TITLE: A-ATOFMS ICE-L DATA ARCHIVE

AUTHORS: Prof. Kimberly A. Prather, Dr. Kerri A. Pratt
University of California, San Diego
9500 Gilman Dr, M/C 0314
La Jolla, CA 92093-0314
PH: 858-822-5312, 858-822-5745
FAX: 858-534-7042
E-MAIL: kprather@ucsd.edu, & kapratt@purdue.edu (or kerripratt2@yahoo.com)


1.0 DATA SET OVERVIEW

This data set includes aerosol size and chemical composition data
obtained using the aircraft aerosol-time-of-flight mass spectrometer
(A-ATOFMS) during clear air and counterflow virtual impactor (CVI)
sampling periods of the Ice in Clouds Experiment - Layer Clouds
(ICE-L) based out of Broomfield, CO from Nov.-Dec. 2007.  Data is
included for the following flights: TF02, TF03, TF04, RF01, RF02,
RF03, RF04, RF05, RF06, RF07, RF09, RF10, RF11, and RF12.


2.0 INSTRUMENT DESCRIPTION

In-situ measurements of the size-resolved chemical composition of
individual submicron residual particles were made using an aircraft
aerosol time-of-flight mass spectrometry (A-ATOFMS) (Pratt et al.,
2009).  The A-ATOFMS measured the vacuum aerodynamic diameter (dva)
and dual-polarity mass spectra of individual particles from ~70-1200
nm in real-time.  Briefly, following a 210Po neutralizer and
pressure-controlled inlet, particles are focused in an aerodynamic
lens system.  Particles are optically detected by two continuous wave
532 nm lasers spaced 6.0 cm apart, providing particle velocity and,
thus, dva; polystyrene latex spheres of known physical diameter from
95-1500 nm were used to complete the single-particle size calibration.
During ICE-L, particles were desorbed and ionized using 266 nm
radiation from a Q-switched Nd:YAG laser operating at ~0.4-0.6 mJ.
Positive and negative ions resulting from individual particles are
detected within the time-of-flight mass spectrometer.


3.0 DATA COLLECTION AND PROCESSING

During ICE-L clear air sampling, particles were obtained using the
Wyoming heated inlet.  The temperature of the Wyoming heated inlet may
be found in the NCAR/NSF C-130 Navigation, State Parameter, and
Microphysics Flight-Level Data under the variable XAERIT.  During most
periods of cloud sampling, particles were obtained as residues of
cloud droplets and ice crystals sampled through the NCAR CVI (Cynthia
Twohy, Oregon State Univ.).  This archived data set does not include
ground-based, interstitial, or smoke plume sampling; these sampling
periods may be obtained upon request.

Single-particle mass spectra and corresponding particle size
information were imported into YAADA (www.yaada.org), a software
toolkit for Matlab (The MathWorks, Inc.).  An adaptive resonance
theory-based clustering method (ART-2a) (Song et al., 1999) was used
to classify single-particle mass spectra with a vigilance factor of
0.80, learning rate of 0.05, and 20 iterations.  ART-2a classifies
particles into separate clusters based on the presence and intensity
of ion peaks in individual single-particle mass spectra.  Resulting
ART-2a clusters were classified into 12 general particle types:
biomass burning, organic carbon (OC), aromatic, amine, elemental
carbon-organic carbon (ECOC), elemental carbon (EC), biogenic or
inorganic-OC, salt, dust, metal, sulfate-nitrate, and sulfuric acid.
General particle classes are defined by the characteristic chemical
species or possible source; these labels do not necessarily reflect
all of the species present within a particular particle type.  Mass
spectral peak identifications correspond to the most probable ions for
a given m/z ratio based on previous ATOFMS lab and field studies; the
peak area of a specific m/z is related to the amount of a specific
species on each particle (Bhave et al., 2002).  Using ART-2a, 89% of
the total clear air and CVI particles were classified and are included
in this data set.

Biomass burning particles are generally characterized by an intense
potassium ion peak at m/z 39(K+), less intense carbonaceous marker
ions (eg. m/z 12(C+), 27(C2H3+), 36(C3+), 37(C3H+), -26(CN-)), as well
as sulfate (m/z -97(HSO4-)) and often nitrate (m/z 62(NO3-)) (Silva et
al., 1999).  These particles are likely from wildfires, prescribed
burns, and residential wood burning.  The mass spectra of the OC
particles were usually dominated by OC marker ions, including m/z
27(C2H3+/CHN+) and 37(C3H+)(Silva et al., 2000); the corresponding
negative ions often featured nitrate (m/z -46(NO2-), -62(NO3-),
-125(H(NO3)2-)), sulfate (m/z -80(SO3-), -97(HSO4-)), and sulfuric
acid (m/z -177(HSO4SO3-) and -195(H2SO4HSO4-)) markers (Miller et al.,
2005).  A subset of OC particles, termed aromatic particles, were
characterized by OC marker ions, aromatic fragment ions, such as(m/z
51(C4H3+), 63(C5H3+), 77(C6H5+), 91(C7H7+), 115(C9H7+), 165(C13H9+),
189(C15H9+), 219(C17H15+)), polycyclic aromatic hydrocarbon (PAH)
molecular ions (m/z 178(phenanthrene/anthracene),
202(pyrene/fluoranthene)), and often organic nitrogen, nitrate, and
sulfate (Silva et al., 2000).  Particles with a dominant amine
signature were often described by OC, ammonium, nitrate, sulfate, and
amine markers: m/z 58(C2H5NHCH2+), 59(trimethylamine), and
142(C8H16NO+) (Angelino et al., 2001).  ECOC particles were
characterized by carbon cluster ions (Cn+), OC, potassium, nitrate,
sulfate, and sulfuric acid.  EC particles not containing OC were
distinguished by both positive and negative ion carbon clusters ions
(Cn+, Cn-) and often potassium, nitrate, and sulfate.  The mass
spectra of the biogenic/inorganic-OC particles were characterized by
OC with sodium, potassium, and/or calcium, and often with organic
nitrogen, nitrate, and phosphate; a fraction of these particles were
likely from biogenic sources (Fergenson et al., 2004).  The positive
ion mass spectra of the salt particle type were dominated by sodium
(m/z 23(Na+)), potassium, calcium (m/z 40(Ca+)), magnesium (m/z
24,25,26(Mg+)), and sodium chloride clusters (m/z 81,83(Na2Cl+),
97,99(Na2ClO+)); the corresponding negative ions were characterized by
chloride, nitrate, and sulfate, in addition to m/z -16(O-), -26(CN-),
-42(CNO-), -43(CH3COH-/HCHO-), and -93,-95,-97(NaCl2-).  The mineral
dust mass spectral signature was usually dominated by potassium,
calcium, sodium, and aluminum, in addition to often OC, nitrate,
phosphate, and sulfate. The metal particles contained strong
signatures of iron, barium (m/z 138(Ba+), 154(BaO+), 155(BaOH+)), or
lithium (m/z 7(Li+)) often with contributions from calcium, OC,
nitrate, phosphate, and/or sulfate.  Particles containing sulfate and
nitrate and for qhich no positive ion mass spectra were collected are
listed as "sulfate-nitrate"; these particles generally follow the
trends of the OC particle type.  The sulfuric acid particles were
characterized by only negative ion mass spectra consisting of m/z
-97(HSO4-), -177(HSO4SO3-), -195(H2SO4HSO4-), and -293((H2SO4)2SO3-).

The chemical composition (mixing state) of these general particle
types varied flight to flight and within flights.  To give an
indication of the relative amount of sulfate, nitrate, ammonium, and
chloride on these particles, the absolute peak areas of sulfate (m/z
-97, HSO4-), nitrate (m/z -62, NO3-), ammonium (m/z 18, NH4+), and
chloride (m/z -35, Cl-) have been exported for each particle in this
dataset.


4.0 DATA FORMAT

The accompanying data set file is a column delimited ASCII file.  Each
row represents an individual particle characterized by the A-ATOFMS.
Column 1 is a flag for either clear air (0) or CVI (1) sampling.
Column 2 is a flag for the general chemical/source characterization of
the particle; the codes of the particle classes, described above, are
as follows:

1 = biomass burning
2 = organic carbon
3 = aromatic
4 = amine
5 = elemental carbon-organic carbon
6 = elemental carbon
7 = biogenic or inorganic-organic carbon
8 = salt
9 = dust
10 = metal
11 = sulfate-nitrate
12 = sulfuric acid

Column 3 is the time stamp for each particle analyzed; the time shown
has been corrected for approximate sampling delay times.  The time is
given in UTC as MONTH/DAY/YEAR HOUR:MINUTE:SECOND.  Column 4 is the
vacuum aerodynamic diameter of each particle, given in nm.  Columns
5-8 are absolute peak areas of sulfate (m/z -97, HSO4-), nitrate (m/z
-62, NO3-), ammonium (m/z 18, NH4+), and chloride (m/z -35, Cl-),
respectively, given in arbitrary units.  Higher numbers indicate more
sulfate, etc., on the specific particle.  Generally, peak areas
greater than 5000 generally indicate that the particles has likely
been aged in the atmosphere (Sullivan et al., 2007).


5.0 DATA REMARKS

The analysis method used herein to classify or group the particles
gives a first approximation for the general types of particles present
at different time periods.  Since ART-2a only classified 89% of the
total particles, the authors highly encourage users to contact them to
work together to incorporate missing particles during time periods of
interest.  In addition, as this was the first aircraft deployment of
the A-ATOFMS, we ask all users of this data to contact Prof. Kimberly
Prather and Kerri Pratt and consider co-authorship.


6.0 REFERENCES

Angelino, S., Suess, D.T., Prather, K.A. Formation of aerosol
particles from reactions of secondary and tertiary alkylamines:
Characterization by aerosol time-of-flight mass
spectrometry. Environ. Sci. Technol. 2001, 35, (15), 3130-3138.

Bhave, P.V., Allen, J.O., Morrical, B.D., Fergenson, D.P., Cass, G.R.,
Prather, K.A. A field-based approach for determining ATOFMS instrument
sensitivities to ammonium and nitrate, Environ. Sci. Technol. 2002,
36, (22), 4868-4879.

Fergenson, D. P., Pitesky, M. E., Tobias, H. J., Steele, P. T.,
Czerwieniec, G. A., Russell, S. C., Lebrilla, C. B., Horn, J. M.,
Coffee, K. R., Srivastava, A., Pillai, S. P., Shih, M. T. P., Hall,
H. L., Ramponi, A. J., Chang, J. T., Langlois, R. G., Estacio, P. L.,
Hadley, R. T., Frank, M., Gard, E. E. Reagentless detection and
classification of individual bioaerosol particles in
seconds. Anal. Chem. 2004, 76, (2), 373-378.

Miller, T.M., Ballenthin, J.O., Viggiano, A.A., Anderson, B.E., Wey,
C.C. Mass distribution and concentrations of negative chemiions in the
exhaust of a jet engine: Sulfuric acid concentrations and observation
of particle growth. Atmos. Environ. 2005, 39, 3069-3079.

Pratt, K.A., Mayer, J.E., Holecek, J.C., Moffet, R.C., Sanchez, R.O.,
Rebotier, T., Furutani, H., Gonin, M., Fuhrer, K., Su, Y., Guazzotti,
S., Prather, K.A. (2009) Development and characterization of an
aircraft aerosol time-of-flight mass spectrometer. Anal. Chem., 2009,
In Press.

Silva, P.J., Liu, D.Y., Noble, C.A., Prather, K.A. Size and chemical
characterization of individual particles resulting from biomass
burning of local Southern California
species. Environ. Sci. Technol. 1999, 33, (18), 3068-3076.

Silva, P.J., Prather, K.A. Interpretation of mass spectra from organic
compounds in aerosol time-of-flight mass
spectrometry. Anal. Chem. 2000, 72, (15), 3553-3562.

Song, X.H., Hopke, P.K., Fergenson, D.P., Prather, K.A. Classification
of single particles analyzed by ATOFMS using an artificial neural
network, ART-2A. Anal. Chem. 1999, 71, 860-865.

Sullivan, R.C., Guazzotti, S.A., Sodeman, D.A., Prather, K.A. Direct
observations of the atmospheric processing of Asian mineral
dust. Atmos. Chem. Phys. 2007, 7, 1213-1236.