Cost-benefit Analysis of Ultra-Low Sulfur Jet Fuel
by
Stephen Kuhn
B.S. in Mechanical Engineering, Carnegie Mellon University, 2008
Submitted to the Department of Aeronautics and Astronautics
in Partial Fulfillment of the Requirements for the Degree of
Master of Science in Aeronautics and Astronautics
at the
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..........................
Au th o r........... .....
"i"epart nent of Aeronauhcs and Astronautics
lay 24, 2010
Certified by..........
Ian A. Waitz
Jerome C. Hunsaker Professor of Aeron
Department Head
Thesis Supervisor
cs and Astronautics
Accepted by............
David L. Dar oI
Professor of Aeronautics and Astronautics
Associate Department Head
Chair, Committee on Graduate Students
2
Cost-benefit Analysis of Ultra-Low Sulfur Jet Fuel
by
Stephen Kuhn
B.S. in Mechanical Engineering, Carnegie Mellon University, 2008
Submitted to the Department of Aeronautics and Astronautics
in Partial Fulfillment of the Requirements for the Degree of
Master of Science in Aeronautics and Astronautics
Abstract
The growth of aviation has spurred increased study of its environmental impacts and the possible
mitigation thereof. One emissions reduction option is the introduction of an Ultra Low Sulfur
(ULS) jet fuel standard for global commercial aviation. A full cost-benefit analysis, including
impacts on air quality, climate, operations, and lifecycle costs is necessary to justify such a policy.
The cost of a ULS jet fuel policy is well-characterized by the adoption of ULS diesel fuel, similar to
jet fuel, for ground transportation in the US and elsewhere. The cost of hydrodesulfurization
(HDS), the process used to remove sulfur from fuel, is projected to be between 4 and 7 cents per
gallon of jet fuel. With 2006 levels of domestic fuel consumption, this translates to a yearly cost of
HDS of $540-$940 million within the US. The climate and air quality benefits are characterized by
several earth-atmosphere models, which isolate the perturbation of aviation emissions.
Comparisons among models, which employ different modeling methods and assumptions as well as
different spatial resolution, provide some cross-validation, as well as characterizing the degree of
uncertainty in the state of the science. This thesis focuses in detail on the CMAQ (Community
Multi-scale Air Quality) model, used by the Environmental Protection Agency (EPA) to support
regulatory impact assessment. Other models, their results, and efforts at inter-model comparison are
also discussed. Benefits are monetized through valuing the reduction in premature mortality from
reduced concentrations of ground-level particulate matter (PM).
The central finding from CMAQ is that with nominal health impact parameters, a global ULS jet
fuel policy is predicted to save 110 lives per year in the US when considering full flight emissions, a
14% reduction in aviation-attributable mortality resulting in an estimated monetary benefit of $800
million.
Acknowledgments
During the work entailed in this thesis, I received support and assistance from many people, some
of whom I would like to thank here.
My advisor Ian Waitz has been instrumental in guiding the course of the ULS project in the face of
setbacks and over the course of much spirited debate. He has been a pleasure to work for, and I've
learned a great deal from him. His style is well-suited to bringing out the best in the people he works
with. Special thanks as well to Steven Barrett, whose knowledge of atmospheric chemistry and tools
has been invaluable, and whose assistance and time has been generously gifted.
Thanks to my friends and colleagues in the PARTNER lab, with whom I have discussed much.
Thanks to Tudor Masek, who helped answer many questions well after he had left us; Elza BrunelleYeung, who brightened the windowless and decrepit expanse of my first office, and helped in many
other ways besides; Akshay Ashok, to whom I gladly yield the mantle of pseudo-computer expert;
Pearl Donohoo, who suffered mightily, and lived to fight another day; Rhea Liem, whose behaviors
can only be described as stochastic; Jim Hileman, a project and lab leader who has helped guide
many students' work; Russ Stratton and Matthew Pearlson, who along with Jim and Pearl researched
and compiled the cost part of the study; and all the rest I knew and worked with.
Thanks as well to the scientists and engineers at UNC, FAA, EPA, Cambridge, Harvard, Stanford,
and elsewhere who helped to make, run, and interpret the models used in the study. There were
many labors and countless emails; I appreciate the time and effort put in by all.
I would also like to thank the MIT Aeronautics and Astronautics Department for helping to make
my time at MIT interesting.
Table of Contents
Abstract..................................................................................................................3
Acknowledgm ents.............................................................................................
4
Table of Contents ..................................................................................................
5
List of Figures ..................................................................................................
8
List of Tables.....................................................................................................
11
List ofSym bols .................................................................................................
12
Introduction.............................................................................................14
1
1.1
Context .........................................................................................................................
14
1.2
Motivation .................................................................................................................
14
1.3
Contributions ............................................................................................................
15
1.4
Thesis Structure ........................................................................................................
15
2
2.1
2.2
Setup .......................................................................................................
15
CMAQ .......................................................................................................................
B ackg ro u n d ............................................................................................................................
2 .1.1
2 .1.2 M o d el Scen ario s....................................................................................................................16
2 .1.3 M o d el D o m ain ......................................................................................................................
15
15
Aviation Inventories ..................................................................................................
2 .2 .1 In tro d u ctio n ..........................................................................................................................
2.2.2 Species P opulation ..........................................................................................................
2.2.2.1 AEDT/SAGE, FOA3 and Emissions Indices ......................................................
2.2.2.2 NOx by Flight Mode...................................................................................................22
2 .2 .2 .3 Lightn ing N O x .................................................................................................................
2.2.2.4 H ydrocarbon Split........................................................................................................
2.2.3 Processing Methodology ................................................................................................
20
20
21
21
17
24
27
28
2.3
28
Background Inventories........................................................................................
28
2 .3 .1 B ackgro u n d ............................................................................................................................
2.3.2 Species Population from National Emissions Inventory....................29
.......................................... 29
2.3.2.1 2001 N E I vs. 2005 N E I...................................................
. .......................................... 29
2.3.2.2 Surface Concentrations ..................................................
30
2.3.2.3 V ertical profiles ............................................................................................................
2.4
GEOS-Chem Boundary Conditions .....................................................................
2 .4 .1 B ackgro u n d ............................................................................................................................
2.4.2 GEOS2CMAQ and methodology .....................................................................................
2 .4.3 V erificatio n ............................................................................................................................
34
34
34
35
2.5
Meteorological Data..................................................................................................38
2 .5 .1 B ackgro u n d ............................................................................................................................
38
2 .5 .2
2.6
V ertical Pro files.....................................................................................................................40
Particle-Bound Water and Dry vs. W et PM ..........................................................
40
2.7
Population Data and H ealth Impacts....................................................................42
2.7.1
Global Population of the World (GPW) and Global Rural-Urban Mapping Project
(GR UM P) ............................................................................................................................................
42
2.7.2 Concentration Response Functions....................................................
...................
43
2.7.3 Monetary Value of a Statistical Life (VSL).......... ..................................................
46
2.8
M odeling Context .....................................................................................................
2.8.1
Earth-Atmosphere Modeling of Aviation....................................................................
2.8.2 Atmospheric Aerosols ....................................................................................................
2.8.3 Multi-Model Approach..................................................................................................
46
46
47
50
FullFlightEmissionsAssessment........................................................
52
3.1
Context ......................................................................................................................
52
3.2
Changes in Surface Concentration and M ortalities..............................................52
3
4
L TO Emissions Assessm ent.....................................................................57
4.1
Context ......................................................................................................................
4.2
Changes in Surface Concentration and M ortalities..............................................57
5
Clim ate Feedbacks................................................................................
57
62
5.1
Background on Climate Feedbacks......................................................................
62
5.2
Background on GATOR-GCM OM .....................................................................
62
5.3
Future W ork ..............................................................................................................
64
6
ULS Cost and OperationsAnalysis ..........................................................
64
6.1
Sulfur Content of ULS Jet Fuel.................................................................................64
6.2
Production Costs of ULS Jet Fuel..........................................................................66
6 .2 .1 B ackgro u n d ............................................................................................................................
6.2.2 Detailed Cost Analysis .....................................................................................................
6.2.2.1 Normalized Crack Spread of Diesel and Jet Fuels .................................................
6.2.2.2 Supply and Demand ..................................................................................................
6.2.2.3 The Cost of a Gallon of Diesel..................................................................................
6.2.2.4 Historic Price Difference in Diesel Fuels ...............................................................
6.2.2.5 Processing Costs..........................................................................................................
6.2.2.6 Limitations of this Analysis ......................................................................................
6.2.3 Production Cost Estimate ..............................................................................................
66
67
69
69
69
70
70
70
71
Potential Usability Concerns and Benefits with ULS Jet Fuel..............................
71
6.3
CMA Q Sensitvity Studies......................................................................
74
7.1
CM AQ models ..........................................................................................................
74
7.2
Introduction ..............................................................................................................
75
7
7.3
7.4
7.5
8
8.1
Vertical Diffusion and Advection..........................................................................
7 .3 .1 B ackgro u n d ............................................................................................................................
..............................
7.3.2 Vertical Diffusion: EDDY vs. ACM 2.........................................
7.3.3 Vertical Advection: PPM vs. YAM O ................................................................................
Static vs. Changing Background Conditions........................................................
7 .4 .1
C o n tex t...................................................................................................................................7
7.4.2
Dominance of GEOS-Chem Boundary Conditions .......................................................
76
76
77
78
79
9
79
Lightning N Ox .........................................................................................................
80
Comparisons to EmpiricalD ata ............................................................
83
Context ......................................................................................................................
83
Speciated M odel Attainm ent Test ........................................................................
8 .2 .1 M eth o d o lo gy .........................................................................................................................
8 .2 .2 R e su lts ....................................................................................................................................
8.2.3 EPA FRM monitor sites......................................................................................................86
8.2.4 Comparisons of Outputs to M onitor Data..................................................................
83
83
84
Limitations and Future Work ...................................................................
89
9.1
Scope/Feedback .......................................................................................................
89
9.2
Uncertainty................................................................................................................90
9.3
Other Differences......................................................................................................90
9.4
UT/LS Transport in CM AQ ..................................................................................
9.5
Proposed Paths Forward........................................................................................92
8.2
9
10
86
91
Conclusion...............................................................................................94
10.1
CTM Findings...........................................................................................................94
10.2
Cost/Benefit Assessm ent......................................................................................
94
A PPEND ICES.................................................................................................
95
Appendix A: CMA Q Inventory and Model Species ...........................................
95
95
A.1
Aircraft Emissions Inventory ................................................................................
A.2
Background Inventory and Concentrations ..........................................................
102
A.3
CM AQ Outputs......................................................................................................
106
Appendix B: CMA Q Processes.........................................................................
B.1
Carbon-Bond IV Processes and Reactions M odeled ............................................
108
108
Appendix C : CMA Q R esults ............................................................................
113
A.1
Additional Aviation Results ....................................................................................
113
A.2
Computational Tim e/Complexity...........................................................................117
Bibliography......................................................................................................
119
List of Figures
Figure 1: CMAQ horizontal domain used in the ULS study (Masek, 2008).......................................18
Figure 2: CMAQ vertical structure (CMAQ 4.6 Operational Guidance Document, 2009) .............
19
Figure 3: Emission indices of NO, N02, and HONO from a CFM56-3B1 engine (Wood, Herndon,
Tim ko, Yelvington, & M iake-Lye, 2008)................................................................................................
23
Figure 4: Vertical Profile of N from Lightning NO over the CMAQ US domain ..............
25
Figure 5: Vertical columnar sum of N [g N/yr] from lightning emissions of NO for 2006 ............
26
Figure 6: NASA NSSTC space-based observation of #flashes/km^2/yr., 1995-2000 (Where
Ligh tn in g S trik es) .............................................................................................................................................
26
Figure 7: 2006 background NH3 and PM2.5 concentration showing several co-located hotspots ....30
Figure 8: Comparison of 2001 and 2005 NEI emissions profiles over CMAQ US domain............31
Figure 9: NEI emissions trends of several key species (National Emissions Inventory Air Pollutant
T ren d s D ata, 2 0 09) ..........................................................................................................................................
32
Figure 10: Seasonal Variability in the 2001 NEI for several key emissions species ...........................
33
Figure 11: Ground level 03 concentrations [ppbV] from GEOS-Chem (inside the black box) and
the CMAQ BCON created from this GEOS-Chem run (outside the black box)
.........
.............. 36
Figure 12: Ground level 03 concentrations in a.), and at ~860hPa (layer 10) in b.); ground level
HNO3 concentrations in c.), and at ~860hPa in d.) ...................................
37
Figure 13: Background H2SO4 showing higher concentrations at domain boundary at ground level
............................................................................................................................................................................
37
Figure 14: Full aviation delta in ground-level NH3 and H2SO4, showing clear boundary layer effects
............................................................................................................................................................................
38
Figure 15: Vertical profiles of US average temperature, cloud water mixing ratio, rain water mixing
ratio, and w ater vapor mixing ratio .........................................................................................................
40
Figure 17: CMA Q dom ain population.....................................................................................................
43
Figure 18: Health impact pathway for air quality assessment (Rojo, 2007)........................................43
Figure 19: Baseline cardiopulmonary and lung cancer mortalities .....................................................
45
Figure 20: Spatial and temporal scale of atmospheric processes (Seinfeld & Pandis, 2006) ...........
47
Figure 21: Global Radiative Forcing from All Anthropogenic Sources of Emissions (Lee, et al.,
2 0 0 9 ) ..................................................................................................................................................................
49
Figure 22: Global Radiative Forcing from Aviation Emissions (Lee, et al., 2009) ..............
50
Figure 23: Full aviation FRM PM2.5 and ULS reduction in same, full aviation (static BCON) FRM
PM 2.5, and post-SM AT FRM PM 2.5...........................................................................................................54
Figure 24: Full aviation S04, N03, and NH4, and ULS reduction in same .....................................
55
Figure 25: Full aviation BC and ORG, and ULS reduction in same ...................................................
56
Figure 26: Speciated components of full aviation-attributable change in FRM PM2.5 and mortalities
56
............................................................................................................................................................................
Figure 27: Speciated components of ULS aviation-attributable change in FRM PM2.5 and mortalities
57
............................................................................................................................................................................
Figure 28: LTO aviation FRM PM2.5 and ULS LTO reduction in same, LTO aviation (static
BCON) FRM PM2.5, post-SMAT FRM PM2.5........................................................................................58
Figure 29: LTO aviation S04, N03, NH4, and ULS reduction in same....................59
Figure 30: LTO aviation BC and ORG, and ULS reduction in same......................................................60
Figure 31: Speciated components of LTO aviation-attributable change in FRM PM2.5 and
61
m o rtalitie s ..........................................................................................................................................................
Figure 32: Speciated components of ULS LTO aviation-attributable change in FRM PM2.5 and
61
mo rtalitie s ..........................................................................................................................................................
Figure 33: Jet A average fuel sulfur content in US (Taylor, Feb. 2009), as plotted by (Hileman, et al.,
65
20 0 9 ) ..................................................................................................................................................................
Figure 34: JP-8 fuel sulfur content as reported by PQI (DESC-BP, 1999-2007), as plotted by
66
(Hilem an , et al., 2 009) .....................................................................................................................................
Figure 35: Jet A-1 average fuel sulfur content in UK (Rickard, June 2008), as plotted by (Hileman, et
al., 2 0 09 ) ............................................................................................................................................................
66
Figure 36: D iesel prices and production...................................................................................................
68
Figure 37: Sensitivity of results to vertical diffusion parameter ...........................
78
Figure 38: Dominance of GEOS-Chem boundary conditions ................................................................
79
Figure 39: Sensitivity of results to LN O x.................................................................................................
82
Figure 40: Full aviation post-SMAT speciated results in comparison to pre-SMAT model outputs.. 85
Figure 41: Full aviation SMAT constituent contribution to FRM PM2.5 in comparison to pre-SMAT
5
m o d el o u tp u ts...................................................................................................................................................8
Figure 42: EPA FRM monitor sites used in SMAT software (Abt Associates, Inc., 2009)........86
Figure 43: Comparison of CMAQ monthly average ground-level FRM-hydrated PM2.5 to quarterly
(native) official FRM monitor data....................................................................
........
........
88
Figure 44: Comparison of monthly average ground-level FRM-hydrated PM across models to
quarterly (native) official FRM monitor data..........................................................................................
89
Figure 45: APMT-AQ projections of changes in FRM PM2.5 (a.) and b.)) in comparison to LTOonly (static B C O N ) results from CM A Q .....................................................................................................
91
Figure 46: AEDT vertical profiles of US yearly aircraft emissions .....................................................
97
Figure 47: CMAQ processed input vertical profiles of US yearly total aircraft emissions................99
Figure 48: CMAQ processed input vertical profiles of US yearly total aircraft emissions (continued)
.........................................................................................................................................................................
10 0
Figure 49: CMAQ 2006 yearly average background species concentrations .......................................
106
Figure 50: Hierarchical relationship between major species in CB-IV (Isukapalli, 1999)..................
109
Figure 51: Full aviation-attributable changes in precursor and oxidizing gases ..................................
115
Figure 52: Aviation's relative contribution to ambient concentrations of several species ................. 116
List of Tables
Table 1: Scenarios included in the design of experiment......................................................................
17
Table 2: CMAQ sigma layers used for ULS, with sea-level referent corresponding pressures/altitudes
............................................................................................................................................................................
20
T ab le 3: E mission s in dices..............................................................................................................................22
Table 4: Calculation of total N02 and NOx emissions from a CFM56-3B1 during LTO cycle
(Wood, Herndon, Timko, Yelvington, & Miake-Lye, 2008) ...............................................................
24
Table 5: Speciated split of NOx into CMAQ species.................................................................................24
Table 6: Hydrocarbon split into CB-IV species ..........................................................................................
28
Table 7: GEOS-Chem to CMAQ species mapping (Moon, Park, Li, & Byun, 2008)...........34
Table 8: CMAQ/MM5 meteorological parameters and mechanisms.................................................
Table 9:
P coefficient values (O stro, 2004) .............................................................................................
39
44
Table 10: Summary Model Comparison...................................................................................................51
Table 11: Refining and crude oil costs of a wholesale gallon of diesel, May 2002 - August 2009 (Spot
Pric e s, 2 0 10) ......................................................................................................................................................
69
Table 12: CM A Q schem es/m odels...............................................................................................................74
Table 13: ULS CMAQ "baseline" options (CMAQ 4.6 Operational Guidance Document, 2009).....75
Table 14: Sensitivity studies scenarios proposed.....................................................................................76
T able 15: P roposed future w ork ....................................................................................................................
92
Table 17: Full aviation total aircraft emissions from AEDT.....................................................................95
Table 18: Full aviation total aircraft emissions CMAQ processed input totals ................
98
Table 19: Corresponding molecular weights for HC species and groupings in Table 18 i.e. (MF/SF)
............................................................................................................................................................................
98
Table 20: Aircraft emissions pre-processed, speciated hydrocarbon profile (FAA OEE & EPA
OT AQ , 2 0 09) ................................................................................................................................................
100
Table 21: Background inventory species and descriptions .....................................................................
103
Table 22: CMAQ4.6 concentration output species and descriptions (Isukapalli, 1999) .................... 106
T able 23: CB -IV reactions (Isukapalli, 1999)............................................................................................
110
List of Symbols
AEDT - Aviation Environmental Design Tool
APMT - Aviation Portfolio Management Tool
AQ - Air Quality
AQM - Air Quality Model
BC - Black Carbon (soot)
BCON - Boundary Conditions
CAEP - Committee on Aviation Environmental Protection
CBIV - Carbon Bond IV (4)
CBV - Carbon Bond V (5)
CCN - Cloud Condensation Nuclei
CH4 - Methane
CI - Confidence Interval
CMAQ - Community Multi-scale Air Quality Model
CRC - Coordinating Research Council
CRF - Concentration Response Function
CTM - Chemistry and Transport Model
EC - Elemental Carbon (soot, also called BC)
El - Emissions Index
EIA - Energy Information Agency
EPA - Environmental Protection Agency
EPAct - Energy Policy Act
FAA - Federal Aviation Administration
FOA3 - First Order Approximation Version 3.0
FRM - Federal Reference Method
FSC - Fuel Sulfur Content
GATOR-GCMOM - Gas, Aerosol, TranspOrt, Radiation, General Circulation, Mesoscale, Ocean
Model
GCM - Global Climate Model
GHG - Greenhouse Gas
GEOS - Goddard Earth Observing System
HC - Hydrocarbon
HDS - Hydrodesulfurization
HOx - OH and applied radicals
HSD - High-Sulfur Diesel
ICAO - International Civil Aviation Organization
ISORROPIA - CMAQ inorganic gas-aerosol equilibrium model, from the Greek for "equilibrium"
IMPROVE - Interagency Monitoring of PROtected Visual Environments
IPCC - Intergovernmental Panel on Climate Change
LNOx - Lightning-induced Nitrogen Oxides
LOSU - Level of Scientific Understanding
LS - Lower Stratosphere
LSD - Low-Sulfur Diesel
LTO - Landing and Takeoff Cycle (<1 000m AGL)
MM5 - Mesoscale Model Version 5
NEI - National Emissions Inventory
NH3 - Ammonia
NH4 - Ammonium ion
N03 - Nitrate ion
NOx - Nitrogen Oxides (NO + N02)
03 - Ozone
OH - Hydroxyl radical
ORG - Organics
PBL - Planetary Boundary Layer
PM - Particulate Matter
PM2.5 - Particulate Matter smaller than 2.5 micrometers in diameter (as nearly all aviationattributable aerosols are)
PMFO - Primary Fuel Organics
PMSO - Primary Sulfur Organics
nvPM - non-volatile Particulate Matter
PBL - Planetary Boundary Layer
ppbV - parts per billion Volumetric
ppm - parts per million
RF - Radiative Forcing
RH - Relative Humidity
SAGE - System for assessing Aviation's Global Emissions
SANDWICH - Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous mass Hybrid
material balance
SMAT - Speciated Model Attainment Test
S04 - Sulfate ion
SOx - Sulfur Oxides
STN - Speciation Trends Network
SULF - Sulfuric Acid (H2SO4)
THC - Total Hydrocarbon
TOG - Total Organic Gases
ug/m^3 - micrograms per meter cubed
ULS - Ultra Low Sulfur
ULSD - Ultra Low Sulfur Diesel
ULSJ - Ultra Low Sulfur Jet
UT - Upper Troposphere
1 Introduction
1.1
Context
Global aviation has historically been a fast-growing industry.
Its rate of growth has outstripped the
growth of gross domestic product by a factor of 2.4 between 1960 and 1999 (IPCC, 1999), and
despite a lull in the past decade due to terrorist attacks and economic downturn, steady growth is
projected into the future. Concerns related to the noise, climate, and air quality impacts of aviation
are increasingly voiced.
On a global scale, aircraft emissions of greenhouse gases (GHGs) and precursors contribute to
climate change (IPCC, 1999).
On a regional scale, aircraft emissions impact air quality, with
resulting health impacts (ICAO, 2007). Poor air quality has been linked to premature mortality (US
EPA, 2006), cardiovascular and pulmonary hospital admissions, and asthma aggravation (US EPA,
2004), among other ailments.
Aviation is largely unique among the anthropogenic sources of emissions in that its emissions extend
well into the atmosphere, to the free troposphere and jet stream, and even into the lower
stratosphere, resulting in impacts that can be felt over a larger distance and longer time than groundbased emissions sources. Long-lived species (compounds) and precursors can be transported across
oceans and continents.
1.2
Motivation
Reductions in aircraft emissions per flight can be effected in one of three ways; changes in
operational procedures for greater efficiency; changes in aircraft technology; and changes in fuel
composition.
Operational changes have been the subject of much research and development, and
are embodied in the systems used to route and manage commercial aviation. Such systems are
developed at great cost over many years, and often require the development of new technology,
including the installation of new avionics equipment in aircraft. Airframe and engine improvements
are likewise emphasized in improving fuel efficiency and performance, but a given airframe and
engine are typically used in the fleet for 30 or more years (Morrell & Dray, 2009), and new
technology takes many years to develop. Furthermore, retrofitting existing aircraft is typically
prohibitively expensive. Therefore changes in operational procedures and aircraft technology are
felt only gradually, over decades.
In contrast, changes in fuel composition result in immediate
changes in emissions. In the case of ULS fuels, which are assumed in this study to contain 15ppm
sulfur, the technology and infrastructure for HDS exists from the EPA-mandated conversion of
diesel to ULS diesel for most types of US ground transportation. The adoption of ULS diesel has
phased into effect since 2006 (US EPA, 2006) (US EPA, 2006) and entails an identical chemical
process.
Projected costs, and especially capital expenditures, are much less speculative for the
adoption of ULS jet fuel than for most other emissions mitigation options for aviation.
The precursor gases NOx and SOx are the two largest sources of aviation-attributable ground level
particulate matter (PM). The species oxidize ammonia (NH3) existing in the atmosphere from nonaviation sources to form sulfates and nitrates. Other sources of aviation-attributable PM include
soot (black carbon, or BC), largely dependent on aromatic and paraffmic fuel content, and organic
compounds, which typically account for less than 5-10% of aviation-attributable PM in this and
prior studies (Rojo, 2007), (Masek, 2008). NOx is created in the aircraft engine combustor as a
byproduct of the combustion of air (~78% N2) and fuel at high temperature. Its emission is
dictated entirely by the processes within the combustor. As such, NOx reduction is dependent on
the implementation of new engine technology in the fleet. SOx is created during combustion as
sulfur in the fuel is oxidized. It is therefore possible to completely control SOx emissions by
changing fuel composition.
1.3
Contributions
This thesis presents some results from the study of a switch to ULS jet fuel. It addresses original
work from several parties, which is labeled as such where applicable. Four earth-atmosphere models
were used to predict the climate and air quality benefits of a ULS jet fuel policy. These include
GATOR-GCMOM (Gas, Aerosol, TranspOrt, Radiation, General Circulation, Mesoscale, Ocean
Model), GEOS-Chem (Goddard Earth Observing System Chemistry Model), TOMCAT, and
CMAQ (Community Multi-scale Air-Quality Model). This thesis describes some results from
several of these models, but focuses primarily on the CMAQ model. The setup, inputs, and outputs
of the CMAQ model are described in detail. The mining and integration of data, where results from
different models are compared together, are original to this thesis.
1.4
Thesis Structure
This thesis is organized into 11 sections. Introduction and setup of the model runs are followed by
results from full-flight and landing-takeoff (LTO)-only emissions scenarios. A general discussion of
results when climate feedbacks are modeled is included. Afterwards follows analysis of sensitivities
to various parameters in CMAQ, as well as comparisons of model data to empirical data. Then
limitations, future work, and conclusions are presented.
2
Setup
2.1
CMAQ
2.1.1
Background
The EPA's Community Multiscale Air Quality (CMAQ) model is a three-dimensional Eulerian
atmospheric chemistry and transport modeling system that simulates ozone, acid deposition,
visibility, and fine particulate matter throughout the troposphere (CMAQ 4.6 Operational Guidance
Document, 2009). Like all Eulerian computational air quality models, on a fundamental level,
CMAQ outputs concentration fields that are the solutions of systems of partial differential equations
for the time-rate of change in species concentrations due to a series of individual physical and
chemical processes, which are aggregated. Air quality models integrate our understandings of the
complex processes that affect the concentrations of pollutants in the atmosphere. Establishing the
relationships among meteorology, chemical transformations, emissions, and removal processes in
the context of atmospheric pollutants is the fundamental goal of such a model (Seinfeld & Pandis,
2006). CMAQ uses a set of modular process solvers to accomplish this, which incorporate models
developed in the literature (Table 12 in Section 7).
2.1.2
Model Scenarios
The procedure for studying a particular phenomenon or perturbation, such as the effect of aircraft
emissions on ground-level PM and ozone, is as follows. A scenario is run (Run 1) in which every
source of emission - biogenic and anthropogenic, industrial, transportation, etc. - is included, except
the perturbation.
This is the so-called "background" inventory and is compiled from the EPA's
National Emissions Inventory (NEI) for the US, and from various sources for the rest of the world.
An inventory is a gridded or spatial accounting of emissions rates (i.e. in g/s or mol/s, depending on
the phase of the species, using accounting of hourly changing emissions profiles). In this case, the
background omits any aviation emissions. The model is run for a given amount of time, processes
evolve, and concentrations are saved (i.e. in ug/m^3 or ppbV, depending on the phase of the
species). Another scenario is run in which the perturbation inventory, in this case the aircraft
emissions inventory, is added to the background inventory (Run 2). The interactions of the aircraft
emissions with background species and emissions, including NH3, 03, OH, and S02, among
others, are captured. The model is run, and concentrations are saved. Run 1 is subtracted from Run
2, and the impacts of aviation emissions within the wider atmospheric system are isolated.
For
aviation, this difference, or "delta" case change in concentration, is typically a small fraction of the
absolute, or ambient, concentrations (the output of Run 2); depending on location, for all species of
PM, aviation-attributable concentrations are at least two orders of magnitude lower than ambient,
and often much lower still. Some representative plots of this relative contribution are provided in
Appendix C.
In addition to the background, there are four aviation scenarios for the ULS project, listed in Table
1. All models used in the study scoped these scenarios at the least, though domains and durations
varied, and additional variations were added for different models. The full aviation, or "baseline",
scenario represents full flight emissions with current fuel compositions. Fuel compositions are
discussed in detail in Section 6.1. The ULS full aviation scenario represents full flight emissions with
a ULS fuel composition. Until recently, it was thought that LTO (landing and takeoff, <1km above
ground level) emissions contributed to the bulk of ground-level PM. However, that has been called
into question by (Barrett, Britter, & Waitz, 2009) and the finding that cruise emissions may
contribute between two and 10 times the amount of ground-level PM as LTO emissions, as
predicted by GEOS-Chem. A secondary objective of this study is to further examine the relative
impact of cruise emissions. As such, scenarios were included in which LTO-only aircraft emissions
were modeled, as well as LTO-only emissions with a ULS fuel policy. These also provide valuable
comparisons to previous LTO-only studies.
A three-month "spin-up" was included for each scenario. This provides sufficient time for
concentration gradients and transport processes to develop before the period of study. For CMAQ,
that period is the full year of 2006. All aviation inventories for the project, as well as the
meteorological data for CMAQ and GEOS-Chem are from 2006. The background inventory is
notably based on the 2001 NEI. Over the US, GATOR-GCMOM uses the 2005 NEI, and GEOSChem uses the 1999 NEI. A comparison of the 2001 and 2005 NEIs is provided in Section 2.3.2
for several CMAQ species. It was impractical to harmonize the NEI years across models due to
general availability at the time, but the differences between models are far more significant than the
differences between the background inventories.
The boundary conditions (BCON) for CMAQ (the "picture frame" concentrations at the edge of the
domain) were derived from global GEOS-Chem runs in which the scenario was also allowed to
change. For instance, the inventory for the global GEOS-Chem run that was used for the
background scenario CMAQ BCON also included no aviation emissions. Two additional scenarios
beyond the design of experiment were included for CMAQ in which the full aviation and LTO
scenarios used static BCON; that is to say, the background BCON were used for these scenarios,
eliminating differences in BCON flux in the delta case, and isolating the component of change over
the US that is due to US-only changes in aircraft emissions.
Table 1: Scenarios included in the design of experiment
2.1.3
Background
Spin-up (Oct-Nov 2005), Scenario Gan-Dec 2006)
Full aviation, or "baseline"
ULS full aviation
Spin-up (Oct-Nov 2005), Scenario Gan-Dec 2006)
Spin-up (Oct-Nov 2005), Scenario Gan-Dec 2006)
LTO aviation
ULS LTO aviation
Spin-up (Oct-Nov 2005), Scenario (Jan-Dec 2006)
Spin-up (Oct-Nov 2005), Scenario Gan-Dec 2006)
Model Domain
The CMAQ grid is a Lambert conformal conic projection covering the continental United States
and parts of Canada, Mexico, and the Caribbean. The 3-D grid is of size 148 x 112 x 35, constituted
of 148 cells from western to eastern boundaries, and 112 grid cells from northern to southern, of 36
x 36km within the projection, extending upwards into 35 vertical layers. The domain boundary is
not rectilinear in latitude/longitude coordinates, so e.g. the western border spans a range of
longitudes. This can be visualized in the CMAQ plots in Section 8. The vertical layers are finer at
ground level and become coarser as they ascend, with eight layers in the lowest km and an upper
limit of 1OOhPa (-15-17km). The parameters of the Lambert projection, including the standard
parallels, central meridian, and latitude of origin, are shown in Figure 1. Lambert conformal
projections minimize two-dimensional distortion of the three-dimensional portion of the Earth's
surface over the US domain, with zero distortion along the standard parallels, at 33N and 45N.
Lambert projections are also favored in general for aviation because a straight line drawn on the
projection approximates the great-circle route between endpoints that aircraft generally attempt.
Figure 1: CMAQ horizontal domain used in the ULS study (Masek, 2008)
The CMAQ vertical domain is a non-hydrostatic sigma coordinate system, described by a set of
dimensionless parameters, denoted sigmas by convention, defined in Equation 1.
=
*
P-Prop
Pgroundref' Prop
Each vertical column has a layer structure that is normalized to the difference between the reference
pressure at mean ground-level and 1OOhPa. In essence, this allows the vertical coordinate system to
be terrain following. The sigmas (which have values between 1, at ground level, and 0, at 1OOhPa)
are then used to determine the pressure at the edges of each vertical layer. As each of the 148 x 112
vertical columns have the same number of layers, and yet different mean ground altitudes (and
therefore pressures), each vertical column has a unique grid. This can be visualized in Figure 2. The
sigmas themselves are based on the lowest levels of the existing CMAQ 2001 NEI used in prior
studies, as well as the reduced vertical domain of GEOS-5 (Goddard Earth Observing System
Model-5) for general boundary condition compatibility. For the purposes of displaying the results of
this study, the vertical grid normalized to sea level was taken as the referent for plots of domainaverage concentration by pressure. This results in a uniform aggregation of all surface layer
concentrations into the same vertical bin, which would otherwise not be the case. A more rigorous
method would require paired-in-time-and-space summation, which is prohibitively intensive for the
quantity of data generated, and which would conflict with the method of display of other models in
the study (which also use sigma, as opposed to eta or hybrid sigma-eta coordinates, in which the
ground layer varies).
Pr =const
=~~~hx
p'X(
X____
h=/-c
)X
MSL
Figure 2: CMAQ vertical structure (CMAQ 4.6 Operational Guidance Document, 2009)
The sigma layers in Table 2 are used to create the pressures at top and bottom of a vertical grid cell.
For example, the bottom of layer 1 is described by the sigma 1.000, and the top by 0.9950. The
bottom of layer 2 is described by 0.9950, and the top by 0.9900, etc. The pressure and altitude for a
vertical column normalized to sea level are also listed, where the pressure and altitude describe the
top of the layer. These pressures and altitudes vary, however, with the ground altitude for a given
vertical column.
Table 2: CMAQ sigma layers used for ULS, with sea-level
pressures/altitudes
referent corresponding
1
0.6377
682.59
3,260
20
0.5959
644.35
3,652
60.3
21
0.5541
606.17
4,144
995.23
121.0
22
0.5123
567.99
4,662
0.9600
976.96
243.4
23
0.4706
529.89
5,208
6
0.9400
958.69
408.9
24
0.4288
491.71
5,787
7
0.9100
931.29
619.6
25
0.3871
453.62
6,404
8
0.8830
906.62
865.1
26
0.3453
415.53
7,063
9
0.8663
891.37
1,057
27
0.3036
377.34
7,772
10
0.8495
876.02
1,207
28
0.2619
339.25
8,539
11
0.8328
860.76
1,359
29
0.2071
289.19
9,516
12
0.8161
845.50
1,513
30
0.1593
245.52
10,686
13
0.7993
830.16
1,670
31
0.1187
208.43
11,837
14
0.7770
809.79
1,856
32
0.0844
177.10
12,951
15
0.7492
784.39
2,099
33
0.0553
150.52
14,025
16
0.7213
758.91
2,377
34
0.0305
127.86
15,065
17
0.6934
733.42
2,663
35
0.0095
108.68
16,070
18
0.6656
708.03
2,957
0.0000
100.00
16,819
(ground
layer)
1.0000
1013.5
2
0.9950
1008.9
20.1
3
0.9900
1004.4
4
0.9800
5
The comprehensive set of CMAQ settings used for such parameters as chemical mechanism,
diffusion, advection, aerosol module, etc. are described in more detail in Table 13 in Section 7, the
section on CMAQ sensitivity to changes in those parameters.
2.2
Aviation Inventories
2.2.1
Introduction
Aviation emissions inventories convert flight data into rates of species emission, using information
about the flight mode (i.e. throttle level and altitude), engine and airframe type, fuel type, and
temperature, pressure and other environmental conditions. Chord-level flight data is scaled based
on relations governed by these parameters, and aggregated to the grid structure. Aviationattributable PM is grouped into two categories: primary PM and secondary PM created from
aviation-attributable precursors. Primary PM is created in the combustor and turbine itself; at the
plane of the exit nozzle, the species are extant. Secondary emissions are formed in the plume of the
engine exhaust and elsewhere once the plume has dissipated, by reactions of the precursor gases
NOx and SOx with existing species in the atmosphere (e.g. OH, 03, H20, and later, gaseous
ammonia (NH3)) on a scale of meters to many kilometers, from seconds to many hours (IPCC,
1999). The aerodynamic diameter of jet turbine aircraft PM is extremely small in size, with bimodal
peaks in the distribution usually occurring at 30 and 100nm (0.3 and lum); as such all aviation
emissions are considered PM2.5 (particulate matter with 2.5um or smaller diameter) (Petzold,
Dopelheuer, Brock, & Schroder, 1999). The emission of C02 (a greenhouse gas, or GHG), H20
(as contrail), and CO (integral in the CO--OH--CH4 cycle) are significant when considering climate
effects.
2.2.2
Species Population
2.2.2.1
AEDT/SAGE, FOA3 and Emissions Indices
Aviation emissions are constituted of several species generally grouped into categories. From an air
quality perspective, the key species are SOx and NOx, as well as black carbon (BC) and primary fuel
organics (PMFO). SOx and NOx are precursor gases that lead to secondary PM (S04, N03, and
NH4), and BC and PMFO are primary PM. Hydrocarbons representing unburnt or partially burnt
fuel are also included. Total monthly and yearly emissions of all pre- and post-processed species are
listed in Appendix A.1. CO is included in the CMAQ emissions, although H20 and C02 are not.
For a chemistry and transport model (CTM) not including climate feedbacks or radiative forcing
(RF), aircraft emissions of H20 are insignificant (contrails are not treated), and C02 is stable and
non-reactive.
The creation of these species is governed by emissions indices (Els) based on fuel burn numbers
and other relations categorized in the ICAO's (International Civil Aviation Organization) FOA3
(First Order Approximation version 3.0). The System for assessing Aviation's Global Emissions
(SAGE) makes use of these relations, in conjunction with global individual flight information, to
create speciated inventories of aviation emissions. SAGE is a high fidelity computer model used to
predict aircraft fuel burn and emissions for all commercial (civil) flights globally in a given year
(DOT/FAA, 2005a). SAGE is part of the Aviation Environmental Design Tool (AEDT), a
software system that dynamically models aircraft performance in space and time to produce fuel
burn, emissions and noise. The outputs of AEDT/SAGE (referred to hereafter as AEDT) for 2006
over the US CMAQ domain are quantified in Appendix A.1. AEDT has been extensively validated
against actual data from US airlines and been found to predict fuel burn with less than 5% error on
average (DOT/FAA, 2005b).
Standardized techniques for directly measuring volatile and primary aircraft emissions do not exist
(Wayson, Fleming, & lovinelli, 2009). As such, a first order approximation was derived. The
present iteration of that approximation is version 3.0, denoted FOA3.0, allowing for independent
prediction of volatiles and non-volatiles on a more theoretical basis. The more conservative FOA3a
approximation was not used for this study. Once an accepted, repeatable method for direct
measurement of PM emissions is established, and the fleet is sufficiently represented, the FOA
methodology will eventually become obsolete. However, currently, a species-specific emission index
is multiplied by the amount of fuel burn per unit time to yield a species emission rate. These
relations are quantified in Table 3.
Table 3: Emissions indices
NOx
AEDT(NOx)
AEDT(NOx)*0.997
Soot (>1 km)
AEDT(fuel burn)*0.035
AEDT(fuel burn)*0.035*0.997
Soot (<1 km)
AEDT(PMNV from FOA3)
AEDT(PMNV from FOA3)*0.997
SO2
AEDT(fuel burn)*1.176
AEDT(fuel burn)*0.02940*0.997
AEDT(fuel burn)*0.03675
AEDT(fuel burn)*0.0009188*0.997
CO 2
AEDT(fuel burn)*3150
AEDT(fuel burn)*3150*0.997
HC
AEDT(HC)
AEDT(HC)*0.997
CO
AEDT(CO)
AEDT(CO)*0.997
PMFO (>1 km)
AEDT(fuel burn)*0.015
AEDT(fuel burn)*0.015*0.997
PMFO (<1 kin)
AEDT(PMFO from FOA3)
AEDT(PMFO from FOA3)*0.997
S(VI) as H 2 SO
4
A baseline fuel sulfur content of 600ppm and ULS fuel sulfur content of 15ppm were assumed. 2%
of fuel sulfur is assumed to be emitted as S(VI) (as H2SO4 for CMAQ). The 0.997 ULS emissions
correction factor and 3159 g C02/kg-fuel El correspond to an anticipated 0.3% increase in the
gravimetric fuel energy density, based on relationships derived from the DESC JP-8 database
(DESC-BP, 1999-2007) using data from 2002-2007, and an assumed 1% loss in volumetric energy
density. Note that AEDT uses a C02 emissions index of 3155 C02/kg-fuel.
2.2.2.2
NOx by Flight Mode
AEDT outputs unspeciated NOx. NOx is comprised of the nitrogen oxides, NO and N02. NO
reacts in the presence of 02 to produce N02, the production of which initiates chemical reactions
creating 03 (Seinfeld & Pandis, 2006). A dynamic equilibrium exists in which N02 is further photodissociated during the day to NO (Pickering, Huntrieser, & Schumann, 2008). Inter-conversion
between NO and NO2 is fairly rapid, with a tropospheric timescale of -5 minutes (Seinfeld &
Pandis, 2006). Given this equilibrium, the ratio of NO/NO2 in aircraft emissions is of secondary
importance, so long as the number of N molecules are conserved (NOx is expressed in AEDT on
an NO2 mass basis). However, for CMAQ, NO and NO2 must be speciated in the aircraft
emissions inventory. The fraction of NOx emitted as NO vs. N02 is dependent on the operating
condition of the engine, as well as ambient conditions. At lower throttle levels i.e. idling during taxi,
N02 dominates; at higher throttle levels, such as takeoff, climb-out, and cruise, NO dominates.
This is corroborated through several campaigns in the literature i.e. (Wood, Herndon, Timko,
Yelvington, & Miake-Lye, 2008), (Herndon, et al., 2004), (Wormhoudt, Herndon, Paul, Miake-Lye,
& Wey, 2007).
0.01
0
W
20
40
60
100
Engine Power (% rated thrust)
Figure 3: Emission indices of NO, N02, and HONO from a CFM56-3B1 engine (Wood,
Herndon, Timko, Yelvington, & Miake-Lye, 2008)
OH-driven oxidation of NO results in a small percentage of aircraft NOx emissions manifested as
nitrous acid (HONO) (Miake-Lye, et al., 2002); for this study a uniform value of 0.8% was adopted.
Figure 3 shows the split of NOx into NO, N02, and HONO by engine power level for a CFM563B1 engine in (Wood, Herndon, Timko, Yelvington, & Miake-Lye, 2008). This data, as well as the
values quantified in Table 4 by flight mode, and similar values in (Wormhoudt, Herndon, Paul,
Miake-Lye, & Wey, 2007), resulted in the speciated split by flight mode listed in Table 5. However,
there is still a significant variance in values adopted for studies across industry; (Chapter 3 Emission Sources, 2006) (Chapter 3 - Emission Sources, 2006), an Air Quality (AQ) study of
Heathrow Airport, used a cruise split with a significantly higher level of N02.
Table 4: Calculation of total N02 and NOx emissions from a CFM56-3B1 during LTO cycle
(Wood, Herndon, Timko, Yelvington, & Miake-Lye, 2008)
LTD phase
time in mode
(miIn)
fuel flow rate
{kg/engine/s)
NO2 El
{g/kg fuel)
NOx El
(g/kg fuel)
total NO2
(kg)
total NOx
(kg)
0.08
0.63
0.06
0.13
0.80
0.49
approach
4
0.29
1.09
7.04
idle
26
0.114
2.98
3.26
takeoff
0.7
0.946
1.41
17.52
climb-out
2.2
0.792
1.29
14.66
totals per engine/LTO
ICAO
" NOx emission indices are expressed as NO2 equivalents. The overall NO2/NOx ratio is 0.24.
0.58
0.70
1.53
3.30
3.60
Table 5: Speciated split of NOx into CMAQ species
Taxi
7%
Takeoff
100%
91.36% / 7.94% / 0.8%
Climb-out
85%
90.47%
/
8.73%
/
0.8%
89.28%
/
9.92%
/
0.8%
Cruise-climb
89.28% / 9.92% / 0.8%
Cruise
Cruise-descent
-
Approach
30%
Landing Ground Roll with
Reverse Thrust
2.2.2.3
8.5% / 90.7% / 0.8%
89.28% / 9.92% / 0.8%
83.8%
83.8%
/
15.4%
15.4/
/
0.8%
0.8%
Lightning NOx
Lightning is characterized by localized heating and ionization of air, resulting in the dissociation of
N2 and 02, and the formation of NOx (Pickering, Huntrieser, & Schumann, 2008). As lightning is
a significant source of NOx at the same altitudes as aircraft emissions, its inclusion was deemed
important to the study. Sensitivity of results to the inclusion of lightning NOx (LNOx) is discussed
in Section 7.5.
(Schumann & Huntrieser, 2007) have provided a comprehensive summary of the current state of
understanding of LNOx, citing a probable range of values in total global emissions from the
literature of 2-8 Tg N/yr, with a likely value of 5 Tg N/yr (values listed as equivalent nitrogen mass
per year). The IPCC adopted a value of 5 Tg N/yr, with a range of uncertainty of 2-13 Tg N/yr
(JPCC: Climate Change 2001, 2001). The GEOS-Chem model, which was used to create a lightning
NOx inventory for CMAQ, predicted total global emissions of 6.16 Tg N/yr, with emissions over
the CMAQ domain constituting 0.98 Tg N/yr thereof (Figure 4). GEOS-Chem emits 100% of
LNOx as NO; the CMAQ LNOx inventories are likewise constituted solely of NO. The value of
0.98 Tg N/yr in LNOx emissions over the CMAQ domain in Figure 4 exceeds that of aviation
emissions of NO at 0.633 Tg N/yr (listed in Appendix A.1). Seasonal variation manifests correctly
as increased emissions in the summer months, followed by spring, fall and then winter.
US Vertical Profile of Nfrom Lightning [g] 2006; Total = 9.83E 11 g
500
600
700
800
900
0
1
2
3
4
5
6
7
8
9
10
10
x 10
Figure 4: Vertical Profile of N from Lightning NO over the CMAQ US domain
Figure 5 depicts the spatial distribution of predicted lightning activity in GEOS-Chem by summing
all yearly LNOx emissions over the vertical domain of each horizontal grid cell. The 4 degree
latitude by 5 degree longitude (4x5) grid cells of the GEOS5 domain is clearly evident in the
Lambert conformal conic projection of the CMAQ domain.
2006 yearly sumof vertical columnar LNOx as NO [g]
Min = 6.533385e+03 @ (98,4)
Max = 2.231915e+08 @ (22,89)
Mean = 6.181459e+07
x 10
12.2
0
20
40
,
60
80
Column Number
100
120
140
Figure 5: Vertical columnar sum of N [g N/yr] from lightning emissi ons of NO for 2006
The spatial distribution agrees well with empirical measurement of the frequency of lightning flashes
from space-based optical instrumentation, shown in Figure 6. The GEOS-Chem lightning module
was indeed written to be brought in line with the Lightning Imaging Sensor/Optical Transient
Detector (LIS/OTD) space-based instruments (GEOS-Chem v8-02-01 Online User's Guide).
-150
-120
-90
-60
-30
0
30
60
90
120
150
Figure 6: NASA NSSTC space-based observation of #flashes/km^2/yr., 1995-2000 (Where
Lightning Strikes)
2.2.2.4
Hydrocarbon Split
AEDT outputs a single unspeciated value for aircraft hydrocarbon (HC) emissions, denoted total
hydrocarbons (THC). Hydrocarbons are emitted as the product of incomplete combustion
processes. They can react photochemically with NOx to form ozone, a key component of smog in
urban areas (primarily from ground transportation) (Seinfeld & Pandis, 2006). CMAQ groups
hydrocarbons into families, including aldehydes and formaldehydes, simple chains like olefins and
paraffins, and aromatics like toluenes and xylenes. A split of these species into CMAQ HC
groupings is shown in Table 6, along with their mass fractions, and a full listing of these species by
IUPAC nomenclature can be found in Table 20. A description of all CMAQ species by name is
provided in Appendix A.1.
THC must be converted to total organic gases (TOG) by a scaling factor (1.156 in this case) and
multiplied by a split factor (SF) for molar-based emissions (Baek, Arunachalam, Holland, Adelman,
& Hanna, 2007). For each species and grouping, the split factor is the product of the mass fraction
and a scaling factor divided by the molecular weight, where the scaling factor is used to allocate a
species into one or more groupings, based largely on the number and type of carbon bonds (Ryan,
2002).
Emission [mol/s] = THC[g/s] * 1.156[TOG/THC] * SF
(2)
For a given grouping, the sum of all the species split factors for that grouping (many of which are
zero) yields a total grouping split factor. For instance, propylene (C3H6) is equally divided between
PAR and OLE (paraffins and olefins), and its split factors contribute to the summed split factor of
both PAR and OLE (but not any others). All HC emissions are expressed in molar emissions rates
i.e. in moles/s. The average molecular weight of all the species in a grouping is the mass fraction
(MF) divided by the SF. ETH (ethene or ethylene) and MEOH (methanol) are treated explicitly, not
as a grouping of species.
The HC profile in Table 20 was based on a measurement campaign different and more recent than
that used in the 1099 Civil Aviation profile in the EPA SPECIATE database (FAA OEE & EPA
OTAQ, 2009). The SPECIATE database is the EPA repository of volatile organic gas and PM
speciation profiles of air pollution sources (Hsu & Divita Jr., 2009). Table 6 compares the resulting
formatted CMAQ HC profiles, which still retain broad similarity. Notable differences are increased
aromatics in the ULS project profile, as well as slight increases in aldehydes, paraffins, and olefins at
the expense of non-reactive compounds.
Table 6: Hydrocarbon split into CB-IV species
2.2.3
ALD2
0.123467
0.003378
0.080683
0.002175
ETH
0.154590
0.005511
0.154800
0.005519
FORM
0.137595
0.004651
0.163100
0.005396
NR
0.047957
0.003526
0.168566
0.011104
OLE
0.086442
0.002966
0.059377
0.002584
PAR
0.381871
0.026194
0.356527
0.015279
TOL
0.024358
0.000248
0.009277
0.000139
XYL
0.027511
0.000260
0.007470
0.000120
MEOH
0.018052
0.000563
N/A
N/A
Processing Methodology
Traditionally, CMAQ inventories are compiled from formatted ASCII text files using the SMOKE
(Sparse Matrix Operator Kernel Emissions) modeling system (Baek, Arunachalam, Holland,
Adelman, & Hanna, 2007). SMOKE is similar to CMAQ in that it is comprised of modules
implemented in FORTRAN. For this study, however, greater flexibility was needed in assigning
scaling factors, speciated splits by flight mode, custom HC profiles, and lightning NOx emissions. A
way to custom-code the processing and injection of the inventories into CMAQ-ready netCDF
(network Common Data Format) files was needed. MATLAB 2009a and later contains a netCDF
module; this was used to push all processing into a single transparent MATLAB script. The
disadvantage of increased processing time was offset by simple parallel implementation of the script,
as all CMAQ inventory files contain one day of emissions. A SMOKE utility was still used to merge
the background, LNOx, and aviation inventories.
2.3
Background Inventories
2.3.1
Background
In this study, the background emissions inventory is the collection of all anthropogenic and biogenic
sources of emissions, excluding aviation. Background emissions have a significant effect on the
formation of secondary PM. The CMAQ inventory was adapted from that developed for the Clean
Air Interstate Rule (CAIR) using the EPA's 2001 NEI database. The NEI is compiled every three
years and includes emissions from industry, agriculture, mobile sources (including on-road vehicles,
construction equipment, boating and shipping, rail, etc.), and biogenic sources (including VOC
emissions, soil uptake and output, dust, wildfire, wildlife, etc.) (EPA, 2009). It is intended to be a
comprehensive source of all emissions of criteria air pollutants and hazardous air pollutants (HAPs)
within the US.
2.3.2
Species Population from National Emissions Inventory
2.3.2.1
2001 NEI vs. 2005 NEI
The 2001 NEI was compiled for use with the Carbon Bond IV (CB-TV) chemical mechanism. The
mechanism is discussed further in Appendix B. For a separate project, the 2005 NEI was compiled
for use with the Carbon Bond V (CB-V) mechanism, an update of CB-IV with increased treatment
of organics and HAPs, as well as updated rate constants. The differences between the mechanism
versions are discussed in detail in (Luecken, Phillips, Sarwar, & Jang, 2008) and (Yarwood, Rao,
Yocke, & Whtiten, 2008). The reference for results in this paper is CB-IV, as this mechanism was
used for all runs. However, a comparison of several key species in the 2001 and 2005 NEIs is
provided subsequently. There was insufficient time and space to do runs comparing the CB-IV and
CB-V mechanisms and their effect on ULS results for CMAQ.
2.3.2.2
Surface Concentrations
CMAQ results for aviation-attributable species concentrations display several "hotspots," or areas of
elevated concentrations. The reasons for these hotspots may vary, and those of southern California
and New York may be due to locally high air traffic alone; however, a significant cause is elevated
concentrations of background gaseous ammonia (NH3), which is oxidized to form sulfates and
nitrates. As can be seen in Figure 7, PM spikes in SE North Carolina, SE Pennsylvania, N Georgia,
N Mississippi, Louisiana, and California's Central Valley are co-located with increased levels of
NH3. Many of these same peaks show up when looking at aviation-attributable PM. Ammonia is
the primary basic (i.e. reducing) gas in the atmosphere, and after N2 and N20 is the most abundant
nitrogen-containing component in the atmosphere (Seinfeld & Pandis, 2006). Its destruction
coincides with nearly all elevated levels of aviation-attributable PM. In this respect, the background
inventory exerts perhaps its most potent influence. Animal waste is estimated to be the primary
source of ammonia emissions, followed by ammonification of humus, emission from soils, losses of
NH3-based fertilizers from soils, and industrial emissions. For example, the NH3 spike in SE
North Carolina corresponds to the state's largest and most concentrated hog farming region, in a
state that is one of the country's largest producers (Hog & Pig County Estimates, 2008). Further
background surface concentration plots are provided in Appendix A.2
Max - 2.125e+01 @(53.21)
Ii
2006yearlyaverage
FRMPM2.5Iug/m^31
background
Min-000@(11) Max- 3.12601 @(14.67) Mean- 5.7825e+00
2006yearlyaverage
NH3jppbVJ
background
Min - 000@(1 1)
Mean - 6.9110e-01
70
002S
as
Ito
20
00
so
so
100
120
100
140
120
140
2006yearlyavg
(tullaviation-no aviation)FRMPM25[ug/m^31
Min--8.023e-03@(13,69) Max- 1.741e-01@(67.130) Mean-2.3919e-02
2006yearlyavg
NH3[ppbVl
(tullaviation- no aviation)
Min--3.250e-02@(4624) Max-1.976e-03@(13.69) Mean--22032e-03
e&OB
0
40
go
so
C06LMW*A
100
120
140
0
20
40
W0
W
1W
Figure 7: 2006 background NH3 and PM2.5 concentration showing several co-located
hotspots
2.3.2.3
Vertical profiles
In contrast to aviation, most other sources of emissions (those in the background) are confined to
the planetary boundary layer (PBL), a region of the atmosphere variably extending up to about 1km
(-900hPa on the plots below), characterized by rapid turbulence and strong vertical mixing (Seinfeld
& Pandis, 2006). Figure 8 shows the variability in several key species between the 2001 and 2005
NEIs. S02 and the H2SO4 into which it reacts show peaks above the ground layer from the
plumes of coal-fired power plant/industrial flues. Such spikes are visible at the same altitudes for
NOx and primary sulfates. All species show strong spikes or maxima at the ground level.
US Total S02 Emissions [Tg]
US Total N02 Emissions [Tg]
US Total H2SO4 Emissions [Tg]
900
900
920
920
940
940
2001
JI
-%--
CUI
r
S960
-
2005
'
960
960
1000
0
2
6
4
0
US Total NO Emissions [Tg]
0.05
0.1
0.15
0.2
0
920
920
940
940 1 \
940
960
960
960
960
960
960
1000
1000
15
10
A
1000
0.05
0.1
0.15
0.2
0
900
900
920
920
920
940
940
940
960
960
960
960
960
960
1000
10001
1000
1.5
1
0.5
2
0
0.2
0.4
0.6
0.6
0.005
0.01
0.015
US Total CO Emissions [Tg]
US Total PEC Emissions [Tg]
900
0
2
920
\
0
US Total POA Emissions [Tg]
1.5
900
900
5
1
US Total PNO3 Emissions [Tg]
US Total PSO4 Emissions [Tg]
900
0
0.5
50
100
150
US Total NH3 Emissions [Tg]
900
920
940
5
960
960
1000
0
2
4
6
Figure 8: Comparison of 2001 and 2005 NEI emissions profiles over CMAQ US domain
The trend in downwards emissions from the 2001 to 2005 NEI reflects increases in efficiency,
improved technology, and regulation in the last several decades, which have overcome increases in
capacity. Indeed, the EPAs historical data for national emissions trends (National Emissions
Inventory Air Pollutant Trends Data, 2009) shows a steady downward trend in S02, NOx, VOCs
and CO emissions dating from 1970 (the earliest year for which an NEI was compiled), with a more
than 50% reduction over the past four decades in those species (Figure 9). NH3 emissions have
remained largely static, while PM10 and PM2.5 have also shown decreases during the years for
which data is provided.
40
35
30
25
-2kNH3
-x-NOx
20
-PM10
-e-PM2.5
15
-+-SO2
--
VOC
YEAR
Figure 9: NEI emissions trends of several key species (National Emissions Inventory Air
Pollutant Trends Data, 2009)
....
....
......
US Total S02 Emissions [Tg]
900
US Total H2SO4 Emissions [Tg]
US Total NO2 Emissions [Tg]
1
1000
0
0.2
0.6
0.4
0
0.8
US Total NO Emissions [Tg]
0.005
0.01
0
0.015
US Total PSO4 Emissions [Tg]
900,
900.
920
920
920
940
940
940
960
960
960
980
980
980
1000
1000
0
1.5
1
0.5
0
0.005
0.01
0.2
US Total PNO3 Emissions [Tg]
900
1000
0.15
0.1
0.05
1
0.5
0
0.015
x 10
US Total POA Emissions [Tg]
900
900
900
US Total CO Emissions [Tg]
US Total PEC Emissions [Tg]
9501
1000
1000
0
0.05
0.1
0.15
0.2
0
0.02
0.04
0.06
0
5
10
15
US Total NH3 Emissions [Tg]
900
920
940
S960
980
1000
0
0.2
0.4
0.6
0.8
Figure 10: Seasonal Variability in the 2001 NEI for several key emissions species
As can be seen in Figure 10, there is little seasonal variability in most key species, with the exception
of NH3, which shows a more than 200% increase from a winter to summer month. In the absence
of other mitigating effects, this would point to higher PM formation in the summer months.
Indeed, background concentrations of PM2.5 increase nearly 50% from winter to summer. The
extent to which this represents correlation or causation is difficult to quantify, but the role of NH3
in PM formation is well-established.
2.4
GEOS-Chem Boundary Conditions
2.4.1
Background
Several species exhibit sufficiently long atmospheric residence times that transport from outside the
CMAQ domain, i.e. from Europe or Asia/Pacific, impacts local concentrations.
The desire to
capture this interaction leads to the use of boundary conditions that are created by a model with
global scope. GEOS-Chem was used to create the boundary conditions for CMAQ. The GEOSChem runs were perturbed with the same scenario as the CMAQ runs for which they provided the
boundary condition. For example, the global GEOS-Chem inventory for the run used to create the
CMAQ full aviation ULS scenario boundary conditions contained full aviation emissions with a ULS
fuel policy. Sensitivity to the boundary conditions is documented in Section 7. GEOS-Chem was
run on a 4x5 degree global grid. Further details about the model can be found in (GEOS-Chem v802-01 Online User's Guide).
2.4.2
GEOS2CMAQ and methodology
GEOS2CMAQ is a modular Fortran utility written to map the GEOS-Chem grid to CMAQ
boundary conditions. CMAQ boundary conditions are a simple additional "picture frame" layer of
concentrations in the grid around the CMAQ domain. The utility performs horizontal and vertical
spatial interpolation of the GEOS-Chem grid to the CMAQ grid, as well as species mapping (listed
in Table 7), outputting concentrations every three hours (Moon, Park, Li, & Byun, 2008). Note that
CMAQ has no vertical boundary condition, i.e. there is no vertical transport across the 1OOhPa
upper layer boundary. Further description of GEOS-Chem species can be found in (GEOS-Chem
v8-02-01 Online User's Guide). Description of all CMAQ species can be found in Appendix A.
Table 7: GEOS-Chem to CMAQ species mapping (Moon, Park, Li, & Byun, 2008)
CMAQ Species
Species.
,GEOS-Chem
N02
NOx
03
Ox -NOx
CO
CO
N205
N205
HNO3
HNO3
PNA
HNO4
H202
H202
UMHP
MP
NTR
R4N2
FORM
CH20
ALD2
0.5*ALD2 + RCHO
PAR
0.333*PRPE + ALK4 + 0.5*C3H8 + 0.2*C2H6 + ACET + MEK + RCHO
OLE
0.333*PRPE
PAN
PAN + PPN + PMN
ISOP
0.2*ISOP
ISPD
MACR + MVK
TERP
ALPH + LIMO + ALCO
S02
S02 + DMS
NH3
NH3
SGTOT TRP 1
0.2*SOG1 + 0.2*SOA1 + 0.2*SOG2 + 0.2*SOA2 + 0.2*SOG3 + 0.2*SOA3
SGTOT TRP_2
0.8*SOG1 + 0.8*SOA1 + 0.8*SOG2 + 0.8*SOA2 + 0.8*SOG3 + 0.8*SOA3
ASO4J
S04 + MSA
2.4.3
ANO3J
NIT
ANH4J
NH4
AORGBJ
0.847*SOA1 + 0.904*SOA2 + 1.24*SOA3
AORGPAJ
0.0545*OCPI + 0.0545*OCPO
AECJ
BCPI + BCPO
A25J
0.145*DST1 + 0.145*DST2
ANAJ
1.03*SALA
ANAK
1.03*SALC
ACLJ
1.03*SALC
ACLK
1.03*SALC
ASOIL
0.29*DST3 + 0.29*DST4
Verification
A simple verification of the GEOS2CMAQ outputs was conducted by plotting the raw GEOSChem outputs for the background case on the same plot as the ring of CMAQ grid cells forming the
CMAQ BCON. Yearly averages were used, and two species at two levels were tested: 03 and
HNO3, at ground level and ~860hPa (10th layer), plotted in Figure 12. An exhaustive proof was
not deemed necessary, as this verifies species and vertical mapping of the implementation of the code,
which has itself already been verified at length by (Moon, Park, Li, & Byun, 2008). The species were
chosen because they have significant concentrations across nearly the entire CMAQ domain
boundary. Note that results need not match exactly, as a less rigorous vertical mapping was
performed for this test than that of GEOS2CMAQ (which uses a non-hydrostatic vertical structure
created with time-changing pressures in the meteorological data, not from a static definition, as well
as other more complex corrections). Nonetheless, results agree very well, both spatially and in
magnitude. The efficacy of the comparison diverges the more ground elevation increases from sea
level, and the higher the vertical layer; for that reason, two lower layers were used. Figure 11 shows
the CMAQ boundary conditions highlighted outside the GEOS-Chem outputs that were used to
create them.
0
100
50
150
Column Number
Figure 11: Ground level 03 concentrations [ppbV] from GEOS-Chem (inside the black box)
and the CMAQ BCON created from this GEOS-Chem run (outside the black box)
c.)
d.)
100
COWNumbe,
ISO
Figure 12: Ground level 03 concentrations in a.), and at -860hPa (layer 10) in b.); ground
level HNO3 concentrations in c.), and at -860hPa in d.)
Of some note, however, is the background concentration of H2SO4, which is the only species that
shows a clearly higher concentration at the boundary than within the domain (as shown in Figure
13). The difference also manifests in the full aviation delta scenario (Figure 14b). H2SO4 from the
boundaries can be transported to react with NH3 to form (NH4)2SO4, contributing to increased
PM within the domain
background H2SO4 [ppbV] 2006 yearly average
Min = 000 @ (1,1) Max = 5.906e-03 @ (23,66) Mean = 3.4887e-04
0.01
0.009
0.008
0.006
0.005
0.004
0.003
0.002
0.001
0
0
0
20
40
60
80
Column Number
100
120
140
Figure 13: Background H2SO4 showing higher concentrations at domain boundary at
ground level
Boundary condition gradients are also evidenced in the delta scenarios. This occurs when GEOSChem predicts a larger aviation-attributable perturbation to a species than CMAQ that is able to
propagate to the domain boundary. The most marked examples include NH3 (Figure 14a), in which
elevated concentrations propagate from the northern boundary, and H2SO4 (Figure 14b), in which
elevated concentrations may propagate from the eastern boundary, though weather generally moves
west to east over the domain. These ground level plots do not speak to the transport processes in
the free troposphere and jet stream, but they do indicate significant boundary effects, which may
extend upwards. NH3 in particular drives PM formation in CMAQ. Elevated concentrations of
NH3 in GEOS-Chem for full aviation are particularly illustrative. Aviation can only act as a sink for
NH3, as it is not produced by aviation, but is destroyed by reaction with SOx and NOx to form
NH4, as is shown throughout the rest of the CMAQ domain. Knowing this, it may be possible to
ascribe some of the contribution from the boundary conditions to artifacts in the model results.
The swath of reduced concentration that breaks the elevated levels, roughly centered at column 50,
is in fact due to the reaction of NH3 to form PM, i.e. the swath of elevated difference in emissions
from column 20 to 100 is continuous. This can be seen most clearly in the LTO-only plots in Figure
38 in Section 7.4, in comparing the runs with static and changing boundary conditions. The
elevated NH3 emissions develop a gradient that extends southeastwards to the main N03 reaction
regions of the Midwest.
b.)
a.)
(fullaviation- noaviation)NH3{ppbV)
2006yearlyavg
Min- -3.250e-02
@(46.24) Max- 1,976e-03
@(13,69) Mean- -2.2032e-03
100
(full aviation-noaviation)H2S04[ppbV)
2000yearlyavg
Mi -- 1.308e-05@(67l0a) Max- 703505@(23,148) Mean-i.9035e-0X
--
0.02
0.015
10
001
0,005
1E
60
40
40
0.01
20
20
-0.015
0
0
0
00
4
40
00
s0
800
Column
Number
i00
120
14
040
00
20
40
t0
80
100
120
140
Column
Number
Figure 14: Full aviation delta in ground-level NH3 and H2SO4, showing clear boundary
layer effects
2.5
Meteorological Data
2.5.1
Background
Meteorological data, or met data, is of integral importance in an atmospheric model. It dictates the
advection of species by wind, wet deposition by precipitation, pressure, temperature, relative
humidity (and the gas-particle equilibrium that is so dependent upon it), degree of photolytic
reaction shielding from clouds, and other parameters that strongly influence the reaction and
persistence of species. CMAQ met data is created from the outputs of the Mesoscale Model version
5 (MM5), a model designed to simulate or predict mesoscale or regional scale atmospheric
circulation (Chapter 8: MM5). The MM5 parameters used are listed in Table 8. CMAQ met data is
not affected by emissions, meaning feedbacks are not included e.g. changes in cloud formation with
changes in aerosol cloud condensation nuclei (CCN). The CMAQ met data is derived from the
same source as that used for GEOS-Chem; however GATOR and TOMCAT used different data
(self-created in the case of GATOR). As an interesting aside, the MM5 data is sufficiently wellresolved in space and time that it accurately modeled the path of Hurricane Ernesto in AugustSeptember of 2006, the only hurricane to make extended landfall along the east coast in the 2006
season.
Table 8: CMAQ/MM5 meteorological parameters and mechanisms
Meteorology-Chemistry Interface Processor
MCIP v3.3 (frozen 08/01/2007)
Input Meteorology
MM5 v3.6.1
Cumulus Parameterization
Grell (Grell & Devenyi, 2002)
Microphysics
Simple Ice (Dudhia) (Dudhia, 1989)
Longwave Radiation
RRTM (Mlawer, Taubman, Brown, Iacono, &
Clough, 1997)
Shortwave Radiation
Dudhia (Dudhia, 1989)
PBL Scheme
MRF (Hong & Pan, 1996)
Surface Layer Scheme
Standard
Land-Surface Scheme
OSU or NOAH Land-Surface Model (Mitchell,
2000)
Land Use Classification
USGS (Andersen, Hardy, Roach, & Witmer,
1976)
3D Analysis Nudging
On
SFC Analysis Nudging
Off
OBS Nudging
Off
2.5.2
Vertical Profiles
US Average Cloud Water Mixing Ratio [g/g] 2006
US Average Temperature [K] 2006
1000 L
200
A
220
240
280
260
300
0
0.5
1
1.5
2.5
2
-s
x10
US Average Rain Water Mixing Ratio [g/g] 2006
0
0.5
1
1.5
US Average Water Vapor Mixing Ratio [g/g] 2006
0
0.005
0.01
0.015
x10 x15
Figure 15: Vertical profiles of US average temperature, cloud water mixing ratio, rain water
mixing ratio, and water vapor mixing ratio
Figure 15 shows several domain- and monthly-averaged vertical profiles of several key parameters.
As a domain average, the data is not amenable to comparison with other models, but it shows the
extent of intra-annual variability in the met data in CMAQ. For instance, decreased rain water
mixing ratio in July below -500hPa may imply higher PM concentrations from reductions in wet
deposition, in the absence of other mitigating effects.
2.6
Particle-Bound Water and Dry vs. Wet PM
The EPA's federal reference method (FRM), based on the SANDWICH model (Sulfate, Adjusted
Nitrate, Derived Water, Inferred Carbonaceous mass Hybrid material balance), dictates that particlebound water (PBW) attached to sulfate, nitrate, and ammonium be included in the accounting of
PM2.5 mass.
C
PM2.5FM = OCM,=
+ BC + S04+ N0
3
FRm
+ NH 4 Fm + H20 + crustal + 0.5
(3)
Here, OCM,,b is organic carbon mass by difference (i.e. it is derived last), crustal is soil and dust, and
0.5 is a blank mass constant. The blank mass and crustal disappear when the background is
subtracted from an aviation scenario.
The PM2.5 FRM sampler is specified by design. It contains a sharp-cut particle size separator and a
detailed monitoring protocol. PM2.5 mass is determined gravimetrically by weighing the pre- and
post-sampling filters that have been equilibrated for a minimum of 24 hours at standardized
conditions of 20-23'C and 30-40% relative humidity (RH). Collectively, the FRM is intended to
provide reproducible mass concentrations and to determine compliance with standards (Frank,
2006).
In this study, dry PM is taken to be the sum of sulfates, nitrates, ammonium, black carbon, and
organics. PBW is output by CMAQ, though it is not output in all models. (Frank, 2006) plots PBW
as 0.24 * (S04 + NH4), yielding a good first-order approximation derived from measurements at six
diverse FRM monitor sites. A more complicated fitted relation varying with S04, NH4, and N03 is
used in the EPA Model Attainment Test Software (MATS), which represents the standard technique
for allocating PBW in hydrated PM for reporting PM concentrations. The relation itself is
comprised of more than twenty terms, conditional on the degree of neutralization (DON), defined
as the amount of NH4-associated-with-SO4 divided by the total amount of S04 (Abt Associates,
Inc., 2009). However, a comparison of the outputs of the first order approximation described in
(Frank, 2006) and that described in the MATS documentation shows good agreement, and as the
first order approximation is easier to use, and sufficiently accurate to represent the PBW in the total
PM values used to derive the epidemiological Concentration Response Functions (CRFs) upon
which health impacts are based, it is used exclusively in this thesis to hydrate PM for all models.
A comparison of the CMAQ output PBW (AH2OI + AH2OJ) to the SANDWICH approximation,
shows a significant divergence, including a notable bias towards higher water concentrations in
nitrate-heavy regions in CMAQ (Figure 16), which are also generally characterized by higher relative
humidity. However, the transformation to wet PM should reflect the underlying assumptions in the
FRM-monitor-derived epidemiological CRFs. Hence, that method is used to create hydrated PM,
with the caveat that it under-predicts model values.
b.)
a.)
yearlyaverage
)ug/m'3]2006
approx.)
PBW(SANDWICH
(fullaviation- background)
Mean-21846860-03
Min--,8115650-03@(11,69) Max- G49399-02@(67,130)
2006yearlyavg
H20 (PBW)(ug/m^3)
(fullaviation-no aviation)
Min- -4,98ge-03@(1368) Max- 1.931e-01@(73.85) Man -2.257le-02
.00
.n4000010
0
20
.z
so
go
ColumNurVer
we
a
4
Colisnn
Nunew
Figure 16: Comparison of CMAQ output PBW (a) to FRM-derived PBW (b) (note the
different scales)
2.7
2.7.1
Population Data and Health Impacts
Global Population of the World (GPW) and Global Rural-Urban Mapping
Project (GRUMP)
Population is used to derive health incidences for a change in concentration within a given grid cell
through a relation known as the concentration response function (CRF). The Center for
International Earth Science Information Network (CIESIN) of Columbia University hosts data
demonstrating the spatial distribution of human populations across the globe (Center for
International Earth Science Information Network (CIESIN), 2005). The highest resolution data is
available under the Global Rural-Urban Mapping Project (GRUMP) at 30 arc-seconds (-1km).
Slightly coarser information is available in the Global Population of the World (GPW) datasets, at
2.5 arc-minutes (-5km). Global baseline health incidences were calculated by (Barrett, Britter, &
Waitz, 2009) at this resolution, and so this dataset was adopted exclusively for all models in this
study, with population for the year 2000. A comparison of GRUMP and GPW data gridded to the
CMAQ domain show very similar results. The data is compiled by CIESIN from censuses and
surveys administered for political or administrative units. The approach uses a simple proportional
allocation of administrative unit population totals over grid cells (Deichmann, Balk, & Yetman,
2001).
Total338 Mlion
IoglO0popoiaton)
0AQ doooiln
Min.ltt@(1,1) M 6.702692e.00@(67,l30) Moot, lot
338.45M
Population
[persons)Total:
CMAQ
domain
Min- 0 @(1,1) Max-5.043031e06@ (67,130) Men- 2.041795e+04
100
4
A6
44
3
404
60
Figur 17:CMAQ
omai popuatio
80'
2
Ar
esons Fnctons(C
Conentaton
60
2.7.2 Concntraton Rspons
0
Z84O0
0
20
20
0
MPllio
Aidmint c(op~toncenotati33n.4f
s)reatechnge
6
zo
40
80
8
Number
Column
100
00
20
10
Funtion
40
10
100
80
68
0
100
20
2
120
40
140
80
so
100
120
140
Number
C01um
Figure 17: CMAQ domain population
2.7.2
Concentration Response Functions
Concentration Response Functions (CRFs) relate changes in ambient concentration of PM to
changes in health incidences. Figure 18 illustrates the role of CRFs in deriving health cost end
points from changes in emissions.
Chong"eI aemntr
conoentndionsm
Ernhionmodeling
"
changeo In heolah
endpoint incdewme
rQUN
Pokey scengio
Aambiet concetration
Aheahh costs = cmissions x.
ACMssions
health mcidence
..
x
cost
aambicn onccntration hcalth incidcne
Figure 18: Health impact pathway for air quality assessment (Rojo, 2007)
Long-term PM exposure is associated with respiratory illness in children, cardiopulmonary mortality
in adults (Pope & Dockery, 2006), premature mortality (US EPA, 2006), lung cancer (Ostro, 2004),
cardiovascular and pulmonary hospital admissions, and asthma aggravation (US EPA, 2004). A
2004 Journal of American Medicine study estimated total yearly PM-attributable mortalities in the
US at between 22,000 and 52,000 (Mokdad, Marks, Stroup, & Gerberding, 2004). The shape of the
CRFs and the existence of a no-effects threshold level have been the subject of much study in the
evolution and evaluation of air quality standards (Pope & Dockery, 2006). Exponential and
piecewise functions have been used, but (Pope & Dockery, 2006) state that the shapes are generally
linear or weakly log-linear with no well-defined threshold. A World Health Organization (WHO)
report recommends the use of the following log-linear CRF (Ostro, 2004):
RR = [(X + 1)/(Xo + 1)]f
(4)
Here, RR is the relative risk, X is the annual mean concentration of PM2.5 under the aviation
P
is an empirical
scenario, X 0 is the annual mean background concentration of PM2.5, and
coefficient for each health condition. Low, nominal and high values are cited in Table 9. For this
study, health incidences were derived only for cardiopulmonary mortality and lung cancer in
individuals over the age of 30, based on the methodology in (Barrett, Britter, & Waitz, 2009).
However, (Sequiera, 2008) has shown that 97% of annual monetized health costs from aviation are
due to premature mortality, and of those more than 99% are due to premature mortality in adults
over 30 (as opposed to infants < 1 year old). Therefore, the overwhelming majority of aviationattributable health costs are included.
Table 9:
P coefficient values
(Ostro, 2004)
Low
Nominal
High
Cardiopulmonary Mortality
0.0562
0.15515
0.2541
Lung Cancer
0.08563
0.23218
0.37873
1
Aviation-attributable mortalities are derived by scaling existing incidences by the reduction in relative
risk:
-mortalities= baseline * [1 - [(X + 1)/ (X + 1)] fl]
(5)
Here, baseline is the total number of deaths by a given cause (here, lung cancer or cardiopulmonary
mortality), compiled from WHO Global Burden of Disease (GBD) reports (Barrett, Britter, &
Waitz, 2009). Global and US-only lung cancer and cardiopulmonary mortalities are plotted in Figure
19 on a logarithmic scale, showing highest global incidences in India, China and Europe. In the US,
the WHO yearly lung cancer mortality figure of ~176,000 closely matches data from the US Center
for Disease Control (CDC) for 2006 at 158,599 (Lung Cancer, 2006). The difference may be
explained by the inclusion of mortalities from parts of Canada, Mexico, and the Caribbean in the
CMAQ domain, as well as different sample years.
Baselinecardiopulmonary
Incidences
from PM2.5[ogl0(mortalities)] Total- 14.135Million
Baselinelung cancer incidences [og10(mortalities)] Total - 1.262Million
6
1N
N
NV
N
N
0: S
E
0
'E
9000E 120'E 150
E101
20',
I
Beg
o
90',
60
30',
0
30 E 60' E 90' E 120 E 150*E 180*E
Totl 76
s
Baseline ncden
941120a d l0(muities]
Total
[og 678 s)]
a dacancer
mon deces
L,0
O
0'N-0
60
-6
2a
m
mema
66
-2L
1.5
z6
40
40
GOMH4ind VOf(,s byx OH14in2the00presence
ag
0.5 of20 N--x.
20
0
,
20
40
60
s0
Column
Number
100
120
140
a
2D
MortII@(~ aieA0.5 from3e.0@6,10 aain-atriutbl
40
so
80
Column
Number
100
120
140
Figure 19: Baseline cardiopulmonary and lung cancer mortalities
In addition to PM, O3 in the troposphere is a pollutant with adverse health impacts; only in the
stratosphere does it play a positive role, where much higher concentrations contribute to the
absorption of UV radiation. Ozone is produced in the troposphere by photochemical oxidation of
CO CH4, and VOCs by OH in the presence of NOx. Mortalities from aviation-attributable
changes in 03 at the surface are not within the scope of this thesis. However, (Ratliff, et al., 2009)
found changes in aviation-attributable mortalities and other health incidences from 03 to be neutral
or even slightly beneficial on balance, reflecting both increases and decreases in ground
concentrations depending on conditions.
Potential differential toxicity between the species of PM is not treated, which may be important to
the extent that aviation-attributable PM differs in chemical composition and size with respect to the
polluted urban air upon which epidemiological CRFs are derived. PM precursor pollutants emitted
from aviation are not different than those emitted from other combustion sources, implying that the
impacts of secondary aerosols are comparable to non-aviation sources. However, aviation BC
emissions are typically finer than non-aviation sources, and recent studies indicate that ultra-fine BC
may have particularly high toxicity (Liao & Chio, 2008); as such, aviation BC impacts may be more
important than results from the present analysis indicate (Barrett, Britter, & Waitz, 2009).
2.7.3
Monetary Value of a Statistical Life (VSL)
Once mortalities have been derived from the CRFs, it is necessary to assign a monetary cost per
death. The EPA uses a hedonic value of a statistical life (VSL).
Hedonic valuations measure the
willingness of an individual to pay to avoid increased risk. For example, if an individual is willing to
pay $600 to avoid a 1 in 10,000 chance of dying, that individual's life is statistically valued at $6
million.
In its 2000 Guidelines, the EPA advocates a VSL of $7.4 million in 2006 dollars (EPA,
2000); however, there is quite a bit of variability in the literature.
The Aviation Portfolio
Management Tool (APMT) uses a mean value of $6.3 million in 2000 dollars, which translates again
to $7.4 million in 2006 dollars. That value is used exclusively in this study for valuing US mortalities.
Elsewhere around the world, such a valuation is inflated, and project results are provided only in
non-monetized mortalities.
2.8
Modeling Context
2.8.1
Earth-Atmosphere Modeling of Aviation
The atmosphere itself presents a range of spatial scales in its motions that spans eight orders of
magnitude, from eddies on the scale of centimeters, to huge air mass movements of continental size.
(Seinfeld & Pandis, 2006).
CMAQ is a regional, or mesoscale model.
Mesoscale denotes
phenomena occurring on scales of tens to hundreds of kilometers, such as land-sea breezes,
mountain-valley winds, and migratory high- and low-pressure fronts (Seinfeld & Pandis, 2006).
Several species are important in analyzing the production of aviation-attributable PM. NOx, S02,
tropospheric 03, and aerosols all have lifetimes of days to several weeks. Strongly oxidizing species
such as H02, H202, and OH (the most important oxidizing species for tropospheric compounds)
are short-lived but integral to the production of sulfates and nitrates from SOx and NOx.
For
example, HNO3 reacts with NH3 to form NH4NO3, or ammonium nitrate; HNO3 is produced by
the reaction of N02 and OH. OH is itself formed during the day by the reaction of H20 and
01D, created from photolysis of 03. In the analysis of CMAQ results, the following species were
examined as significant in the study of aviation-attributable PM: S02, NO, N02, 03, HNO3,
H2SO4, aerosols (S04, N03, NH4, BC, ORG), OH, H202, and NH3. The temporal and spatial
scale of reaction of several of these species are plotted in Figure 17.
Urban or Regional or Synoptic to
Local scale Mesoscale| Global scale
Microscale
ng-lived .
100 yr
species
CFCs)
NPI
CH4
CH 1CCl1,
10 yr I
~
kOCH3Br
--
Lived Species
L0*Aerosols
Trop O
1Iday
.@s02
H2O2
day* -I NI x
t
Mixing Tim
Intra-hemisphenc
Mixing Time
Bounday Layer
Mixing Time
--
./~
~
Shr-lived
ioo
[
DMS
Ih
Inter-hemispheric
* Co '
Modertely Long
u
100
g
*CH3Oj
O
gCH
H02
ON03
Im
l10m
100m
1km
10km 1(K0kmlOOWkrnIO,OOkm
Spatial Scale
Figure 20: Spatial and temporal scale of atmospheric processes (Seinfeld & Pandis, 2006)
One of the most important atmospheric cycles in the study of aviation's impacts is the creation and
destruction of 03, which is integral to the oxidation of precursors to PM. Ozone in the troposphere
is generated from two major types of precursors: VOCs and NOx (Seinfeld & Pandis, 2006). 03
formation is initiated by the reaction of OH with organics, and a subsequent interwoven network of
free-radical reactions is catalyzed by NOx. Aviation is the sole significant source of VOC emissions
above the PBL, and one of the two major sources of NOx emissions, along with lightning. The
significance of the role NOx plays as a catalyst can be seen in Section 7 describing the sensitivity of
results to lightning NOx, in which the removal of lightning NOx results in a significant increase in
oxidizing capacity from the NOx created by aviation, especially in the summer months.
2.8.2
Atmospheric Aerosols
Particulate matter is any substance, except pure water, that exists as a liquid or solid in the
atmosphere under normal conditions and is of microscopic or submicroscopic size but larger than
molecular dimensions. In studying aviation, we usually refer to "dry" and "wet," or hydrated PM.
Wet PM contains water that has condensed onto a particle, inflating its mass. In analyzing the
health impacts of PM the particle-bound water (PBW) contributes mass, but the health effects are
correlated with the amount of hydration assumed in the FRM as described earlier (30%-40% RH
and 20-23'C).
A full description of atmospheric particles requires specification of not only their concentration but
also their size, chemical composition, phase (liquid or solid), and morphology (Seinfeld & Pandis,
2006).
In spite of the specific processes that affect particulate aging, the usual residence time of
particles in the lower atmosphere does not exceed several weeks. Very close to the ground, removal
is effected by dry deposition and settling on surfaces. Above 100m, precipitation scavenging
(incorporation into cloud droplets during formation of precipitation) is the predominant removal
mechanism.
Differences in meteorology between models have significant effects on aerosol
concentration. Particles that can become activated to grow to cloud droplets in the presence of a
super-saturation of water vapor are termed cloud condensation nuclei (CCN) (Seinfeld & Pandis,
For sulfur compounds with an estimated average residency of one week, the troposphere is
2006).
not even well-mixed vertically.
Particles less than 2.5um in diameter are generally referred to as "fine" and those greater than 2.5um
diameter as "coarse." The fine and coarse particle modes originate separately, are transformed
separately, are removed from the atmosphere by different mechanisms, require different techniques
for their removal from sources, have different chemical composition, have different optical
properties, and differ significantly in their deposition patterns in the respiratory tract. Therefore the
distinction between fine and coarse particles is a fundamental one in the physics, chemistry,
measurement and health effects of aerosols (Seinfeld & Pandis, 2006). That being said, the CRFs
used in this study do not distinguish between type of PM, which is as of yet an unquantified
uncertainty. CMAQ contains three size bins, or modes, of aerosol. The nucleation (or nuclei) mode
comprises particles with diameters up to 10nm. The Aitken mode spans particles from 10nm to
100nm (0.1um) in diameter. These modes account for the large majority of particles by number, but
only a small percentage of particles by mass and are lumped together in CMAQ. The accumulation
mode, spanning particles from 0.1um to 2.5um in diameter, usually accounts for most of the aerosol
surface area and a substantial part of the mass. Particles in the accumulation mode are formed from
coagulation of smaller particles; at this size, particle removal mechanisms are least efficient, hence
particles tend to accumulate (Seinfeld & Pandis, 2006).
The coarse mode is comprised of particles
larger than 2.5um in diameter; however, essentially all aviation emissions are smaller than 2.5um.
As discussed in the section on climate feedbacks, atmospheric aerosols can have either a warming or
a cooling effect on the atmosphere, depending on cloud interactions, altitude and optical properties.
Several past studies have found aerosols to have a net cooling effect.
In particular, in the 2009
authors update of the IPCC reports, (Lee, et al., 2009) predict that total global radiative forcing (RF)
from aerosols exert a net cooling effect from direct backscattering and some types of cloud
formation that's nearly 75% that of the warming effect from elevated C02, notably with uncertainty
bounds falling entirely on the side of cooling (Figure 21).
However, the level of scientific
understanding (LOSU) and characterization of these processes is cited as medium to low.
In
general, however, there is thought to be a tradeoff between the air quality and climate effect of
aerosols, as increased PM (and the health incidences it brings) nonetheless combat global warming
through negative RF. Any such effect, however, is far from symmetric, as the spatial patterns of
warming from GHGs and cooling from aerosols are quite different.
Global Radiative Forcing Components in 2005
RF values
RF Terms
Long-lived
greenhouse gases
1
LHalocarbons
SOzone-005
CD
OzntaopeicToopei
Stratospheric water
vapour from CH 4
Surface albedocarbon
SraeabdLadueon
<
Total
aerosol
1..
effect
Solar irradiance
Global
High
Global
High
[-0.15 to 0.051
0.35 [0.25 to 0.65]
Continental
to global
Med
1
-0.56 [-0.9 to -0. 1]
Contgnenal
Medo
1
-0.7 [-1.8 to -0.3]
Continental
Low
0.01 [0.003 to 0.03]
Continental
Low
0.12 [0.06 to 0.30]
Global
LOW
1
1
1
-1
0
Med
Low
-
Total net
anthropogenic.
-2
1.66 [1.49 to 1.831
0.48 [0.43 to 0.53]
0.16 [0.14 to 0. 18]
Local to
continental
I
Linear contrails
1t
Spatial scale LOSU
-0.2 -0.4 to 0.0
0.1 [0.0 to 0.2]
snow
Direct effect
Directblffect
Cloud albedo
2
(W M )
1
Radiative Forcing (W rn- 2 )
[0.
to global
to .]
2
Figure 21: Global Radiative Forcing ftom All Anthropogenic Sources of Emissions (Lee, et
al., 2009)
Aviation-attributable aerosols are emitted over a larger range of altitudes than other anthropogenic
sources and contribute to induced upper-altitude cirrus cloudiness. (Lee, et al., 2009) predict sulfate
aerosols from aviation have a cooling effect, but the LOSU is low.
Sulfate aerosols in the upper troposphere and stratosphere from volcanic emissions have had
profound climate effects.
In 1991, the eruption of Mt. Pinatubo in the Philippines and
corresponding injection of -i0Tg
i02 into the tropical stratosphere resulted in an average cooling
of the earth by
adiC
the following year (Crutzen, 2006). Stratospheric aerosols have significant
longer residence times due to low cross-tropopause transport. Indeed, a leading strategy for
geoengineering, or the deliberate manipulation of the climate to combat the effects of global
warming, is the injection of sulfate precursors into the upper stratosphere. The Nobel Prize winning
chemist Paul Crutzen has posited that a (sizeable) continuous stratospheric sulfate loading of 5.3Tg
S would be sufficient to counteract the 4 W/m^2 RF from a doubling of atmpospheric C02
concentrations (Crutzen, 2006). By comparison, total global aviation output of SOx is on the order
of 0.1Tg S.
Aviation Radiative Forcing Components in 2005
[(Wm)
RF Terms
00280
Carbon dioxide
produiction
Methane
Ozoe0263
I
pro
Neduction
I SU
Globa
igh
9hemisphei
jj
-LT9
L
Global
(0.0115)
Kemnspheric
-0.0028
Water vapour
Sulphate aerosol
LO
scale
Conflnenital
tonphn
(0.219)
-0.0125Md
obal
(4,0104)
0
Total NOx
Spatial
LOW
Best estimate
Sulhae arool
I
~-0.0048
L2Estimate
values)
o -- 90% confidence
Local
gloalto
Low
Local
Low
'(IPCCAR4
Soot 0aeroso
001
0.0034
0-0025)
Linear contrails
Induce cirrus0033
cloudiness
0.055
(0.0478)
Total aviation
(Excl. induced cirrus)
Total aviation
-0.08
-0.04
0.08
0
0.04
Radiative Forcing (W rn- 2 )
Low
Local to
hemispheic
Very
Low
ll
0.078_Global
7l
(Inct induced cIrrus)
to global
Low
continental
L
Low
0.12
Figure 22: Global Radiative Forcing from Aviation Emissions (Lee, et al., 2009)
2.8.3
Multi-Model Approach
Given the uncertainties of some of the processes that affect aviation's impact on air quality and
climate, it is desirable to introduce a number of independently-developed models for some measure
of cross-comparison. The Intergovernmental Panel on Climate Change (IPCC) used at least 18
distinct models, some with one or more versions. The models varied significantly in resolution,
upper-most altitude, coupling, and other key parameters (Randall, et al.). Considerable confidence
can be developed as model predictions can be verified by empirical data. Studying an individual
perturbation to the atmosphere, such as aviation, defies easy comparison to empirical data, though
comparisons of the baseline case for each model to FRM monitor data is presented in Section 8 as a
way of aggregating the differences in model processes and inventories. Table 10 summarizes
differences in model resolution, data, duration, and scope.
Table 10: Summary Model Comparison
GATOR
global
4x5 degree
GATOR
GATOR
1x1 degree
US-nested
GEOSChem
global
58 layers, 8
Coupled
layers in
meteorology
bottom km,
created in
~19km
model
ceiling
45 layers
'
14 layers in
bottom km
Coupled
Culd
meteorology
created in
model
GEOS-5
reduced, 47
layers, 8
2006 GEOS5
4x5 degree
layers in
meteorology,
bottom km,
static
80km
12 years,
looped
2006
aircraft
emissions
1 +1
CMAQ 3x36 km
CMAQ
36x36 k
2006 GEOS5
bottom km,
oo
meteorology,
static
..
(~15km)
TOMCAT
2.8x2.8
degree
35 layers,
10hPa
..
ceiling
(-32km)
Static boundary conditions,
includes nearly all of the Pacific
month (Jan,
' and Atlantic Oceans above 15N
Jl 06
in domain
Tropospheric + stratospheric
chemistry transport model
(CTM), no climate feedbacks,
1 year199NJbcgon
(2006)
1999 NEI background
inventory over US, default
GEOS-Chem inventories
ceiling
35 layers,
18 layers in
Coupled
meteorology/ chemistry, climate
feedbacks, more detailed/nearexplicit physical and chemical
processes than other models,
computationally expensive in
comparison, 2005 NEI
background over US
elsewhere
1 year + 3
Tropospheric CTM, no climate
feedbacks, nested with varying
month
spin-up
GEOS-Chem boundary
conditions, 2001 NEI
background inventory
No treatment of BC, NH4
Full Flight Emissions Assessment
3
Context
3.1
The inclusion of full flight emissions in AQ studies has been a recent development. The APMT Air
Quality (AQ) module (hereinafter APMT-AQ), which used CMAQ to create a response surface to
the perturbation of aviation emissions, and EPAct studies used aviation inventories with LTO-only
emissions. However, (Barrett, Britter, & Waitz, 2009) have used GEOS-Chem to show that cruise
level emissions can have significant effects on ground concentrations of PM in CTMs, a finding
borne out in the CMAQ results comparing full flight and LTO-only emissions. The addition of
cruise emissions with varying boundary conditions (to reflect the scenario) results in increased
surface concentrations of PM by a factor of -5.4. The addition of cruise emissions in comparison
to LTO-only emissions both with static BCON (background BCON) results in increased surface
concentrations of PM by a factor of -2.3. Therefore, there are both significant CMAQ modelspecific reasons for increased PM, as well as boundary condition transport/effects.
Changes in Surface Concentration and Mortalities
3.2
When including full flight emissions, the main findings from CMAQ are as follows:
e
When considering US + non-US, full flight emissions, a ULS fuel policy results in a 20%
reduction of domain-average ground-level PM2.5, with 110 lives saved (a 14% reduction).
*
Reducing
direct aircraft
sulfate emissions
(as oxidized aviation-attributable
S02) to
essentially zero results in a reduction in aviation-attributable S04 of ~50%. The rest is a
result of aviation-attributable NOx reacting with VOCs to form 03, which feeds OH, which
oxidizes background S02 to S04. The chain of reaction is thus NOx + VOCs -*
{03} +
{OH} + S02 -+ {S03} + H20 -* {H2SO4} + NH3 ->
(NH4)2SO4. This effect is discussed further in Section 8 describing the sensitivity of results
to LNOx, as well as in Section 2.
ho -+
*
{02 + [O} + H20] -+
This limit on sulfate reduction is also a key finding of the corresponding GEOS-Chem runs,
which set the upper limit at ~40%.
*
Background ammonia (NH3) drives the formation of N03 and NH4 in the US, which
constitute almost 50% of aviation-attributable PM.
*
As a percentage of all sources of particulate matter, global full aviation-attributable
concentrations average about 0.4% in the US, with a maximum of 1.3% (Appendix C).
*
The application of SMAT yields very similar results, with a marginal reduction in mortalities
and marginal increase in concentration, indicating a good general agreement in model
baseline and FRM monitor concentrations (Section 8.2).
There has been some contention that these results overestimate the significance of cruise emissions
due to an overly diffusive vertical transport scheme, for both CMAQ and GEOS-Chem. CMAQ
used a (comparatively) simple first order local closure K-theory eddy diffusion scheme. Sensitivity
tests (described in further detail in Section 7) in which the value of K was set to zero above the PBL
showed negligible differences in domain average vertical profiles, and minor differences in surface
concentrations, including with respect to a more sophisticated local + non-local closure scheme
(ACM2). Indeed, changes in concentration and mortalities between full aviation and LTO from BC,
a non-reactive aviation emission that can be treated as a tracer, are relatively low, with a 30%
reduction in ground-level concentration and less than 10% reduction in mortalities. As emissions of
BC are much higher at cruise altitude (see Appendix A), this implies modest vertical transport
downwards, with quick removal of BC at altitude through wet deposition. Nonetheless, significant
aviation-attributable concentrations exist between the top of the PBL and cruise altitudes, where
emissions are sparse. Some initial comparisons have shown subsidence rates (W-component wind)
in CMAQ (and by extension GEOS-Chem, given the provenance of the met data), may be higher
than that in GATOR. Subsequent phases of the study will seek to investigate this and other effects
further. It is fair to say that given these results, and a comparison of the runs in which the boundary
conditions were varied and static, GEOS-Chem is driving the results of CMAQ when cruise
emissions are considered.
Indeed, domain average concentration of PM2.5 is approximately five
times higher with varying BCON than with static BCON.
Note that all domain average concentrations were taken using a subset of the total CMAQ grid
displayed, windowed tightly on the CONUS landmass and bounded by 125W, 67W, 25N, and 51N.
Min- -8.023e-03@(13,69)
(ugim^31
- 797(290to1304)
2006yearlyavg.mortalities
Max- 1 741e-01@ (67.130) Mean-2 3919e-02
(fullaviation- noaviation)FRMPM25
to 160)
2006avg,mortalities
- 110(40
fullaviationULSreductionin FRMPM25ug/m^31
Mn
-
78e-02@(1468)
Mao
4195e-02@(4622
0,0
00
01
Z
20
40
0
0
s
o
004
003
008
20
20
0
Mean 47415e03
0
01
so
s0o
ComnNumber
100
120
140
0
0
20
40
so
so
Columnuber
1
= 106(7210325)
avg.mortalities
FRMPM25)ug/m31 2006yeariy
- no aviation)
(fullaviation(sBCON)
(full aviation (sBCON) - no aviation) FRM PM25 [ug/m^3] 2006 yearly avg, mortalities - 198 (72 to 325)
Min-000@(1,1) Max- 9.577e-02@(67,130) Mean- 5.1547e-03
(fullaviation- noaviation,SMAT)FRMPM2.5[ug/m^3]
2006avg.morts.- 783(285to1281)
Min- -8.300e-03
@(13,69) Max- 3.542e-01
@(53,21) Mean- 2.4608e-02
1so
0
20
40
so
so
100
120
140
Column
Number
Column
Number
Figure 23: Full aviation FRM PM2.5 and ULS reduction in same, full aviation (static
BCON) FRM PM2.5, and post-SMAT FRM PM2.5
By component, the largest reductions with ULS are seen in S04, as was expected. However,
significant aviation-attributable S04 concentrations remained, almost exclusively in the East and
Southeast, where background S02 and VOCs are highest, and OH concentrations are depressed
from more rapid oxidation. Background S04 adheres to the same pattern of elevated concentration.
A smaller but non-trivial reduction in mortalities was seen from decreased NH4, primarily in the
West, where a greater percentage of S04 was removed with a ULS fuel policy. BC and ORG are
negligibly affected, confined to boundary condition effects.
4
(fullaviation- noaviation)S04[ug/m^3]
2006yearlyavg,mortalities
- 174(63to 284)
@(13,69) Max- 3.073e-02
@(46,22) Mean- 7.4230e-03
Min- -4.079e-03
0
20
40
0
80
Column
Number
100
120
140
- 85 (31to140)
full aviationULSreductionin S04 [ug/m312006avg,mortalities
Min- 4923e-03 @(13,69) Max- 2.865e-02
@ (46.22) Mean-3.6228e-03
0
20
40
60
0
ColumnNumber
100
120
140
(fullaviation- noaviation)N03 [ug/m^3]2006yearlyavg.mortalities
- 342(124to560)
Min- -1.144e-03@(43.47) Max- 8.211e-02
0(60.18) Mean- 7.9871e-03
full aviationULSreductionin N03 lug/m^312006avg.mortalities
- -18(-7to-30)
Min- 9.789"43 0(72.116) Max-2 716e-030(54.22) Mean- -5.6634e-04
CohannNumber
ColumNumber
(full aviation -no aviation) NH4 (ug/m^3)
2006yearly avg mortalities - 142 (52 to 233)
Min- -1.902e-03@(13,69) Max- 30114e-02@(67.130) Mean-39471e-03
full aviation ULS reduction in NH4 (ug/m^3] 2006avg. mortalities - 19 (7 to 31)
Min. -2.283e-03@(14,68) Max - 7803e-03 @(46.22) Mean - 7.101le-04
0035
'002
0 02
01015
10005
101
0
20
40
60
s0o
Column
Number
100
120
20
140
40
80
Coklum
Nurrbe
60
100
120
140
Figure 24: Full aviation S04, N03, and NH4, and ULS reduction in same
(fullaviation-no aviation)
BC [ug/nm3]
2006yearlyavg.mortalities
- 14(5 to22)
Min - 1.092e-04 @(13,68)
Max - 1.797e-02@(67,130)
fullaviationULSreductionin BC[ug/m^3]2006avg,mortalities
- 0 (0 to 0)
Mean - 1.5474e-04
Min - -1.298e-04@(13,69)
Max- 1.797e-04@(13,62)
Mean- 57789e-06
100
20
40
60
so
Column
Number
100
120
140
0
20
40
60
80
Cokonn
Number
100
120
140
- -1 (0to-2)
in ORGIppbV]2006avg,mortalities
fullaviationULSreduction
@(13,62) Mean- -. 7975a-05
Min- 3,126e-03@(94,103) Max- 1.030e-03
- 55(20to 90)
ORG[ppbV)2006yearlyavg.mortalities
(fullaviation-no aviation)
@(40,111) Mean- 1.9604e-03
@(88,5) Max- 1.800e-02
Min- -6.621e-04
0.01
0.00"
0-002
0.001
0.01
140
20
40
s0
s0
100
120
140
Column
Number
Column
Numbern
Figure 25: Full aviation BC and ORG, and ULS reduction in same
The contribution of each constituent species of FRM PM2.5 to total mortalities and concentration is
shown in Figure 26. N03 shows higher concentrations in population centers in the Midwest and
East, dominating total mortalities.
Full Aviation Mortalities
ORG
7%
BC
1%
Full Aviation dConc
ORG
8%
BC
1%U
Figure 26: Speciated components of full aviation-attributable change in FRM PM2.5 and
mortalities
The constituents of ULS aviation-attributable FRM PM2.5 are shown in Figure 27, in which N03
exerts a larger influence at the expense of S04. Other species contributions remain largely
unchanged from those of the full aviation scenario.
tTLS Aviation dConc
ULS Aviation Mortalities
ORG
BC 8%
2%
BC
1%
Figure 27: Speciated components of ULS aviation-attributable change in FRM PM2.5 and
mortalities
4 LTO Emissions Assessment
Context
4.1
LTO-only emissions are defined as those occurring below 1km above ground level. They have a
more localized effect given their emission within the PBL, where strong vertical mixing reduces
residence times. Prior aviation AQ studies have almost exclusively included LTO-only emissions.
Providing a comparison of full aviation results to LTO-only results provides some continuity in our
understanding of aviation's impact.
Changes in Surface Concentration and Mortalities
4.2
The main findings from CMAQ when considering LTO-only emissions are as follows:
e
When considering US + non-US, LTO-only flight emissions, a ULS fuel policy results in a
28% reduction in domain-average ground-level PM2.5, with 60 lives saved (a 32%
reduction).
*
Reducing
direct aircraft sulfate emissions
(as oxidized aviation-attributable
S02) to
essentially zero results in a reduction in aviation-attributable sulfates of almost 90%,
including more than 90% of mortalities that would be attributable to aviation-related sulfate
PM. This is a significantly larger relative reduction in S04 with ULS than that of the full
aviation scenario.
*
Background ammonia (NH3) drives the formation of N03 and NH4 in the US, which
constitute almost 60% of LTO-aviation-attributable PM and more than 70% of mortalities.
*
The application of SMAT yields very similar results, with marginal increases in domainaverage concentration and mortalities, indicating a good general agreement in baseline and
FRM monitor concentrations (Section 8.2).
(LTOaviation- noaviation.SMAT)FRMPM2.5
lug/m^3]2006avg,morts.- 188(68to308)
Min- -7.808e-03
@(13,68) Max- 1.080e-01@(46,22) Mean- 4.6618e-03
Figure 28: LTO aviation FRM PM2.5 and ULS LTO reduction in same, LTO aviation (static
BCON) FRM PM2.5, post-SMAT FRM PM2.5
By component, the largest reductions with ULS are seen in S04, as was expected. Again, however,
significant aviation-attributable S04 concentrations remained, almost exclusively in the East and
Southeast (due to the role of NOx in increasing the oxidative capacity of the atmosphere through
ozone and OH, leading to greater conversion of S04 from non aviation sources). In contrast to the
full aviation scenario, in which ULS reduced S04-attributable mortalities by only ~50%, LTO
aviation S04-attributable mortalities were cut by more than 90%. A smaller but significant
reduction in mortalities was seen from decreased NH4, primarily in the West, where a greater
percentage of S04 (as (NH4)2SO4) was removed with a ULS fuel policy.
negligibly affected, confined primarily to boundary condition effects.
BC and ORG are
LTO aviation ULSreduction in SO4 [ug/m^3] 2006avg, mortalities - 36 (13to 60)
@ (46.22) Mean-8 6166e-04
Min- -4.810e-03 @(13.74) Max-2 536e-02
(LTO aviation - no aviation) S04 [ug/m^31 2006yearly avg. mortalities - 39 (14 to 64)
Min - 3215e-03 @(13,68) Max - 2.702e-02 @(46,22) Mean - 98271e-04
too
40
20
20
i0
ColumNurme
- 74 (27to121)
(LTOaviation- noaviation)N03|ugm^312006 yearlyavg.mortalities
Max- 2.87le-02 @(58.19) Mean- 1.9207e-03
Min- -1.877e-03@(36.28)
20
40
00
SO
Coiumn
Nuer
Oe
100
120
20
40
st
Column
Number
60
100
120
140
LTO aviation ULSreduction in N03 [ug/m^31 2006avg. mortalities - 1 (0 to 1)
Min - -2.452e-03 6M46.22) Max - 6.342e-03 @ (94.24) Mean - -5 6563e-05
ColumNumber
+
Max- 1.402e-02@(67.130)
60
0
140
- 32(12to52)
NH4[ug/m^3]2006yearlyavg,mortalities
(LTOaviation- no aviation)
Min - -1.179e-03@(13,68)
40
i
100
Mean- 7.3203e-04
I2N
I4"
- 11 (4to18)
2006avg.mortalities
LTOaviationULSreductionin NH4(ug/m^3]
Min - -2.119e-03@(13,74)
20
40
Max- 6810e-03@(46.22)
60
90
Column
Number
Figure 29: LTO aviation S04, N03, NH4, and ULS reduction in same
10o
Mean- 1.4938e-04
120
140
LTOaviationULSreduction
in BC)ug/m^3]
2006avg,mortalities
- 0 (0to0)
Min- -1,647e-04@(13.74) Max- 1.330e-04
@(13,62) Mean- 3.8011e-06
(LTOaviation- no aviation)
BC [ug/m^3]
2006yearlyavg.mortalities
- 13(5 to21)
@(13,68) Max- 1.794e-02
Min- -7.554e-05
@(67,130) Mean- 1.080le-04
100
D
20
40
s
o
100
120
0
140
20
40
Column
Number
20
40
60
s0
Column
Number
100
120
140
Numbu
Column
(LTOaviation- no aviation)
ORG[ppbV]2006yearlyavg,mortalities
- 12(4to19)
Min- 1.126e-03
@(41.109) Max- 1,081s-02
@(67.1301 Mean- 3.0160e-04
0
00
60
100
LTOaviationULSreduction
in ORG(ppbV]2006avg.mortalities
- 1(0 to 2)
@(87.881 Max- 1.388s-03
0013.681 Mean- 2834a-as
Min- -9.715e-04
120
0
20
Column
Number
Figure 30: LTO aviation BC and ORG, and ULS reduction in same
The contribution of each constituent species of FRM PM2.5 to total mortalities and concentration is
shown in Figure 31. N03 again shows higher concentrations in population centers in the Midwest
and East, dominating total mortalities. By domain-average concentration, S04 contributes a
significantly larger fraction to PM2.5 than in the full aviation scenario.
60
LTO Aviation dConc
LTO Aviation Mortalities
ORG
ORG
6%1m
BC
,%a
Figure 31: Speciated components of LTO aviation-attributable change in FRM PM2.5 and
mortalities
The constituents of ULS LTO-only aviation-attributable FRM PM2.5 are shown in Figure 32, in
which N03 again exerts a larger influence at the expense of S04, which is almost entirely
eliminated.
PBW
4%
PBW
Aviation Mortalities
ULS
LTO Aviation dConc
4%
BC
3%
4%
2%
Figure 32: Speciated components of ULS LTO aviation-attributable change in FRM PM2.5
and mortalities
5
Climate Feedbacks
5.1
Background on Climate Feedbacks
Climate effects from aviation (IPCC, 1999) include:
*
Increased radiative forcing (RF) from C02, an established GHG with a high level of
understanding of its creation and effects
e
Increased RF from contrails, linear ice clouds formed in the wake of aircraft in regions with
sufficient humidity. Contrails reflect more space-bound infrared radiation back to earth than
earth-bound radiation back to space and exert a wholly warming effect at night. In 1992, it
was estimated that by annual average, contrails covered 0.10% of the earth's surface.
e
Increased RF from induced cirrus cloudiness, or clouds formed of ice crystals above -6km
and estimated to cover nearly 30% of the earth. Cirrus clouds can condense around
persistent contrails.
*
Increased RF from aircraft emissions of water vapor, a greenhouse gas
e
Increased RF from NOx-catalyzed ozone production
e
Decreased RF from methane reduction by NOx-catalyzed ozone production, which also
produces the hydroxyl radical OH, which breaks down CH4 into C02 and H20, which are
weaker GHGs than CH4.
*
It is thought that sulfate aerosols result in decreased RF through direct backscattering and
reduction in optical aerosol depth, as well as indirectly by acting as cloud condensation
nuclei (CCN), though the sign of this indirect effect depends on the type of cloud formation.
There was a low LOSU of this result in the IPCC report on global aviation, and the finding
of negative total RF is questioned by the results from GATOR in this study.
*
Increased RF from soot aerosols, since the (black) carbon of soot absorbs solar radiation
and decreases albedo when deposited, especially over snow.
It can be seen that some clouds have a net cooling effect by reflecting more solar radiation back to
space than they trap heat from the earth; others have a net warming effect by trapping energy near
the surface.
On balance, clouds exert a significant cooling effect, although this varies by region.
Low-level, white clouds reflect sunlight, thereby preventing sunlight from reaching the Earth, which
exerts a cooling effect, whereas high, convective clouds absorb energy from below at higher
temperatures than they radiate energy into space from their tops and thereby exert a warming effect
(Seinfeld & Pandis, 2006). Cloud formation as a function of the density of aerosols, which function
as CCN, can therefore either increase or decrease RF depending on conditions.
5.2
Background on GA TOR-GCMOM
GATOR-GCMOM is described and evaluated for the application to aircraft climate and air quality
effects in
(acobson
et al, 2010a) and
(Jacobson et al, 2010b). Briefly, it is a one-way-nested (feeding
information from coarser to finer domains) global-regional Gas, Aerosol, Transport, Radiation,
General Circulation, Mesoscale, and Ocean Model that simulates climate, weather, and air pollution
in all domains. It treats emissions, urban, tropospheric, and stratospheric gas photochemistry, sizeand composition-resolved aerosol microphysics, size- and composition-resolved cloud microphysics,
size- and composition-resolved cloud-aerosol interactions, sub-grid cumulus cloud thermodynamics,
grid-scale stratiform thermodynamics accounting for sub-grid variations in energy and moisture,
spectral UV, visible, near-IR, and thermal-IR radiative transfer for heating rates and photolysis,
dynamical meteorology, 2-D ocean dynamics, 3-D ocean diffusion, 3-D ocean chemistry, oceanatmosphere exchange, soil, vegetation, road, rooftop, snow, and sea-ice energy transfer, and soil and
vegetation moisture transfer, among other processes (PARTNER Project 27 Internal Interim
Report, 2010).
With respect to aircraft, the model accounts for the microphysical evolution of contrails from all
commercial aircraft worldwide at the sub-grid scale (at their actual size). Specific sub-grid processes
treated include:
* spreading and shearing of individual aircraft exhaust plumes (Naiman, Lele, Wilkerson, &
Jacobson, 2010)
* the calculation of a unique supersaturation for each plume based on emitted and ambient
water vapor, ambient temperature, and emitted and ambient particle size distributions
* time-dependent, discrete size-resolved ice deposition and condensation/freezing onto sizeresolved aerosols
* size-resolved coagulation to create and grow linear contrails at the sub-grid scale
e
*
discrete size-resolved evolution of grid-scale cirrus and other clouds from the remnants of
sub-grid linear contrails
radiative effects of sub-grid linear contrails as a function of ice crystal size and composition
and contrail plume shape.
Two discrete (multiple size bins) aerosol distributions, each with multiple components, were treated
here on the grid scale - an emitted fossil-fuel soot (EFFS) distribution and an internally-mixed (IM)
distribution. EFFS particles could coagulate with other EFFS particles, IM particles, or discrete sizeand composition-resolved liquid, ice, or graupel hydrometeor particles. EFFS or IM particles could
also serve as nuclei for new hydrometeor particles. Once sub-grid contrails dissipated due to subgrid plume expansion, dilution, and water sublimation, their aerosol cores were released to the EFFS
size distribution on the grid scale. Aerosol cores released from sub-grid contrails to the grid scale
eventually either coagulated with aerosol particles from other sources or hydrometeor particles or
served as cloud nuclei. Grid-scale aerosol particles and their components were tracked within
hydrometeor particles, both through cloud formation and precipitation (PARTNER Project 27
Internal Interim Report, 2010).
5.3
Future Work
At this time, no conclusions can yet be made from GATOR as to the monetized health impacts
from ULS fuel that result from also including climate feedbacks. There is significant intra-annual
variability in the nested US-Ocean runs, and it is thought that the global runs are insufficiently
resolved to carry through mortalities in the cost-benefit analysis. Further runs and tests have been
proposed for GATOR (PARTNER Project 27 Internal Interim Report, 2010).
6
6.1
ULS Cost and Operations Analysis
Sulfur Content of ULS Jet Fuel
This section presents work cited from (PARTNER Project 27 Internal Interim Report, 2010). It
was adapted from work conducted and reported by Dr. James Hileman and others and is not an
original contribution of this thesis. It is included here since it will be drawn upon in deriving
conclusions.
The current specification for Jet A (and Jet A-1) fuel sulfur content (FSC) is 0.30% or 3,000 ppm;
however, the actual fuel sulfur levels typically fall below this value, with both spatial and temporal
variation. The Coordinating Research Council (CRC) sponsored a voluntary survey of the fuel sulfur
content of jet fuel leaving refineries to determine if the incorporation of the US ULS diesel standard
would impact jet fuel sulfur content (Taylor, Feb. 2009). The results from the survey, presented in
Figure 33, show that the eastern US refineries produced jet fuel with reduced fuel sulfur content
concurrently with the incorporation of the ULS diesel standard. The data also shows the regional
variation in the fuel sulfur content of jet fuel leaving US refineries. The highest sulfur contents were
recorded in the Gulf coast while the lowest values were recorded on the west coast. The sulfur
content values for the west coast showed considerable variability with a minimum weighted monthly
mean value of 116 ppm and a maximum value of 573 ppm. The U.S. Military's annual jet fuel
quality survey (Defense Energy Support Center (DESC), Petroleum Quality Information System
(PQIS) and (DESC-BP, 1999-2007)) presented in Figure 34, shows that sulfur content within jet fuel
varies by location and has been changing with time. Individual samples of jet fuel have sulfur levels
ranging from under 10 ppm to over 2000 ppm. The DESC survey collects data from U.S. military
bases around the world including Europe and the Pacific. In comparison, in the United Kingdom,
as can be seen in Figure 35, the mean jet fuel sulfur content has fluctuated between 360 ppm and
640 ppm over the past 20 years (Rickard, June 2008). In 2007, the mean for the United Kingdom
was 500 ppm with a standard deviation of 600 ppm. This demonstrates the international variability
of jet fuel sulfur content.
................
......
x Nationwide aGulf 0 East A West
1000
0
800
600
0
-
0
0
XD
x
a
D
0
0
x
ex x
-
a
x
_
xx
Ax
X
A
0
XX
xx
x
xxa
X
X X
A
A
*
400 -
0
010
0
x
@0
AAA
A
AS @0.00
200 dA
Sep-05 Jan-06 Jun-06 Nov-06 Apr-07 Sep-07 Feb-08
US East
446
321
US Gulf
858
800
US West
240
395
Nationwide
709
677
Figure 33: jet A average fuel sulfur content in US (Taylor, Feb. 2009), as plotted by
(Hileman, et al., 2009)
60
50l
1
40
30
-
20
-
10 -
0
1800
2400
Fuel Sulfur Content (ppm)
3000
2002
530
514
2003
580
525
2004
666
504
2005
668
581
2006
754
648
2007
762
590
Figure 34: JP-8 fuel sulfur content as reported by PQI (DESC-BP, 1999-2007), as plotted by
(Hileman, et al., 2009)
7001
0
E 600
500
0
.0
0 S400
0
0@
*
@0e
0
S
S
0
-*
.0
300
200
LL
100
0i
1985
___
- --------------
1990
1995
2000
2005
Year
Figure 35: Jet A-1 average fuel sulfur content in UK (Rickard, June 2008), as plotted by
(Hileman, et al., 2009)
6.2
Production Costs of ULS Jet Fuel
6.2.1
Background
Desulfurization processing units are well established in the petroleum industry to remove sulfur
containing chemical species from middle distillate products. Ultra-low sulfur middle distillates, such
as ULS gasoline, diesel and jet, require harsher processing conditions than low-sulfur products and
increase refining costs. Additional hydrogen gas, process energy, and capital expenses for new or
upgraded reactors are necessary to operate at more severe processing conditions.
During the transition to ULS diesel (ULSD), some refineries left the diesel market due to their
processing capabilities and the economics of ULS production. Additional capital and operating
costs for producing diesel shifted production to other products, such as gasoline, jet, and heating
fuels. However, by the time the proposed ULS jet (ULSJ) specification rolls out, nearly all products
leaving the refinery gate must be ULS. In an effort to estimate the costs of ULSJ production, diesel
prices were examined to determine the production costs during the transition to ULSD. Diesel and
jet fuels exhibit nearly identical crack spread profiles. As a result, refining costs for diesel are
believed to be a good surrogate for jet fuel.
Prior to the ULSD transition the Energy Information Agency (EIA) conducted a comprehensive
study to evaluate economic impacts and create estimates for both the refinery and end-user costs
(EIA, 2001) (EIA, 2001). The EIA report explored several ULSD production strategies and
assumptions taking into account detailed refinery supply and demand information. The additional
cost of ULSD was estimated to be between 3.1 and 8.3 cents per gallon depending on refiner
capacity, existing on-road diesel production, feed stock quality, and existing hydro-treating capacity.
These models assumed a 5.2% after-tax return on investment, production of -7ppm ULSD at the
refinery gate, and world crude oil price of no more than 58.57 cents per gallon ($24.60 per barrel) in
2010.
In reality the world price of crude oil increased well beyond predicted prices during the phase-in
period of ULSD production. For example, the spot price of West Texas Intermediate at Cushing,
Oklahoma increased from 46 cents per gallon in December of 2001 to 318.76 cents per gallon in
March 2008 (Spot Prices, 2010). The price then fell to 97 cents per gallon in January 2009, 65%
higher than what the EIA predicted. Additionally the U.S. price for natural gas, used as a process
fuel and in the desulfurization process, followed a similar price trend and reached historic high
prices. The increased cost of process fuel increased the cost of refining diesel during the phase in
period and is discussed below.
Additionally, the transition to ULSD fuel occurred during some of the most dramatic events in
recent history. The terrorist attacks of September 11 and the wars in Iraq and Afghanistan sent
crude oil prices to unprecedented high levels. Hurricane Katrina's devastating effects on the Gulf
Coast took several refineries off line resulting in diminished supply levels. These market-influencing
events affected energy and fuel prices significantly.
6.2.2
Detailed Cost Analysis
Figure 36 was created with historical fuel prices, and refinery production data obtained from the
EIA. The figure shows crack spread, diesel fuel production levels, the component costs of diesel,
and the price differential of diesel fuels.
...................
0.6
0.5
0.4
L1.0
-jet Crack Spread
0.3
Diesel Crack Spread
0.2
U.S
---
Product Supphed Of
DIsonlate Fuel 0, 0 to 15
ppm Sulfur (Thousand
GaHion)
450
350
0i
-0-
250
IL
150
U S. Product Supplied of
Distwilate fuel Od. Greater
than 15 to 500 ppm
Sulfur (Thousand Gallon)
U.S
Product Supplied of
Ddnllate fuel Oil, Greater
Than SOO ppm Sulfur
(thousand Gallon)
50
.50
400
8
350
300'
41 S
'Jo.
Crude
20
Ott
(cents/gal)
lRehmnt cents/gal)
Wholiesale Pnce (cents/gal)
I
150
100
so
0
25
20
I
15
wholesae DetatLS-HS)
($0.01/ga)
10
wholesale Delta(tuL-tts)
(So01/gal)
Figure 36: Diesel prices and production
-
-
Wholesale Delta(ULS-L5s
($0 01/gal)
6.2.2.1
Normalized Crack Spread of Diesel and Jet Fuels.
The normalized crack spread of diesel and jet fuels was calculated by subtracting the monthly
wholesale price from the Cushing, OK spot price of crude oil, and diving by the spot price of crude
(Diesel Fuel Explained, 2009)
Crack Spread= Pricee
-
Pricec'u
(6)
It can be seen from Figure 36a that the crack spreads are very similar. The average crack spread is
0.24 percent for both fuels. The average cost of crude was 91.31 cents/gal, and 112.8 cents/gal and
113.23 cents/gal for jet and diesel respectively. The refining costs of ULSD are therefore believed
to be a good basis for estimating the costs of ULSJ production.
6.2.2.2
Supply and Demand
High Sulfur Diesel (HSD) is a substitute for heating oil, and production fluctuates with seasonal
variations (higher in the winter). HSD is also used in off-road applications such as locomotives and
agricultural machinery. Low Sulfur Diesel (LSD) is used for on-road trucks and buses. It has been
replaced by ULSD for on-road use. Figure 36b shows that the production of ULSD was reported
beginning in January 2004. It ramps up quickly in June 2006, and over took LSD production in
September 2007. All diesels will be ULSD in 2011.
6.2.2.3
The Cost of a Gallon of Diesel
Wholesale prices at the refinery gate include the cost of crude and refining. Figure 36c plots the
monthly US average cost the reseller pays for refining and crude from May 2002 through August
2009. These prices do not include distribution costs, taxes, or retail mark-up which are typically
included in retail prices. Figure 36c also shows that the costs of refining increased through May
2008. The cost of crude also increases through the same time period. In addition to the capital costs
and additional process energy, the cost of all refining processes increases because of the rising costs
of crude and natural gas. Table 11 reports the average, maximum, and minimum costs of producing
a gallon of diesel fuel.
Table 11: Refining and crude oil costs of a wholesale gallon of diesel, May 2002 - August
2009 (Spot Prices, 2010)
Average [percentage]
100.0
23.89
76.11
Average [cents/gal]
167.84
40.1
127.74
Maximum [cents/gal]
383.05
92.48
307.58
Minimum [cents/gal]
65.97
6.65
57.48
6.2.2.4
Historic Price Difference in Diesel Fuels
It can be seen from Figure 36d that the difference between the cost of LSD and HSD was relatively
stable from May 2002 through June 2004, exhibiting typical seasonal variations. The difference then
began to increase significantly because of the high price of crude oil, natural gas, and amortization of
capital investments made for ULSD production. The high price difference of ULSD compared to
HSD in early 2007 ranges between approximately 13 and 25 cents per gallon. After the summer of
2007 prices fell and stabilized with some fluctuations in the winter of 2008.
6.2.2.5
Processing Costs
US monthly wholesale diesel prices from the EIA were used to estimate the cost of processing
ULSD. The US average wholesale monthly diesel price is available from January 1994 for high
(>500ppm) and low (15-500ppm) sulfur diesel fuels, and from January 2007 through July 2009 for
ultra-low sulfur (<15ppm). The cost of ULSD production was found by averaging the difference
between the monthly wholesale prices of ULSD and high-sulfur diesel (HSD). Overall, the average
price difference of ULSD from HSD is 6.7 cents per gallon and from LSD is 3.3 cents per gallon.
In spite of the unforeseen high costs of feedstocks, these production costs are within the range
given by EIA estimates and all sensitivity analysis.
6.2.2.6
Limitations of this Analysis
There are several limitations of this analysis. First, ULSD production prices are reported beginning
in January 2007, though small scale ULSD production is reported to begin in January 2004 with
approximately 40.5 million gallons produced. The production then ramped up significantly to three
billion gallons in August 2006. Capital costs for new equipment and increased labor costs were
amortized during this phase in period and were difficult to observe directly with the cost of ULSD.
Second, this is an incomplete data set of national averages. ULSD has only been produced for a
couple of years, and the full costs of ULSD transition will not be fully realized until 2011 when
100% production of ULSD for on- and off-road applications is achieved. Additionally, the data
obfuscates regional differences in costs, sales, and production disruptions such as scheduled
downtime caused by machinery malfunctions, accidents, or severe
weather. Moreover, every refinery is different, and some refiners enter the market, or leave
maintenance, unexpected
depending on the economics and capabilities of their equipment. Therefore, this analysis assumes
that the average cost of ULSD is spread among all refineries, not just ULSD producers.
Third, this does not take into account imports and exports of diesel and jet fuels. International
market for diesel can affect domestic prices and production. Similarly, jet fuel is traded on the open
international market.
Lastly, HSD, LSD, and ULSD supply and demand changes throughout the phase in period as
production levels change. Some production changes are attributed to refineries leaving the diesel
market due to the increased operating and capital costs for harsh ULS processing conditions. This is
complicated by the fact that prices may shift due to seasonal demand changes, and because these
products are substitutes for heating oil and non-road applications.
6.2.3
Production Cost Estimate
Based on a review of the literature supporting the EPA ULS diesel rulemaking, (Hileman, et al.,
2009) found, "the additional marginal cost of producing and distributing ULS diesel fuel from thenstandard diesel fuel was estimated during rule-making at an additional $0.054 per gallon. This
estimated cost is comprised of a refinery desulfurization cost of $0.041 per gallon, a distribution cost
of $0.011 per gallon, and lubricity additives with costs of $0.002 per gallon (US EPA, 2000a) (US
EPA, 2000a) and (EIA, 2001) (EIA, 2001). Recently, (US EPA, 2006) (US EPA, 2006) reiterated that
'[ULS] diesel fuel costs an additional $0.04 to $0.05 per gallon to produce and distribute.' Given the
similarities between jet fuel and diesel fuel, the experience gained in producing ULS diesel fuel is
relevant."
Based on the analysis of historical data, the cost of ULSD production was estimated to range
between three and 25 cents per gallon. The average cost was 6.7 cents per gallon and 3.3 cents per
gallon for desulfurization to ULSD from HSD and LSD, respectively. The cost of desulfurization
process changes over time due to the amortization of equipment and labor costs, as well as
fluctuations in the cost of process energy. Even with unprecedented high costs of crude oil and
natural gas, the average production costs are within the ranges given by the EIA report estimates
and all sensitivity analysis.
Based on the literature review and data analysis, the expected increase in cost of ULS jet fuel is
between 4 and 7 cents per gallon. Statistics from the Bureau of Transportation Statistics' National
Transportation Statistics show that in 2006, the most recent year for which data is provided (and the
nominal study year for this modeling effort), the domestic commercial aviation fleet consumed
13.458 billion gallons of fuel (Fuel Consumption by Mode of Transportation, 2007). The HDS of
that amount of fuel translates to a yearly marginal cost of between $540 million and $940 million.
6.3
Potential Usability Concerns and Benefits with ULS Jet Fuel
This section presents an excerpt on ULS jet fuels from (Hileman, et al., 2009).
The main compatibility and operability concerns in switching to ULS fuels are energy density,
lubricity, fuel leakage due to reduced elastomeric swelling, and pipeline compatibility.
Within diesel fuels, the HDS process results in a small loss in volumetric fuel economy because of
the approximately 1% loss in energy density (US EPA, 2000b) (US EPA, 2000b), (US EPA, 2006)
(US EPA, 2006), (BP, 2006) (BP, 2006), (Exxon, Undated) and (Chevron, 2007). This decrease was
observed for ULS diesel fuels, and it is expected that a ULS jet fuel would have a similar loss. In
(Hileman, et al., 2009), relationships of fuel energy density as a function of hydrogen content were
used to estimate the impact of deep HDS on the specific energy of jet fuel. A 1% loss in energy
density corresponds to a change from 34.8 MJ/L to 34.4 MJ/L. It was assumed that a desulfurized
jet fuel would follow the trends for hydrocarbons derived from the PQIS database; thus, a 1%
change in energy density should be accompanied by an increase in mean hydrogen content from
13.8 percent to 14.0 percent and a 0.3% increase in mean specific energy. The net result is that 1
percent more fuel volume, but 0.3% less fuel weight, is required to fly a typical distance. These
estimates take into account the reduction in fuel consumption that would be associated with reduced
aircraft weight.
HDS can result in a reduction in fuel lubricity, as it results in the removal of the compounds that
provide jet fuel its natural lubricity. However, as noted by (Chevron, 2004), low sulfur or aromatics
levels in jet fuel are not necessarily signs of inadequate lubricity. The boundary lubricity of jet fuel
cannot be predicted from bulk physical or chemical properties, it can only be measured in a specially
designed test apparatus. Fuels with similar sulfur and aromatics content can have different lubricity.
Further, a near-zero-sulfur, synthetic fuel has been made with similar lubricity properties to
conventional jet fuel without the use of fuel additives (Moses & Wilson III, Evaluation of Sasol
Synthetic Kerosene for Suitability as Jet Fuel, Dec. 2003).
If the lubricity of a ULS jet fuel were found to be less than desired, lubricity additives may be added
to the fuel such that the end user does not notice a difference, such as has been done with ULS
diesel fuel (BP, 2006) (BP, 2006), (Exxon, Undated) and (Chevron, 2007). The additives contain
esters (10-50 ppm) or fatty acids (20-250 ppm) (Chevron, 2007). For example, all of Exxon's diesel
fuels have incorporated lubricity additives since 2005 (Exxon, Undated). These fuels all meet the
diesel-fuel standard ASTM D975 (ASTM Standard Specification for Aviation Turbine Fuels, 2007a).
A framework for lubricity additives already exists in aviation; US military standards for jet
propellants 4, 5, and 8 (JP-4, JP-5, and JP-8) require a corrosion inhibitor/lubricity improver.
Lubricity and corrosion inhibitors can also be added to Jet A-1 (Chevron, 2004). For this purpose,
the US military has approved RPS 613, produced by Champion Technologies, and DCI-4A, by
Octel Starreon, which are also used in civil aviation (Moses & Wilson III, Evaluation of Sasol
Synthetic Kerosene for Suitability as Jet Fuel, Dec. 2003). However, additives usually have not been
used in Jet A (Chevron, 2004).
The use of ULS fuel may lead to a reduction in certain maintenance costs (Edwards, et al., July
2004). A low-sulfur fuel may have increased thermal stability, which would reduce the formation of
corrosive deposits, possibly leading to increased component life (US EPA, 2000a) (US EPA, 2000a).
The reduced aromatic content of a ULS fuel could lead to reduced combustor-liner temperatures
and increased combustor life. (Moses & IKarpovich, Fuel Effects on Flame Radiation and HotSection Durability, 1988) conducted a set of combustor experiments with fuels of varying hydrogen
contents, and they found that a 1% decrease in fuel-hydrogen content resulted in a combustor life
reduction of between 20% and 80% (ULS fuels have slightly higher hydrogen contents). These
results are based on a reduced-fidelity model for older combustors, but the trends should extend to
newer designs. The reason for this increase in life is that the wall temperature of the combustor is
influenced by the emitted radiant energy and the formation of soot in the primary zone of the
combustor. Both of these are related to hydrogen content (Martel & Angello, 1973), (Shirmer &
Quigg, 1965), (Lefebvre, 1984) and (Moses & Karpovich, Fuel Effects on Flame Radiation and HotSection Durability, 1988). It thus appears that a higher-hydrogen fuel such as that resulting from
HDS may lead to increased combustor life.
At present, available testing data are insufficient to provide a foundation for reliable quantitative
estimates of the potential impact of ULS Jet A fuel on maintenance costs. Specifically, further study
is required to determine whether there is an appreciable positive or negative impact.
Current Jet A, with its relatively high sulfur content, causes problems for pipeline operators. They
must use extra steps to flush out sulfur contamination after shipping high-sulfur fuels, such as Jet A,
to avoid contamination of low-sulfur fuels, such as ULS diesel e.g. (EIA, 2001). A transfer to ULS
Jet A would ease this problem and enable more-efficient pipeline system use, providing a benefit
that might be passed on to aviation consumers. ULS jet fuel may also benefit the U.S. Department
of Defense (DoD) in its goal of reducing the number of different fuels required during military
operations, and it could potentially lead to lower overall procurement and maintenance costs. The
U.S. Army uses JP-8 in its diesel equipment, but new diesel engines are designed for ULS diesel
fuels. These engines will encounter problems if operated on higher-sulfur fuels. To use current JP-8,
which has ~700 ppm sulfur content, the U.S. Army may need to special order diesel engines that can
tolerate higher sulfur levels. However, if Jet A were ULS, then JP-8 would likely become ULS as
well, and then off-the-shelf diesel engines could be used.
The specification for Jet A, ASTM D1655 (ASTM Stamdard Specification for Diesel Fuel Oils,
2007b) has a maximum limit for fuel sulfur content, but not a minimum limit. As such, a ULS jet
fuel falls within the current specification outlined by ASTM D1655. As previously noted, the degree
of hydrotreatment that is necessary to achieve a ULS fuel can affect lubricity. The D1655 standard
does not have a lubricity requirement, and lubricity issues can be overcome through the use of
appropriate fuel additives.
7
CMAQ Sensitivity Studies
7.1
CMAQ models
CMAQ uses a number of models to describe atmospheric processes, developed by academic and
government organizations and cited in the literature, summarized in Table 12 with references. These
include:
" Carbon Bond IV gas-phase equilibrium model, developed by the EPA for use in urban and
regional photochemical modeling
e
ISORROPIA II gas-aerosol equilibrium model for inorganic PM, developed by the Georgia
Institute of Technology with assistance from the National Oceanic and Atmospheric
Agency (NOAA); ISORROPIA is also an option in GEOS-Chem in replacement of the
default RPMARES scheme (GEOS-Chem v8-02-01 Online User's Guide)
e
Dry deposition Regional Acid Deposition Model
* MM5 Meteorological Model, developed by the National Center for Atmospheric Research
(NCAR), using NASA Global Modeling and Assimilation Office (GMAO) GEOS-5 data,
which is also used in GEOS-Chem.
Table 12: CMAQ schemes/models
Carbon Bond IV (CB-IV)
Gas-phase equilibrium model (Gery, Whitten, Killus, &
Dodge, 1989)
ISORROPIA II
Gas-aerosol equilibrium model for inorganic PM (Nenes,
Christodoulos, & Pandis, 1998), (ISORROPIA v1.7
Reference Manual, 2006)
SOA
Organic PM model (Odum, Hoffmann, Bowman, Collins,
Flagan, & Seinfeld, 1996)
RADM (Regional Acid Deposition
Model)
Dry-deposition model (Stockwell, Middleton, & Chang,
1990), (Wesely, 1989)
RADM Bulk
Aqueous Chemistry model
MM5
Meteorological model (Grell, Dudhia, & Stauffer, 1995)
K-theory Eddy
Vertical Diffusion method
Piecewise Parabolic Method (PPM)
Vertical/Horizontal Advection method (Colella &
Woodward, 1984)
7.2
Introduction
In line with its modular nature, active development community, and serial updates of modules,
CMAQ has several different options or versions for diffusion, advection, and chemistry. Table 13
shows the options used by default in all the results presented.
Table 13: ULS CMAQ "baseline" options (CMAQ 4.6 Operational Guidance Document,
2009)
Lightning NOx from 4x5 global GEOSChem added into background inventory as
100% NO
Lightning NOx included
Background inventory year and source
2001 EPAct
ModInit - chemistry/ transport timestep
init - air density-based scheme for mass-conserving
initialization
advection
adjustmmsscse
denrate - adjust the vertical advection error term
from the meteorology model by air density from the
chemistry/transport module
ero
ModHadv - horizontal advection
hppm - Piecewise Parabolic method
ModVadv - vertical advection
vppm - Piecewise Parabolic method
multiscale - diffusion coefficient based on local wind
ModHdiff - horizontal diffusiondeomtn
deformation
ModVdiff - vertical diffusion
eddy - calculate vertical diffusion using eddy
dfuiiyter
diffusivity theory
ModPhot - photolysis
phot - photolysis included
ModPing - plume-in-grid
ping-noop - plume-in-grid not included
ebi cb4 - use the Euler Backward Iterative solver
optimized for the carbon bond-IV mechanism
ModAero - aerosol chemistry
aero4 - 4th generation modal CMAQ aerosol model
with extensions for sea salt emissions and
thermodynamics
ModAdepv - aerosol deposition velocity
aero-depv2 - second generation CMAQ aerosol
deposition velocity routine
ModCloud - cloud module for modeling
cloudacm - RADM-based cloud processor using
impacts of clouds on deposition, mixing,
asymmetric convective model to compute
photolysis, and aqueous chemistry
convective mixing
Mechanism - specifies the combination of
gas-phase, aerosol, and aqueous phase
cb4_ae4_aq
chemical mechanisms
Tracer
tracO - no tracer specified
In order to characterize the sensitivity of CMAQ results to options that may have a significant effect
on vertical transport, several additional test cases were run (and proposed). These test cases entail
changing a single mechanism from the "baseline" values listed in Table 13. For each test, the full
aviation scenario with static BCON only was considered to better isolate the importance of the
mechanism within the model. Four months only were considered due to time and space constraints.
Likewise, not all proposed sensitivity tests were carried out.
In particular, a full aviation and
background run with the CB-V mechanism necessitated the reprocessing of nearly all data involved,
including met data, aircraft, and background inventories. The other scenarios were limited by lack
of storage space and time. In subsequent phases of the project, when time and further computing
resources will exist, it may be useful to revisit these. There is also direct applicability to future
aviation studies using CMAQ.
Table 14: Sensitivity studies scenarios proposed
(full aviation (static BCON)
no aviation) (completed)
Lightning NOx not included
Jan, Apr, Jul, Nov 2006
Vertical Diffusion - acm2
)
.
(fl
noaviation
aviation)(tB(limited)
(asymmetric convective model,
vrin2
version 2)
Jan, Apr, Jul, Nov 2006
Vertical and Horizontal
(full aviation (static BCON) -
Advection - YAMO
no aviation) (proposed)
(Yamartino-Blackman cubic
Jan, Apr, Jul, Nov 2006
scheme)
(full aviation (static BCON) -
2005 EPAct inventory, CB-V
no aviation) (proposed)
mechanism, CMAQ4.7
7.3
Vertical Diffusion and Advection
7.3.1
Background
Oct 2005-Dec 2006
The equations and assumptions for the diffusion and transport schemes in CMAQ are described in
detail in (Byun, Young, Pleim, Odman, & Alapaty).
Briefly, diffusion is here defined as eddy or
turbulent mixing on a sub-grid scale. Advection is defined (somewhat differently than it is
sometimes taken to be in the literature) as both horizontal and vertical transport of the matter in one
grid cell to another, through wind or convection. Dry deposition is another transport process that
results in the settling of aerosols to the ground over time, under gravitational force alone. Wet
deposition occurs when aerosols wash out in precipitation.
Vertical Diffusion: EDDY vs. ACM2
7.3.2
On the advice of Dr. Daewon Byun of NOAA, K-theory eddy diffusivity was used for all final
results in lieu of the default asymmetric convective model, version 2 (ACM2). By K-theory eddy
diffusivity, it is meant that a first order approximation for turbulent diffusion is taken, in which it is
assumed that turbulent fluxes of a quantity flow down the mean gradient of that quantity at a rate of
flow proportional to a local eddy diffusivity coefficient K. Such schemes assume that turbulent
eddies are sub-grid scale and hence can be described by the local K coefficient. Second or higher
order relations (variances, covariances, etc.) are subsumed by the first order approximation. ACM2
combines local eddy diffusivity with a non-local scheme that is better able to represent the supergrid-scale components of diffusion (Pleim, 2007). It was initially proposed that the vertical diffusion
scheme in CMAQ was excessively diffusive given the high relative contribution of cruise emissions
to surface PM. Therefore a sensitivity test was conducted in which three schemes were compared.
In addition to local eddy diffusivity and ACM2, eddy diffusivity with all turbulence coefficients K set
to zero above the PBL was also custom-coded for comparison. This artificially induces zero vertical
diffusion in the free troposphere, which is a not unreasonable approximation in any case, as the free
troposphere is usually non-turbulent, or only intermittently turbulent, with subsidence rates on the
order of 0.1-1 cm/s (Seinfeld & Pandis, 2006).
(U
3
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003
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(full
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- background)
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averageSC, varyingyd1 schemes(average
OCT-DEC
2005)
0.1
20
0.09
100
00
0.03
0.02
Z
300 -
0
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Kr>0BL~
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0.32
0
0
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0
00
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10
0.2
0.4
140
0.0
038
1
concentration
ug/m3
1.2
14A
10
1.8
X10'
Figure 37: Sensitivity of results to vertical diffusion parameter
The results are shown in Figure 35. Note that these results used slightly different aviation emissions
processing than that used for the final results. There was no relative difference, however, and so the
results should hold in general for any study of aviation with CMAQ. A period of three months was
chosen as sufficient to allow downward gradients to develop to steady state. The result is that all
three schemes showed quite similar behavior, resulting in changes in ground level concentrations of
only a few percent. The artificially depressed K and eddy diffusivity schemes resulted in the lowest
concentrations (diverging by less than a percent) and ACM2 in the highest. It is interesting that the
zero diffusion scheme resulted in slightly higher ground concentrations, implying as much or more
upwards transport as downwards, something that's borne out in time-lapse vertical transport movies
of non-reactive BC. The noticeable divergence of all three schemes occurred only roughly within
the PBL. The existence of concentrations between cruise altitude and ground level, where emissions
are sparse, implies subsidence in lieu of an "overly diffusive" numerical method.
7.3.3
Vertical Advection: PPM vs. YAMO
On the advice of Dr. Daewon Byun of the NOAA, a piecewise parabolic method (PPM) was
adopted as a vertical advection scheme, in lieu of the default Yamartino-Blackman cubic scheme.
The Piecewise Parabolic Method is a numerical technique using discrete, monotonic parabolas fit to
the zonal average of a variable using information from neighboring averages, providing for accurate
representation, particularly of sharp gradients. The Yamartino-Blackman cubic scheme (YAMO)
replaces a parabola with a cubic spline as the grid-cell interpolating polynomial for vertical advection
but uses PPM for horizontal advection. There was insufficient time and space to conduct a
sensitivity test of the differences between these methods, though early phases of the study used
YAMO, and the final results were run with PPM; however, other parameters differed between runs.
Qualitatively, given the magnitude of these other differences and the evolution of CMAQ model
results throughout the study, the advection scheme may have a more significant effect than the
diffusion scheme. Additional sensitivity studies isolating the effects of advection from cruise may be
profitable. Analysis of hourly data, showing surface concentrations, as well as vertical profiles
averaged by latitude and longitude, in time-lapse movie format, has been of some value in
understanding these effects.
7.4
Static vs. Changing Background Conditions
7.4.1
Context
Most aviation modeling work done to date with CMAQ has used constant boundary conditions.
However, as the scope of this work has been to study a global change in policy, it was necessary to
create a differing set of boundary conditions for each aviation scenario, by perturbing the global
GEOS-Chem runs used to create the BCON with the corresponding scenario.
7.4.2
Dominance of GEOS-Chem Boundary Conditions
talities
- 812(295to 1328)
avg mor
(tull aviation- noaviation)FHMPM25[ugm 3| 2006yearlIy
Mean- 2 3919e-02
@(1369) Mao-i1 741e-0Ot@0(67.130)
Min. -8.023e-03
(full aviation (sBCON) - no aviation) FRM PM25 (ug/m^312006 yearly avg. mortalities - 213 (77 to 348)
Min.0000@1,1)
Max- 9.577e-02@(67,130)
Mean- 5.1547e-03
100
10000
1
s0
000
o24
2 4
vg otlte
06yal
M5)gm3
oaito)02
sCN
(LOaito
40
6o
00
s
Number
Column
100
120
140
- 201(73to 329)
(LTOaviation- noaviation)FRMPM25[ug/m^32006yearlyavg,mortalities
@(67,130) Mean-4.4404e-03
@(13,68) Max- 9.473e-02
Min- -5.766e03
s
0
20
40
60
00
Column
Number
100
120
140
(L TO aviation (sBCON) -no aviation) FRM PM25 [ug/m^3] 2006 yearly avg, mortalities - 125 (45 to 204)
Min- 000@(11)
@ (67,130) Mean- 2.0521e-03
Max- 1.064e-01
100.
0
20
40
60
00
40
Column
Numberx
60
0
120
140
Coiumn
Number
Figure 38: Dominance of GEOS-Chem boundary conditions
Figure 38 shows that using changing boundary conditions results in ~4.6 times higher surface PM in
the full aviation scenario and ~2.2 times higher surface PM in the LTO-only aviation scenario. The
dominance of the GEOS-Chem boundary conditions can manifest in one of two ways. Firstly, PM
can be transported directly into the domain from the ring of boundary cells. Secondly, precursors
can be transported into the domain and react to form PM. In the boundary condition analysis
described in Figure 7, NH3 and H2SO4 developed significant gradients along the boundaries that
could result in elevated concentrations of PM as the species react into NH4 and S04, whereas PM
species did not develop these gradients (see Appendix C). In particular, the northern swath of
elevated PM extending from north of Montana to the lower Midwest was traced to significantly
elevated NH3 (even in the aviation-impact-only delta plot, sometbing that is difficult to explain) that
was advected southeastwards into the main aviation-attributable PM zones in the Midwest and
Southeast. In the LTO-only scenario in Figure 38, if one were to discount this vector of
southeasterly elevated concentrations, due wholly or in part to the elevated NH3, the static and
changing BCON runs may be quite similar.
7.5
Lightning NOx
Lightning creates NOx emissions in the troposphere of the same order of magnitude at cruise
altitude as aviation (see Section 2 and Appendix A). Given this significant source of emissions at
upper altitudes, which would otherwise be lacking in CMAQ, an LNOx emissions inventory was
created from GEOS-Chem. A comparison of full aviation (static BCON) delta results (for runs in
which both the full aviation and background cases contained no LNOx, and boundary conditions
were kept constant) with full aviation delta results (in which both the full aviation and background
cases contained LNOx) is presented in Figure 39. It was thought desirable to limit the scenario to
static boundary conditions to isolate model-specific effects (from boundary condition effects). Note
that these results used slightly different aviation emissions processing than that used for the final
results.
(fullaviation(sBCON)
- noaviation)FRMPM25[Ug/m^3]
JANaverage
Min- 8.322e-03@(74,121) Max- 8.641e-02
@(46,22) Mean- 1.0104e-03
(full aviation(staticBCON,noLNOx)- no aviation(noLNOx))FRMPM25[ug/m^3]JANaverage
Min. -6.657e-03
@(37,44) Max- 8.669e-02 @(46,22) Mean- 8.7584e-04
0.1
100
mm0.1
0.09
0.09
100
00
400.04
0-0.
am
Column
0
40
as
so
80
Column
Number
100
Nubo
100.
ol03Nnio
140
0
0
0
20
40
so
00
Column
Number
100
ooo
ian
(fullaviation(sBCON)
- noaviation)FRMPM25fug/m^31
JULaverage
Min - -2195e-03 @ (33.140) Max- 2.303e1 @(67.130) Mean- 5.8182e-03
(lull aviation(staticBCON,noLNOxo-no aviation(noLNOx))FRMPM25(og/m^3]
JULaverage
Mmn-t-166e-03 @(34,142) Max- 5.672e-01
@(67.130) Mean.- 2 7389e-02
009
100
0,08
80
00
J
I
000
aor
00%
6
-005
004
003
0.01
20
40
so
0
100
120
140
0
0
002
0 01
40
s0
00
100
120
140
Column
Nomber
ColumNumber
(fullaviation(sBCON)
- noaviation)SO4[ugim^31
JULaverage
@ (67.130)Mean- 2.8512e-03
Min- -1.281e-03@(92,24) Max- 6.479e-02
(full aviation (static BCON.no LNOx)- noaviation(noLNOx))
S04(ug/m^3JULaverage
Min- -8.391e-04
@ (92.24) Max- 9611e-02
@ (58,113) Mean- 1 1270e-02
40
00
20
40
100
140
120
20
40
60
Column
Number
^
(fullaviation(sBCON)
- noaviation)N03 lugim31JULaverage
k
un.
04A0-non [AA
al A.- - S97Ao-na
s0o
Colum Number
100
120
140
N03lug/m^3]JULaverage
(lullaviation(staticBCONnoLNO) - noaviation(noLNOx))
Min- -2030e-03@(51.33)Max- 3.378e-01
@(29.93) Mean-50073e03
RAI - .1 D01.-m G1111 411
co1
0.00 1
100
006
0006
0
20
40
so
00
Colum Number
100
120
m.
140
0
a
20
I
40
60
so
Column
Number
100
120
140
JULaverage
- noaviation)OHIppbV]
(fullaviation(sBCON)
0 (59.59) Mean- 1.2599e-07
Min- 4.555e-06
0 (46.221 Max- 6.227e-06
Column
Number
Figure 39: Sensitivity of results to LNOx
Results of the sensitivity study indicate that LNOx may have a significantly greater impact in the
study of aviation than was previously expected. Although there was a modest (though nonnegligible) decrease in aviation-attributable PM2.5 concentrations by domain average when no LNOx
emissions were included in a winter month, there was a very large increase in S04, 03, and PM2.5 for
a summer month. The small impact in the winter months can be explained given the much smaller
amount of lightning emissions in the winter, coupled with lower photo-dissociation rates, which
create and destroy 03, feeding OH and the oxidation of S02. In the summer months, lightning
emissions and photolysis rates are much higher, and the inclusion of NO from lightning at altitude
apparently leads to a nearly saturated oxidation pathway in which the NOx and VOCs from aviation
VOCs react with NO to create N02, which bypasses the normal
mechanism in which 03 is destroyed to maintain this equilibrium. 03 is both created and destroyed
by photolysis, in a way that results in relatively low 03 levels; however, when VOCs block a path to
03 destruction, levels of 03 rise. As aviation is the only source of VOCs above the PBL, but not
have a much smaller effect.
the only source of NOx, its effect on increased 03 is dampened; however, when it becomes the only
source of both, its impact is magnified. This can be seen in the huge, 16-fold increase in domain
average aviation-attributable surface 03 in July when no LNOx is included. Increased 03 levels
result in increased OH (again, by nearly 20-fold) and oxidation of precursor gases to PM.
Interestingly, this elevated value for 03 is quite similar to yearly average aviation-attributable 03
(though not quite so prominently of OH) when boundary conditions are not held constant
(Appendix C).
8
8.1
Comparisons to Empirical Data
Context
In contrast to studies of cumulative effects on the atmosphere and climate that can use monitor data
to gauge the efficacy of a model, studying the small perturbation of aviation defies easy verification
of how well the models are doing in their predictions. In the aftermath of the September 11
terrorist attacks, when planes were grounded for more than three days, scientists looked at the effect
of a lack of aviation on contrail formation and the resulting changes in temperature this brought
(Travis, Carleton, & Lauritsen, 2004), but it was too short a period to isolate air quality effects,
especially from cruise emissions, given the transport times, and it is thought that the effect may not
have been significant relative to natural meteorological variations (Kalkstein & Balling Jr., 2004).
In general, models are evaluated by paired-in-time-and-space comparisons of model predictions to
monitors (ground based stations, radiosondes, aircraft measurement, space-based optical
instruments, etc.). This cannot isolate the impact of aviation, but it does provide a means of
comparing the broader differences in model processes and inventories. Aggregating these
measurements to temporal averages is not rigorous, as it doesn't measure the standard deviation of
instantaneous model outputs to monitor measurements; however, as the CRFs and mortality
calculations assume lengthy exposure, they are useful in gauging the efficacy of model predictions of
totalmortalities from PM, and are technically interestingin any case.
8.2
Speciated Model Attainment Test
8.2.1
Methodology
The Speciated Model Attainment Test (SMAT) is used as EPA best practice to bring model results
in line with the Federal Reference Method (FRM) for computing PM (which method is used in any
official EPA figures for PM concentration), as well as shifting results to reflect differences between
model outputs and measured monitor concentrations. SMAT maintains the PM species fractions in
the ambient speciated data, interpolating monitor data, of irregular distribution across the US, to
each model data grid cell on a quarterly basis. Put more simply, one has to trust the model in its
prediction of the total relative magnitude of a perturbation, since monitor data only determines the
baseline, i.e. there is no isolating the contribution of aviation in monitor data. However, the species
fraction split of the monitor data can be mapped to the baseline case of the model data, and the
extent of the difference between model and monitor baselines assessed. In this way, the SMAT
process can alter both the total magnitude and species fractions of an aviation scenario.
Results
8.2.2
Full aviation results retain good agreement pre- and post-SMAT as shown in Figure 40, both
spatially and in magnitude. The main shift is an increase in ORG at the expense of N03 as shown
in Figure 41. This may be attributed to a good general agreement between CMAQ baseline and
FRM monitor concentrations. LTO-only results show similar good agreement. Note that monitor
data was only used from 2006, and all other settings were kept at the software defaults.
- 797(290to1304)
2006yearlyavg.mortalities
(fullaviation-no aviation)FRMPM25[ug/m^3]
@(67.130) Mean-2.3919e-02
Max. 1741)-01
Min- -8.023e-03@(13.89)
SMAT)FRMPM2.5[ug/m'3) 2006avg,morts.- 783(285to 1281)
(full aviation-no aviation.
@ (13.69) Max- 3.542e-01@ (53,21) Mean- 2.4608e-02
Min - 8.300e-03
0 07
0406
004
0,03
& 02
001
0
20
4
W
we0
O
colum Number
zo
40
Go
60
W
ColumnX
04
- 342(124to560)
2006yearlyavg.mortalities
(full aviation- noaviation)N03 [ug/m^3]
Mn-1.144e-03@(43,47) Max- 8,211e-02@(60,18) Mean-7.9871e-03
2006avg,morts.- 263(96to 430)
SMAT)N03[ug/m^3]
(full aviation-no aviation,
Min--1300e-03@(36.28) Max-2.373e-01
@(53,21) Mean-6.1534e-03
0
in0
Camrn Nuneer
I
Numer
0
100
12a
140
I
(full aviation - no aviation) S04 [ug/m^312006 yearly avg. mortalities - 174(63 to 284)
Min - -4.079e-03 @ (13,69) Max - 3.073e-02 @ (46.22) Mean - 7.4230e-03
(full aviation -no aviation SMAT) S04 )ug/m^3] 2006avg. morts.- 188(68 to 307)
Mon- -5,900e-03@(13,69) Max- 5.320e-02@(46.22) Mean - 74226e-03
0035
oO
0025
so
4002
-0015
0005
1001
0
20
40
0
0
Cok.irwiri
NL"Ilw
100
12o
140
(full aviation - no aviation. SMAT) NH4 lugim^3| 2006avg morts - 136 (49to 222)
Min - -2.900e-03@(78.20) Max - 7.740e-02@(53,21) Mean - 4 2242e-03
o
0
2
i
0
-go
5e
o
Colim Nuff6w
(full aviation -no aviation) NH4 lugJm^312006 yearly avg. mortalities - 142(52 to 233)
Min - -1.902e-03@(13.69) Max - 3.011e-02@(67,130) Mean - 39471e-03
003
*005
100
0025
0025
0 02
0
001
001
0
1
20
0
20
40
s0
10
Ca0m Nurmbe
100
0
0 005
20
120
40
s0
0
Colurm
Number
100
120
140
Figure 40: Full aviation post-SMAT speciated results in comparison to pre-SMAT model
outputs
SMAT Full Aviation dConc
Full Aviation dCone
ORG
8%
BC
1% ,
BC
1%
Figure 41: Full aviation SMAT constituent contribution to FRM PM2.5 in comparison to
pre-SMAT model outputs
8.2.3
EPA FRM monitor sites
There are ~1200 FRM monitor sites across the country, of which -250 are in the STN (Speciation
Trends Network) and ~165 in the IMPROVE (Interagency Monitoring of PROtected Visual
Environments) network, which provide speciation of PM (Abt Associates, Inc., 2009). Data from
locations with species fractions information is interpolated to the larger number of sites which can
only measure total PM.
Figure 42: EPA FRM monitor sites used in SMAT software (Abt Associates, Inc., 2009)
8.2.4
Comparisons of Outputs to Monitor Data
The comparisons between quarterly average monitor data and pre-SMAT quarterly average model
data for the absolute baseline is shown in Figure 43. Note that because the monitor sites are
irregularly spaced, a natural neighbor interpolation based on Voronoi tessellation was chosen to
display concentrations from monitor sites across the domain. Note that FRM PM here denotes dry
PM that has been (lightly) hydrated using the (Frank, 2006) first order PBW approximation. This
under-predicts model apportionment of PBW by as much as an order of magnitude or more in
CMAQ, but is nonetheless the assumption used in the monitor data from which the epidemiological
CRFs are derived. It can be seen that CMAQ successfully picks up seasonal changes in PM and
matches several regional and urban hotspots, with generally good agreement with monitors by
magnitude across the domain.
OfficialFF monitorPM2.51stquarter2006[ug/m^3]
7
Min 2 412 e+00@(35.2,-113,5)
Max = 2.0846e+01@(30.2,-113.2)
Mean
NaN
(fullaviation+ background)
FRMPM2.5|ugim^3 1 s quarler2006
Mm- 4 8915e-01@(184,121 7) Max- 3 6097e+O01
@)(256.
100.3)Mean- 46120e.00
do
10
0
40 W
130W
120'W
110W
lo0W
90'W
80W
70'W
60*W
OfficialFIRMmonitorPM2.52ndquarter2006[ug/^3]
Mn - 3,3995e+00 @(37.8,107,2) Max = 2 4915e+01@(38.0,117,0) Mean
(fullaviation+ background)
FRIMPM25
NaN
lug/m^312nd quarter2006
Min - 1 0318o+00@(18 4 -121 7) Max - 4 4406e+01@(490 1230) Mean - 5 4841e+00
so
I0
140W
130'W
120'W
110*
W
100*W
90'W
80W
70'W
60'W
OfficialFRMmonitorPM2.53rd quarter2006[ug/^3]
@(33.5,-84.5)
Mean = NaN
Min 4.3519e+00@(43.0,-112.8)
Max - 2.1160e+01
(full aviation + background)FRM PM2.5 |ug/m^3 3rd quarter 2006
Min - 7 2195e-01@(18.4-121 7) Max - 36369e+01 @(45 5,73.4) Mean - 6.9539e+00
10
5
140W
130'W
120*W
110'W
100'W
90'W
80' W
70'W
60 WY
OfficialFRMmonitorPM2.54thquarter2006[ug/^3]
Max= 2.7943e+01@(38.0,-117.0)
Mean
Min 2.7558e+00@(44.5,-106.5)
(fullaviation+ background)
FRMPM2.5[ug/m^3)
4thquarter2006
Min- 4.3789e-01@(18.4,-121,7)
Max- 4.0290e+01
@(25.6,-100.3)
Mean- 5.5036e+00
NaN
Fj
14....*.
W 1'W..
140W
130*W
2'
120*W
11W
10W
1lltW
100,W
0W.'70W
go'W
80*W
70'W
w
rt~r;w
0W
60 W
Figure 43: Comparison of CMAQ monthly average ground-level FRM-hydrated PM2.5 to
quarterly (native) official FRM monitor data
The aggregation of the differences in all model processes and inventories is reflected in the baseline
concentrations predicted by each model (i.e. full aviation + background). A comparison of the
results of each model to actual FRM monitor data is possible. Although this does not speak to
what would happen if some emission is perturbed, or the feedbacks it might create, it is nonetheless
a valuable comparison when fundamental differences between models are examined. The three
leading proposed tasks for future work have been comparison of the background inventories,
comparisons of model processes, and comparison of numerical methods. The aggregate of these
three things is the model baseline results. Note that GEOS-Chem results were interpolated from
their native 4x5 resolution. GATOR and CMAQ are displayed by native grid cell size. The months
of January and July alone were chosen given the unavailability of other months in the US-ocean
nested GATOR runs.
OfficialFRMmonitorPM2.51st quarter 2006[ugl3]
Max = 2.0846e+01@(30.2-113.2) Mean = NaN
Min= 2.4127e+00@(35.2,-113.5)
14W...... ........
........ .......
OfficialFRMmonitorPM2.53rd quarter 2006[ug/^3]
Mean = NaN
Max= 2.1160e+01@(33.5,-84.5)
Min= 4.3519e+00 @(43.0,-112.0)
..... ....... ..........
I
40'W
130'W
120'W
1 10'W
10'W
go*W
80'W
70'W
60'W
GATOR(full aviation+ background)
JUL2006wet PM25[ug/m^3]
+ background)
JAN2006wetPM25[ug/m^3]
GATOR(fullaviation
Mean- 8,0535e+00
@(44.2-79.8)
Max- 3.6301e+01
Min- 8.7864e-01@(16.2-208.8)
""i
-A*IC*
112-n
A
11 kA-^
" ..
-
0 Aac-Ain
LWA
o_700
kA-
.
-An
4111-
30
25
20
15
10
5
0
GEOS-Chem
(full aviation + background)
JUL2006wetPM2.5[ug/m^31
Min- 1.7195e+00Max- 1.5579e+01
Mean- 4.84950+00
GEOS-Chem
(full aviation+ background)
JAN2006wetPM2.5[ug/m^3]
Min- 1.0481e+00 Max- 1.7943*+01Mean- 4.3443e+00
JAN2006
FRMPM2.5(ugim^31
(fullaviation+ background)
@(18.4,121.7) Max- 4.5148e+0l @(25.6-100.3)Mean- 4.1668e+00
Min- 1 93700-01
(fullaviation+background)
FRMPM2.5Iugm^3] JUL2006
Mn - 6.8681e-01@(18,4.-121.7)
Max- 3,9229e+01@(40.5,-73.8)
Mean- 6.5300e+00
30
* i
lie
I:
*in
10
*r
Figure 44: Comparison of monthly average ground-level FRM-hydrated PM across models
to quarterly (native) official FRM monitor data
9 . Limitations and Future Work
9.1
Scope/Feedback
There is inherent difficulty in establishing areas of agreement and disagreement when each model
has differing scope, resolution, variable duration, and degree of independence. In establishing the
relative importance of climate feedbacks, for example, it is necessary to separate the CTM-only and
climate-perturbed components of model results, a capability that is currently lacking. Indeed, this is
one of many proposed tasks for future work. In addition, CMAQ results are not independent when
80% or more of the full aviation results are attributable to boundary condition effects from GEOSChem. As TOMCAT does not fully treat PM, in assessing mortalities for monetized benefits as of
this moment, CTM-only results reduce largely to that of 4x5 GEOS-Chem runs, given their
dominance in CMAQ. High-resolution US-nested GEOS-Chem runs are also being conducted and
when completed will provide a truer comparison to CMAQ results, providing a solid foundation for
the estimation of CTM-only effects. Assuming the nested GEOS-Chem results hold with CMAQ to
first order, and that these combined results might be taken as an approximation for the CTM-only
portion of the GATOR results (which they may not), preliminary results suggest that the climate
effects from GATOR are significantly larger than the chemistry/transport effects.
GEOS-Chem and CMAQ use static meteorological data based on the NASA GMAO (Global
Modeling and Assimilation Office) GEOS-5 model. As many of the climate feedback effects from
GATOR are due to aviation-attributable changes in precipitation and clouds that are created within
GATOR, this is a significant difference between models. It is known that aviation has a significant
effect on cloud formation, as particulate emissions function as cloud condensation nuclei (CCN).
9.2
Uncertainty
The most fundamental source of uncertainty in the study is the extent to which CMAQ accurately
describes the atmospheric processes that result from aviation. It is known that CMAQ does not
treat feedbacks or changes to weather and cloud formation, which GATOR has shown may be
significant. There is no way of quantifying this uncertainty, and so it remains a qualitative caveat in
any model results. The aviation inventories themselves have a small level of uncertainty on the
order of 5%, which is not carried through to the CIs (Confidence Intervals) in the final results.
Likewise, the background inventories derived from EPA's 2001 NEI are assumed to be
representative, and the quantification of any uncertainty in these inventories is beyond the scope of
this study. It is known that using a 2001 NEI to model a 2006 study year results in higher domainaverage background concentrations, with somewhat lower peaks in some species; however, given the
range of inventory years in other models and data sets, this is of lesser relative significance. For
instance, population totals were only available for the year 2000. The health impacts CRFs result in
a value for nominal mortalities, which is bounded by 95% confidence low and high values. This
uncertainty is carried through in the conclusions as a range of calculated benefits.
9.3
Other Differences
Comparisons to APMT-AQ
outputs show significantly higher concentrations for full flight
APMT-AQ is currently derived from training runs that include LTO-only
emissions; however, even the LTO-only results show (less) significantly higher aviation-attributable
concentrations than APMT-AQ. This can be explained given that APMT-AQ used exclusively static
emissions results.
boundary conditions for all the CMAQ training runs used to create the response surface. In
comparing the LTO-only static BCON case to APMT-AQ, agreement is quite good (with slightly
higher domain average but moderately lower mortalities), as can be seen in Figure 45.
The
...............
................
.
...
differences can likely be explained by different aircraft inventories and diffusion/transport settings
used within CMAQ, as well as the domain used for averaging (see below).
(full aviation- noaviation)FRMPM2.5[ug/m^32006yearly
avg.mortalities
- 172
Min- -1288e-02
@(64.39) Max- 8.590e-02 @(46.22) Mean- 1.0902e-03
ULSreduction
InFRMPM2.5[ug/m^3|2006
yearly
avg mortalities
- 72
Min - -3.921e-03@
(63,46) Max- 3.768e-02
@(42.24) Mean- 6.08540-04
0.1
0.00
0.07
0U
0,01
0
20
40
60
0
100
120
140
20
CO~nNumr W
40
00
00
100
120
140
Cou1mnNumber
c.)
- 125(45to204)
-no aviation)FRMPM25[ug/m^3|2006yearly avg.mortalities
(ILTO
aviation(sBCON)
Min-000@(1.1) Max- 1.064e-01
@(67,130) Mean-2.0521e-03
t044
0.0
001
ao
alummer
20
44
00
so
Cooo umbe.r
100
120
140
Figure 45: APMT-AQ projections of changes in FRM PM2.5 (a.) and b.)) in comparison to
LTO-only (static BCON) results from CMAQ
In order to apply a consistent means of averaging over dissimilar domains across models, a tightly
bound box on the CONUS landmass described by 125W, 67W, 25N, and 51N was used in this
project. This must be a caveat in comparison of domain averages to other projects for which this
was not done, such as all APMT-AQ work.
9.4
UT/LS Transportin CMA(
CMAQ is a nominally tropospheric-only CTM that nonetheless extends to 1OOhPa, which in
northern latitudes lies well above the tropopause. The characterization of upper troposphere/lower
stratosphere (UT/LS) transport may be poorly defined, and indeed is accounted for in some
corrections i.e. setting all concentrations above the tropopause in CMAQ to the GEOS-Chem
concentrations at the tropopause (GEOS-Chem v8-02-01 Online User's Guide).
9.5
Proposed Paths Forward
When it became apparent that significant model differences necessitated further work to understand
the reasons for these differences, a list of proposed tasks was created. These tasks were further
weighted across the working group to isolate those that would be most beneficial, given constraints
on time and manpower. These are briefly summarized in Table 15, listed from the top in order of
the degree to which each project member thought them useful, where each member received one
vote.
Table 15: Proposed future work
,Argnments
Test/T ask
Comparison of background
Background emissions have a strong effect through
emissions
interactions with aviation emissions
Run GATOR with and without
This has been central to the argument of the relative
passive tracer to separate climate
importance of climate feedbacks, also tests cross-tropopause
feedbacks from transport
transport; is it possible to run in CTM-only mode, given that
this may not necessarily isolate climate effects?
Expand scope of high-resolution
In progress
GATOR runs for further
scenarios/durations
Characterize cross-tropopause
Not likely to be main source of difference, but useful given
transport in each model
30% of emissions are in LS, may be possible to roll in with
passive tracer test
Run CTMs with perturbed wet
removal parameters
Useful in understanding cloud processes across models,
assessing sensitivity of results to wet removal process, would
need to define uncertainty range, where/when it rains - not
easy to fix
Create inventory of processes
modeled
Information in literature, likely highly qualitative; however,
may be a good investment for future work using these models
Create inventory of numerical
Likely to provide insight, given the importance of numerical
methods used in each model for
each process
methods in a model, but information alone is still qualitative,
may be a good investment for future work using these models
Comparison of 7-Be observations
The only proposed empirical test of aviation
with model performance
emissions/transport processes, may be a good proxy, but
depends on wet removal
Comparison of 03, CO, and PAN
May be a useful proxy for general model performance for
vertical profiles with data
vertical mixing, but we are most interested in modeling
perturbation, not baseline; however, this is a good sanity check
as well, been done before
Evaluation of met model outputs
Perform constant mixing ratio test
Compare against satellite data
products
Do vertical grid resolution
sensitivity study for CTMs
Assess inter-annual variability
Perform rotating plume test
Introduce a Lagrangian transport
test
Proceed without CMAQ
Introduce additional coupled
chemistry-climate model
Introduce CMAQ hemispheric
model
Do RF calculations for GEOSChem results
Important to have confidence in met models, given their
significance, but such evaluation has been done before; both
CMAQ and GEOS-Chem use NASA GEOS-5 data derived in
part from satellite observations, likely about as good as it gets
for static weather
Already known for GEOS-Chem; routine, easy, could be used
to compare vertical transport
CALIPSO and MODIS AOD for aerosols aloft, OMI for
N02 and HCHO aloft, essentially model validation
Unlikely to be significant driver, all models have roughly
similar high resolution in PBL, lower in free trop; may be
useful for sensitivity
Aviation-attributable changes usually smaller than inter-annual
changes, but would entail much rework to do an entirely
different year
Not much bang for the buck, may need to be custom-coded,
some references in literature already, direct test of numerical
method used, some think "simpler is better" in trying to do a
comparison, Rastigejev (citation) paper would seem to obviate
importance of test
Would identify transport trajectories, but a lot of work to
implement (and take away from other work), can't do
nonlinear chemistry, difficult for vertical transport
CMAQ currently dominated by GEOS-Chem boundary
condtions, but CMAQ needed for urban resolution and
fidelity to population over US domain; CMAQ is precisely
designed for regional AQ, the central focus of valuation;
would focus science primarily on GEOS-Chem and GATOR
and add CMAQ tests only where time/utility allows
Depends on availability, potentially a lot of work to
implement, but currently GATOR has no comparison for
climate feedbacks, will be done via ACCRI, but may add to
general confusion currently
Would be a better comparison for the GATOR US-Oceannested runs, but code is not yet mature, may be issues even if
it were, based on comparison to an intercontinental version of
CMAQ
Simplified stratospheric chemistry is not really a problem since
you want delta 03, would provide a comparison to
TOMCAT, doesn't really address any central issue
10
Conclusion
10.1
CTM Findings
Both GEOS-Chem and CMAQ show that when a global full flight emissions ULS policy is studied,
the benefits, as monetized reduction in premature mortality from reduced PM2.5 in the US, are of
the same order of magnitude as the costs in the US. Reducing the fuel sulfur content by nearly 98%
results in a significant reduction in aviation-attributable S04; however, there is an upper bound on
this reduction due to aviation NOx emissions and resulting oxidation of background S02, predicted
to be ~40-50%.
As other PM species are only marginally affected by a ULS fuel policy, this
effectively limits the total reduction in aviation-attributable PM and mortalities to ~10-20%.
Runs in which boundary condition effects were isolated suggest that transport of above-LTO
emissions to ground occurs both with and without changing boundary conditions. Given the
vertical transport and circulation times, it is thought that cruise-level emissions from Europe and
Asia may contribute to elevated concentrations over North America. Indeed, using changing
boundary conditions to model global full aviation results in a higher ratio of full aviation to LTO
aviation domain average surface PM concentration, with a value of -5.4. However, runs in which
both full and LTO aviation scenario boundary conditions were held static still show a ratio of ~2.3,
indicating significant transport of US emissions to ground-level in the US. LNOx emissions were
found to have a significant dampening effect on the contribution aviation emissions are able to exert
by partially saturating oxidative capacity at altitude. The climate and weather feedback effects of a
ULS fuel policy, as well as the significance of their omission in the CTMs, remains an interesting
subject of ongoing study.
10.2
Cost/Benefit Assessment
The cost of HDS is projected to be between 4 and 7 cents per gallon of jet fuel. With 2006 levels of
domestic jet fuel consumption, this translates to a yearly cost of $540-$940 million. CMAQ predicts
that a global ULS jet fuel policy saves 110 lives per year in the US (with a 95% CI of 40-180) when
considering full flight emissions, a 14% reduction in aviation-attributable mortality resulting in a
nominal monetary benefit of $800 million (corresponding to a 95% CI of $300-$1300 million). This
valuation does not monetize other operational benefits of such a switch, such as pipeline transport,
military diesel operations, maintenance, combustor lifetime, and the introduction of technology
relevant to the use of biofuels for aviation. Even so, the monetized health impact benefits compare
favorably to the cost of HDS.
APPENDICES
Appendix A: CMAQ Inventory and Model Species
A.1
Aircraft Emissions Inventory
Table 16 shows the full list of CMAQ aircraft inventory species and a brief description thereof,
followed by monthly totals of the pre-processed AEDT outputs showing variation in emissions
throughout the year. This is followed by vertical profiles of the AEDT outputs, a table of postprocessed CMAQ species throughout the year, and vertical profiles of the same. Finally, the preprocessed, speciated hydrocarbon emissions inventory (showing species splits as a fraction of THC)
is shown.
Table 16: Aircraft inventory species
CO
Carbon Monoxide
NO
Nitrogen Oxide
N02
Nitrogen Dioxide
ALD2
Grouping of Acetaldehyde and higher aldehydes
ETH
Ethene (CH2=CH2)
FORM
Formaldehyde and related
MEOH
Methanol
NR
Non-Reactive Hydrocarbon
OLE
Grouping of Olefin Carbon Bond (C=C)
PAR
Grouping of Paraffin Carbon Bond (C-C)
TOL
Toluene (C6H4-CH3) and related
XYL
Xylene (C6H5-(CH3)2) and related
S02
Sulfur Dioxide
POA
Primary Organic Aerosols
PEC
Primary Elemental Carbon
PSO4
Primary Sulfates
SULF
Sulfuric Acid (H2SO4)
HONO
Nitrous Acid
Table 17: Full aviation total aircraft emissions from AEDT
FEB
4.19
2.12E-2
3.21E-3
5.36E-2
1.53E-4
2.74E-4
2.50E-4
3.36E-3
13.2
5.18
4.93E-3
MAR
APR
4.81
4.64
2.46E-2 3.65E-3
2.36E-2 3.44E-3
6.14E-2
5.97E-2
1.76E-4
3.14E-4
2.88E-4
3.86E-3
15.2
5.95
5.66E-3
1.69E-4
3.03E-4
2.79E-4
3.72E-3
14.7
5.74
5.46E-3
MAY
4.69
2.37E-2 3.49E-3
6.03E-2
1.71E-4
3.06E-4
2.85E-4
3.75E-3
14.8
5.80
5.52E-3
JUN
4.66
2.37E-2 3.44E-3
6.OOE-2
1.70E-4
3.04E-4
2.83E-4
3.73E-3
14.7
5.76
5.48E-3
JUL
4.86
2.40E-2 3.46E-3
6.31E-2
1.77E-4
3.18E-4
2.94E-4
3.90E-3
15.4
6.02
5.72E-3
AUG
4.94
2.49E-2 3.61E-3
6.41E-2
1.80E-4
3.23E-4
3.OOE-4
3.95E-3
15.6
6.11
5.81E-3
SEP
4.54
2.33E-2 3.37E-3
5.87E-2
1.65E-4
2.96E-4
2.76E-4
3.62E-3
14.3
5.61
5.34E-3
OCT
4.71
2.45E-2 3.56E-3
6.10E-2
1.71E-4
3.07E-4
2.86E-4
3.76E-3
14.9
5.83
5.54E-3
NOV
4.52
2.33E-2 3.40E-3
5.81E-2
1.64E-4
2.95E-4
2.73E-4
3.61E-3
14.3
5.59
5.31E-3
DEC
4.77
2.36E-2 3.48E-3
6.14E-2
1.73E-4
3.12E-4
2.82E-4
3.82E-3
15.1
5.90
5.61E-3
2006
55.9
0.720
0.00204
0.00365
0.00337
0.0447
17.7
69.1
0.0657
0.283
0.0414
............
......
!t
.......
...
.......
2006 US Aircraft FB Emissions [Tg]
2006 US Aircraft HC Emissions [Tg]
2006 US Aircraft CO Emissions [Tg]
a-
400
t
600
1000
2006 US Aircraft NOx Emissions [Tg]
0.1
0.05
0.015
0.01
0.005
0
0.015
0.01
0.005
0
2006 US Aircraft PMNV Emissions [Tg] 2006 US Aircraft PMFO Emissions [Tg
200
200
400
600
"NOx
800
0.01
1000
0
1000
"w
0
0.03
0.02
-
--
2
4
-
4
a
6
2
0
4
x10
x10
2006 US Aircraft PM Emissions [Tg]
200
""" ----- ~
1000 I-
-
0
2
...-..
M
4
6
200
200
-0
1
1000
-1
2
3
4
-4
x10
2006 US Aircraft SOx Emissions [Tg]
200
n
sox
---
1000
0
1
2
3
-6
2006 US Aircraft H20 Emissions [Tg]
2006 US Aircraft C02 Emissions
1000 ft0
6
6
4
Figure 46: AEDT vertical profiles of US yearly aircraft emissions
-
M0
-
--
2
4
6
4
8
Table 18: Full aviation total aircraft emissions CMAQ processed input totals
Outputs
CO [Tg]
NO [Tg]
NO2 [Tg2
5.14E-2
4.71E-2
6.58E-3
6.04E-3
4.78E-4
5.98E-4
5.33E-4
7.00E-5
2.85E-4
OLE [Tg]
3.35E-4
FEB
2.24E-2
2.12E-2
4.60E-4
5.72E-4
5.09E-4
6.71E-5
2.73E-4
3.20E-4
MAR
2.46E-2
5.40E-2
6.93E-3
5.19E-4
6.50E-4
5.78E-4
7.63E-5
3.10E-4
3.65E-4
JAN
4
[P3
Tg] ETH T4
FO RM[g
4
E
T]
4
APR
2.36E-2
5.25E-2
6.73E-3
4.92E-4
6.16E-4
5.46E-4
7.17E-5
2.92E-4
3.43E-4
MAY
2.37E-2
5.30E-2
6.81E-3
4.96E-4
6.24E-4
5.54E-4
7.29E-5
2.96E-4
3.48E-4
JUN
JUL
2.37E-2
5.27E-2
6.79E-3
4.92E-4
6.16E-4
5.46E-4
7.17E-5
2.92E-4
3.43E-4
5.55E-2
5.63E-2
7.13E-3
7.25E-3
4.92E-4
6.20E-4
5.50E-4
7.23E-5
2.94E-4
3.45E-4
AUG
2.40E-2
2.49E-2
5.14E-4
6.46E-4
5.74E-4
7.52E-5
3.07E-4
3.60E-4
SEP
2.33E-2
5.16E-2
6.65E-3
4.83E-4
6.03E-4
5.37E-4
7.06E-5
2.86E-4
3.38E-4
OCT
2.45E-2
5.36E-2
6.91E-3
5.05E-4
6.37E-4
5.66E-4
7.40E-5
3.02E-4
3.55E-4
NOV
2.33E-2
5.10E-2
6.58E-3
4.87E-4
6.07E4
5.42E-4
7.11E-5
2.89E-4
3.40E-4
DEC
2.36E-2
5.40E-2
6.94E-3
4.96E-4
6.20E-4
5.54E-4
7.29E-5
2.96E-4
3.22E-4
2006
0.283
0.633
0.0814
0.00592
0.00741
0.00659
0.000866
0.00352
0.00411
POA [Tg]f
PEC-[Tg
Outputs
PAR [Tg] TOL [Tg-]
XYL [Tg]-SO2 [Tg]1
PSO04 [,Tg]. SULF [Tg,] HONO-[g
1.48E-3
1.42E-3
9.35E-5
8.97E-5
1.06E-4
1.02E-4
5.36E-3
2.72E-4
1.66E-4
2.98E-4
1.67E-4
4.67E-4
FEB
4.93E-3
2.50E-4
1.53E-4
2.74E-4
1.54E-4
4.28E-4
MAR
1.61E-3
1.02E-4
1.16E-4
5.66E-3
2.88E-4
1.76E-4
3.14E-4
1.77E-4
4.92E-4
APR
1.52E-3
9.61E-5
1.09E-4
5.46E-3
2.79E-4
1.69E-4
3.03E-4
1.71E-4
4.77E-4
MAY
1.54E-3
9.76E-5
1.11E-4
5.52E-3
2.85E-4
1.71E-4
3.06E-4
1.72E-4
4.82E-4
4.8E-4
JAN
JUN
JUL
1.51E-3
9.61E-5
1.09E-4
5.48E-3
2.83E-4
1.70E-4
3.04E-4
1.71E-4
1.53E-3
9.66E-5
1.10E-4
5.72E-3
2.94E-4
1.77E-4
3.18E-4
1.79E-4
5.05E-4
AUG
1.59E-3
1.01E-4
1.15E-4
5.81E-3
3.00E-4
1.80E-4
3.23E-4
1.82E-4
5.13E-4
SEP
1.49E-3
9.42E-5
1.07E-4
5.34E-3
2.76E-4
1.65E-4
2.96E-4
1.67E-4
4.70E-4
OCT
1.57E-3
9.95E-5
1.13E-4
5.54E-3
2.86E-4
1.71E-4
3.07E-4
1.73E-4
4.88E-4
NOV
1.50E-3
9.52E-5
1.08E-4
5.31E-3
2.73E-4
1.64E-4
2.95E-4
1.66E-4
4.65E-4
DEC
1.54E-3
9.73E-5
1.10E-4
5.61E-3
2.82E-4
1.73E-4
3.12E-4
1.75E-4
4.91E-4
2006
0.0183
0.00116
0.00132
0.0657
0.00337
0.00204
0.00365
0.00205
0.00576
Table 19: Corresponding molecular weights for HC species and groupings in
Table 18 i.e. (MF/SF)
105.8
97.44
14.58
29.14
13.60
32.04
29.59
28.05
36.55
[g/mol]
grouping explicit grouping explicit grouping grouping grouping grouping grouping
2006 US Aircraft CO Emissions [Tg]
0.1
0.05
0
2006 US Aircraft ALD2 Emissions [Tg]
2006 US Aircraft NO Emissions [Tg]
0
0.05
0.1
0.15
2006 US Aircraft N02 Emissions [Tg]
0
0.2
0.01
0.005
0.015
0.02
2006 US Aircraft ETH Emissions [Tg] 2006 US Aircraft FORM Emissions [Tg]
FORM
2
- -
-- Jm -
1000
0
--
6
4
.4
2006 US Aircraft MEOH Emissions [Tg]
2006 US Aircraft NR Emissions [Tg]
1000
0
xl10
x10
x 10
x105
2
4
6
6
2006 US Aircraft OLE Emissions [Tg]
0
2
x10
Figure 47: CMAQ processed input vertical profiles of US yearly total aircraft emissions
2005 US Aircraft PAR Emissions [Tg]
2005 US Aircraft TOL Emissions [Tg]
2006 US Aircraft XYL Emissions [Tg]
200
200
200
',
"-XYL
)eIo
.
-
400
400
600
600
600
500
innn
-
I-
1000 1 -----------------------------------0
2
4
6
1000
6
0
2
4
6
.6
-4
x 10
x106
2006 US Aircraft S02 Emissions [Tg]
x10
2006 US Aircraft POA Emissions [Tg]
2006 US Aircraft PEC Emissions [Tg]
POA
200
400
600 X"I"*.*~
c
1
600
1000
0
1
2
3
4
0
1
2
3
4
-4
x10
x10
0
2
4
6
-4
-4
2006 US Aircraft SULF Emissions [Tg] 2006 US Aircraft HONO Emissions [Tg]
200
""
"
-200
--
0
0
2
2
4
4
6
6
000
0
is.,--e
.5
0.5
1
1
.-5
1.5
Figure 48: CMAQ processed input vertical profiles of US yearly total aircraft emissions
(continued)
Table 20: Aircraft emissions pre-processed, speciated hydrocarbon profile (FAA OEE &
EPA OTAQ, 2009)
Ethylene
0.15458986
28.05316
Acetylene
0.039385952
26.03728
2
C2H2
Ethane
0.005214505
30.06904
2
C2H6
Propylene
0.045336437
42.07974
3
C3H6
Propane
0.000780871
44.09562
3
C3H8
Isobutene/1-Butene
0.017538274
56.10632
4
C4H8
1,3-Butadiene
0.016869627
54.09044
4
C4H6
cis-2-Butene
0.002104593
56.10632
4
C4H8
3-Methyl-1-butene
0.001403439
70.1329
5
C5H10
100
C2H4
1-Pentene
0.007760686
70.1329
5
C5H10
2-Methyl-1-butene
0.001744648
70.1329
5
C5H10
n-Pentane
0.00198433
72.14878
5
C5H12
trans-2-Pentene
0.003593968
70.1329
5
C5H1O
cis-2-Pentene
0.002757017
70.1329
5
C5H1O
2-Methyl-2-butene
0.001846216
70.1329
5
C5H1O
4-Methyl-1-pentene
0.000858179
84.15948
6
C6H12
2-Methylpentane
0.004084956
86.17536
6
C6H14
2-Methyl-1-pentene
0.000428122
84.15948
6
C6H12
1-Hexene
0.00736025
84.15948
6
C6H12
trans-2-Hexene
0.000371141
84.15948
6
C6H12
Benzene
0.01681482
78.11184
6
C6H6
1-Heptene
0.004384568
98.18606
7
C7H14
n-Heptane
0.000638894
100.20194
7
C7H16
Toluene
0.006421156
92.13842
7
C7H8
1-Octene
0.002757017
112.21264
8
C8H16
n-Octane
0.000624801
114.22852
8
C8H18
Ethylbenzene
0.001742866
106.165
8
C8H1O
m-Xylene/p-Xylene
0.002821783
106.165
8
C8H1O
Styrene
0.003094253
104.14912
8
C8H8
o-Xylene
0.001659872
106.165
8
C8H1O
1-Nonene
0.002455358
126.23922
9
C9H118
n-Nonane
0.000623583
128.2551
9
C9H20
Isopropylbenzene
3.96117E-05
120.19158
9
C9H12
n-Propylbenzene
0.00066586
120.19158
9
C9H12
m-Ethyltoluene
0.001926704
120.19158
9
C9H12
p-Ethyltoluene
0.000802048
120.19158
9
C9H12
1,3,5-Trimethylbenzene
0.000675821
120.19158
9
C9H12
o-Ethyltoluene
0.000817972
120.19158
9
C9H12
1,2,4-Trimethylbenzene
0.004377359
120.19158
9
C9H12
1-Decene
0.001846216
137.1921245
10
C1OH20
n-Decane
0.003201988
142.28168
10
C10H22
1,2,3-Trimethylbenzene
0.001327673
120.19158
9
C9H12
n-Undecane
0.004441511
156.30826
11
C11H24
n-Dodecane
0.004615541
170.33484
12
C12H26
n-Tridecane
0.005354028
184.36142
13
C13H28
C14-alkane
0.00186031
198.388
14
C14H30
C15-alkane
0.00177053
212.41458
15
C15H32
n-tetradecane
0.00416355
198.388
14
C14H30
101
C16-alkane
0.001459826
226.44116
16
C16H34
n-pentadecane
0.001726267
212.41458
15
C15H32
n-hexadecane
0.000486609
226.44116
16
C16H34
C18-alkane
1.76775E-05
254.49432
18
C18H38
n-heptadecane
8.84283E-05
240.46774
17
C17H36
phenol
0.007261785
94.11124
6
C6H50H
naphthalene
0.00541181
128.17052
10
C1OH8
2-methyl naphthalene
0.002061886
142.1971
11
C11H10
1-methyl naphthalene
0.002466177
142.1971
11
C11H1O
dimethylnapthalenes
0.000898492
156.22368
12
C12H12
C4-Benzene + C3-aroald
0.006564325
134.21816
10
C1OH14
C5-Benzene+C4-aroald
0.003241136
148.24474
11
C11H16
Methanol
0.018051895
32.04186
1
CH30H
Formaldehyde (FAD)
0.123081099
30.02598
1
CH20
Acetaldehyde (AAD)
0.042718224
44.05256
2
C2H40
Acetone
0.003693477
58.07914
3
C3H60
Propionaldehyde
0.007265856
58.07914
3
C3H60
Crotonaldehyde
0.012909514
70.08984
4
C4H60
Butyraldehyde
0.001481767
72.10572
4
C4H80
Benzaldehyde
0.004695067
106.12194
7
C7H60
Isovaleraldehyde
0.000406083
86.1323
5
C5H100
Valeraldehyde
0.003064793
86.1323
5
C5H100
o-Tolualdehyde
0.002872119
120.14852
8
C8H80
m-Tolualdehyde
0.003471951
120.14852
8
C8180
p-Tolualdehyde
0.000602286
120.14852
8
C8H80
Methacrolein
0.005362609
70.09
4
C4H60
Glyoxal
0.018164641
58.03608
2
C2H202
Methylglyoxal
0.015032806
72.06266
3
C3H402
acrolein
0.024493139
56.06326
3
C3H40
C-10 paraffins
0.141565
142.28168
10
C10H22
C-10 oleffins
0.056626
140.2658
10
C1OH20
Decanal
0.056626
156.2652
10
C1OH200
dodecenal
0.028313
184.31836
12
C12H240
A.2
Background Inventory and Concentrations
It has been established that the background inventory has a significant effect on the reaction of
aviation-attributable species. The list of post-processed 2001 NEI CMAQ background species is
shown in Table 21, along with a brief description of the species. This is followed by yearly average
102
surface plots of background concentrations of several significant species and groupings of species,
which are driven by the background inventory.
Table 21: Background inventory species and descriptions
Grouping of Acetaldehyde and higher aldehydes
Carbon Monoxide
Ethene (CH2=CH2)
Formaldehyde (CH20) and related
ALD2
CO
ETH
FORM
ISOP
NH3
NO
Isoprene (C5H8)
Ammonia
Nitrogen Oxide
Nitrogen Dioxide
Non-Reactive Hydrocarbon
Grouping of Olefin Carbon Bond (C=C)
Grouping of Paraffin Carbon Bond (C-C)
N02
NR
OLE
PAR
Primary Elemental Carbon
Coarse Particulate Matter
Fine Particulate Matter
Primary Nitrates
Primary Organic Aerosols
Primary Sulfates
Sulfur Dioxide
Sulfuric Acid (H2SO4)
Grouping of Terpenes
Toluene (C6H4-CH3) and related
Xylene (C6H5-(CH3)2) and related
PEC
PMC
PMFINE
PNO3
POA
PSO4
S02
SULF
TERPB
TOL
XYL
lug/m^3)
backgroundNH3 [ppbV]2006 yearlyaverage
Max - 2.125e+01 @(53.21) Mean- 6.9110e-01
Mn -000@(1,1)
2006yearly average
backgroundFRM PM2.5
Min - 000@(1 1) Max -3126e+01 @(1467) Mean - 5,7825e+00
404
u0
Com
CokorNorreor
0
O
C
N0
ICok
103
N0
o
N.,,t&o
00
4
background
S02IppbV]
2006yearlyaverage
Min - 000@ (1.1) Max- 1.365e+01
@ (30,92) Mean- 8.1943e-01
background
N02 (ppbV
2006yearlyaverage
Min- 000@(1,1) Max- 3.786e+01
@(67,130) Mean- 2.1725e+00
60
40
40
20
20
--~~~~
~~
0
20
- -~--~
- ---
40
-~
60
00
100
120
0
140
m
o
Column
Numbor
ColumnNumber
background
NO[ppbV]2006yearlyaverage
Min- 000@(1,1) Max- 2.598e+01
@ (67,130) Mean- 2.0451e-01
background
N03 lug/m^3]2006yearlyaverage
Min-000 @(1,1) Max- 3.609e+00
@(64,125) Mean- 3.2596e-01
5
4.5
100
4
so
-5
60
20
0
20
40
s0
00
Column
Number
100
120
140
0
background
S04 [ug/m^3]
2006yearlyaverage
Min-0000@(1,1) Max- 7.837e+00
@(43,106) Mean- 1.7349e+00
0
40
00
00
ClumnNumber
100
120
140
background
H2SO4[ppbV]2006yearlyaverage
Min- 000@(1,1) Max- 5.906e-03
@(23,66) Mean- 3.4887e-04
-r
20
0
0
40
50
C
0
000
000
140
1
00
a
ColumnNumber
20
100
40
Column
Number
104
120
140
background
NH4[ugm^3) 2006yearlyaverage
Mean- 5.4595e-01
Min- 000@(1,1) Max- 2.975e+00@(64.125)
background
HNO3[ppbVj2006yearlyaverage
Max- 3.098e+00@(29,95)
Mean-5.9693e-01
Min-000@(1.1)
60
2.s
0:
00___
20
40
10g
120
Cokmn
Numbner
background
OH[ppbV]
2006yearlyaverage,
Min-000@(1.1) Max- 1.375e-04@(36.60) Mean-5.1958e-05
u
uy
a
n
NIr
Colukm
e
140
I,
0
20
100
40
120
ComnNumber
background
H202 [ppbVj
2006yearlyaverage
Min-000@(1.1) Max- 2.889e+00@(16.43) Mean- 1.3072e+00
40
v
60
00
100
120
140
CokumNumber
background
COIppbV2006yearlyaverage
2006yearlyaverage
background
03 [ppbv)
@(30,101) Mean-3.7612e+01
Min- 000@(1.1) Max- 4.859e+01
Ain- n
Wn
1i 11
Ma.. 7071.4006i7 1
Um
Moan.1
017a4n
250
150
too
0
Coumn
Numbr
105
20
40
60
so
0
CoklunNumber
100
120
140
background
BC[ug/m^3]200Byearly
average
Min- 000@(1,1) Max- 4.098e+00
@(94,24) Mean- 1.9054e-01
0
20
40
u
uu
Column
Number
100
background
H20(PBW)[ug/m^3]2006yearlyaverage
Min- 000@(1,1) Max- 1.488e+01
@(67,130) Mean- 3.2276e+00
0
0
120
background
ORG[ppbV]2006yearlyaverage
Min- 000@ (1,1) Max- 1.S04e+01
@(94,24) Mean- 1.2037e+00
0
0
20
xe
40
00
100
120
-
-
- -
20
40
bu
n
ColumnNumber
background
VOC(ppbVl
2006yearly average
Min- 000@(1,1) Max- 1.828e+02
@(67,130) Mean- 2.6936e+01
140
0
20
40
ColumnNumber
so
0
100
120
140
Column
Number
Figure 49: CMAQ 2006 yearly average background species concentrations
Note that in Figure 49, PBW is directly output from the model, not derived using the lightly
hydrated FRM method.
A.3
CMAQ Outputs
CMAQ particulate outputs are divided into three modes: Aitken, accumulation, and coarse
(described in Section 2). Aitken mode particulates are denoted by "J", accumulation mode by "I",
and coarse mode by "K". A brief description of all CMAQ species and groupings treated in the
model is listed in Table 22.
Table 22: CMAQ4.6 concentration output species and descriptions (Isukapalli, 1999)
SpeciesNierid
NO2
NO
Nitrogen Oxide
Nitrogen Dioxide
O
03
Ground Potential Atomic Oxygen (singlet)
Ozone
N03
01D
Energized Atomic Oxygen (singlet)
Nitrate
106
OH
H02
N205
HNO3
HONO
PNA
Hydroxide
Hydroperoxyl radical
Dinitrogen Pentoxide
Nitric Acid
Nitrous Acid
Peroxynitric Acid
H202
CO
FORM
ALD2
Hydrogen Peroxide
C203
X02
PAN
PACD
PAR
XO2N
ROR
NTR
OLE
FACD
AACD
ETH
TOL
CRES
T02
OPEN
CRO
XYL
MGLY
ISOP
ISPD
S02
SULF
UMIHP
TERP
AS04J
ASO4I
ANH4J
ANH4I
ANO3J
ANO3I
AORGAJ
AORGAI
Carbon Monoxide
Formaldehyde (CH2O) and related
Acetaldehyde and higher aldehydes
Peroxyacyl radical (CH3C(O)OO)
NO-to-NO2 Operation
Peroxyacyl Nitrate
Higher Peroxycarboxylic Acid
Grouping of Paraffin Carbon Bond (C-C)
NO-to-Nitrate Operation
Secondary Organic Oxy radical
Alkylnitrate (>C3)
Grouping of Olefin Carbon Bond (C=C)
Formic Acid
Higher Carboxylic Acid
Ethene (CH2=CH2)
Toluene (C6H4-CH3) and related
Cresol and higher molecular weight Phenols
Toluene-hydroxyl radical adduct
High molecular weight aromatic oxidation ring fragment
Methylphenoxy radical
Xylene (C6H5-(CH3)2) and related
Methylglyoxal (CH3C(O)C(O)H)
Isoprene (C5H8)
Products of Isoprene reactions
Sulfur Dioxide
Sulfuric Acid (H2SO4)
Methyl Hydroperoxide
Terpenes
Accumulation mode Sulfates
Aitken mode Sulfates
Accumulation mode Ammonium
Aitken mode Ammonium
Accumulation mode Nitrates
Aitken mode Nitrates
Accumulation mode Secondary Anthropogenic Organics
Aitken mode Secondary Anthropogenic Organics
AECJ
AECI
Accumulation mode Secondary Biogenic Organics
Accumulation mode Secondary Biogenic Organics
Accumulation mode Elemental Carbon
Aitken mode Elemental Carbon
A25J
Aitken mode unspecified anthropogenic mass
AORGBJ
AORGBI
107
ACORS
ASOIL
NUMATKN
NUMACC
NUMCOR
SRFATKN
SRFACC
AH2OJ
AH2OI
ANAJ
ACLJ
ANAK
ACLK
ASO4K
NH3
SGTOT_ALK
SGTOTOLI_1
SGTOTOLI_2
SGTOTXYL_1
SGTOTXYL_2
SGTOTCSL
SGTOTTOL_1
SGTOT TOL_2
SGTOT TRP 1
SGTOTTRP_2
HCL
Other coarse mass
Soil (dust)
Aitken mode number
Accumulation mode number
Coarse mode number
Aitken mode surface area
Accumulation mode surface area
Accumulation mode Water
Aitken mode Water
Accumulation mode Sodium
Accumulation mode Chloride
Coarse mode Sodium
Coarse mode Chloride
Coarse mode Sulfate
Ammonia
Combined Gas + Particle Alkanes
Combined Gas + Particle SOA precursor species produced by alkene reactions 1
Combined Gas + SOA precursor species produced by alkene reactions 2
Combined Gas + Particle Xylenes 1
Combined Gas + Particle Xylenes 2
Combined Gas + Particle Cresol
Combined Gas + Particle Toluenes 1
Combined Gas + Particle Toluenes 2
Combined Gas + Particle Terpenes 1
Combined Gas + Particle Terpenes 2
Hydrochloric Acid
Appendix B: CMAQ Processes
B.1
Carbon-Bond IVProcesses and Reactions Modeled
The default chemical mechanism used for all ULS runs is Carbon Bond IV (CB-IV), developed
mainly for urban smog and regional air quality mode (Isukapalli, 1999), (Morris & Myers, 1990).
The mechanism is a hybrid of explicit chemistry, surrogate approximations, and lumped or
generalized chemistry designed to simulate the features of urban smog chemistry. Explicit chemistry
is used for the inorganic and carbonyl species and the chemistries of ethene, isoprene, and
formaldehyde. The ethene chemistry, for example, is treated explicitly because it reacts much slower
than other alkenes, it is a large fraction of common hydrocarbon emissions, and that it yields a high
fraction of formaldehyde. Isoprene is an alkene but it is treated explicitly also because its reactions
with free radicals are much faster, and it forms a prominent part of biogenic emissions in rural areas.
Many peroxy radicals have been lumped into a single X02 universal peroxy radical. The lumping
method of carbon-bonds is used mainly for paraffins and olefins. Molecular surrogates toluene and
xylene are used for higher aromatic compounds. For instance, a complex molecule with both
108
aromatic and alkene structures might be represented with a combination of TOL, OLE, and PAR
surrogates. Also, some of the rates and the stoichiometries of some incorporated reactions depend
upon the atmospheric composition of reacting hydrocarbons (Isukapalli, 1999).
The hierarchical relationship between the major species in the carbon bond mechanism is depicted
in Figure 50. The most complex species, in terms of their oxidation products (paraffins, olefins,
isoprene and aromatic hydrocarbons), are placed at the highest levels. The simplest species, in terms
of molecular complexity (NOx, HOx, CO and formaldehyde), are placed at the lowest levels
(Isukapalli,1999). Table 23 presents the chemical reactions that constitute the CB-IV mechanism.
Note that in Table 23, h V denotes the addition of a photon of light of frequency V in a photolytic
reaction, and M denotes an additional reagent.
Figure 50: Hierarchical relationship between major species in CB-IV (Isukapalli, 1999)
109
Table 23: CB-IV reactions (Isukapalli, 1999)
Reacti
No.
R1 ]NO2 +h
R2
O
Reaction
..
NO + O
0 3
021
R3
3 + NO
R4
O+NO2
NO
R5
O+
N02
N03
R6
O+NO
R7
NO2 + O3
N02
[
N02
NO3
R8
03 + h
..
0
R9
03 + h y
-.
01D
R10
01D
R1 1
01D + H2O
R12
03 + OH
R13 ]
03 + HO2
---
OH
FN3 + hu
.--
0.89 N02 + 0.89 0 + 0.11 NO
R15 ]INO3 + NO
-.
2 N03
R16 ]NO3
.--
NO + N02
R14
+ NO2
_0-+_O
.-.
2 OH
HO2
R17
N03 + N02
R18
N2O5 + H20
R19
N205
N03 + N02
R20
NO + NO
2 N02
R21
NO + N2 H20
2 HNO2
R22
NO + OH
HNO2
R23
HNO2 +h
R24
OH + HNO2
N205
--
2 HNO3
NO + OH
.....+
NO2
110
HNO2 + HNO2
NO + N02
N02 + OH
HN03
OH + HNO3
NO3
H02 + NO
OH + N02
H02 + N02
PNA
PNA
H02 + N02
OH + PNA
N02
H02 + H02
H202
H02 + H02 H20
H202
H202 + hy
2 OH
OH + H202
H02
OH + CO
H02
FORM + OH
H02 + CO
FORM + hy
2 HO2 + CO
FORM + hV
CO
FORM + 0
OH + HO2 + CO
FORM + N03
HNO3 + HO2 + CO
ALD2 + O
C303 + OH
ALD2 + OH
C2O3
ALD2 + N03
C2O3 + HNO3
ALD2 + hv
FORM + 2 H02 + CO + X02
C203 + NO
FORM + NO2 + HO2 + XO2
C203 + N02
PAN
PAN
C2O3 + NO2
C203 + C203
2 FORM + 2 XO2 + 2 H02
C203 + H02
0.79 FORM + 0.79 X02 + 0.79 H02 + 0.79 OH
111
OH
FORM + X02 + H02
PAR + OH
0.87 X02 + 0.13 XO2N + 0.11 H02 + 0.11 ALD2
+ 0.76 ROR - 0.11 PAR
ROR
0.96 X02 + 1.1 ALD2 + 0.94 H02 + 0.04 XO2N
+ 0.02 ROR - 2.1 PAR
ROR
ROR + NO
H02
2
0 + OLE
0.63 ALD2 + 0.38 H02 + 0.28 X02 + 0.3 CO
+ 0.2 FORM + 0.02 XO2N + 0.22 PAR + 0.2 OH
OH + OLE
FORM + ALD2 - PAR + X02 + H02
02 + OLE
0.5 ALD2 + 0.74 FORM + 0.22 X02 + 0.1 OH
+ 0.33 CO + 0.44 H02 - PAR
N02 + OLE
0.91 X02 + FORM + 0.09 XO2N + ALD2
+ N02 - PAR
O + ETH
FORM + 1.7 H02 + CO + 0.7 X02 + 0.3 OH
OH + ETH
X02 + 1.56 FORM + 0.22 ALD2 + H02
03 + ETH
FORM + 0.42 CO + 0.12 H02
TOL + OH
0.44 H02 + 0.08 X02 + 0.36 CRES + 0.56 T02
T02 + NO
0.9 N02 + 0.9 H02 + 0.9 OPEN
T02
CRES + H02
OH + CRES
0.4 CRO + 0.6 X02 + 0.6 H02 + 0.3 OPEN
CRES + N03
CRO + HNO3
CRO + N02
OH + XYL
0.7 H02 + X02 + 0.2 CRES + 0.8 MGLY
+ 1.1 PAR + 0.3 T02
OPEN + OH
X02 + 2 CO + 2 H02 + C203 + FORM
112
R71
OPEN + h v
_Ly1
R72
OPEN + O3
_
C2O3 + HO2 + CO
110.03 ALD2 + 0.62 C203 + 0.7 FORM + 0.03 X02
+ 0.69 CO + 0.08 OH + 0.76 H02 + 0.2 MGLY
R73
OH + MGLY
-y
R74
MGLY + hv
_y
R75
OII
+ISOP
..
XO2 + C2O3
C2O3 + HO2 + CO
-
1
0.6 H02 + 0.8 ALD2 + 0.55 OLE + X02
+ 0.5 CO + 0.45 ETH + 0.9 PAR
R76
X02 + FORM + 0.67 H02 + 0.13 XO2N
OH + ISOP
+ ETH + 0.4 MGLY + 0.2 C2O3 + 0.2 ALD2
R77
FORM + 0.4 ALD2 + 0.55 ETH + 0.2 MGLY
03 + ISOP
+0.1 PAR +0.06 CO +0.44 HO2 +0.1 OH
R78
NO3 + ISOP
XO2N
R79
XO2 + NO
NO2
R80
XO2 + XO2
R81
XO2N + NO
R82
XO2 + HO2]I
.....
Appendix C : CMAQ Results
A.1
AdditionalAviation Results
Note that similar sets of plots for all scenarios, in addition to monthly averages, were made and can
be accessed on request, but cannot be shown here given their number. Likewise, movies of several
species and meteorological parameters were made on hourly time steps for the full 15 months of
study, which help to illustrate boundary condition effects, the importance of meteorological data, the
extent of transport and residence times, transient initial condition times, plume transport, vertical
mixing, and other key effects.
113
I
2006yearlyavg
-no aviation)N2 JppbVl
(full aviation
Min - -7.612e-02@(59.111) Max-1446e+00@(67.130) Mean- -1,5102e-02
(full aviation -no aviation) S02 (ppbV] 2006yearly avg
Min - -2.908e-03 @(58,112) Max - 1,780e-01 @ (67.130) Mean - -2.1936e-04
0.0
0
40
0
Uin
-
60
to
ColumNumber
100
0
0
140
120
40
20
40
Cakmm Nmb+
(full aviation - no aviation) HNO3 [ppbV)2006yearly avg
Min - -5.680e-04@(98.126) Max - 4.991e-02 @(41,109) Mean - 1.0647e-02
9
100
zu
Column_
Numer
120
_
so
G0
CoblmNuiber
to0
10
14
(full aviation - no aviation) H2SO4[ppbV 2006yearly avg
Min- -1,308-05@(67,108) Max- 7.035e-05@(23,148) Mean-1.9035046
(full aviation - no aviation) NO [ppbV] 2006yearly avg
Max - 4.760e-01@ (67.130) Mean - -1.22160-02
6(2A.941
M7W-1
100
20
140
120
Uin.
0
140
_
114
G0
my
Numlber
Column
(full aviation - no aviation) OH [ppbV] 2006yearly avg
Mean - 6.0023e-07
Max- 4.18e-06 @(18.132)
-1 AirA.M006221
20
40
s0
0
I
(fullaviation-no aviation)H202[ppbV]2006yearlyavg
Mean- 8.5872e-03
Min- 6.761e-04@(41.109) Max- 2.464e-02@(16,121)
03 [ppbv]2006yearlyavg
(fullaviation- no aviation)
Mean-10576e+00
Min- 000@(1,1) Max- 1.716e+00@(57.25)
0
20
40
59
0
P
20
40
sx
so
100
120
140
Column
Nume&
2006yearlyavg
(full aviation- noaviation)VOC[ppbV]
@ (20.45) Max- 1.861e+00@ (67.130) Mean- -8.8552e-02
Min- -1.989e-01
(tull aviation- noaviation)CO[ppbV)2006yearlyavg
Min- -2.188e+00 @(53,52) Maxu-5501e+00@(67,130) Mean- -18567e+00
20
40
s0
x0
-2
100
0
120
20
40
s0
so
100
120
140
CoumNumere
Figure 51: Full aviation-attributable changes in precursor and oxidizing gases
2006yearlyaverage
/ fullaviation)S04 1%contribution)
((fullaviation-no aviation)
@0(1369) Max- 1.970e+000@(46,22)Mean-5.0803e-01
Min- -2.034e-01
2006yearlyaverage
((fullaviation- noaviation)/ fullaviation)FRMPM251%contribution
- 1239e+000(4524) Mean- 41363e-01
Uin - 1 qO7-Al1 i I Ro Mulax
0i4
02Z
0
02
120
20
140
40
60
so
ColumNumbe
115
100
120
140
{(fullaviation- noaviation)
/ fullaviation)N03 [%contribution
2006yearlyaverage
Min- -4.670e+00
@(30,37) Max- 1.044e+01
@(17,36) Mean- 2.0845e+00
0
20
40
en
so
100
120
{(fullaviation- noaviation)/ fullaviation)VOC[%contribution|
2006yearlyaverage
Min- -1.613e+00@(22,6)
Max- 1.256e+00
@(4622) Mean- -4.8183e-01
140
20
Column
Number
40
en
n0
1-lu
-- nb
((fullaviation-no aviation)/full aviation)BC[%contribution)
2006yearly average
Min- -6.360e42
@(13,68) Max- 1.131e+00
@(18,132) Mean- 9.4334e-02
{(fullaviation- noaviation)/ fullaviation)NH4[%contribution)
2006yearlyaverage
Min- -2.802e-01
@(13,69) Max- 3.309e+00
0(60,15) Mean- 8.5163e-01
0
go
20
40
60
80
100
120
140
umnb
erN
Columum
((full aviation- noaviation)/ full aviation)03 [%contribution]
2006yearlyaverage
Min- 000@ (11) Max- 4.220e+00 @ (63,42) Mean- 2.7368e+00
{(fullaviation- no aviation)
/ full aviation)OH[%contribution)
2006yearlyaverage
Min- -6.476e+00
@(46,22) Max- 7.093e+000 (18,132) Mean- 1.1040e+00
0 0
20
40
ColumnNumber
Figure 52: Aviation's relative contribution to ambient concentrations of several species
116
A.2
Computational Time/Complexity
The resolution, time step, number of reactions treated (and degree of explicit treatment), the
numerical method used and the manner in which it is implemented all affect computational
complexity. For this work, each CMAQ scenario was run on an 8-core E5530 using 1066 DDR3
RAM and Ethernet-attached PCI-e 16-bay RAID external storage formatted to XFS. Parallelized
CMAQ is implemented with MPI (Message Passing Interface). With 148 x 112 x 35 = 580,160 grid
cells, and using the CB-IV mechanism, each day of CCTM output takes about 25 minutes (20-30
minutes) to compute (after several optimizations of the storage arrays for better concurrent use I/O
performance). In comparison, some similarly resolved CB-V runs appear to take about twice as
long.
117
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