ON THE WATER UPTAKE OF ATMOSPHERIC AEROSOL
PARTICLES
A Thesis
Presented to
The Academic Faculty
by
Terry L. Lathem
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy in the
School of Earth and Atmospheric Sciences
Georgia Institute of Technology
December 2012
ON THE WATER UPTAKE OF ATMOSPHERIC AEROSOL
PARTICLES
Approved by:
Professor Athanasios Nenes, Advisor
School of Chemical & Biomolecular
Engineering and School of Earth &
Atmospheric Sciences
Georgia Institute of Technology
Professor Kostas Konstantinidis
School of Civil & Environmental
Engineering and School of Biology
Georgia Institute of Technology
Professor Paul H. Wine
School of Chemistry and Biochemistry
and School of Earth & Atmospheric
Sciences
Georgia Institute of Technology
Bruce E. Anderson
Chemistry and Dynamics Branch/Science
Directorate
NASA Langley Research Center
Professor Rodney Weber
School of Earth & Atmospheric Sciences
Georgia Institute of Technology
Date Approved: 15 October 2012
To my parents and family...
iii
ACKNOWLEDGEMENTS
The research in this dissertation is the culmination of numerous collaborations with colleagues in the laboratory and field, many of whom have become dear friends. First, I would
like to thank my advisor Prof. Thanos Nenes for being an excellent mentor and working
with me to provide research opportunities that I could have only dreamed of as an undergraduate looking to make my impact in the scientific community. He has challenged me to
think openly and critically, explore my own research ideas, instilled within me a deep passion for science and learning, and provided the encouragement and drive for getting results
when the road was most challenging. Second, I would like to thank my many mentors at
the NASA Langley Aerospace Research Center, including Dr. Bruce Anderson, Eddie Winstead, Dr. Lee Thornhill, Dr. Andreas Beyersdorf, and Dr. Luke Ziemba. These gentlemen
introduced me to the field of aircraft field research and enabled me to learn instruments
and take research flights to places I would have never thought possible. They made even
the most stressful scientific environments into enjoyable and memorable moments. Thank
you to Dr. Faye McNeill, Dr. Allison Schwier, and Dr. Neha Sareen for introducing me to
aerosol chamber research and enabling me to apply my skills to novel research questions.
Thank you to Dr. Josef Dufek, Dr. Prashant Kumar and Dr. Irina Sokolik for helping me
turn a class project paper on volcanic ash into a significant research publication, enabling
me to gain increasing diversity in the field. Thank you to the Nenes Research Group, including Dr. Akua Asa-Awuku, Dr. Luz Padró, Dr. Vlassis Karydis, Dr. Tomi Raatikainen, Dr.
Richard Moore, Shannon Capps, Kate Cerully, Ricardo Morales, Jack Lin, Peng Liu, and
James Hite, for your many contributions in data analysis, presentation and paper critiques,
and research advice, which have all contributed to the work presented herein.
And finally, I thank my friends and family for offering their love, support, and encouragement. To my parents for teaching me to work hard, strive for excellence and to never
give up. To my older brothers, Steve and Josh, whom I owe my competitiveness to always
iv
be better and not settle. And to my friends in Atlanta and Gainesville who pushed me to
try new things, increased my perspective on life, and who were friends in faith.
v
TABLE OF CONTENTS
DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
iii
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
iv
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
LIST OF FIGURES
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ix
SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
I
II
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.1
Aerosols, Pollution, and Climate Change . . . . . . . . . . . . . . . . . . .
1
1.2
Aerosol Chemical Properties . . . . . . . . . . . . . . . . . . . . . . . . . .
2
1.3
Water Uptake and Hygroscopicity of Soluble Aerosol . . . . . . . . . . . .
3
1.4
Water Uptake and Hygroscopicity of Insoluble Aerosol . . . . . . . . . . .
7
1.5
Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
WATER VAPOR DEPLETION IN THE DMT CONTINUOUS FLOW
CCN CHAMBER: EFFECTS ON SUPERSATURATION AND DROPLET
GROWTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
2.2
Theory of water vapor depletion effects . . . . . . . . . . . . . . . . . . . .
12
2.2.1
Depletion effects on supersaturation . . . . . . . . . . . . . . . . . .
13
2.2.2
Depletion effects on droplet size at the exit of the growth chamber
14
2.2.3
Simplified expressions for condensation effects on s, Dp . . . . . . .
15
2.3
Instrument and Droplet Growth Models . . . . . . . . . . . . . . . . . . .
17
2.4
Experimental Determination of Depletion Effects . . . . . . . . . . . . . .
24
2.5
Summary and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
III ANALYSIS OF CCN ACTIVITY OF ARCTIC AEROSOL AND CANADIAN BIOMASS BURNING DURING SUMMER 2008 . . . . . . . . 32
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
3.2
Experimental Measurements . . . . . . . . . . . . . . . . . . . . . . . . . .
39
3.2.1
Aircraft Measurements . . . . . . . . . . . . . . . . . . . . . . . . .
39
3.2.2
Submicron Particle Number Density and Size Distribution Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
vi
3.2.3
Submicron Particle Chemical Composition Measurements . . . . .
41
3.2.4
Gas Phase Measurements . . . . . . . . . . . . . . . . . . . . . . . .
42
3.2.5
CCN Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
44
3.3.1
Inferring Particle Hygroscopicity
. . . . . . . . . . . . . . . . . . .
44
3.3.2
Supersaturation Depletion Correction . . . . . . . . . . . . . . . . .
46
3.3.3
Air Mass Source Type Identification . . . . . . . . . . . . . . . . .
48
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
3.4.1
Aerosol Physical and Chemical Properties by Source Type . . . . .
51
3.4.2
Particle Hygroscopicity, κ . . . . . . . . . . . . . . . . . . . . . . .
56
3.4.3
CCN Closure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
3.4.4
Aging and Evolution of Biomass Burning Aerosol . . . . . . . . . .
63
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
IV HYGROSCOPIC PROPERTIES OF VOLCANIC ASH . . . . . . . . .
68
3.3
3.4
3.5
V
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
4.2
Experimental Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
4.3
Data Analysis Methodology . . . . . . . . . . . . . . . . . . . . . . . . . .
71
4.4
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
72
4.5
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73
NEW PARTICLE FORMATION IN TROPICAL CYCLONES CAN
PROMOTE RAPID INTENSIFICATION THROUGH EYEWALL SEEDING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
5.2
In-Situ Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
80
5.3
Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85
5.4
Conclusions and Implications
. . . . . . . . . . . . . . . . . . . . . . . . .
86
VI CONCLUSIONS, IMPLICATIONS, AND FUTURE DIRECTIONS .
88
APPENDIX A
— CONTRIBUTIONS TO COAUTHOR PAPERS OF
SIGNIFICANCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
VITA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
vii
LIST OF TABLES
1
2
3
4
5
6
Operating conditions and aerosol characteristics considered in the CFSTGC
instrument simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
Summary of aerosol microphysical and chemical properties for each source
type. Median size (Dp , nm) and standard deviation (σg ) are from lognormal
fits to the merged UHSAS and SMPS distributions. Total volume of Organic
Carbon (OC) and the Water-soluble Organic Matter (WSOM) fraction of the
OC are percentages calculated from total HR-ToF-AMS and PILS-WSOC
mass loadings (Section 3.2.3). Inferred particle hygroscopicity, κ, is from
measured CCN and size distributions while calculated κ is from AMS and
WSOM composition (Section 3.3.1). Median CN and CCN concentrations
are reported in cm−3 at STP. CCN and Activation Ratio (AR, CCN/CN)
are reported at 0.55% instrument supersaturation. Biomass burning is abbreviated as BB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
Summary of CCN closure errors (%) for the three closure scenarios tested:
1) Internally mixed aerosol with hygroscopicity of organics parameterized
by κorg = 0.12εW SOM , 2) Internally mixed aerosol with organics assumed
completely insoluble with κorg = 0 and 3) Externally mixed aerosol with
hygroscopicity of organics parameterized by κorg = 0.12εW SOM . Supersaturation and Activation Ratio (CCN/CN) are denoted as s and AR. . . . . .
61
FHH adsorption model parameters and exponent comparisons determined
for analyzed volcanic ash samples . . . . . . . . . . . . . . . . . . . . . . . .
74
Summary of average aerosol hygroscopicity parameters (κ) observed for the
experiments of Schwier et al. (2011). Sodium Oleate is denoted as SO. . . .
98
Hygroscopicity parameter, κ, values for the different chamber and flowtube
experiments for methylglyoxal (MG) and acetaldehyde (AC) as presented in
Sareen et al. (2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
100
viii
LIST OF FIGURES
1
2
3
4
5
6
Predicted centerline temperature (black line) and supersaturation (symbols)
profiles in the CFSTGC for different levels of aerosol flowing through the
instrument. Chamber conditions correspond to Q = 0.5 L min−1 , wall temperature gradient 16 Km−1 and 506.5 mb chamber pressure. Aerosol is composed of (NH4 )2 SO4 , and follows a lognormal distribution with 100 nm modal
diameter and 1.6 geometric standard deviation. αc is assumed unity. The
thickness of the temperature profile line represents the variability across all
simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
Predicted centerline average droplet diameter and relative dispersion (inset
graph) in the CFSTGC for different levels of aerosol flowing through the
instrument. Chamber and aerosol characteristics are identical to those of
Figure 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
Predicted supersaturation depletion (left panels) and droplet size depression ratios (right panels) as a function of CCN concentration (defined as the
droplet concentration exiting the flow chamber). Results shown for αc = 1
(Panels a, b), αc = 0.06 (Panels c, d), and αc = 0.01 (Panels e, f). Each
panel shows simulations for Q = 0.5 L min−1 , wall temperature gradient 8, 16
K m−1 , chamber pressure 500, 750, 1000 mb and (NH4 )2 SO4 aerosol following a lognormal distribution with 40, 100 nm modal diameter, 1.6 geometric
standard deviation and total concentration from 1 to 2×104 cm−3 . . . . . .
22
Predicted average droplet diameter at the exit of growth chamber vs. supersaturation (with depletion effects considered) for Q = 0.5 L min−1 , αc = 1
(Panel a), Q = 0.5 L min−1 , αc = 0.06 (Panel b), Q = 1.0 L min−1 , αc =
0.06 (Panel c), and Q = 0.5 L min−1 , αc = 0.01 (Panel d). Triangles denote
simulations that correspond to “zero CCN” conditions (defined as aerosol
concentration below 500 cm−3 ). . . . . . . . . . . . . . . . . . . . . . . . . .
23
Instrument setup for (a) “zero CCN” instrument supersaturation calibration,
and, (b) polydisperse CCN activation experiments. . . . . . . . . . . . . . .
25
Measured CFSTGC responses to increasing levels of CCN in the flow chamber. Instrument operation was kept at Q = 0.5 L min−1 , chamber pressure
980 mb and (NH4 )2 SO4 aerosol following a lognormal distribution with 40, 70
nm modal diameter, 1.6 geometric standard deviation and total concentration from 1 to ∼ 3×104 cm−3 . (a) droplet size depression ratios as a function
of CCN concentration (b) Predicted vs. measured droplet size depression
ratios as a function of CCN concentration, for Q = 0.5 L min−1 , P = 980
mb and so = 0.62% (αc = 1.0 is assumed in the simulations). (c) Measured
supersaturation depletion as a function of CCN concentration. (d) Average
droplet size measured at the optical particle counter vs. instrument supersaturation (considering depletion effects). Triangles denote simulations that
correspond to “zero CCN” conditions (defined as CCN concentration below
500 cm−3 ). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
ix
7
8
9
10
11
12
13
14
s
so
D
p
vs Dpo
for (a) all the simulations presented in Figure 3, and, (b) the experiments presented in Figure 6a. Dashed lines correspond to ±10% deviation
from the diagonal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
Aircraft flight trajectories for the research flights of ARCTAS-B on the NASA
DC-8, colored by air mass source type. Red colors denote biomass burning,
green colors denote boreal forest background, blue colors denote Arctic background, and purple colors denote anthropogenic industrial pollution. . . . .
48
Average dry particle size distributions (dN/dlogDp) for each air mass source
type and average aerosol volume fractions (%) calculated from the HR-TOFAMS and PILS-WSOC mass loadings (inset). Water-soluble organic matter
(WSOM) is calculated from PIL-WSOC (Section 3.2.3), and water-insoluble
organic matter (WIOM) is taken as the difference between WSOM and total
organic carbon measured by the HR-TOF-AMS. Error bars on size distributions denote one standard deviation from the mean. . . . . . . . . . . . . .
51
Aerosol (CN) particle concentration (diameter > 10 nm) (a), CCN particle
concentration at 0.55% supersaturation (b) and CCN/CN activation ratios at
0.55% supersaturation (c). Horizontal bars and corresponding values in the
boxes represent median concentrations, the box defines the first and third
quartiles, and the bars represent the 10th and 90th percentiles. Biomass
burning is abbreviated BB. . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
CN (a) and CCN (b) number concentration (cm−3 STP) as a function of
latitude, colored by source type, for all ARCTAS-B research flights. . . . . .
56
κ inferred from CCN measurements vs. κ calculated from HR-ToF-AMS
and PILS-WSOC chemical composition for each source type assuming that
composition is size-independent and internally mixed. Error bars denote one
standard deviation from the mean. Dashed lines indicate reported literature κ values for aged biomass burning (BB) (Engelhart et al., 2012) and
boreal forest aerosol (Sihto et al., 2011). Shaded area indicates the standard
deviation in the reported literature values of κ. . . . . . . . . . . . . . . . .
57
CCN closure plot for each source type assuming externally mixed aerosol with
soluble organics. Solid line represents the 1:1 agreement, while the dashed
lines represent ±50% error bars. . . . . . . . . . . . . . . . . . . . . . . . .
63
Inferred κorg vs. HR-ToF-AMS derived O:C ratios. Markers denote observed relationships published by Chang et al. (2010) in rural Egbert, Ontario, Canada (green) and Jimenez et al. (2009) for a variety of ambient and
laboratory organic aerosols (blue). Shaded area represents the reported standard deviation from the mean. Black triangle represents the average values
observed during the 2008 ARCPAC campaign for biomass burning influenced
air masses (Moore et al., 2011). Red circles indicate average values observed
for biomass burning plumes sampled during ARCTAS flight 18. . . . . . . .
64
x
15
16
17
18
19
20
CCN activation data, supersaturation vs. critical dry diameter, for the different volcanic ash samples presented in Table 4. Solid symbols represent
the ash samples, with errors bars showing uncertainty in the critical dry diameter. Solid lines represent FHH adsorption activation theory fits, while
dashed lines represent values of constant κ-Köhler hygroscopicity, ranging
from completely insoluble (κ = 0) to highly soluble (κ = 0.7). . . . . . . . .
75
Flight tracks (upper panels) and representative eye transects (lower panels)
through Hurricane Earl at Category 4 intensity on August 30, 2010. NASA
DC-8 flight track at 10 km is colored by the concentration of ultrafine condensation nuclei (UFCN) (a) and NOAA WP-3D flight track at 2-6 km is
colored by the concentration of Cloud Condensation Nuclei (CCN) at 0.6%
supersaturation (b). Underlying GOES satellite imagery is for the estimated
time of the first pass through the eye and hurricane motion is NW. Lower
panels show the concentration (C) of UFCN (blue), CCN (green), and cloud
droplet number concentrations (CDNC, black) through one eye transect, with
the eye indicated where the measured flight level wind speed is minimum. .
81
DC-8 dropsonde sounding through the eye of Hurricane Earl on 30 August,
2010 at 19:18 UTC. The low level inversion is marked by the increase in
temperature and divergence of the temperature and dewpoint at ∼760 mb,
which corresponds to ∼1.6 km. . . . . . . . . . . . . . . . . . . . . . . . . .
82
DC-8 dry aerosol size distributions at 10 km through Hurricane Earl at Category 4 intensity on 30 August, 2010. The number concentration (dN/dlogdp)
versus dry aerosol size (dp ) is measured with a Scanning Mobility Particle
Sizer (SMPS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83
Maximum Ultrafine Condensation Nuclei (UFCN, > 3 nm) (a) and Cloud
Condensation Nuclei at 0.6% supersaturation (b) as a function of TC intensity. Each data point represents the maximum concentration observed during
a transect through the eye. TC intensity is obtained from the NOAA HURDAT database corresponding to the time stamp of the data collected within
the eye. UFCN is measured by the NASA DC-8 at ∼10 km altitude, while
CCN is measured by the NOAA WP-3D at ∼3 km altitude for Earl, Tomas
and NASA DC-8 at ∼10 km altitude for Karl. . . . . . . . . . . . . . . . . .
84
Weather and Research Forecasting (WRF) model simulations of an idealized
tropical cyclone (TC) vortex. Minimum surface pressure (a) and maximum
wind speed (b) are shown as a function of simulation time for the control
simulation (black), seeding the eyewall with 3,000 cm−3 of cloud condensation nuclei (green), and seeding the eyewall with 20,000 cm−3 of cloud
condensation nuclei (red). . . . . . . . . . . . . . . . . . . . . . . . . . . . .
86
xi
21
22
23
Simulated droplet size as a function of observed droplet size. Simulations in
the upper plot account for water vapor depletion effects, but these are ignored in the simulations shown in the lower plot. The marker color is based
on instrument supersaturation. The black square markers represent average data from the calibration experiments and simulations. The gray boxes
show the approximate extent of observed and predicted (without water vapor depletion) droplet size variability for the three common supersaturations
(Raatikainen et al., 2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . .
96
Schematic of experimental set-up as shown in Sareen et al. (2012). Gas phase
methylglyoxal and/or acetaldehyde and ammonium sulfate seed aerosols are
introduced into the chamber maintained at 62-67% relative humidity (RH).
At the outlet of the chamber, particles are analyzed with a Scanning Mobility
Particle Sizer (SMPS), consisting of a Differential Mobility Analyzer (DMA)
and Condensation Particle Counter (CPC), and Continuous-Flow Streamwise
Thermal Gradient Cloud Condensation Nuclei Counter (CFSTGC). . . . .
99
NASA GRIP DC-8 flight tracks used for bacterial and fungal cell quantification, as described in DeLeon-Rodriguez et al. (2012). . . . . . . . . . . . . .
101
xii
SUMMARY
The feedbacks among aerosols, clouds, and radiation are important components for
understanding Earth’s climate system and quantifying human-induced climate change, yet
the magnitude of these feedbacks remain highly uncertain. Since every cloud droplet in the
atmosphere begins with water condensing on a pre-existing aerosol particle, characterizing
the ability of aerosols to uptake water vapor and form cloud condensation nuclei (CCN) are
key to understanding the microphysics behind cloud formation, as well as assess the impact
aerosols have on the Earth system. Through a combination of controlled laboratory experiments and field measurements, this thesis characterizes the ability of atmospheric aerosols
to uptake water vapor and become CCN at controlled levels of water vapor supersaturation.
The origin of the particle water uptake, termed hygroscopicity, is also explored, being from
either the presence of deliquescent soluble material and/or adsorption onto insoluble surfaces. The data collected and presented is comprehensive and includes (1) ground samples
of volcanic ash, collected from six recent eruptions re-suspended in the laboratory for analysis, (2) laboratory chamber and flow-tube studies on the oxidation and uptake of surface
active organic compounds, and (3) in-situ aircraft measurements of aerosols from the Arctic background, Canadian boreal forests, fresh and aged biomass burning, anthropogenic
industrial pollution, and from within tropical cyclones in the Atlantic basin.
The water uptake of most ambient particles is shown to be driven primarily by soluble
organics, which represent more than 50% of the aerosol volume in all sources and regions
sampled. Due to their complexity, organic aerosols and organic chemical effects have been
one of the largest sources of uncertainty for CCN prediction in models; however, it is shown
that accurate CCN predictions from theory can be obtained by simply parameterizing the
hygroscopicity of the organics by the volume fraction of the water-soluble components, such
that the exact organic speciation does not need to be known. New organic chemical effects
from surface active organics were also explored, where gas-phase surface-active compounds
xiii
were found to uptake onto wet aerosol and facilitate cloud droplet formation. The hygroscopicity of volcanic ash was also quantified in the laboratory for the first time. While
volcanic ash is insoluble, it is shown that the ash still uptakes water efficiently through adsorption onto the ash surface, similar to atmospheric dust. The identified ash water uptake
has important implications for ash plume and transport modeling, as ash-water interactions
can influence plume buoyancy and ash aggregation efficiency and most current models do
not account for any ash water uptake mechanisms.
A performance assessment of the existing CCN instrumentation and methods was also
conducted, where an important bias in the Droplet Measurement Technologies (DMT) CCN
counter was discovered. The calibrated instrument supersaturation was found to decrease
with increasing concentrations of CCN, starting at 5 × 103 cm−3 . The DMT CCN counter
has become the international standard for collecting CCN measurements, often in heavily
polluted environments, such that this supersaturation depletion bias has become an important consideration for accurate CCN measurements. The supersaturation depletion was
parameterized as a function of CCN concentration, where it was found that the supersaturation decreases with a linear dependence on the concentration of CCN. Methodologies for
both preventing and correcting for this bias are presented, and the resulting errors in CCN
prediction and hygroscopicity resulting from this bias are quantified. Characterization of
this instrument bias has also led to the development and advancement of a detailed growth
kinetics model for the DMT CCN instrument, where the kinetics of the water vapor uptake
can be accurately quantified for the first time.
Finally, we also present the first in-situ observations of CCN and aerosol within multiple stages of a tropical cyclone, from tropical storm through Category 4 hurricane. High
resolution CCN spectra and aerosol concentrations were sampled with NASA DC-8 and
NOAA WP-3D aircraft during the 2010 Atlantic hurricane season. Significant increases
in aerosol and CCN with tropical cyclone intensity were identified, with concentrations in
the eye region exceeding typical background concentrations by 1-3 orders of magnitude.
In the upper troposphere, new particle formation was identified for the first time over the
strongest hurricanes, with the concentrations of particles highest in the well-developed eye.
xiv
In the lower troposphere, significant enhancements in CCN were observed, suggesting rapid
growth of the newly formed particles or entrainment of additional particles into the eye.
Due to eye-eyewall mixing, it is shown that these aerosol and CCN enhancements have the
potential to seed the eyewall convection and convectively invigorate the storm. This newly
discovered new particle formation and eyewall seeding may be a microphysical explanation
for the formation of convective hot towers, which are a sign of rapid intensification.
All the data presented herein, varying widely in scope from volcanic ash to biomass
burning to hurricanes, was utilized to better understand and model the mechanisms of water uptake onto these different aerosol particles. Having a more thorough understanding
of aerosol water uptake will enable a more accurate representation of cloud droplet number concentration in global models, which can have important implications on reducing
the uncertainty of aerosol-cloud-climate interactions, as well as aid in reducing additional
uncertainties in aerosol transport, atmospheric lifetime, and impact on storm dynamics.
xv
CHAPTER I
INTRODUCTION
1.1
Aerosols, Pollution, and Climate Change
Atmospheric aerosols (suspended particulate matter) are produced either directly by anthropogenic and natural sources (e.g., combustion, dust, soot, biological particles), or indirectly
by various photochemical processes and gas phase reactions (secondary organic aerosol).
Aerosols and anthropogenic pollution are viewed as having a substantial impact on both
air quality and climate worldwide. The issues of aerosols gain further complexity as we begin to understand how increasing levels of atmospheric aerosols and pollutants can change
cloud properties, modify the radiation balance, alter precipitation patterns, and produce
acid rain (Ramanathan et al., 2001). The net effect becomes one of global scale, and the
connections between air pollution and climate remain at the forefront of current research
due to the uncertainties regarding the potential modifications to both regional and global
climate. Aerosols directly influence climate through the scattering and absorption of incoming solar radiation and the absorption, scattering, and emission of outgoing thermal
radiation. The ability of aerosols to become cloud condensation nuclei (CCN) or ice nuclei
(IN) and form cloud droplets or ice crystals indirectly affects climate through modification of cloud reflectivity and lifetime (Twomey, 1977; Albrecht, 1989), resulting in greater
climate cooling. As aerosol concentrations are increased, the same amount of ambient water vapor is distributed over a greater number of particles, thus greater concentrations of
smaller droplets are formed. This leads to denser, brighter clouds that can reflect more incoming solar radiation back into space (first indirect effect, Twomey, 1977), and the smaller
cloud droplets are more likely to persist longer in the atmosphere, thereby increasing cloud
lifetime (second indirect effect, Albrecht, 1989). The combined aerosol direct and indirect
effects result in a probable cooling of the climate system that is comparable to greenhouse
1
warming; yet, a large uncertainty in quantifying aerosol indirect effects remains (Intergovernmental Panel on Climate Change, 2007). The uncertainties associated with estimates
of the indirect effects are exacerbated by the complexity and variability of atmospheric
aerosols. The variability of aerosols in both space and time is immense, and understanding the exact chemical speciation of any given aerosol remains an extremely difficult task
and an active area of research. It is for these reasons that an understanding of CCN and
the associated direct and indirect climate forcing are closely linked to an understanding of
aerosol physical and chemical properties, and how these properties facilitate water uptake.
1.2
Aerosol Chemical Properties
An important challenge for aerosol-cloud interaction studies is the treatment of complex
aerosol chemical properties in atmospheric models without adding significant computational burden or uncertainty. Knowledge of the chemical composition of aerosol particles is
essential to determine not only the aerosol sources and production mechanisms, but also to
understand and predict the direct and indirect aerosol radiative effects. However, an accurate determination of the aerosol composition in current climate models is too computationally demanding because of the vast numbers of evolving compounds present in atmospheric
aerosol. Additional uncertainties arise because many of these compounds are unknown,
not individually measurable with current techniques, or have insufficient information on
their thermodynamic properties. Organic compounds are the most poorly understood, yet,
organic material (OA) often represents more than half of the mass for submicron particles
worldwide (Jimenez et al., 2009), which makes organics a critical source of uncertainty for
current models. Numerous studies have sought to reduce this complexity by developing
chemical composition parameterizations, such that the exact chemical speciation does not
have to be known.
For aerosol water uptake and CCN activity, the influence of aerosol composition can
be efficiently represented by a single hygroscopicity parameter, κ, which simply expresses
the affinity of a given aerosol particle to uptake water vapor (Petters and Kreidenweis,
2
2007). The κ for atmospheric particles can range anywhere from ∼ 0 for insoluble, wettable species to 1.3 for sodium chloride (Petters and Kreidenweis, 2007). However, some
insoluble particles, like dust and volcanic ash, can exhibit significant hydrophilicity from
the presence of clays and surface porosity, such that these species can activate at much
lower supersaturations than expected for κ = 0 (Kumar et al., 2011a; Lathem et al., 2011).
An understanding of particle hygroscopicity has become one of the standards for predicting aerosol water uptake, CCN activity, and modeling the impacts of atmospheric aerosol.
However, many uncertainties in particle hygroscopicity remain, such as characterizing the
hygroscopicity of the organic compounds, determining how particle hygroscopicity evolves
with atmospheric aging processes, and quantifying the regional and temporal variability of
the aerosol hygroscopicity at key regions of the world, such as the Arctic.
1.3
Water Uptake and Hygroscopicity of Soluble Aerosol
An important link among the physics and chemistry of aerosols, cloud formation, and
indirect radiative forcing are CCN, atmospheric particles upon which water condenses.
The tendency of an atmospheric aerosol to uptake water vapor, act as a CCN, and form
a cloud droplet is theoretically described by Köhler theory, which is a function of the
aerosol dry size, chemical composition, and ambient water vapor supersaturation. Köhler
theory describes the hygroscopic growth and CCN activation of soluble aerosol particles
as a function of relative humidity or water vapor supersaturation, respectively (Köhler ,
1936; Seinfeld and Pandis, 2006). The equilibrium state of a soluble aerosol particle in the
atmosphere is a function of two competing effects: the Kelvin effect, which represents the
enhanced equilibrium vapor pressure of a curved surface relative to a flat surface, and the
Raoult effect, which represents the depression in equilibrium water vapor pressure over an
aqueous solution, relative to pure water, resulting from dissolved species. Smaller particles
have greater curvature and correspondingly higher equilibrium vapor pressure, while the
contribution of solute from the particle reduces the water activity and lowers the equilibrium
vapor pressure. The Kelvin and Raoult effects are combined in the Köhler equation to
establish the equilibrium between the particle and water vapor:
3
[
4ρw Ms
sc =
3
ρs Mw νi Ddry
(
4σMw
3RT ρw
)3 ]1/2
(1)
where ρw is the density of water, ρs the density of solute, Mw the molar mass of water,
Ms the molar mass of solute, νi the effective van’t Hoff dissociation factor, Ddry the dry
particle diameter, R the gas constant, T the absolute temperature, and σ the surface tension
of the solution droplet. Here, the standard convention of assuming the surface tension of the
solution drop is equal to that of pure water (σ = σw ) is used, which is a good approximation
for dilute droplets unless they contain considerable amounts of strong surfactants.
For soluble particles, Petters and Kreidenweis (2007) suggested replacing the term
ρs Mw νi
ρw Ms
with an adjustable parameter, κ, termed the hygroscopicity parameter. κ expresses
the affinity of a material for water and is a simple way to parameterize the solute term of
Köhler theory, such that Equation 1 becomes:
[
4
sc =
3
κDdry
(
4σw Mw
3RT ρw
)3 ]1/2
(2)
If the particle is composed of many compounds, the total particle κ is calculated as the
volume fraction weighted average κ of the individual aerosol components:
κ=
∑
εj κ j
(3)
j
where κj and εj represents the κ and volume fraction of chemical species j, respectively.
Alternatively, the bulk aerosol κ can be inferred from measurements of CCN activity
and particle size distributions (e.g., Jurányi et al., 2010; Moore et al., 2011). Equation 2
allows a direct inference of the volume averaged particle κ, provided the supersaturation and
dry particle size are known, and assuming the particle chemical composition is internally
mixed. First, the critical diameter for activation, Dpc , must be determined by integrating
the particle size distribution, starting from the largest size bin, until the concentration
matches the measured CCN concentration at a given supersaturation, using Equation 4:
∫
∞
NCCN =
nCN dDp
Dpc
4
(4)
where NCCN is the measured CCN number concentration and nCN is the particle size
distribution function. The Dpc can then be substituted into Equation 2 to determine κ, at
a set instrument supersaturation.
The use of Köhler theory and κ to describe the water uptake and CCN activation of
inorganic aerosol species, as well as predominately soluble low molecular weight organics,
has been fairly well characterized (Cruz and Pandis, 1997; Raymond and Pandis, 2002,
2003; Kumar et al., 2003; Broekhuizen et al., 2004a). Since a significant fraction of organics
present in aerosol can also be hygroscopic (Hennigan et al., 2008; Fu et al., 2009; Duplissy
et al., 2011), an understanding of the particle κ and CCN activity requires understanding
the hygroscopicity contribution from organics, κorg . The value of κorg can range from zero
for insoluble species to ∼ 0.3 for the water-soluble carbon extracted from secondary organic
aerosol (SOA) and biomass burning aerosols (Wang et al., 2010; Padró et al., 2010; AsaAwuku et al., 2010). Several recent studies have confirmed that κorg is an important variable
for understanding and predicting the CCN activity of atmospheric aerosol. These studies
account for κorg in CCN predictions by making various assumptions about the relative
fractions of insoluble and soluble organic species, as well as varying the κorg until ideal
closure is achieved, (e.g., Bougiatioti et al., 2009, 2011; Moore et al., 2011; Dusek et al.,
2011; Martin et al., 2011). Assuming the organics to be insoluble (κorg = 0) has generally
resulted in better CCN predictions for regions with low organic mass fractions that are not
directly influenced by anthropogenic emissions (Chuang et al., 2000; Roberts et al., 2002;
Medina et al., 2007). However, in most other regions there is mounting evidence that a
substantial fraction of organics are water-soluble and contribute to CCN activity, especially
in regions where the organics dominate the aerosol mass and the water-soluble content is
highest (Novakov and Corrigan, 1996; Mircea et al., 2002; Asa-Awuku et al., 2009, 2008;
Wozniak et al., 2008; Kuwata et al., 2008; Chang et al., 2008). Carbonaceous compounds
can make up 70 - 90% of the total aerosol mass with 10 - 70% water-soluble (Sullivan et al.,
2006; Yamasoe et al., 2000).
While these studies have managed to account for organics in CCN closure, an explicit
determination of κorg is far from trivial, owing to the complexity of the organic aerosol
5
fraction and its tendency to evolve with atmospheric oxidative processing and aerosol aging (Jimenez et al., 2009). Numerous studies have sought to reduce this complexity by
estimating κorg based upon other easily measured aerosol parameters, without having to
explicitly know the detailed organic speciation. One method has been to parameterize the
κorg with respect to the fraction of water-soluble organic carbon (WSOC) or water-soluble
organic matter (WSOM) (Padró et al., 2010; Engelhart et al., 2011; Asa-Awuku et al.,
2011), as it is the soluble organics that are most hygroscopic. These studies suggest that
κorg = (0.25 ± 0.05)εorg , where εorg is the volume fraction of total organic aerosol which
is water-soluble. Another method has been to correlate κorg with the Oxygen to Carbon
Ratio, O:C (or ion signal m/z 44 expressed as a fraction of total organic signal f44 as a surrogate of O:C), determined from an aerosol mass spectrometer (AMS) (Jimenez et al., 2009;
Chang et al., 2010; Duplissy et al., 2011; Lambe et al., 2011), where the κorg is expected to
increase with the O:C, which is proportional to the mass fraction of oxygenated functional
groups that should increase the solubility of the organic species.
In previous laboratory and chamber studies, the SOA κ has been observed to correlate
strongly with the AMS O:C ratio (or f44 ) (Jimenez et al., 2009; Duplissy et al., 2011). These
studies suggest that atmospheric photochemical oxidation may increase the overall SOA or
κorg as a result of increased aerosol oxygenation and reduced organic aerosol volatility.
Chang et al. (2010) report a linear relationship between the κorg and O:C ratio for organic
aerosol in rural Egbert Ontario, Canada, such that κorg = (0.29 ± 0.05)(O:C), although
they stress the need for further studies to characterize if this relationship holds for a wider
range of O:C and aerosol types and locations. Engelhart et al. (2012) report an increasing
trend in total primary aerosol κ with increasing f44 for the aging of fresh biomass burning
emissions in a smog chamber. These relationships between O:C and κorg do not necessarily
imply O:C controls κorg , but rather that O:C is a proxy for aerosol molecular properties,
such as molar volume and solubility, that control κorg . Very few studies to date have all
of the available data to fully link measurements of WSOC, O:C and κorg with changes in
CCN activity, which is an area of focus for this thesis.
6
1.4
Water Uptake and Hygroscopicity of Insoluble Aerosol
CCN activity and cloud drop formation from soluble particles has long been recognized and
studied as one of the primary pathways for cloud formation via the well established Köhler
theory; however, recent studies have confirmed that even insoluble particles, such as dust
and volcanic ash, can be hygroscopic through the presence of clays, surface porosity and
physical adsorption processes (Kumar et al., 2011a; Lathem et al., 2011). Prior to these
studies, cloud droplet activation from insoluble particles was generally neglected in models.
Kumar et al. (2011a) report laboratory measurements of CCN activation from a variety
of dust and desert soil samples from Northern Africa, East Asia/China, and North America
and show that dust particles do not require deliquescent material to act as CCN in the
atmosphere, but rather uptake water vapor through physical adsorption onto the dust surface. Lathem et al. (2011) extended the approach of Kumar et al. (2011a) and showed that
insoluble volcanic ash also activates through physical water adsorption onto the ash surface.
Theoretically, this surface adsorption can be effectively described by the Frenkel-Halsey-Hill
adsorption activation theory (FHH-AT):
[
4σw Mw
− AF HH
sc =
RT ρw Dp
(
Dp − Ddry
2DH2 O
)−BF HH ]
(5)
where sc is the supersaturation, Ddry is the dry CCN diameter, DH2 O is the diameter of a
water molecule, Dp is the droplet diameter, and all other variables are as defined in Equation
1. AF HH and BF HH are two adjustable parameters that describe the contribution of water
vapor adsorption to CCN activity. BF HH is a measure of the particle hydrophilicity with
a more hydrophilic particle having a lower BF HH value. AF HH and BF HH are determined
by least squares fitting of the observed sc , Ddry to maxima of the FHH-AT water vapor
equilibrium curves as described in Sorjamaa and Laaksonen (2007); Kumar et al. (2009a,b,
2011a).
The CCN activity of insoluble dust and ash particles has important implications for
regional and global climate modeling. The adsorption parameters determined can be incorporated into existing global climate models to improve the representation of dust activation
7
as well as volcanic plume transport models to improve predictions of ash microphysics,
atmospheric transport, and impacts.
1.5
Dissertation Outline
The goal of this dissertation is to understand the mechanisms by which atmospheric particles
uptake water vapor and to develop parameterizations for models that aid in reducing the
uncertainty of aerosol-cloud-climate interactions. An understanding of instrument biases is
used to improve existing measurements of CCN, droplet growth kinetics, and inferences of
particle hygroscopicity. Observations of aerosols and CCN are expanded in regions where
such observations are limited and uncertainty is high. These measurements of aerosol and
CCN are then utilized to better understand both the physical and chemical mechanisms by
which a variety of atmospheric aerosols uptake water vapor. Current microphysical theories
are tested to understand the impact of particle chemistry (organics) on CCN activation and
additional parameterizations are developed to simplify the role of water-soluble organics
and incorporate water uptake of insoluble ash particles.
Chapter 2 begins with a through characterization of a supersaturation depletion bias
discovered with the DMT CCN counter, one of the international standards for measuring
CCN concentrations. The experiments and methodology outlined in Chapter 2 will aid in
understanding and preventing supersaturation depletion biases in future measurements of
CCN, and may help explain some of the large errors observed in existing data collected in
high concentration environments. Chapter 3 presents a thorough characterization of aerosols
and CCN in the Arctic environment during summer, with measurements of boreal forest
and Arctic backgrounds, fresh and aged biomass burning, and anthropogenic industrial
pollution collected during research flights onboard a NASA DC-8 aircraft. The observations
were spatially extensive, characterizing a large portion of the summertime Arctic, from
50 - 85◦ N and 40 - 130◦ W. Aerosol physical and chemical data are coupled with CCN
observations to understand and parameterize the contribution of water-soluble organics
on CCN using Köhler theory. Additionally, the instrument supersaturation was corrected
for water vapor depletion biases, as outlined in Chapter 2, and the resulting biases in
8
CCN closure and inferred κ are reported. In Chapter 4, the first reported laboratory
study on the CCN activity of volcanic ash particles is utilized to understand the water
uptake onto insoluble ash and to develop a physically-based parameterization for the water
uptake using adsorption theory. In Chapter 5, the first observations of aerosol and CCN
within multiple stages of tropical cyclone development, from tropical storm to Category 4
hurricane intensity, are reported. A new hypothesis for hurricane intensification is proposed,
where new particle formation observed within the eye of strong hurricanes seeds eyewall
convection with potentially important implications for sustaining hurricane intensity or
promoting subsequent rapid intensification. Additional significant research efforts, leading
to high impact coauthor publications are presented in Appendix A, where the methods
and results presented in Chapters 1-5 are utilized to develop collaborative studies to further
investigate (1) the kinetics of aerosol water uptake, (2) the impact of surface active organics
on particle κ and oxidative aging, and (3) characterization of biological particles above the
tropical cyclone environment. Finally, Chapter 6 discusses the impact of this dissertation
on improving the understanding of particle water uptake through detailed measurements of
aerosol and CCN. Studies that have utilized or benefited from the measurements and results
provided in this dissertation are discussed and future research directions are identified.
9
CHAPTER II
WATER VAPOR DEPLETION IN THE DMT CONTINUOUS FLOW
CCN CHAMBER: EFFECTS ON SUPERSATURATION AND
DROPLET GROWTH
The Continuous-Flow Streamwise Thermal-Gradient Cloud Condensation Nuclei Counter
(CFSTGC) is a commercially-available instrument that is widely used for laboratory and
field measurements of cloud condensation nuclei (CCN). All studies to date assume that
the supersaturation profile generated in its growth chamber is not influenced by the condensation of water vapor upon the growing CCN. The validity of this assumption, however,
has never been systematically explored. This work examines when water vapor depletion
from CCN can have an important impact on supersaturation, measured CCN concentration
and droplet growth. A fully coupled numerical flow model of the instrument is used to
simulate the water vapor supersaturation, temperature, velocity profiles and CCN growth
in the CFSTGC for a wide range of operation and CCN concentrations. Laboratory CCN
activation experiments of polydisperse calibration aerosol (with a DMT CFSTGC operated
in constant flow mode) are used to evaluate the simulations. The simulations and laboratory experiments are then generalized using a scaling analysis of the conditions that lead to
supersaturation depletion. We find that CCN concentrations below 5000 cm−3 (regardless
of their activation kinetics or instrument operating conditions) do not decrease supersaturation and outlet droplet diameter by more than 10%. For larger CCN concentrations, a
simple correction can be applied that addresses both the depression in supersaturation and
droplet size.
1
This chapter published as: Lathem, T. L. and Nenes, A. (2011), Water vapor depletion in the DMT
Continuous Flow CCN Chamber: Effects on supersaturation and droplet growth, Aerosol Science and Techc 2011 Taylor & Francis. Reproduced
nology, 45, 604–615, doi:10.1080/02786826.2010.551146. Copyright ⃝
with permission.
10
2.1
Introduction
The Continuous-Flow Streamwise Thermal Gradient Cloud Condensation Nuclei (CCN)
Chamber (CFSTGC; Roberts and Nenes, 2005) and its commercialization by Droplet Measurement Technologies (Lance et al., 2006; Rose et al., 2008) has enabled large strides in
understanding and parameterizing the CCN activity of atmospheric aerosol. This is in large
part due to the flexibility and fast time response of the instrument, which allows its use in a
number of configurations to complement aerosol and cloud studies. Used as a “counter” in
constant flow mode (e.g., Roberts and Nenes, 2005; Lance et al., 2006; Rose et al., 2008) or
as a “spectrometer” in scanning flow mode (Moore and Nenes, 2009), it provides the total
concentration of CCN as a function of supersaturation. When coupled with a differential
mobility analyzer (DMA) operated in voltage-stepping (e.g., Lance, 2007; Petters et al.,
2009; Rose et al., 2010) or voltage-scanning mode (Moore et al., 2010), size-resolved CCN
measurements enable the parameterization of composition impacts on cloud droplet formation (Rose et al., 2010; Petters et al., 2009; Petters and Kreidenweis, 2007; Wex et al., 2007;
Padró et al., 2007; Lance, 2007; Asa-Awuku et al., 2010), the characterization of chemical
aging and mixing state of aerosol (Roberts et al., 2010; Cubison et al., 2008; Kuwata et al.,
2008; Lance, 2007), and provide insight on the molar volume and surfactant characteristics
of the water-soluble carbonaceous aerosol fraction (e.g., Padró et al., 2007; Engelhart et al.,
2008; Asa-Awuku et al., 2009, 2010).
The CFSTGC has also been used to study the activation kinetics of ambient CCN, using
the final activated CCN drop size at the end of the growth column as the metric of CCN
growth rate. One approach, Threshold Droplet Growth Analysis (TDGA; Engelhart et al.,
2008; Sorooshian et al., 2008; Bougiatioti et al., 2009; Asa-Awuku et al., 2009; Lance et al.,
2009; Murphy et al., 2009) uses a “reference” critical supersaturation - droplet size curve
to define the minimum size of droplets that are produced from rapidly-activating CCN
(such as ammonium sulfate and sodium chloride). If ambient CCN produce droplets that
lie below the reference curve (i.e., produce smaller droplets than the reference at a given
supersaturation), the ambient CCN are then said to exhibit slower activation kinetics than
the reference. If kinetic delays are detected, then knowledge of the aerosol size distribution
11
and hygroscopicity can then be combined with a numerical model of the instrument to
parameterize the slow activation kinetics in terms of a water-vapor uptake coefficient (e.g.,
Asa-Awuku et al., 2009) or a dissolution timescale (e.g., Chuang, 2006). When size-resolved
CCN measurements are carried out, the observed droplet distribution at the exit of the
chamber can be inverted (using an instrument model) to provide distributions of kinetic
parameters (Ruehl et al., 2008, 2009) which provide particularly powerful insight on the
kinetic heterogeneity of atmospheric CCN.
A basic assumption in all studies using the CFSTGC is that the water vapor field in
the growth chamber is not affected by the condensation of water onto the growing CCN.
While this is generally a good assumption when the concentration of CCN is low (e.g., when
size-resolved CCN experiments using a DMA are carried out), the extent of these depletion
effects at higher CCN concentrations have not been systematically quantified to date. This
is especially important for CCN studies in polluted environments (or other conditions of
high CCN). Water vapor depletion effects can also affect the final activated droplet size,
which can bias studies of activation kinetics carried out with the CFSTGC. This study
aims at understanding and quantifying water vapor depletion effects on supersaturation
and droplet growth in the CFSTGC using a comprehensive combination of instrument
theoretical analysis and laboratory activation experiments.
2.2
Theory of water vapor depletion effects
We first proceed with a simple scaling analysis to determine when water vapor depletion
in the instrument can impact supersaturation and droplet size at the exit of the chamber.
For this, we first determine the “zero CCN” limit of supersaturation and droplet growth
(corresponding to very small CCN concentrations flowing through the instrument). The
perturbation about the “zero CCN” limit is then determined as a function of chamber CCN
concentration (and other relevant variables, such as flow rate and streamwise temperature
gradient) to express depletion effects. In the analysis, we assume that air flows with a total
rate Q through the growth chamber of radius R and wall temperature gradient G. We
also assume that supersaturation depletion is primarily from condensational loss of water
12
vapor to the CCN; the release of latent heat has a second order impact on supersaturation
(numerical simulations in Section 2.3 confirm this).
2.2.1
Depletion effects on supersaturation
The saturation ratio, S =
P
Ps ,
of a fluid “material point” flowing along the centerline of the
chamber (P , Ps are the partial and saturation pressure of water, respectively) can change
over time as follows:
dS
d
=
dt
dt
(
P
Ps
)
[
1 dP
P dPs
1 dP
dPs
=
− 2
=
−S
Ps dt
Ps dt
Ps dt
dt
]
(6)
where d/dt denotes the material (or Lagrangian) derivative of a material point property.
Equation 6 expresses the change in saturation ratio from fluctuations in water vapor concentration (P ) and temperature (Ps ). From the chain rule,
dz
dt
is the average flow velocity ∼
Q
πR2
dPs
dt
=
dPs dT dz
dT dz dt ;
(Roberts and Nenes, 2005), and,
dPs
dT
assuming
=
∆Hv Ps
Rg T 2
dT
dz
= G,
from the
Clausius-Clapeyron Equation (where T is the temperature and ∆Hv , Rg are the enthalpy
of evaporation and specific gas constant for water, respectively) we obtain:
dPs
∆Hv Ps
Q
=
G
dt
Rg T 2 πR2
(7)
Substitution of Equation 7 into Equation 6 gives:
[
1 dP
∆Hv Ps
dS
Q
=
−
G 2S
2
dt
Ps dt
Rg T
πR
Here,
dP
dt
]
(8)
can be expressed as the sum of water vapor supply from transport, Ṡ, and
condensational loss, Ċ, from the activated CCN:
[
1
∆Hv Ps
Q
dS
=
Ṡ − Ċ −
G 2S
2
dt
Ps
Rg T
πR
]
(9)
When the concentration of CCN, N , approaches zero, then Ċ → 0; assuming the flow
field is also developed,
dS
dt
≈ 0 and Equation 9 becomes:
Ṡ =
Q
∆Hv Ps
G 2 So
2
Rg T
πR
13
(10)
where So denotes the maximum (or “effective”) saturation ratio in the instrument for “zero
CCN” conditions (determined from instrument calibration; Section 2.4).
Here, Ṡ is controlled by the transport of water vapor from the chamber walls, and should
not depend on N . Hence, Equation 10 can be substituted into Equation 9 to give the general
supersaturation depletion equation:
dS
Ċ
∆Hv GQ
(So − S) −
=
2
2
dt
πR Rg T
Ps
If S along a streamline is assumed to be in a dynamical steady-state,
(11)
dS
dt
≈ 0. With this
and expressing the saturation ratio in terms of supersaturation (S = s+1 and So = so +1),
Equation 11 becomes:
s = so −
πR2 Rg T 2
Ċ
∆Hv GQPs
(12)
Equation 12 expresses the supersaturation depletion (in the developed flow region of the
chamber) from the condensational growth of activated CCN.
2.2.2
Depletion effects on droplet size at the exit of the growth chamber
The average size of activated CCN, Dp , at the exit of the growth chamber (which experimentally is measured in the optical particle counter of the CFSTGC) is taken as the
characteristic diameter for expressing droplet size reduction in our analysis. Dp can be
obtained from the integration of the droplet growth equation (Seinfeld and Pandis, 2006)
over the particle residence time in the instrument (Roberts and Nenes, 2005):
∫
Dp2
=
Dc2
+2
Γs(t)dt
(13)
τ
where Dc , τ are the characteristic critical wet diameter and the residence time of the CCN
in the instrument, respectively; s(t) denotes the streamwise supersaturation profile that
particles are exposed to while flowing in the growth chamber, and Γ is a growth parameter
that depends on droplet size and the water vapor mass transfer coefficient (Seinfeld and
Pandis, 2006; Nenes and Seinfeld , 2003).
14
Assuming Equation 12 applies at every point in the axial direction, it can be introduced
into Equation 13 to give:
∫
Dp2 = Dc2 + 2
Γso (t)dt − 2
τ
∫
Γ
τ
πR2 Rg T 2
Ċdt
∆Hv GQPs
(14)
where so (t) denotes the supersaturation profile at “zero CCN” concentration (corresponding
to the instrument “steady-state” supersaturation; Roberts and Nenes, 2005; Lance et al.,
2006).
(
The average droplet size at “zero CCN” concentration, Dpo =
Dc2
)1/2
∫
+ 2 Γso dt
, is
τ
given by Equation 14 for Ċ → 0. When higher concentrations of CCN flow in the chamber,
water vapor depletion leads to a lower s, hence lower Dp than Dpo :
2
Dp2 = Dpo
−2
∫
Γ
τ
2.2.3
πR2 Rg T 2
Ċdt
∆Hv GQPs
(15)
Simplified expressions for condensation effects on s, Dp
Equation 12 and Equation 15 give the impact of CCN growth on supersaturation and
droplet size at the exit of the growth chamber; more convenient forms can be derived if
Ċ is explicitly written in terms of Dp , N and Γ . Assuming that the droplets formed can
be divided into n classes of concentration Ni and wet diameter Dpi (that increases in the
streamwise direction), Ċ can be expressed at a given point in the flow chamber as
[
]
R∗ T ρw d π ∑
dP
π R ∗ T ρw ∑
3
2 dDpi
Ċ =
=
Ni Dpi
=
Ni Dpi
dt
Mw dt 6 n
2 Mw
dt
n
(16)
where R∗ , Mw , ρw are the universal gas constant, the molar mass and density of liquid water,
respectively. From the droplet growth equation (Roberts and Nenes, 2005),
this and the definition of average droplet size D̄p =
Ċ =
1
N
∑
n
=
Γs
Dpi ;
with
Ni Dpi , Equation 16 becomes
π R∗ T ρw
ΓN D̄p s
2 Mw
Combination of Equation 17 and Equation 12 eventually gives
15
dDpi
dt
(17)
s
1
=
Φ
so
1 + 2 ΓN D̄p
where Φ =
π 2 R 2 Rg R ∗ T 3 ρ w
∆Hv GQPs Mw .
(18)
Applying the binomial expansion up to first order, (1 + x)−1 ≈
1 − x, Equation 18 is approximated by:
s
Φ
≈ 1 − ΓN D̄p
so
2
(19)
To express the depression in instrument (i.e., maximum) supersaturation, Equation 19
should be applied in the vicinity of the supersaturation entry length (Lance et al., 2006),
where all the CCN have activated but experienced the least amount of growth. Further
downwind in the chamber, Equation 19 still applies but predicts increasingly larger supersaturation depletion because D̄p increases by condensation of water vapor. In fact,
Equation 19 suggests that supersaturation decreases downwind of its maximum value as
ds
dz
∼ so Φ2 ΓN
dD̄p
dz .
Simplification of the droplet size depression equation results from combination of Equation 17 and Equation 15:
Dp2
=
2
Dpo
−
∫
2
2
Dpo
ΦΓ N D̄p sdt =
τ
− ΦΓ N
∫
∫
2
(20)
D̄p sdt
τ
D̄p sdt
¯ = τ∫
Defining the column-average droplet size, D̄
p
, then
sdt
∫
¯ ∫ sdt and
D̄p sdt = D̄
p
τ
τ
τ
Equation 20 becomes:
2
¯
Dp2 = Dpo
− ΦΓ2 N D̄
p
where
Dp2
∫
2
sdt ≈ Dpo
−
τ
∫
Φ
¯ D2
ΓN D̄
p p
2
(21)
≈ 2 Γsdt (a valid approximation for CCN that exhibit considerable growth
τ
∫
beyond their critical wet diameter, i.e., Dc2 << 2 Γsdt; (Nenes and Seinfeld , 2003).
τ
Manipulation of Equation 21 gives
(
Dp
Φ
¯
= 1 + ΓN D̄
p
Dpo
2
)−1/2
where the truncated expansion (1 + x)−1/2 ≈ 1 −
16
x
2
≈1−
Φ
¯
ΓN D̄
p
4
is used.
(22)
Equation 19 and Equation 22 are remarkably similar. Both expressions would exhibit
identical dependence on N , Φ and Γ (hence imply that
s
so
≈
Dp
Dpo )
¯ ∼ 2D̄ . Numerical
if D̄
p
p
simulations (Section 2.3) and experimental observations (Section 2.4) suggest this is often
the case.
2.3
Instrument and Droplet Growth Models
The CFSTGC instrument model (Roberts and Nenes, 2005; Lance et al., 2006) numerically
integrates the Navier-Stokes equations and the heat and water vapor conservation equations
to predict the velocity, pressure, water vapor supersaturation, and temperature fields in the
instrument. The model has been shown to successfully simulate instrument behavior over
a wide range of operating conditions (Lance et al., 2006; Rose et al., 2008). Inputs to the
model are total volumetric flow rate (Q), sheath-to-aerosol flow ratio (SAR), pressure (P ),
and the inner wall streamwise temperature gradient (G) between the exit and entrance of
the column. The simulated supersaturation, velocity, and temperature profiles are used
to compute the activation and growth of CCN (with a user-defined size distribution and
composition) as they flow through the chamber. Integration of the CCN growth equations is
accomplished with the LSODE (Livermore Solver for ordinary differential equations) solver
(Hindmarsh, 1983). The loss of water vapor from the gas phase onto the growing CCN
and associated latent heat are then allowed to affect the gas phase water vapor and heat
balances (Nenes et al., 2001; Roberts and Nenes, 2005). For more details about the modeling
framework, the solution algorithm and the instrument characteristics, refer to Roberts and
Nenes (2005) and Lance et al. (2006).
To study supersaturation depletion effects, we vary the temperature gradient, total flow
rate, pressure, aerosol concentration, size distribution characteristics, and, the water vapor
uptake coefficient for the combinations of these parameters given in Table 1. The flow
field in the growth chamber is discretized onto 100 grid points in the streamwise direction
and 100 grid points in the radial direction. The temperature of the air (both sheath and
aerosol flows) entering the column is 293 K and the sheath-to-aerosol ratio in the growth
chamber is 10:1. Aerosol flows through the instrument that follows a single lognormal
17
Table 1: Operating conditions and aerosol characteristics considered in the CFSTGC instrument simulations
Property
Instrument Conditions
P (mb)
G (K m−1 )
Q (L min−1 )
Aerosol Properties
Dg (nm)
σg
Na (cm−3 )
αc
Values Considered
1000, 750, 500
6, 8, 11, 16∗
0.5, 1.0
40, 100
1.6
1, 5×102 , 103 , 5×103 , 104
2×104 , 3×104 , 5×104
1.0, 0.06, 0.01
*Inlet-outlet temperature difference of 3, 4, 5.5, 8 K
mode of ammonium sulfate aerosol with a geometric mean dry diameter, Dg , of 0.04-0.1
µm, geometric standard deviation, σ g , of 1.6, and number concentration, Na , between 1
and 5×104 cm−3 . The size distribution is discretized onto 50 bins equally spaced in logsize between 20 nm and 1 µm. The water vapor uptake coefficient, αc, (used in Γ to
express differences in activation kinetics of the CCN) was varied between 0.01 and 1.0,
which represents the range of activation kinetics observed to date in carbonaceous and
atmospheric aerosol (e.g., Ruehl et al., 2008, 2009; Asa-Awuku et al., 2009). A total of over
1100 simulations were completed.
Figure 1 presents a typical example of the effects of CCN on instrument supersaturation
and temperature. Shown are predicted centerline supersaturation and temperature profiles
in the CFSTGC for different levels of aerosol concentration. Chamber conditions correspond
to Q = 0.5 L min−1 , wall temperature gradient 16 Km−1 (i.e., a 8K difference between the
entry and exit of the growth chamber) and 506.5 mb chamber pressure. Aerosol is assumed
to be composed of (NH4 )2 SO4 , with Dg = 100 nm, σ g = 1.6 and αc = 1. The thickness
of the temperature profile line represents the variability of the quantity across simulations,
arising from latent heat released by the water vapor condensing upon the growing droplets.
The temperature profile is largely unaffected by the aerosol up to concentrations of 104
18
Figure 1: Predicted centerline temperature (black line) and supersaturation (symbols) profiles in the CFSTGC for different levels of aerosol flowing through the instrument. Chamber
conditions correspond to Q = 0.5 L min−1 , wall temperature gradient 16 Km−1 and 506.5
mb chamber pressure. Aerosol is composed of (NH4 )2 SO4 , and follows a lognormal distribution with 100 nm modal diameter and 1.6 geometric standard deviation. αc is assumed
unity. The thickness of the temperature profile line represents the variability across all
simulations.
19
cm−3 (maximum temperature increase of 0.07K), with a negligible impact on the streamwise temperature gradient and supersaturation (less than 2% relative change compared to
“zero CCN” conditions). The supersaturation profile for the lowest aerosol concentration
(1 cm−3 ) corresponds to the “zero CCN” solution; supersaturation develops after passing
of its characteristic entry length (Lance et al., 2006), reaches a maximum value (∼ 0.33%)
and slowly decays from the effects of increasing the absolute temperature in the streamwise
direction (Roberts and Nenes, 2005). Increasing the CCN concentration above 1000 cm−3
begins to impact the supersaturation profile. Consistent with the scaling analysis of Section 2.2, both maximum supersaturation and its decay are equally affected. However, the
location of the maximum in the instrument is not affected, so that the length of chamber
available for growth is unaffected by the CCN concentration level.
A change in supersaturation is expected to induce a change in the size of droplets
exiting the column. This is shown in Figure 2, which presents the predicted centerline
droplet diameter as a function of distance from the chamber inlet for the simulations of
Figure 1. Clearly, the “zero CCN” simulation provides the largest droplet sizes; increasing
CCN concentrations depresses droplet size by more than 30% at the exit of the chamber.
Also shown in the inset graph is the droplet relative dispersion (defined as the width of the
droplet distribution normalized by the average diameter); it decreases in the flow direction,
consistent with the diffusional narrowing expected for the droplets during their residence
in the chamber. Despite the variation of average droplet diameter and relative dispersion
with CCN concentration, the former tend to be uniquely correlated. This suggests that the
droplet size distribution is strongly linked to the level of supersaturation that eventually
develops in the CFSTGC (regardless if depletion effects are important).
Figure 3 presents the predicted supersaturation depletion (Figure 3a, c, and e) and
droplet size depression ratios (Figure 3b, d, and f) as a function of CCN concentration
(defined as the droplet concentration exiting the flow chamber in each simulation). Each
panel shows simulations for Q = 0.5 L min−1 , wall temperature gradient 8, 16 K m−1 ,
chamber pressure 500, 750, 1000 mb and (NH4 )2 SO4 aerosol with Dg = 40 and 100 nm, σg
= 1.6, and Na from 1 to 2×104 cm−3 . Results are shown for αc = 1 (Figure 3a and b), αc =
20
Figure 2: Predicted centerline average droplet diameter and relative dispersion (inset graph)
in the CFSTGC for different levels of aerosol flowing through the instrument. Chamber and
aerosol characteristics are identical to those of Figure 1.
0.06 (Figure 3c and d), and αc = 0.01 (Figure 3e and f). The behavior seen in all these plots
are consistent with the analysis of Section 2.2, as both s/so and Dp /Dpo scale linearly with
N , with remarkably similar slopes. The diverse set of instrument operating conditions and
aerosol characteristics considered in the simulations introduce a relatively small variability
¯
in s/so and Dp /Dpo ; this implies that changes in G and Q induce responses in D̄p and D̄
p
so that ΦD̄p ≃ const. (Equation 22). The sensitivity to αc however is important; a lower
value of the parameter tends to reduce the intensity of supersaturation depletion effects.
This is expected because reduction of αc reduces Γ, hence s/so and Dp /Dpo at constant N
(Equations 19, 22). Based on Figure 3, a 10% reduction in s and Dp is predicted for N ∼
4×103 for rapidly activating aerosol (αc = 1), N ∼ 104 for intermediate (αc = 0.06), and
N ∼ 3.5×104 for slowly activating aerosol (αc = 0.01).
Given that the “supply” rate of water vapor in the instrument is constrained by the
operation conditions (G, transport timescale of water vapor), condensational effects can only
act to reduce the s profile in the flow chamber. This means that if condensational depletion
21
Figure 3: Predicted supersaturation depletion (left panels) and droplet size depression ratios
(right panels) as a function of CCN concentration (defined as the droplet concentration
exiting the flow chamber). Results shown for αc = 1 (Panels a, b), αc = 0.06 (Panels c,
d), and αc = 0.01 (Panels e, f). Each panel shows simulations for Q = 0.5 L min−1 , wall
temperature gradient 8, 16 K m−1 , chamber pressure 500, 750, 1000 mb and (NH4 )2 SO4
aerosol following a lognormal distribution with 40, 100 nm modal diameter, 1.6 geometric
standard deviation and total concentration from 1 to 2×104 cm−3 .
22
Figure 4: Predicted average droplet diameter at the exit of growth chamber vs. supersaturation (with depletion effects considered) for Q = 0.5 L min−1 , αc = 1 (Panel a), Q =
0.5 L min−1 , αc = 0.06 (Panel b), Q = 1.0 L min−1 , αc = 0.06 (Panel c), and Q = 0.5 L
min−1 , αc = 0.01 (Panel d). Triangles denote simulations that correspond to “zero CCN”
conditions (defined as aerosol concentration below 500 cm−3 ).
23
of supersaturation notably affects outlet droplet sizes, it will also affect the maximum (hence
effective) supersaturation in the instrument. The simulations (Figure 3) and theoretical
analysis strongly supports this. Furthermore, while the presence of water vapor depletion
may affect the shape of the supersaturation profile somewhat (Figure 1), the strongest
effect on outlet droplet size arises from the reduction in effective supersaturation (i.e.,
the magnitude of the maximum). This is clearly shown in Figure 4, which shows the
average droplet diameter predicted at the exit of growth chamber vs. supersaturation (with
depletion effects considered) for the range of Q, αc , Na and Dg presented in Table 1. If
water vapor depletion significantly affected the shape of the supersaturation profile in the
instrument, it would also change the relationship between s and Dp (keeping Q, αc and
Dg constant). However, this is not the case, as simulations corresponding to “zero CCN”
conditions (Na < 500 cm−3 ; filled triangles) follow the same s vs. Dp relationship as for
simulations with high Na .
2.4
Experimental Determination of Depletion Effects
To experimentally determine supersaturation depletion and outlet droplet size depression,
we carry out two types of experiments: a “standard” instrument calibration (such as those
described in Lance et al. (2006) and Rose et al. (2008)) to determine the supersaturation.
We then proceed with polydisperse CCN activation experiments to quantify the effects
of water vapor depletion on supersaturation and droplet size. In all experiments we use
(NH4 )2 SO4 aerosol, generated by atomization of an aqueous solution of the salt.
The instrumentation setup used to determine the “zero CCN” supersaturation level in
the instrument is shown in Figure 5a. Polydisperse dry aerosol is charge-neutralized using a
Po-210 neutralizer and introduced into a differential mobility analyzer (DMA, TSI 3081L)
for size classification by electrical mobility. The classified aerosol is then split between a
condensation particle counter (CPC, TSI 3010) for measurement of total aerosol (condensation nuclei, CN) concentration, and a DMT CFSTGC (serial number 002) to measure CCN
concentrations. In order to maintain a sample flow rate of 1 L min−1 through the DMA,
filtered air is supplied to the classified aerosol stream or to the CPC stream (the latter
24
Figure 5: Instrument setup for (a) “zero CCN” instrument supersaturation calibration,
and, (b) polydisperse CCN activation experiments.
being preferable in cases where low aerosol concentrations limit the counting statistics in
the CFSTGC). In this study, the aerosol classification is operated as in a Scanning Mobility
Particle Sizer (SMPS; Wang and Flagan, 1989), where the voltage applied to the DMA is
exponentially scanned over time. The TSI Aerosol Instrument Manager control software is
used to scan the voltage, manage data acquisition in the CPC and carry out the inversion
to provide the aerosol number size distribution. The timeseries of CN and CCN counts obtained during a voltage ramp is then inverted into a “CCN activation curve” using scanning
mobility CCN analysis (SMCA, Moore et al., 2010) which provides the ratio of particles that
are CCN-active as a function of their dry mobility diameter. The critical supersaturation
(using Köhler theory) of the particle with dry diameter, d50 (at which 50% of the classified
aerosol act as CCN) is then used to characterize the instrument supersaturation:
(
sc =
where A =
4Mw σ
R ∗ T ρw ,
B =
ϕs νs ρs Mw d3s
,
ρw Ms
4A3
27B
)1/2
(23)
σ is the droplet-air surface tension at the point of
activation, and, ϕs , νs , and ρs are the osmotic coefficient, stoichiometric van’t Hoff factor,
25
and density of the solute, respectively. ϕs accounts for the incomplete solute dissociation
and was calculated for (NH4 )2 SO4 using the ion-interaction approach of Pitzer and Mayorga
(1973) with parameters taken from Clegg and Brimblecombe (1988). This supersaturation
calibration procedure is repeated for a range of instrument operating conditions (column
temperature gradient, flow rate and column pressure). The particle concentration in the
CFSTGC during SMCA (generally less than 500 cm−3 ) is low enough for water vapor
depletion effects to be negligible; hence the calibrated supersaturation corresponds to the
“zero CCN” supersaturation levels.
The polydisperse activation experiments are completed using the experimental setup
shown in Figure 5b; polydisperse aerosol is generated with an atomizer and dried by a series
of diffusional dryers. The aerosol stream is then sampled by a SMPS and the CFSTGC.
The flow rate in the instrument was kept at Q = 0.5 L min−1 , ambient pressure 980
mb, and several temperature gradients corresponding to a “zero CCN” supersaturation
range between 0.23% and 0.62%. The concentration and size distribution of the aerosol
is varied by changing the atomizer pressure and concentration of salt solution placed in
the atomizer. A single-mode aerosol is typically generated that is well-described with a
lognormal distribution, with σg = 1.6, Dg varying between 40 nm and 70 nm, and Na
ranging from ∼5×102 to 5×104 cm−3 . Supersaturation depletion effects in the CFSTGC
are quantified by determining the effective supersaturation in the instrument for each CCN
concentration level measured. This is done by determining the characteristic dry size,
d∗ , for which integration of the SMPS size distribution (from d∗ to the highest resolved
diameter, ∼300 nm) matches the average CCN concentration observed during the SMPS
scan. d∗ is then used to characterize the instrument supersaturation, s, (through application
of Equation 23) from which s/so is calculated. Droplet growth depletion is quantified by
calculating the average droplet size measured by the instrument OPC as a function of the
total CCN concentration, normalized with the “zero CCN” droplet size (also determined
from polydisperse activation experiments, under conditions of low particle concentration,
less than 500 cm−3 ).
Results of the polydisperse activation experiments are presented in Figure 6. Figure 6a
26
presents measured droplet size depression ratios, Dp /Dpo , as a function of CCN concentration. All the activation experiments conducted collapse onto a narrow band that follows a
linear trend; the slope of Dp /Dpo vs. N (-2.53×10−5 cm3 ) is close to the numerical predictions for aerosol with αc = 1.0 (-1.90×10−5 cm3 ; Figure 3b). When measured droplet
depression ratio is placed against predictions (Figure 6b), an excellent agreement can be
obtained; even extrapolation of the simulations to higher CCN concentrations fits the experimental data well.
Figure 6c presents the supersaturation depletion as a function of CCN concentration
in the instrument. Consistent with simulations, the activation experiments collapse onto
a narrow band that follows a linear trend; the slope of s/so vs. N (-1.94×10−5 cm3 ) is
close to the numerical predictions for aerosol with αc = 1.0 (-2.01×10−5 cm3 ; Figure 3a).
Finally, the average droplet diameter measured by the instrument OPC vs. supersaturation
(with depletion effects considered) is shown in Figure 6d. In agreement with simulations
(Figure 4), the relationship between s and Dp is not affected by the level of CCN in the
instrument, as “zero CCN” observations (i.e., Na < 500 cm−3 ; filled triangles) follow the
same curve as for measurements at high CCN concentration.
All the evidence presented above strongly suggests that the theoretical analysis presented
in sections 2.2 and 2.3 is a realistic representation of water vapor depletion effects in the
CFSTGC. Against initial expectations, the coupling of supersaturation, CCN number and
outlet droplet size gives rise to a remarkably simple relationship between supersaturation
and droplet size depletion. This is shown in Figure 7, which presents s/so vs Dp /Dpo
from the simulations (Figure 7a) and the observations (Figure 7b). For the wide range of
conditions considered,
s
so
≈
Dp
Dpo
to within 10%.
The analysis presented does not consider the effect of coincidence errors for measurements of CCN at high concentrations; for the instrumentation used, a maximum coincidence
error of 10% can occur for concentrations up to 6000 cm−3 (Droplet Measurement Technologies, 2005). The consistency between the theoretical analysis and instrument observations
suggests that coincidence errors, if present, constitute a secondary effect on the observed
instrument response.
27
Figure 6: Measured CFSTGC responses to increasing levels of CCN in the flow chamber. Instrument operation was kept at Q = 0.5 L min−1 , chamber pressure 980 mb and
(NH4 )2 SO4 aerosol following a lognormal distribution with 40, 70 nm modal diameter, 1.6
geometric standard deviation and total concentration from 1 to ∼ 3×104 cm−3 . (a) droplet
size depression ratios as a function of CCN concentration (b) Predicted vs. measured droplet
size depression ratios as a function of CCN concentration, for Q = 0.5 L min−1 , P = 980
mb and so = 0.62% (αc = 1.0 is assumed in the simulations). (c) Measured supersaturation depletion as a function of CCN concentration. (d) Average droplet size measured at
the optical particle counter vs. instrument supersaturation (considering depletion effects).
Triangles denote simulations that correspond to “zero CCN” conditions (defined as CCN
concentration below 500 cm−3 ).
28
D
p
Figure 7: sso vs Dpo
for (a) all the simulations presented in Figure 3, and, (b) the experiments
presented in Figure 6a. Dashed lines correspond to ±10% deviation from the diagonal.
29
2.5
Summary and Implications
All published studies with the DMT CFSTGC assume that the supersaturation generated
in the instrument is not influenced by the condensation of water vapor upon the growing
CCN. This study evaluates this assumption and examines the conditions for which depletion
effects become important for supersaturation, measured CCN concentration and droplet
growth. An analysis is carried out with a fully coupled numerical model and laboratory
CCN activation experiments using a commercial instrument.
We find that condensational depletion of water vapor does not impact supersaturation
and droplet size by more than 10% (which is comparable to the supersaturation uncertainty
quoted for the CFSTGC (e.g., Rose et al., 2008) if the CCN are present at concentrations
below 5000 cm−3 . If the CCN exhibit slower activation kinetics than (NH4 )2 SO4 aerosol,
higher concentrations can be present in the instrument before depletion effects become
important. Remarkably, depletion effects do not significantly alter the shape of the supersaturation distribution in the instrument growth chamber, so that the relationship between
instrument supersaturation and outlet droplet size does not change (regardless of the extent
of supersaturation depletion). The above suggests that the majority of atmosphericallyrelevant CCN measurements with the DMT CFSTGC may not be substantially affected
by supersaturation depletion. However, high CCN concentrations can occur with enough
frequency (especially if sampling polydisperse CCN in polluted environments, or in laboratory experiments with high particulate loads) that a methodology needs to be developed to
address the issue.
There are a number of approaches that can be used to eliminate biases from supersaturation depletion. One approach is to avoid depletion effects all together by maintaining
a low concentration of CCN in the instrument during laboratory calibrations and atmospheric sampling. Low concentrations of CCN can be achieved in-situ by either performing
size-resolved CCN measurements (e.g., SMCA) or through controlled dilution of the sample
stream with filtered air before entering the CFSTGC. Another approach is to account for
depletion biases using the relationship of s/so versus CCN presented in this manuscript;
the effective supersaturation then corresponds to the measured CCN level and droplet size
30
– and is equivalent to measurements carried out under “zero CCN conditions”. The degree
of correction is sensitive to the activation kinetics of the ambient aerosol, but an analysis
with the instrument model (such as carried out in this manuscript) can largely account for
this uncertainty. Supported by the available body of evidence (e.g., Bougiatioti et al., 2009;
Lance et al., 2009; Ruehl et al., 2008, 2009), rapid activation kinetics (i.e., αc comparable
to that of (NH4 )2 SO4 aerosol ∼ 0.1) can be assumed a priori when the aerosol is aged,
sampled from a humid environment or contains large amounts of soluble material.
The analysis carried out here can be extended to the different operation modes and
implementations of the CFSTGC design (e.g., Ruehl et al., 2008; Moore et al., 2010) or any
other CCN instrument design (such as those analyzed in Nenes et al. (2001)). Regardless
of the method used to measure CCN concentrations, this study shows that depletion effects
need to be carefully understood for unambiguous measurements of CCN activity and droplet
activation kinetics.
31
CHAPTER III
ANALYSIS OF CCN ACTIVITY OF ARCTIC AEROSOL AND
CANADIAN BIOMASS BURNING DURING SUMMER 2008
Instruments and researchers on the NASA DC-8 aircraft characterized the aerosol properties, chemical composition, and cloud condensation nuclei (CCN) concentrations of the
summertime Arctic during the 2008 NASA Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) campaign. Air masses characteristic of
fresh and aged biomass burning, boreal forest, Arctic background, and anthropogenic industrial pollution were sampled. Observations were spatially extensive (50 - 85◦ N and 40 130◦ W) and exhibit significant variability in aerosol and CCN concentrations. The chemical
composition was dominated by highly oxidized organics (66 − 94% by volume), more than
half of which were water-soluble. The aerosol hygroscopicity parameter, κ, ranged between
κ = 0.08 and 0.32 for all air mass types. Industrial pollution had the lowest κ of 0.08 ± 0.01,
while the Arctic background had the highest and most variable κ of 0.32 ± 0.21, resulting
from a lower and more variable organic fraction. Both fresh and aged (long-range transported) biomass burning air masses exhibited remarkably similar κ (0.18 ± 0.13), consistent
with observed rapid chemical and physical aging of smoke emissions in the atmosphere,
even in the vicinity of fresh fires. The organic hygroscopicity (κorg ) was parameterized
by the volume fraction of water-soluble organic matter (εW SOM ), with a κ = 0.12, such
that κorg = 0.12εW SOM . Assuming bulk (size-independent) composition and including
the κorg parameterization enabled CCN predictions to within 30% accuracy for nearly all
environments sampled. The only exception was for industrial pollution from Canadian oil
sands exploration, where an external mixture and size-dependent composition was required.
2
This chapter published as: Lathem, T. L., Beyersdorf, A. J., Thornhill, K. L., Winstead, E. L., Cubison,
M. J., Hecobian, A., Jimenez, J. L., Weber, R. J., Anderson, B. E., and Nenes, A. (2012), Analysis of
CCN activity of Arctic aerosol and Canadian biomass burning during summer 2008, Atmos. Chem. Phys.
Discuss., 12, 24677-24733, doi:10.5194/acpd-12-24677-2012, 2012. Reproduced under the Creative Commons
Attribution 3.0 license.
32
Aerosol mixing state assumptions (internal vs. external) in all other environments did not
significantly affect CCN predictions; however, the external mixing assumption provided the
best results, even though the available observations could not determine the true degree
of external mixing. No correlation was observed between κorg and O:C. A novel correction of the CCN instrument supersaturation for water vapor depletion, resulting from high
concentrations of CCN, was also employed. This correction was especially important for
fresh biomass burning plumes where concentrations exceeded 1.5×104 cm−3 and introduced
supersaturation depletions of ≥ 25%. Not accounting for supersaturation depletion in these
high concentration environments would therefore bias CCN closure and inferred κ by up to
50%.
3.1
Introduction
The Arctic is a particularly vulnerable region of Earth with a large warming trend and high
sensitivity to climate forcing (Law and Stohl , 2007; Shindell and Faluvegi , 2009; Screen
et al., 2012), largely due to the strong albedo-sea ice feedback. Aerosol species have the
potential to modify these critical feedbacks and affect the sea ice albedo by altering the
heat balance of the atmosphere and surface, either directly through their ability to scatter
and absorb solar radiation, or indirectly through their ability to uptake atmospheric water
vapor and form cloud droplets and ice crystals. It is well known that the Arctic environment can accumulate aerosol through pollution transported from northern mid-latitude
continents, with Arctic haze, black carbon deposition onto snow and bioaccumulation of
heavy metals (e.g., mercury) being observed (Jacob et al., 2010). As industrial activities
expand toward the mid-latitudes and higher Arctic, their closer proximity and emissions
may further accelerate Arctic warming. There is an urgent need to characterize the source
of these pollutants in the Arctic environment, to understand their physical and chemical
properties, their evolution over time, and what direct and indirect impacts they may have
on regional and global climate (Jacob et al., 2010).
The Arctic atmosphere is affected by three dominant sources of aerosol pollution: 1)
33
long range transport of emissions from mid-latitude continents, 2) biomass burning emissions from summertime boreal forest fires in North America and Eurasia, and 3) natural
or anthropogenic sources in the Arctic. The transport of pollution from mid-latitude continents can occur throughout the year, but occurs most frequently during the winter-spring
transition, when increased cyclonic activity enables the pollution to be transferred along
warm conveyor belts into the vertically stratified Arctic atmosphere (Shaw , 1995; Stohl ,
2001; Fuelberg et al., 2003; Scheuer et al., 2003; Quinn et al., 2007). These Arctic haze
layers can persist for weeks and have been attributed to both industrial sources (Law and
Stohl , 2007) and biomass burning (Stohl et al., 2006, 2007; Quinn et al., 2008; Warneke
et al., 2009, 2010). The frequency and intensity of transport emissions, particularly from
Asia, may also be modulated by large scale climate variability; for example, strong El Niño
conditions are known to enhance the transport of pollutants into the Arctic (Fisher et al.,
2010).
Boreal forest fires have also become increasingly recognized as a dominant source of
transported aerosol to the Arctic, especially during the active summer fire season (Warneke
et al., 2009, 2010). These emissions are expected to increase in the future, as fire occurrence
and intensity increase with a warming climate (Reid et al., 2005). The emissions and
properties of the smoke can be highly variable depending significantly on the type of biomass
burned, but are known to contribute substantially to cloud condensation nuclei (CCN) (e.g.,
Reid et al., 2005; Pratt et al., 2011; Engelhart et al., 2012) and black and light-absorbing
carbon (Matsui et al., 2011). This variability leads to increased uncertainty in predicting
the direct and indirect impacts of biomass burning emissions on Arctic climate. Finally,
particles can be directly emitted into the Arctic atmosphere, arising from a variety of
natural or anthropogenic sources. Natural emissions of organic-rich materials, microgels,
and dimethyl sulfide (DMS) have been observed above the open ocean surface or open leads
in pack ice (Charlson et al., 1987; Leck and Persson, 1996; Leck et al., 2002; Martin et al.,
2011; Orellana et al., 2011), and additional natural sources (such as secondary organic
aerosol production from oxidation of biogenic volatile organic compounds) can arise from
the boreal forest ecosystems (Spracklen et al., 2008; Slowik et al., 2010). As sea ice coverage
34
decreases and boreal forests expand into the more temperate climate zones (Elmendorf
et al., 2012), these emissions are suspected to play an increasingly important, yet uncertain,
role in Arctic climate. Industrial activities can also directly contribute aerosol into the
Arctic environment, and with oil exploration and transportation through open waters in
these regions increasing, it is also expected that these emissions will become increasingly
important in the future (McLinden et al., 2012).
The existing in-situ measurements of Arctic aerosol have been conducted through various ground-based, ship and airborne observation platforms to capture both the temporal
(ground studies) and spatial (airborne/ship studies) variability of the aerosol (e.g., Leck
et al., 2002; Kammermann et al., 2010; Martin et al., 2011; Brock et al., 2011; Moore et al.,
2011; Heintzenberg and Leck , 2012). However, comprehensive in-situ measurements of the
Arctic are limited, primarily because of the remoteness and harsh conditions that can make
long term sampling challenging. Observed CCN number concentrations have historically
been less than 100 cm−3 in the pristine Arctic background air, but can reach values as high
as 103 cm−3 in haze layers indicative of long-range transport of pollutants (Moore et al.,
2011, and references therein), or as low as 1 cm−3 (Mauritsen et al., 2011). The large
variability in Arctic CCN number concentration is a result of varying particle sources and
chemical composition throughout the years, as well as efficient loss mechanisms (Browse
et al., 2012). The resulting seasonality in aerosol mass, composition, and CCN is an important effect that models still have difficulty accurately representing (e.g., Shindell et al.,
2008).
While observations of aerosol and CCN in the Arctic are sparse and difficult to obtain,
studying their impacts on Arctic climate through models can be even more challenging. It is
well known that increased CCN concentrations can lead to the production of more numerous
and smaller cloud drops, which can result in optically thicker clouds that tend to reflect
more incoming solar radiation back to space (Twomey, 1977). However, since the Arctic is
generally characterized by an already highly reflective surface of ice and snow, the surface
cooling effect from increased cloud reflectivity is thought to be small (Tietze et al., 2011).
A distinctly different, and perhaps more significant, indirect effect from increased CCN
35
has recently been proposed for thin Arctic clouds, where increased aerosol has been shown
to increase the longwave emissivity of the cloud, thereby warming the surface (Garrett
and Zhao, 2006; Lubin and Vogelmann, 2006; Alterskjær et al., 2010). Furthermore, it
has been suggested that CCN may be important for modulating ice formation processes
in mixed-phase clouds, leading to greater cloud lifetime, increased cloud emissivity, and
reduced precipitation (Lance et al., 2011). A lack of understanding of ice nuclei (IN) and
ice initiation processes may also be one of the reasons regional and global climate models
are not able to accurately reproduce Arctic clouds and the Arctic radiation budget (Prenni
et al., 2007).
An important challenge for aerosol-cloud interaction studies is the treatment of complex
aerosol chemical properties in atmospheric models without adding significant computational
burden or uncertainty. Many recent studies have shown that, for CCN activity, the influence
of aerosol composition can be efficiently represented by a single hygroscopicity parameter,
κ, which simply expresses the affinity of a given aerosol particle for water (Petters and Kreidenweis, 2007). The κ for atmospheric particles can range anywhere from ∼ 0 for insoluble,
wettable species to 1.3 for sodium chloride (Petters and Kreidenweis, 2007). However, some
insoluble particles, like dust and volcanic ash, can exhibit significant hydrophilicity from
the presence of clays and surface porosity, such that these species can activate at much
lower supersaturations than expected for κ = 0 (Kumar et al., 2011a; Lathem et al., 2011).
The availability of existing observational data is not sufficient to effectively constrain the κ
for Arctic aerosol, particularly its regional and seasonal variability. It has been suggested
by Andreae and Rosenfeld (2008) that the effective κ for continental aerosol is 0.3 ± 0.1,
consistent with global modeling studies (e.g., Pringle et al., 2010). However, neither study
provides estimates of κ for the Arctic environments and Moore et al. (2012) show that the
sensitivity of CCN to aerosol composition and number in the Arctic is unusually high, where
cloud droplet number concentration uncertainty to CCN prediction errors can exceed 70%.
This high sensitivity underscores the need to understand and quantify the regional and
seasonal changes in Arctic κ, which can be one of the primary drivers of CCN prediction
uncertainty.
36
There is overwhelming evidence that organics can represent more than 50% of the total
aerosol mass in the Arctic (Quinn et al., 2009; Shaw et al., 2010; Frossard et al., 2011; Moore
et al., 2011). Since a significant fraction of organics can also be hygroscopic (Hennigan et al.,
2008; Fu et al., 2009; Duplissy et al., 2011), an understanding of the particle κ and CCN
activity requires understanding the hygroscopicity contribution from organics, κorg . The
value of κorg can range from zero for insoluble species to ∼ 0.3 for the water-soluble carbon
extracted from secondary organic aerosol (SOA) and biomass burning aerosols (Wang et al.,
2010; Padró et al., 2010; Asa-Awuku et al., 2010). Several recent studies have confirmed that
κorg is an important variable for understanding and predicting the CCN activity of Arctic
aerosol. These studies account for κorg in CCN predictions by making various assumptions
about the relative fractions of insoluble and soluble organic species, as well as varying the
κorg until ideal closure is achieved, (e.g., Bougiatioti et al., 2009, 2011; Moore et al., 2011;
Dusek et al., 2011; Martin et al., 2011). For example, Moore et al. (2011) found the lowest
CCN prediction errors (< 20%) for Alaskan springtime Arctic aerosol when the aerosol
population was assumed to be an external mixture with moderately hygroscopic organics
(κorg = 0.11). On the other hand, Martin et al. (2011) studied Arctic aerosol over the
summertime pack ice (> 85◦ N) and found low CCN prediction errors by assuming the
aerosol to be internally mixed with primarily insoluble organics (κorg = 0.02). The large
differences in κorg between the two studies and differences in assumed mixing state highlight
potentially large regional or seasonal variability in Arctic aerosol properties and motivate a
need for a more comprehensive understanding of the factors leading to this variability.
Determination of κorg is far from trivial, owing to the complexity of the organic aerosol
fraction and its tendency to evolve with atmospheric oxidative processing and aerosol aging (Jimenez et al., 2009). Numerous studies have sought to reduce this complexity by
estimating κorg based upon other easily measured aerosol parameters, without having to
explicitly know the detailed organic speciation. One method has been to parameterize the
κorg with respect to the fraction of water-soluble organic carbon (WSOC) or water-soluble
organic matter (WSOM) (Padró et al., 2010; Engelhart et al., 2011; Asa-Awuku et al.,
2011), as it is the soluble organics that are most hygroscopic. These studies suggest that
37
κorg = (0.25 ± 0.05)εorg , where εorg is the volume fraction of total organic aerosol which is
water-soluble. Another method has been to correlate κorg with the O:C (or f44 as a surrogate of O:C) determined from an aerosol mass spectrometer (AMS) (Jimenez et al., 2009;
Chang et al., 2010; Duplissy et al., 2011; Lambe et al., 2011), where the κorg is expected to
increase with the O:C, which is proportional to the mass fraction of oxygenated functional
groups that should increase the solubility of the organic species. In previous laboratory
and chamber studies, the SOA κ has been observed to correlate strongly with the AMS
O:C ratio (or f44 ) (Jimenez et al., 2009; Duplissy et al., 2011). These studies suggest that
atmospheric photochemical oxidation may increase the overall SOA or κorg as a result of
increased aerosol oxygenation and reduced organic aerosol volatility. Chang et al. (2010)
report a linear relationship between the κorg and O:C ratio for organic aerosol in rural
Egbert Ontario, Canada, such that κorg = (0.29 ± 0.05)(O:C), although they stress the
need for further studies to characterize if this relationship holds for a wider range of O:C
and aerosol types and locations. Engelhart et al. (2012) report an increasing trend in total
primary aerosol κ with increasing f44 for the aging of fresh biomass burning emissions in
a smog chamber. These relationships between O:C and κorg do not necessarily imply O:C
controls κorg , but rather that O:C is a proxy for aerosol molecular properties, such as molar
volume and solubility, that control κorg . Very few studies to date have all of the available
data to fully link measurements of WSOC, O:C and κorg with changes in CCN activity.
In this study, we present observations and provide model parameters for the physical,
chemical, and hygroscopic properties of summertime Arctic aerosol from dominant source
components (including Arctic background, boreal forests, biomass burning influences, and
anthropogenic industrial emissions). These measurements were collected during 2008 as
part of the NASA ARCTAS (Arctic Research of the Composition of the Troposphere from
Aircraft and Satellites) campaign and cover the summertime Arctic, from 50 - 85◦ N and 40 130◦ W. The measured physical and chemical properties of the aerosol are used to constrain
the aerosol hygroscopicity. An aerosol-CCN closure study for each source is presented,
using assumptions and simplifications most utilized by global climate models. Additionally,
we investigate the aging of the aerosol, specifically for biomass burning, by exploring the
38
relationship between particle κ, oxidation state, and the fraction of water-soluble organic
matter. Finally, we explore the importance of supersaturation depletion in determining κ
and the CCN spectra. As first shown by Lathem and Nenes (2011), high concentrations of
CCN (> 5 × 103 cm−3 ) can significantly deplete the calibrated instrument supersaturation,
and the resulting biases in CCN closure and inferred κ this depletion causes are investigated
and a method for correcting these biases is introduced.
3.2
3.2.1
Experimental Measurements
Aircraft Measurements
The 2008 ARCTAS mission was in support of the 4th International Polar Year (IPY) collaborative effort POLARCAT (Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, of Climate, Chemistry, Aerosols, and Transport). An extensive
overview and description of the mission is given by Jacob et al. (2010) and the accompanying detailed meteorological overview and modeled air mass back trajectories is provided by
Fuelberg et al. (2010). The results presented here represent nine flights from the summertime
ARCTAS-B deployment phase on the NASA Dryden Flight Research Center DC-8 aircraft
platform, which took place from 26 June to 14 July 2008. Flights were based out of Cold
Lake, Alberta, Canada (54◦ N, 110◦ W) and included transits to and from Thule, Greenland
(77◦ N, 69◦ W), and, a local flight out of Thule over Summit, Greenland (73◦ N, 39◦ W). The
primary objective of ARCTAS-B was to investigate and characterize fresh boreal forest fire
emissions in Canada, as well as their evolution and transport into the Arctic environment.
A majority of the aerosol sampling occurred within the boundary layer and lower free troposphere, with fresh biomass burning emissions sampled primarily in the boundary layer
within a few hours after emission. In addition to the fresh boreal fire emissions, several
additional environments and sources were characterized, including boreal forest and Arctic
backgrounds, transported Siberian biomass smoke, and direct sampling of industrial pollution from Canadian oil sands operations at Fort McMurray. A map of the flight tracks,
colored by the measured air mass type, is shown in Figure 8, with details on the source
identification methods provided in Section 3.3.3.
39
In-situ measurements of aerosol properties (number density, size distributions, optical
properties, and CCN) were measured aboard the DC-8 as part of the NASA Langley Aerosol
Research Group Experiments (LARGE), with instrumentation mounted inside the cabin and
optimized for reduced pressure (Thornhill et al., 2008; Chen et al., 2011). Isokinetic flow was
maintained through the aerosol sampling inlet by determining the ideal flow based on realtime measurements of air speed, static pressure, and temperature, which minimizes inertial
sampling biases (Chen et al., 2011). The University of Hawaii solid diffuser inlet (UH-SDI)
was window mounted at the forward right section of the aircraft ahead of all engines and
has a 50% sampling efficiency at 5 µm dry aerodynamic diameter (Clarke et al., 2007;
McNaughton et al., 2007). CCN sampling transmission efficiency was determined from field
calibration experiments to be > 90% for particles larger than 30 nm.
3.2.2
Submicron Particle Number Density and Size Distribution Measurements
Particle number density (CN) was measured with several TSI condensation particle counters (CPC), including a TSI CPC-3010 and CPC-3772 to measure total CN greater than
10 nm, and a TSI CPC-3025 (ultrafine CPC) to measure total CN greater than 3 nm. Dry
aerosol size distributions were measured independently via a Droplet Measurement Technologies Ultra High Sensitivity Aerosol Spectrometer (DMT-UHSAS; Cai et al., 2008) and
a customized Scanning Mobility Particle Sizer (SMPS) system. The DMT-UHSAS provides
aerosol size distributions for the 0.07 - 1 µm dry diameter range at a time resolution of 1 Hz
and was calibrated in the field using polystyrene latex spheres (PSL). Error in the UHSAS
PSL calibration due to the refractive index difference between PSL and ambient particles
was investigated by comparing with size calibrations using ammonium sulfate particles. A
UHSAS sizing bias from using the PSL calibration was found to not be important for the
dry particle size range investigated here. Due to operating issues at low pressure, only
DMT-UHSAS data below ∼2130 m flight altitude was used in data analysis. The custom
SMPS system consisted of a TSI Long Differential Mobility Analyzer (DMA) column, sheath
and aerosol flow control, and a CPC-3010. The SMPS system provided size distributions
over the dry diameter range of 8-250 nm with a scan time of 105 seconds. SMPS sizing
40
was calibrated with PSL and reported mobility diameters corrected for changes in ambient
temperature and pressure. SMPS concentrations were also corrected for multiple charge
distributions at ambient pressure and for diffusion losses in the SMPS and CPC systems.
For altitudes lower than 2130 m, the UHSAS data was averaged over the SMPS scan
time, and both distributions were merged to provide a full size distribution from 8 nm to
1 µm diameter. The merged distributions for every flight were then visually inspected to
ensure the diameter bins were properly merged and that the concentration in the region
of size overlap between the two distributions agreed to within 15%. The integrated counts
from the merged particle size distributions were then scaled to match the total CN from the
CPC-3010 (although the merged SMPS-UHSAS distributions agreed with the CPC-3010
counts to within 25%). There is inherent variability associated with aircraft measurements;
to ensure that this variability did not induce biases, the variability in CN over a given
SMPS scan was a metric to determine if the SMPS scan was representative of a constant
or changing environment. If the CN standard deviation over an SMPS scan time exceeded
50% of the average CN reported for that scan, then the scan was not included in the data
analysis. This type of SMPS filtering was most important for sampling biomass burning
plumes, where in a given scan time, both background air and a plume could be sampled
within a single 105 second scan. To ensure representative sampling of environments, only
scans corresponding to a single air mass type (e.g., only background or only plume) were
included in the various source type analysis.
3.2.3
Submicron Particle Chemical Composition Measurements
Measurements of the non-refractory submicron aerosol composition were made with an
Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, DeCarlo
et al., 2006) with a pressure controlled inlet (Bahreini et al., 2008), as described in Cubison
et al. (2011). Bulk aerosol mass loadings for sulfate, nitrate, ammonium and organic aerosol
components were calculated from the mass spectra and recorded at 12 second data intervals,
with estimated relative uncertainties on the order of 34% and 38% for inorganic and organic
components, respectively (Bahreini et al., 2009; Middlebrook et al., 2012). Additionally,
41
for the purpose of CCN analyses, the organic aerosol mass spectra were further utilized
to provide information on the oxygenation state of the aerosol, via the O:C ratio, which
can be a proxy for both particle aging and hygroscopicity (Jimenez et al., 2009; Duplissy
et al., 2011). A small fraction of the nominally inorganic AMS species could be due to
organosulfates, organonitrates, or amines (Farmer et al., 2010).
Submicron water-soluble organic carbon (WSOC) measurements were made with a
particle-into-liquid-sampler (PILS) coupled to a total organic carbon (TOC) analyzer (Sullivan et al., 2006). The PILS-WSOC provided three second integrated measurements of the
concentration of WSOC. A WSOC to organic carbon (OC) ratio, WSOC/OC, is calculated
by comparing the amount of WSOC measured by the PILS to the amount of organic matter
(OM) measured by the AMS. For this study, WSOC is converted to WSOM by multiplying
by an OM/OC ratio of 1.6. A OM/OC ratio of 1.6 is consistent with moderately aged
organics with an O:C ratio of ∼0.4 (Aiken et al., 2008) and biomass smoke particles (Hand
et al., 2010). However, it should be noted that there is considerable variability in reported
OM/OC ratios for organic compounds depending on the relative contribution of primary
and secondary organic aerosol sources, with reported values ranging from 1.2-2.4 (Turpin
and Lim, 2001; Russell , 2003; Aiken et al., 2008). To account for this uncertainty in the
OM/OC ratio, sensitivity studies were performed using OM/OC ratios of 1.6 and 2.2. It
was found that increasing the OM/OC ratio from 1.6 to 2.2 would lead to a 15% maximum
increase in the reported WSOM fraction (Table 2). This increase in the WSOM fraction
would increase the reported CCN closure errors (Table 3) by an average of 4% and increase
the calculated κ by an average of 0.02 (Section 3.4.2), which are both within the reported
variability.
3.2.4
Gas Phase Measurements
Jacob et al. (2010) provides a complete list of all gas-phase measurements made aboard the
NASA DC-8 during ARCTAS. Select measurements were used in this study to classify air
masses by source type, being carbon monoxide (CO), carbon dioxide (CO2 ), acetonitrile
(CH3 CN), methane (CH4 ), sulfur dioxide (SO2 ), nitrogen oxides (NOx = NO + NO2 ), and
42
oxidized nitrogen (NOy ). CH3 CN and CO were used as biomass burning and combustion
tracers, whereas SO2 and NOx /NOy were used to isolate anthropogenic industrial sources.
The full details of how each gas phase species was used in the source type analysis is provided
in Section 3.3.3.
3.2.5
CCN Measurements
CCN measurements were made with a Droplet Measurement Technologies (DMT) continuousflow, streamwise thermal-gradient CCN counter (hereafter referred to as CCNC; Roberts and
Nenes, 2005; Lance et al., 2006; Lathem and Nenes, 2011; Raatikainen et al., 2012). The
CCNC was located within the LARGE instrument rack, sharing sampling lines with the
SMPS and CPC-3772, such that the size distribution and total number concentration of
the aerosol could be directly related to the CCN measurements. The CCNC generates a
controlled and precise supersaturation within a cylindrical column, which is continuously
wet with water and experiences a linear temperature gradient generated by applying a temperature difference (∆T) between the column top and bottom. As particles flow through
the centerline of the column and are exposed to this supersaturation, they can uptake the
water vapor and activate to form cloud drops, dependent upon their size and chemical composition. The CCNC measures the total number density and size distribution of activated
cloud drops in the diameter size range of 1 - 10 µm, with a reporting frequency of 1 Hz.
During ARCTAS, the CCNC was operated at a constant flow rate of 0.5 L min−1 and a
constant pressure of 450 hPa. The pressure was maintained by use of a flow orifice and active
pressure control system, which compensates for changes in ambient pressure due to changes
in aircraft altitude. High altitude sampling, in which the ambient pressure dropped below
450 hPa, was not included in the analysis presented herein, because pressure fluctuations
affect instrument supersaturation. At a constant flow rate and pressure, the instrument
supersaturation scales linearly with ∆T (Roberts and Nenes, 2005), and so the applied ∆T
was increased in discrete steps between 5.8 and 12.3 K to provide a calibrated supersaturation range of 0.20 - 0.57%. Data collected during instrument transient operation, during
change in instrument supersaturation, or when flow or instrument pressure fluctuated by
43
more than 0.5% about the mean were excluded from analysis (Asa-Awuku et al., 2011).
The instrument supersaturation was calibrated before, during and after the field experiment with size-selected ammonium sulfate particles via Scanning Mobility CCN Analysis
(SMCA) following the procedures of Moore et al. (2010). The relative error in calibrated
supersaturation for the ARCTAS experiment is reported to be 0.05% (absolute). The total
uncertainty in reported CCN number concentrations derived from counting statistics and
instrument operating parameter variability (temperature, pressure, flow) is 7-16% for CCN
concentrations above 100 cm−3 STP (Moore et al., 2011). The calibrated instrument supersaturation has been corrected for supersaturation depletion, which becomes important
for CCN concentrations above 5 × 103 cm−3 (Lathem and Nenes, 2011). The methodology
for the supersaturation depletion correction is presented in Section 3.3.2.
3.3
3.3.1
Methods
Inferring Particle Hygroscopicity
The concentration of CCN represents the fraction of aerosol particles that form droplets
in cloudy air parcels and depends on aerosol dry size, chemical composition, and ambient
water vapor supersaturation. Quantitatively, the level of water vapor above which a particle
acts as a CCN is given by Köhler theory (Köhler , 1936). Expressed in terms of critical
supersaturation, sc (where sc = RH - 1, and RH is the fractional relative humidity), theory
gives:
[
4ρw Ms
sc =
ρs Mw νi Dp3
(
4σMw
3RT ρw
)3 ]1/2
(24)
where ρw is the density of water, ρs the density of solute, Mw the molar mass of water, Ms
the molar mass of solute, νi the effective van’t Hoff dissociation factor, Dp the dry particle
diameter, R the gas constant, T the absolute temperature, and σ the surface tension of
the solution droplet. Here, the standard convention of assuming the surface tension of the
solution drop is equal to that of pure water (σ = σw ) is used, which is a good approximation
for dilute droplets unless they contain considerable amounts of strong surfactants.
Petters and Kreidenweis (2007) suggested replacing the term
44
ρs Mw νi
ρw Ms
with an adjustable
parameter, κ, termed the hygroscopicity parameter. κ expresses the affinity of a material
for water and is a simple way to parameterize the solute term of Köhler theory, such that
Equation 24 becomes:
[
4
sc =
κDp3
(
4σw Mw
3RT ρw
)3 ]1/2
(25)
If the particle is composed of many compounds, the total particle κ is calculated as the
volume fraction weighted average κ of the individual aerosol components:
κ=
∑
εj κ j
(26)
j
where κj and εj represents the κ and volume fraction of chemical species j, respectively.
This approach treats the particles as internal mixtures, where all particles have identical chemical composition for the entire size range. In this study, the average composition
is derived from bulk submicron mass concentrations provided by the HR-ToF-AMS and
PILS-WSOC. Composition is approximated as a mixture of ammonium sulfate, ammonium
nitrate, ammonium bisulfate, sulfuric acid, water-soluble organics, and water-insoluble inorganics (taken as the difference between total OA and WSOM). BC is neglected from this
analysis due to its low mass fraction (e.g., Kondo et al., 2011). The inorganic fraction of
the aerosol is partitioned between neutral and acidic sulfate species using the molar ratio
of ammonium ions to sulfate and nitrate ions, RSO4 , and mass balance following the procedures of Nenes et al. (1998) and Moore et al. (2011). For RSO4 > 2, the sulfate is present
as ammonium sulfate, while for 1 < RSO4 < 2, the sulfate is present as a mixture of ammonium sulfate and ammonium bisulfate. At RSO4 < 1, the sulfate is present as a mixture
of ammonium bisulfate and sulfuric acid. Mass fractions of each constituent are converted
into volume fractions (required by Equation 26) using the known densities of the inorganic
constituents and assuming an organic density of 1400 kg m−3 (Reid et al., 2005; King et al.,
2007; Moore et al., 2011; Kuwata et al., 2012). Apart from εorg , Equation 26 also requires
knowledge of the κ of the organic component, κorg . A number of approaches are tested to
estimate and understand the evolution of κorg (Section 3.4.3 and Section 3.4.4).
45
Alternatively, the bulk aerosol κ can be inferred from measurements of CCN activity
and particle size distributions (e.g., Jurányi et al., 2010; Moore et al., 2011). Equation 25
allows a direct inference of the volume averaged particle κ, provided the supersaturation and
dry particle size are known, and assuming the particle chemical composition is internally
mixed. First, the critical diameter for activation, Dpc , must be determined by integrating
the particle size distribution, starting from the largest size bin, until the concentration
matches the measured CCN concentration at a given supersaturation, using Equation 27:
∫
∞
NCCN =
nCN dDp
(27)
Dpc
where NCCN is the measured CCN number concentration and nCN is the particle size
distribution function from the SMPS and UHSAS merged distributions. The Dpc can then
be substituted into Equation 25 to determine κ, at a set instrument supersaturation.
3.3.2
Supersaturation Depletion Correction
Lathem and Nenes (2011) show that the calibrated CCNC supersaturation can be considerably depleted when the concentration of CCN is high (> 5×103 cm−3 ). When not accounted
for, depletion effects can introduce significant biases in CCN closure, inferences of particle
hygroscopicity, and droplet activation kinetics (Raatikainen et al., 2012). We correct for
such depletion biases following the procedures of Lathem and Nenes (2011) and the updated
model of Raatikainen et al. (2012). The experiments of Lathem and Nenes (2011) were performed at ambient pressure while the ARCTAS data was collected at 450 hPa; therefore,
additional high CCN concentration calibration experiments were conducted at 450 hPa to
parameterize the influence of increasing CCN concentration on the calibrated instrument
supersaturation at lower pressure. The experimental procedures were identical to Lathem
and Nenes (2011), with the addition of a pressure control system to lower and control the
CCN pressure at 450 hPa. Polydisperse ammonium sulfate particles with total concentration varying between 101 - 105 cm−3 were passed through the CCN instrument to determine
the impact on the calibrated supersaturation and droplet size. At each concentration level,
the Dpc was calculated from the measured CCN and dry particle size distribution, and used
46
in Equation 25 with a prescribed κ for ammonium sulfate of 0.6 to determine the effective
instrument supersaturation, s. The lowest CCN concentration levels (< 102 cm−3 ) were
used to characterize the reference calibrated supersaturation, s0 , at a given set of operation
parameters with no depletion effects. The CCN data and dry particle size distributions were
also used in a fully-coupled CCN instrument model (Raatikainen et al., 2012) to simulate
the expected changes in supersaturation and droplet size, which were in agreement with
the experimental observations. The laboratory experiments and model identified a linear
decrease in supersaturation for increasing CCN, which was parameterized for the ARCTAS
data set (450 hPa, correlation coefficient of 0.9) as:
s
= 1 − 1.61 × 10−5 NCCN
s0
(28)
where s is the instrument supersaturation when the concentration of CCN, NCCN , is high;
and s0 is the supersaturation for NCCN approaches zero. Equation 28 was then used to
correct the calibrated supersaturation values (which are equivalent to s0 ) for supersaturation
depletion. For example, concentrations of CN and CCN routinely exceeded 1.5 × 104 cm−3
during the sampling of fresh biomass burning plumes (Figure 10), resulting in a
s
s0
≤ 0.75,
or a supersaturation depletion of ≥25%.
Small changes in the supersaturation can significantly bias the CCN inferred κ, due to
the dependence of sc on the product κDp3 in Equation 25, leading to strongly increasing κ
as s decreases for the same Dp . The relative change in κ arising from a given change in
supersaturation,
s
s0
, can be represented by:
[
κ − κ0
∆κ
s
=
=1−
κ
κ
s0
]2
(29)
where κ0 is the κ inferred assuming s0 represents the instrument supersaturation. Following
Equation 29, if
s
s0
≈ 0.75, then
∆κ
κ
≈ 0.44, which indicates the κ0 at the original uncorrected
calibrated s0 would be underpredicted by 44%.
47
3.3.3
Air Mass Source Type Identification
The nine research flights for ARCTAS-B were classified into three primary aerosol types,
representing the dominant sources of pollution into the Arctic environment. Flight tracks,
color coded by source type, are shown in Figure 8. Median size distributions and chemical
composition are shown in Figure 9, while median concentration and activation efficiency
are shown in Figure 10. A numerical summary of the physical and chemical properties of
each source type, useful for model inputs, is provided in Table 2. A detailed analysis of all
the data follows.
Latitude
80
70
60
50
-140
-120
-100
-80
Longitude
-60
-40
Figure 8: Aircraft flight trajectories for the research flights of ARCTAS-B on the NASA
DC-8, colored by air mass source type. Red colors denote biomass burning, green colors
denote boreal forest background, blue colors denote Arctic background, and purple colors
denote anthropogenic industrial pollution.
48
49
σg1
1.55 ± 0.02
2.16 ± 0.08
2.05 ± 0.04
1.37 ± 0.04
1.45 ± 0.01
Dp1
146 ± 2
48 ± 1
84 ± 2
31 ± 1
52 ± 1
Fresh BB
Aged BB
Arctic
Boreal Forest
Industrial Pollution
Air Mass Type
154 ± 2
107 ± 3
-
-
-
Dp2
2.19 ± 1.3
1.75 ± 0.04
-
-
-
σg2
84 ± 3
94 ± 2
66 ± 22
78 ± 14
93 ± 4
Total OC
95 ± 10
91 ± 22
87 ± 25
80 ± 16
57 ± 19
WSOM Fraction
0.08 ± 0.01
0.17 ± 0.14
0.32 ± 0.21
0.18 ± 0.10
0.18 ± 0.16
κ Inferred
CCN/CN) are reported at 0.55% instrument supersaturation. Biomass burning is abbreviated as BB.
0.22 ± 0.02
0.16 ± 0.03
0.34 ± 0.17
0.24 ± 0.10
0.11 ± 0.04
κ Calculated
2229
651
514
709
7832
CN
341
319
247
217
7778
CCN
WSOM composition (Section 3.3.1). Median CN and CCN concentrations are reported in cm−3 at STP. CCN and Activation Ratio (AR,
(Section 3.2.3). Inferred particle hygroscopicity, κ, is from measured CCN and size distributions while calculated κ is from AMS and
soluble Organic Matter (WSOM) fraction of the OC are percentages calculated from total HR-ToF-AMS and PILS-WSOC mass loadings
(σg ) are from lognormal fits to the merged UHSAS and SMPS distributions. Total volume of Organic Carbon (OC) and the Water-
Table 2: Summary of aerosol microphysical and chemical properties for each source type. Median size (Dp , nm) and standard deviation
0.15
0.55
0.52
0.35
0.89
AR
3.3.3.1
Fresh and Aged Biomass Burning
Following Hecobian et al. (2011), Hornbrook et al. (2011), Moore et al. (2011), and Kondo
et al. (2011), biomass burning plume air masses were identified by regions where both of
the time averaged CO and CH3 CN concentrations were greater than 175 ppbv and 0.2
ppbv, respectively. Biomass burning was further separated into fresh and aged, based on
sampling location and aerosol loadings. All fresh biomass burning emissions were sampled in
the region of active fires in Canada within the boundary layer, while aged biomass burning
was sampled over the high Arctic, indicating long-range transport from Siberia (Fuelberg
et al., 2010). Cubison et al. (2011) characterizes the increase in O:C with aging for the fire
plumes, and confirms that the fresh plumes aged substantially in several hours of daytime
transport, and that the long-range transported plumes were substantially more aged than
the plumes originating in Canada. As reported by Singh et al. (2010), the observed physical
and chemical properties of the fire plumes were similar, despite variations in burn locations,
fuel types, and meteorological conditions, as is discussed further in Section 3.4.2.
3.3.3.2
Boreal Forest and Arctic Background
Air masses of the clean background boreal forest and Arctic were characterized by both
time averaged CO and CH3 CN concentrations being less than 170 ppbv and 0.1 ppbv,
respectively (Moore et al., 2011). The background data was further separated into boreal
forest and Arctic backgrounds according to latitude of the sample and underlying vegetation.
Flights over forested regions South of 66◦ N were classified as boreal forest and flights over
ice, sea-ice or open ocean North of 66◦ N were classified as Arctic background.
3.3.3.3
Anthropogenic/Industrial Aerosol
Limited measurements of anthropogenic aerosol were intercepted during ARCTAS-B, with
the most notable sample occurring during Flight 23 in the vicinity of the Canadian oil sands
operations at Fort McMurray (56.7◦ N, 111.4◦ W). An up to threefold increase in CN particle
concentration (against background levels) was observed (Figure 10). Significant increases
in SO2 , CO, and NOx were also observed above background levels (Simpson et al., 2010).
50
5
Fresh BB
5
5 2
11
4
40
dN/dlogDp
dN/dlogDp
4
3
10
53
2
10
10
3
10
2
10
100 1000
Dp [nm]
100 1000
Dp [nm]
5
10
Arc!c Background
2
5
9
Boreal Forest
1 4 1
8
10
19
dN/dlogDp
4
10
3
67
10
1
1
10
dN/dlogDp
1
5 4
12
10
13
10
2
57
10
4
10
3
10
2
10
1
86
1
10
10
100 1000
Dp [nm]
5
10
dN/dlogDp
Aged BB
10
10
4
100 1000
Dp [nm]
WIOM
Industrial Pollu!on
10
WSOM
13 2 4
10
Ammonium Bisulfate
3
Ammonium Sulfate
10
2
10
Sulfuric Acid
81
Ammonium Nitrate
1
10
100 1000
Dp [nm]
Figure 9: Average dry particle size distributions (dN/dlogDp) for each air mass source
type and average aerosol volume fractions (%) calculated from the HR-TOF-AMS and
PILS-WSOC mass loadings (inset). Water-soluble organic matter (WSOM) is calculated
from PIL-WSOC (Section 3.2.3), and water-insoluble organic matter (WIOM) is taken as
the difference between WSOM and total organic carbon measured by the HR-TOF-AMS.
Error bars on size distributions denote one standard deviation from the mean.
3.4
3.4.1
Results and Discussion
Aerosol Physical and Chemical Properties by Source Type
The physical and chemical properties of each Arctic source type are summarized below, as
well as in Table 2. Median CN number densities are reported from a CPC-3010, median
51
CCN number densities are reported at 0.55% (±0.05%) supersaturation from the CCNC,
size distributions are reported from the SMPS and UHSAS merged distributions, and volume averaged chemical composition are provided by the HR-ToF-AMS and PILS-WSOC.
A supersaturation of 0.55% was chosen to present CCN data due to greater data availability across all source types and to provide an upper limit for the CCN concentration and
activation ratios expected in these regions.
Figure 11 shows plots of CN and CCN concentrations as a function of latitude for all
flights, colored by the aerosol source type. It is clear that the highest concentrations occur
within 50-60◦ N latitude and that biomass burning and industrial emissions can significantly
enhance the concentrations in this region by 1-2 orders of magnitude compared to background levels. It is also important to note the wide range of variability in CN and CCN
above 70◦ N, where concentrations varied from a few as 10 cm−3 to greater than 103 cm−3 ,
which is a level of variability that could have significant impacts in the Arctic environment,
where concentrations are generally less than 100 cm−3 (Moore et al., 2012).
3.4.1.1
Fresh and Aged Biomass Burning
As shown in Figure 10, fresh biomass burning aerosol sampled in Canada had median CN
number densities of 7832 cm−3 . Median CCN concentrations and activation ratio (at 0.55%
supersaturation) were 7778 cm−3 and 0.89, respectively. Aged biomass burning emissions
were sampled over the high Arctic (83◦ N, indicating long range transport from Siberia)
and had median CN and CCN number densities of 709 cm−3 and 217 cm−3 , respectively,
resulting in a median activated fraction of 0.35. The lower activated fraction of the aged
biomass burning is mostly attributable to a smaller size mode, likely as a result of wet
depositional losses and heterogeneous chemical processing with transport (Brock et al.,
2004; Dunlea et al., 2009). The median size for fresh biomass burning aerosol was 146±2
nm, whereas it was only 48±1 nm for the aged biomass burning (Figure 9, Table 2). The
median size of the aged biomass burning aerosol sampled over the high Arctic is much
smaller than that observed by Moore et al. (2011) over Alaska, where a mean diameter of
189 nm was observed.
52
(a)
(b)
4
4
10
10
7778
6
5
4
-3
CCN Concentration (cm )
-3
CN Concentration (cm )
7832
6
5
4
3
2229
2
3
10
709
6
5
4
651
514
3
2
3
10
6
5
4
3
319
341
3
2
247
2
2
217
2
10
Fresh BB
Aged BB
Boreal Forest
Arctic
1.0
10
Industrial
Fresh BB
Aged BB
Boreal Forest
Arctic
Industrial
(c)
0.89
CCN/CN Activation Ratio
0.8
0.6
0.55
0.52
0.4
0.35
0.2
0.15
0.0
Fresh BB
Aged BB
Boreal Forest
Arctic
Industrial
Figure 10: Aerosol (CN) particle concentration (diameter > 10 nm) (a), CCN particle
concentration at 0.55% supersaturation (b) and CCN/CN activation ratios at 0.55% supersaturation (c). Horizontal bars and corresponding values in the boxes represent median
concentrations, the box defines the first and third quartiles, and the bars represent the 10th
and 90th percentiles. Biomass burning is abbreviated BB.
Organics dominated the non-refractory aerosol volume in both fresh and aged biomass
burning, representing, on average, 93% and 78%, respectively (Figure 9). Of the organic
component, a significant fraction of the OC was WSOM (57% for fresh biomass burning
and 80% for aged biomass burning), which is in agreement with existing observations of
biomass burning OC and WSOM (Fu et al., 2009; Kondo et al., 2011). WSOM significantly
increased for aged biomass burning, which is consistent with expectations of organic species
becoming more polar and soluble with oxidative aging in the atmosphere (Ng et al., 2011;
Duplissy et al., 2011) and the observed increase of O:C and reduction of primary species
53
with aging for these biomass burning plumes (Cubison et al., 2011). Kondo et al. (2011)
also found that a large majority of the sampled biomass burning particles did not contain
significant amounts of black carbon (average volume fraction 2-4%), suggesting the biomass
burning particles were well aged. Ammonium sulfate, ammonium bisulfate, ammonium
nitrate, and sulfuric acid represent less than 10% of total volume; however, these species
tended to increase in the aged biomass burning up to 22% total volume (Figure 9). Similar
compositions for aged biomass burning were observed in the Alaskan Arctic by Moore et al.
(2011), and for fresh biomass burning over Wyoming (Pratt et al., 2011), with organics
representing 70-80% of aerosol volume and mass.
3.4.1.2
Boreal Forest and Arctic Background
The median number density of CN and CCN in both the boreal forest and Arctic background
environments were generally low and less variable. For the boreal forest, median CN number
densities were 651 cm−3 and CCN number densities were 319 cm−3 , resulting in a median
activated fraction of 0.55. For the Arctic background, median CN number densities were 514
cm−3 and CCN number densities were 247 cm−3 , resulting in a median activated fraction of
0.52. The particle size distributions for both background environments were bimodal, with
peaks in both the accumulation and Aitken modes (Figure 9, Table 2), as also observed
by Moore et al. (2011) and Brock et al. (2011) for the Alaskan Arctic during 2008. Since
these areas are devoid of primary combustion particles and the lifetime of Aitken mode
particles is short, these observations suggest the importance of new particle formation in
these environments.
For the boreal forest, organics constituted 94% of the aerosol volume, with 91% of the OC
being WSOM. Sulfuric acid, ammonium bisulfate and ammonium nitrate represented 5%,
2% and 1% of the total aerosol volume, respectively. For the Arctic background, organics
were less dominant, constituting only 66% of the total volume, with 87% of the OC being
WSOM. The remaining volume was divided between 18% sulfuric acid, 13% ammonium
bisulfate, and 2% ammonium nitrate. The Arctic aerosol organic component measured in
this study was higher than the 36% reported by Martin et al. (2011) over the summertime
54
high Arctic pack ice and 51% measured by Moore et al. (2011) in the springtime Alaskan
Arctic, but had overall high variability (66±22%, Table 2). The larger organic fraction
during the summer as compared to the spring is likely due to the increased importance of
biogenic SOA and biomass burning sources in the summer.
3.4.1.3
Anthropogenic/Industrial Aerosol
The median CN number density for the industrial aerosol plume in the vicinity of Fort
McMurray was 2229 cm−3 and the median CCN number density was 341 cm−3 , resulting in
a median activated fraction of only 0.15. This low activated fraction suggests the particles
are either too small or insufficiently hygroscopic to activate. The particle size distribution
suggests the former is the likely reason, as the distribution was bimodal, with the dominant
mode at 52 nm. Another mode was present at 154 nm, indicating external mixing of the
industrial pollution with the boreal forest background. The anthropogenic particles are too
small to be efficient CCN near the source, but these industrial emissions could potentially be
a large source of CCN further downwind as the particles age and their mean size increases
as the SO2 from the plumes is converted to sulfate.
AMS data indicated 84% of the aerosol volume of the particles in the industrial plumes
was organic, with 95% of this OC being WSOM. However, this chemical composition is
more likely representative of the larger, boreal forest background mode, since the mass of
the accumulation mode is estimated to be 10 times larger than that of the small mode, and
possibly also since the AMS has limited detection efficiency for particles less than 50 nm
(Jimenez et al., 2003). In fact, the measured chemical composition is in excellent agreement
with measurements from the boreal forest background (Figure 9, Table 2). This confirms
that the smaller, industrial mode represented a very small fraction of the submicron mass,
and that the larger boreal forest background aerosol dominated the bulk composition. When
using the measured size distributions and chemical composition to predict CCN, significant
overprediction biases occurred on the order of 150% (Section 3.4.3, Table 3). This confirms
the industrial emissions are externally mixing with the aerosol background and altering the
aerosol composition beyond its natural state.
55
90
90
Biomass burning
Background
Industrial pollution
(a)
80
Latitude
80
Latitude
Biomass burning
Background
Industrial pollution
(b)
70
70
60
60
50
50
10
1
2
3
4
10
10
10
CN Concentration
10
1
2
3
10
10
10
CCN Concentration
4
Figure 11: CN (a) and CCN (b) number concentration (cm−3 STP) as a function of latitude,
colored by source type, for all ARCTAS-B research flights.
3.4.2
Particle Hygroscopicity, κ
In Figure 12, inferred κ from measurements of CCN activity and particle size distributions
are compared with κ calculated directly from HR-ToF-AMS and PILS-WSOC composition,
using Equation 25 and Equation 26 and assuming κorg = 0.12εW SOM . Despite differences
in the physical and chemical properties of the aerosol from the different environments, the
inferred κ from CCN measurements was fairly uniform with κ = 0.1 − 0.32, consistent
with organics dominating particle water uptake and also consistent with the κ = 0.1 − 0.3
reported for various air mass types sampled by Moore et al. (2011) in the spring Alaskan
Arctic. However, the variability in the inferred and calculated κ for the Arctic background
was large, with κ = 0.32 ± 0.21. This variability is mostly attributed to large variations in
the volume fractions of the inorganic species, which varied from 6% to 70%. Because the
inorganic species have much higher κ (κ = 0.61 − 0.9) compared to the organics (κ = 0.12)
(Petters and Kreidenweis, 2007), large variations in inorganic volume fraction is expected
to have large impacts on aerosol κ. Some of the variability could also arise from the
assumptions of size-independent composition and internally mixed aerosol. High variability
in Arctic aerosol κ has also been observed in several previous studies, such as Martin et al.
(2011) who inferred a total κ for the high Arctic of 0.33±0.13 and Kammermann et al.
(2010) who measured at the remote subarctic Stordalen mire in Sweden and found κ to
56
vary between 0.07 - 0.21.
Fresh BB
Aged BB
Boreal forest
Arctic background
Industrial pollution
0.6
Inferred κ
0.5
0.4
0.3
0.2
0.1
Aged BB (Engelhart et al., 2012)
Boreal Forest (Sihto et al., 2011)
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Calculated κ
Figure 12: κ inferred from CCN measurements vs. κ calculated from HR-ToF-AMS and
PILS-WSOC chemical composition for each source type assuming that composition is sizeindependent and internally mixed. Error bars denote one standard deviation from the
mean. Dashed lines indicate reported literature κ values for aged biomass burning (BB)
(Engelhart et al., 2012) and boreal forest aerosol (Sihto et al., 2011). Shaded area indicates
the standard deviation in the reported literature values of κ.
Surprisingly, the inferred κ was invariant between aged biomass burning plumes transported from Siberia and fresh biomass burning smoke sampled directly over active fires in
Canada, with both having CCN inferred κ ≈ 0.18 ± 0.13. A κ of ∼0.2 for biomass burning suggests that water uptake for the particles is determined by soluble organics, which
dominate the aerosol volume (Section 3.4.1). Recent laboratory studies suggest that very
fresh biomass burning particles are less hygroscopic (have a lower κ) than aged biomass
57
burning, but the κ can be highly variable (κ = 0.02 − 0.8) based on the fuel type, inorganic
to organic fraction, and burning conditions (Petters et al., 2009; Carrico et al., 2010; Engelhart et al., 2012). Engelhart et al. (2012) also found evidence for rapid evolution of the
κ of primary biomass burning aerosol by performing smog chamber studies of smoke from
fuels representative of North American wildfires. They found the κ for the primary biomass
burning aerosol to be highly variable between 0.06 and 0.6 depending on fuel type, but all
samples generally converged to a value of 0.2±0.1 after just a few hours of photochemical
aging, which is in excellent agreement with our ambient observations. This laboratory and
ambient data, together with in-situ chemical evolution data (Cubison et al., 2011), strongly
suggests that atmospheric processing can quickly alter the diverse aerosol properties, such
that they converge to a similar κ and CCN activity in the atmosphere. Such rapid aging and
evolution of aerosol could be significant for modeling studies, as this dramatically reduces
the complexity and variability of κ and aging processes required for model representation.
The inferred κ do not always agree with the κ calculated based on HR-ToF-AMS and
PILS-WSOC bulk chemical composition, assuming the κorg = 0.12εW SOM . The inferred κ
for the fresh biomass burning and boreal forest background aerosol are slightly higher than
the calculated values, but generally within the level of variability, and also in agreement with
other published studies (Sihto et al., 2011; Engelhart et al., 2012). Size-varying chemical
composition or non-CCN active particles could also result in discrepancies between the
inferred and calculated κ, since both approaches used in Figure 12 assume an internally
mixed, bulk composition (e.g., Cerully et al., 2011). This type of bias was only observed
for the industrial pollution case, where the aerosol was significantly externally mixed and
large discrepancies between the different κ was observed (Figure 12).
The inferred κ for the industrial pollution (κ = 0.08) is much lower than calculated (κ
= 0.22), with much less overall variability. The primary reason for this difference is that the
bulk composition measurements from the HR-ToF-AMS and PILS-WOSC are dominated
by the larger particle sizes, where most of the aerosol mass resides (refer to Section 3.4.1.3).
Size dependent composition or non-CCN active modes could also reduce the CCN inferred
κ, which would not be represented by the bulk composition derived κ. For example, for
58
the industrial pollution, the chemical composition suggests a predominately soluble organic
composition (81% by volume, Figure 9) and thus a high calculated κ, whereas the CCN
activity indicates a much lower κ, likely attributable to the industrial mode at ∼52 nm being
weakly to non-hygroscopic. As discussed further in Section 3.4.3, the industrial mode κ
needs to be ∼0.06 for CCN closure to be obtained. Size resolved HR-ToF-AMS composition
in such environments is desired, but was unavailable during this flight period, due to the
use of a fast “plume capture” mode that does not include size-resolved measurements.
3.4.3
CCN Closure
Predictions of CCN concentrations in climate models require simplifying assumptions, particularly in the description of chemical composition and aerosol mixing state, and the resulting uncertainty in indirect forcing from their application needs to be quantified (e.g.,
Sotiropoulou et al., 2007; Moore et al., 2012). To address this need for the Arctic environment, CCN closure studies were performed for each of the regional aerosol types using
Köhler theory, testing assumptions regarding aerosol mixing state and hygroscopicity of the
organics. In this study, we test the applicability of bulk (size-independent) composition for
internally or externally mixed aerosol populations, which represent some of the simplest, yet
robust and efficient, descriptions of aerosol CCN activity for global models. Additionally,
the organic component of the aerosol was treated as either completely insoluble (κorg =
0) or the measured WSOC volume fraction, εW SOC , was converted to εW SOM and used
to parameterize the organic hygroscopicity as κorg = 0.12εW SOM . This approach assumes
that only the WSOM fraction of the organic is hygroscopic, and the κ of the WSOM is
assigned a value of 0.12, determined by assuming an organic density of 1400 kg m−3 , an
organic molar mass of 0.2 kg mol−1 , and a van’t Hoff factor of unity (Engelhart et al., 2008;
Asa-Awuku et al., 2010; Engelhart et al., 2011; Moore et al., 2011). A similar approach was
used by Bougiatioti et al. (2009), where excellent CCN closure was achieved in the East
Mediterranean by using measured WSOC and using κorg = 0.16εW SOC .
Three different closure scenarios were tested, including: 1) internally mixed aerosol
with insoluble organics, 2) internally mixed aerosol with soluble organics, and, 3) externally
59
mixed aerosol, where each particle contains either only organic species (of which a fraction is
soluble) or only inorganic species. For each assumption of aerosol mixing state and chemical
composition, the particle κ was calculated and used with Equation 25 to determine the
critical dry particle activation diameter, Dpc , at a given supersaturation. The number of
CCN was then derived from the Dpc by integrating the measured size distribution from
Dpc to the largest measured size, using Equation 27. The predicted CCN for each closure
scenario was then compared to measurements from the CCNC. The relative error between
the measured and predicted CCN concentrations was used to assess the performance of each
closure scenario and determine which simplifications best matched observations.
A summary of the closure errors for each closure scenario and source type is shown
in Table 3. Treating the organics as insoluble resulted in significant underpredictions in
CCN number, especially for the fresh biomass burning (-44±16%) and boreal forest background (-45±8%) environments. This highlights the importance of the WSOM fraction in
correctly determining the CCN activity of the Arctic aerosol, as has also been shown by
Asa-Awuku et al. (2011) for urban pollution in Texas and by Bougiatioti et al. (2009, 2011)
for well-aged aerosol sampled in Crete, Greece. Including the measured WSOM (with hygroscopicity represented as κorg = 0.12εW SOM ) improved closure for an internally mixed
aerosol assumption, but in some cases also led to a general overprediction of CCN (8-30%,
on average). These overprediction errors decreased to less than 25% for all environments,
except industrial pollution, when the aerosol was assumed to be externally mixed (Figure 13). These findings are consistent with Moore et al. (2012), who compare reported
closure errors from 36 different published CCN studies around the world and find closure
errors to generally improve for an assumption of externally mixed aerosol with soluble organics, even though data were not always present to determine the aerosol mixing state.
It is also important to note the extremely high observed CCN concentrations (up to 104
cm−3 ) for fresh biomass burning (Figure 13). At these high concentration levels, supersaturation depletion becomes significant (> 25%), and must be accounted to ensure unbiased
CCN measurements. Neglecting the supersaturation depletion in the biomass burning environments biases the reported CCN prediction errors by an additional 10-20%, highlighting
60
the importance to accurately quantify and account for this measurement artifact in high
concentration environments.
Table 3: Summary of CCN closure errors (%) for the three closure scenarios tested: 1) Internally mixed aerosol with hygroscopicity of organics parameterized by κorg = 0.12εW SOM ,
2) Internally mixed aerosol with organics assumed completely insoluble with κorg = 0
and 3) Externally mixed aerosol with hygroscopicity of organics parameterized by κorg =
0.12εW SOM . Supersaturation and Activation Ratio (CCN/CN) are denoted as s and AR.
Air Mass Type
Internal Mixture
WSOM OC Insoluble
External Mixture
WSOM
0.57±0.17
0.87±0.15
0.90±0.15
25±38
-4±24
-17±11
-35±18
-46±19
-51±12
12±30
-14±26
-24±12
0.45±0.14
0.38±0.15
30±19
16±11
-20±9
-17±11
20±15
2±7
0.32±0.14
0.46±0.18
0.60±0.17
31±11
19±20
-2±16
-41±9
-45±6
-48±9
24±15
13±17
-10±17
0.33±0.15
0.51±0.17
16±23
8±18
-19±17
-24±15
0±25
-2±15
0.08±0.04
0.15±0.02
195±67
158±57
14±7
18±19
153±37
105±39
AR
Fresh Biomass Burning
s = 0.26 ± 0.05 (%)
s = 0.37 ± 0.05 (%)
s = 0.48 ± 0.05 (%)
Aged Biomass Burning
s = 0.23 ± 0.05 (%)
s = 0.53 ± 0.05 (%)
Boreal Forest
s = 0.34 ± 0.05 (%)
s = 0.42 ± 0.05 (%)
s = 0.55 ± 0.05 (%)
Arctic
s = 0.42 ± 0.05 (%)
s = 0.57 ± 0.05 (%)
Industrial Pollution
s = 0.42 ± 0.05 (%)
s = 0.57 ± 0.05 (%)
CCN closure for the Arctic background was within -1 ± 15% for the external aerosol
case (Table 3), which is in agreement with the findings of Moore et al. (2011) for the Alaskan
Arctic in spring; however, an externally mixed aerosol may not always be representative of
the Arctic background in summer. Martin et al. (2011) found an internally mixed assumption to provide the best CCN closure for the Arctic in summer, while Kammermann et al.
(2010) achieved closure to within 11% regardless of the assumed mixing state. The closure
error for fresh biomass burning and boreal forest also varied with supersaturation, tending
towards larger underpredictions (up to 24%) at higher supersaturation. This is suggestive
of a size-dependent chemical composition, where the smaller particles (which activate at
61
higher supersaturation) may have a higher soluble fraction and/or higher κ (e.g., Agarwal
et al., 2010; Bougiatioti et al., 2011). The observed CCN underpredictions at high supersaturation could be reduced if the κ for the biomass burning WSOM at smaller sizes is higher
than the prescribed bulk value of 0.12. Previous studies of water-soluble extracts from
biomass burning and secondary organic aerosol support a higher κ for the water-soluble
fraction, such that κorg = (0.25 ± 0.05)εorg (Asa-Awuku et al., 2008; Padró et al., 2010;
Engelhart et al., 2011; Asa-Awuku et al., 2011). While a higher κ of 0.25 for the WSOM
might help explain the CCN underpredictions for biomass burning at high supersaturation
(Table 3), using a bulk κ of 0.25 for the rest of the data set would lead to more significant
overpredictions. Furthermore, Moore et al. (2011) found excellent CCN closure for a κorg
= 0.11 in the Alaskan Arctic, which suggests the organic hygroscopicity in the Arctic might
be lower. From these examples, it is important to note that a bulk, size-averaged κ for the
organic or WSOM is not always suitable for every environment.
It is also important to note that even an external mixture assumption is not sufficient
to correctly predict the CCN concentrations for the industrial pollution case, with overprediction errors of 105-153%. In this case, the bulk composition measurements are most likely
inconsistent with the actual size varying aerosol composition, due to the small mass fraction
represented by the Aitken mode. Assuming the HR-ToF-AMS bulk composition applies to
both modes of the measured size distribution (52 nm and 154 nm, respectively) is incorrect,
since this assumption implies that a large fraction of the hydrocarbon-like aerosols and BC
in the fresh industrial plumes are hygroscopic and can serve as CCN, which leads to the
large overprediction errors. Actually, the κ of the smaller industrial mode at 52 nm is much
less and would have to be close to 0.06 in order to accurately predict CCN concentrations
in this environment. These results are in agreement with Wang et al. (2010) who find that
mixing state strongly influences the calculated CCN only when weak or non-hygroscopic
species (such as primary organic aerosol or black carbon) contribute a substantial fraction
of the aerosol volume, as is the case for these industrial emissions.
These results indicate, that for the majority of air masses representative of the Arctic,
CCN can be accurately predicted by assuming bulk aerosol chemical composition and using
62
4
-3
CCN Predicted [cm ]
10
Fresh BB
Aged BB
Arctic Background
Boreal Forest Background
Industrial Pollution
10
10
3
2
10
2
3
10
10
-3
CCN Observed [cm ]
4
Figure 13: CCN closure plot for each source type assuming externally mixed aerosol with
soluble organics. Solid line represents the 1:1 agreement, while the dashed lines represent
±50% error bars.
an external mixture of pure inorganic and organic species, with the organic hygroscopicity
defined by the WSOM. The only exceptions are in the vicinity of strong point sources of
industrial pollution, where a separate κ for each mode of the size distribution is required,
and for fresh biomass burning aerosol, where smaller sizes may have higher WSOM κ.
3.4.4
Aging and Evolution of Biomass Burning Aerosol
Condensation of secondary species (e.g., sulfate, nitrate, organics) onto particles is an important, and potentially dominant, pathway for the transformation from primary biomass
burning particles into more hygroscopic ones. Although SOA formation in these biomass
burning plumes did not lead to a net increase in organic mass (Cubison et al., 2011; Hecobian et al., 2011), net aging was observed in a few hours after emission, which likely involved
63
gas-phase oxidation of semivolatile species (Cubison et al., 2011), and possibly also redistribution of primary semivolatile species across different particles (Marcolli et al., 2004).
Organic species are expected to become more polar and soluble with oxidative aging in the
atmosphere (Ng et al., 2011; Duplissy et al., 2011), such that a correlation between κorg
and O:C exists (Jimenez et al., 2009; Lambe et al., 2011).
0.30
Jimenez et al. (2009)
Chang et al. (2010)
ARCPAC, Moore et al. (2011)
ARCTAS Flight 18
0.25
κorg
0.20
0.15
0.10
0.05
0.00
0.2
0.4
0.6
O:C
0.8
1.0
Figure 14: Inferred κorg vs. HR-ToF-AMS derived O:C ratios. Markers denote observed
relationships published by Chang et al. (2010) in rural Egbert, Ontario, Canada (green)
and Jimenez et al. (2009) for a variety of ambient and laboratory organic aerosols (blue).
Shaded area represents the reported standard deviation from the mean. Black triangle
represents the average values observed during the 2008 ARCPAC campaign for biomass
burning influenced air masses (Moore et al., 2011). Red circles indicate average values
observed for biomass burning plumes sampled during ARCTAS flight 18.
Figure 14 shows the relationship between the inferred κorg from CCN measurements and
the HR-ToF-AMS derived O:C ratio for the direct sampling of fresh biomass burning aerosol
for ARCTAS Flight 18 in the vicinity of Lake MacKay in Saskatchewan, Canada. κorg does
64
not strongly increase with O:C as expected; however, the variability in observed O:C is
not sufficiently large to observe significant changes in κorg . The range of O:C observed,
0.5 for fresh biomass burning emissions and 0.7 for more aged ones (Cubison et al., 2011),
confirms the aerosol is composed of oxidized organics (Aiken et al., 2008); however, the
inferred values of κorg (0.1 ± 0.05) are mostly lower than predicted by the Chang et al.
(2010) parameterization and observations reported by Jimenez et al. (2009) (shaded areas
of Figure 14). Evidence for fast physical aging of the ARCTAS biomass burning black carbon
aerosol was also observed by Kondo et al. (2011), who observed black carbon particles to
be thickly coated with secondary organic species 1-2 hr after emission. Any further aging
or increase in aerosol oxidation for these fresh biomass burning plumes was not observed
during the timescale of these aircraft measurements; however, additional aging was observed
for the transported Asian plumes (Cubison et al., 2011). Similarly low κ values at high O:C
ratios and organic volume fraction (> 80%) were also observed by Moore et al. (2011) in
the Alaskan Arctic, with total aerosol κ approaching a limit of 0.15 ± 0.05 for an O:C of
1.0 (Figure 14).
3.5
Conclusions
Measurements of aerosol properties, chemical composition, and CCN for nine research flights
during the ARCTAS-B campaign captured the main sources of aerosol in the summertime
Arctic, including samples from fresh and aged biomass burning, boreal forest background,
Arctic background, and industrial emissions. The observations show significant variability in
total CN and CCN concentrations, with biomass burning and industrial emissions increasing
particle concentrations by 1-2 orders of magnitude against the pristine Arctic background. A
significant fraction of the aerosol in all environments (66 - 94% by volume) is made of organic
species, with the highest organic loads present in the boreal forest background and biomass
burning samples. Of this organic carbon, greater than 57% is water-soluble, contributing
significantly to particle hygroscopicity and CCN activity. The Arctic background had the
highest and most variable inferred hygroscopicity parameter, κ, of 0.32 ± 0.21, consistent
with a lower (and more variable) observed organic aerosol fraction. This high variability
65
suggests that the Arctic κ may exhibit large regional and seasonal variability arising from
variations in transport and organic loadings, and this variability could be significant for
understanding and parameterizing the CCN activity of Arctic aerosol. Future Arctic field
studies should seek to further quantify the regional and seasonal variability in κ.
The inferred κ for aged and fresh biomass burning were both consistent and representative of aged organics with a κ = 0.18 ± 0.13, consistent with the observations that emissions
of primary aerosol directly over active fires rapidly age in the atmosphere on the timescale
of a few hours (Cubison et al., 2011), converging the diverse primary aerosol properties to
the κ and CCN activity close to those of aged organics. A correlation between κorg and O:C
was not observed for the fresh biomass burning aerosol, suggesting that additional aging
does not increase the hygroscopicity of the organic species, and a limit of κorg of 0.1 ± 0.05
resulting from oxygenation is approached within a few hours after emission. Such rapid aging has also been observed in chamber studies of fresh biomass burning aerosol (Engelhart
et al., 2012) and in other in-situ measurements in the Arctic (Moore et al., 2011; Cubison
et al., 2011), which suggests that this could be a potentially significant finding for reducing
the complexity required for global atmospheric models to simulate atmospheric aging of
biomass burning. Rather than needing to know the hygroscopic properties of aerosol from
individual fires, an understanding of the overall aged particles after a few hours may be all
that is required, as it is the aged smoke that forms the regional and mixed hazes that most
influence climate.
CCN closure was assessed using Köhler theory along with compositional simplifications
in aerosol mixing state and organic hygroscopicity (κorg ) employed by global models. The
best CCN predictions (< 25% relative error, on average, for all environments) were achieved
by assuming a size-average chemical composition, an external mixture of inorganics and organics, and with the hygroscopicity of the organics parameterized by the κ of the WSOM
fraction, where κorg = 0.12εW SOM . Neglecting the water-soluble organics resulted in systematic underpredictions of CCN activity on the order of 30-50%. Therefore, the assumption
that the WSOM component drives hygroscopicity for the entire organic fraction is sufficient
66
for the Arctic environment, with only measurements of the OC and WSOC present in atmospheric particles needed for accurate CCN predictions. The only exception is in the vicinity
of the Canadian oil sands industrial pollution, where a size-dependent chemical composition
and external mixture (represented as a separate κ for each mode of the distribution) was
needed for CCN closure. It is important to characterize and understand the impacts of
these industrial emissions, as they are expected to increase significantly in the future with
increased Arctic oil exploration and shipping (McLinden et al., 2012).
These measurements are for a fairly large geographical area (50-85◦ N, 40-130◦ W) of the
Arctic, but for limited temporal sampling in the summertime (26 June - 14 July, 2008).
Aerosol sources in the Arctic are known to have a seasonal cycle, where changes in longrange transport, solar irradiance, and ambient temperature can significantly influence the
chemical composition and hygroscopicity of the aerosol. However, these results are consistent with measurements conducted during the Alaskan Arctic during the spring (Moore
et al., 2011), which suggest the overall hygroscopicity ranges between κ = 0.1 - 0.32. The
variability in Arctic κ and CCN is likely to be driven by variations in the water-soluble
organic content. Including real-time measurements of the WSOM fraction can significantly
improve CCN predictions, and a simple parameterization of the κorg is all that is required,
which can greatly simplify model inputs and potentially reduce the uncertainty of aerosol
water uptake and cloud radiative forcing in global climate models. A novel correction of
the CCN instrument supersaturation for water vapor depletion resulting from high concentrations of CCN was also applied. Care should be taken to account for supersaturation
depletion in high concentration environments, such as biomass burning plumes, because a
supersaturation depletion of just 30% (arising from CCN concentrations of 104 cm−3 ) can
bias CCN closure prediction errors by an additional 20% and result in underestimating κ
by as much as 50%.
67
CHAPTER IV
HYGROSCOPIC PROPERTIES OF VOLCANIC ASH
Limited observational data exist on the physical interactions between volcanic ash particles
and water vapor; yet it is thought that these interactions can strongly impact the microphysical evolution of ash, with implications for its atmospheric lifetime and transport, as
well as formation of water and ice clouds. In this study, we investigate for the first time, the
hygroscopic properties of ultra-fine volcanic ash (<125 µm diameter) from the eruptions of
Mt. St. Helens in 1980, El Chichón in 1982, Tungurahua in 2006, Chaitén in 2008, Mt.
Redoubt in 2009, and Eyjafjallajökull in 2010. The hygroscopicity of the ash particles is
quantified by their ability to nucleate cloud drops under controlled levels of water vapor
supersaturation. Evidence presented strongly suggests that ash takes up water efficiently
via adsorption and a simple parameterization of ash hygroscopicity is developed for use in
ash plume and atmospheric models.
4.1
Introduction
Explosive volcanic eruptions release large quantities of ash particles and gases into the atmosphere, which can be transported over long distances and have a strong influence on the
environment and climate (Sparks et al., 1997; Schumann et al., 2011). Ash impacts are
largely determined by complex microphysical processes that are challenging to study in-situ
(e.g., Schumann et al., 2011; Hobbs et al., 1981), so most knowledge on plume composition
and size distribution comes from post-eruption analysis of ash deposits. Considerable uncertainties remain on the concentration and size distribution of particles emitted by volcanoes,
as well as their microphysical interactions (Textor et al., 2006; Delmelle et al., 2007).
Ash particles are usually highly angular shards of amorphous glass produced during
3
This chapter published as: Lathem, T.L., Kumar, P., Nenes, A., Dufek, J., Sokolik, I.N., Trail, M.,
and Russell, A. (2011), Hygroscopic Properties of Volcanic Ash, Geoph.Res.Lett., VOL. 38, L11802, 4
c 2011 American Geophysical Union. Reproduced with
PP., doi:10.1029/2011GL047298, 2011.. Copyright ⃝
permission.
68
fragmentation or comminution, with a majority ranging from nm to mm in size (Dufek
and Manga, 2008; Heiken and Wohletz , 1985). Ash composition reflects that of the source
magma, ranging from rhyolitic (high silica content) to basaltic (low silica content). Siliceous
volcanic ash can also contain crystal shards from the primary magma, lithic materials from
the volcanic conduit margins, and salts formed in the eruptive column or entrained remnants
of brines from hydrothermal systems (Delmelle et al., 2007). These salts can be hygroscopic
and increase the ability of ash to take up water vapor, which is the dominant gas emitted
during most explosive volcanic eruptions (Textor et al., 2006). Water vapor concentrations
can increase further from the upward transport of evaporated groundwater or glacial ice
and from the entrainment of moist environmental air (Herzog et al., 1998). If water vapor
concentration is high enough to form liquid coatings on ash, important implications arise for
the aggregation, atmospheric lifetime, and detection ability of the ash (Prata et al., 2001;
Textor et al., 2006). Furthermore, activation of ash into cloud droplets or formation of ice
crystals can notably increase plume temperature and buoyancy through latent heat release
(Herzog et al., 1998), as well as indirectly affect climate by contributing cloud condensation
nuclei (CCN) and ice nuclei (IN) (Seinfeld and Pandis, 2006).
The occurrence of wetted ash aggregates and frozen hydrometeor fallout is observational
proof of ash-water interactions (Sparks et al., 1997; Hobbs et al., 1981), yet limited data
exist to quantify the ability of ash to take up water vapor. Delmelle et al. (2005) performed
high resolution N2 and H2 O adsorption-desorption experiments on six different volcanic
samples. All samples exhibited high specific surface areas, were more reactive towards H2 O
than N2 , and formed a monolayer of H2 O at 0.05 - 20 % relative humidity. This led to the
assumption in many microphysical studies that ash is always covered by a liquid layer (e.g.,
Textor et al., 2006). However, the high temperature and strong competition for water vapor
among the high concentration of ash particles in the plume may deplete the supersaturation,
so that few (if any) particles can have complete coverage of water, especially in the near
vent region. In this study, we comprehensively characterize ash-water interactions over
a wide range of water vapor saturations, using CCN activity measurements of samples
collected from a variety of recent eruptions. We then determine the physics governing the
69
observed hygroscopicity and develop a parameterization for use in plume microphysical and
atmospheric models.
4.2
Experimental Methods
Ground samples of volcanic ash were collected from the eruptions of Mt. St. Helens (1980),
El Chichón (1982), Tungurahua (2006), Chaitén (2008), Mt. Redoubt (2009), and Eyjafjallajökull (2010) and were selected to encompass a wide range of location, composition,
crystallinity and eruptive style. All samples were collected near the volcano with the exception of Mt. Redoubt (∼ 130 km downwind). The ash was collected within two days of
the eruption for Mt. St. Helens, El Chichón, and Mt. Redoubt. Tungurahua, Chaitén, and
Eyjafjallajökull samples were each collected months after the eruption. The influence of
weathering is tested by comparing an original 1980 Mt. St. Helens deposit (collected two
days after the eruption) to a sample collected in 2009. Each ash sample is dry sieved to an
ultra-fine mode (< 125 µm diameter) for subsequent analysis.
Ash hygroscopicity is measured following the methodology of Kumar et al. (2011a) and
Moore et al. (2010). Ash aerosol is generated by placing three grams of dry ash in a 1000 mL
sealed Erlenmeyer flask connected to a Burrell Wrist Action Shaker (Model 75). Filtered air
flows into the shaking flask that generates dry, polydisperse aerosol by mechanically dispersing the ash particles. The dry ash aerosol is sent to a Differential Mobility Analyzer (DMA,
TSI Model 3081) for size classification by electrical mobility (with sheath flow 2.3 L min−1
and aerosol flow 0.45 L min−1 ). The DMA outputs monodisperse aerosol that is then split
and sent to a Condensation Particle Counter (CPC, TSI Model 3010), that measures the
total concentration of particles (CN), and a Droplet Measurement Technologies Continuous
Flow Streamwise Thermal Gradient CCN chamber (DMT CFSTGC) (Roberts and Nenes,
2005) for measuring the concentration of particles that act as cloud condensation nuclei
(CCN) over a range (0.2% to 1.0%) of water vapor supersaturation (SS). SS is controlled
in the DMT CFSTGC by varying the flow rate and temperature gradient applied to its
growth chamber and is calibrated with an ammonium sulfate (AS) salt standard (Moore
et al., 2010).
70
The voltage applied to the DMA for size selection is changed over time, allowing the
sampling of aerosol with diameters between 20 and 850 nm over 180 seconds. Scanning
Mobility CCN Analysis (SMCA, Moore et al., 2010) is then used to invert the data and
determine the activation efficiency (CCN/CN ratio) as a function of dry particle size. The
effects of particle multiple charging and non-sphericity are accounted for as described in
Moore et al. (2010) and Kumar et al. (2011a). The resulting dry diameter uncertainty is ±
20%, calculated as shown in Kumar et al. (2011a) based on an average ash shape factor of
1.3 (Schumann et al., 2011; Riley et al., 2003). The dry diameter for which 50% of the ash
particles act as CCN (for a given SS) in the CFSTGC is called the critical diameter, ddry ;
any ash particle larger than ddry will act as a CCN.
4.3
Data Analysis Methodology
Ash hygroscopicity can originate from soluble salts present in the particles, and, from the
adsorption of water vapor on the ash surface. The relative importance of each, together
with the dry particle size, controls the SS required to form a cloud drop. Köhler theory
(KT) can be used to predict SS when soluble salts are the dominant contributor to hygroscopicity (Seinfeld and Pandis, 2006). Solute effects can be parameterized in terms of
a “hygroscopicity parameter”, κ (Petters and Kreidenweis, 2007). If the ash composition
does not vary with size, KT suggests that SS ∼ ddry xKT , with xKT ∼ -1.5. Adsorption
activation theory (AT) can be used to predict SS when the adsorption of water vapor dominates particle hygroscopicity (Kumar et al., 2009b, 2011a). The formulation of AT in this
study adopts the Frenkel, Halsey and Hill (FHH) adsorption isotherm (Lowell et al., 2004;
Sorjamaa and Laaksonen, 2007) and uses two empirical parameters, AF HH and BF HH , that
are constrained by the CCN experiments. FHH-AT also predicts SS ∼ ddry xF HH , but xF HH
is between -0.8 and -1.1 (Kumar et al., 2009b, 2011a), meaning the slope of SS vs. ddry is
less steep than observed for KT.
x
exp
For each ash sample, SS vs. ddry is fitted to a power law function of the form SS = Cddry
,
where C and xexp are fitting constants. Additionally, the data are parameterized by FHHAT (Kumar et al., 2009b, 2011a) to determine the AF HH , BF HH adsorption parameters
71
and xF HH . Comparison of xexp to xF HH and xKT determines which theory (FHH-AT or
KT) best describes the observed hygroscopicity of each volcanic ash sample (Table 4).
4.4
Results and Discussion
Measured SS vs. ddry data are presented in Figure 15 for all samples considered. Ash
samples are denoted by points, and the FHH-AT model fits to the data are shown as solid
lines. Results for ammonium sulfate salt are shown as a reference for KT. Black dashed
lines indicate lines of constant κ, and are used to gauge the ash hygroscopicity. Figure 15
demonstrates that volcanic ash is hygroscopic and CCN active, even at atmospherically
relevant SS. Despite large variations in composition and eruptive dynamics, the activation
data for ash are quite consistent, and have a lower hygroscopicity than what Kumar et al.
(2011a) observed for atmospheric mineral dust aerosol and clays (with most ash κ ≤ 0.03
and most dust κ ≤ 0.05). The lower apparent hygroscopicity of volcanic ash relative to dust
originates from differences in water adsorption capacity arising from differences in molecular
structure. Consistent with Bolis et al. (1985), the water adsorption capacity of crystalline
quartz (present in dust) is greater than the water adsorption of amorphous silica (present in
volcanic ash). The sole exception is Mt. Redoubt ash, which exhibits an average apparent
κ ∼ 0.15, which is higher than any other dust or ash samples studied to date. This could
be the result of more complex particle morphology or size dependent composition. For
example, an enrichment of solute (salts) in the ultra-fine Redoubt ash of up to 20% volume
fraction would be sufficient to explain a κ ∼ 0.15, therefore it is possible the hygroscopicity
of Redoubt ash is a combination of KT and FHH-AT (Kumar et al., 2011a,b).
A comparison of the slopes of SS vs. ddry (Figure 15) reveals slopes that deviate significantly from classical KT behavior (SS ∼ ddry −1.5 ), suggesting that solute does not contribute
substantially to the observed ash hygroscopicity. In fact, the CCN activity of the volcanic
ash samples can be well represented by the FHH-AT model fits (Figure 15, solid lines) and is
consistent with the study of Delmelle et al. (2005). Bulk IC analysis of all ash samples (not
shown) further supports adsorption as the dominant mechanism, as all soluble ions (at less
than 0.5% by mass), are not sufficient to explain the observed hygroscopicity. Additionally,
72
the similarity between Mt. St. Helens 1980 and 2009 samples (agreement of xexp in Table 4
to within 3%) suggests that weathering does not significantly alter the observed activation
behavior.
For all volcanic ash samples, FHH-AT represents the observed CCN activity (xexp ) to
within 12% (Table 4). This results in an average AF HH = 2.22 ± 0.99 and a BF HH = 1.25
± 0.18, with uncertainty including the influence of particle non-sphericity. Excluding the
Mt. Redoubt sample, AF HH = 2.41 ± 0.93 and a BF HH = 1.31 ± 0.12. As shown in Kumar
et al. (2009b, 2011a), the BF HH adsorption parameter dominates the activation physics,
with AF HH of secondary importance. Therefore, the relatively small variation in BF HH
among a variety of volcanic eruptions is encouraging for future modeling applications and
suggests that this simple parameterization of adsorption could be applied for all volcanic
ash.
Measurement of subsaturated ash water uptake (e.g., with Hygroscopic Tandem Differential Mobility Analysis; HTDMA) can be used to futher test the robustness of FHH-AT
because it predicts a considerably different growth response than KT (e.g., Kumar et al.,
2009a). For example, using FHH-AT parameters from CCN activity measurements (Table 4) and assuming 90% relative humidity (i.e., S = 0.9), Mt. Redoubt ash grows 5 ± 1 %
relative to its dry diameter. If the same particles follow KT with κ = 0.15, a much larger
growth is anticipated, about 31 ± 1 %. The other ash samples would generally exhibit
smaller humidifed growth, between 2 and 5 % at S = 0.9. A future study will focus on
hygroscopic closure against these predictions.
4.5
Conclusions
In this study, the hygroscopicity of ash aerosol generated from in-situ deposits of six different
volcanic eruptions is experimentally quantified and, for the first time, a simple parameterization for ash-water interactions is provided. A combination of CCN activation data and IC
analysis suggests adsorption of water vapor onto the ash surface gives rise to the observed
hygroscopicity. This work confirms that the surface of insoluble ash particles efficiently
adsorbs water as first suggested by Delmelle et al. (2005). Despite large variations in ash
73
74
Sample (Eruption Year)
Eyjafjallajökull (2010)
Mt. Redoubt (2009)
Chaitén (2008)
Tungurahua (2006)
El Chichón (1982)
Mt. St. Helens (1980)
Mt. St. Helens (1980)
* Approximate location
Location
Iceland
Alaska, USA
Chile
Ecuador
Mexico
Washington, USA
Washington, USA
Sample (Lat, Lon)
(63.546, -19.662)
(59.435, -151.709)
(-42.814, -72.646)
(-1.518, -78.488)
(17.360, -93.228)*
(46.232, -122.152)
(46.248, -122.178)*
Sample Age
3 weeks
2 days
1 month
28 months
2 days
29 years
2 days
AF HH
0.74
1.05
1.87
2.96
2.91
3.00
3.00
BF HH
1.09
0.94
1.41
1.42
1.27
1.36
1.29
xexp
-0.96
-1.14
-0.89
-0.83
-0.76
-0.74
-0.76
xF HH
-0.96
-1.09
-0.90
-0.87
-0.84
-0.85
-0.85
Table 4: FHH adsorption model parameters and exponent comparisons determined for analyzed volcanic ash samples
Figure 15: CCN activation data, supersaturation vs. critical dry diameter, for the different
volcanic ash samples presented in Table 4. Solid symbols represent the ash samples, with
errors bars showing uncertainty in the critical dry diameter. Solid lines represent FHH
adsorption activation theory fits, while dashed lines represent values of constant κ-Köhler
hygroscopicity, ranging from completely insoluble (κ = 0) to highly soluble (κ = 0.7).
75
composition across samples, a single set of adsorption parameters (AF HH = 2.41 ± 0.93 and
BF HH = 1.31 ± 0.12) can describe the ash hygroscopicity with sufficient accuracy for use in
volcanic plume and atmospheric climate models, with the end goal to improve predictions
of ash microphysics, transport and impacts.
76
CHAPTER V
NEW PARTICLE FORMATION IN TROPICAL CYCLONES CAN
PROMOTE RAPID INTENSIFICATION THROUGH EYEWALL
SEEDING
Skill in hurricane intensity prediction is a major challenge and is thought to be limited
by the inability of hurricane models to fully resolve all the scales of atmospheric motion,
by a poor understanding of physical processes involved in rapid intensification, and, to
limited observational data. The role of the microphysical scale in tropical cyclone (TC)
development and intensification is poorly understood and often overlooked by models. To
address this knowledge gap, we conducted unprecedented observations of aerosols and cloud
condensation nuclei (CCN) from within multiple stages of TC development, from tropical
storm to Category 4 hurricane intensity. We find strong evidence for new particle formation generated by intensifying TCs, which leads to orders of magnitude enhancement in
total aerosol and CCN concentrations within the well-developed eye region, with estimated
aerosol growth rates in the eye column of 10 nm hr−1 . It is hypothesized that particles
located within the eye can seed the eyewall convective region, leading to observed enhancements of cloud droplet number concentrations (CDNC) right at the eye-eyewall boundary
throughout the eye column. We hypothesize that eyewall convective seeding during the TC
intensification contributes to its rapid intensification. Idealized cyclone simulations support
this hypothesis, giving rise to a previously unknown and natural mode of intensification
that could also be modulated by long-range transport of anthropogenic pollution.
5.1
Introduction
Extensive research and modeling efforts have significantly improved tropical cyclone (TC)
track forecasts over the past few decades, yet such efforts have not led to improvements
in the forecasting of TC intensity, especially during periods of rapid intensification (RI)
(Rogers et al., 2006; Hamill et al., 2012). Lack of refined skill in intensity forecasts leads
77
to considerable risk and uncertainty in the level of preparedness, especially for TCs making
landfall. Errors in TC intensity prediction are thought to be the combined result of limitations in modeling, observations, and the fundamental theory governing TC development
and intensification (McFarquhar and Black , 2003; Khain et al., 2010; Rosenfeld et al., 2012).
It is thought that understanding the interaction and feedbacks between all scales will be
critical for improving TC intensity forecasts.
Most research on TC development and intensification has focused primarily on the
large-scale environment or vortex structure, such as understanding the role of underlying
sea-surface temperature, upper-level vertical wind shear, and low-level relative humidity
(Rogers, 2010). The microphysical scale has often been disregarded, owing to the scarcity of
in-situ measurements of aerosol and cloud properties in TCs, and, to the high computational
cost of microphysical simulations, which forces timely forecasts to be completed using coarse
resolution without microphysical parameterizations (Khain et al., 2010; Davis et al., 2011).
There is mounting evidence, however, suggesting that resolving the microphysical scale may
not only improve TC intensity predictions, but also explains some of the past forecast errors
(Davis et al., 2011; Rosenfeld et al., 2011).
The importance of the microphysical scale in TC intensity originates from the fact that
TCs are energized by latent heat that is released from the condensation of water vapor
onto existing atmospheric aerosol particles (Rosenfeld et al., 2012). The latent heat release
in TCs depends upon the rate of cloud microphysical processes, such as the activation
of cloud drops and ice crystals, which form upon aerosol particles serving as either cloud
condensation nuclei (CCN) or ice nuclei (IN). It is well known that increases in aerosol and
CCN concentrations can lead to the development of smaller cloud drops that precipitate
less efficiently (Twomey, 1977; Albrecht, 1989) and that these smaller cloud drops lead to
convective invigoration of tropical convection (e.g., Khain et al., 2010; Rosenfeld et al.,
2012). Recent modeling studies have also shown a link between microphysical processes
and TC intensity, where seeding the outer rainband convection with aerosols and cloud
condensation nuclei (CCN) generally weakens TC intensity through redistribution of latent
heat and development of cold pools near the surface (Zhang et al., 2007; Rosenfeld et al.,
78
2007; Khain et al., 2008; Zhang et al., 2009; Khain et al., 2010; Rosenfeld et al., 2011; Carrio
and Cotton, 2011; Rosenfeld et al., 2012; Krall and Cotton, 2012). However, this mechanism
still remains to be demonstrated in TC observations, nor is this the only possible seeding
mechanism.
In this study, we provide unprecedented in-situ aircraft observations of aerosol and CCN
from multiple stages of TC development (from tropical storm to Category 4 hurricane),
collected as part of two field campaigns during the 2010 Atlantic Hurricane Season: the
NOAA Intensity and Forecasting Experiment (IFEX) on the NOAA WP-3D aircraft and
the NASA Genesis and Rapid Intensification Processes (GRIP) experiment on the NASA
DC-8 aircraft. Our observations provide the first known evidence of new particle formation
(NPF) occurring within the TC environment, which provides a significant natural source of
aerosol and CCN that become concentrated in the eye of well-developed systems. A clear
correlation between the observed aerosol number, CCN and CDNC with TC intensity is
discovered, where the concentrations of aerosol and CCN increase significantly in the eye and
the CDNC increases within the eyewall with increasing TC intensity (Figure 16, Figure 19).
We propose a new role for convective invigoration and possible RI of TCs, resulting from
the mixing of the particle-rich air from NPF between the eye and the eyewall. The resulting
flux of high concentrations of aerosol and CCN effectively seeds and invigorates the eyewall
convection, as shown by the 1-2 order of magnitude increases in CNDC right at the eyeeyewall boundary of Hurricane Earl in Figure 16 (lower panels). This seeding can result in
a positive feedback on TC intensity that may also be a microphysical explanation for the
formation of the convective bursts and convective hot towers observed during RI (Steranka
et al., 1986; Montgomery et al., 2006; Rogers, 2010; Jiang, 2012). Our hypothesis is further
supported by simulations of an idealized vortex with the Weather Research and Forecasting
Model (WRF), where the eyewall of the vortex is seeded with the measured concentrations
of CCN and an intensification of the vortex compared to the control simulation is observed
(Figure 20).
79
5.2
In-Situ Observations
The in-situ data collected includes multiple flights through three named storms from the
2010 Atlantic Hurricane season, covering a wide range and full evolution of TC intensity, including (1) ten flights on the evolution of Hurricane Earl from a Tropical Storm to Category
4 Hurricane from 29 August, 2010 to 2 September, 2010 (2) three flights on the evolution
of Hurricane Karl from Tropical Storm to Category 3 Hurricane from 14-17 September,
2010 and (3) two flights on the evolution of Tropical Storm Tomas from 4-6 November,
2010. The NASA DC-8 measured the aerosol number concentrations, dry aerosol size distributions, CDNC, and CCN spectra (at 0.15 - 0.65% supersaturation every 30 seconds) of
the high altitude storm environment (∼10 km), while the NOAA WP-3D measured CDNC
and CCN spectra (at 0.15 - 0.65% supersaturation every 30 seconds) of the low-level storm
environment (2 - 6 km).
The spatial distributions of aerosol and CCN observed during Hurricane Earl at peak
Category 4 intensity on 30 August, 2010 are shown in Figure 16 with underlying GOES
satellite imagery. Figure 16a shows the representative flight track of the NASA DC8 at ∼10
km colored by the concentration of ultrafine (> 3 nm) aerosol particles, while Figure 16b
shows the flight track of the NOAA WP-3D at 3 km, colored by the concentration of CCN at
0.6% supersaturation. The spatial distribution reveals a clear and significant enhancement
in total aerosol and CCN concentration within the well-developed eye. Representative
transects through the eye at both levels are shown in the lower panels of Figure 16, where
the concentration enhancements of aerosol and CCN can be clearly correlated with the
decrease in wind speed within the eye, while the increase in CDNC occurs at the eyewall
boundary. The total aerosol concentrations observed within the eye at 10 km exceed 5 × 104
cm−3 , while CCN concentrations observed within the eye at 3 km exceed 3 × 103 cm−3 .
These aerosol and CCN concentrations are 1-3 orders of magnitude higher than observed
in the background environment, suggesting that the TC environment produces a significant
concentration of new aerosol and CCN. The newly formed aerosol particles can grow to
CCN relevant sizes during the subsidence transport through the eye column, with average
growth rates of 10 nm hr−1 estimated for an average eye subsidence of 1 m s−1 . Analysis of
80
(b)
24
24
22
22
20
Latitude
Latitude
(a)
18
20
18
16
16
14
14
-68
0
-66
-64
Longitude
4000
-62
-68
0
8000
Wind [kts]
Wind [kts]
20
40
20
0
0
CCN (0.6% S)
CDNC
4
10
-3
C [cm ]
-3
C [cm ]
UFCN
CDNC
10
1000
60
40
10
500
CCN [cm ]
UFCN [cm ]
10
-62
-3
-3
10
-66
-64
Longitude
2
0
-2
10
10
10
4
2
0
-2
9:30 PM
8/30/2010
7:22 PM
8/30/2010
Figure 16: Flight tracks (upper panels) and representative eye transects (lower panels)
through Hurricane Earl at Category 4 intensity on August 30, 2010. NASA DC-8 flight
track at 10 km is colored by the concentration of ultrafine condensation nuclei (UFCN) (a)
and NOAA WP-3D flight track at 2-6 km is colored by the concentration of Cloud Condensation Nuclei (CCN) at 0.6% supersaturation (b). Underlying GOES satellite imagery is
for the estimated time of the first pass through the eye and hurricane motion is NW. Lower
panels show the concentration (C) of UFCN (blue), CCN (green), and cloud droplet number
concentrations (CDNC, black) through one eye transect, with the eye indicated where the
measured flight level wind speed is minimum.
temperature and dew point temperature from dropsondes taken within the eye of Hurricane
Earl indicate that both aircraft were sampling above the low-level eye inversion located at
∼760 mb (∼1.6 km) (Figure 17), which suggests the influence from wind-driven sea-spray
81
would be minimal and there must be another source for the observed particle and CCN
enhancements.
700
Temperature
Dewpoint
P [mb]
750
800
850
900
950
15
20
ºC
25
30
Figure 17: DC-8 dropsonde sounding through the eye of Hurricane Earl on 30 August,
2010 at 19:18 UTC. The low level inversion is marked by the increase in temperature and
divergence of the temperature and dewpoint at ∼760 mb, which corresponds to ∼1.6 km.
Further analysis of the size distribution of the aerosol particles collected by the NASA
DC-8 aircraft provides strong evidence that the source of these particles is from new particle
formation (NPF), which is occurring above the TC environment and concentrated in the
eye. The mean size of particles observed in all the transects through the eye of Hurricane
Earl is < 15 nm, which is a classic sign of NPF (Figure 18). The upper free troposphere
and lower stratosphere are known sources for NPF due to low temperatures and low preexisting aerosol surface area, with NPF occurring through possible ion-induced nucleation
(Kulmala, 2003; Lee et al., 2003), homogenous nucleation (Weber et al., 1999), interstitially
within cirrus clouds (Lee et al., 2004; Kazil et al., 2007), or from the outflow of tropical
convection (Weigel et al., 2011). Furthermore, hurricane updrafts and large scale motions
82
can provide the necessary water vapor and aerosol precursors from lower altitudes (Young
et al., 2007) such that NPF occurs from photochemical oxidation of gaseous species by OH.
30x10
Eye Pass 1
Eye Pass 2
Eye Pass 3
3
-3
dN/dlogdp [cm ]
25
20
15
10
5
0
8 9
2
3
4
5
10
dp [nm]
Figure 18: DC-8 dry aerosol size distributions at 10 km through Hurricane Earl at Category
4 intensity on 30 August, 2010. The number concentration (dN/dlogdp) versus dry aerosol
size (dp ) is measured with a Scanning Mobility Particle Sizer (SMPS).
The observed enhancements in aerosol from NPF were not only consistent through
multiple flights within Hurricane Earl over the course of several days, but additional NPF
was observed during the RI phase of Hurricane Karl on September 16, 2010, suggesting
that the hurricane environment is conducive for NPF (Figure 19a). High concentrations of
small aerosol and CCN (> 103 cm−3 ) were also observed within the eye of Hurricane Erin
in 2001 (Hudson and Simpson, 2002), where it was originally thought the concentrations
were the result of pollution aerosols. However, these observations are remarkably consistent
with our measurements of NPF and provide further evidence that intensifying TCs are a
natural source for new particles. This link between TC intensification and NPF is shown
83
more clearly in Figure 19a, where the observed concentration of ultrafine aerosol is plotted
as a function of the hurricane intensity, obtained from the NOAA HURDAT database. A
clear trend of increasing NPF with increasing hurricane intensity is observed for both Earl
and Karl (Figure 19a). NPF is not observed during flights through Tropical Storm Karl
on 14 September, 2010 or the weak Category 1 Hurricane Earl on 29 August, 2010, but
significant NPF is observed during the RI of Hurricane Earl on 30 August, 2010 and the
RI of Hurricane Karl on 16 September, 2010. The trend of increasing CCN measured at 3
km (Figure 19b) is similar to the trend of increasing NPF at 10 km, which suggests that
the aerosol particles produced by NPF can grow to become efficient CCN as they subside
through the eye column, or additional large particles get entrained into the eye as TCs
intensify. Due to eye-eyewall mixing (Kossin et al., 2002; Persing and Montgomery, 2003;
Montgomery et al., 2006; Cram et al., 2007), these significant enhancements in aerosol and
CCN within the eye may seed and convectively invigorate the eyewall convection, leading
to a microphysical feedback which either sustains the storm intensity or possibly leads to
RI of the TC.
10
5
(a)
10
4
6
4
2
10
3
6
4
Karl
Earl
2
10
2
-3
2
10
(b)
6
4
Maximum CCN [cm ]
-3
Maximum UFCN [cm ]
6
4
4
2
0
1
2
3
4
TC Intensity Category
10
3
6
4
2
10
2
6
4
Karl
Earl
Tomas
2
10
1
0
5
1
2
3
4
TC Intensity Category
5
Figure 19: Maximum Ultrafine Condensation Nuclei (UFCN, > 3 nm) (a) and Cloud
Condensation Nuclei at 0.6% supersaturation (b) as a function of TC intensity. Each data
point represents the maximum concentration observed during a transect through the eye.
TC intensity is obtained from the NOAA HURDAT database corresponding to the time
stamp of the data collected within the eye. UFCN is measured by the NASA DC-8 at ∼10
km altitude, while CCN is measured by the NOAA WP-3D at ∼3 km altitude for Earl,
Tomas and NASA DC-8 at ∼10 km altitude for Karl.
84
5.3
Model Results
To test our hypothesis that the observed aerosol and CCN enhancements from NPF may
directly impact hurricane intensity, we use the Weather and Research Forecasting (WRF)
model to develop an idealized TC vortex and test the impact of seeding CCN within the
eyewall of this vortex. After the idealized TC vortex is established, a “seeded” experiment
is conducted where the concentration of CCN within the eyewall is increased from the background of 250 cm−3 to a level consistent with our CCN observations at 0.6% supersaturation
(3 × 103 cm−3 ). This is likely a conservative estimate, as the observed aerosol concentrations are an order of magnitude higher and the updraft velocities within the eyewall are
sufficiently high to achieve supersaturations > 0.6%, and so a higher “seeded” experiment
is also conducted at 2×104 cm−3 . These WRF TC seeding experiments are used to only
demonstrate the feasibility of our hypothesis and test whether or not aerosol seeding leads
to any microphysical impacts on TC intensity.
A sample of the WRF modeling results are shown in Figure 20, where these results
correspond to seeding at an initial condition corresponding to a period where the idealized
TC is in a transitional state of strengthening from a Category 2 intensity, consistent with
the measurements of Hurricane Earl from 29 August, 2010 to 2 September, 2010. The
simulation was run for an additional 40 hours after the initial seeding and the changes in
the minimum surface pressure (Figure 20a) and maximum wind speed (Figure 20b) were
investigated. As shown in Figure 20, the seeded experiments (green and red traces) result in
a stronger period of RI from hours 10-20 and subsequently maintains the TC consistently
at a stronger intensity compared to the control (black trace). Multiple ensemble runs
at different initial conditions along the intensity transition region of the idealized vortex
provided similar results (not shown), while ensembles run at the maximum intensity did not
show any further intensification, as the microphysical seeding impact for storms once they
have reached peak intensity is likely minimal. This suggests that the timing of the seeding
is critical and has maximum impact for weak TCs prone to RI. Therefore, eyewall seeding
from new particle formation could be one possible trigger for RI, which is a completely new
mechanism overlooked by all current forecast models.
85
Min Sfc. Pressure [mb]
(a)
Simulation Time [hr]
Max Wind Speed [kts]
(b)
Simulation Time [hr]
Figure 20: Weather and Research Forecasting (WRF) model simulations of an idealized
tropical cyclone (TC) vortex. Minimum surface pressure (a) and maximum wind speed (b)
are shown as a function of simulation time for the control simulation (black), seeding the
eyewall with 3,000 cm−3 of cloud condensation nuclei (green), and seeding the eyewall with
20,000 cm−3 of cloud condensation nuclei (red).
5.4
Conclusions and Implications
The implications of this study are significant, as we have proposed a new mode of natural
intensification of TCs that has not previously been considered or included in any intensity
forecast. The discovery of new particle formation and the corresponding enhancements
of aerosols and CCN within the eye of intensifying TCs is a novel finding, as the traditional view has been that the eye was a relatively particle free region as most particles
were removed by precipitation before reaching the center of the storm. Our hypothesis that
86
the new particle formation seeds the eyewall and leads to convective invigoration is supported by observational evidence of enhancements in cloud droplet number concentrations
at the eye-eyewall boundary and WRF idealized TC vortex simulations that show significant increases in TC intensity upon seeding the eyewall with the observed concentrations
of CCN. The experimental and modeling results presented motivate a strong need for more
thorough microphysical measurements within TCs, including additional measurements of
particle chemistry and gas phase species to unambiguously confirm the presence of new particle formation, to quantify the new particle formation rates, and to develop the necessary
microphysical parameterizations for hurricane forecast models. The new particle formation
may be modulated by anthropogenic pollution (gas-phase precursors or aerosol) which suggests a new role for anthropogenic influences on TC intensity, especially for systems making
landfall, which should also be explored.
87
CHAPTER VI
CONCLUSIONS, IMPLICATIONS, AND FUTURE DIRECTIONS
In this thesis, advancements in laboratory and in-situ measurements are used to better
understand the water uptake and cloud condensation nuclei (CCN) ability of atmospheric
aerosol particles. Aerosol water uptake is quantified in terms of the aerosol hygroscopicity
parameter, κ, which is a widely used parameterization for water-uptake employed by models.
Many uncertainties in κ still exist, which are the combined result of the chemical complexity
of atmospheric aerosol, the tendency of κ to evolve with atmospheric aging and transport,
and limited observations to constrain the regional and temporal variability of κ at critical
regions of the world. To address these gaps, this thesis (1) characterizes key biases in the
instrumentation used to measure CCN activation and infer aerosol κ, (2) measures the CCN
activity and κ of aerosol at regions of the world where data are scarce or non-existent and
of particle types previously overlooked, and (3) develops new parameterizations to describe
the water uptake, for both insoluble ash particles and soluble organics.
In Chapter 2, a detailed theoretical and experimental analysis of a supersaturation depletion bias in the Droplet Measurement Technologies (DMT) CCN counter was presented.
It was shown that high concentrations of CCN (> 5×103 cm−3 ) can significantly deplete
the instrument supersaturation below the expected calibration based on standard operating
conditions (e.g., flow, temperature gradient, pressure), because the condensation of water
to the growing droplets is faster than the rate of water vapor supplied by the CCN instrument. Since the DMT CCN counter has become one of the international standards
for collecting CCN measurements, often in polluted environments where the concentration
of CCN is high, the identification and characterization of this bias represents a significant
advancement to the field that has aided in the collection of more precise measurements of
CCN, droplet activation kinetics, and inferences of particle κ. An example of the impact of
supersaturation depletion on CCN closure predictions and inferences of particle κ is shown
88
in Chapter 3, where extremely high CCN measurements were sampled directly over Canadian forest fires. CCN concentrations in these biomass burning plumes exceeded 104 cm−3 ,
leading to supersaturation depletions > 25%, which if unaccounted for, led to an error in
CCN predictions using Köhler theory of up to 20% and underpredictions in CCN inferred
κ of up to 50%. As shown in Chapters 2 and Appendix A, supersaturation depletion also
significantly impacts the measured droplet size, since droplet size is tightly coupled to the
supersaturation that develops in the instrument growth chamber. This bias in droplet size
has significant implications for droplet activation kinetic studies, where these studies investigate small changes in droplet size and attribute these changes to kinetic limitations on
droplet growth, which can occur through slow dissolution or development of hydrophobic
films on the surface of growing droplets. It has been shown that the supersaturation depletion bias has to be considered for unambiguous determination of droplet activation kinetics,
as most of the observed variability in activated droplet size is actually due to changes in
the instrument supersaturation. The theoretical and experiential analysis of supersaturation depletion presented in Chapter 2 was critical for the development and validation of
the CCN instrument model developed by Raatikainen et al. (2012), which has enabled the
most accurate inferences of droplet activation kinetics to date. The methods and strategies
for preventing or correcting supersaturation depletion described in Chapters 2 and 3 are
currently being employed by research groups around the world to improve CCN measurements and to explain existing errors in CCN measurements sampled in high concentration
environments.
In Chapters 3-5, aerosols and CCN are thoroughly characterized from a wide variety
of environments, including the Arctic, samples of volcanic ash, and within tropical cyclones. These measurements represent significant advancements to the understanding of
these critical environments and sources of aerosol and serve as either the first or among
the most thorough measurements conducted. In Chapter 3, a thorough characterization
from dominant sources of aerosol and CCN to the Arctic environment during summer were
characterized, including measurements of boreal forests, Arctic background, fresh and aged
89
biomass burning, and anthropogenic industrial pollution. It was shown that the CCN activity and κ was significantly influenced by highly oxidized, water-soluble organics, which
represented more than 50% of the total aerosol volume in all environments. While knowing
the exact organic speciation has been a historical challenge for models, it was shown that
only knowledge of the water-soluble fraction was required for accurate CCN predictions using Köhler theory. The parameterization developed, based on a water-soluble fraction and
associated κ, represents a potentially significant advancement and simplification of organics for global models. Furthermore, it was shown that the aging and evolution of biomass
burning aerosol was rapid, such that the fresh biomass particle κ quickly converged to that
of aged biomass burning on the timescale of a couple of hours. This also represents another
simplification for global models, as results suggest that models do not need to understand
and characterize the full evolution of biomass burning κ, but rather only need to represent the properties of the aged biomass burning κ. Further field experiments are needed to
characterize water-soluble organics and their contribution on CCN predictions and κ to test
whether the current parameterization holds in other environments outside the Arctic and to
also determine the factors contributing to differences in water-soluble organic κ. Additional
modeling studies are also needed to further test the validity of this parameterization against
observations.
The first measurements of the water uptake and κ of six different types of volcanic ash
particles was presented in Chapter 4. Measurements of the dry ash particle size, composition, and CCN activities were used to understand the physical and chemical mechanisms
by which insoluble ash particles activate into CCN. The work presented confirms that the
surface of insoluble volcanic ash particles efficiently adsorbs water, such that the surface
porosity and hydrophilicity enables the insoluble ash particle to activate at much lower
supersaturations than previously thought. The data were further utilized to develop a
parameterization for the water uptake based on the Frenkel, Halsey and Hill adsorption
isotherm, which describes the ash hygroscopicity with sufficient accuracy for use in volcanic
plume and atmospheric climate models, with the end goal to improve predictions of ash
microphysics, transport, and impacts. These parameterizations are currently being utilized
90
to better understand experimental evaluations of ash aggregation efficiency (Telling and
Dufek , 2011; Van Eaton et al., 2012), as well as to develop model parameterizations for the
activation and transport of volcanic ash in the atmosphere (Ghan et al., 2011). Due to the
significant economic and travel disruptions associated with the uncertainty in the volcanic
ash cloud from the Eyjafjallajökull eruption in 2010, gaining a better understanding of ash
transport is critical, and ash water uptake, which is currently disregarded, could have a
significant impact on ash lifetime. Additional field studies of either dust or volcanic ash
will be important to test the laboratory derived adsorption activation parameterizations,
where a combined theory including Köhler and adsorption activation (e.g., Kumar et al.,
2011b) will likely be most representative for aged dust and volcanic ash particles in the
atmosphere.
In Chapter 5, the first and most comprehensive measurements of both aerosol and
CCN within multiple stages of tropical cyclone (TC) development was presented. The
observations were used to identify the first known evidence for new particle formation
occurring within the eye of intensifying TCs, where significant increases in both aerosol and
CCN were observed with increasing TC intensity. These increases in aerosol and CCN within
the eye, being 1-3 orders of magnitude higher than the background environments, provides
direct observational evidence that strong tropical storms produce new particles. It was
hypothesized that new particle formation can directly seed the eyewall convection, leading
to possible convective invigoration, which can either sustain the TC intensity or promote
rapid intensification. This hypothesis was supported by observations of order of magnitude
enhancements in cloud droplet number concentrations right at the eye-eyewall boundary
and by idealized TC vortex model simulations which showed consistent TC intensification
as a result of eyewall seeding at levels consistent with observations. The implications of
this study are significant, as we have proposed a new mode of natural intensification of
TCs that has not previously been considered or included in any intensity forecast. The new
particle formation may be modulated by anthropogenic pollution (gas-phase precursors or
aerosol) which suggests a new role for anthropogenic influences on TC intensity, especially
for systems making landfall. The experimental and modeling results presented in Chapter
91
5 motivate a strong need for more thorough microphysical measurements within TCs of not
only aerosols and CCN, but additional measurements of particle chemistry and gas phase
species to unambiguously confirm the presence of new particle formation and to develop
the necessary microphysical parameterizations for hurricane forecast models.
In addition to the primary thesis work presented in Chapters 2-5, Appendix A presents
the results and implications arising from high impact collaborative studies with multiple
government and university research groups. The studies included (1) development of a
droplet growth kinetics model to study water vapor uptake kinetics of ambient aerosol, (2)
laboratory flow tube and chamber studies to investigate the impact of surface active organics on particle κ and aging, and (3) the first known characterization of biological particles
present in the upper atmosphere before, during, and after TCs. The droplet growth model
includes the algorithms for supersaturation depletion identified in Chapters 2 and 3, and
allows for precise inferences of the water vapor uptake coefficient from CCN measurements.
The model is currently being applied to CCN data sets collected around the world to determine the first global database for the water vapor uptake coefficient used by models and
parameterizations of cloud droplet formation. Surface active organics were shown to impact
oxidative aging by ozone, where oxidation did not lead to increasing particle κ as expected.
Uptake of gas-phase surface active organics onto wet aerosol, however, led to significant enhancements in aerosol κ, providing for the first known evidence of gas-phase volatile organic
compounds (VOCs) directly influencing particle κ through surface uptake and lowering of
surface tension. Models currently do not account for either of these mechanisms, and this
work will facilitate additional studies and development of coordinated field studies to determine whether these processes occur in the atmosphere where the concentration of VOCs
is high. Finally, the presence of significant bacterial microorganisms in the upper troposphere suggests than TCs may generate and redistribute a significant amount of biological
material, which may have direct impacts on cloud microphysics and the biosphere. Since
little is known of particles generated by hurricanes or of the biological particles of the upper
atmosphere, this study represents a significant advancement to the current understanding
in multiple scientific fields.
92
All the data presented herein, varying widely in scope from volcanic ash to biomass
burning to hurricanes, was utilized to better understand and model the mechanisms of water uptake onto these different aerosol particles. Having a more thorough understanding
of aerosol water uptake will enable more accurate representation of cloud droplet number
concentrations in global models, which can have important implications on reducing the uncertainty of aerosol-cloud-climate interactions, as well as additional uncertainties in aerosol
transport, atmospheric lifetime, and impact on storm dynamics.
93
APPENDIX A
CONTRIBUTIONS TO COAUTHOR PAPERS OF SIGNIFICANCE
In addition to the primary thesis work of Chapters 2-5, this appendix highlights the results
of several collaborative projects undertaken during this thesis and summarizes this author’s
intellectual contributions that were provided to these studies. These collaborations included the development, design, testing, and analysis of laboratory experiments conducted
at Georgia Tech, NASA Langley Research Center, and Columbia University. These experiments were first utilized to further explore supersaturation depletion and droplet growth
kinetics, which aided the development and validation of a droplet growth model for the
DMT CCN counter, as described in Appendix A.1. At Columbia University, the author
used the techniques presented in Chapters 1-3 to develop a CCN chamber study, where
the impact of oxidative aging and partitioning of surface-active organics on particle hygroscopicity (particle-water uptake) was explored (Appendix A.2). During the NASA GRIP
campaign, described in Chapter 5, the author collaborated with the Schools of Civil and
Environmental Engineering and Biology at Georgia Tech, where bio-filters were prepared
and collected during the campaign to aid in the first ever study of the microbiome of the
upper troposphere and assess the atmospheric implications of these species (Appendix A.3).
A.1
Modeling Droplet Growth Kinetics: Role of Supersaturation Depletion
Cloud droplet activation and the subsequent growth are kinetic processes limited by water
vapor diffusion. Water vapor uptake, described in cloud microphysical models by a condensation or mass accommodation coefficient (αc ), is a highly uncertain variable. Little is
known about the spatial and temporal distribution of αc and whether or not potential kinetic limitations arising from chemical effects or transport limitations exist. To address this
issue, a cloud droplet activation and growth kinetics model for the DMT CCN counter was
developed (Raatikainen et al., 2012). To aid in the development of this model, this author
94
contributed extensive field experience, knowledge of the instrument design and transient
behaviors, and field and laboratory data. The supersaturation depletion bias presented in
Chapter 2 was a critical step in understanding the observed droplet size variability and the
ARCTAS data set (described in Chapter 3) was used to test and develop the supersaturation depletion algorithm used by the model. In addition, many detailed calibrations of the
DMT CCN counter were conducted by this author to further explore and parameterize the
impact of supersaturation depletion, to investigate instabilities in temperature, flow and
pressure of the instrument, and to calibrate the optics of the particle counter, which were
also assisted by Dr. Richard Moore and Jack Lin.
The high concentrations of aerosol and CCN observed in the ARCTAS biomass burning
plumes were used to show the importance of water vapor depletion effects in determining
the observed variability in droplet size. As shown in the lower panel of Figure 21, there are
clear variations in the observed droplet size, which are represented by the widths of the gray
boxes. When water vapor depletion is accounted for, the correlation between the observed
and predicted droplet sizes becomes clear (upper plot in Figure 21). The conclusion from
this correlation is that water vapor depletion can explain the majority of changes in the
average droplet size. If unaccounted for, the effect of water vapor depletion could have
been interpreted as a change in the water vapor uptake coefficient. As a result, no kinetic
limitations were observed in the ARCTAS data, as all droplet depressions were a result
of water vapor depletion and not changes in water vapor uptake kinetics. A follow-up
kinetic study, including a synthesis of numerous field data sets collected around the world,
is currently in progress to show that, for most most regions of the world, activation kinetics
are fast with αc ∼0.2. Most of the observed droplet size variability in these environments is
indeed driven by water vapor depletion and other instrument transients, which the model
can now accurately predict and simulate.
95
)m
8
a) Water vapor depletion
m( ezis telpord detalumiS
effects accounted for
7
6
5
0.30
)m
8
0.40
0.50
Instrument supersaturation (%)
4
b) No water vapor
m( ezis telpord detalumiS
depletion effects
7
6
5
4
2
3
4
mm)
5
Observed droplet size (
Figure 21: Simulated droplet size as a function of observed droplet size. Simulations in the
upper plot account for water vapor depletion effects, but these are ignored in the simulations shown in the lower plot. The marker color is based on instrument supersaturation.
The black square markers represent average data from the calibration experiments and
simulations. The gray boxes show the approximate extent of observed and predicted (without water vapor depletion) droplet size variability for the three common supersaturations
(Raatikainen et al., 2012).
A.2
Laboratory Investigations of Surface-Active Organics: Role in Hygroscopicity, κ
Surface-active molecules tend to partition to the gas-liquid interface of aqueous drops as
a result of interactions between hydrophilic and hydrophobic groups. Surface active compounds are prevalent in tropospheric aerosols, but understanding the impact of surfactants
on particle water uptake (primarily a result of the formation of organic films) is challenging. Such organic films have long been thought to influence CCN activation and subsequent
droplet growth (e.g., Shulman et al., 1996; Chuang, 2003). Through a collaboration with Dr.
Faye McNeill at Columbia University, the authors used a combination of aerosol chamber,
flow tube reactor, and CCN measurements to study the water uptake potential of aerosol
particles in the presence of surfactants, including liquid-phase sodium oleate and oleic acid
96
and gas-phase methylglyoxal and acetaldehyde. This author’s role in these studies was to
aid in the design and execution of the experiments, including visiting Columbia University
and training the McNeill Research Group on the CCN instrument calibration, operation,
data analysis and interpretation of results. The resulting publications (Schwier et al., 2011;
Sareen et al., 2012) represent an equal contribution among all authors in regards to experiments and data analysis.
In the first study, Schwier et al. (2011), a flow tube reactor was used to study the effect of
ozone oxidation on the κ and CCN activity of aerosol particles containing mixtures of sodium
oleate and oleic acid with inorganic salts (sodium chloride or sodium sulfate). Pure oleic acid
is a common unsaturated fatty acid present in atmospheric aerosols, which upon oxidation
by ozone is known to lead to increases in particle hygroscopicity, κ (e.g., Broekhuizen et al.,
2004b; Shilling et al., 2007), thus enhancing water uptake and increasing CCN activity as
discussed in Chapter 1. However, whether the trend of increasing κ is preserved when
oleic acid is internally mixed with other electrolytes (also common in atmospheric aerosols)
was addressed in this study. It was found that particles containing roughly one monolayer
of sodium oleate/oleic acid had similar κ to the pure salt (sodium chloride or sodium
sulfate) particles. Increasing the organic concentration by an order of magnitude only
slightly depressed κ (Table 5). The most interesting finding was that oxidation of the
sodium oleate particles by exposure to ozone at atmospherically relevant pH (controlled by
addition of H2 SO4 ) led to more significant decreases in the κ and CCN activity (Table 5),
which opposes the conventional view that oxidation increases κ, as described in Section
1.3. This effect, which is more pronounced at the higher organic concentrations, suggests
that atmospheric oxidation may not always enhance particle κ of internally mixed inorganicorganic aerosols, as is traditionally expected. The chemical explanation for the increase in κ
with oxidation is related to the breakage and disappearance of a surface-active organic film,
which initially decreases the vapor pressure over the particles by reducing surface tension.
Upon oxidation, the resulting semi-volatile products partition to the gas-phase and result
in the disappearance of the surface-active film, which increases the vapor pressure and leads
to the observed increase in κ.
97
Table 5: Summary of average aerosol hygroscopicity parameters (κ) observed for the experiments of Schwier et al. (2011). Sodium Oleate is denoted as SO.
Experiment
κ (avg)
NaCl
NaCl, 1ppm O3
0.001 M SO, NaCl
0.01 M, SO, NaCl
0.001 M SO, Nacl, H2 SO4
0.001 M SO, NaCl, H2 SO4 , 1 ppm O3
0.01 M SO, NaCl, H2 SO4
0.01 M SO, NaCl, H2 SO4 , 1 ppm O3
1.378±0.025
1.094±0.045
1.187±0.034
0.869±0.062
1.138±0.051
1.065±0.054
0.971±0.079
0.786±0.024
Na2 SO4
Na2 SO4 , 1 ppm O3
0.001 M SO, Na2 SO4
0.01 M SO, Na2 SO4
0.001 M SO, Na2 SO4 , H2 SO4
0.001 M SO, Na2 SO4 , H2 SO4 , 1 ppm O3
0.01 M SO, Na2 SO4 , H2 SO4
0.01 M SO, Na2 SO4 , H2 SO4 , 1 ppm O3
0.872±0.016
0.909±0.044
0.708±0.037
0.682±0.025
0.748±0.011
0.709±0.026
0.615±0.029
0.548±0.024
In the second study, Sareen et al. (2012), the reactive uptake of common volatile organic
compounds (VOCs, methylglyoxal and acetaldehyde) by wet aerosols and cloud droplets
is explored through a coupled CCN-SOA chamber study (Figure 22). Once absorbed,
these gaseous VOCs may react to form low-volatility secondary organic aerosol (SOA), thus
impacting the chemical composition, κ, and CCN activity of the seed aerosol particles (Kroll
et al., 2005; Duplissy et al., 2008; Ervens and Volkamer , 2010). Few studies have focused
on the impact of SOA generated within the aqueous phase, even though the SOA generated
through aqueous-phase chemistry is likely to be highly oxygenated and surface active (Sareen
et al., 2010; Schwier et al., 2010), and thus facilitate CCN activity by increasing κ. Sareen
et al. (2012) investigates the change in κ and CCN activity of acidified ammonium sulfate
seed aerosols upon exposure to gas-phase methylglyoxal or acetaldehyde, at 250 ppb or 8
ppb levels (with the 8 ppb level being at the most atmospherically relevant concentration).
Significant enhancements in CCN activity were observed on the timescale of just a couple of
hours, quantified by a 15% reduction in the critical diameter for activation for the reacted
98
particles compared to pure ammonium sulfate seed particles, and an increase in the particle
κ by as much as 65% (Table 6). These enhancements are attributed to the adsorption
of methylglyoxal and acetaldehyde to the gas-aerosol interface, leading to surface tension
depression of the aerosol. This study represents the first direct experimental evidence that
the uptake of relatively insoluble, volatile organics gases by atmospheric aerosol particles
may lead to enhanced CCN activity. An important implication of this study is that gasphase surfactants may be an important and overlooked source of enhanced CCN activity in
the atmosphere.
Figure 22: Schematic of experimental set-up as shown in Sareen et al. (2012). Gas phase
methylglyoxal and/or acetaldehyde and ammonium sulfate seed aerosols are introduced
into the chamber maintained at 62-67% relative humidity (RH). At the outlet of the chamber, particles are analyzed with a Scanning Mobility Particle Sizer (SMPS), consisting of
a Differential Mobility Analyzer (DMA) and Condensation Particle Counter (CPC), and
Continuous-Flow Streamwise Thermal Gradient Cloud Condensation Nuclei Counter (CFSTGC).
A.3
Investigating the Microbiome of the Upper Troposphere in Tropical
Cyclones
The NASA GRIP observations presented in Chapter 5 were further utilized to investigate
the composition and abundance of microorganisms in the mid-to-upper troposphere before,
during, and after the occurrence of Tropical cyclones (TCs) Earl and Karl. Flight tracks used
for the analysis are shown in Figure 23, which represents one of the first studies to investigate
the concentration and type of biological particles present in the upper troposphere (at ∼ 10
99
Table 6: Hygroscopicity parameter, κ, values for the different chamber and flowtube experiments for methylglyoxal (MG) and acetaldehyde (AC) as presented in Sareen et al.
(2012).
Experiment
κ (avg)
(NH4 )2 SO4
5 hr 250 ppb MG
3 hr 250 ppb MG
3 min 250 ppb MG
5 hr 8 ppb MG
3 min 8 ppb MG
5 hr 250 ppb AC
5 hr 8 ppb AC
5 hr 8 ppb AC, 8 ppb MG
0.60±0.18
0.81±0.24
0.74±0.22
0.63±0.18
0.55±0.16
0.59±0.18
0.81±0.24
0.72±0.22
0.71±0.19
km). Filter samples of the ambient air sampled by the NASA DC-8 aircraft were collected
on Whatman cellulose nitrate membranes of 0.22 µm pore size and 47 mm diameter and
shipped to Georgia Tech for the DNA extraction and bacterial and fungal cell quantification,
as described in DeLeon-Rodriguez et al. (2012). This authors role in this study was to aid
in the design and execution of the filter collections, assist with the interpretation of the
data in the context of the aerosol and CCN measurements outlined in Chapter 5, and to
calculate back-trajectories using NOAA HYSPLIT to aid in the source-type identifications
of continental versus marine influences.
As reported in DeLeon-Rodriguez et al. (2012), approximately 20% of the aerosol particles observed within the 0.25-1 µm diameter range were viable bacterial cells, which was
significantly higher than expected. These bacterial cells exceeded fungal cells by at least
an order of magnitude, suggesting that bacterial cells may be an important and previously
underestimated fraction of micron-sized atmospheric aerosols. These findings suggest that
TCs could not only be source of new particle formation (as shown in Chapter 5), but they
may also promote the generation and re-distribution of significant concentrations of bacterial cells, which can get lofted up in to the upper troposphere from convective updrafts.
Since bacterial cells and microorganisms are often better ice nuclei (IN) than other nonbiological particles (Vali et al., 1976) and may also be sources for giant CCN (Bauer et al.,
100
Hurricane Earl Track
Hurricane Karl Track
5
4
3
2
1
Latitude ºN
25
0
Hurricane Intensity
30
20
15
10
F06 (Gulf)
F09 (Earl)
F10 (Earl)
F20 (Karl)
F21 (Karl)
F22 St. Croix Transit
-100
-90
-80
-70
Longitude ºW
-60
Figure 23: NASA GRIP DC-8 flight tracks used for bacterial and fungal cell quantification,
as described in DeLeon-Rodriguez et al. (2012).
2003), these biological particles generated and lofted by TCs may have additional impacts
on cloud microphysics that should continue to be explored further.
101
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VITA
Terry Lathem was born in Gainesville, Georgia, in 1984 to Richard and Janet Lathem. He
attended North Hall High School in Gainesville Georgia, graduating among the top five of
his class in 2002. He began his undergraduate studies at Gainesville State College, where he
studied physics for two years and received both the Dean’s Award and Physics Student of
the Year Award before transferring to Georgia Tech in 2004. Terry graduated Georgia Tech
with a B.S. from the School of Earth and Atmospheric Science, obtaining highest honors
and graduating at the top of his undergraduate class in the Fall of 2006. After pursuing a
Langely Aerospace Research Summer Scholar Internship at the National Aeronautics and
Space Administration’s Langley Research Center under the direction of Dr. Bruce E. Anderson, he continued his studies at Georgia Tech, entering the Ph.D. program of the School
of Earth and Atmospheric Science under the direction of Professor Athanasios Nenes in the
Fall of 2007. As a graduate student at Georgia Tech, Terry was supported by graduate
research fellowships from the Langley Aerospace Research Center of the National Aeronautics and Space Administration, the National Science Foundation, and the prestigious
Georgia Tech Institutional Fellowship. During his graduate studies, he received 12 awards
and honors, including three best presentation awards at national conferences, four NASA
group achievement and team leader awards, and five Georgia Tech and School of Earth
and Atmospheric Science Awards (Quarter Century Award, President’s Research Award,
Chair’s Award, John Bradshaw Award, and Glenn Cass Award). He is the author or coauthor of seven peer-reviewed journal publications with six additional publications currently
in preparation or review. He will graduate in October of 2012 with a Ph.D. in Earth and
Atmospheric Science. In November of 2012, Terry will begin an Associate Scientist position
at the Phillips 66 Research Center based in Bartlesville, Oklahoma, where he will work in
the sustainability/techonologies water and air quality division.
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