overwater lapse rates used in convective mixing

height growth 0 0

6 IWARM COARE warm layer computation 0 0

6 JWAT1 Beginning landuse type defining water 999 55

6 JWAT2 Ending landuse type defining water 999 55

6 MNMDAV Max averaging radius (number of grid ce lls) 1 1

6 NFLAGP Method for precipitation interpolation (2 = 1/r**2) 2 2

6 NUMTS Max number of stations in temperature interpolations 5 5

6 SIGMAP Precip radius for interpolations (km) 100 100

6 TGDEFA Default over

- water capping lapse rate (K/m)

- 0.0045

- 0.0045

6 TGDEFB Default over

- water mixed layer lapse rate (K/m)

- 0.0098

- 0.0098

6 THRESHL Threshold buoyancy flux required to sustain convective

mixing height growth overland 0.05 0.0

6 THRESHW Threshold buoyancy flux required to sustain convec tive

mixing height growth overwater 0.05 0.05

6 TRADKM Radius of temperature interpolation (km) 500 500

### 33 Input

Group Variable Description Default Value Recommended Value

6 ZIMAX Maximum over

- land mixing height (m) 3000 3000

6 ZIMAXW Maximum over

- water mixing heigh (m) 3000 3000

6 ZIMIN Minimum over

- land mixing he ight (m) 50 50

6 ZIMINW Minimum over

- water mixing height (m) 50 50

### 34 

## Appendix B CALPUFF Parameter Values

### 35

Table B

- 1. CALPUFF Parameter Values



### CALPUFF input settings.

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

1 Run Control METRUN Do we run all periods (1) or a subset (0)? 0 0

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning month User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning ho ur User Defined 1

1 IRLG Length of run (hours) User Defined 8760

1 XBTZ Base Time Zone User Defined 0

1 NSECDT Length of modeling time step (seconds) 3600 3600

1 NSPEC Number of species modeled (for MESOPUFF II

chemistry) 5 8

1 NSE Number of spe cies emitted 3 5

1 ITEST Flag to stop run after Setup Phase 2 2

1 MRESTART Restart options (0 = no restart) allows splitting

runs into smaller segments 0 0

1 NRESPD Number of periods in Restart 0 0

1 METFM Format of input meteorology (1 = CALM ET, 2 =

ISC) 1 1

1 AVET Averaging time lateral dispersion parameters

(minutes) 60 60

1 PGTIME PG Averaging time 60 60

2 Tech Options MGAUSS Near

- field vertical distribution (1 = Gaussian) 1 1

2 MCTADJ Terrain adjustments to plume path (3 = Plume

path) 3 3

2 MCTSG Do we have subgrid hills? (0 = No) allows

CTDM

- like treatment for subgrid scale hills 0 0

2 MSLUG Near

- field puff treatment (0 = No slugs) 0 0

2 MTRANS Model transitional plume rise? (1 = Yes) 1 1

2 MTIP Treat stack tip down wash? (1 = Yes) 1 1

2 MBDW Method to simulate downwash not used

### 36

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

(1=ISC,2=PRIME)

2 MSHEAR Treat vertical wind shear? (0 = No) 0 0

2 MSPLIT Allow puffs to split? (0 = No) 0 0

2 MCHEM MESOPUFF

- II Chemistry? (1 = Yes) 1 1

2 MAQCHEM Aqueous phase transformation 0 0

2 MWET Model wet deposition? (1 = Yes) 1 1

2 MDRY Model dry deposition? (1 = Yes) 1 1

2 MTILT Plume Tilt (gravitational settling) 0 0

2 MDISP Method for dispersion coefficients

(2=micromet,3 = PG) 3 3

2 MTURBVW Turbulence cha racterization? (Only if MDISP =

1 or 5) 3 3

2 MDISP2 Backup coefficients (Only if MDISP = 1 or 5) 3 3

2 MTAULY Method for Sigma y Lagrangian timescale 0 0

2 MTAUADV Method for Advective

- Decay timescale for

Turbulence 0 0

2 MCTURB Method to compute sigma v,w using micromet

variables 1 1

2 MROUGH Adjust PG for surface roughness? (0 = No) 0 0

2 MPARTL Model partial plume penetration? (0 = No) 1 1

2 MTINV Elevated inversion strength (0 = compute from

data) 0 0

2 MPDF Use PDF for convective di spersion? (0 = No) 0 0

2 MSGTIBL Use TIBL module? (0 = No) allows treatment of

subgrid scale coastal areas 0 0

2 MBCON Boundary conditions modeled 0 0

2 MFOG Configure for FOG model output 0 0

2 MREG Regulatory default checks? (1 = Yes) 1 1

3 S pecies List CSPECn Names of species modeled (for MESOPUFF II

must be SO2

- SO4

- NOX

- HNO3

- NO3) User Defined SO2,SO4,NOX,HNO3,NO3,

NH3,PM10,PM25

3 Specie

Names Manner species will be modeled

Species,Modeled,Emitted,Dry Deposited,Group User Define d SO2,1,1,1,0

SO4,1,0,2,0

NOX,1,1,1,0

HNO3,1,0,1,0

NO3,1,0,2,0

### 37

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

NH3,1,0,1,0

PM10,1,1,2,0

PM25,1,1,2,0

3 Specie

Groups Grouping of species if any User Defined N/A

3 CGRUP

3 CGRUP

4 MapProjection XLAT1 Latitude of 1 st standard parallel 3 0N

4 XLAT2 Latitude of 2 nd standard parallel 60N

4 PMAP Map Projection UTM LCC

FEAST False Easting 0.0 0.0

FNORTH False Northing 0.0 0.0

4 DATUM Datum

- region WGS

- G WGS

- G

4 NX Number of east

- west grids of input meteorology User Defined 199

4 NY Number of north

- south grids of input

meteorology User Defined 145

4 NZ Number of vertical layers of input meteorology User Defined 10

4 DGRIDKM Meteorology grid spacing (km) User Defined 4.

4 ZFACE Vertical cell face heights of input mete orology User Defined 0.,20.,40.,80.,160.,320.,640.,

1200.,2000.,3000.,4000.

4 XORIGKM Southwest corner (east

- west) of input User Defined meteorology

- 360.

4 YORIGIM Southwest corner (north

- south) of input User Defined meteorology

- 588.

4 IUTMZN UT M zone User Defined N/A (17)

4 UTMHEM Hemisphere for UTM Projection N N

4 RLAT0 Latitude of Projection Origin User Defined 23.0N

4 RLON0 Longitude of Projection Origin User Defined 158.0W

4 IBCOMP Southwest X

- index of computational domain User De fined 1

4 JBCOMP Southwest Y

- index of computational domain User Defined 1

4 IECOMP Northeast X

- index of computational domain User Defined 199

4 JECOMP Northeast Y

- index of computational domain User Defined 145

4 LSAMP Use gridded receptors? (T = Yes) F F

### 38

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

4 IBSAMP Southwest X

- index of receptor grid User Defined

4 JBSAMP Southwest Y

- index of receptor grid User Defined

4 IESAMP Northeast X

- index of receptor grid User Defined

4 JESAMP Northeast Y

- index of receptor grid User Defined

4 MESHDN Gridded recpetor spacing =

DGRIDKM/MESHDN 1 1

5 Output Options ICON Output concentrations? (1 = Yes) 1 1

5 IDRY Output dry deposition flux? (1 = Yes) 1 1

5 IWET Output west deposition flux? (1 = Yes) 1 1

5 IT2D 2D Temperature 0 0

5 IRHO 2D Density 0 0

5 IVIS Output RH for visibility calculations (1 = Yes) 1 0

5 LCOMPRS Use compression option in output? (T = Yes) T T

5 ICPRT Print concentrations? (0 = No) 0 0

5 IDPRT Print dry deposition fluxes (0 = No) 0 0

5 IWPRT Print wet deposition fluxes (0 = No) 0 0

5 ICFRQ Concentration print interval (1 = hourly) 1 1

5 IDFRQ Dry deposition flux print interval (1 = hourly) 1 1

5 IWFRQ West deposition flux print interval (1 = hourly) 1 1

5 IPRTU Print output units (1 = g /m**3; g/m**2/s; 3 =

ug/m3, ug/m2/s) 1 3

5 IMESG Status messages to screen? (1 = Yes) 1 2

5 LDEBUG Turn on debug tracking? (F = No) F F

5 IPFDEB First puff to track 1 1

5 NPFDEB (Number of puffs to track) (1) 1

5 NN1 (Met. Period to start outp ut) (1) 1

5 NN2 (Met. Period to end output) (10) 10

7 Dry Dep Chem Dry Gas Dep Chemical parameters of gaseous deposition

Species,Diffusivity,Alpha

Star,Reactivity,Mesophyll,Henry’s User Defined SO2,0.1509,1000.,8.,0.,0.04

NOX,0.1656,1.,8,.5.,3.5

HNO3,0.1628,1.,18.,0.,8E

- 8

NH3,0.234,0.2,4.,0.,3.59E

- 4

### 39

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

8 Dry Dep Size Dry Part. Dep

Chemical parameters of particulate deposition

Species,Mass Mean Diameter,Geometric Std D User Defined SO4,0.48.,2.,

NO3,0.48,2.,

PM10,10.,0.,

PM25,2.5,0.,

9 Dry De p Misc RCUTR Reference cuticle resistance (s/cm)

30 30

9 RGR Reference ground resistance (s/cm) 10 10

9 REACTR Reference reactivity 8 8

9 NINT Number of particle

- size intervals 9 9

9 IVEG Vegetative state (1 = active and unstressed;

2=active and stressed) 1 1

10 Wet Dep Wet Dep Wet deposition parameters

Species, Liquid Precip, Frozen Precip

User Defined SO2,3.0E

- 05,0.

SO4,1.0E

- 04,3.0E

- 05

NOX,0.,0.

HNO3,6.0E

- 05,0.0

NO3,1.0E

- 04,3.0E

- 05

NH3,8.0E

- 05,0.0

PM10,1.0E

- 04,3.0E

- 05

PM25,1.0E

- 04,3.0E

- 05

11 Chemistry MOZ Ozone background? (0 = constant background

value; 1 = read from ozone.dat) 1 0

11 BCKO3 Ozone default (ppb) (Use only for missing data) 80. 12*40.

11 BCKNH3 Ammonia background (ppb) 10 1.5

11 RNITE1 Nightt ime SO2 loss rate (%/hr) 0.2 0.2

11 RNITE2 Nighttime NOx loss rate (%/hr) 2 2

11 RNITE3 Nighttime HNO3 loss rate (%/hr) 2 2

11 MH2O2 H2O2 data input option 1 1

11 BCKH2O2 Monthly H2O2 concentrations 1 not used

BKPMF Fine particulate concentra tion 12 * 1.00 not used

OFRAC Organic fraction of Fine Particulate 2*0.15, 9*0.20, 1*0.15 not used

VCNX VOC / NOX ratio 12 * 50.00 not used

12 Dispersion SYTDEP Horizontal size (m) to switch to time

dependence 550 550

12 MHFTSZ Use Heffter for ve rtical dispersion? (0 = No) 0 0

### 40

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

12 JSUP PG Stability class above mixed layer 5 5

12 CONK1 Stable dispersion constant (Eq 2.7

- 3) 0.01 0.01

12 CONK2 Neutral dispersion constant (Eq 2.7

- 4) 0.1 0.1

12 TBD Transition for downwash algorithms (0.5 = I SC) 0.5 0.5

12 IURB1 Beginning urban landuse type 10 10

12 IURB2 Ending urban landuse type 19 19

12 ILANDUIN Land use type (20 = Unirrigated agricultural

land) 20 20

12 ZOIN Roughness length (m) 0.25 0.25

12 XLAIIN Leaf area index 3.0 3.0

12 ELEVIN Met. Station elevation (m above MSL) 0.0 0.0

12 XLATIN Met. Station North latitude (degrees)

- 999.0

- 999.0

12 XLONIN Met. Station West longitude (degrees)

- 999.0

- 999.0

12 ANEMHT Anemometer height of ISC meteorological data

(m) 10.0 10 .0

12 ISIGMAV Lateral turbulence (Not used with ISC

meteorology) 1 1

12 IMIXCTDM Mixing heights (Not used with ISC meteorology) 0 0

12 XMXLEN Maximum slug length in units of DGRIDKM 1.0 1.0

12 XSAMLEN Maximum puff travel distance per sampling

st ep (units of DGRIDKM) 1.0 1.0

12 MXNEW Maximum number of puffs per hour 99 99

12 MXSAM Maximum sampling steps per hour 99 99

12 NCOUNT Iterations when computing Transport Wind

(Calmet & Profile Winds) 2 2

12 SYMIN Minimum lateral dispersion of n ew puff (m) 1.0 1.0

12 SZMIN Minimum vertical dispersion of new puff (m) 1.0 1.0

12 SVMIN Array of minimum lateral turbulence (m/s) 6 * 0.50,6*0.37 6 * 0.50, 6*0.50

12 SWMIN Array of minimum vertical turbulence (m/s) 2*(0.20,0.12,0.08,0.06,0.03,

0.016) 2*(0.20,0.12,0.08,0.06,0.03,

0.016)

12 CDIV (1), (2) Divergence criterion for dw/dz (1/s) 0.0,0.0 0.0,0.0

12 WSCALM Minimum non

- calm wind speed (m/s) 0.5 0.5

### 41

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

12 XMAXZI Maximum mixing height (m) 3000 3000

12 XMINZI Minimum mixing height (m ) 50 50

12 WSCAT Upper bounds 1 st 5 wind speed classes (m/s) 1.54,3.09,5.14,8. 23,10.8 1.54,3.09,5.14,8. 23,10.8

12 PLX0 Wind speed power

- law exponents 0.07,0.07,0.10,0.15,0.35,0.55 0.07,0.07,0.10,0.15,0.35,0.55

12 PTGO Potential temperature gradi ents PG E and F

(deg/km) 0.020,0.035 0.020,0.035

12 PPC Plume path coefficients (only if MCTADJ = 3) 0.5,0.5,0.5,0.5,0.35,0.35 0.5,0.5,0.5,0.5,0.35,0.35

12 SL2PF Maximum Sy/puff length 10.0 10.0

12 NSPLIT Number of puffs when puffs split 3 3

1 2 IRESPLIT Hours when puff are eligible to split Hour 17=1 Hour17=1

12 ZISPLIT Previous hour’s mixing height(minimum)(m) 100.0 100.0

12 ROLDMAX Previous Max mix ht/current mix ht ratio must

be less then this value for puff to split 0.25 0.25

12 NS PLITH Number of puffs when puffs split horizontally 5 5

12 SYSPLITH Min sigma

- y (grid cell units) of puff before horiz

split 1.0 1.0

12 12 SHSPLITH Min puff elongation rate per hr from wind shear

before horiz split 2.0 2.0

12 CNSPLITH Min conc g/m3 be fore puff may split horizontally 1.0E

- 07 1.0E

- 07

12 EPSSLUG Convergence criterion for slug sampling

integration 1.00E

- 04 1.00E

- 04

12 EPSAREA Convergence criterion for area source

integration 1.00E

- 06 1.00E

- 06

12 DSRISE Step length for rise integrat ion 1.0 1.0

12 HTMINBC 500.0 500.0

12 RSAMPBC 10.0 10.0

12 MDEPBC 1 1

13 Point Source NPT1 Number of point sources User Defined

13 IPTU Units of emission rates (1 = g/s) 1 3

### 42

Table B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Descri ption Default Value a Recommended Value

13 NSPT1 Number of point source

- species combinations 0 0

13 NP T2 Number of point sources with fully variable

emission rates 0 0

13 Point

Sources Point sources characteristics User Defined

14 Area Source Area Sources Area sources characteristics User Defined

15 Volume Source Volume Volume sources characterist ics User Defined Sources

16 Line Source Line Sources Buoyant lines source characteristics User Defined

17 Receptors NREC Number of user defined receptors User Defined

17 Receptor

Data Location and elevation (MSL) of receptors User Defined

### 43 

## Appendix C CALPOST Parameter ValuesTable C

- 1. CALPOST Parameter Values



### The CALPOST utility will be used for computation of visibility parameters using the parameters in this

table.

Table C

- 1: CALPOST Parameter Values

Input

Group Variable Description Default Value Recommended Value

1 ASPEC Species to process VISIB VISIB

1 METRUN Option to run subset of met file 0 1

1 BTZONE Base time zone User Defined 0

1 ILAYER Layer/deposition code (1 = CALPUFF concentrations;

- 3 = wet+dry

deposition fluxes) 1 1

1 A Scaling factor scalar 0.0 0.0

1 B Additive factor 0.0 0.0

1 LBACK Add Hourly Background Concentrations/Fluxes? F F

1 NO2CALC Specify a single NO2/Nox ratio 1 1

1 RNO2NOX Ratio of NO2 to NOx 1.0 1.0

1 MSOURCE Process only total reported contribu tion 0 0

2 MVISCHECK Confirm conformity to FLAG 2008 Configuration 1 1

2 MFRH Particle growth curve for hygroscopic species 4 4

2 RHMAX Maximum relative humidity (%) used in particle growth curve 98 98

2 LDRING Report results by Discrete receptor Ring, if Discrete Receptors used.

(T = true) T T

Modeled species to be included in computing the light extinction

2 LVSO4 Include SO4? T T

2 LVNO3 Include NO3? T T

2 LVOC Include Organic Carbon? T F

2 LVPMC Include Coarse Particles? T T

2 LVPMF Inclu de Fine Particles? T T

2 LVEC Include Elemental Carbon? T F

2 LVNO2 Include NO2 T T

2 LVBK when ranking for TOP

- N, TOP

- 50, and Exceedance tables Include

BACKGROUND? T T

2 SPECPMC Species name used for particulates in MODEL.DAT file: COARSE = PM C PM10

2 SPECPMF Species name used for particulates in MODEL.DAT file: FINE = PMF PM25

Extinction Efficiencies (1/Mm per ug/m**3)

2 EEPMC PM COARSE = 0.6 0.6

2 EEPMF PM FINE = 1.0 1.0

2 EEPMCBK Background PM COARSE 0.6 0.6

2 EESO4 SO4 = 3.0 3.0

2 EENO3 NO3 = 3.0 3.0

2 EEOC Organic Carbon = 4.0 4.0

2 EESOIL Soil = 1.0 1.0

2 EEEC Elemental Carbon = 10.0 10.0

2 EENO2 NO2 Gas = 0.1755 0.1755

2 LAVER Method used for 24

- hr avg % change light extinction F F

2 MVISBK Method used for backgro und light extinction (2 = Hourly RH

adjustment; 6 = FLAG seasonal f(RH)) 8 8

2 RHFAC Monthly RH adjustment factors from FLAG (unique for each Class I

area) Yes if 6 N/A

Background monthly extinction coefficients (FLAG) unique for each

Class I area

2 BKSO4 Sulfate See Table 5 (NH 3 ) 2 SO 4Table C

- 1: CALPOST Parameter Values

Input

Group Variable Description Default Value Recommended Value

2 BKNO3 Nitrate See Table 5 NH 4 NO 3

2 BKPMC Coarse particulate See Table 5 CM

2 BKOC Particulate organic carbon See Table 5 OM

2 BKSOIL Soil See Table 5 Soil

2 BKEC Particulate elemental carbon See Table 5 EC

2 M8_MODE Method 8 Mode 5 5

2 BKSALT Background salt concentration See Table 2 Sea Salt

2 RHFSML Small ammonium sulfate and nitrate particle f(RH) See Table 7

2 RHFLRG Large ammonium sulfate and nitrate particle f(RH) See Table 6

2 RHFSEA Sea salt f(RH) See Table 8

2 BEXTRAY Extinction due to Rayleigh scattering 10.0 10.0

3 LDOC Print documentation image F F

Averaging time(s) reported

3 L1PD Averaging period of model output F F

3 L1HR 1

- hr averages F F

3 L3HR 3

- hr averages F F

3 L24HR 24

- hr averages T T

3 LRUNL Run length (annual) F F

3 LT50 Top 50 table for each averaging time selected T F

3 LTOPN F T

3 NTOP 4 1

3 ITOP 1 1

3 LEXCD Threshold exceedance count F T

3 THRESH1 1

- hour threshold

- 1.0

- 1.0

3 THRESH3 3

- hour threshold

- 1.0

- 1.0

3 THRESH24 24

- hour threshold

- 1.0 0.5

3 THRESHN N

- hour threshold

- 1.0

- 1.0

3 NDAY Accumulated Period (days) 0 0

3 NCOUNT Number of exceedances allowed 1 1

Table C

- 2: POSTUTIL (Repartition) Parameter Values



### The CALUTIL will be used to perform HNO3/NO3 repartitioning using the parameters in this table.

Table C

- 2: POSTUTIL (Repartition) Parameter Values

Input

Group Variable Description Default Value Recommended

Value

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning month User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning hour User Defined 1

1 NPER Length of run (hours) User Defined 8760

1 NSPECINP Number of species to process User Defined 7

1 NSPECOUT Number of species to w rite User Defined 7

1 NDUPLCT Stop if duplicate species names 0 0

1 NSCALED Number of CALPUFF data files to be scales 0 0

1 MNITRATE Recompute HNO3/NO3 concentration partition 0 1

1 BKNH3 Background ammonia concentrations 10. 1.5

2 ASPECI Species to i nput User Defined SO2,SO4,NOX,HNO3,Table C

- 2: POSTUTIL (Repartition) Parameter Values

Input

Group Variable Description Default Value Recommended

Value

NO3,PM10,PM25

2 ASPECO Speceis to output User Defined SO2,SO4,NOX,HNO3,

NO3,PM10,PM25



### Table C

- 3. Background Extinction Coefficients (20% Best Days).

Background Extinction Coefficients (20% Best Days)

Class I Are a (NH 4 ) 2

SO 4 μg/m3 NH 4 N

O 3 Μg/m3 OM

μg/m3 EC

μg/m3 Soil

μg/m3 CM

μg/m3 Sea

Salt

μg/m

3 Rayleig

h

Mm

- 1

Haleakala

National Park

(HALE1) 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawaii

Volcanoes

National Park

(HAVO1) 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10



### Table C

- 4. Monthly f L 

### (RH)

– Large (NH 4 

### ) 2 

### SO4 and NH 4 

### NO 3 

### Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Haleakala National Park (HALE1) 2.34 2.27 2.25 2.20 2.13 2.09 2.16 2.14 2.10 2.19 2.33 2.30

Hawaii Volcanoe s National Park

(HAVO1) 2.56 2.45 2.47 2.47 2.47 2.45 2.53 2.59 2.55 2.57 2.78 2.55



### Table C

- 5. Monthly f S 

### (RH)

– Small (NH 4 

### ) 2 

### SO 4 

### and NH 4 

### NO 3 

### Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Haleakala Nati onal Park (H ALE 1) 2.98 2.85 2.81 2.72 2.60 2.53 2.65 2.63 2.56 2.69 2.95 2.89

Hawaii Volcanoes National Park

(HAVO1) 3.35 3.10 3.14 3.13 3.14 3.11 3.24 3.42 3.34 3.38 3.76 3.34



### Table C

- 6. Monthly f SS 

### (RH)

– Sea Salt Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Haleakala National Park (HALE1 ) 3.52 3.43 3.40 3.35 3.25 3.18 3.29 3.27 3.22 3.33 3.52 3.47

Hawaii Volcanoes National Park

(HAVO1) 3.80 3.68 3.71 3.72 3.72 3.69 3.79 3.83 3.79 3.83 4.08 3.80

# Subject

- to

- Best Available Retrofit Technology (BART)

and Reasonable Progress (RP) Prioritization

Modeling Protocol for the State of Hawaii

Application of the CALPUFF Modeling System

Prepared for:

Hawaii State Depart ment of Health

Environmental Management Division

Clean Air Branch Prepared by:

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, Colorado 80007



### 30 November 2009

### i 

## Table of Contents

Section No. Page No.

1 INTRODUCTION AND PRO TOCOL OBJECTIVES

................................ ................................ .... 1

1.1 I NTRODUCTION

................................ ................................ ................................ ................................ ............................ 1



### 2 MODELING APPROACH

................................ ................................ ................................ ................. 2

2.1 C LASS I A REAS

................................ ................................ ................................ ................................ ............................ 3

2.2 P OLLUTANTS TO C ONSIDER

................................ ................................ ................................ ................................ ......... 5

2.3 E MISSIONS AND S TACK D A TA

................................ ................................ ................................ ................................ ...... 5

2.4 N ATURAL B ACKGROUND

................................ ................................ ................................ ................................ ............. 6

2.5 V ISIBILITY C ALCULATION

................................ ................................ ................................ ................................ ........... 6

2.6 M ODEL E XECUTION

................................ ................................ ................................ ................................ ..................... 7

2.6.1 Subject

- to

- BART and RP Prioritization Analysis

................................ ................................ ................................ .. 7

2.6.2 Implementing Subject

- to

- BART/RP Prioritization Mod eling Analysis

................................ ................................ . 8



### 3 VISIBILITY MODELING SYSTEM

................................ ................................ .............................. 10

3.1 CALMET

................................ ................................ ................................ ................................ ................................ .......... 10

3.1.1 Meteorological Data

................................ ................................ ................................ ................................ ............. 12

3.1.2 CALMET Control File Settings

................................ ................................ ................................ ........................... 17

3.2 CALPUFF

................................ ................................ ................................ ................................ ................................ . 18

3.2.1 Emissions

................................ ................................ ................................ ................................ ............................. 18

3.2.2 Ozone Background

................................ ................................ ................................ ................................ ............... 19

3.2.3 Ammonia Background

................................ ................................ ................................ ................................ ......... 19

3.2.4 Receptor Locations

................................ ................................ ................................ ................................ ............... 19

3.3 CALPOST AND VISIBILITY POST

- PROCESSING

................................ ................................ .............................. 19



### 4 INTERPRETATION OF RE SULTS

................................ ................................ ............................... 20

5 REFERENCES

................................ ................................ ................................ ................................ ... 21

APPENDIX A CALMET PA RAMETER VALUES

................................ ................................ ............... 23

APPEND IX B CALPUFF PARAMET ER VALUES

................................ ................................ .............. 28

APPENDIX C CALPOST P ARAMETER VALUES

................................ ................................ ............. 37

### ii 

## List of Figures

Figure No. Page No.

Figure 1 BART

- Eligible Facility Locations in Proximity to Class I Areas.

................................ ................................ ................... 5

Figure 2. Subject

- to

- BART/ RP Prioritization Flowchart

................................ ................................ ................................ ............. 9

Figure 3: Modeled 36/12/4 km MM5 Domain.

................................ ................................ ................................ ............................ 16

Figure 4. Modeled 4km MM5 Domain.

................................ ................................ ................................ ................................ ....... 17

### iii 

## List of Tables

Table No. Page No.

Table 1. BART

- eligible Sources.

................................ ................................ ................................ ................................ .................. 3

Table 2. Federal Mandatory Class I Areas in the Modeling Domain.

................................ ................................ ........................... 3

Table 3. 20% Best Natural Condition s

– Concentrations and Rayleigh Scattering By Class I Area.

................................ ............. 6

Table 4

. CALPUFF Modeling System

................................ ................................ ................................ ................................ ........ 10

Table 5. MM5 Vertical Domain Specification.

................................ ................................ ................................ ........................... 15

### 1

PURPOSE

This P rotocol is prepared for the State of Hawaii and describes the application of the

CALPUFF Model for determining whether a Best Available Retrofit Technology

(BART)

- eligible source is deemed Subject

- to

- BART under the Regional Haze (RH)

Rule, 40 CFR 308. It may also be used to prioritize other significant emitting sources to

be considered for the Reasonable Progress (RP) control evaluation.



## 1 INTRODUCTION AND PROTOCOL OBJECTIVES

1.1 

## I NTRODUCTION



### Under the federal RH Rule, 40 CFR 308, Hawaii is required to de velop a plan to establish

Reasonable Progress goals for incremental visibility improvements in achieving natural

background conditions within its two Federal Class I areas. Natural conditions exist

when a Class I area is absent of anthropogenic or man

- mad e air pollution emissions. In

establishing RP goals, Hawaii will need to consider emission reduction measures for

specified source groups or activities including the application of BART for existing major

stationary sources.

On 6 July 2005, the U.S. Envi ronmental Protection Agency (EPA) published final

amendments to its 1999 RH Rule in the Federal Register, including 40 CFR Part 51,

Appendix Y, the final guidance for BART determinations ( BART Guideline; EPA, 2005;

70 FR 39104

- 39172). The rule applies to any BART

- eligible source that ―emits any air

pollutant which may reasonably be anticipated to cause or contribute to any impairment

of visibility‖ in any mandatory Class I federal area. States retain the authority to exempt

certain BART

- eligible sources b ased on dispersion modeling demonstrating that the

source cannot reasonably be anticipated to cause or contribute to visibility impairment in

a Class I area. Hawaii has elected to use this modeling option for BART

- eligible sources,

and reserves this model ing option to prioritize evaluation of other potential sources

contributing to visibility impairment in addressing RP goals.

According to 40 CFR Part 51, Appendix Y, a BART

- eligible source is considered to

―contribute‖ to visibility impairment in a Class I area if the modeled 98 th 

### percentile

change in deciviews is equal to or greater than the ―contribution threshold.‖ Any BART

eligible source, through modeling, determined to cause or contribute to visibility

impairment above the threshold in any Class I ar ea is deemed subject to BART. The

EPA BART Guidelines suggest a contribution threshold of no greater than a 0.5 change

in deciview be used. Hawaii has chosen to use the recommended 0.5 deciview threshold

for Subject

- to

- BART determination and RP prioritiz ation.

The U.S. EPA Region 9, U.S. National Park Service (NPS), and the U.S. Fish and

Wildlife Service (FWS) were consulted during the development of this Protocol, which

follows very closely the BART Guideline.

### 2

1.2 

## O BJECTIVES



### The objectives of this Pr otocol are to provide:

A consistent approach in determining which BART

- eligible source is deemed

Subject

- to

- BART, requiring the 5

- factor analysis pursuant to 40 CFR Part 51,

Appendix Y, Guidelines for BART Determinations under the RH Rule (BART

Guideline) ;

A consistent approach which may be used in prioritizing identified RP

- eligible

sources to be evaluated under the 4

- factor analysis pursuant to EPA’s Guidance,

Setting Reasonable Progress Goals Under the Regional Haze Program, June 1, 2007;

A clearly deli neated modeling methodology;

A common CALMET/CALPUFF/POSTUTIL/CALPOST modeling configuration;

and

A common expectation for reporting modeling results.

The Protocol is not intended to define the engineering analyses required by the US EPA’s

BART Guidance, nor address model alternatives to the CALPUFF model.



## 2 MODELING APPROACH

This section describes the components or information required for the CALPUFF

modeling and the overall process in determining whether a source is anticipated to cause

or contribute to visibility impairment to Hawaii’s federal Class I areas. Eligible source

criteria, Class I area identification, pollutants considered, emissions data required, natural

background conditions, visibility impairment determination, and the analysis process are

covered here.



## 1.1 BART

- E LIGIBLE 

## /RP

- E LIGIBLE 

## S OURCES



### Sources to be evaluated in this protocol include BART

- eligible sources and identified

RP

- Eligible sources. BART

- eligible sources determined to cause or contribute to

visibility impairment in any Class I area will be subject to further air pollution control

analyses, similarly with RP

- eligible sources.

The term ―BART

- eligible emission unit‖ refers to any single emission unit that meets the

following criteria:

1) Emits greater than 250 tons/year of a visibility

- impairing pollutant;

2) Was put into place between August 7, 1962 and August 7, 1977; and

3) Falls within one of the 26 listed source categories in the guidance.

### 3

A ―BART

- eligible source‖ is the total of all BART

- eligible emission units at a sin gle

facility. If a source has several emission units, only those that meet the BART

- eligible

criteria are included in the definition of ―BART

- eligible source.‖

A ―RP

- eligible source‖ is the total of all significant emitting emission units/process at a

si ngle facility that are suspected of causing or contributing to visibility impairment as

identified by the Hawaii State Department of Health (DOH)/EPA.

Hawaii identified nine BART

- eligible facilities shown in Table 1. RP

- eligible sources to

be considered for the first RH planning period are under evaluation. Priority will initially

be placed on major emitting emission units within similar BART categories, located

within a BART

- eligible source or near a Federal Class I area.

Table 14

. BART

- eligible Sources

.

Facilities

Chevron Refinery

Tesoro Refinery

HECO

– Kahe

HECO

– Waiau

HC&S Puunene

MECO

– Kahului

HELCO

– Kanoelehua Hill

Hu Honua Bioenergy, LLC

- Pepeekeo

(formerly Hilo Coast)

Kauai Utilities

– Port Allen

For purpose s of this modeling protocol, references made to a BART

- eligible source will

apply to an identified RP

- eligible source if not explicitly stated. The modeling

methodology to be used will essentially be the same for determining Subject

- to

- BART

sources and pr ioritizing sources for RP evaluation. This protocol follows the BART

Guideline, and may extend the evaluation to identified RP

- eligible sources, except that

the modeling analysis serves to prioritize RP

- eligible sources for review, rather than

exempting these sources from further review as with BART

- eligible sources.

2.8 Class I Areas

The mandatory Class I federal areas in Hawaii that could be impacted by BART

- eligible

sources, are presented in Table 2. Refer to Figure 1 below showing the location of the

C lass I areas in relation to BART

- eligible facilities/sources.

Table 15 . Federal Mandatory Class I Areas in the Modeling Domain.

Class I Area IMPROVE Monitor Federal Land Manager

Haleakala National Park HALE 1 National Park Service

Hawaii Volcanoes National Park HAVO 1 National Park Service

### 4

The Haleakala National Park consists of 30,183 acres in central to eastern portions of the

island of Maui, 24,719 acres of which are designat ed wilderness. Park elevations range

from 3,055 m (10,023 ft) at the summit of Haleakala to sea level at the mouth of

Kipahulu Valley on the extreme eastern boundary. The Park thus includes a wide variety

of climate zones, from high mountain elevations abo ve the Marine Boundary Layer, to

leeward lowlands that are dry relative to windward lowlands at the same elevation.

The Haleakala National Park IMPROVE site is HALE1, located at an elevation of 1,158

m (3,799 ft) on the northward slope of Haleakala, ~5.7 km northwest of the Park

Boundary.

The Hawaii Volcanoes National Park IMPROVE Site is HAVO1, located on the east to

southeastern slopes of Mauna Loa on the island of Hawaii. More specifically, it is

situated on the northeastern rim of the Kilauea crater . The site elevation is 1,204 m (3,949

ft), some 124 m above the crater floor at 1080 m (3,542 ft). One of the major terrain

feature affecting meteorology and air quality at the monitoring site, other than the

erupting Kilauea volcano, is the 4,170 m (13, 681 ft) high Mauna Loa cone, about 35 km

(22 mi) to the west of the monitoring site.

The IMPROVE data is used to represent measured pollutant impacts at the two National

Parks.

### 5

Figure 5 BART

- Eligible Facility Locations in Proxi mity to Class I Areas.

### 5

2.9 Pollutants to Consider

The BART Guideline specifies that sulfur dioxide (SO2), oxides of nitrogen (NOx) and

direct particulate matter (PM) emissions, including both PM10 and PM2.5 should be

included for the Subject

- to

- BART modelin g analyses.

The BART Guideline also discusses the inclusion of volatile organic compound (VOC),

ammonia and ammonia compounds as visibility impairing pollutants. These pollutants

will be considered separately if it is determined that these emissions fro m a BART

eligible source are reasonably anticipated to cause or contribute to visibility impairment.

2.10 Emissions and Stack Data

The BART Guideline states, ―the emission estimates used in the models are intended to

reflect steady

- state operating conditions during periods of high capacity utilization.‖

These emissions should not generally include start

- up, shutdown, or malfunction

emissions. The BART Guideline recommends that states use the 24

- hour average actual

emission rate from the highest emitting day of the meteorological period modeled.

Hawaii requested source information of the highest emitting day between calendar years

2003 to 2007 from BART

- eligible sources and will similarly request the same

information from identified RP

- eligible sources.

De pending on the availability of emissions data, the following emissions information

(listed in order of priority) will be used with CALPUFF for the Subject

- to

- BART and RP

prioritization modeling:

24

- hour average actual emission rate from the highest emitti ng day within the

modeling period (preferred) based on representative CEM or stack test data; or

24

- hour average emissions calculated using emission factors specified in Title V

permits.

24

- hour average emissions calculated using AP

- 42 emission factors or other

equivalent methods.

Allowable emissions (maximum 24

- hour allowable).

DOH will work with sources to develop appropriate emission rates.

If plant

- wide emissions for SO 2 

### , NO

x 

### , and PM 10 

### , from eligible sources, are less than the

significant emission rat e (SER) used for Prevention of Significant Deterioration review,

emissions of that pollutant will not be included in the Subject

- to

- BART and RP

prioritization modeling. However, if plant

- wide emissions exceed the SERs for these

pollutants, then all emissi ons of that pollutant from individual emission units will be

evaluated even if emissions are below the SER for an individual emission unit.

### 6

2.11 Natural Background

The BART Guideline references EPA’s ―Guidance for Estimating Natural Conditions

under the Regio nal Haze Rule‖ (EPA, 2003a). This guidance lists three sets of Natural

Conditions corresponding to Annual Average, Best 20% Days and Worst 20% Days.

Due to limited access to available meteorological data, the 20% best natural days will be

used, in lieu o f annual average natural days, to represent natural visibility background in

Class I areas. This definition of natural background is consistent with the intent of the

BART Guideline (Federal Register Vol. 70, No. 128, pf 39125). The specific natural

back ground values used in this protocol are drawn from the Federal Land Managers’ Air

Quality Related Values Workgroup (FLAG) Phase I Report

– Revised (FLAG, 2008).

Class I specific background extinction coefficients are presented in Table 3. The option

pres ented in EPA’s guidance for refining the default visibility background will not be

used in this protocol.

Table 16 . 20% Best Natural Conditions

– Concentrations and Rayleigh Scattering

By Class I Area.

Background Extinction Coeffic ients (20% Best Days)

Class I Area (NH 4 ) 2 SO 4

μg/m3 NH 4 NO 3

Μg/m3 OM

μg/m3 EC

μg/m3 Soil

μg/m3 CM

μg/m3 Sea

Salt

μg/m3 Rayleigh

Mm

- 1

Haleakala

National Park 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawaii

Volcanoes

National Park 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10



### 2.12 Visibility Calculation

The CALPUFF modeling techniques presented in this protocol will provide ground level

concentrations of visibility impairing pollutants. The concentration estimates from

CALPUFF are used with the FLAG referenced IMPROVE equation to calculate the

extinction coefficien t ( b

ext 

### ) , as shown below.

b ext 

### = 2.2 × f S 

### (RH) × [Small Sulfate] + 4.8 × f L 

### (RH) × [Large Sulfate]

• 2.4 × f

S 

### (RH) × [Small Nitrate] + 5.1 × f L 

### (RH) × [Large Nitrate]

• 2.8 × [Small Organic Mass] + 6.1 × [Large Organic Mass]

• 10 × [Elemental Carbon]

• 1 × [Fine Soil]

• 0.6 × [Coarse Mass]

• 1.7 × f

SS 

### (RH) × [Sea Salt]

• Rayleigh Scattering (Site Specific)

• 0.33 × [NO 2 

### (ppb)] {or as: 0.1755 × [NO 2 

### (μg/m 3 

### )]}

Where:

[ ] indicates concentrations in μg/m 3

### 7 f S 

### (RH) = Relative humidity adjustment factor for small sulfate and nitrate

f L 

### (RH) = Relative humidity adjustment factor for large sulfate and nitrate

f SS 

### (RH) = Relative humidity adjustment factor for sea salt

For Total Sulfate < 20 μg/m 3 

### : [Large Sulfate] = ([Total Sulfate] / 20 μg/m 3 

### ) × [Total S ulfate]

For Total Sulfate ≥ 20 μg/m 3 

### :

[Large Sulfate] = [Total Sulfate]

And:

[Small Sulfate] = [Total Sulfate]

– [Large Sulfate]



### To calculate large and small nitrate and organic mass, substitute ({Large, Small,

Total} {Nitrate, Organic Mass}) for Sulf ate.

The monthly relative humidity adjustment factors from the above equation are found in

Appendix C.

The change in visibility for the Subject

- to

- BART and RP prioritization analyses will be

compared against the 20% best natural background conditions. The delta

- deciview, dv,

value is calculated from the source’s contribution to extinction, b ext (source) 

### , and

background extinction, b ext(bkg) 

### , as follows:

dv = 10 ln (b ext(bkg) 

### +b ext (source) 

### ) / b ext(bkg) 

### )

2.13 Model Execution

2.13.1 Subject

- to

- BART and RP Priorit ization Analysis

The Subject

- to

- BART/RP prioritization modeling will be used to determine which

eligible sources are reasonably anticipated to cause or contribute to visibility impairment

at any Class I area. This protocol uses Option 1 in Section III of the BART Guideline for

this analysis. This option is the Individual Source Attribution Approach. With this

approach, each BART

- and RP

- eligible source is modeled separately and the impact on

visibility impairment in any Class I area is determined.

For determining the visibility threshold, the recommendations in the BART Guideline are

followed to assess whether a BART

- or RP

- eligible source is reasonably anticipated to

cause or contribute to any visibility impairment in a federal Class I area. According to

the BART Guideline:

“A single source that is responsible for a 1.0 deciview change or more should be

considered to “cause” visibility impairment; a source that causes less than a 1.0

deciview change may still contribute to visibility impairment and th us be subject to

BART. As a general matter, any threshold that you use for determining whether a source

“contributes” to visibility impairment should not be higher than 0.5 deciviews.

### 8

As recommended in the BART Guideline, if a single source causes a 0.5 deciview or

greater change from natural background, then that source is determined to be reasonably

anticipated to contribute to any visibility impairment in a Class I area and will be subject

to BART. For this single source analysis, the Subject

- to

- BART or RP prioritization

modeling will not consider the frequency, magnitude, and duration of impairment.

Outside the work of this protocol, DOH will make a preliminary determination whether

an eligible source is Subject

- to

- BART or should be prioritized for R P evaluation by

following the screening methodology described in Section III, Option 2 of the BART

Guideline. Based on a 0.5 deciview threshold, if a source calculated Q/D < 10, an

eligible source would be exempt from further BART analysis or will not be prioritized for

RP evaluation at least for the first RH planning period.

Q = Potential Emissions (tons/year) of SO 2 

### +NOx+PM 10 

### from all eligible units

within a facility

D = Distance (kilometers) between the eligible source and the boundary of the

nearest C lass I area

Generally, if a source is a smaller emitter or is located far from a Class I area, and its Q/D

is less than 10, the source is not expected to contribute to visibility impairment at a Class

I area.

2.13.2 Implementing Subject

- to

- BART/RP Prioritizatio n Modeling Analysis

Alpine Geophysics, LLC (Alpine) will perform the Subject

- to

- BART modeling in

accordance with this modeling protocol for the BART

- eligible sources and the initial set

of identified RP

- eligible sources. Figure 2 is a flow chart of how i ndividual facilities will

be evaluated for its eligible emission units. There could be cases where a facility could

have both BART

- eligible and RP

- eligible emission units.

Consideration for allowing a more refined Subject

- to

- BART analysis in the event a

source is found to be Subject

- to

- BART or RP evaluation is not a part of this protocol’s

scope.

### 9

Figure 6 . Subject

- to

- BART/ RP Prioritization Flowchart

Note: “Pre

- BART” units = identified “RP

- eligible” units.

### 10



## 3 VISIBIL ITY MODELING SYSTEM

In general, the Subject

- to

- BART/RP prioritization modeling using the CALPUFF suite of

programs will follow the procedures and recommendations outlined in two documents:

the IWAQM (Interagency Workgroup on Air Quality Models) and the FL AG (Federal

Land Managers Air Quality Related Values Workgroup) reports (EPA 1998, FLAG

2008). Tables listing the modeling parameters for each CALPUFF module are located in

the Appendices.

The specific CALPUFF programs and their version numbers that wil l be used in both the

Subject

- to

- BART/RP prioritization modeling are presented in Table 4. The CALPUFF

modeling system is the regulatory approved versions except for the CALPOST processor.

CALPOST version 6.221 will be used to enable this application to use the most up

- to

date FLAG (6

- 29

- 2008) guidance. The CALMET model will be run using the August 20,

2009, ―EPA

- FLM Recommended CALMET input File Values.‖

The CALMET meteorological domain, as described below, covers the entire Hawaiian

Island chain.

T able 17

. CALPUFF Modeling System

Program Version Level

CALMET 5.8 060911

CALPUFF 5.8 060911

CALPOST 6.221 080724

POSTUTIL 1.56 070627



### 3.1 CALMET

Input requirements for the CALMET model include various meteorological and

geop hysical data sets, and a control input file with appropriate settings. Required

meteorological data include surface, upper

- air, and precipitation observations, and

mesoscale model output data fields. Specifics of these data are provided below.

Geophysical input data include terrain elevation and land

- use data. Though CALMET

may be run with mesoscale model meteorological data, alone (i.e., no observations), the

EPA modeling guideline recommends ―blending‖ observations with the mesoscale model

fields. There fore, Hawaii will include observations in a blended approach.

The dispersion modeling will use CALMET wind fields for the calendar year 2005.

While a minimum of three years of mesoscale meteorological model output is

recommended for conducting the initi al step in the first

- level analysis, meteorological

data sets for more than 2005 were unavailable for the modeling domain under this

contract. The CALMET wind fields for 2005 will cover the entire island chain and

### 11

sufficiently encompass both Class I areas within 300km of any BART

- and RP

- eligible

facility included in this analysis.

Land Use Processing : CALMET requires both land use/land cover and terrain height

data for the application region. These are generated using the CALMET CTGPROC,

TERREL and MA KEGEO as described below.

TERREL is the terrain pre

- processor that averages terrain features to the

modeling grid resolution; TERREL constructs the basic properties of the gridded

domain and defines the coordinates upon which meteorological data are store d.

Key parameters include specification of grid type, location, resolution and terrain

elevation.

CTGPROC computes the fractional land use for the modeling grid resolution.

Land use characteristics for each grid cell are assigned using CTGPROC. The

prim ary variable adjustment associated with CTGRPOC is selection of an

appropriate land use database. Version 2.0 of the North American Land Cover

Characteristics database will be used.

MAKEGEO is the final pre

- processor that combines the terrain and land us e data

for input to CALMET. Generating the appropriate MAKEGEO.INP control file

requires only minimal alteration of the default assignments. Key modifications

include specifying domain attributes and ensuring input files are correctly

referenced.

Terrain : CALMET requires both terrain height and land use/land cover for the

application region. These are generated using the CALMET CTGPROC, TERREL and

MAKEGEO processors. The terrain data will be created using the TERREL (version

3.684 level 070327) processo r and the Shuttle Radar Topography Mission (SRTM)

- GTOPO 30 second (~1 km) resolution dataset.

Land Use: The land use data set was created using the Composite Theme Grid

CTGROC processor (version 2.682, level 070430) and the United States Geological

Surv ey (USGS) Global Land Cover Characterization (GLCC) version 2.0 database. The

GLCC database is available at 30 second (~1km) resolution.

Observed Data Processing: Observation data from both available surface and upper

- air

stations in the modeling will be used. The surface data will be taken from the NOAA

Techniques Development Lab (TDL) database and the upper

- air data from the NOAA

Forecast Systems Lab (FSL) database.

CALMET Model Options : The options used in the CALMM5 processing will generally

fol low the FLAG (FLAG, 2008) and IWAQM (EPA, 1998) guidance. Specific

CALMET input values will be updated using the August 20, 2009, ―EPA

- FLM

Recommended Input File Values.‖

### 12

3.3.1 Meteorological Data

3.3.1.1 Mesoscale Model Data

Alpine was contracted to apply the Pe nnsylvania State University/National Center for

Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1993; Grell et

al., 1994: UCAR, 2003a) for calendar year 2005 to provide data for the

CALMET/CALPUFF modeling as well as for additional modeling and analytical studies

conducted for the Hawaiian Islands.

A complete description of the modeling approach and a statistical evaluation of the

annual MM5 model are described in McNally and Wilkinson, 2008. A brief description

of the modeling approach fo llows:

Model Selection : The publicly available non

- hydrostatic version of MM5 (version

3.7.4) was used for this modeling study. Preprocessor programs of the MM5 modeling

system including TERRAIN , REGRID, LITTLE_R, and INTERPF (UCAR, 2003b) were

used to develop model inputs.

Horizontal Domain Definition: The computational grid is presented in Figures 3 and 4.

The outer 36km domain (D01) has 165 x 129 grid cells, selected to provide boundary

conditions to the 12km domain. The mid

- scale 12km domain (D02 ) has 226 x 226 grid

cells, selected to maximize the coverage of the western region around the Hawaiian

Islands. The 4km domain (D03) has 208 x 154 grid cells, selected to cover the Island

chain with a greater than 100km buffer around the perimeter. The projection was in

Lambert Conformal Coordinates (LCC) with a grid projection pole of 23 o



### , - 158 o 

### with true

latitudes of 30 o 

### and 60 o



### .

Vertical Domain Definition: The MM5 modeling was based on 34 vertical layers with

an approximately 38 meter deep surface l ayer. The MM5 vertical domain is presented in

both sigma and height coordinates in Table 5.

Topographic Inputs: Topographic information for the MM5 was developed using the

NCAR and the United States Geological Survey (USGS) terrain databases. The grid w ill

be based on the 2 min (~4 km) Geophysical Data Center global data interpolated to the

model grid using a Cressman

- type objective analysis scheme. To avoid interpolating

elevated terrain over water bodies, after the terrain databases are interpolated o nto the

MM5 grid, the NCAR graphic water body database is used to correct elevations over

water bodies.

Vegetation Type and Land Use Inputs: Vegetation type and land use information were

developed using the most recently released PSU/NCAR databases pro vided with the

MM5 distribution. Standard MM5 surface characteristics corresponding to each land use

category were employed.

### 13

Atmospheric Data Inputs: Two choices for the first guess meteorological fields were

available: (1) the NCEP Global Tropospheric Final Analysis; and (2) the NCAR/NCEP

Reanalysis Project (NNRP) data (UCAR, 2007). Though the NNRP data were used in a

previous study of Hawaii (Yang and Chen, 2003; Yang et. al. 2005), the NNRP data have

relatively coarse 2.5x2.5 degree resolution wherea s the NCEP Final Analysis data have

much higher 1x1 degree resolution. For the current modeling study, the NCEP Final

Analysis dataset was used.

Available surface and upper

- air observations were used in the objective analyses. These

data were incorporat ed into the analyses datasets following the procedures outlined by

Stauffer and Seaman (1990) and were quality

- inspected by MM5 pre

- processors using

automated gross

- error checks and ―buddy‖ checks. In addition, radiosonde soundings

were subjected to verti cal consistency checks. The synoptic

- scale data used for the

initialization (and in the analysis nudging discussed later) were obtained from the

conventional National Weather Service (NWS) twice

- daily radiosondes (UCAR, 2008a)

and 3

- hr NWS surface observa tions (UCAR, 2008b).

Water Temperature Inputs: The water temperature data will be taken from the NCEP

RTG global one

- half degree analysis (Gemmill et al., 2007; NCEP, 2008).

FDDA Data Assimilation: This simulation will use both analysis and observation based

nudging. For the 36km and 12km grids analysis nudging coefficients of 2.5x10

- 4 

### and

1.0x10

- 4 

### were used for winds and temperature, respectively. For the 4km grid analysis

nudging to observed winds from the NCAR DS472.0 TDL dataset (UCAR, 2008c) was

employed with a coefficient of 1.0x10

- 3



### .

Physics Options : Sensitivity testing using approximately two

- week long episodes in

early February 2005 and early July 2005 were performed to help select the best

performing physics options (McNally, 2008). Alt hough the performance was similar for

the different options, the best performing configuration was the version used by the

University of Hawaii and the National Weather Service (Yang and Chen, 2003; Yang et.

al. 2005. Namely:

Warm Rain;

Grell Cumulus Par ameterization;

MRF PBL;

NOAH Land Surface Model; and

Cloud Radiation.

Application Methodology : The MM5 model was executed in 5

- day blocks initialized at

12Z every 4 days with a 90 second time step. Model results were output every 60

minutes and output f

iles are split at 24 hour intervals. Twelve (12) hours of spin

- up were

included in each 4

- day block before the data were used in the atmospheric simulations

and subsequent model performance evaluation.

### 14

3.3.1.2 CALMM5 Processing

The final step in the meteorologic al processing is to reformat the MM5 data using the

CALMM5 processor. CALMM5 will be run for the 4km MM5 nest to extract the whole

region, less a few grid cells around the perimeter that are contaminated by MM5

boundary artifacts.

### 15

Table 18 . MM5 Vertical Domain Specification.

k(MM5) sigma pressure.(mb) height(m) depth(m)

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0.250 32500 8621 900

28 0.300 37000 7720 817

27 0.350 41500 6903 750

26 0.400 46000 6153 693

25 0.450 50500 5461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.900 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0 .950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0

### 16

Figure 7 : Modeled 36/12/4 km MM5 Domain.

### 17

Figure 8 . Modeled 4km MM5 Domain.

3.3.2 CALMET Control File Settings

These CALMET wind fields will be used by all BART

- and RP

- eligible sources within

the modeling domain for both the Subject

- to

- BART and RP prioritization modeling. The

wind fiel ds will be provided by Alpine using CALMET. Details of the parameter settings

in CALMET are provided in Appendix A to this document; however, the major

assumptions are summarized below.

5) The initial

- guess fields will use the 4

- km MM5 outputs, forecast f rom hours 13

– 108 of each 5 day run segment, January

– December, 2005.

6) Both the Subject

- to

- BART and RP prioritization modeling will utilize the wind

fields at 4km resolution.

### 18

7) There will be 10 vertical layers with face heights of 0, 20, 40, 65, 120, 200, 4 00,

700, 1200, 2200, and 4000 meters.

8) 

### CALMET will be run using surface, buoy and upper air observations. Upper air

data from the LIH (WBAN 22536) site on Kauai and the ITO (WBAN 21504) site

on Hilo will be used. Buoy data collected from the National Data Buoy Center

for sites 51001, 51002, 51003, 51004, KLIH1 and OOUHI will be used and

surface data from 14 surface stations in the TDL DS472.0 dataset will be used.

3.4 CALPUFF

The CALPUFF modeling will use Version 5.8. This protocol generally follows the

rec ommendation of the IWAQM and FLAG guidance documents. Details of the

parameter settings in CALPUFF are provided in Appendix B to this document; however,

the major features are summarized below:

8) The calendar year 2005 CALMET input file will be developed by Alpine

Geophysics, LLC and be provided as input

- ready to CALPUFF.

9) The Subject

- to

- BART and RP prioritization modeling will examine the visibility

impairment on Class I areas within 300km of each single source or facility. The

computational modeling doma in will be sufficient to include all Class I areas

within a 300km radius of a source.

10) Pasquill

- Gifford Dispersion coefficients will be used.

11) MESOPUFF

- II chemistry algorithm will be used.

12) Puff splitting will not be used, following the recommendations of the FLMs.

13) Source elevations will be based on the same terrain files as the receptor

elevations.

14) The CALUTIL will be used to perform HNO3/NO3 repartitioning using the

parameters in Appendix B.

CALPUFF will be applied separately for each Class I Area (Haleakal a National Park and

Hawaii Volcanoes National Park) on the island of Maui and Hawaii.

3.4.1 Emissions

Section 2.4 above presents the emissions and stack data that is required from the

facilities. This section only discusses the emissions estimates needed in CALPUFF.

Primary emission, species will include available input species of PM, SO 2 

### , SO 4 

### , and NO

x 

### ;

and the additional modeled species HNO 3 

### and NO 3 

### . SO 2 

### emissions will be reviewed to

ensure ―double

- counting‖ is avoided.

The primary PM species will be tre ated as follows:

Filterable:

Elemental Carbon (EC) (<2.5 μm)

### 19 PM Fine (PMF) (<2.5 μm)

PM Coarse (PMC) (2.5

– 10 μm)

Condensable:

Organic Carbon (SOA)

Inorganic Aerosol (SO 4 

### )

The condensable fraction will be treated as primary emissions in the CALPUFF inp ut file

and assumed to be 100% in the PM 2.5 

### fraction (see NPS Web site listed below).

Facility specific speciation emissions data will be used when provided. The following

size distributions will be used in CALPUFF:

Pollutant Mean diameter Standard dev iation

SO 4 

### , NO 3 

### , PMF, SOA, EC 0.48 2

PMC 2.5 5

3.4.2 Ozone Background

Background ozone will be assumed at an annual constant level of 40 ppb.

3.4.3 Ammonia Background

Because the Hawaiian Island chain does not fit easily within the three land use values

p roposed in IWAQM (10 parts per billion (ppb) for grasslands, 0.5 ppb for forest, and 1

ppb for arid lands), ammonia background concentrations will be determined in

consultation with the participating FLMs. A review of the ammonia emissions inventory

for H awaii in comparison with mainland agricultural areas indicate a background of 1.5

ppb to be representative based on agricultural and livestock activity.

3.2.4 Receptor Locations

Predicted visibility impacts will be computed at two sets of receptors; a se t of receptor

locations for the Class I Areas within the domain which are available for download from

the National Park Service Web site http://www2.nature.nps.gov/air/Maps/Receptors/i ndex.cfm

, 

### Source and receptor elevations will be based on actual elevations. The elevations for the

grid points will be taken from the CALMET modeling.

3.5 CALPOST AND VISIBILITY POST

- PROCESSING

The CALPOST processing will use Version 6.221 and POSTUTIL V ersion 1.56. Details

of the parameter settings in CALPOST and POSTUTIL are provided in Appendix C, with

some of the major features summarized below for calculating visibility impairment:

### 20

7) For the visibility calculation, Method 8, Mode 5 (MVISBK=8, M8_MOD E=5)

will be employed. This method uses monthly average relative humidity and

f(RH) values for each Class I area, which are based on data from FLAG (FLAG

2008).

8) Particulate species for the visibility analysis will include SO 4 

### , NO 3 

### , EC, OC,

PMF, and PMC, a s reported in the CALPOST output files.

9) POSTUTIL will not be used to speciate modeled PM 10 

### concentrations, as PM 10 

### is

speciated into its components (PMF, PMC, SOA, EC, SO 4 

### ) and entered as

primary emissions in CALPUFF. However, HNO 3 

### /NO 3 

### partition option in

POSTUTIL will be used

10) Natural background extinction calculations will use the 20% best natural days for

each Class I area in the modeling domain. Tables providing the monthly f(RH)

and 20% best days coefficients are provided in Appendix C, and are based on data

from FLAG (FLAG 2008).

1. The 98 th 

### percentile value will be calculated for all BART

- and RP

- eligible

sources at each mandatory Class I area.

11) The contribution threshold has the implied level of precision equal to the level of

precision reported by CALPOST. Therefore, the 98 th 

### percentile value will be

reported to three decimal places.



## 4 INTERPRETATION OF RESULTS

The change in visibility impairment for the Subject

- to

- BART/RP prioritization modeling

is based on the increase in the haze index from a BA RT

- or RP

- eligible source relative to

natural background, defined as the 20% best natural visibility days for each Class I area.

This definition of natural background is consistent with the intent of the BART Guideline

(Federal Register Vol. 70, No. 128, pf 39125). Although the use of the average 20%

natural visibility days was subsequently allowed (EPA 2006, Memorandum from Joseph

W. Paisie to Kay Prince dated July 19, 2006. Subject Regional Haze Regulations and

Guidelines for BART Determinations), the 20% best natural visibility days is being used

due to the availability of one year’s worth of meteorological data. Three years worth of

meteorological data is recommended by guidance.

The U.S. EPA recommends using the 98 th 

### percentile value from the distr ibution of values

containing the highest modeled delta

- deciview ( dv) value for each day of the simulation

from all modeled receptors at a given Class I area. The 98 th 

### percentile dv value will be

determined with the 8 th 

### highest value for the year modeled (2005).

This 8 th 

### highest value will be compared to the contribution threshold ( dv≥0.5 dv). If

there are more than seven (7) days with values greater than the contribution threshold in

any single meteorological year for any of the Class I areas, then th e source is considered

Subject

- to

- BART or will be considered for RP control evaluation in the first RH planning

period.

### 21



## 5 REFERENCES

40 CFR Part 51, Appendix W. Guidelines on Air Quality Models

Dudhia, J., 1993. ―A Non

- hydrostatic Version of the Penn Stat e/NCAR Mesoscale

Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front‖, Mon.

Wea. Rev., Vol. 121. pp. 1493

- 1513.

EPA (U.S. Environmental Protection Agency) 1998. Interagency Workgroup on Air

Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for

Modeling Long Range Transport Impacts, EPA

- 454/R

- 98

- 019, December 1998.

EPA 2003. Guidance for Estimating Natural Visibility Conditions under the Regional

Haze Rule, EPA

- 454/B

- 03

- 005, September, 2003.

Federal Register, Vo l. 70, No. 128. Regional Haze Regulations and Guidelines for Best

Available Retrofit Technology (BART) Determinations. pp. 39104

– 30172, July 6, 2005.

FLAG 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)

2000. Phase I Report. Dec ember 2000.

FLAG 2008. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)

2008. Phase I Report

— Revised. June 27, 2008 draft.

Gemmill, William, Bert Katz and Xu Li, 2007. Daily Real

- Time Global Sea Surface

Temperature

- High Resolution An alysis at NOAA/NCEP. NOAA/NWS/NCEP/MMAB

Office Note Nr. 260, 39 pp.

Grell, G. A., J. Dudhia, and D. R. Stauffer. 1994. A Description of the Fifth Generation

Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN

- 398

- STR,

138 pp.

McNally, D.E., 2008,‖MM5 Sensitivity to Alternate Configurations over the Hawaiian

Islands‖, prepared for the Hawaii State Department of Health, Environmental

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada,

CO.

McNally, D.E, and J.G. Wilkinson 2008, ‖MM5 Application for 2005 Over the

Hawaiian Islands‖, prepared for the Hawaii State Department of Health, Environmental

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada,

CO.

NCEP, 2008. Real

- time, global, sea surface temperature (RTG_SST) analysis. 10

- July.

polar.ncep.noaa.gov/sst/oper/Welcome.html

### 22

NOAA, 2009. Mauna Loa Observatory. 29

- April, mlo.noaa.gov/livedata/livedata.html.

UCAR, 2003a. MM5 Community Model: Current Release. 09

- April.

www.mmm.ucar.edu/mm5/overviewCurrent.html

UCAR, 2003b. MM5 Community Model: MM5 Modeling System Overview. 10

- April.

www.mmm.ucar.e du/mm5/overviewProgram.html

UCAR, 2008a. ds353.4 Home Page. NCEP ADP Global Upper Air Observation Subsets,

January 1973

– continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds353.4.

UCAR, 2008b. ds464.0 Home Page. NCEP ADP Global Surface Obs ervations, February

1975

– continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds464.0.

UCAR, 2008c. ds472.0 Home Page. TDL U.S. and Canada Surface Hourly

Observations, daily 1976Dec

– cont. CISL Research Data Archive.

dss.ucar.edu.datasets.ds4 72.0.

### 23 

## Appendix A CALMET Parameter Values

### 24

Table A

- 1. CALMET Parameter Values

The CALMET model will be run using the August 20, 2009, ―EPA

- FLM Recommended

CALMET input File Values.‖

Input

Group Variable Description Default Value Recommended Value

0 DIADAT Input file: preprocessed surface temperature data

(DIAG.DAT) User Defined

0 GEODAT Input file: Geophysical data (GEO.DAT) User Defined User Define

0 LCFILES Convert file name to lower case User Defined

0 METDAT Output file (CALMET.DAT ) User Defined

0 METLST Output file (CALMET.LST) User Defined

0 MM4DAT Input file: MM4 data (MM4.DAT) User Defined

0 NOWSTA Input files: Names of NOWSTA overwater stations User Defined 6

0 NUSTA Number of upper air data sites User Defined 2

0 PACDAT Output file: in Mesopuff II format (PACOUT.DAT) User Defined

0 PRCDAT Input file: Precipitation data (PRECIP.DAT) User Defined

0 PRGDAT Input file: CSUMM prognostic wind data (PROG.DAT) User Defined

0 SEADAT Input files: Names of N OWSTA overwater stations

(SEAn.DAT) User Defined

0 SRFDAT Input file: Surface data (SURF.DAT) User Defined

0 TSTFRD Output file (TEST.FRD) User Defined

0 TSTKIN Output file (TEST.KIN) User Defined

0 TSTOUT Output file (TEST.OUT) User Defin ed

0 TSTPRT Output file (TEST.PRT) User Defined

0 TSTSLP Output file (TEST.SLP) User Defined

0 UPDAT Input files: Names of NUSTA upper air data files (UPn.DAT) UPn.DAT

0 WTDAT Input file: Terrain weighting factors (WT.DAT) User Defined

1 CLDDAT Input file: Cloud data (CLOUD.DAT) User Defined Not used

1 IBDY Beginning day User Defined

1 IBHR Beginning hour User Defined

1 IBMO Beginning month User Defined

1 IBTZ Base time zone User Defined 0

1 IBYR Beginning year User Def ined

1 IRLG Number of hours to simulate User Defined User Define

1 IRTYPE Output file type to create (must be 1 for CALPUFF) 1 1

1 ITEST Flag to stop run after Setup Phase 2 2

1 LCALGRD Are w

- components and temperature needed? T T

1 MREG Text to options to conrim they conform to regulatory options User Defined 1

2 PMAP Map Projection UTM LCC

2 FEAST False easting at the projection origin 0.0 0.0

2 FNORTH False northing at the projection origin 0.0 0.0

2 DATUM WGS

- G, NWS

- 27, NWS

- 84, ESR

- S,... WGS

- G

2 DGRIDKM Grid spacing User Defined 4

2 UTMHEM Hemisphere for UTM projection N N

2 IUTMZN UTM Zone User Defined User Define

2 LLCONF When using Lambert Conformal map coordinates

- rotate

winds from true north to map north? F F

2 NX Numbe r of east

- west grid cells User Defined 199

2 NY Number of north

- south grid cells User Defined 145

2 NZ Number of vertical layers User Defined 10

2 RLAT0 Latitude used if LLCONF = T User Defined 23.0N

2 RLON0 Longitude used if LLCONF = T User Def ined 158.0W

2 XLAT1 Latitude of 1 st standard parallel User Defined 30

2 XLAT2 Latitude of 2 nd standard parallel User Defined 60

2 XORIGKM Southwest grid cell X coordinate User Defined

- 360

### 25 Input

Group Variable Description Default Value Recommended Value

2 YORIGKM Southwest grid cell Y coordinate User Defined

- 588

2 ZFACE Vertical cell face heights (NZ+1 values) User Defined 0,20,40,80,160,320,640,

1200,2000,3000,4000

3 IFORMO Format of unformatted file (1 for CALPUFF) 1 1

3 LSAVE Save met. data fields in an unformatted file? T T

4 ICLOUD Is cloud data to be input as gridded fields? (0 = No) 0 0

4 IFORMC Format of cloud data (2 = formatted) 2 2

4 IFORMP Format of precipitation data (2 = formatted) 2 2

4 IFORMS Format of surface data (2 = formatted) 2 2

4 NOOBS Use or non

- use of surface, overwater , upper observations User Defined 0

4 NPSTA Number of stations in PRECIP.DAT User Defined 14

4 NSSTA Number of stations in SURF.DAT file User Defined 14

5 ALPHA Empirical factor triggering kinematic effects 0.1 0.1

5 BIAS Surface/upper

- air weight ing factors (NZ values) NZ*0 NZ*0

5 CRITFN Critical Froude number 1 1

5 DIVLIM Maximum acceptable divergence 5.00E

- 06 5.00E

- 06

5 FEXTR2 Multiplicative scaling factor for extrap surface obs to uppr

layrs NZ*0.0 NZ*0.0

5 ICALM Extrapolate surface c alms to upper layers? (0 = No) 0 0

5 IDIOPT1 Compute temperatures from observations (0 = True) 0 0

5 IDIOPT2 Compute domain

- average lapse rates? (0 = True) 0 0

5 IDIOPT3 Compute internally inital guess winds? (0 = True) 0 0

5 IDIOPT4 Read surface w inds from SURF.DAT? ( 0 = True) 0 0

5 IDIOPT5 Read aloft winds from UPn.DAT? (0 = True) 0 0

5 IEXTRP Extrapolate surface winds to upper layers? (

- 4 = use

similarity theory and ignore layer 1 of upper air station data)

- 4

- 4

5 IFRADJ Adjust winds usin g Froude number effects? (1 = Yes) 1 1

5 IKINE Adjust winds using kinematic effects? (1 = Yes) 0 0

5 IOBR Use O’Brien procedure for vertical winds? (0 = No) 0 0

5 IPROG Using prognostic or MM

- FDDA data? (0 = No) 0 14

5 ISLOPE Compute slope flows? ( 1 = Yes) 1 1

5 ISTEPPG Timestep (hours) of the prognostic model input data 1 1

5 ISURFT Surface station to use for surface temperature (between 1

and NSSTA) User Defined 9

5 IUPT Station for lapse rates (between 1 and NUSTA) User Defined 1

5 IUPWND Upper air station for domain winds (

- 1 = 1/r**2 interpolation

of all stations)

- 1

- 1

5 IWFCOD Generate winds by diagnostic wind module? (1 = Yes) 1 1

5 KBAR Level (1 to NZ) up to which barriers apply NZ 10

5 LLBREZE Use Lake Breeze module F F

5 LV ARY Use varying radius to develop surface winds? F F

5 METBXID Station IDs in the region User Defined

5 NBAR Number of Barriers to interpolation User Defined 0

5 NBOX Number of Lake Breeze regions User Defined 0

5 NINTR2 Max number of stations for interpolations (NA values) 99 99

5 NITER Max number of passes in divergence minimization 50 50

5 NLB Number of stations in region User Defined 0

5 NSMTH Number of passes in smoothing (NZ values) 2, 4*(NZ

- 1) 2,4*(NZ

- 1)

5 R1 Relative weight at s urface of Step 1 field and obs User Defined 50

5 R2 Relative weight aloft of Step 1 field and obs User Defined 100

5 RMAX1 Max surface over

- land extrapolation radius (km) User Defined 100

5 RMAX2 Max aloft over

- land extrapolation radius (km) User Defined 200

5 RMAX3 Maximum over

- water extrapolation radius (km) User Defined 200

5 RMIN Minimum extrapolation radius (km) 0.1 0.1

### 26 Input

Group Variable Description Default Value Recommended Value

5 RMIN2 Distance (km) around an upper air site where vertical

extrapolation is excluded (Set to

- 1 if IEXTRP = ±4) 4

- 1

5 RPROG Weighting factor for CSUMM prognostic wind data User Defined 0

5 TERRAD Radius of influence of terrain features (km) User Defined 15

5 XBBAR X coordinate of Beginning of each barrier User Defined 0

5 XBCST X Point defining the coastline (staight line) User Defined 0

5 XEBAR X coordinate of Ending of each barrier User Defined 0

5 XECST X Point User Defined 0

5 XG1 X Grid line 1 defining region of interest User Defined 0

5 XG2 X Grid line 2 User Defined 0

5 YBBAR Y coordinate o f Beginning of each barrier User Defined 0

5 YBCST Y Point User Defined 0

5 YEBAR Y coordinate of Ending of each barrier User Defined 0

5 YECST Y Point User Defined 0

5 YG1 Y Grid line 1 User Defined 0

5 YG2 Y Grid Line 2 User Defined 0

5 ZUPT Depth of domain

- average lapse rate (m) 200 200

5 ZUPWND Bottom and top of layer for 1 st guess winds (m) 1, 1000 1.,1000.

6 CONSTB Neutral mixing height B constant 1.41 1.41

6 CONSTE Convective mixing height E constant 0.15 0.15

6 CONSTN Stable m ixing height N constant 2400 2400

6 CONSTW Over

- water mixing height W constant 0.16 0.16

6 CUTP Minimum cut off precip rate (mm/hr) 0.01 0.01

6 DPTMIN Minimum capping potential temperature lapse rate 0.001 0.001

6 DSHELF Coastal/shallow water length scale 0 0

6 DZZI Depth for computing capping lapse rate (m) 200 200

6 FCORIOL Absolute value of Coriolis parameter 1.00E

- 04 1.00E

- 04

6 HAFANG Half

- angle for looking upwind (degrees) 30 30

6 IAVET Conduct spatial averaging of temperature? (1 = True ) 1 1

6 IAVEZI Spatial averaging of mixing heights? (1 = True) 1 1

6 ICOARE Overwater surface fluxes method and parameters 10 0

6 ICOOL COARE cool skin layer computation 0 0

6 ILEVZI Layer to use in upwind averaging (between 1 and NZ) 1 1

6 ILUOC3D Land use category ocean in 3D.DAT datasets 16 16

6 IMIXH Method to compute the convective mixing height

- 1

- 1

6 IRAD Form of temperature interpolation (1 = 1/r) 1 1

6 IRHPROG 3D relative humidity from observations or from prognostic

data 0 1

6 ITPROG 3D temps from obs or from prognostic data? 0 2

6 ITWPROG Option for overwater lapse rates used in convective mixing

height growth 0 0

6 IWARM COARE warm layer computation 0 0

6 JWAT1 Beginning landuse type defining water 999 55

6 JWAT2 Ending landu se type defining water 999 55

6 MNMDAV Max averaging radius (number of grid cells) 1 1

6 NFLAGP Method for precipitation interpolation (2 = 1/r**2) 2 2

6 NUMTS Max number of stations in temperature interpolations 5 5

6 SIGMAP Precip radius for inte rpolations (km) 100 100

6 TGDEFA Default over

- water capping lapse rate (K/m)

- 0.0045

- 0.0045

6 TGDEFB Default over

- water mixed layer lapse rate (K/m)

- 0.0098

- 0.0098

6 THRESHL Threshold buoyancy flux required to sustain convective

mixing height growth overland 0.05 0.0

6 THRESHW Threshold buoyancy flux required to sustain convective

mixing height growth overwater 0.05 0.05

6 TRADKM Radius of temperature interpolation (km) 500 500

### 27 Input

Group Variable Description Default Value Recommended Value

6 ZIMAX Maximum over

- land mixing height (m) 3000 3000

6 ZIMAXW Max imum over

- water mixing heigh (m) 3000 3000

6 ZIMIN Minimum over

- land mixing height (m) 50 50

6 ZIMINW Minimum over

- water mixing height (m) 50 50

### 28 

## Appendix B CALPUFF Parameter Values

### 29

Table B

- 1. CALPUFF Parameter Values



### CALPUFF input settings.

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

1 Run Control METRUN Do we run all periods (1) or a subset (0)? 0 0

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning mo nth User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning hour User Defined 1

1 IRLG Length of run (hours) User Defined 8760

1 XBTZ Base Time Zone User Defined 0

1 NSECDT Length of modeling time step (seconds) 3600 3600

1 NSPE C Number of species modeled (for MESOPUFF II

chemistry) 5 8

1 NSE Number of species emitted 3 5

1 ITEST Flag to stop run after Setup Phase 2 2

1 MRESTART Restart options (0 = no restart) allows splitting

runs into smaller segments 0 0

1 NRESPD Number of periods in Restart 0 0

1 METFM Format of input meteorology (1 = CALMET, 2 =

ISC) 1 1

1 AVET Averaging time lateral dispersion parameters

(minutes) 60 60

1 PGTIME PG Averaging time 60 60

2 Tech Options MGAUSS Near

- field vertical distribut ion (1 = Gaussian) 1 1

2 MCTADJ Terrain adjustments to plume path (3 = Plume

path) 3 3

2 MCTSG Do we have subgrid hills? (0 = No) allows

CTDM

- like treatment for subgrid scale hills 0 0

2 MSLUG Near

- field puff treatment (0 = No slugs) 0 0

2 MT RANS Model transitional plume rise? (1 = Yes) 1 1

2 MTIP Treat stack tip downwash? (1 = Yes) 1 1

2 MBDW Method to simulate downwash not used

### 30

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

(1=ISC,2=PRIME)

2 MSHEAR Treat vertical wind shear? (0 = No) 0 0

2 MSPLIT Allow puffs to split? (0 = No) 0 0

2 MCHEM MESOPUFF

- II Chemistry? (1 = Yes) 1 1

2 MAQCHEM Aqueous phase transformation 0 0

2 MWET Model wet deposition? (1 = Yes) 1 1

2 MDRY Model dry deposition? (1 = Yes) 1 1

2 MTILT Plume Tilt (gravitational settling) 0 0

2 MDISP Meth od for dispersion coefficients

(2=micromet,3 = PG) 3 3

2 MTURBVW Turbulence characterization? (Only if MDISP =

1 or 5) 3 3

2 MDISP2 Backup coefficients (Only if MDISP = 1 or 5) 3 3

2 MTAULY Method for Sigma y Lagrangian timescale 0 0

2 MTAUADV Met hod for Advective

- Decay timescale for

Turbulence 0 0

2 MCTURB Method to compute sigma v,w using micromet

variables 1 1

2 MROUGH Adjust PG for surface roughness? (0 = No) 0 0

2 MPARTL Model partial plume penetration? (0 = No) 1 1

2 MTINV Elevated inversion strength (0 = compute from

data) 0 0

2 MPDF Use PDF for convective dispersion? (0 = No) 0 0

2 MSGTIBL Use TIBL module? (0 = No) allows treatment of

subgrid scale coastal areas 0 0

2 MBCON Boundary conditions modeled 0 0

2 MFOG Configure for FOG model output 0 0

2 MREG Regulatory default checks? (1 = Yes) 1 1

3 Species List CSPECn Names of species modeled (for MESOPUFF II

must be SO2

- SO4

- NOX

- HNO3

- NO3) User Defined SO2,SO4,NOX,HNO3,NO3,

NH3,PM10,PM25

3 Specie

Names Manner speci es will be modeled

Species,Modeled,Emitted,Dry Deposited,Group User Defined SO2,1,1,1,0

SO4,1,0,2,0

NOX,1,1,1,0

HNO3,1,0,1,0

NO3,1,0,2,0

### 3 1

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

NH3,1,0,1,0

PM10,1,1,2,0

PM25,1,1,2,0

3 Specie

Groups Grouping of species if any User Defined N/A

3 CGRUP

3 CGRUP

4 MapProjection XLAT1 Latitude of 1 st standard parallel 30N

4 XLAT2 Latitude of 2 nd standard parallel 60N

4 PMAP Map Projection UTM LCC

FEAST False Easting 0.0 0.0

FNORTH False Northing 0.0 0.0

4 DATUM Datum

- region WGS

- G WGS

- G

4 NX Number of east

- west grids of input meteorology User Defined 199

4 NY Number of north

- south grids of input

meteorology User Defined 145

4 NZ Number of vertical layers of input meteorology User Defined 10

4 DGRIDKM Meteorology grid spacing (km) User Defined 4.

4 ZFACE Vertical cell face heights of input meteorology User Defined 0.,20.,40.,80.,160.,320.,640.,

1200.,2000.,3000.,4000.

4 XORIGKM Southwest corner (east

- west) of input User Defined meteorology

- 360.

4 YORIGIM Sout hwest corner (north

- south) of input User Defined meteorology

- 588.

4 IUTMZN UTM zone User Defined N/A (17)

4 UTMHEM Hemisphere for UTM Projection N N

4 RLAT0 Latitude of Projection Origin User Defined 23.0N

4 RLON0 Longitude of Projection Origin User Defined 158.0W

4 IBCOMP Southwest X

- index of computational domain User Defined 1

4 JBCOMP Southwest Y

- index of computational domain User Defined 1

4 IECOMP Northeast X

- index of computational domain User Defined 199

4 JECOMP Northeast Y

- inde x of computational domain User Defined 145

4 LSAMP Use gridded receptors? (T = Yes) F F

### 32

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

4 IBSAMP Southwest X

- index of receptor grid User Defined

4 JBSAMP Southwest Y

- index of receptor grid User Defined

4 IESAMP Northeast X

- index of receptor g rid User Defined

4 JESAMP Northeast Y

- index of receptor grid User Defined

4 MESHDN Gridded recpetor spacing =

DGRIDKM/MESHDN 1 1

5 Output Options ICON Output concentrations? (1 = Yes) 1 1

5 IDRY Output dry deposition flux? (1 = Yes) 1 1

5 IWET Output west deposition flux? (1 = Yes) 1 1

5 IT2D 2D Temperature 0 0

5 IRHO 2D Density 0 0

5 IVIS Output RH for visibility calculations (1 = Yes) 1 0

5 LCOMPRS Use compression option in output? (T = Yes) T T

5 ICPRT Print concentrations? (0 = No) 0 0

5 IDPRT Print dry deposition fluxes (0 = No) 0 0

5 IWPRT Print wet deposition fluxes (0 = No) 0 0

5 ICFRQ Concentration print interval (1 = hourly) 1 1

5 IDFRQ Dry deposition flux print interval (1 = hourly) 1 1

5 IWFRQ West dep osition flux print interval (1 = hourly) 1 1

5 IPRTU Print output units (1 = g/m**3; g/m**2/s; 3 =

ug/m3, ug/m2/s) 1 3

5 IMESG Status messages to screen? (1 = Yes) 1 2

5 LDEBUG Turn on debug tracking? (F = No) F F

5 IPFDEB First puff to track 1 1

5 NPFDEB (Number of puffs to track) (1) 1

5 NN1 (Met. Period to start output) (1) 1

5 NN2 (Met. Period to end output) (10) 10

7 Dry Dep Chem Dry Gas Dep Chemical parameters of gaseous deposition

Species,Diffusivity,Alpha

Star,Reactivity,Me sophyll,Henry’s User Defined SO2,0.1509,1000.,8.,0.,0.04

NOX,0.1656,1.,8,.5.,3.5

HNO3,0.1628,1.,18.,0.,8E

- 8

NH3,0.234,0.2,4.,0.,3.59E

- 4

### 33

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

8 Dry Dep Size Dry Part. Dep

Chemical parameters of particulate deposition

Species,Mass Mean Diameter,Geometric Std D User Defined SO4,0.48.,2.,

NO3,0.48,2.,

PM10,10.,0.,

PM25,2.5,0.,

9 Dry Dep Misc RCUTR Reference cuticle resistance (s/cm)

30 30

9 RGR Reference ground resistance (s/cm) 10 10

9 REACTR Reference reactivity 8 8

9 NINT Number of particle

- s ize intervals 9 9

9 IVEG Vegetative state (1 = active and unstressed;

2=active and stressed) 1 1

10 Wet Dep Wet Dep Wet deposition parameters

Species, Liquid Precip, Frozen Precip

User Defined SO2,3.0E

- 05,0.

SO4,1.0E

- 04,3.0E

- 05

NO X,0.,0.

HNO3,6.0E

- 05,0.0

NO3,1.0E

- 04,3.0E

- 05

NH3,8.0E

- 05,0.0

PM10,1.0E

- 04,3.0E

- 05

PM25,1.0E

- 04,3.0E

- 05

11 Chemistry MOZ Ozone background? (0 = constant background

value; 1 = read from ozone.dat) 1 0

11 BCKO3 Ozone default (ppb) (Use only for missing data) 80. 12*40.

11 BCKNH3 Ammonia background (ppb) 10 1.5

11 RNITE1 Nighttime SO2 loss rate (%/hr) 0.2 0.2

11 RNITE2 Nighttime NOx loss rate (%/hr) 2 2

11 RNITE3 Nighttime HNO3 loss rate (%/hr) 2 2

11 MH2O2 H2O2 data input option 1 1

11 B CKH2O2 Monthly H2O2 concentrations 1 not used

BKPMF Fine particulate concentration 12 * 1.00 not used

OFRAC Organic fraction of Fine Particulate 2*0.15, 9*0.20, 1*0.15 not used

VCNX VOC / NOX ratio 12 * 50.00 not used

12 Dispersion SYTDEP Horizon tal size (m) to switch to time

dependence 550 550

12 MHFTSZ Use Heffter for vertical dispersion? (0 = No) 0 0

### 34

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

12 JSUP PG Stability class above mixed layer 5 5

12 CONK1 Stable dispersion constant (Eq 2.7

- 3) 0.01 0.01

12 CONK2 Neutral dispersion constant (Eq 2.7

- 4) 0.1 0.1

12 TBD Transition for downwash algorithms (0.5 = ISC) 0.5 0.5

12 IURB1 Beginning urban landuse type 10 10

12 IURB2 Ending urban landuse type 19 19

12 ILANDUIN Land use type (20 = Unirrigated agricultural

land) 20 20

12 ZOIN Roughness length (m) 0.25 0.25

12 XLAIIN Leaf area index 3.0 3.0

12 ELEVIN Met. Station elevation (m above MSL) 0.0 0.0

12 XLATIN Met. Station North latitude (degrees)

- 999.0

- 999.0

12 XLONIN Met. Station West longitude (degrees)

- 9 99.0

- 999.0

12 ANEMHT Anemometer height of ISC meteorological data

(m) 10.0 10.0

12 ISIGMAV Lateral turbulence (Not used with ISC

meteorology) 1 1

12 IMIXCTDM Mixing heights (Not used with ISC meteorology) 0 0

12 XMXLEN Maximum slug length in un its of DGRIDKM 1.0 1.0

12 XSAMLEN Maximum puff travel distance per sampling

step (units of DGRIDKM) 1.0 1.0

12 MXNEW Maximum number of puffs per hour 99 99

12 MXSAM Maximum sampling steps per hour 99 99

12 NCOUNT Iterations when computing Trans port Wind

(Calmet & Profile Winds) 2 2

12 SYMIN Minimum lateral dispersion of new puff (m) 1.0 1.0

12 SZMIN Minimum vertical dispersion of new puff (m) 1.0 1.0

12 SVMIN Array of minimum lateral turbulence (m/s) 6 * 0.50,6*0.37 6 * 0.50, 6*0.50

12 SWMIN Array of minimum vertical turbulence (m/s) 2*(0.20,0.12,0.08,0.06,0.03,

0.016) 2*(0.20,0.12,0.08,0.06,0.03,

0.016)

12 CDIV (1), (2) Divergence criterion for dw/dz (1/s) 0.0,0.0 0.0,0.0

12 WSCALM Minimum non

- calm wind speed (m/s) 0.5 0.5

### 35

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

12 XMAXZI Maximum mixing height (m) 3000 3000

12 XMINZI Minimum mixing height (m) 50 50

12 WSCAT Upper bounds 1 st 5 wind speed classes (m/s) 1.54,3.09,5.14,8. 23,10.8 1.54,3.09,5.14,8. 23,10.8

12 PLX0 Wind speed power

- law exponents 0.07,0.07,0.10,0 .15,0.35,0.55 0.07,0.07,0.10,0.15,0.35,0.55

12 PTGO Potential temperature gradients PG E and F

(deg/km) 0.020,0.035 0.020,0.035

12 PPC Plume path coefficients (only if MCTADJ = 3) 0.5,0.5,0.5,0.5,0.35,0.35 0.5,0.5,0.5,0.5,0.35,0.35

12 SL2PF Maxim um Sy/puff length 10.0 10.0

12 NSPLIT Number of puffs when puffs split 3 3

12 IRESPLIT Hours when puff are eligible to split Hour 17=1 Hour17=1

12 ZISPLIT Previous hour’s mixing height(minimum)(m) 100.0 100.0

12 ROLDMAX Previous Max mix ht/curr ent mix ht ratio must

be less then this value for puff to split 0.25 0.25

12 NSPLITH Number of puffs when puffs split horizontally 5 5

12 SYSPLITH Min sigma

- y (grid cell units) of puff before horiz

split 1.0 1.0

12 12 SHSPLITH Min puff elongation rate per hr from wind shear

before horiz split 2.0 2.0

12 CNSPLITH Min conc g/m3 before puff may split horizontally 1.0E

- 07 1.0E

- 07

12 EPSSLUG Convergence criterion for slug sampling

integration 1.00E

- 04 1.00E

- 04

12 EPSAREA Convergence criterion for are a source

integration 1.00E

- 06 1.00E

- 06

12 DSRISE Step length for rise integration 1.0 1.0

12 HTMINBC 500.0 500.0

12 RSAMPBC 10.0 10.0

12 MDEPBC 1 1

13 Point Source NPT1 Number of point sources User Defined

13 IPTU Units of emission rates ( 1 = g/s) 1 3

### 36

T able B

- 1. CALPUFF Parameter Values

Input

Group Group

Description Variable Description Default Value a Recommended Value

13 NSPT1 Number of point source

- species combinations 0 0

13 NPT2 Number of point sources with fully variable

emission rates 0 0

13 Point

Sources Point sources characteristics User Defined

14 Area Source Area Sources Area sources ch aracteristics User Defined

15 Volume Source Volume Volume sources characteristics User Defined Sources

16 Line Source Line Sources Buoyant lines source characteristics User Defined

17 Receptors NREC Number of user defined receptors User Defined

17 Receptor

Data Location and elevation (MSL) of receptors User Defined

### 37 

## Appendix C CALPOST Parameter ValuesTable C

- 1. CALPOST Parameter Values



### The CALPOST utility will be used for computation of visibility parameters using the parameters in this

table.

Table C

- 1: CALPOST Parameter Values

Input

Group Variable Description Default Value Recommended Value

1 ASPEC Species to process VISIB VISIB

1 METRUN Option to run subset of met file 0 1

1 BTZONE Base time zone User Defined 0

1 ILAYER Layer /deposition code (1 = CALPUFF concentrations;

- 3 = wet+dry

deposition fluxes) 1 1

1 A Scaling factor scalar 0.0 0.0

1 B Additive factor 0.0 0.0

1 LBACK Add Hourly Background Concentrations/Fluxes? F F

1 NO2CALC Specify a single NO2/Nox ratio 1 1

1 RN O2NOX Ratio of NO2 to NOx 1.0 1.0

1 MSOURCE Process only total reported contribution 0 0

2 MVISCHECK Confirm conformity to FLAG 2008 Configuration 1 1

2 MFRH Particle growth curve for hygroscopic species 4 4

2 RHMAX Maximum relative humidity (%) used i n particle growth curve 98 98

2 LDRING Report results by Discrete receptor Ring, if Discrete Receptors used.

(T = true) T T

Modeled species to be included in computing the light extinction

2 LVSO4 Include SO4? T T

2 LVNO3 Include NO3? T T

2 LVOC Include Organic Carbon? T F

2 LVPMC Include Coarse Particles? T T

2 LVPMF Include Fine Particles? T T

2 LVEC Include Elemental Carbon? T F

2 LVNO2 Include NO2 T T

2 LVBK when ranking for TOP

- N, TOP

- 50, and Exceedance tables Include

BACKGROUND? T T

2 SPECPMC Species name used for particulates in MODEL.DAT file: COARSE = PMC PM10

2 SPECPMF Species name used for particulates in MODEL.DAT file: FINE = PMF PM25

Extinction Efficiencies (1/Mm per ug/m**3)

2 EEPMC PM COARSE = 0.6 0.6

2 EEPMF PM FINE = 1.0 1.0

2 EEPMCBK Background PM COARSE 0.6 0.6

2 EESO4 SO4 = 3.0 3.0

2 EENO3 NO3 = 3.0 3.0

2 EEOC Organic Carbon = 4.0 4.0

2 EESOIL Soil = 1.0 1.0

2 EEEC Elemental Carbon = 10.0 10.0

2 EENO2 NO2 Gas = 0.1755 0.1755

2 LAVER Method u sed for 24

- hr avg % change light extinction F F

2 MVISBK Method used for background light extinction (2 = Hourly RH

adjustment; 6 = FLAG seasonal f(RH)) 8 8

2 RHFAC Monthly RH adjustment factors from FLAG (unique for each Class I

area) Yes if 6 N/A

Ba ckground monthly extinction coefficients (FLAG) unique for eachTable C

- 1: CALPOST Parameter Values

Input

Group Variable Description Default Value Recommended Value

Class I area

2 BKSO4 Sulfate See Table 5 (NH 3 ) 2 SO 4

2 BKNO3 Nitrate See Table 5 NH 4 NO 3

2 BKPMC Coarse particulate See Table 5 CM

2 BKOC Particulate organic carbon See Table 5 OM

2 BK SOIL Soil See Table 5 Soil

2 BKEC Particulate elemental carbon See Table 5 EC

2 M8_MODE Method 8 Mode 5 5

2 BKSALT Background salt concentration See Table 2 Sea Salt

2 RHFSML Small ammonium sulfate and nitrate particle f(RH) See Table 7

2 RHFLRG L arge ammonium sulfate and nitrate particle f(RH) See Table 6

2 RHFSEA Sea salt f(RH) See Table 8

2 BEXTRAY Extinction due to Rayleigh scattering 10.0 10.0

3 LDOC Print documentation image F F

Averaging time(s) reported

3 L1PD Averaging period o f model output F F

3 L1HR 1

- hr averages F F

3 L3HR 3

- hr averages F F

3 L24HR 24

- hr averages T T

3 LRUNL Run length (annual) F F

3 LT50 Top 50 table for each averaging time selected T F

3 LTOPN F T

3 NTOP 4 1

3 ITO P 1 1

3 LEXCD Threshold exceedance count F T

3 THRESH1 1

- hour threshold

- 1.0

- 1.0

3 THRESH3 3

- hour threshold

- 1.0

- 1.0

3 THRESH24 24

- hour threshold

- 1.0 0.5

3 THRESHN N

- hour threshold

- 1.0

- 1.0

3 NDAY Accumulated Period (days) 0 0

3 NCOUNT Number o f exceedances allowed 1 1

Table C

- 2: POSTUTIL (Repartition) Parameter Values



### The CALUTIL will be used to perform HNO3/NO3 repartitioning using the parameters in this table.

Table C

- 2: POSTUTIL (Repartition) Parameter Values

Input

Group Variable Descri ption Default Value Recommended

Value

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning month User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning hour User Defined 1

1 NPER Length of run (hours) User Defined 8760

1 NSPECI NP Number of species to process User Defined 7

1 NSPECOUT Number of species to write User Defined 7

1 NDUPLCT Stop if duplicate species names 0 0Table C

- 2: POSTUTIL (Repartition) Parameter Values

Input

Group Variable Descri ption Default Value Recommended

Value

1 NSCALED Number of CALPUFF data files to be scales 0 0

1 MNITRATE Recompute HNO3/NO3 concentration partit ion 0 1

1 BKNH3 Background ammonia concentrations 10. 1.5

2 ASPECI Species to input User Defined SO2,SO4,NOX,HNO3,

NO3,PM10,PM25

2 ASPECO Speceis to output User Defined SO2,SO4,NOX,HNO3,

NO3,PM10,PM25



### Table C

- 3. Background Extinction Coefficients ( 20% Best Days).

Background Extinction Coefficients (20% Best Days)

Class I Area (NH 4 ) 2

SO 4 μg/m3 NH 4 N

O 3 Μg/m3 OM

μg/m3 EC

μg/m3 Soil

μg/m3 CM

μg/m3 Sea

Salt

μg/m3 Raylei

gh

Mm

- 1

Haleakala National Park

(HALE1) 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawai i Volcanoes

National Park (HAVO1) 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10



### Table C

- 4. Monthly f L 

### (RH)

– Large (NH 4 

### ) 2 

### SO4 and NH 4 

### NO 3 

### Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Haleakala National Park (HA LE1) 2.34 2.27 2.25 2.20 2.13 2.09 2.16 2.14 2.10 2.19 2.33 2.30

Hawaii Volcanoes National Park

(HAVO1) 2.56 2.45 2.47 2.47 2.47 2.45 2.53 2.59 2.55 2.57 2.78 2.55



### Table C

- 5. Monthly f S 

### (RH)

– Small (NH 4 

### ) 2 

### SO 4 

### and NH 4 

### NO 3 

### Relative Humidity Adjustment Fa ctor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Haleakala National Park (H ALE 1) 2.98 2.85 2.81 2.72 2.60 2.53 2.65 2.63 2.56 2.69 2.95 2.89

Hawaii Volcanoes National Park

(HAVO1) 3.35 3.10 3.14 3.13 3.14 3.11 3.24 3.42 3.34 3.38 3.76 3 .34



### Table C

- 6. Monthly f SS 

### (RH)

– Sea Salt Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Haleakala National Park (HALE1 ) 3.52 3.43 3.40 3.35 3.25 3.18 3.29 3.27 3.22 3.33 3.52 3.47

Hawaii Volcanoes Na tional Park 3.80 3.68 3.71 3.72 3.72 3.69 3.79 3.83 3.79 3.83 4.08 3.80(HAVO1)

### Appendix B.

This document is also available in the DOCKET E PA

- R09

- OAR

- 2012

- 0345: Approval and Promulgation

of Implementation Plans; Hawaii; Regional Haze Federal Implementation Plan.in Section C.3 Documents

Related to Modeling to Support the Subject to BART and Reasonable Progress.

C.3.b MM5 Application for 2005 Over the Hawaiian Islands, prepared for Hawaii State Department

of Health, Environmental Management Division, Clean Air Branch Prepared by: Alpine Geophysics,

LLC., 2008

### Topical Report



# MM5 Application for 2005

Over the Hawaiian Islands

Prepared for:

Hawaii State Department of Health

Environmental Management Division

Clean Air Branch

Prepared by:

Dennis McNally

James G. Wilkinson

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, Colorado 80007

31 October 2008

### i Table of Contents

Section No. Page No.

1 INTRODUCTION

................................ ................................ ................................ ............................ 1

- 1

2 METHODOLOGY

................................ ................................ ................................ ........................... 2

- 1

2.1 M ODEL S ELECTION AND A PPLICATION

................................ ................................ ................................ ..................... 2

- 1

2.2 E VALUATION A PPROACH

................................ ................................ ................................ ................................ .......... 2

- 2



### 3 MM5 PERFORMANCE EVAL UATION RESULTS

................................ ................................ .. 3

- 1

3.1 I NTRODUCTION

................................ ................................ ................................ ................................ ......................... 3

- 1

3.2 T EMPERATURE

................................ ................................ ................................ ................................ .......................... 3

- 2

3.3 M IXING R ATIO

................................ ................................ ................................ ................................ .......................... 3

- 2

3.4 W INDS

................................ ................................ ................................ ................................ ................................ ...... 3

- 3

3.5 P RECIPITATION

................................ ................................ ................................ ................................ ......................... 3

- 3

3.6 M ODEL P ERFORMANCE S UMMARY

................................ ................................ ................................ ........................... 3

- 4



### 4 REFERENCES

................................ ................................ ................................ ................................ ... 18

### ii

List of Figures

Figure No. Page No.

Figure 2

- 1. 36/12/4 km MM5 domain.

................................ ................................ ................................ ................................ ....... 2

- 5

Figure 2

- 2. Hawaii 4km MM5 domain.

................................ ................................ ................................ ................................ ..... 2

- 5

Figure 3

- 1. Spatial mean temperature (deg. C) for January 2005.

................................ ................................ ............................. 3

- 6

Figure 3

- 2. Spatial mean temperature (deg. C) for February 2005.

................................ ................................ ........................... 3

- 6

Figure 3

- 3. Spatial mean temperature (deg. C) for March 2005.

................................ ................................ ............................... 3

- 6

Figure 3

- 4. Spatial mean temperature (deg. C) for April 2005.

................................ ................................ ................................ . 3

- 6

Figure 3

- 5. Spatial mean temperature (deg. C) for May 2005.

................................ ................................ ................................ ... 3

- 7

Figure 3

- 6. Spatial mean temperature (deg. C) for June 2005.

................................ ................................ ................................ ... 3

- 7

Figure 3

- 7. Spatial mean temperature (deg. C) for July 2005.

................................ ................................ ................................ ... 3

- 7

Figure 3

- 8. Spatial mean temperature (deg. C) for August 2005.

................................ ................................ .............................. 3

- 7

Figure 3

- 9. Spatial mean temperature (deg. C) for September 2005.

................................ ................................ ......................... 3

- 8

Figure 3

- 10. Spatial mean temperature (deg. C) for October 2005.

................................ ................................ ........................... 3

- 8

Figure 3

- 11. Spatial mean temperature (deg. C) for November 2005.

................................ ................................ ....................... 3

- 8

Figure 3

- 12. Spatial mean temperature (deg. C) for December 2005.

................................ ................................ ........................ 3

- 8

Figure 3

- 13. Spatial mean mixing ratio (g/kg) for January 2005.

................................ ................................ .............................. 3

- 9

Figure 3

- 14. Spatial mean mixing ratio (g/kg) for February 2005.

................................ ................................ ............................ 3

- 9

Figure 3

- 15. Spatial mean mixing ratio (g/kg) for March 2005.

................................ ................................ ................................ 3

- 9

Figure 3

- 16. Spatial mean mixing ratio (g/kg) for April 2005.

................................ ................................ ................................ .. 3

- 9

Figure 3

- 17. Spatial mean mixing ratio (g/kg) for May 2005.

................................ ................................ ................................ . 3

- 10

Figure 3

- 18. Spatial mean mixing ratio (g/kg) for June 2005.

................................ ................................ ................................ . 3

- 10

Figure 3

- 19. Spatial mean mixing ratio (g/kg) for July 2005.

................................ ................................ ................................ .. 3

- 10

Figure 3

- 20. Spatial mean mixing ratio (g/kg) for August 2005.

................................ ................................ ............................. 3

- 10

Figure 3

- 21. Spatial mean mixing ratio (g/kg) for Septembe r 2005.

................................ ................................ ........................ 3

- 11

Figure 3

- 22. Spatial mean mixing ratio (g/kg) for October 2005.

................................ ................................ ............................ 3

- 11

Figure 3

- 23. Spatial mean mixing ratio (g/kg ) for November 2005.

................................ ................................ ........................ 3

- 11

Figure 3

- 24. Spatial mean mixing ratio (g/kg) for December 2005.

................................ ................................ ........................ 3

- 11

Figure 3

- 25. Spatial vector me an wind speed (m/s) for January 2005.

................................ ................................ .................... 3

- 12

Figure 3

- 26. Spatial vector mean wind speed (m/s) for February 2005.

................................ ................................ .................. 3

- 12

Figure 3

- 2 7. Spatial vector mean wind speed (m/s) for March 2005.

................................ ................................ ...................... 3

- 12

Figure 3

- 28. Spatial vector mean wind speed (m/s) for April 2005.

................................ ................................ ........................ 3

- 12

Figure 3

- 29. Spatial vector mean wind speed (m/s) for May 2005.

................................ ................................ ......................... 3

- 13

Figure 3

- 30. Spatial vector mean wind speed (m/s) for June 2005.

................................ ................................ ......................... 3

- 13

Figure 3

- 31. Spatial vector mean wind speed (m/s) for July 2005.

................................ ................................ .......................... 3

- 13

Figure 3

- 32. Spatial vector mean wind speed (m/s) for August 2005.

................................ ................................ ..................... 3

- 13

Figure 3

- 33. Spatial vector mean wind speed (m/s) for September 2005.

................................ ................................ ................ 3

- 14

Figure 3

- 34. Spatial vector mean wind speed (m/s) for October 2005.

................................ ................................ .................... 3

- 14

Figure 3

- 35. Spatial vector mean wind speed (m/s) for November 2005.

................................ ................................ ................ 3

- 14

Figure 3

- 36. Spatial vector mean wind speed (m/s) for Decem ber 2005.

................................ ................................ ................ 3

- 14

Figure 3

- 37. Accumulated Precipitation (mm) Observations and Model Estimates for January 2005.

................................ ... 3

- 15

Figure 3

- 38. Accumulated Precipitation (mm) Observations and Model Estimates for February 2005.

................................ . 3

- 15

Figure 3

- 39. Accumulated Precipitation (mm) Observations and Model Estimates for March 2005.

................................ ..... 3

- 15

Figure 3

- 40. Accumulated Precipitation (mm) Observations and Model Estimates for April 2005.

................................ ....... 3

- 15

Figure 3

- 41. Accumulated Precip itation (mm) Observations and Model Estimates for May 2005.

................................ ........ 3

- 16

Figure 3

- 42. Accumulated Precipitation (mm) Observations and Model Estimates for June 2005.

................................ ........ 3

- 16

Figure 3

- 43. Accumulated Precipitation (mm) Observations and Model Estimates for July 2005.

................................ ......... 3

- 16

Figure 3

- 44. Accumulated Precipitation (mm) Observations a nd Model Estimates for August 2005.

................................ .... 3

- 16

Figure 3

- 45. Accumulated Precipitation (mm) Observations and Model Estimates for September 2005.

............................... 3

- 17

Figure 3

- 46. Accumulated Precipitation (mm) Observations and Model Estimates for October 2005.

................................ ... 3

- 17

Figure 3

- 47. Accumulated Precipitation (mm) Observations and Model Estimat es for November 2005.

............................... 3

- 17

### iii

Figure 3

- 48. Accumulated Precipitation (mm) Observations and Model Estimates for December 2005.

............................... 3

- 17

### iv List of Tables

Table No. Page No.

Table 2

- 1: MM5 Vertical Domain Specification.

................................ ................................ ................................ ....................... 2

- 4

Table 3

- 1. Bias and error for temperature, mixing ratio, and precipitation and wind speed index of agreement by mon th for

2005 annual Hawaii MM5 simulation.

................................ ................................ ................................ ................................ 3

- 5

### 1

- 1

1. INTRODUCTION

Over the past decade, emergent requirements for direct numerical simulation of urban

and regional scale photochemical and secondary aerosol air quality

— spawned lar gely by

particulate matter (PM 2.5 

### ), ozone and regional haze regulations

—have led to intensified

efforts to construct high

- resolution emissions, meteorological and air quality data sets.

The concomitant increase in computational throughput of low

- cost mode rn scientific

workstations has ushered in a new era of regional air quality modeling. It is now

possible, for example, to exercise sophisticated mesoscale prognostic meteorological

models and Eulerian and Lagrangian photochemical/aerosol models, simulatin g ozone,

sulfate and nitrate deposition, and secondary organic aerosols (SOA) across quite large

geographic regions.

One such model is the Pennsylvania State University/National Center for Atmospheric

Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1 993; Grell et al., 1994:

www.mmm.ucar.edu/mm5 ). MM5 is a limited

- area, non

- hydrostatic, terrain

- following

model designed to simulate mesoscale atmospheric circulation. The model is supported

by several pre

- an d post

- processing programs which are referred to collectively as the

MM5 modeling system.

This document describes a 2005 annual MM5 simulation. The MM5 physics for the

annual simulation were selected based on two, two

- week long MM5 simulations

– (1) for

early February 2005 (a winter

- time episode); and (2) for early July 2005 (a summer

- time

episode). The MM5 physics options that were selected for the annual simulation were

based on those physics options from the two, two

- week long simulations that resulte d in

the best model performance.

Ultimately, it is believed that the he proposed annual 2005 MM5 modeling results will be

used in CALMET/CALPUFF modeling system (TRC, 2008). With that in mind, the

model performance evaluation results presented in this do cument are discussed within the

context of being used in a CALMET/CALPUFF application. However, though the MM5

results can be used in other applications (e.g., Regional Haze studies, Best Available

Retrofit Technology (BART) determinations, State Implemen tation Plans to develop and

test options to resolve air quality issues or support management policy decisions), care

must be taken when applying the current MM5 simulation results in such applications.

### 2

- 1

2. METHODOLOGY

2.1. Model Selection and Application

A brief summary is provided of the MM5 input data preparation procedures that were

used for this meteorological modeling sensitivity exercise. Because of the different

physics options available in MM5, sensitivity testing was performed for a winter (early

Februa ry) and summer (early July) period to select the final configuration for the annual

simulation.

Model Selection : The publicly available non

- hydrostatic version of MM5 (version 3.7.4)

was used for this modeling study. Preprocessor programs of the MM5 mod eling system

including TERRAIN , REGRID, LITTLE_R, and INTERPF (UCAR, 2003b) were used to

develop model inputs.

Horizontal Domain Definition: The computational grid is presented in Figures 2

- 1 and 2

- 2. The outer 36km domain (D01) has 165 x 129 grid cell s, selected to provide boundary

conditions to the 12km domain. The mid

- scale 12km domain (D02) has 226 x 226 grid

cells, selected to maximize the coverage of the western region around the Hawaiian

Islands. The 4km domain (D03) has 208 x 154 grid cells, s elected to cover the island

chain with a buffer greater than 100km around the perimeter of the eight major Hawaiian

Islands. The projection was in Lambert Conformal Coordinates (LCC) with a grid

projection pole of 23 o



### , - 158 o 

### with true latitudes of 30 o 

### and 60 o



### .

Vertical Domain Definition: The MM5 modeling was based on 34 vertical layers with an

approximately 38 meter deep surface layer. The MM5 vertical domain is presented in

both sigma and height coordinates in Table 2

- 1.

Topographic Inputs: Topogra phic information for the MM5 was developed using the

NCAR and the United States Geological Survey (USGS) terrain databases. The grid was

based on the 2 min (~4 km) Geophysical Data Center global data (UCAR, 2002)

interpolated to the model grid using a Cre ssman

- type objective analysis scheme

(Cressman, 1959). To avoid interpolating elevated terrain over water bodies, after the

terrain databases are interpolated onto the MM5 grid, the NCAR graphic water body

database was used to correct elevations over wate r bodies.

Vegetation Type and Land Use Inputs: Vegetation type and land use information was

developed using the most recently released PSU/NCAR databases provided with the

MM5 distribution (UCAR, 2002). Standard MM5 surface characteristics correspondi ng

to each land use category was employed.

Atmospheric Data Inputs: Two choices for the first guess meteorological fields were

available: (1) the NCEP Global Tropospheric Final Analysis; and (2) the NCAR/NCEP

Reanalysis Project (NNRP) data (UCAR, 2007 ). Though the NNRP data were used in a

previous study of Hawaii (Yang and Chen, 2003; Yang et. al. 2005), the NNRP data have

### 2

- 2

relatively coarse 2.5x2.5 degree resolution whereas the NCEP Final Analysis data have

much higher 1x1 degree resolution. For the current modeling study, the NCEP Final

Analysis dataset was used.

Available surface and upper

- air observations were used in the objective analyses . These

data were incorporated into the analyses datasets following the procedures outlined by

Stauffer and Seaman (1990) and were quality

- inspected by MM5 pre

- processors using

automated gross

- error checks and "buddy" checks. In addition, radiosonde soundings

were subjected to vertical consistency checks. The synoptic

- scale data used for the

initialization (an d in the analysis nudging discussed later) were obtained from the

conventional National Weather Service (NWS) twice

- daily radiosondes (UCAR, 2008a)

and 3

- hr NWS surface observations (UCAR, 2008b).

Water Temperature Inputs: The water temperature data were taken from the NCEP RTG

global one

- half degree analysis (Gemmill et al., 2007; NCEP, 2008).

FDDA Data Assimilation: Both analysis and observation based nudging were used in the

simulation. Analysis nudging coefficients of 2.5x10

- 4 

### on the 36km grid doma in and

1.0x10

- 4 

### on the 12km grid domain were used for winds and temperature. For the 4km

grid domain, nudging to observed winds was employed with a coefficient of 1.0x10

- 3



### .

Physics Options : Sensitivity testing using approximately two

- week long episod es in

early February 2005 and early July 2005 were performed to help select the best

performing physics options (McNally, 2008). Although the performance was similar for

the different options, the best performing configuration was the version used by the

University of Hawaii and the National Weather Service ( Yang and Chen, 2003; Yang et.

al. 2005 . Namely:

Warm Rain;

Grell Cumulus Parameterization on 36/12/04km Domains;

MRF PBL;

NOAH Land Surface Model; and

Cloud Radiation.

Application Methodology : The MM5 model was executed in 5

- day blocks initialized at

12Z every 4 days with a 90 second time step. Model results were output every 60

minutes and output files are split at 24 hour intervals. Twelve (12) hours of spin

- up were

included in each 4

- day block before the data were used in the atmospheric simulations

and subsequent model performance evaluation.

2.2. Evaluation Approach

Because only limited observational data were available at the 12km and 36km grid scales,

and because it is very likely that only the 4km domain will be used for subsequent air

quality analyses, only MM5 predictions on the 4km grid were evaluated.

### 2

- 3

The model performance evaluation approach was based on a combination of qualitative

and quantitative analyses using comparisons of observed and predicted values of mixing

ratio, temperature, precipitation and winds. The observed values for winds, temperature,

and water mixing ratio were taken from the NOAA Techniques Development Lab (TDL)

(UCAR, 2008c) surface observations and the National C limatic Data Center (NCDC)

TD3240 (UCAR, 2008d) hourly precipitation databases. For the qualitative model

performance evaluation, the model estimated monthly total precipitation was graphically

compared with the TD3240 precipitation database. For the qua ntitative model

performance evaluation, tabulations of (1) the model bias and error for temperature,

precipitation, and mixing ratio and (2) the index of agreement for the wind fields were

analyzed for the 4km simulations.

### 2

- 4

Table 2

- 1 : MM5 Vertical Domain Specification.



### k (MM5) sigma pressure (Pa) height (m) depth (m)

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0. 250 32500 8621 900

28 0.300 37000 7720 817

27 0.350 41500 6903 750

26 0.400 46000 6153 693

25 0.450 50500 5461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76 600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.900 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0.950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0

### 2

- 5

Figure 2

- 1 . 36/12/4 km MM5 d omain.

Figure 2

- 2 . Hawaii 4km MM5 d omain.

### 3

- 1



## 3. MM5 PERFORMANCE EVALUATION RESULTS

3.1. Introduction

Quantitative and qualitative model performance evaluation results for surface winds,

temper ature, mixing ratio and episode total precipitation are presented and discussed. A

full annual model evaluation is very difficult to summarize in a single document. With

this in mind, this section presents results so potential users of the MM5 prediction s can

independently judge the adequacy of the model simulation. Overall comparisons are

offered herein to judge the model efficacy, but this review does not necessarily cover all

potential user needs and applications.

The statistics that were estimated include the mean bias (or simply bias) and mean

absolute gross error (or simply error) for temperature, mixing ratio and precipitation as

well as the wind speed index of agreement (Willmott, 1982; Willmott et al., 1985). Bias

quantifies the difference be tween the average of a predicted value and the average of an

observed value. If the bias is negative, the model is said to underpredict the mean

observed value. Error quantifies the average of the absolute differences between the

predictions and observat ions. Thus, even if a simulation exhibits no bias, it can still

exhibit large error as the simulation results have offsetting under

- and overpredictions.

Index of agreement quantifies the relative average error between the predictions and

observations. Please note that in the comparisons, vertical level one model estimates that

are predicted at approximately nineteen meters are compared directly with the nominal,

observed temperatures and moistures that are measured at about two meters and wind

speeds an d directions that are measured at about ten meters. As such, model performance

must be judged with this caveat taken into context.

Quantitative meteorological model performance benchmarks have been developed to help

assess the ability of a model to repro duce observed conditions. The benchmarks are

based upon the evaluation of about thirty MM5 and RAMS meteorological simulations of

multi

- day episodes in support of air quality modeling study applications performed over

several years (Emery et al., 2001). In turn, these benchmarks have been adopted for use

in annual meteorological modeling studies. The purpose of these benchmarks is not to

give a pass or fail grade to any one particular meteorological model application, but

rather to put the model results into the proper context of other models and meteorological

data sets. That is, the benchmarks provide a contextual understanding as to how the

model results compare to other model applications run over portions of the continental

United States. Therefore , the benchmarks must be viewed as being guideline and not

bright

- line numbers. These benchmarks include bias and error in temperature and mixing

ratio as well as the wind speed index of agreement (IA) between the model predictions

and observational data bases. The benchmarks for each variable to judge whether

predictions from a meteorological model are on par with previous meteorological

modeling studies are as follows:

Temperature bias: less than or equal to +/

- 0.5 K;

### 3

- 2 Temperature error: less than or equal to 2.0 K

Mixing ratio bias: less than or equal to +/

- 1.0 g/kg

Mixing ratio error: less than or equal to 2.0 g/kg

Wind Speed Index of Agreement (unitless): 0 = worst, 1 = best, 0.6 =

acceptable

Of critical note for the current study

– though meteor ological model performance

benchmarks have been developed to help assess the ability of a model to reproduce

observed conditions, these benchmarks were derived from simulations performed

primarily for continental and limited coastal areas (Emery et al., 20 01). Hence,

application of these model performance benchmarks is suspect for the current study as

the MM5 simulations were performed over the Hawaiian Islands. However, it is still

prudent to mention these benchmarks as they do provide a reference point for the model

performance evaluation.

3.2. Temperature

Review of temperature bias statistics in Table 3

- 1 indicates that MM5 consistently

underpredicts temperatures throughout 2005. MM5’s best performance is for January

where the bias is

- 0.47 K, and the wor st performing month is August where the bias is

- 1.66 K. Over the entire year, MM5 has a bias of

- 1.07 K. Table 3

- 1 also shows the

temperature error statistics to be a low of 1.75 K in November and a high of 2.32 K in

August.

Figures 3

- 1 through 3

- 12 gr aphically compare the spatial mean observed and predicted

temperatures for each month of 2005. Qualitatively, MM5 does better at predicting the

nighttime low temperatures as compared to its ability to predict the daytime high

temperatures. In fact, MM5 p erforms quite poorly at predicting the daytime high

temperatures in that it seldom is able to generate spatial mean temperatures in excess of

25 C where as the observed spatial mean temperatures routinely exceed 25 C reaching

highs of 31 C in September (Fi gure 3

- 9).

The temperature performance though poor is not wholly unexpected as establishing such

features as an appropriate marine boundary layer are critical to accurately predict coastal

meteorology. Indeed, initialization of limited area prognostic me teorological models

over the data

- sparse ocean and coastal regions remains a difficult problem (e.g., Leidner

et al., 2001). Substantial effort is likely necessary to correct the inadequate temperature

performance of MM5 for the current simulation. Such efforts may i nclude the

incorporation of highly resolved vertical and horizontal grid structure s (Stauffer and

Seaman, 1999), updating land use characteristics, or revision to the deep soil

temperatures or other force

- restore parameterizations (Boucouvala et al., 2003).

3.3. Mixing Ratio

Table 3

- 1 presents the mixing ratio bias and error statistics by month for the 2005 MM5

simulation. January is the trailing month in terms of performance with a bias of

- 1.23

g/kg and an error of 1.59 g/kg. The remaining mont hs show biases of between

- 0.96 g/kg

### 3

- 3

for May and 0.18 g/kg for December and errors from 1.07 g/kg from August to1.37 g/kg

for April. For the entire period, the bias is

- 0.63 g/kg, and the error is 1.22 g/kg.

Figure 3

- 13 through 3

- 24 graphically compare t he spatial mean observed and predicted

mixing ratios by month for 2005. Qualitatively, MM5 does reasonably well at predicting

both the highs and lows of the spatial mean mixing ratios, though there is a small

underprediction bias. Further, MM5 does well at predicting the diurnal pattern of the

spatial mean mixing ratios. Overall MM5 does reasonably well at predicting mixing ratio

for all months of 2005 with no month being overtly better or worse than other months.

3.4. Winds

Table 3

- 1 presents the wind speed index of agreement by month for 2005 with May

exhibiting the lowest performance at 0.63 and January and September exhibiting the best

performance at 0.73. Annually, MM5 exhibits an index of agreement of 0.69.

Figures 3

- 25 through 3

- 36 graphically compare the observed and predicted vector mean

wind speeds by month for 2005. Qualitatively, there is mixed performance with MM5

doing better at predicting the high wind speeds in some months than others or with MM5

doing better at predicting the low wind speed in some months over other months. In

January (Figure 3

- 25), for example, MM5 follows the diurnal trend though it does have

periods of overprediction of the high winds (e.g., 07

- 08 January) and low winds (e.g., 23

- 24 January). In June (Figure 3

- 30), MM5 h as a general trend to underpredict the high

wind speeds and overpredict the low wind speeds. However, for all months, MM5 does

reasonably well at predicting the diurnal trend. Overall, as with mixing ratio, MM5 does

reasonably well at predicting wind spe eds.

3.5. Precipitation

Table 3

- 1 presents the bias and error statistics for precipitation by month for the 2005

MM5 simulation. In terms of bias, June shows none and January shows the largest bias

of

- 8.88 mm. In terms of error, January is the lowest perform ing month with 10.24 mm,

and December is the best performing month with 2.63 mm. Annually, MM5 exhibits a

bias 0

- 4.00 mm and an error of 6.69 mm.

Another way of examining qualitative precipitation performance is to examine spatial

fields of accumulated observed and predicted precipitation. The graphics are prepared by

plotting the modeled episode estimated precipitation overlaid with color coded markers at

locations where precipitation measurements were reported. Episode accumulated

precipitation for e ach month of 2005 is presented in Figures 3

- 37 though 3

- 48.

For January (Figure 3

- 37), it can be seen that the poorer performance reflected in the bias

and error statistics in Table 3

- 1 is due to the underpredictions of precipitation on all the

islands except Kaua`i. MM5 precipitation predictions improve in February (Figure 3

- 38)

over the islands of Hawai`i and Maui, then revert to poorer performance in March (Figure

3

- 39) over the islands of Kaua`i, O`ahu, and Moloka`i. In April (Figure 3

- 40), May

(Fi gure 3

- 41), June (Figure 3

- 42), July (Figure 3

- 43), and August (Figure 3

- 44), MM5

performance is relatively consistent. In September (Figure 3

- 45) through November

### 3

- 4

(Figure 3

- 47), MM5 precipitation performance degrades slightly, and then improves

slightly for December (Figure 3

- 48). In general, however, MM5 does tend to predict

both the magnitudes and locations of high precipitation throughout the year.

3.6. Model Performance Summary

A 2005 annual MM5 simulation over the Hawaiian Islands has been performed and a

model performance evaluation has been conducted. The model performance lies within

the guidelines for mixing ratio and wind field index of agreement. In regards to mixing

ratio, MM5 does reasonably well at predicting the diurnal structure of the observ ed

mixing ratios though it slightly underpredicts observed values throughout the year. As

with its ability to predict observed mixing ratios, MM5 does reasonably well at predicting

the observed wind speeds diurnal structure with slight underprediction and overprediction

biases throughout the year depending on the month. In terms of precipitation, MM5 does

reasonably well at predicting both the magnitudes and locations of the high precipitation

throughout the year.

The surface temperature evaluation shows that for many months and for the annual

average, the bias and error are outside of the guideline metrics. This result is not

unexpected since the performance metrics were derived from largely continental,

episodic applications of MM5. For this applicati on the model is being applied to a very

challenging problem with nearly all coastal monitors near very steep orography.

Therefore, a small difference in wind flow can determine if a monitor is influenced by

land or maritime air parcels.

Given the reasona ble model performance of the MM5 predicted winds, precipitation, and

mixing ratio, they are adequate for use in a CALMET/CALPUFF application. Though

MM5 has less skill at replicating temperature observations, the Hawaiian Islands, have a

relative high, st able temperature range over the year that is roughly 15 C to 30 C; and

given that the CALMET/CALPUFF modeling system uses temperature primarily in

buoyancy, deposition, and atmospheric stability calculations (and in some cases, nitrate

chemistry), the MM5 predictions for the current 2005 simulation are likely sufficient for

the task intended in a CALMET/CALPUFF application. Again, care must be taken if the

current 2005 MM5 simulation results are to be used in other applications.

### 3

- 5

Table 3

- 1 . Bias and error for temperature, mixing ratio, and precipitation and wind speed index of

agreement by month for 2005 annual Hawaii MM5 simulation.

Month Temperature (K) Mixing Ratio (g/kg) Precipitation (mm) Wind

Bias Er ror Bias Error Bias Error IA

Jan

- 0.47 1.81

- 1.23 1.59

- 8.88 10.24 0.73

Feb

- 0.76 1.82

- 0.63 1.31

- 5.71 7.35 0.71

Mar

- 0.71 1.95

- 0.67 1.20

- 8.65 9.43 0.69

Apr

- 1.44 2.01

- 0.94 1.37 0.11 5.80 0.67

May

- 1.57 2.16

- 0.96 1.35

- 1.81 4.22 0.63

Jun

- 1.44 2 .07

- 0.65 1.16 0.00 6.55 0.69

Jul

- 1.49 2.05

- 0.47 1.06

- 2.17 6.03 0.66

Aug

- 1.66 2.32

- 0.38 1.07

- 0.77 4.57 0.70

Sep

- 1.31 1.98

- 0.59 1.10

- 6.58 8.86 0.73

Oct

- 0.81 1.90

- 0.44 1.05

- 6.39 8.31 0.66

Nov

- 0.62 1.75

- 0.78 1.21

- 5.10 6.31 0.71

Dec

- 0.53 2.07 0.18 1.14

- 2.05 2.63 0.69

Annual

- 1.07 1.99

- 0.63 1.22

- 4.00 6.69 0.69

### 3

- 6 Figure 3

- 1 . Spatial mean temperature (deg. C) for January 2005. Figure 3

- 2

. Spatial mean temperature (deg. C) for February 2005.

Figure 3

- 3 . Spatial mean temperature (deg. C) for March 2005. Figure 3

- 4 . Spatial mean temperature (deg. C) for April 2005.

### 3

- 7 Figure 3

- 5 . Spatial mean temperature (deg. C) for May 2005. Figure 3

- 6 . Spatial mean temperature (deg. C) for June 2005.

Fi gure 3

- 7 . Spatial mean temperature (deg. C) for July 2005. Figure 3

- 8 . Spatial mean temperature (deg. C) for August 2005.

### 3

- 8 Figure 3

- 9 . Spatial mean temperature (deg. C) for September 2005. Figure 3

- 10 . Spatial mean temperature (deg. C) for October 2005.

Figure 3

- 11 . Spatial mean temperature (deg. C) for November 2005. Figure 3

- 12 . Spatial mean temperature (deg. C) for December 2005.

### 3

- 9 Figure 3

- 13 . Spatial mean mixing ratio (g/kg) for January 2005. Figure 3

- 14

. Spatial mean mixing ratio (g/kg) for February 2005.

Figure 3

- 15

. Spatial mean mixing ratio (g/kg) for March 2005. Figure 3

- 16 . Spatial mean mixing ratio (g/kg) for April 2005.

### 3

- 10 Figure 3

- 17 . Spatial mean mixing ratio (g/kg) for May 2005. Figure 3

- 18 . Spatial mean mixing ratio (g/kg) for June 2005.

Figure 3

- 19 . Spatial mean mixing ratio (g/kg) for July 2005. Figure 3

- 20 . Spatial mean mixing ratio (g/kg) for August 2005.

### 3

- 11 Figure 3

- 21 . Spatial mean mixing ratio (g/kg) for September 2005. Figure 3

- 22

. Spatial mean mixing ratio (g/kg) for October 2005.

Figure 3

- 23 . Spatial mean mixing ratio (g/kg) for November 2005. Figure 3

- 24 . Spatial mean mixing rat io (g/kg) for December 2005.

### 3

- 12 Figure 3

- 25 . Spatial vector mean wind speed (m/s) for January 2005. Figure 3

- 26 . Spatial vector mean wind speed (m/s) for February 2005.

Figure 3

- 27 . Spatial vector mean wind speed (m/s) for March 2005. Figure 3

- 28 . Spatial vector mean wind speed (m/s) for April 2005.

### 3

- 13 Figure 3

- 29 . Spatial vector mean wind speed (m/s) for May 2005. Figure 3

- 30 . Spatial vector mean wind speed (m/s) for June 2005.

Figure 3

- 31 . Spatial vector mean wind speed (m/s) for July 2005. Figure 3

- 32 . Spatial vector mean wind speed (m/s) for August 2005.

### 3

- 14 Figure 3

- 33 . Spatial vector mean wind speed (m/s) for September 2005. Figure 3

- 34 . Spatial vector mean wind speed (m/s) for October 2005.

Figure 3

- 35 . Spatial vector mean wind speed (m/s) for November 2005. Figure 3

- 36 . Spatial vector mean wind speed (m/s) for December 2005.

### 3

- 15

Figure 3

- 37 . Accumu lated Precipitation (mm) Observations and Model Estimates for January 2005. Figure 3

- 38 . Accumulated Precipitation (mm) Observations and Model Estimates for February 2005.

Figure 3

- 39 . Accumulated Precipitation (mm) Observations and Model Estimates for March 2005. Figure 3

- 40 . Accumulated Precipitation (mm) Observations and Model Estimates for April 2005.

### 3

- 16

Figure 3

- 41 . Accumulated Precipitation (mm) Observations and Model Estimates for May 2005. Figure 3

- 42 . Accumulated Precipitation (mm) Observations and M odel Estimates for June 2005.

Figure 3

- 43 . Accumulated Precipitation (mm) Observations and Model Estimates for July 2005. Figure 3

- 44 . Accumulated Precip itation (mm) Observations and Model Estimates for August 2005.

### 3

- 17

Figure 3

- 45 . Accumulated Precipitation (mm) Observations and Model Estimates for September 2005. Figure 3

- 46 . Accumulated Precipitation (mm) Observations and Model Estimates for October 2005.

Figure 3

- 47 . Accumulated Precipitation (mm) Observations and Model Estimates for November 2005. Figu re 3

- 48 . Accumulated Precipitation (mm) Observations and Model Estimates for December 2005.

### 18 

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