OCS Study

BOEM 2018-020

U.S. Department of the 

Interior

Bureau of Ocean Energy 

Management

Alaska OCS Region

Eastern Research Group, 

Inc.

8950 Cal Center Drive

Suite 230

Sacramento, CA 95826

Ramboll

773 San Marin Drive

Suite 2115

Novato, CA 94998

Arctic Air Quality Impact Assessment 

Modeling Study:

Final Project Report
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

[Page Intentionally Left Blank]
OCS Study

BOEM 2018-020

Arctic Air Quality Impact Assessment 

Modeling Study:

Final Project Report

Prepared by:

Eastern Research Group, Inc.

8950 Cal Center Drive, Suite 230

Sacramento, CA 95826

Ramboll

773 San Marin Drive, Suite 2115

Novato, CA 94998

Prepared under Contract Number M13PC00014 for:

U.S. Department of Interior

Bureau of Ocean Energy Management

3801 Centerpoint Drive, Suite 500

Anchorage, AK 99503-5823

U.S. Department of the 

Interior

Bureau of Ocean Energy 

Management

Alaska OCS Region

Eastern Research Group, 

Inc.

8950 Cal Center Drive

Suite 230

Sacramento, CA 95826

Ramboll

773 San Marin Drive

Suite 2115

Novato, CA 94998

June 13, 2018
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

Disclaimer

Study concept, oversight, and funding were provided by the U.S. Department of the Interior, 

Bureau of Ocean Energy Management, Environmental Studies Program, Washington, DC, under 

Contract Number M13PC00014. This report has been technically reviewed by BOEM and it has 

been approved for publication. The views and conclusions contained in this document are those 

of the authors and should not be interpreted as representing the opinions or policies of the U.S. 

Government, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use.

This report discusses a conservative “full build-out scenario” that describes air emissions sources 

associated with potential future oil and gas exploration, development, and production activities 

on the Beaufort Sea and Chukchi Sea Outer Continental Shelf. The elements of this scenario are 

included for the purpose of analysis and do not necessarily represent expected activities.

May be cited as: 

Fields Simms, P., B. Do, B. Brashers, Ph.D., T. Stoeckenius, and R. Morris. 2018. Arctic Air 

Quality Impact Assessment Modeling Study: Final Project Report. Prepared by Eastern 

Research Group, Inc., Sacramento, CA for U.S. Dept. of the Interior, Bureau of Ocean 

Energy Management, Alaska OCS Region, Anchorage, AK.

OCS Study BOEM 2018-020. 58 pp.

Authors:

Eastern Research Group

Paula Fields Simms

Bebhinn Do

Ramboll

Bart Brashers

Till Stoeckenius

Ralph Morris

Acknowledgements

This report was made possible with the guidance and oversight of the following individuals: 

From BOEM OCS Alaska: Dr. Heather Crowley, and Virgilio (VJ) Maisonet-Montanez; and, 

members of the Arctic Air Quality Impact Assessment Modeling Study Science Review Group: 

Dr. David Allen, Dr. Brian Lamb, and Mr. Tom Moore.

Also, the authors would like to acknowledge the significant technical contributions that these 

individuals made to the Study: From ERG: Richard Billings, Marty Wolf, Mike Pring, Regi 

Oommen, Darcy Wilson, Lindsay Dayton, and Noel Hilliard; and, from Ramboll: Joe Knapik, 

Jay McAlpine, Timothy Sturtz, Jaegun Jung, Bonyoung Koo, and Tejas Shah.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

iii

Table of Contents

Section Page

I. INTRODUCTION ............................................................................................................I-1

II. TASK 2: METEOROLOGICAL DATA EVALUATION............................................. II-1

A. Meteorological Data Evaluation ......................................................................... II-1

i. Summary of Approach............................................................................ II-1

ii. Final Findings ......................................................................................... II-2

B. Model Performance Evaluation .......................................................................... II-3

i. Summary of Approach............................................................................ II-3

ii. Final Findings ......................................................................................... II-4

C. Model Justification Report.................................................................................. II-5

i. Summary of Approach............................................................................ II-6

ii. Final Findings ......................................................................................... II-7

III. TASK 3: EMISSION INVENTORIES..........................................................................III-1

A. Summary of Approach.......................................................................................III-1

i. Baseline Inventory .................................................................................III-2

ii. Projections Inventory.............................................................................III-5

B. Final Findings ..................................................................................................III-11

i. Baseline Inventory ...............................................................................III-11

ii. Projections Inventory...........................................................................III-12

C. Emissions Inventory Uncertainty and Opportunities for Improvement...........III-14

D. Spatial and Temporal Allocation of Emissions ...............................................III-16

IV. TASK 4: ATMOSPHERIC DISPERSION MODELING .............................................IV-1

A. Summary of Approach.......................................................................................IV-1

B. Final Findings ....................................................................................................IV-4

V. TASK 5: PHOTOCHEMICAL/DISPERSION FAR-FIELD POLLUTANT MODELING

......................................................................................................................................... V-1

A. Summary of Approach........................................................................................ V-1

B. Final Findings ..................................................................................................... V-5

VI. TASK 6: EMISSION EXEMPTION THRESHOLD EVALUATION .........................VI-1

A. Summary of Approach.......................................................................................VI-1

B. Final Findings ....................................................................................................VI-5

VII. REFERENCES ............................................................................................................ VII-1
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

iv

List of Tables

Table III-1. Sources Included in the BOEM Arctic AQ Modeling Study Emissions 

Inventory....................................................................................................................................III-3

Table III-2. Full Build-Out Projections Scenario for Offshore Oil and Gas Activities.............III-6

Table III-3. Projected Drilling Activity .....................................................................................III-8

Table III-4. Projected Number of Wells....................................................................................III-8

Table III-5. Summary of Baseline Emissions – Criteria Air Pollutants (tons/yr)....................III-12

Table III-6. Summary of Baseline Emissions – Greenhouse Gases and Other Pollutants 

(tons/yr)....................................................................................................................................III-12

Table III-7. Selected Baseline Emissions from Onshore Oil and Gas Sources.......................III-12

Table III-8. Summary of Emissions Projections – Criteria Air Pollutants (tons/yr) ...............III-13

Table III-9. Summary of Emissions Projections – Greenhouse Gases and Other Pollutants 

(tons/yr)....................................................................................................................................III-13

Table III-10. Emissions Projections from Offshore Sources (tons/yr) ....................................III-14

Table IV-1. Impact Ranges for Point Sources...........................................................................IV-5

Table IV-2. Impact Ranges for Area Sources............................................................................IV-5

Table IV-3. Impact Ranges for Line Sources ............................................................................IV-5

Table V-1. Source Groups for CAMx Source Apportionment................................................... V-4

Table VI-1. Summary of Mandatory Equipment Under Each Scenario....................................VI-2

Table VI-2. Hourly Emission Levels for Synthetic Sources......................................................VI-2

Table VI-3. Summary Statistics for Distance to Shore..............................................................VI-3

Table VI-4. MERPs Based on Hypothetical Sources ................................................................VI-5
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

v

List of Figures

Figure I-1. Regional Map Depicting OCS Planning Areas Including Location of the Arctic AQ 

Modeling Study.............................................................................................................................I-1

Figure II-1. Moderate Resolution Imaging Spectroradiometer (MODIS) SST Composite Image 

for September 7-8, 2010 ............................................................................................................. II-3

Figure II-2. BOEM Arctic WRF Onshore METSTAT 4 Kilometer Domain Wind Speed 

Performance ................................................................................................................................ II-4

Figure II-3. BOEM Arctic WRF Onshore METSTAT 4 Kilometer Domain Wind Direction 

Performance ................................................................................................................................ II-5

Figure II-4. Arctic WRF 4 Kilometer Modeling Domain........................................................... II-6

Figure III-1. Offshore Projected Development Areas................................................................III-7

Figure IV-1. Location of Electricity Generating Facilities........................................................IV-2

Figure IV-2. Example of Generalized Shoreline Receptors Along the North Slope .................IV-3

Figure IV-3. North Slope Communities Included in the Near-field Dispersion Modeling .......IV-3

Figure V-1. CAMx Nested Modeling Domains at Horizontal Resolutions of 36 km, 12 km, 

and 4 km...................................................................................................................................... V-2

Figure VI-1. Synthetic Source Placement..................................................................................VI-4
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

vi

Abbreviations and Acronyms

ADEC Alaska Department of Environmental Conservation

ADM atmospheric dispersion model(ing)

AERCOARE meteorological pre-processor for AERMOD using the COARE over-water 

algorithm

AERMET meteorological pre-processor for AERMOD

AERMOD American Meteorological Society/United States Environmental Protection 

Agency regulatory model for dispersion

AMNWR Alaska Maritime National Wildlife Refuge

ANWR Alaska National Wildlife Refuge

AOCSR Alaska Outer Continental Shelf Region

APCA Anthropogenic Precursor Culpability Assessment

AQ air quality

AQRP Air Quality Regulatory Program

BC boundary condition

BCF/yr billion cubic feet per year

BOEM Bureau of Ocean Energy Management

CALMET meteorological pre-processor for CALPUFF

CALPUFF California Puff regulatory air pollution dispersion model

CAMx Comprehensive Air Quality Model with Extensions

CAP criteria air pollutant(s)

CB05TUCL Carbon Bond 2005 version with toluene and chlorine chemistry updates

CB6r2 Carbon Bond 6 revision 2 photochemical mechanism

CH4 methane

CMAQ Community Multi-scale Air Quality Model

CMV commercial marine vessel

CO carbon monoxide

CO2 carbon dioxide

CO2e carbon dioxide equivalent

COARE Coupled Ocean-Atmosphere Response Experiment

DAT deposition analysis threshold

DEASCO3 Deterministic and Empirical Assessment of Smoke’s Contribution to Ozone

DOI U.S. Department of the Interior

DPP development and production plan

dv deciview

EDMS Emissions and Dispersion Modeling System

EET emission exemption threshold

EP exploration plan

EPA U.S. Environmental Protection Agency

ERG Eastern Research Group, Inc.

FAA Federal Aviation Administration

FBO full build-out

FINN NCAR Fire INventory

FPSO Floating production storage and offloading

FY future year
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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G&G geological and geophysical

GBS gravity-based structure(s)

GHG greenhouse gas(es)

GHGRP Greenhouse Gas Reporting Program

GIS geographic information system

GREET Greenhouse gases, Regulated Emissions, and Energy use in Transportation model

GSE ground support equipment

GWP global warming potential

HAP hazardous air pollutant(s)

HFC hydrofluorocarbon(s)

H1H high first high

H2S hydrogen sulfide

kg/ha/yr kilogram per hectare per year

km kilometer

kW-hr kilowatt-hour

LTO landing and takeoff

MDA8 maximum daily running 8-hour average

MEGAN Model of Emissions of Gases and Aerosols from Nature

MERP Modeled Emission Rates for Precursors

METSTAT Meteorological/Statistical program

MISR Multi-angle Imaging SpectroRadiometer

MMbbl/yr million barrels per year

MMIF mesoscale model interface

MMM Mesoscale Meteorological Modeling

MMS Minerals Management Service

MODIS Moderate Resolution Imaging Spectroradiometer

MOVES Motor Vehicle Emission Simulator model

NAAQS National Ambient Air Quality Standards

NCAR National Center for Atmospheric Research

NCDC National Climatic Data Center

NEI National Emissions Inventory

NEPA National Environmental Policy Act

NH3 ammonia

NOAA National Oceanic and Atmospheric Administration

NOx nitrogen oxides

NO2 nitrogen dioxide

NSB North Slope Borough

NWS National Weather Service

N2O nitrous oxide

O&G oil and gas

OCD Offshore and Coastal Dispersion model

OCS Outer Continental Shelf

OCSLA OCS Lands Act

OSAT Ozone Source Apportionment Technology

PBL planetary boundary layer

PFC perfluorocarbon(s)
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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PGM photochemical grid model(ing)

PM10 particulate matter with an aerodynamic diameter of less than or equal to 

10 micrometers 

PM2.5 particulate matter with an aerodynamic diameter of less than or equal to 

2.5 micrometers 

ppb parts per billion

PRISM Parameter-elevation Regressions on Independent Slopes Model

PSAT PM Source Apportionment Technology

PSD Prevention of Significant Deterioration

Q-Q Quantile-Quantile

QA quality assurance

QC quality control

RFD reasonably foreseeable development

RHC Robust Highest Concentration

SF6 sulfur hexafluoride

SIL significant impact level

SMOKE Sparse Matrix Operator Kernel Emissions model

SO2 sulfur dioxide

SRG Science Review Group

SST sea surface temperature

TAF Terminal Area Forecast

TAPS Trans-Alaska Pipeline System

UAF University of Alaska, Fairbanks

ULSD ultra-low sulfur diesel

VMT vehicle miles traveled

VOC volatile organic compound

WRAP Western Regional Air Partnership

WRF Weather Research and Forecasting model

μg/m3 micrograms per cubic meter 

μm micrometer
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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I. INTRODUCTION

The Bureau of Ocean Energy Management (BOEM) Alaska Outer Continental Shelf Region 

(AKOCSR) has the delegated authority to regulate stationary sources of emissions from oil and 

gas activities proposed within the Chukchi Sea and Beaufort Sea Outer Continental Shelf (OCS)

Planning Areas adjacent to the North Slope Borough (NSB) of Alaska. Proposed operators on the 

Alaska OCS are required to comply with the U.S. Department of the Interior (DOI)/BOEM Air 

Quality Regulatory Program (AQRP), established under 30 CFR part 550, subpart C, and BOEM 

has the obligation to implement the authority provided in OCS Lands Act (OCSLA) Section 

5(a)(8). Figure I-1 shows the Alaska OCS area and NSB of Alaska.

Figure I-1. Regional Map Depicting OCS Planning Areas Including Location of the 

Arctic AQ Modeling Study

(Select historical lease areas are indicated in green.) Source: BOEM, 2014
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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The overall objective of the BOEM Arctic Air Quality Impact Assessment Modeling Study 

(Arctic AQ Modeling Study or study) is to facilitate BOEM’s assessment of potential air quality 

impacts from oil and gas exploration, development, and production on the Alaska OCS and 

related onshore activities. This study uses computerized atmospheric dispersion modeling and 

photochemical grid modeling to estimate increases in concentrations of emissions from existing 

and proposed anthropogenic activities. The air quality analysis is only as comprehensive and 

accurate as the emission inventory on which the analysis is based, and only as accurate as the 

meteorological dataset applied to disperse and transport the pollutants.

The Arctic AQ Modeling Study is organized into the following tasks:

• Task 1: Science Review Group

• Task 2: Conduct Meteorological Dataset Evaluation

• Task 3: Prepare Emission Inventories

• Task 4: Conduct Atmospheric Near-Field Dispersion Modeling

• Task 5: Conduct Photochemical/Dispersion Far-Field Pollutant Modeling

• Task 6: Prepare Emission Exemption Thresholds Evaluation

• Task 7: On-Call Statistical Support

Eastern Research Group, Inc. (ERG) and Ramboll conducted this study under BOEM Contract 

M13PC00014.

The study began in September 2013 and is scheduled to be completed in September 2018.

During this 5-year period, the study team developed protocols and task-specific draft and final 

reports (for Tasks 2 through 6), which were reviewed and commented on by BOEM and the 

Science Review Group (SRG) prior to being finalized. Also, the study team developed numerous 

memos and two Interim Progress Reports (August 2015 and October 2016), which documented 

interim progress related to the main task reports.

This report contains the background, methods used, and results obtained for the main task 

activities covering the meteorological data set evaluation (Task 2), emissions inventory 

development (Task 3), near-field atmospheric dispersion modeling (Task 4), photochemical grid 

modeling (Task 5), and the emission exemption thresholds (EET) evaluation including the 

far-field dispersion modeling analysis (Task 6).
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II. TASK 2: METEOROLOGICAL DATA EVALUATION

The goals of the Arctic AQ Modeling Study require a three-dimensional, physically-consistent 

meteorological dataset to drive the photochemical and dispersion models. Typically, this is 

supplied by running a prognostic (forecasting) model in “hindcast” mode, with sufficient 

observations to constrain the model. The current state-of-the-art prognostic meteorological 

model is the Weather Research and Forecasting (WRF) model.

Ramboll conducted an initial assessment of four existing WRF datasets under Task 2 and 

concluded that none of the datasets were sufficient to support the goals of the study. Ramboll 

performed a new WRF run specifically to support the study goals. The assessment methods for 

the initial evaluation and for the evaluation of the final dataset were very similar and are 

described below.

The final WRF output was used in a Model Justification Report (Ramboll Environ, 2017), where 

the performance of the WRF-based American Meteorological Society/United States 

Environmental Protection Agency regulatory model for dispersion (AERMOD) and California 

Puff regulatory air pollution dispersion model (CALPUFF) dispersion models were compared to 

the performance of the U.S. Environmental Protection Agency (EPA) regulatory-default model 

for offshore and coastal dispersion (OCD). This same final WRF dataset is also used in the nearfield dispersion modeling (Task 4), photochemical grid modeling (Task 5), and the EET 

evaluation and far-field dispersion modeling (Task 6).

A. Meteorological Data Evaluation

i. Summary of Approach

Four potential WRF Arctic datasets existed as of the beginning of the study: Alpine Geophysics 

Chukchi WRF, Ohio State/National Center for Atmospheric Research (NCAR) WRF, University 

of Alaska, Fairbanks (UAF) Mesoscale Meteorological Modeling (MMM) WRF, and 

EPA/BOEM WRF for the WRF-AERCOARE project. The first two datasets were eliminated 

based on temporal coverage: the Alpine dataset covered only three summers, and the Ohio State 

dataset was saved at 3-hourly resolution (hourly data are required for the BOEM Arctic AQ 

Modeling Study).

Ramboll collected several observational datasets, including the 31-year dataset from UAF; the 

DS-3505 Integrated Surface Global Hourly dataset from National Climatic Data Center (NCDC); 

upper-air data from National Oceanic and Atmospheric Administration (NOAA) radiosondes, 

Japan Agency for Marine-Earth Science and Technology research vessels, the Endeavor Island 

Vertical Profiler; and satellite-based cloud-cover retrievals. These observations were compared 

to WRF model output to assess the model’s ability to reproduce realistic atmospheric conditions.

Ramboll evaluated the surviving two WRF dataset candidates both quantitatively and 

qualitatively. The quantitative (statistical) portion used Ramboll’s Meteorological/Statistical 

(METSTAT) program that analyzes paired in-time and in-space statistics. Ramboll analyzed 

onshore and offshore datasets separately for wind speed, wind direction, temperature, and 

humidity (both absolute water vapor mixing ratio, and relative humidity) performance. Ramboll 

also analyzed offshore datasets for sea surface temperature (SST) performance.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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The qualitative analysis involved plotting total monthly precipitation fields for each month of the 

year, for each WRF dataset. These were compared to the Parameter-elevation Regressions on 

Independent Slopes Model (PRISM) 30-year normals for each month. A further qualitative 

analysis involved plotting monthly averages of cloud cover fraction from each WRF simulation

and comparing the results to the Multi-angle Imaging SpectroRadiometer (MISR) “first look” 

cloud cover product. Because this cloud cover retrieval is somewhat limited, due to its 

requirement of visible light, the innermost WRF domain over the North Slope was not well 

covered in the winter months. 

ii. Final Findings

The METSTAT analysis results comparing both onshore and offshore observational datasets to 

each WRF dataset showed they were reasonably similar. The EPA/BOEM WRF dataset from the 

AERCOARE project (a BOEM/EPA project to develop a meteorological pre-processor for 

AERMOD using the Coupled Ocean-Atmosphere Response Experiment [COARE] over-water 

algorithm) showed slightly better performance than the UAF MMM WRF dataset.

While not a perfect comparison, the comparison to the PRISM 30-year normals showed that the 

UAF MMM WRF dataset substantially overpredicts precipitation. Comparison with the MISR 

satellite-based cloud cover product was inconclusive for the winter months, but did suggest a 

slight overprediction of cloudiness in the UAF MMM WRF dataset.

Ramboll found a more serious problem with the EPA/BOEM WRF dataset related to SST, a 

required input to WRF. The simulation used an SST dataset that did not represent the Mackenzie 

River outflow well, which can be up to 15 °C warmer than water in the Beaufort Sea during the 

summer. The warm, fresh water “floats” over the cold, salt water and creates a buoyant plume 

that can be seen far from shore (see Figure II-1). Due to a lack of local measurements and 

persistent clouds, this feature was not captured well in WRF, leading to reduced vertical stability 

and increased atmospheric convection. As a result, there were notable differences between 

modeled and measured meteorological parameters in the region.

The final recommendation of the Meteorological Dataset Evaluation document was to re-run 

WRF with an alternate SST dataset, after testing the accuracy of the various high-resolution SST 

products. This resulted in a new WRF dataset, called the BOEM Arctic WRF dataset (Brashers, 

et al., 2015).
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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Figure II-1. Moderate Resolution Imaging Spectroradiometer (MODIS) SST Composite 

Image for September 7-8, 2010

B. Model Performance Evaluation

The BOEM Arctic WRF dataset, developed by Ramboll, spans 5 years (2009-2013), and was 

based on the previous EPA/BOEM WRF dataset with updates to improve performance in 

Northern Alaska. The Model Performance Evaluation document describes the WRF settings and 

input datasets, and presents an evaluation of how well WRF matched the observations (Brashers 

et al., 2016).

i. Summary of Approach

Ramboll subjected the BOEM Arctic WRF dataset to the same assessments as the candidate 

WRF datasets, described above: both quantitative and qualitative assessments. The quantitative 

assessment included separate onshore and offshore METSTAT analyses. The qualitative

assessment examined vertical profiles taken at Point Barrow, from research vessels offshore, and

from the Shell thermal profiler on Endeavor Island. Both PRISM precipitation and MISR cloud 

cover qualitative evaluations were also performed, though in this case the WRF precipitation was 

averaged by month over the full 5-year simulation, to better match the available PRISM 30-year 

normals. The UAF MMM WRF dataset was not subjected to the same 5-year average by month 

comparison with the PRISM 30-year normals, because the aim of the original comparison was to 

compare the single overlapping year between the two datasets (2009).
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ii. Final Findings

The BOEM Arctic WRF meteorological model simulation, for January 2009 through December 

2013, reproduced the observed surface meteorological variables reasonably well. The wind 

direction performance was improved over previous meteorological datasets for the same region. 

Sample METSTAT-based “soccer” plots for winds are shown in Figure II-2 (speed) and 

Figure II-3 (direction) for the onshore assessment. The model performance offshore seemed 

worse, due in part to lower spatial and temporal resolution of available measurements.

Vertical profiles also performed well, accurately reproducing observed conditions of coastal 

interactions in the planetary boundary layer. The 5-year average precipitation amounts for each 

month were consistent with the PRISM 30-year normal. Where and when valid satellite cloud 

retrievals were available, WRF-predicted cloud amount compared reasonably well. Based on 

Ramboll’s experience, the BOEM Arctic WRF modeling’s performance provides a sound basis 

for developing meteorological inputs for the study modeling.

Figure II-2. BOEM Arctic WRF Onshore METSTAT 4 Kilometer Domain Wind Speed 

Performance
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Figure II-3. BOEM Arctic WRF Onshore METSTAT 4 Kilometer Domain Wind Direction 

Performance

C. Model Justification Report

As stated in 30 CFR 550.218(e), air dispersion modeling of sources under BOEM jurisdiction 

must be conducted using a model that is approved by the BOEM Director and in accordance with 

the guidelines in Appendix W of 40 CFR part 51, often called the U.S. EPA Guideline on Air 

Quality Models (i.e., 2017 EPA Guideline) (U.S. EPA, 2017).

The OCD is the preferred dispersion model for overwater sources for short-range transport 

(source-to-receptor distances less than 50 kilometers [km]). The 2017 EPA Guideline does not 

currently list a preferred dispersion model for long-range (source-to-receptor distances greater 

than 50 km) transport over water, but the previous version (2005) listed CALPUFF as the 

preferred model for long-range transport over land (U.S. EPA, 2005). AERMOD is the preferred 

dispersion model for over-land short-range modeling. The last substantial change to the OCD 

model was in 1997, almost two decades ago (though there was a small bug fix released in 2000). 

AERMOD, unlike OCD, has undergone continuous updates to both its scientific algorithms and 

its input and output formats.

Section 8.4.5 of the 2017 EPA Guideline allows for the use of prognostic meteorological model 

output data where there is no National Weather Service (NWS) station near the emissions source, 

or where it is unfeasible or too expensive to measure site-specific meteorological data. In 

offshore situations, suitable meteorological datasets near a desired emission source are often 

unavailable or prohibitively expensive to obtain.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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In the Model Justification Report (Ramboll Environ, 2017), Ramboll evaluated the preferred 

model for short-range transport of emissions from offshore sources, OCD, to two alternative 

models, AERMOD and CALPUFF, using prognostic WRF in place of observed meteorology.

i. Summary of Approach

There were two main components of the Model Justification Report: an evaluation using 

available offshore tracer studies; and, a “consequence” evaluation using the 5-year WRF dataset. 

Ramboll used WRF data to supply the meteorology to OCD, CALPUFF, and AERMOD 

simulations, for both components of the report.

For the tracer studies, Ramboll compared model results to observed concentrations of an inactive 

tracer. For the consequence evaluation, there were no observed concentrations available; 

CALPUFF and AERMOD results were compared to the regulatory-default model OCD. The 

release parameters and emission rates for the tracer studies were taken from the Minerals 

Management Service (MMS) project to enhance CALPUFF (Earth Tech, 2006). The release 

parameters and emission rates for the consequence analysis were taken from the BOEM/EPA 

WRF-AERCOARE project (ENVIRON, 2012a; ENVIRON, 2012b), and four synthetic sources 

were each placed 25 km from shore in the Beaufort Sea and 40 km from shore in the Chukchi 

Sea, with receptors placed both along the shoreline and along the state seaward boundary as 

shown in Figure II-4.

Figure II-4. Arctic WRF 4 Kilometer Modeling Domain

The tracer studies were performed in the 1980s and though they often had phases in multiple 

seasons, the total number of hours is limited. Half of the data were from periods before SST 

sensors were first deployed on satellites, which limited WRF’s ability to accurately reproduce the 
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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meteorology (due to very coarse SST inputs). The tracer studies were from Cameron, LA (July 

1981 and February 1982); Carpinteria, CA (September 1985); Pismo Beach, CA (December 

1981 and June 1982); and Ventura, CA (September 1980 and January 1981). These are the same 

tracer studies that were used for benchmark testing and development of OCD and CALPUFF.

Ramboll used the EPA mesoscale model interface (MMIF) program to convert the WRF output 

to the meteorological file formats required by each dispersion model. MMIF supports CALPUFF 

and AERMOD directly. The AERMOD modeling system has its own meteorological preprocessor named AERMET, and the CALPUFF modeling system has its own meteorological 

pre-processor named CALMET. MMIF replaced both AERMET and CALMET for this study. 

Although CALMET has reasonable support for overwater situations, AERMET has certain 

assumptions and simplifications that make it unsuitable for overwater use.

Ramboll wrote Fortran programs to convert MMIF output for AERCOARE to the overwater 

meteorological file format required by OCD, and to convert the AERMOD surface file (*.SFC) 

to the overland meteorological file format required by OCD.

Ramboll assessed model performance using a series of statistical measures, both following the 

EPA’s Cox-Tikvart method, and using Quantile-Quantile (Q-Q) plots, Robust Highest 

Concentration (RHC), fractional factor of two, and geometric correlation coefficient, mean, and 

variance.

ii. Final Findings

For the tracer studies, the analysis showed that AERMOD, OCD, and CALPUFF perform 

differently at each individual tracer site, but that no model is an overall poor performer. When all 

the data are used or when the data are grouped by season, AERMOD and OCD model 

performance are comparable and AERMOD outperforms OCD in the winter, perhaps when the 

spatial gradients in stability and planetary boundary layer (PBL) heights are weakest. The results 

presented in the tracer study analysis suggest AERMOD is a comparable to, if not better than, the 

OCD model.

For the consequence analysis, there was better agreement between AERMOD and OCD 

concentrations than between CALPUFF and OCD, especially for the highest concentrations, 

which carry the most weight for regulatory purposes. From a model configuration perspective, 

this analysis revealed that area sources in OCD agree more closely with volume sources in 

AERMOD compared to area sources in AERMOD. At the high end of the concentrations, 

AERMOD tends to agree well (not underpredict) compared to OCD.

CALPUFF has a slight (for point sources) to larger (for area and volume sources) tendency to 

underpredict compared to OCD. It is also possible that OCD overpredicts compared to 

CALPUFF – because these are hypothetical sources with no corresponding observed 

concentrations, it is only possible to identify that there are differences. OCD is presumed to be 

the reference because it is the current regulatory-default dispersion model for overwater 

situations. At short source-receptor distances, CALPUFF’s area and volume sources are similar 

to OCD’s area source. At longer source-receptor distances, CALPUFF area and volume sources 

differ from OCD’s area source, but it cannot be determined which is more correct. None of the 
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

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tracer studies’ releases could be characterized by volume or area sources, so it is not possible to 

compare any of the model’s performance to observations for these source types. Given that 

OCD’s area source algorithm appears to be very similar to a “pseudo point source” (a point 

source with a large stack diameter and a small stack exit velocity), it might be expected that the 

volume sources would agree, but there would be little expectation that the area source types 

would agree between models; the area source algorithms between the three models are just too 

different.
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III. TASK 3: EMISSION INVENTORIES

One of the first steps in the Arctic AQ Modeling Study, and in support of subsequent air quality 

modeling analyses, was to develop a comprehensive air emissions inventory that accurately 

estimates emissions within the study area encompassing the North Slope region and adjacent 

waters of the Beaufort Sea and Chukchi Sea Planning Areas (see Figure I-1). 

ERG developed the emissions inventory, which was completed in December 2014 and 

documented in the Final Task 3 Report (Fields Simms et al., 2014).

A. Summary of Approach

ERG first developed a detailed protocol in developing the emissions inventory for the study. The 

protocol provided details on the scope of the inventory, the procedures and data to be used for 

estimating emissions. The draft emissions inventory protocol was prepared, reviewed, and 

commented on by BOEM and the SRG. ERG incorporated responses to these comments into the 

final method used for the inventory, which is described below. 

The scope of the inventory was defined as follows:

• Baseline – The year for which the most recent, credible, and reliable information was 

available. To the greatest extent possible, 2012 data were used, but these were sometimes 

augmented with data from other years (including 2011 and 2013).

• Future scenario – Future year sources and activities that are reasonably foreseeable and 

expected to continue for an extended period of time. Projected (future year) emissions 

were estimated for use in evaluating impacts anticipated from potential future oil and gas 

exploration, development, and production activities on the Arctic OCS. ERG projected 

future emissions based on information and guidance provided by BOEM for a year in the 

future when projected offshore operations are reasonably expected to be fully built out, or 

referred to hereafter as the full build-out (FBO) scenario (BOEM, 2014). ERG estimated 

annual emissions (i.e., baseline and future emissions), and developed temporal profiles 

for use in air quality modeling.

• Pollutants – The air pollutants that contribute to air quality and health and visibility 

concerns, including: criteria air pollutants (CAPs) and precursors; hazardous air 

pollutants (HAPs, as defined by the Clean Air Act, Title III); greenhouse gases (GHGs, 

including carbon dioxide [CO2], methane [CH4], nitrous oxide [N2O], sulfur hexafluoride 

[SF6], hydrofluorocarbons [HFCs], and perfluorocarbons [PFCs]); hydrogen sulfide 

(H2S); and ammonia (NH3).

• Sources – The sources operating within the inventory domain, including stationary 

sources located in North Slope communities and oil fields, on-road motor vehicles, 

nonroad equipment, marine vessels and other offshore (oil- and gas-related) sources (i.e., 

both OCS and near shore in state waters), the Trans-Alaska Pipeline System (TAPS) and 

airports. Also, emissions from other sources were estimated based on their potential 

influence on air quality concentrations, including dust from paved and unpaved portions 
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of the Dalton Highway and other roads located in communities and the oil fields. 

Table III-1 lists the source groups and categories included in the Arctic AQ Modeling 

Study emissions inventory and the associated air pollutants. Note that emissions from 

biogenic sources (e.g., nitrogen oxides [NOx] from soils, etc.) and geogenic sources (e.g., 

oil seeps, wildfires, etc.) will be estimated in a future stage in this study.

• Domain – The geographic area in which the emission sources are located. For the Arctic 

AQ Modeling Study emissions inventory, the domain encompasses the Arctic OCS, 

including the Chukchi and Beaufort Seas, near shore state waters (within 3 nautical miles 

of the coast), and the NSB. 

i. Baseline Inventory

The baseline emissions inventory represents air emissions from the sources operating in the 

Arctic OCS, including the Chukchi and Beaufort Seas, near-shore state waters (within 3 nautical 

miles of the coast), and the NSB, over the course of a year. ERG generally developed the 

baseline emissions inventory using data from 2012, however, because it was necessary to use 

data from other years in order to compile a comprehensive inventory, the initial annual inventory 

is also referred to as the “baseline” emissions inventory.

a. Offshore Sources

Offshore activities during 2012 included oil and gas seismic surveys, exploratory drilling, and 

support helicopters visiting survey vessels and exploratory drilling rigs to deliver supplies and 

transfer personnel. Also, commercial marine and research vessels operated in the Beaufort and 

Chukchi Seas during this time, although these were not directly related to oil and gas 

exploration. To estimate vessel emissions, ERG applied activity data to appropriate Greenhouse 

gases, Regulated Emissions, and Energy use in Transportation (GREET) model marine vessel 

emission factors (ANL, 2013) and HAP speciation profiles (U.S. EPA, 2013a). For estimating 

helicopter and aircraft landing and takeoff (LTO) emissions, ERG used the Federal Aviation 

Administration’s (FAA’s) Emissions and Dispersion Modeling System (EDMS) (FAA, 2013) 

where aircraft specific data were available, and EPA methods provided in the 2011 National 

Emissions Inventory (NEI) where only generic aircraft type (i.e., commercial carriers, air taxis, 

general aviation, and military aircraft) activity were available (U.S. EPA, 2013a).
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Table III-1. Sources Included in the BOEM Arctic AQ Modeling Study Emissions Inventory

Group and Category CAPs HAPs GHGs H2S NH3

Offshore Oil 

& Gas 

Activities

Seismic survey and supply vessels ✓ ✓ ✓ ✓

Seismic support helicopters ✓ ✓ ✓

On-ice seismic survey equipment ✓ ✓ ✓ ✓

Exploratory drilling – drill ships, jackups ✓ ✓ ✓ ✓

Exploratory drilling – fleet support vessels ✓ ✓ ✓ ✓

Platform construction and support vessels ✓ ✓ ✓ ✓

Island construction and support vessels ✓ ✓ ✓ ✓

Production platform operation ✓ ✓ ✓ ✓

Platform support – supply and support vessels ✓ ✓ ✓ ✓

Platform support – helicopters ✓ ✓ ✓

Pipelaying and support vessels ✓ ✓ ✓ ✓

Offshore –

Other

Commercial marine vessels ✓ ✓ ✓ ✓

Research vessels ✓ ✓ ✓ ✓

Onshore Oil 

& Gas

Fields

Seismic survey equipment ✓ ✓ ✓ ✓

Drilling/exploration ✓ ✓ ✓ ✓

Well pads ✓ ✓

Processing plants, gathering centers, etc. ✓ ✓ ✓ ✓

Support (injection, seawater treatment) ✓ ✓ ✓ ✓

Airports Aircraft and helicopters ✓ ✓ ✓ ✓

Ground support equipment ✓ ✓ ✓ ✓

TransAlaska 

Pipeline 

System 

Pump stations (1-4) ✓ ✓ ✓ ✓

On-road patrol vehicles ✓ ✓ ✓ ✓

Aerial surveillance aircraft ✓ ✓ ✓ ✓

TAPS fugitives ✓ ✓ ✓ ✓

Natural gas supply line fugitives ✓ ✓ ✓ ✓

Pigging operations ✓ ✓ ✓ ✓

Pipeline replacement, repair ✓ ✓ ✓ ✓

Onshore 

Non-Oil & 

Gas 

Activities

Power plants ✓ ✓ ✓ ✓

Industrial/commercial/institutional/residential 

fuel combustion

✓ ✓ ✓ ✓

On-road motor vehicles ✓ ✓ ✓ ✓

Nonroad mobile sources ✓ ✓ ✓ ✓

Road dust ✓

Waste burning ✓ ✓ ✓ ✓

Wastewater treatment ✓

Fuel dispensing ✓ ✓ ✓

Power plants ✓ ✓ ✓ ✓

Industrial/commercial/institutional/residential 

fuel combustion

✓ ✓ ✓ ✓

Spills OCS pipeline spills ✓ ✓ ✓ ✓

Platform spills ✓ ✓ ✓ ✓
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b. Onshore Sources

In addition to offshore sources, the emissions inventory includes estimates for onshore emission 

sources within the North Slope region and adjacent waters of the Beaufort Sea and Chukchi Sea 

Planning Areas. These sources include the North Slope oil and gas fields, as well as onshore 

sources located in eight nearby villages and elsewhere on the North Slope (e.g., airports, the 

TransAlaska Pipeline System, non-oil and gas related stationary and mobile sources, etc.).

Due to the wide variation in the types of onshore sources operating on the North Slope, their 

emissions inventory methods varied significantly. ERG used various methods and data to 

estimate baseline onshore source emissions, by source type, as follows:

• Seismic survey equipment – ERG estimated emissions based on estimated quantity of 

ultra-low sulfur diesel (ULSD) consumption, based on geological and geophysical 

(G&G) permit data (BOEM, 2012) and EPA WebFIRE emission factors (U.S. EPA, 

2013b).

• Exploratory drilling – ERG estimated emissions based on estimated quantity of ULSD 

used in drilling rig engines, heaters, and boilers, based on permit data from the Alaska 

Department of Environmental Conservation (ADEC) and EPA WebFIRE emission 

factors (U.S. EPA, 2013b).

• Oil and gas production – ERG estimated emissions using data from the 2011 NEI (U.S. 

EPA, 2013a), data reported under subpart W of the Greenhouse Gas Reporting Program 

(GHGRP), ADEC permit data for sources that were too small to report to the NEI, and 

applying scaling factors to estimate emissions for some pollutants and sources that were 

not part of the permit data.

• Airports, aircraft, and ground support equipment – ERG estimated emissions for 

11 airports using either aircraft-specific or air-carrier-specific data provided by the 

airports or the FAA’s Terminal Area Forecast (TAF) data (FAA, 2014), and the FAA’s

EDMS. For the Deadhorse Airport, ERG discovered that the original baseline inventory 

had omitted commercial airline traffic (i.e., only general aviation had been accounted 

for). To correct this omission, ERG used aircraft-specific data from the FAA and reran 

the EDMS using the correct commercial data. This procedure and the associated results 

were documented in a memo (ERG, 2015a).

• TransAlaska Pipeline System – Pump Stations 1 through 4 are the only TAPS pump 

stations located in the NSB; ERG obtained emissions for Pump Stations 1, 3 and 4 from 

the 2011 NEI (Pump Station 2 was inactive). ERG estimated fugitive pipeline emissions 

using national production-based emission factors and a scaling factor (i.e., TAPS mileage 

within the North Slope vs. national pipeline mileages) (BTS, 2014). ERG estimated 

emissions from on-road motor vehicles and nonroad equipment used for pipeline 

operation and maintenance using the same methods as described below for on-road 

mobile sources nonroad sources, and assuming the levels of activity needed to operate 

and maintain the pipeline. Also, ERG estimated emissions for pipeline aerial surveillance 

by helicopters by assumed air time, and number of trips, and using the FAA’s EDMS 

(FAA, 2013).
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• Non-oil and gas stationary, point, and area sources – In general, for the combustion 

sources, ERG estimated emissions by multiplying the relevant activity data by EPA 

WebFIRE emission factors (U.S. EPA, 2013b); ERG estimated HAP emissions using 

speciation fractions from the SPECIATE database (U.S. EPA, 2014a). For road dust, 

ERG estimated emissions by multiplying unpaved road vehicle miles traveled (VMT) by 

emission factors derived from empirical equations found in AP-42, Section 13.2.2 (U.S. 

EPA, 1995).

• On-road motor vehicles and nonroad mobile sources – For on-road motor vehicles, ERG 

estimated emissions using emission factors from EPA’s Motor Vehicle Emission 

Simulator (MOVES) model (U.S. EPA, 2014b) with local meteorological and vehicle 

activity data for VMT and fuel consumption. For the nonroad mobile sources (i.e., 

snowmobiles and all-terrain vehicles), ERG used the EPA’s NONROAD2008a model to 

derive emission factors based on fuel consumption (U.S. EPA, 2009).

ii. Projections Inventory

To help BOEM assess impacts on air quality from future oil and gas exploration, development 

and production on the Alaska OCS, as well as in near-shore state waters, ERG also developed 

future year emissions inventory projections. The future year emissions inventory projections 

covered sources and activities in Arctic OCS that are reasonably foreseeable, and expected to 

continue for an extended period of time. The projections reflect a future scenario as defined by 

BOEM and representative of a single future year when offshore operations are anticipated to be 

fully built out (i.e., FBO scenario) (BOEM, 2014). The sources and their respective levels of 

activity associated with the full build-out scenario are summarized in Table III-2.

The methods used to estimate emissions for the baseline inventory were also used to estimate 

emissions for the projections inventory. For the sources that are the same in both inventories,

only the activity data changed for the projections inventory. Also, there are additional sources in 

the projections inventory (i.e., pipelaying, gravel island, and offshore platform construction and 

operation) that are not present in the baseline inventory.

a. Offshore Sources

The offshore projection scenario developed by BOEM includes two sites in the Chukchi Sea, and 

four sites in the Beaufort Sea as noted in Figure III-1. The figure also shows the anticipated 

location of the Liberty (gravel) Island, discussed below. Because the projection scenario does not 

identify specific vessels and aircraft to be used, actual periods of activities, or actual vessel 

traffic patterns, ERG made several assumptions.
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Table III-2. Full Build-Out Projections Scenario for Offshore Oil and Gas Activities

Beaufort Sea Chukchi Sea

Projected Production

Production: Gas BCF/yr 167 115

Production: Oil, Condensate MMbbl/yr 132 204

Total Platform wells 215 260

Total Subsea Wells 34 90

Projected Activities and Duration: B1, B2, B3, and B4 = Development Areas in the Beaufort Sea; C1 and C2 = Development Areas in the Chukchi Sea

Seismic Surveys July – October 8-week run includes survey vessel, support, and scout 

vessels; support helicopters

4-week run includes survey vessel, support, and scout 

vessels; support helicopters

December – May 1 on-ice operation lasting 4 weeks None

Geohazard Surveys July – October 8-week run includes survey and support vessels 8-week run includes survey and support vessels

Geotechnical Surveys 8-week run includes survey and support vessels 8-week run includes survey and support vessels

Exploratory Drilling July – October

• 1 jackup at B3 and 1 jackup at B4

• Support vessels, icebreaker, spill response, helicopters

• 2 drill ships at C1

• Support vessels, icebreaker, spill response, helicopters

Offshore Pipeline 

Construction July – October

• 44 miles of new construction 

• Pipelaying vessel, dredge ships, support vessels, 

helicopters

• 40 miles of new construction 

• Includes pipelaying vessel, dredge ships, support 

vessels, helicopters

Platform Construction

July – October

(Gravel island 

construction

December – May)

• 1 gravity-base system constructed at B1

• 1 gravity-base system constructed at B2

• 1 gravel island at Liberty location

• Support vessels, icebreaker, helicopter support, gravel 

trucks

• 1 gravity- based system constructed at C1

• Support vessels, icebreaker, helicopters 

• Subsea well construction

• 2 jackups at B2 and 1 jackup at B3

• Support vessels, helicopter support

• Subsea well construction

• 1 jackup and 2 drill ships at C1 

• Support vessels, helicopters

Production Platform 

Operation

Throughout the 

Year

• 1 platform at B1; 27 on platform wells

• 2 platforms at B2; 81 on platform wells, 23 subsea wells

• 1 platform at B3; 54 on platform wells, 11 subsea wells

• 1 platform at B4; 54 on platform wells, providing a total 

of 215 on platform wells and 34 subsea wells

• Platform equipment, support vessels, helicopter support

• 2 platforms at C1; 260 on platform wells and 90 subsea 

wells

• Platform equipment, support vessels, helicopters

• Production at Liberty Island; 32 wells

• Platform equipment, support vessels

None

BCF/yr = billion cubic feet per year; MMbbl/yr = million barrels per year.

(Source: BOEM, 2014)
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ERG made the following assumptions regarding the methods and data to estimate projected 

offshore source emissions, by source type:

• Seismic survey vessels and support helicopters – ERG estimated emissions using the 

baseline inventory vessel method. Activity data (i.e., number of trips, distances) were 

based on assumptions shown in Table III-2 and locations shown in Figure III-1.

• Exploratory drilling – ERG estimated emissions using the same method as was used for 

the baseline inventory. Activity data were provided by BOEM (BOEM, 2014) and are 

summarized in Table III-3.

• Pipelaying and support vessels – ERG estimated support vessel emissions using the 

baseline inventory vessel method. Vessel characteristics were developed based on 

pipelaying vessels that operate in other areas of the Arctic. Each pipelaying vessel was 

assumed to require four support vessels and operate 24 hours/day laying pipe at a rate of 

1 mile/day.

Figure III-1. Offshore Projected Development Areas

(Select historical lease areas are indicated in green.) Source: BOEM, 2014
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Table III-3. Projected Drilling Activity

Activity 

Type

Development 

Areas Vessel Type Number of Vessels

Wells per 

Vessel

Days per 

Well

Total 

Days

Exploratory

B3 Jackup 1 2 38 76

Support Fleet 1 2 38 76

B4 Jackup 1 1 38 38

Support Fleet 1 1 38 38

C1 Drillship 2 2 38 152

Support Fleet 1 2 38 76

Subsea Well

B2 Jackup 2 3 38 228

Support Fleet 1 3 38 114

B3 Jackup 1 3 38 114

Support Fleet 1 3 38 114

C1

Drillship 2 3 38 228

Jackup 1 3 38 114

Support Fleet 1 3 38 114

(Source: BOEM, 2014)

• Platform construction – BOEM’s full build-out scenario included construction of gravitybased structures (GBS) built to withstand winter ice flows. ERG estimated emissions for 

vessels involved in towing the GBS to the sites, positioning the platform, ballasting the 

base, as well as for support vessel and helicopter activities. ERG assumed levels of 

activity such as: platform towing at a rate of 2 mph; 40 hours each for tugs to put 

platforms in place; and support vessels operating during the remainder of the open water 

season (2.5 months).

• Platform operations – ERG derived emissions profiles from available data from some of 

the larger offshore platforms operating in Cook Inlet, Alaska, based on data compiled 

from the 2011 NEI and the EPA’s GHGRP data. ERG applied the average Cook Inlet 

platform emissions per well to the projected number of production wells for each 

development area based on BOEM’s scenario as shown in Table III-4. 

• Spills – BOEM anticipates that there may be emissions associated with spills from future 

oil and gas exploration, development and production activities in the Chukchi and 

Beaufort Seas. BOEM’s scenario included estimates of the potential spill quantities of 

diesel and crude; ERG estimated emissions based on evaporation curves (i.e., percentage 

of evaporation relative to time after spill). Also, ERG estimated emissions from 

associated spill response vessels by assuming days required to clean up spills of various 

quantities and hours/day of operation by each vessel (i.e., tug boats, larger vessels).

Table III-4. Projected Number of Wells

Location Well Type Development 

Areas

Number of 

Wells 

Beaufort Sea On-platform B1 27

Beaufort Sea On-platform B2 81

Beaufort Sea On-platform B3 54

Beaufort Sea On-platform B4 54
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Table III-4. Projected Number of Wells

Location Well Type Development 

Areas

Number of 

Wells 

Beaufort Sea Subsea B2 23

Beaufort Sea Subsea B3 11

Chukchi Sea On-platform C1 260

Chukchi Sea Subsea C1 9

Liberty Island - - 32

(Source: BOEM, 2014)

b. Onshore Sources

ERG projected onshore emissions that represent anticipated future year emissions for sources 

that can reasonably be expected to be constructed and/or operated during a future year that is 

consistent with the offshore projection scenario. These sources, along with the methods and data 

used by ERG to estimate the projected emissions, are described below. 

Several sources and activities are not addressed in this future year scenario because any 

prediction of future activities for these sources would be highly speculative at this time. These 

sources include existing onshore oil and gas production facility activities and several non-oil/gas 

related stationary point and mobile sources (i.e., these sources were included in the impacts 

modeling, as discussed below in Sections III.B and V.A). Also, no future (post-2012) regulations 

are anticipated to reduce future emissions from the existing onshore oil and gas production 

facilities and the existing non-oil/gas related stationary point and mobile sources, with one 

exception: Tier 4 diesel manufacturer emission standards that came into effect in 2014. Although 

these standards will serve to reduce emissions from affected engines after 2012 as older engines 

are replaced, the rate of turnover is difficult to predict. Therefore, ERG did not estimate these 

reductions, which will provide a conservatively high estimate of these emissions for modeling.

• New oil and gas production facilities – Four future production facilities are either 

currently under construction, are permitted for construction, or are reasonably expected to 

be built: Greater Moose’s Tooth Unit 1 (AECOM, 2013), and Point Thomson Production 

Facility (ADEC, 2014), CD-5 Satellite at Alpine (ADEC, 2014). The method used to 

estimate projected emissions from each of these facilities was based on ADEC 

construction permits, BLM EIS, and actual emissions estimates for similar existing 

facilities.

• New pipeline construction and operations – Construction emissions for the two new 

onshore pipelines to be constructed and operated to transport new offshore production to 

the TAPS and the existing feeder pipelines were estimated based on construction 

emissions associated with Greater Moose’s Tooth Unit 1 (AECOM, 2013).

• Liberty Island construction and drilling – Liberty Island will be planned as a selfcontained offshore drilling/production facility located on a conventional gravel island 

with pipelines to shore. The island is planned to be built in Foggy Island Bay in the 

Beaufort Sea in approximately 21 feet of water. The future emissions projected to be 
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emitted by Liberty Island will be due to its construction, followed by drilling and 

production operations. ERG estimated construction emissions for Liberty Island based 

upon construction emissions associated with Greater Moose’s Tooth Unit 1 (AECOM, 

2013). ERG derived the emissions estimates for the Liberty Island drilling operation from 

the Kuparuk River Transportable Drilling Rigs Renewal Application (ADEC, 2012), and 

the peak number of production wells to be drilled as provided in the BOEM scenario 

(BOEM, 2014).

• Airport and aircraft emissions – ERG projected the base year airport/aircraft emissions 

into future years using the FAA’s Air Traffic Activity from the FAA’s Aerospace 

Forecast Fiscal Years 2012-2032 (FAA, 2014).

• New support facilities – In support of increased aircraft activities, a number of additional 

facilities would need to be built, including the following: an Exploration Base, an Air 

Support Base, and a Search and Rescue Base. Also, a new Supply Boat Terminal would

be built to support offshore production in the Chukchi Sea. ERG based the construction 

emissions from these four facilities on construction emissions for Greater Moose’s Tooth 

Unit 1 (AECOM, 2013), scaled based on the ratio of the proposed facility footprint 

divided by the Greater Moose’s Tooth Unit 1 pad footprint. ERG used only emissions 

from the ice roads, gravel roads and pads, and facilities installation construction activities 

(both on-road motor vehicle and nonroad equipment) for this estimate.

• Trans-Alaska Pipeline System – The future year increased production would affect some 

of the existing emissions associated with the TAPS. According to Alyeska Pipeline 

Service Company statistics, the 2012 TAPS throughput was slightly over 200 million 

barrels (Alyeska, 2013), so the future year increased production would effectively double 

the TAPS throughput. A review of pump station inventories from ADEC’s online Point 

Source Emissions Inventory indicated a general trend of decreased pump station 

emissions with decreasing throughput (ADEC, 2014). Conversely, increased throughput 

would result in increased emissions. ERG estimated future year emissions for pump 

stations, pipeline fugitives, and natural gas supply line fugitives by doubling the 2012 

emissions. ERG assumed that future year emissions would not increase for on-road 

patrols, aerial surveillance, pigging operations, and pipeline replacement and repair.

• Non-oil and gas stationary point and area sources – Several point sources as well as 

commercial/institutional fuel combustion and residential fuel combustion used heating oil 

with a high sulfur content (i.e., not ULSD) in the baseline inventory. To account for the 

future use of ULSD in these sources, ERG reduced the baseline sulfur dioxide (SO2) 

emissions by 99.4 percent (i.e., corresponding to a shift from 2,500 ppm sulfur content of 

heating oil to 15 ppm sulfur content of ULSD) to represent the projections inventory for 

these sources.

• Man-camp construction and operation – Subsequent to the submittal of the original 

emissions inventory as reported in the Final Task 3 Report (Fields Simms et al., 2014), 

BOEM requested the addition of a 100-person man-camp (both construction and 

operation) that would support the Chukchi Sea development. ERG added these emissions 

to the projection inventory only (i.e., and not the baseline inventory) and documented the 
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emissions in the “new source” memo (ERG, 2015a). ERG based the operation emissions

for the Chukchi Sea man-camp on similarly sized man-camp located near Nuiqsut in 

support of the Greater Moose’s Tooth Unit 1 development (AECOM, 2013). Assumed 

operating sources consisted of two continuously operating diesel-fired IC engines, each 

consuming 32 gallons of fuel per area. ERG estimated emissions using per gallon 

emission factors developed for Greater Moose’s Tooth Unit 1 (AECOM, 2013). ERG did 

not estimate man-camp construction emissions for Greater Moose’s Tooth Unit 1 (likely 

due to the modular nature of the man-camp structures); construction emissions were also 

not estimated for the Chukchi Sea man-camp.

• Supply boat operation – BOEM also requested the addition of emissions from a supply 

boat traveling from Dutch Harbor to Chukchi Sea Development Areas C1 and C2. ERG 

based supply boat emissions upon operating parameters of an example vessel (Harvey 

Champion) (i.e., route distance, cruising speed, vessel rated power, and operating load 

factor) provided by BOEM (Crowley, 2015a; Crowley, 2015b). ERG based estimates of 

supply boat operation emissions for operations within the Chukchi Sea Planning Area 

upon shipping lane segments obtained from the U.S. Army Corps of Engineers (USACE, 

2014).

B. Final Findings

This section presents the results of the Arctic AQ Modeling Study emissions inventory for the 

baseline emissions and the future projections. In addition to the results provided in the emissions 

inventory report published in December 2014, ERG provided a set of Emission Inventory Tables 

in Excel format for BOEM’s use in December 2015.

i. Baseline Inventory

Tables III-5 and III-6 summarize the baseline emissions inventory for CAPs, GHGs, and other 

pollutants (i.e., HAPs, H2S, and NH3). In the baseline emissions inventory, offshore sources 

include emissions from seismic survey vessels, drilling rigs, and survey/drilling support aircraft 

and vessels; commercial marine vessels (CMV); and research vessels. Onshore sources include 

oil and gas activities (i.e., seismic surveys, exploratory drilling, and oil and gas production); 

airports, aircraft, and ground support equipment (GSE); TAPS; and non-oil and gas related 

stationary and mobile sources. These tables show that emissions from onshore sources in the 

baseline inventory are much larger (i.e., by two orders of magnitude for most pollutants) than 

emissions from offshore sources. This result is not unexpected given that the offshore sources 

that operated during this time were limited to a very small number of sources as compared to the 

onshore sources.
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Table III-5. Summary of Baseline Emissions – Criteria Air Pollutants (tons/yr)

Sector Pollutant

NOx SO2 VOC CO PM10 PM2.5 Pb

Offshore 1,816.3 38.2 106.0 248.6 35.8 27.2 0.005

Onshore 45,811.7 1,243.1 2,918.4 14,073.4 35,647.2 4,774.5 0.244

Total 47,628.0 1,281.3 3,024.4 14,322.0 35,683.0 4,801.7 0.250

Table III-6. Summary of Baseline Emissions – Greenhouse Gases and Other 

Pollutants (tons/yr)

Sector Greenhouse Gases Other Pollutants

CO2 CH4 N2O CO2e

a HAPs H2S NH3

Offshore 139,982.5 0.8 6.5 141,932.6 18.1 0.0 0.7

Onshore 13,570,837.3 8,792.4 29.1 13,799,316.2 398.5 16.4 4.4

Total 13,710,819.8 8,793.2 35.6 13,941,248.8 416.6 16.4 5.2

a Calculated using global warming potentials (GWPs) from IPCC, 2007.

Table III-7 shows the baseline emissions inventory for the onshore oil and gas sector, by source 

category. This table provides the total emissions (tons/yr) by pollutant and source category 

within the onshore oil and gas sector, as well as the percentage of the total pollutant emissions 

contributed by each source category. Production accounts for the majority of emissions 

generated within the sector.

Table III-7. Selected Baseline Emissions from Onshore Oil and Gas Sources

Pollutant

Exploratory 

Drilling

Oil and Gas 

Production

Seismic Survey 

Equipment Total

NOx

Tons/yr 1,388.2 42,260.1 144.1 43,792.4

Percent of Total 3% 97% <1% 100%

SO2

Tons/yr 42.1 1,049.0 9.5 1,100.6

Percent of Total 4% 95% 1% 100%

VOC

Tons/yr 354.2 1,707.2 2.7 2,064.1

Percent of Total 17% 83% <1% 100%

CO

Tons/yr 318.0 8,967.5 31.0 9,316.5

Percent of Total 3% 96% <1% 100%

PM10

Tons/yr 19.0 1,168.6 10.1 1,197.7

Percent of Total 2% 98% <1% 100%

CO2e

Tons/yr 108,823.1 13,185,512.4 5,390.1 13,299,725.6

Percent of Total <1% 99% <1% 100%

ii. Projections Inventory

Tables III-8 and III-9 summarize the emissions inventory projections for the CAPs, GHGs, and 

other pollutants (i.e., HAPs, H2S, and NH3). These tables show projection emissions for the 
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offshore sources based on BOEM’s scenario (BOEM, 2014) and for the onshore sources 

reasonably expected to occur and that are affected by increased offshore production and

exclusive use of ULSD fuel in selected onshore point and area sources. 

Note that the projected emissions described in this section do not represent all future year 

projected emissions. The projected emissions include only those sources and activities that are 

expected to change (i.e., increase or decrease) in the future. Furthermore, the future year 

projected emissions should not simply be added to the 2012 emissions of the sources that are not 

expected to change to calculate total future year emissions because onshore oil and gas emissions 

from existing facilities, and emissions from construction and operation emissions from new 

facilities will likely not all occur during the same year. Work conducted by ERG and Ramboll

during the modeling phase of the BOEM Arctic AQ study defined which specific emission 

sources were modeled to assess future air quality impacts, including emissions from existing 

facilities and from construction and operation of new facilities. See Section V.A below for more 

detail.

These tables show that the emissions projected for the offshore sources are distributed nearly 

equally across sources anticipated to be operating in the Beaufort and Chukchi Seas in the future.

Table III-8. Summary of Emissions Projections – Criteria Air Pollutants (tons/yr)

Sector

Pollutant

NOx SO2 VOC CO PM10 PM2.5 Pb

Offshore – Beaufort Sea 7,474.2 561.3 417.8 1,484.6 174.5 144.5 0.017

Offshore – Chukchi Sea 6,988.8 774.0 354.5 1,531.5 174.1 150.5 0.014

Onshorea 17,365.4 364.0 945.2 7,528.5 971.5 898.4 0.021

Total 31,828.4 1,699.3 1,717.5 10,544.6 1,320.1 1,193.4 0.052

a

Includes only emissions from new sources and from sources expected to change under the projection scenario (i.e., 

future new oil and gas production facilities; new pipelines; Liberty (gravel) Island; airports, aircraft, and supply 

boat terminal; TAPS; and certain non-oil and gas stationary point and area sources).

Table III-9. Summary of Emissions Projections – Greenhouse Gases and Other 

Pollutants (tons/yr)

Sector

Greenhouse Gases Other Pollutants

CO2 CH4 N2O CO2e

a HAPs H2S NH3

Offshore –

Beaufort Sea 1,293,500.1 52,375.3 181.9 2,657,097.2 68.3 0 2.3

Offshore –

Chukchi Sea 1,534,029.2 73,618.2 242.5 3,446,759.1 56.2 0 1.8

Onshoreb18,371,080.1 26,602.2 76.9 19,059,046.5 80.7 0 0.002

Total 21,198,609.3 152,595.7 501.4 25,162,902.8 205.1 0 4.1

a Calculated using global warming potentials (GWPs) from IPCC, 2007.

b

Includes only emissions from new sources and from sources expected to change under the projection scenario 

(i.e., future new oil and gas production facilities; new pipelines; Liberty (gravel) Island; airports, aircraft, and 

supply boat terminal; TAPS; and certain non-oil and gas stationary point and area sources).
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Table III-10 shows the projected offshore emissions by source for the CAPs. The largest 

contributors to the projected offshore emissions are platform operation, resupply of drilling 

vessels, pipelaying activities, production support, and drilling vessels.

Table III-10. Emissions Projections from Offshore Sources (tons/yr)

Source

Criteria Air Pollutant

NOx SO2 VOC CO PM10 PM2.5 Pb

Survey Operations 553.8 0.5 28.3 62.9 8.6 6.3 0.001

Exploratory Drilling 6,550.8 12.3 442.1 1,043.2 138.3 102.2 0.021

Pipelaying and Support Vessels 1,705.1 1.0 87.0 191.4 26.3 19.3 0.004

Platform Construction 537.9 0.6 30.5 62.5 14.0 10.3 0.002

Platform Operations and 

Support Vessels 5,088.6 1,311.5 183.1 1,653.1 159.9 155.4 0.002

Spills 26.8 9.4 1.2 3.1 1.5 1.3 0.0002

Total 14,463.0 1,335.3 772.3 3,016.1 348.6 295.0 0.031

C. Emissions Inventory Uncertainty and Opportunities for Improvement

Two general areas of uncertainty in this study’s inventory, and basically characteristic of any 

large-scale “bottom up” inventory of this type (i.e., based on the collection of data for each 

source in the domain) include: 

• Use of emission factors combined with activity data (e.g., amount of fuel combusted, 

vessel activity kilowatt-hours [kW-hrs], aircraft travel distances, etc.) resulting in an 

approximate estimate of emissions, and not reflective of actual emissions with the same 

accuracy that direct source tests would yield.

• Limitations are due to the availability of source-specific data, such that surrogate data 

from similar sources is needed to ensure completeness of the inventory in terms of 

sources covered and pollutants included.

ERG identified specific areas of emissions inventory uncertainty, along with suggestions for 

ways to improve these specific estimates in future inventory versions, as provided below. These 

uncertainties and associated recommendations focus on the most significant sources (i.e., 

contributing the most emissions or combination of emissions) in the baseline and projections 

inventories.

• Baseline inventory:

o Oil and gas production (greatest NOx, volatile organic compound [VOC], SO2, 

carbon monoxide [CO], carbon dioxide equivalent [CO2e], and HAP emissions) –

Uncertainty is associated with applying scaling factors to estimate emissions for 

some pollutants that were not available from ADEC permits (or 2011 NEI and 

GHGRP data) for smaller units. It is possible that ADEC now has permit data 
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collected in 2014 for these sources that could be used to update these estimates. 

Also, surveys of North Slope operations could provide better data and coverage of 

emissions as compared to what was last reported to the GHGRP, since these 

reporting requirements have recently changed.

o Unpaved road dust re-entrainment (greatest particulate matter with 

aerodynamic diameter of less than or equal to 10 micrometers [μm] [PM10] and 

particulate matter with aerodynamic diameter of less than or equal to 2.5 μm 

[PM2.5] emissions) – Uncertainty is associated with the lack of robust local silt and 

moisture content samples for the North Slope villages, which are used as inputs to 

the unpaved road dust algorithm to estimate emissions. Silt and moisture content 

sampling could be conducted in the North Slope villages following the sampling 

procedures detailed in Appendix C.1 and C.2 of U.S. EPA’s AP-42 (U.S. EPA, 

1995) to provide more accurate inputs to the equation, and thus more accurate 

emission estimates for these sources.

• Projections inventory:

o New oil and gas production facilities (high NOx, VOC, CO, PM10, PM2.5, and 

CO2e emissions) – Uncertainty is associated with the emissions estimated for the 

planned Chukchi coast processing production base facility, and the lack of permit 

data on the sizes, unit types, or controls that may be put in place at the proposed 

facility. An initial construction permit application for this facility will be 

extremely beneficial to inform the projected emissions inventory.

o Offshore exploratory drilling, and platform operations and support vessels

(high NOx, SO2, and VOC emissions) – Uncertainty is associated with the 

following assumptions and surrogate data used to project emissions:

▪ Assumptions of the number of support and scout vessels (for surveys, 

exploratory drilling, platform construction, production platforms, and 

pipelaying).

▪ Surrogates used for vessel characteristics, and the number of helicopter 

trips (for surveys, exploratory drilling, platform construction, and 

production platforms).

▪ Assumptions made for all vessel power rating and load factors, dredging 

vessel operating hours, and surrogate dredging vessels (for pipelaying).

▪ Surrogate data used for gravity-based structures, then adjusted downward 

for Arctic conditions (for platform construction).

▪ Surrogate data used from a platform in Cook Inlet (for platform 

production).
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Uncertainty associated with these estimates can be addressed in the future when 

Beaufort/Chukchi platform operators apply for air quality permits. Studies to validate emissions 

such as testing and data logging of activity, throughput, and operating load will also be needed to 

more accurately assess emissions from the future Beaufort/Chukchi platforms.

D. Spatial and Temporal Allocation of Emissions

ERG developed two additional sets of emissions-related data needed for air quality modeling:

• Spatial surrogates used to spatially allocate the baseline and projected emissions 

inventories across the inventory domain.

• Temporal profiles used to distribute the annual baseline and projected emissions 

inventories over various timeframes. 

In total, ERG developed over 550 unique point source latitude/longitude coordinates and nearly 

40 sub-county boundary shapes from the nonpoint, on-road, and nonroad sectors for spatial 

allocation of the baseline and projections inventory within the modeling domain. Each allocation 

was converted to .kmz format, so that they could be visually inspected using Google Earth. The 

procedures followed and data used to develop the spatially allocated emissions were documented 

in a technical memorandum (ERG, 2015b).

ERG compiled the spatially allocated baseline and projections emissions inventory using 

geographic information system (GIS) software. Point source emissions were spatially allocated 

by the facility’s latitude and longitude coordinates and then assigned to a unique 4-km grid cell 

within the modeling domain. For the nonpoint, on-road, and nonroad sectors, ERG developed 

unique boundary shapes for each source category within the North Slope and allocated the 

emissions evenly to 4-km grid cells using the spatial surrogates where there is overlap of a 

boundary area and a 4-km grid cell. Where there is partial overlap of a boundary area and a 4-km 

grid cell, the GIS software apportioned emissions based on area of the overlapping cell; 

therefore, the sum of the emissions of the unique source category within a boundary shape is 

equal to the sum of the emissions in the overlapping 4-km grids. 

Also, ERG converted the annual emissions from the baseline and projections inventory to hourly 

emissions for inputs to the models. ERG implemented the EPA Sparse Matrix Operator Kernel 

Emissions (SMOKE) model (U.S. EPA, 2002) for developing hourly estimates, which includes a 

temporal crosswalk, monthly allocation factors, weekday allocation factors, and daily allocation 

factors. In some instances, default temporal allocation profiles from the SMOKE model were 

used, but if no existing SMOKE profile accurately characterized the temporal allocation of 

particular sources, custom profiles developed by ERG were applied, including to the extent 

feasible profiles reflecting seasonal and diurnal factors unique to the North Slope. 
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IV. TASK 4: ATMOSPHERIC DISPERSION MODELING

ERG conducted local scale, or near-field (within approximately 50 km of the source) 

atmospheric dispersion modeling (ADM) in support of two of the overarching project goals. 

First, the ADM supports the National Environmental Policy Act (NEPA) environmental impact 

air quality assessments, by estimating the impact from project oil and gas production on onshore 

pollution levels. Second, ADM supports the evaluation of the existing emissions exemption 

thresholds (see Task 6). The near-field ADM uses the meteorological dataset developed in 

Task 2 and the emissions inventory developed in Task 3.

A. Summary of Approach

As the first phase of the near-field ADM task, ERG prepared a detailed protocol (ERG, 2014). 

The protocol outlined the procedures to be used for the near-field ADM runs. BOEM and the 

SRG reviewed and commented on the draft ADM protocol. ERG incorporated responses to these 

comments into the final method that ERG used for the modeling in Task 4.

In summary, under Task 4, ERG modeled all near-shore sources (i.e., sources within 50 km of 

the shoreline) in separate modeling runs to assess the impact for each individual source. Each 

source was modeled using the baseline (base year) and FBO emissions scenarios, where 

applicable. For the FBO scenario, the different emissions levels for the construction and full 

production stages were modeled separately. This provides BOEM with an estimate of the 

impacts from individual sources for future comparison to submitted plans. Emission sources

were categorized based on the release shape: point, area, and line. Point sources are facilities 

with distinct release sources (i.e., stacks) that have characteristics specified in the inventory (e.g., 

stack height, release temperature, exit velocity). For example, electric generating facilities 

(Figure IV-1) have a distinct location and release points, and were modeled as point sources. 

Area sources release emissions over an area. For this study, communities were modeled as area 

sources to capture the broad release area of the emissions. Line sources are sources whose 

emissions are released over a distance best represented by a line. For example, fugitive dust 

emissions from the Dalton Highway were modeled as lines.
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IV-2

Figure IV-1. Location of Electricity Generating Facilities

ERG used the AERMOD model (version 16216r) with 5 years (2009-2013) of meteorology 

extracted from the Task 2 WRF simulation results. ERG used EPA-prescribed modeling options 

for regulatory assessment. For nitrogen dioxide (NO2) modeling, ERG used a “Tier 1” approach, 

which assumes all NOx is converted to NO2 to provide a conservatively high estimate of NO2

impacts.

ERG placed receptors at 500-meter intervals along the shoreline and state seaward boundary to 

assess the impact of each source at the jurisdictional interface of BOEM and onshore regulators.

The shoreline receptors follow a generalized coastline definition, rather than a strict shoreline 

definition that would follow every coastal feature. This simplifies the receptor placement by not 

strictly following large coastal features such as bays, lagoons, and mouths of rivers. Figure IV-2

provides an example of the generalized shoreline receptors. ERG also placed receptors in the 

center of the North Slope communities (Figure IV-3) to quantify the onshore impacts in 

population areas.
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Figure IV-2. Example of Generalized Shoreline Receptors Along the North Slope

Figure IV-3. North Slope Communities Included in the Near-field Dispersion Modeling
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B. Final Findings

The Task 4 ADB database produced by ERG provides impact and emissions information by 

source category and facility, where appropriate (Do et al., 2017a). All reported impacts are the 

maximum value of the highest reported concentration at each receptor, also known as the high 

first high (H1H) value. ERG made significance comparisons by comparing the H1H values to the 

EPA prescribed significant impact levels (SILs) and National Ambient Air Quality Standard 

(NAAQS). The database contains separate significance determinations for the shoreline receptors 

and for the state seaward boundary and community receptors. Tables IV-1, IV-2, and IV-3 show 

the range of impacts seen for each of the types of sources (i.e., point, area, line) used in the 

modeling.

Overall, the results of the ADM analysis show low estimated air quality impacts from the criteria 

air pollutants at the shoreline. This is largely due to low emissions from sources or large 

distances from the receptors. The modeling was designed for a conservatively high estimate of 

impact, in that the maximum hourly emission rate was used and comparison to both the NAAQS 

and SIL used the maximum, or H1H impact for assessment. In addition, NO2 modeling runs 

assumed a full conversion of NO to NO2, which provides a conservatively high estimate. Only 

estimates of these 1-hour NO2 impacts showed any source over the SIL; however, these source 

impacts were ultimately a small percentage of the NAAQS and unlikely to cause a violation of 

the NAAQS at these conservatively high levels. Offshore sources had slightly higher impacts at 

the seaward boundary receptor locations, particularly for NO2. However, the values were still 

comparable to shoreline values with respect to the percentage of the NAAQS. While direct PM2.5

estimated impacts are conservatively high, the estimates are lower than what would be seen with 

photochemical modeling, as the estimates do not include the additional mass formed through the 

chemical reaction of SO2 and NOx emissions. The formation of secondary particulate matter 

species can be significant, depending on the meteorological conditions and other pollutant 

present.

These individual source modeling results can be combined to provide a conservatively high 

estimate of cumulative impact in an area. As new plans are submitted, BOEM can use the select 

similar sources from the modeling results and combine them to develop a rough estimate of the 

impacts of the proposed project. This could be used in project planning to suggest projects that 

might cause high impacts and warrant controls or other mitigation measures. The impact 

estimates could also be compared to submitted plan impact levels as a quality check. That is, the 

estimated impact can serve as a baseline level to compare the projects against to gauge whether 

plan impacts seem too high or low. For example, the modeled NO2 estimates assume all NOx is 

retained as NO2, and as such are conservatively high. If a submitted project’s estimates fall 

above an impact estimated from this modeling, even after accounting for any differences in 

emission levels and distances to the point of impact, it would suggest the proposed project 

modeling needs further review.
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IV-5

Table IV-1. Impact Ranges for Point Sources

Pollutant Inventory

Range of Impact (μg/m3)

1 Hour 3-Hour 8-Hour 24-hour Annual

CO Baseline [< 0.01 - 11.70] -- [< 0.01 - 2.88] -- --

Full Build-Out [< 0.01 - 631.60] -- [< 0.01 - 155.41] -- --

NO2

Baseline [< 0.01 - 3.47] -- -- -- [< 0.01 - 0.07]

Full Build-Out [< 0.01 - 36.24] -- -- -- [< 0.01 - 3.97]

PM10

Baseline -- -- -- [< 0.01 - 0.34] --

Full Build-Out -- -- -- [< 0.01 - 0.38] --

PM2.5

Baseline -- -- -- [< 0.01 - 0.25] [< 0.01]

Full Build-Out -- -- -- [< 0.01 - 0.25] [< 0.01]

SO2

Baseline [< 0.01 - 0.14] [< 0.01 - 0.09] -- [< 0.01 - 0.02] [< 0.01]

Full Build-Out [< 0.01 - 7.43] [< 0.01 - 4.61] -- [< 0.01 - 1.15] [< 0.01 - 0.03]

Table IV-2. Impact Ranges for Area Sources

Pollutant Inventory

Range of Impact (μg/m3)

1 Hour 3-Hour 8-Hour 24-hour Annual

CO Baseline [< 0.01 - 0.26] -- [< 0.01 - 0.11] -- --

Full Build-Out [< 0.01 - 0.26] -- [< 0.01 - 0.10] -- --

NO2

Baseline [< 0.01] -- -- -- [< 0.01]

Full Build-Out [< 0.01] -- -- -- [< 0.01]

PM10

Baseline -- -- -- [< 0.01 - 0.55] --

Full Build-Out -- -- -- [< 0.01 - 0.55] --

PM2.5

Baseline -- -- -- [< 0.01 - 0.06] [≤ 0.01]

Full Build-Out -- -- -- [< 0.01 - 0.05] [≤ 0.01]

SO2

Baseline [< 0.01 - 0.02] [< 0.01 - 0.02] -- [≤ 0.01] [< 0.01]

Full Build-Out [< 0.01] [< 0.01 - 0.02] -- [≤ 0.01] [< 0.01]

Table IV-3. Impact Ranges for Line Sources

Pollutant Inventory

Range of Impact (μg/m3)

1 Hour 3-Hour 8-Hour 24-hour Annual

CO Baseline [< 0.01 - 0.19] -- [< 0.01 - 0.07] -- --

Full Build-Out [< 0.01 - 3.90] -- [< 0.01 - 1.79] -- --

NO2

Baseline [< 0.01 - 1.73] -- -- -- [< 0.01 - 0.02]

Full Build-Out [< 0.01 - 24.91] -- -- -- [< 0.01 - 0.03]

PM10

Baseline -- -- -- [< 0.01 - 0.03] --

Full Build-Out -- -- -- [< 0.01 - 0.18] --

PM2.5

Baseline -- -- -- [< 0.01 - 0.01] [< 0.01]

Full Build-Out -- -- -- [< 0.01 - 0.07] [< 0.01]

SO2

Baseline [< 0.01 - 0.35] [< 0.01 - 0.28] -- [< 0.01 - 0.07] [< 0.01]

Full Build-Out [< 0.01 - 0.35] [< 0.01 - 0.28] -- [< 0.01 - 0.07] [< 0.01]
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V. TASK 5: PHOTOCHEMICAL/DISPERSION FAR-FIELD POLLUTANT 

MODELING

Ramboll conducted regional air quality modeling with a photochemical grid model (PGM) to 

quantify air quality conditions associated with existing and potential future development in the 

OCS and the combined (cumulative) impact of OCS sources with other existing and other

reasonably foreseeable future sources (i.e., reasonably foreseeable development or RFD). 

In Task 5, Ramboll evaluated OCS and related source impacts on a regional scale, in this case a 

large area encompassing the North Slope and beyond. Regional scale impacts of interest are 

concentrations of ozone and PM2.5, visibility degradation or regional haze associated with 

elevated levels of light absorbing and scattering particles and gasses, and acid deposition.

Impacts of OCS sources and associated activities within the immediate vicinity (~50 km) of 

individual sources were modeled under Task 4.

A. Summary of Approach

Ramboll performed modeling for both a Base Case and a Future Year (FY) scenario. Both full 

annual scenarios used the 2012 WRF model meteorological fields developed in Task 2. WRF 

results for 2012 were selected from the 2009 – 2013 WRF model output developed in Task 2. 

Year 2012 WRF result were found to be reasonably representative of “typical” meteorological 

conditions in northern Alaska and provided the best opportunity for collecting the wide range of 

activity data needed for estimating emissions from most of the air pollution sources operating on 

the North Slope. Base Case modeling used the Base Case emission inventory and FY modeling 

used the FY emission inventory for the North Slope Borough and adjacent state and federal 

waters developed by ERG under Task 3. In both scenarios, Ramboll combined the Task 3 

inventory with emissions from anthropogenic sources in the remaining portions of the modeling 

domain (primarily Alaska, but also including small portions of northwestern Canada and 

Siberia). Ramboll estimated anthropogenic emissions outside of the NSB and adjacent state and 

federal waters and biogenic, lightning, and wildfire emissions for the entire modeling domain for 

the 2012 Base Case and used without modification for the FY modeling. Ramboll then spatially 

and temporally allocated and merged emissions together in preparation for modeling. Ramboll 

performed modeling over a set of nested domains as shown in Figure V-1 with the inner domains 

having the highest spatial resolution. Ramboll selected the 4 km domain to encompass all of the 

onshore NSB emission sources and to include offshore sources out to a distance of roughly 

200 km or more from shore. For Comprehensive Air Quality Model with Extensions (CAMx), 

Ramboll extracted boundary conditions (BCs) for the 12 km domain from the 36 km simulation 

results and the 12 and 4 km grids were modeled using 2-way nesting (allowing interactions 

between the two grids in both inbound and outbound directions).

Ramboll based specification of the PGM vertical layer structure on the definition of the WRF 

vertical layers structure. Ramboll ran the WRF simulation with 34 vertical layer interfaces (thus 

making 33 vertical layers) from the surface up to 100 mbar (approximately 16 km above mean 

sea level). The WRF model employs a terrain-following coordinate system based on the eta () 

coordinate, which is defined by relative pressure differences between layers. WRF levels are 

more finely stratified near the surface in an attempt to improve simulation of the atmospheric 

boundary layer structure and processes: Instead of the common practice of specifying a surface 
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V-2

layer depth of approximately 40 m, two 12 m layers overlaid by a 16 m layer were used to better 

capture the very stable conditions often observed in the Arctic. Ramboll adopted a layer 

collapsing scheme for the PGM simulations, whereby pairs of upper level WRF layers are 

combined into single PGM layers to improve the PGM computational efficiency while 

maintaining the fine layer structure used in WRF in the lower layers (up to approximately 

1.2 km). This provides for a better simulation of the stable thermal stratification of the boundary 

layer and avoids errors potentially introduced by layer collapsing.

Day-specific BCs for the outermost (36 km horizontal resolution) domain were obtained from a 

2012 simulation of the GEOS-Chem (v10-01) global chemical model (ACMG, 2017). The use of 

an alternative global model (MOZART-4/GEOS5) as a source for the BCs was also explored via 

a test simulation on the 36 km domain with BCs derived from MOZART (NCAR, 2017a). 

Comparisons of Community Multi-scale Air Quality Model (CMAQ) simulations based on 

MOZART BCs with CMAQ simulations based on GEOS-Chem BCs showed generally similar 

results for ozone and PM over northern Alaska. Based on these results and the fact that, in 

contrast to GEOS-Chem, MOZART does not use day-specific values for dust emissions, BCs 

based on the GEOS-Chem model were selected for use in the final model simulations.

Figure V-1. CAMx Nested Modeling Domains at Horizontal Resolutions of 36 km, 12 km, 

and 4 km

Green Grid Lines Represent the GEOS-Chem 2 x 2.5 Degree Global Modeling Grid.
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V-3

Ramboll based the modeling procedures on the U.S. EPA’s current and revised draft modeling 

guidance procedures (U.S. EPA, 2007; U.S. EPA, 2014c). Emissions processing procedures and 

photochemical model configuration are described in a detailed modeling protocol (ENVIRON, 

2014a). BOEM and the SRG reviewed and commented on the Task 5 Modeling Protocol and 

their recommendations were incorporated into the final modeling procedures documented in the

Task 5 Final Report (Stoeckenius et al., 2017).

In this study, BOEM and the SRG applied two PGMs commonly used for ozone and PM 

modeling in the U.S: CAMx version 6.20 with the Carbon Bond 6 revision 2 (CB6r2) 

photochemical mechanism including active excess methane emissions (ENVIRON, 2014b) and 

CMAQ version 5.0.2 (Byun and Ching, 1999) with the Carbon Bond 2005 version with toluene 

and chlorine chemistry updates (CB05TUCL) (CMAQ, 2017). Ramboll performed initial Base 

Case simulations using both CAMx and CMAQ. Both models were found to produce mostly

similar results aside from differences related to sea salt emission estimates. Ramboll then 

performed Refined Base Case and FY simulations using CAMx in order to take advantage of the 

source apportionment technology available in CAMx as described below.

Biogenic emissions were generated using the Model of Emissions of Gasses and Aerosols in 

Nature (MEGAN) version 2.03, developed at the National Center for Atmospheric Research 

(Sakulyanontvittaya et al., 2008). Ramboll obtained meteorological data needed by MEGAN 

from the 2012 WRF output. Ramboll obtained NOX emissions generated by lightning from the 

in-line lightning emissions module contained in the CMAQ PGM. Ramboll obtained sea salt 

emissions from the CAMx sea salt emissions preprocessor.

Ramboll based wildfire emissions on 2012 calendar year estimates of fire emissions from the 

NCAR, which were derived from analysis of fire locations determined by satellite borne 

detectors. Ramboll Environ processed the day-specific Fire INventory from NCAR (FINN)

(NCAR, 2017b) to develop "point sources" of fire emissions using plume rise estimates as a 

function of fire size from the Western Regional Air Partnership’s (WRAP) Joint Fire Science 

Program’s Deterministic and Empirical Assessment of Smoke’s Contribution to Ozone 

(DEASCO3) project. A description of the WRAP 2002 fire plume rise approach can be found in 

Mavko and Morris (2013).

Ramboll used the SMOKE system to prepare emissions from Task 3 for use in the PGM

(CEMPD, 2017). SMOKE consists of a set of programs that convert annual, daily, or hourly 

estimates of emissions at the state or county level to hourly emissions fluxes on a uniform spatial 

grid formatted for input to either the CMAQ or CAMx PGMs. SMOKE integrates emissions 

inventories with source-based temporal, spatial, and chemical allocation profiles to create hourly 

emissions fluxes on a predefined model grid. For elevated sources that require allocation of the 

emissions to the vertical model layers, SMOKE integrates meteorological data to derive dynamic 

vertical profiles.

Ramboll processed emissions by major source category in several different processing “streams” 

to simplify the emissions modeling process and facilitate the quality assurance/quality control 

(QA/QC) of results. SMOKE includes QA and reporting features to keep track of the 

adjustments at each step of emissions processing and to ensure that data integrity is not 

compromised. Ramboll Environ reviewed the SMOKE log files for noteworthy error messages 
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and ensured that appropriate source profiles are being used. In addition, Ramboll reviewed and 

compared SMOKE output summary reports with input emission totals.

Ramboll evaluated results from the Base Case modeling against contemporaneous ambient 

monitoring data to confirm that the model was performing reasonably well in reproducing 

observed air quality conditions. Ramboll then compared results from the Base Case and FY

model runs to quantify expected changes in air quality under the FY scenario as compared to the 

Base Case. In addition, the FY scenario model output included estimates of the contributions of

the specified source groups listed in Table V-1 to predicted ozone and PM2.5 concentrations. 

Ramboll calculated the contributions of emissions from each source group to predicted ozone 

and PM2.5 concentrations using the Ozone Source Apportionment Technology (OSAT) and PM 

Source Apportionment Technology (PSAT) probing tools available in CAMx. The 

Anthropogenic Precursor Culpability Assessment (APCA) version of OSAT was used in the 

future year scenario modeling. APCA differs from OSAT in that it distinguishes between natural 

and anthropogenic emissions; when ozone is formed due to the interaction of biogenic VOC and 

anthropogenic NOx under VOC-limited conditions, a case OSAT would assign the ozone formed 

to the biogenic VOC, APCA recognizes that biogenic VOC is uncontrollable and re-directs the 

ozone formed to the anthropogenic NOx. Thus, APCA assigns ozone formed to natural emissions 

only when it is due to natural VOC interacting with natural NOx emissions.

A detailed description of procedures used to prepare the model-ready emissions inventories and 

run the photochemical grid model are presented together with a detailed description of results in 

a final PGM modeling report (Stoeckenius et al., 2017).

Table V-1. Source Groups for CAMx Source Apportionment

Source Groupa Name Source Categories

A Naturalb Biogenic, Lightning NOx

B

Baseline Oil and 

Gas (O&G)

Existing O&G sources included in Base Year scenario 

of the Arctic AQ Study Emissions Inventory

C

Baseline Other 

Anthropogenic

Existing anthropogenic sources other than O&G 

included in Base Year scenario of the Arctic AQ Study 

Emissions Inventory

D New O&G New O&G sources included in the Future Year 

scenario

E

Outside of NSB 

Anthropogenic

Anthropogenic sources outside of the NSB and 

adjacent state and federal waters

F Fires All wildfires and prescribed burns

G Baseline All Source Groups B and C

H NSB All Source Groups B, C and D

FY Future Year All sources in Future Year scenario

BY 2012 Base Year 2012 Base Year run results

a Results for groups A – H are from the Future Year scenario source apportionment outputs; results for group 

FY are from the Future Year scenario “host model” (all sources combined) outputs; results for group BY are 

from the “host model” (all sources combined) outputs from the Base Year scenario outputs.

b Although sea salt represents a “natural” source of emissions, a tracer for sea salt is not included in PSAT so 

sea salt contributions are not included in Source Group A. 
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B. Final Findings

Analysis of the photochemical modeling results focused on evaluating the impacts of new oil and 

gas sources under the FBO scenario on regional concentrations of ozone and PM2.5 and on acid 

deposition and visibility impacts to Class I and specified Class II areas. Detailed findings are 

described in the final PGM modeling report (Stoeckenius et al., 2017). Highlights of the results 

include:

• Only limited ambient data were available for use in evaluating model performance for the 

Base Case simulation. The simulated seasonal pattern of ozone concentrations over the 

North Slope qualitatively matched the observed pattern although the model consistently 

overpredicts by roughly 10 parts per billion (ppb) during November through February 

and is unable to reproduce the periods of zero or near zero ozone observed during March 

through April. Model performance is much better during June through October. Overall 

performance statistics exhibit low bias and normalized mean error within generally 

accepted regulatory modeling performance criteria. Evaluation of model performance for 

PM2.5 is confounded by highly uncertain sea salt emission values and a complete lack of 

contemporaneous speciated PM2.5 data. 

• Predicted 4th highest maximum daily running 8-hour average (MDA8) ozone 

concentrations along the North Slope under the FBO scenario are generally in the upper 

30s to lower 40s ppb, well below the 70 ppb NAAQS. Ozone source apportionment 

results indicate that anthropogenic sources in the NSB contribute up to 11.4 ppb to these 

predicted ozone design values with nearly all this amount attributable to existing 

(baseline) oil and gas sources. New oil and gas sources contribute a maximum of 3.3 ppb. 

Predicted ozone increases between the Base Case and the FY scenario are limited to a 

maximum of 1.2 ppb along the North Slope. 

• 8th Highest daily average PM2.5 concentrations under the FBO scenario are generally less 

than 5 micrograms per cubic meter (μg/m3) higher than in the Base Case scenario at the 

same location except near Wainwright, where increased support vessel activity and a new 

petroleum processing facility with an assumed 200 MMbbl/yr throughput under the FBO

scenario results in a projected increase of 13.8 μg/m3 which is 40 percent of the 35 μg/m3

NAAQS. Annual average PM2.5 concentrations are predicted to increase by more than 

0.5 μg/m3in the immediate vicinity of new sources, with a maximum increase of 

8.5 μg/m3near Wainwright. The predicted annual average PM2.5 concentration at this 

location (but not elsewhere) exceeds the level of the NAAQS (12 μg/m3) even after 

subtracting the approximate mass attributable to sea salt based on predicted sodium and 

particulate chloride content (see discussion of sea salt sensitivity below).

• For Class I and specified Class II areas, maximum Prevention of Significant 

Deterioration (PSD) pollutant concentration increments (annual average NO2, PM10, and 

SO2; 24-hour average PM10, PM2.5, SO2; and 3-hour average SO2) from new oil and gas 

sources are all below the corresponding Class I and Class II area allowable increments. 

• Incremental visibility impacts at several specified Class II areas from new oil and gas 

sources relative to natural background conditions are modeled to exceed 1.0 deciviews on 
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

V-6

multiple days during the year. Visibility impacts at Denali National Park are modeled to 

exceed 0.5 deciviews on one day out of the year.

• Comparison of annual nitrogen and sulfur deposition attributable to new oil and gas

sources with western (0.005 kilogram per hectare per year [kg/ha/yr]) and eastern 

(0.01 kg/ha/yr) deposition analysis thresholds (DATs) developed by the Federal Land 

Managers for the lower 48 states indicates nitrogen deposition above the 0.01 kg/ha/yr 

DAT in all areas within the NSB and in some areas outside the NSB. Sulfur deposition is 

lower but the maximum sulfur deposition from new oil and gas sources exceeds 

0.01 kg/ha/yr in the Alaska Maritime National Wildlife Refuge (AMNWR) and in the 

Arctic National Wildlife Refuge (ANWR).

• Predicted PM2.5 concentrations are sensitive to large uncertainties in estimated emissions 

of road dust in some populated areas such as Barrow and to sea salt emissions throughout 

the coastal zone. While uncertainties in road dust emissions do not play a significant role 

in evaluating the impacts of new oil and gas sources, a potential overestimation of sea salt 

emissions can result in an overestimation of the impact of NOX emissions on particulate 

nitrate formation, thus adding to the predicted total PM2.5 mass burden, as well as to acid 

deposition and visibility impacts. A sensitivity analysis was conducted using a new 

experimental sea salt emissions preprocessor (Stoeckenius et al., 2017) to help evaluate 

the sensitivity of predicted impacts to uncertainties in sea salt emissions. CAMx 

sensitivity runs were prepared for two different 10-day periods: one in April and one in 

August. Each 10-day simulation consisted of a 3-day spin-up period and seven analysis 

days. Results from these runs were compared with results for the same periods from the 

original CAMx run. Highlights of the sensitivity analysis results include: 

o The revised sea salt emission estimates averaged 96 percent less than in the original 

simulation. While no contemporaneous sodium observations were available with 

which to evaluate the revised sea salt estimates, the much lower sea salt emissions

generated by the new preprocessor provided a convenient benchmark for the 

sensitivity analysis. Comparisons of the revised sodium predictions with historical 

(1997 – 2009) monthly average sodium measured at Barrow and with predictions for 

the corresponding 7-day periods during April and August from the original CAMx 

simulation show a large reduction in overprediction bias towards levels more in line 

with observed values in August but a large underprediction in April.

o Reduced sea salt emissions result in large reductions in particulate nitrate due to 

reduced sodium nitrate formation. This has a corresponding impact on total predicted 

PM2.5 in coastal areas where a large fraction of the predicted PM2.5 is associated with

nitrate. Sensitivity to reduced sea salt emissions are much smaller away from the 

coast.

o Reductions in particulate nitrate resulting from reduced sea salt emissions result in 

lower estimates of visibility impacts in the Alaska National Wildlife Refuge where 

large impacts were predicted from the original model run. However, even with lower 

sea salt emissions, the new oil and gas sources (Source Group D in Table V-1) are 

predicted to result in peak day visibility impacts exceeding the 1 deciview (dv)
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

V-7

threshold at coastal locations in the ANWR on at least a few days during the year. 

The impact of reduced sea salt emissions on predicted visibility impacts in other 

Class I and selected Class II areas is less clear given the limited temporal coverage of 

the sensitivity analysis.

o Impacts of sea salt emission reductions on ozone were minimal; averaging an increase 

of 0.74 ppb during the August period.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VI-1

VI. TASK 6: EMISSION EXEMPTION THRESHOLD EVALUATION

A key objective of the Arctic AQ Modeling Study is to evaluate the current regulatory equations 

in 30 CFR 550.303(d) used to estimate exemption thresholds for offshore source emission rates 

of selected pollutants for the study domain. If this examination shows that the equations can be 

improved relative to the source emission rates of selected pollutants in the study domain, then 

ERG will develop options for revisions of the equations for consideration by BOEM.

Task 6 builds on the modeling analyses conducted by the ERG/Ramboll in Tasks 4 and 5, 

combined with additional modeling to rigorously test the existing emission exemption threshold 

(EET) formulas.

A. Summary of Approach

In general, the EET evaluation approach involves modeling “synthetic” offshore sources in the 

Chukchi and Beaufort Seas. The reason for using hypothetical, but representative, sources is 

twofold: 

• Using synthetic sources avoids the perception that BOEM is calling into question 

previous exemption analysis for existing sources. It is not the intent of this study to 

review previous plans and test the validity of the analysis already conducted for current 

exploration or production operations. 

• Developing synthetic sources allows the flexibility to pair various emission levels with 

various distances to shore to ensure the formula is tested with the full range of possible 

values. By capturing the full range of possibilities, the existing EET formulas will be 

thoroughly tested for any limitations, including combinations of emissions and distance 

to shore not currently leased. 

For the synthetic source emissions levels, ERG developed emission rates for the following 

emissions scenarios that represent: 

• Drilling exploration plan (EP) with well testing.

• Development and production plan (DPP).

• DPP with well testing.

• Production-only DPP.

• Floating production storage and offloading (FPSO) platforms. 

ERG developed all five scenarios for small-, medium-, and large-scale operations using 

calculation methods consistent with submitted EPs and DPPs. 

ERG reviewed publicly available EPs and DPPs from BOEM’s website for typical platform 

configurations and emissions levels submitted to BOEM for approval (BOEM, 2017). ERG then 

used the platform configurations and support vessels from the available plans to construct 
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VI-2

synthetic sources for each of the five scenarios and each operational scale. Table VI-1

summarizes the scenarios and indicates equipment required under each scenario. For example, all 

production operations (Scenarios 2, 3, and 4) include at least one vent or flare (with volumes that

include upsets). 

Table VI-1. Summary of Mandatory Equipment Under Each Scenario

Scenario Description Includes (At Least One)

1 Drilling EP well testing Diesel engines

2 Production and drilling DPP Diesel engine, flare or vent, and fugitives (default of 11,420 

components with light oil stream type)

3

Production and drilling DPP 

with well testing

Diesel engine, flare or vent, and fugitives (default of 11,420 

components with light oil stream type); and well testing

4 Production only Diesel engine, flare or vent, and fugitives (default of 11,420 

components with light oil stream type)

5 FPSOs Diesel engine, flare or vent, and fugitives (default of 11,420 

components with light oil stream type)

After adding all the representative equipment for each scenario, ERG made additional 

modifications to operational hours and activity levels to ensure an adequate variation in the 

annual and hourly emission level to be modeled. Hourly emission rates are consistent with the 

supporting maximum hourly emission rate calculations in the EP and DPP air quality 

spreadsheets (i.e., the total hourly emission rate if all equipment was operated at the same time). 

The emission values were rounded to the nearest hundred to flag that the modeled emissions are 

synthetic sources (e.g., 15,897 would be rounded to 15,900). Table VI-2 presents the initial hour 

emission levels of PM10, PM2.5, SO2, NOx, NH3, VOC, and CO used in modeling. 

Table VI-2. Hourly Emission Levels for Synthetic Sources

Scenario Description Size Emissions (lb/hr)

PM10 PM2.5 SO2 NOx NH3 VOC CO

1 Drilling EP with well testing

L 66 65 631 1,487 0.77 56 356

M 42 41 459 948 0.63 34 224

S 31 31 176 587 0.25 34 142

2 Production and drilling DPP 

L 114 113 669 3,824 0.77 126 600

M 41 40 460 1,076 1.10 138 263

S 32 31 176 600 0.25 139 144

3

Production and drilling DPP 

well testing

L 77 77 506 2,780 0.63 88 346

M 44 44 461 1,001 0.84 140 238

S 32 31 176 600 0.25 139 144

4 Production only DPP

L 14 14 1.7 925 0.05 119 305

M 2.7 2.7 1.7 289 0.05 105 161

S 1.0 1.0 0.9 28 0.01 106 6.6

5 FPSOs

L 15 14 112 676 0.22 125 182

M 17 17 115 671 0.14 135 178

S 13 12 113 517 0.22 21 142

Each emission scenario was modeled for a variety of distances to shore that are representative of 

the active lease blocks in the Chukchi Sea and Beaufort Sea. ERG calculated the distance to 

shore for all the active lease blocks in the Arctic OCS. The active lease blocks were determined 

using the Active Lease Polygons shapefile published on the BOEM “Geographic Mapping Data 
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VI-3

in Digital Format” website (BOEM, 2015) and the ArcGIS proximity toolset to calculate the 

closest distance to shore for each active lease block. Because of barrier islands off the Alaska 

coast, the overall lease block minimum distance to shore of 3 statute miles (4.8 km) determined 

by the analysis periodically fell outside the BOEM Planning Area boundary (i.e., within state 

waters). Therefore, the distances were adjusted to be parallel to the state seaward boundary 

instead of the shoreline. This will ensure all locations selected will be placed just outside state 

waters. Table VI-3 summarizes the distances used for synthetic source placement in modeling. 

Table VI-3. Summary Statistics for Distance to Shore

Distance to Shore 

(statute miles [km])

Distance from 

Seaward Boundary 

(statute miles [km])

Represents

3 [4.8] 1 [1.6] Minimum distance of all lease blocks 

16 [25.7] 13 [20.9] Average distance for Beaufort Sea

22 [35.4] 19 [30.6] 75th percentile distance for the Beaufort Sea

39 [62.8] 36 [57.9] Maximum distance for the Beaufort Sea

60 [96.6] 57 [91.9] 25th percentile distance for all lease blocks

83 [133.6] 80 [128.7] 25th percentile distance for Chukchi Sea

91 [146.5] 89 [143.2] Median distance of all lease blocks

106 [170.6] 103 [165.8] Average/median distance for Chukchi Sea

120 [193.1] 117 [188.3] 75th percentile distance for all lease blocks

163 [262.3] 160 [257.5] Maximum distance for all lease blocks

The proposed synthetic source locations were selected at random from the modeling grid cell that 

fell along the distance lines. All locations are within federal waters in the 4 km WRF domain, 

and coincide with the center of a modeling grid cell. Figure VI-1 shows the proposed synthetic 

source locations in the study area.

ERG modeled the synthetic sources at 50 km or less from the state seaward boundary using 

AERMOD following similar modeling procedures to the ADM modeling in Task 4 (i.e., same 

modeling set and options) in multiple locations in the Chukchi and Beaufort Seas, at multiple 

distances to shore. Synthetic sources at a distance greater than 50 km from the state seaward 

boundary were modeled with CALPUFF. Along the 57-mile contour from the state seaward 

boundary, a subset of sources was modeled with both AERMOD and CALPUFF to determine 

the difference in impact predicted by both models.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VI-4

Figure VI-1. Synthetic Source Placement

Each of the modeled synthetic sources was subjected to the existing EET formulas to determine 

the level of impact at the receptors compared to the established SILs for each NAAQS. The 

results of the exemption formulas were compared to the over/under SILs conclusions from the 

modeling. This produced three outcomes with respect to the EET:

• “Pass” 

o A correct evaluation.

o Emissions from the scenario were above the EET threshold, which indicated 

modeling was needed and the modeling impacts were above the SIL, or emissions 

from the scenario were below the EET threshold, which indicated modeling was 

not needed and the modeled impacts were below the SIL.

• False positive (Type I error)

o Emissions from the scenario were above the EET threshold, which indicated 

modeling was needed, however the modeled impact was below the SIL.

• “Miss” (Type II error) 

o Emissions from the scenario were below the EET threshold, which indicated 

modeling was not necessary, however the modeled impact was over the SIL.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VI-5

B. Final Findings

The existing EET formulas are overly conservative for most annual standards. Specifically, the 

existing formulas suggest modeling is needed more often than is necessary. In addition, for the 

short-term standards, the formulas have a higher “miss” rate. However, the miss rate generally 

does not exceed 15 percent for most of the NAAQS. The 1-hour SO2 and 24-hour NAAQS PM2.5

and PM10 have a miss rate higher than 15 percent. 

Although the emission exemption thresholds generally show they are still protective, BOEM 

may want to consider alternative approaches to reduce the false positive rate to reduce the burden 

on the operator. An alternative approach to the EET formulas would be to utilize the modeling 

information from this task to estimate impacts based on comparable modeled sources. For 

example, an operator could identify a synthetic source modeling run at a comparable emission 

rate and distance to shore as the proposed source to evaluate the likelihood of a significant 

impact. If the comparable synthetic source impacts are below SIL values, then the proposed 

source should be as well. Because not all possible iterations of emissions levels and distances to 

shore were modeled, there would have to be concessions in matching to ensure a protective 

estimate (i.e., conservatively high estimate of impact). For example, a comparable source should 

select a synthetic source with higher emission rates in the absence of an exact match. The results 

from any modeling submitted to BOEM could also be added to the database to provide additional 

sources for comparison. Additionally, the synthetic source database does include a unit emission 

rate source (i.e., 1 gs-1) impact run for three source types: 1) elevated hot stack, 2) short cold 

stack, and 3) vessel characterized as a volume. These results could be scaled and combined to 

estimate a more comparable emissions rate.

For secondary formation, ERG developed conservatively low modeled emission rates for 

precursors (MERPs) (Table VI-4) for use in the Arctic based on three synthetic sources placed in 

both the dispersion and photochemical grid future year modeling (Do et al., 2017). The MERP

guidance from EPA is still in draft form and the EPA is continuing to add to the MERP modeling 

database. As such BOEM will want to continue to coordinate with the EPA on the best methods 

for estimating source contributions to secondary formation in Alaska’s unique climate and 

evaluate EPA modeling results for Alaska. BOEM could coordinate with the ADEC on any 

additional modeling efforts to support MERP development specific to Alaska.

Table VI-4. MERPs Based on Hypothetical Sources

NAAQS SIL

Emission

Rate (tpy)

Estimated 

Impact

MERP

(tpy)

Daily PM2.5 1.2 100 1.18 102

Annual PM2.5 0.2 100 0.22 91

8-hr O3 1.0 100 0.04 2,500
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VII-1

VII. REFERENCES

ACMG, 2017. GEOS-Chem model. Harvard University, Atmospheric Chemistry Modeling 

Group. Internet address: http://wiki.seas.harvard.edu/geos-chem/index.php/Main_Page.

ADEC, 2012. Title V Permit Renewal Application: Transportable Drilling Rigs, ConocoPhillips, 

Alaska Inc. (May).

ADEC, 2014. Point Source Emissions Inventory. Internet address: 

http://dec.alaska.gov/Applications/Air/airtoolsweb/PointSourceEmissionInventory/.

AECOM, 2013. ConocoPhillips Alaska, Inc. – Greater Moose’s Tooth 1 Air Quality Impact 

Analysis. Final. Prepared for ConocoPhillips Company by AECOM, Fort Collins, Colorado 

(October). Internet address: https://eplanning.blm.gov/epl-frontoffice/projects/nepa/37035/49803/54251/2013-10-

18_GMT1_Air_Quality_Impact_Analysis_Conoco_Phillips_AK_Inc.pdf

Alyeska, 2013. Trans-Alaska Pipeline System – The Facts. Alyeska Pipeline Service Company, 

Anchorage, Alaska (May 1).

ANL, 2013. Life Cycle Analysis of Conventional and Alternative Marine Fuels in GREET, 

ANL/ESD-13/10. Argonne National Laboratory (October 25). Internet address: 

https://greet.es.anl.gov/publication-marine-fuels-13.

BOEM, 2012. ION Geophysical 2012 Seismic Survey, Beaufort Sea and Chukchi Sea, Alaska –

Environmental Assessment. OCS EIS/EIA, BOEM Report No. 2012-081. U.S. Department of the 

Interior, Bureau of Ocean Energy Management, Alaska OCS Region (October). Internet address: 

http://www.boem.gov/uploadedFiles/BOEM/About_BOEM/BOEM_Regions/Alaska_Region/En

vironment/Environmental_Analysis/2012_1011_Final_EA_IONSeismicSurvey.pdf.

BOEM, 2014. AQScenarioSummary-rev.docx. Provided by H. Crowley, U.S. Department of the 

Interior, Bureau of Ocean Energy Management (May 12).

BOEM, 2015. Active Lease Polygons [shapefile]. “Geographic Mapping Data in Digital 

Format.” Available at: https://www.data.boem.gov/Main/Mapping.aspx (Accessed June 2, 

2015.)

BOEM, 2017. Exploration Plans and Development and Production Plans. U.S. Department of the 

Interior, Bureau of Ocean Energy Management, Alaska OCS Region. Internet address: 

https://www.boem.gov/akplans/

Brashers, B., J. Knapik, J.D. McAlpine, T. Sturtz, and R. Morris, 2015. Arctic Air Quality 

Modeling Study: Meteorological Dataset Evaluation – Final Task Report. Prepared by Ramboll 

Environ, Lynnwood, WA for U.S. Department of the Interior, Bureau of Ocean Energy 

Management, Alaska OCS Region, Anchorage, AK.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VII-2

Brashers, B., J. Knapik, J.D. McAlpine, T. Sturtz, and R. Morris. 2016. Arctic Air Quality 

Modeling Study: Meteorological Model Performance Evaluation: 2009-2013 BOEM Arctic WRF 

Dataset. Prepared by Ramboll Environ, Lynnwood, WA for U.S. Department of the Interior, 

Bureau of Ocean Energy Management. OCS Study BOEM 2015-049, 50 pp.

BTS, 2014. National Transportation Statistics – Table 1-10 (U.S. Oil and Gas Pipeline Mileage). 

U.S. Department of Transportation, Bureau of Transportation Statistics (April). Internet address: 

https://www.bts.gov/content/us-oil-and-gas-pipeline-mileage.

Byun, D.W. and J.K.S. Ching, ed., 1999, Science Algorithms of the EPA Models-3 Community 

Multi-scale Air Quality (CMAQ) Modeling System, EPA 600/R-99/030. Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC. March. 

CEMPD, 2017. SMOKE Modeling System. Center for Environmental Modeling for Policy 

Development. Internet address: https://www.cmascenter.org/smoke/

CMAQ, 2017. “CMAQv5.0 Chemistry Notes – CB05TUCL”. CMASWIKI. Internet address: 

https://www.airqualitymodeling.org/index.php/CMAQv5.0_Chemistry_Notes#CB05TUCL

Crowley, 2015a. Crowley, Heather (BOEM) email to Paula Fields Simms (ERG). April 2, 2015. 

Subject: Notes for discussion related to emissions inventory & modeling tasks.

Crowley, 2015b. Crowley, Heather (BOEM) email to Paula Fields Simms (ERG). April 27, 

2015. Subject: One more source for the EI.

Do, B., L. Dayton, N. Hilliard, and P. Fields Simms, 2017a. Arctic Air Quality Modeling Study –

Final Near-Field Dispersion Modeling Report. Prepared by Eastern Research Group, Inc., 

Sacramento, CA for U.S. Department of the Interior, Bureau of Ocean Energy Management, 

Alaska OCS Region, Anchorage, AK. OCS Study BOEM 2017-029. 47 pp.

Do, B., L. Dayton, N. Hilliard, and P. Fields Simms, 2017b. Arctic Air Quality Modeling Study –

Evaluation of the Emissions Exemption Thresholds Report. Prepared by Eastern Research Group, 

Inc., Sacramento, CA for U.S. Department of the Interior, Bureau of Ocean Energy Management, 

Alaska OCS Region, Anchorage, AK. OCS Study BOEM 2017-040. 59 pp.

Earth Tech, 2006. Development of the Next Generation of Air Quality Models for the Outer 

Continental Shelf (OCS) Applications, Final Report: Volume 1. Prepared for Minerals 

Management Service, Contract 1435-01-01-CT-31071. March.

ENVIRON, 2012a. Evaluation of the Combined AERCOARE/AERMOD Modeling Approach for 

Offshore Sources. Novato, California: ENVIRON Int. Corp., 773 San Marin Drive, Suite 2115.

ENVIRON, 2012b. Draft User’s Manual: AERCOARE Version 1.0. Prepared for the U.S. 

Environmental Protection Agency, Region 10, Seattle, WA. EPA Contract EP-D-08-102, Work 

Assignment 5-12, EPA 910-R-12-008, October.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VII-3

ENVIRON, 2014a. Arctic Air Quality Impact Assessment Modeling Study – Draft Photochemical 

Modeling Protocol. Prepared by ENVIRON for U.S. Department of the Interior, Bureau of 

Ocean Energy Management, Alaska OCS Region, Anchorage, AK. September 3.

ENVIRON, 2014b. CAMx User’s Guide: Comprehensive Air Quality Model with Extensions, 

Version 6.1. ENVIRON International Corporation, Novato, CA. April.

ERG, 2014. Arctic Air Quality Impact Assessment Modeling Study – Draft Dispersion Modeling 

Protocol. Prepared by ERG, Sacramento, CA for U.S. Department of the Interior, Bureau of 

Ocean Energy Management, Alaska OCS Region, Anchorage, AK. December 2.

ERG, 2015a. “Development of Emissions Estimates for Additional Sources to be Included in the 

BOEM Arctic Air Quality Modeling Study Emission Inventory.” Final Revised Memorandum/ 

Technical Summary. Developed by ERG, Sacramento, CA for U.S. Department of the Interior, 

Bureau of Ocean Energy Management, Alaska OCS Region, Anchorage, AK. August 26.

ERG, 2015b. “Spatial Surrogates and Temporal Profiles for the BOEM Arctic Air Quality 

Modeling Study Emission Inventory.” Final Memorandum/Technical Summary. Developed by 

ERG, Sacramento, CA for U.S. Department of the Interior, Bureau of Ocean Energy 

Management, Alaska OCS Region, Anchorage, AK. August 6.

FAA, 2013. Emissions and Dispersion Modeling Systems (EDMS), Version 5.1.4.1. Federal 

Aviation Administration (August). Internet address: 

http://www.faa.gov/about/office_org/headquarters_offices/apl/research/models/edms_model/.

FAA, 2014. FAA Aerospace Forecast Fiscal Years 2012-2032. HQ-121545. Federal Aviation 

Administration (August). Internet address: https://www.faa.gov/about/office_org/ 

headquarters_offices/apl/ aviation_forecasts/aerospace_forecasts/2012-2032/media/ 

2012%20FAA%20Aerospace%20Forecast.pdf.

Fields Simms, P., R. Billings, M. Pring, R. Oommen, D. Wilson, and M. Wolf, 2014. Arctic Air 

Quality Modeling Study: Emissions Inventory – Final Task Report. Prepared by Eastern 

Research Group, Inc., Sacramento, CA for U.S. Department of the Interior, Bureau of Ocean 

Energy Management, Alaska OCS Region, Anchorage, AK. OCS Study BOEM 2014-1001. 169 

pp.

IPCC, 2007. Fourth Assessment Report: Climate Change 2007. Intergovernmental Panel on 

Climate Change. Internet address: http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch2s2-

10-2.html.

Mavko, M. and R. Morris. 2013. DEASCO3 Project Updates to the Fire Plume Rise 

Methodology to Model Smoke Dispersion. Technical Memo prepared as part of Joint Science 

Form (JSP) project Deterministic and Empirical Assessment of Smoke’s Contribution to Ozone. 

December 3. Internet website: https://wraptools.org/pdf/DEASCO3_Plume_Rise_Memo_

20131210.pdf.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VII-4

NCAR, 2017a. MOZART-4/GEOS-5 model. National Center for Atmospheric Research. Internet 

address: https://www.acom.ucar.edu/wrf-chem/mozart.shtml

NCAR, 2017b. Fire INventory from NCAR (FINN) model. National Center for Atmospheric 

Research. Internet address: https://www2.acom.ucar.edu/modeling/finn-fire-inventory-ncar

Ramboll Environ, 2017. Arctic Air Quality Modeling Study: Model Justification Demonstration 

to Support Outer Continental Shelf Dispersion Modeling in the Arctic. Prepared by Ramboll 

Environ, Inc., Lynnwood, WA for U.S. Department of the Interior, Bureau of Ocean Energy 

Management, Alaska OCS Region, Anchorage, AK. February 3.

Sakulyanontvittaya, T., T. Duhl, C. Wiedinmyer, D. Helmig, S. Matsunaga, M. Potosnak, J. 

Milford, and A. Guenther, 2008. Monoterpene and sesquiterpene emission estimates for the 

United States. Environ. Sci. Techno. 42, 1623–1629. 

Stoeckenius, T., J. Jung, B. Koo, T. Shah, and R. Morris, 2017. Arctic Air Quality Modeling 

Study – Final Photochemical Modeling Report. BOEM 2016-076. Prepared by Ramboll Environ, 

Novato, CA for U.S. Department of the Interior, Bureau of Ocean Energy Management, Alaska 

OCS Region, Anchorage, AK. OCS Study BOEM 2016-076. 111 pp.

USACE, 2014. National Waterway Network (line). U.S. Army Corps of Engineers Navigation 

Data Center, New Orleans, LA. Internet address: 

http://www.navigationdatacenter.us/data/datanwn.htm. (Accessed September 30.)

U.S. EPA, 1995. Compilation of Air Pollution Emission Factors (AP-42) – Volume I: Stationary 

Point and Area Sources, Fifth Edition (various sections). U.S. Environmental Protection Agency 

(January). Internet address: http://www.epa.gov/ttnchie1/ap42/.

U.S. EPA, 2002. Temporal Allocation of Annual Emissions using EMCH Temporal Profiles. 

Prepared by Gregory Stella. April 29, 2002. Internet address: http://www.epa.gov/ttnchie1/ 

emch/temporal/temporal_factors_042902.pdf. (Accessed December 2014.)

U.S. EPA, 2005. Guideline on Air Quality Models, 40 CFR Part 51, Appendix W. November 

2005.

U.S. EPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. EPA-454/B-07-002, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. April.

U.S. EPA, 2009. NONROAD2008a. U.S. Environmental Protection Agency. Internet address: 

http://www.epa.gov/otaq/nonrdmdl.htm.

U.S. EPA, 2013a. 2011 National Emissions Inventory (NEI). U.S. Environmental Protection 

Agency (September 30). Internet address: http://www.epa.gov/ttnchie1/net/2011inventory.html.
Arctic Air Quality Impact Assessment Modeling Study: Final Project Report

VII-5

U.S. EPA, 2013b. WebFIRE Database. U.S. Environmental Protection Agency. Internet address: 

http://cfpub.epa.gov/webfire/.

U.S. EPA, 2014a. SPECIATE 4.4 Database. U.S. Environmental Protection Agency. Internet 

address: http://www.epa.gov/ttnchie1/software/speciate/.

U.S. EPA, 2014b. MOVES2014 (Database version “movesdb20140731”). U.S. Environmental 

Protection Agency. Internet address: http://www.epa.gov/otaq/models/moves/.

U.S. EPA, 2014c. Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals 

for Ozone, PM2.5 and Regional Haze. U.S. Environmental Protection Agency, Research Triangle 

Park, NC. December. Internet website: http://www.epa.gov/ttn/scram/guidance/guide/Draft_O3-

PM-RH_Modeling_Guidance-2014.pdf.

U.S. EPA, 2017. Guideline on Air Quality Models, 40 CFR Part 51, Appendix W. January 2017.