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Atmos. Meas. Tech., 4, 1275–1289, 2011
www.atmos-meas-tech.net/4/1275/2011/
doi:10.5194/amt-4-1275-2011
© Author(s) 2011. CC Attribution 3.0 License.
Atmospheric
Measurement
Techniques
Eddy covariance measurements with high-resolution time-of-flight
aerosol mass spectrometry: a new approach to chemically resolved
aerosol fluxes
D. K. Farmer1, J. R. Kimmel1,2,3, G. Phillips4, K. S. Docherty1, D. R. Worsnop2, D. Sueper1,2, E. Nemitz4, and
J. L. Jimenez1
1CIRES and Dept. of Chemistry and Biochemistry, University of Colorado-Boulder, Boulder, CO, USA
2Aerodyne Research, Inc., Billerica, MA, USA
3Tofwerk AG, Thun, Switzerland
4Center for Ecology and Hydrology (CEH), Edinburgh, Penicuik, UK
Received: 27 October 2010 – Published in Atmos. Meas. Tech. Discuss.: 21 December 2010
Revised: 4 April 2011 – Accepted: 14 June 2011 – Published: 29 June 2011
Abstract. Although laboratory studies show that bio-
genic volatile organic compounds (VOCs) yield substantial
secondary organic aerosol (SOA), production of biogenic
SOA as indicated by upward fluxes has not been conclu-
sively observed over forests. Further, while aerosols are
known to deposit to surfaces, few techniques exist to pro-
vide chemically-resolved particle deposition fluxes. To bet-
ter constrain aerosol sources and sinks, we have developed
a new technique to directly measure fluxes of chemically-
resolved submicron aerosols using the high-resolution time-
of-flight aerosol mass spectrometer (HR-AMS) in a new,
fast eddy covariance mode. This approach takes advan-
tage of the instrument’s ability to quantitatively identify
both organic and inorganic components, including ammo-
nium, sulphate and nitrate, at a temporal resolution of sev-
eralHz. The new approach has been successfully deployed
over a temperate ponderosa pine plantation in California dur-
ing the BEARPEX-2007 campaign, providing both total and
chemically resolved non-refractory (NR) PM1fluxes. Aver-
age deposition velocities for total NR-PM1aerosol at noon
were 2.05±0.04mms−1. Using a high resolution measure-
ment of the NH+
2and NH+
3fragments, we demonstrate the
Correspondence to: J. L. Jimenez
(jose.jimenez@colorado.edu)
first eddy covariance flux measurements of particulate am-
monium, which show a noon-time deposition velocity of
1.9±0.7mms−1and are dominated by deposition of ammo-
nium sulphate.
1 Introduction
Aerosols affect air quality (Martin et al., 2003; Monks et
al., 2009), human health (Dominici et al., 2006; Brook et
al., 2010) and climate (Solomon et al., 2007; Isaksen et al.,
2009), but remain a poorly understood component of the
Earth’s atmosphere. Dry deposition is an important aerosol
sink, influencing particle lifetime. Models currently calcu-
late deposition with parameterizations that have not been suf-
ficiently tested in the real-world (Wesely et al., 2000) leading
to significant differences in the particle loss rates predicted
by different models (Textor et al., 2006). Better measure-
ments and parameterizations of aerosol deposition rates are
important for more accurate aerosol modeling (Kanakidou et
al., 2005). Further, deposition of gas-phase semi-volatile or-
ganic compounds is poorly constrained, and ignoring it may
cause up to 50 % overestimation of secondary organic aerosol
(SOA) in chemical transport models (Bessagnet et al., 2010).
The rate of aerosol movement across the surface-
atmosphere interface, or aerosol flux, affects not only aerosol
Published by Copernicus Publications on behalf of the European Geosciences Union.
1276 D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry
lifetime and atmospheric chemistry, but also surface chem-
istry, particularly when the surface is a forest. Particulate
deposition to ecosystems can be a major nutrient source, af-
fecting nitrogen, phosphorus and calcium cycling (e.g., Lind-
berg et al., 1986; Pett-Ridge, 2009; Vicars et al., 2010). Ni-
trogen is a key component of both anthropogenic and bio-
genic aerosols, and is often a limiting nutrient in temper-
ate forests (Vitousek et al., 1991), the supply of which can
stimulate plant growth and carbon storage in forests (Mag-
nani et al., 2007; Sutton et al., 2008). High nitrogen fertil-
ization levels, however, can reduce forest health and cause
plant death and loss of diversity (Matson et al., 2002; Mag-
ill et al., 2004; Stevens et al., 2004). Further, while particle
fluxes are known to be size dependent (Vong et al., 2010),
they are also expected to be chemically dependent (Erisman
et al., 1997; Ruijgrok et al., 1997). Models typically in-
clude size-dependent particle fluxes, but do not allow for up-
ward fluxes of particles from ecosystem surfaces, let alone
chemically-resolved deposition fluxes. Emissions may arise
from the release of primary biological particles and the gas-
particle conversions in and above vegetation canopies, below
the measurement height.
Fluxes of chemical components in the gas or particle phase
are driven by turbulent eddies in the atmosphere that oper-
ate in the “inertial sub-range”, a range of turbulence typ-
ically corresponding to timescales of seconds to minutes.
Eddy covariance (EC) uses the covariance between vertical
wind speed and species concentration to determine the flux,
and is the most commonly used direct method for measur-
ing surface-atmosphere exchange (Baldocchi et al., 1988).
EC flux measurements over forests are typically taken at 5
to 10Hz in order to capture the smallest eddies that con-
tribute to the flux. Measurements are typically averaged
over 30min, which is long enough to capture the larger flux-
relevant eddies, but not so long as to introduce errors from
atmospheric non-stationarity. A challenge is collecting data
at evenly spaced intervals to reduce errors.
Few instruments are capable of making accurate and pre-
cise in situ measurements with enough sensitivity at 10Hz to
determine aerosol fluxes. While frequently applied to CO2
and other gas phase species, the application of the eddy co-
variance approach to aerosols has been limited by the strin-
gent instrumental requirements: measurements must not only
be portable and free of interference, but they must also be
fast and sensitive enough to capture fluctuations on the time
scale of flux-carrying turbulent eddies (≥5Hz). Fluxes of
total or size-resolved aerosol number (without chemical in-
formation) have been performed for some time (e.g., Katen
et al., 1985; Sievering, 1987; Buzorius et al., 1998; Dorsey et
al., 2002; M˚
artensson et al., 2006; Vong et al., 2010). How-
ever, total and chemically-resolved particle mass fluxes have
lagged behind because most instruments measuring mass or
aerosol chemical composition are far from meeting the rig-
orous requirements for EC, and most chemically-resolved
aerosol flux measurements have been indirect with slower
time resolution approaches (e.g., Nemitz et al., 2004b; Trebs
et al., 2006; Myles et al., 2007; Thomas et al., 2009; Wolff et
al., 2010).
The Aerodyne quadrupole – aerosol mass spectrometer
(Q-AMS) was recently adapted to make EC flux measure-
ments of submicron aerosol chemical species (Nemitz et al.,
2008). Fluxes by Q-AMS are restricted to about ten mass-to-
charge ratios (m/z) with unit m/z resolution, but can include
sulphate, nitrate and markers of both hydrocarbon-like or-
ganic aerosol (HOA) and oxygenated organic aerosol (OOA)
(Nemitz et al., 2008), with the limitation that certain assump-
tions are needed to derive quantitative organic mass fluxes
from the monitoring of a few tracer m/z. Here, we describe
the application of a novel, fast data acquisition system (Kim-
mel et al., 2011) to a high-resolution time-of-flight aerosol
mass spectrometer (HR-AMS), which enables direct eddy
covariance flux measurements of chemically resolved non-
refractory (NR) PM1particles with far more chemical in-
formation that was possible with the Q-AMS. Making flux
measurements at higher mass spectral resolution is necessary
for measuring fluxes of a larger array of chemical compo-
nents, and introduces the potential for measuring ammonium
(NH+
4)fluxes.
2 Methods
2.1 Site
We deployed the HR-AMS in alternating eddy covari-
ance/standard modes in a mid-elevation Sierra Nevada pon-
derosa pine plantation during the BEARPEX-2007 (Bio-
sphere Effects on AeRosols and Photochemistry EXperi-
ment) campaign. BEARPEX-2007 took place at the Univer-
sity of California at Berkeley’s Blodgett Forest Research Sta-
tion (1330m, 38◦53.7180N 120◦38.0410W) between 10 Au-
gust and 3 October 2007. The site has been described in
detail elsewhere (Goldstein et al., 2000; Murphy et al., 2006;
Day et al., 2009). Blodgett Forest is characterized by consis-
tent meteorology in which day-time upslope flows bring air
masses influenced by local pine forests, upwind oak forests,
and the Greater Sacramento Area in the Central Valley of
California (Lamanna et al., 1999; Murphy et al., 2006; Day
et al., 2009). Air flows downslope at night, bringing cleaner
background air to the site. The site and daytime fetch is lo-
cated in a plantation dominated by Pinus ponderosa L. (pon-
derosa pine), which was planted in 1990. The understory
is composed of Ceanothus cordulatus (whitethorn) and Ar-
costaphylus spp. (Manzanita) (Misson et al., 2005). Dur-
ing the BEARPEX-2007 campaign, the canopy had a mean
height of 7.9m; the understory was 2 m. One-sided Leaf
Area Index (LAI) for the full canopy was 5.1 m2m−2. Unless
otherwise specified, the measurements presented here rep-
resent only a subset of the BEARPEX-2007 project, from
12–27 September 2007, during which both the instrument
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D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry 1277
performance and meteorology were consistent. The inlet and
sonic anemometer were 25m above the ground at the top
of a walk-up tower, while the HR-AMS was located in a
temperature-controlled container at the bottom of the tower.
The HR-AMS inlet was shared with a scanning mobility par-
ticle sizer (SMPS), optical particle counter (OPC), and Dust-
Trak; the total flow was controlled by by-pass pumps with
critical orifices to be 28.3 Lpm. Flow rates were measured by
digital TSI flow meters, and found to be consistent through-
out the time period described herein.
2.2 Eddy covariance measurements
The mean vertical turbulent flux (Fc)crossing the measure-
ment plane over a horizontally homogeneous area (e.g., a for-
est) is determined as the covariance of vertical wind speed
(w) and a scalar (such as concentration, c, of a chemical
species) (Baldocchi et al., 1988),
Fc= hw0c0i(1)
The deposition velocity (Vdep)is derived from the flux and
mean concentration as
Vdep =−Fc
¯c(2)
Vertical wind speed was measured with a sonic anemome-
ter (K-style, Applied Technologies, Inc., Longmont, CO,
USA). Particles were sampled adjacent (<20 cm) to the sonic
anemometer through ∼25m of copper tubing (1.27cm OD,
Re≈3500) with a wire mesh screen to avoid insect and de-
bris contamination; residence time in the tubing was ∼4 s.
Losses through the sample system were estimated based on
flow rates and tube dimensions, and were estimated to be
negligible (<5%) for the size range of the AMS for the
BEARPEX conditions. Chemically resolved particle concen-
trations (non-refractory PM1)were measured with an Aero-
dyne High-Resolution Time-of-Flight Aerosol Mass Spec-
trometer (HR-AMS) (DeCarlo et al., 2006; Canagaratna
et al., 2007). The HR-AMS focuses particles in the 50–
1000 nm size range into a narrow beam with an aerodynamic
lens. The size range measured by the HR-AMS is determined
by the transmission efficiency of the lens, and depends on
aerodynamic lens design and operating pressure. However,
comparisons between the AMS and other accepted measure-
ments of submicron aerosol typically show good agreement.
For example, DeCarlo et al. (2008) show correlations be-
tween the AMS and an SMPS with a slope of 0.98±0.01,
suggesting that AMS measurement can be considered non-
refractory PM1. The beam exits the lens into a vacuum
chamber. Particle size is measured by modulating the par-
ticle beam with a rotating mechanical chopper and determin-
ing the particle flight time through the chamber, which is a
function of the vacuum aerodynamic particle size. At the
end of the particle time-of-flight chamber, particles impact a
heated surface (∼600◦C) that flash vaporizes non-refractory
species. The resultant vapor plume is ionized by electron
ionization (EI, 70eV), and ions are transferred to a time-
of-flight mass spectrometer (HTOF, Tofwerk, Switzerland).
The HTOF operates in either a shorter flight path V-mode,
or longer W-mode. The V-mode has higher signal, and is
thus more sensitive, while the W-mode provides mass spec-
tra with twice the resolution.
The acquisition mode of the HR-AMS was alternated ev-
ery 30-min between a standard field AMS data acquisition
mode (“General Alternation Mode”, see e.g. Canagaratna
et al., 2007) and a new flux data acquisition mode (“Flux
Mode”). In the General Alternation Mode, the HR-AMS was
alternated between a 2.5min average of V-mode mass spec-
tra and particle size-segregated data (PToF) and a 2.5min
average of W-mode mass spectra. The m/z calibration was
performed automatically every 2.5min during this standard
acquisition phase. While in Flux Mode, a novel fast mass
spectrometry acquisition system collected particle composi-
tion measurements at 5 or 10Hz. This system is described
in detail by Kimmel et al. (2011). Briefly, high-resolution
V-mode mass spectra (m/z range of 11–428) were acquired
with a save rate of 10Hz without particle size modulation.
Mass spectra of the transmitted particle and gas beam were
acquired continuously for 29min. This 29 min dataset was
preceded and followed (or “bookended”) by 30-s windows
of background measurements, in which the particle and gas
phase beam was blocked by the mechanical chopper. The
difference between the transmitted and averaged background
mass spectra was used for flux analysis. The acquisition soft-
ware forces a time grid based on the computer clock to main-
tain accurate and precise spacing between the start times of
successive measurements. For example, for 10 Hz data col-
lection, the software averaged 92.5ms of mass spectra, with
the remaining 7.5ms used for transferring the mass spec-
trum. Note that the measurement was saved even if data
could not be both acquired and transferred within the 100 ms
window. Saving takes place during the mass spectra averag-
ing for the following datapoint. However, if a measurement
could not begin within 0.1 ms of the end of the previous mea-
surement (i.e., transfer took >7.5ms), it was missed. These
missed points were replaced by interpolated values during
post-acquisition analysis. Throughout the BEARPEX-2007
field project, this setup typically led to <0.5% of the points
being missed during a given half-hour. Sonic anemometer
data were sent to the HR-AMS computer at 20Hz via a dig-
ital serial port connection. The HR-AMS data acquisition
system simultaneously collected wind speed along three axes
and temperature on the same time grid as described for mass
spectra.
The flux software saved mass spectra at 10Hz in three for-
mats: (i) complete high-resolution mass spectra, (ii) mass
spectra with unit m/z resolution, and (iii) the total signal
within a number of specified high-resolution m/z ranges.
Both unit m/z resolution (ii) and high-resolution (iii) data are
determined as the integrated signal within a defined region
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1278 D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry
of the mass spectrum. The center point of the window for
signal summation depends on the ToF-m/z calibration. The
number of points integrated into a unit m/z signal depends on
the HTOF resolution, and is always ≤m/z0.5 of either side
of the center (integer) point. For example, the unit m/z signal
for m/z48 is integrated between m/z47.879 and m/z48.193.
High resolution m/z signals are calculated as the sum of sig-
nals within a sub-integer range of m/z, typically correspond-
ing to a consistently isolated mass spectrum peak such as
NH+
2. Hereafter, any reference to a unit resolution m/z sig-
nal will be preceded by “UR” (e.g., UR m/z 48 will refer to
the unit resolution m/z48 signal). Any reference to a high-
resolution m/z signal will be preceded by “HR” (e.g., HR
m/z 47.9670, or HR SO+).
Note that for both unit and high-resolution (UR and HR)
m/z signals, the calibration of ion flight time to m/z is not
re-adjusted during the fast flux data collection, but relies on
the assumption that the calibration changes negligibly across
the 30-min period. Post-acquisition analysis of raw data con-
firmed that this assumption was met for all BEARPEX-2007
campaign data, but should be re-confirmed for all applica-
tions in other environments, particularly where the instru-
ment is subject to temperature fluctuations.
2.3 Aerosol flux approaches
Operating the HR-AMS in Flux Mode allows us to calcu-
late eddy covariance particle fluxes with three different ap-
proaches:
a. Unit m/z resolution (UR) flux, calculated from unit
m/z signals.
b. High-resolution (HR) fluxes, calculated from either HR
signals that are integrated over a defined window of the
mass spectrum (described above), or fitted HR signals,
in which the signal for a given ion is calculated from
the high-resolution mass spectra by a peak fitting proce-
dure as described elsewhere (e.g., DeCarlo et al., 2006;
M¨
uller et al., 2010).
c. Species fluxes, in which a fragmentation pattern is ap-
plied to the mass spectra, sub-dividing UR (or HR)
peaks into chemical components before calculating
fluxes. This calculation is mathematically identical to
the standard AMS data processing that produces, for ex-
ample, aerosol organic, sulphate, and nitrate concentra-
tions (Allan et al., 2004; Canagaratna et al., 2007).
For example, the aerosol sulphate flux could be determined
as the flux of (a) UR m/z 48, (b) HR SO+ion (peak cen-
tered at m/z 47.9670), or (c) a sum of HxOyS+
zfragments
(Canagaratna et al., 2007). In approach (a), the UR flux as-
sumes that sulphate is the only contributing signal to the flux
at UR m/z 48. Nemitz et al. (2008) validated this approach
for sulphate by comparing flux signals obtained at multiple
m/z thought to be dominated by sulphate. To avoid confu-
sion, we will hereafter refer to ions observed in the mass
spectrometer by their chemical formula (e.g. SO+, NH+
2)and
chemical species present in aerosol by their complete names
(e.g. sulphate, ammonium). Note that all HR fluxes described
herein were calculated from HR signals integrated over a de-
fined m/z range.
2.4 Calculations
Particle fluxes are calculated for each of the three approaches
(i.e., UR, HR, and species fluxes) following a time lag cor-
rection. The time lag between the sonic anemometer and
HR-AMS is primarily determined by the flow rate through
the inlet tubing. For the BEARPEX-2007 inlet configura-
tion, this was approximately 4s. A more precise determina-
tion of time lag can be made with an autocorrelation analysis
(Farmer et al., 2006; Nemitz et al., 2008). Time-lag deter-
mination through auto-correlation analysis can lead to flux
over-estimation in noisy data limited by counting statistics,
because it systematically tries to maximize the flux (Taipale
et al., 2010). Thus, we used autocorrelation for a sub-set of
UR signals throughout the BEARPEX-2007 datatset to find
an average time lag for the data. This single determined lag-
time of 3.8s was then applied universally for all measure-
ments described herein.
Fluxes and deposition velocities are calculated from the
signal by Eqs. (1) and (2). Note that the HR-AMS collects
signal in (bits×ns)/extraction, and the initial flux is calcu-
lated via Eq. (1) in (bits×ns)/extraction ms−1. This is con-
verted to deposition velocity (mms−1)via Eq. (2). The de-
position velocities can be reconverted to flux in more typical
units of µgm−2s−1by multiplying by average mass concen-
trations derived from the standard HR-AMS analysis for ei-
ther the flux period, or the average from the data collected
before and after the half-hour flux measurements. This is
mathematically identical to converting every 10 Hz datapoint
into a mass concentration from a raw signal and calculating
the flux using the mass concentration time series (Nemitz et
al., 2008).
Three corrections are applied to the data:
1. Sonic anemometer rotation. To account for the sonic
anemometer not being perfectly level with the ground
and for slope effects from the surrounding area, we also
apply a two-dimensional rotation to wind speed in the
three axes.
2. WPL correction. The HR-AMS measures particle mass
concentrations, rather than mixing ratios; the Webb-
Pearman-Leuning (WPL) correction is thus necessary to
account for the changes in air density caused by fluctua-
tions in water vapor (Webb et al., 1980). Corrections for
density fluctuations due to temperature are typically ig-
nored for flux measurements with long inlet lines as the
tubing is expected to dampen temperature fluctuations
Atmos. Meas. Tech., 4, 1275–1289, 2011 www.atmos-meas-tech.net/4/1275/2011/
D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry 1279
(Rannik et al., 1997; Nemitz et al., 2008; Ahlm et al.,
2009). For this dataset, the WPL correction is positive
(upwards), reducing the total aerosol mass deposition
flux by 0.1 %, with an average correction of +0.03%.
In recent studies, inlet lines have typically been dried
for aerosol composition measurements, which should
remove, or at least reduce, the WPL correction. Dur-
ing BEARPEX-2007, we decided not to dry the inlet
because of the low ambient humidity at this site; thus,
the WPL correction needs to be considered.
3. Gas-phase corrections. The HR-AMS measures both
the aerosol- and gas-phases, although the former is en-
riched by a factor of ∼107compared with the latter.
For concentration measurements, the gas-phase contri-
bution is subtracted from the signal by estimating the
average contribution from the air beam signal strength
derived at m/z 28 (N+
2)and subtracting the signals due
to, for example, oxygen and argon (O+
2, Ar+)(Allan et
al., 2004). However, this subtraction does not work for
short-term fluctuations. Thus, if a gas-phase molecule
has a substantial flux, it may contribute to the observed
particle flux (Nemitz et al., 2008). Water and CO2are
the most likely candidates for such interference. As de-
scribed above for the WPL correction, drying the in-
let would remove the water flux interference. Dur-
ing BEARPEX-2007, subtracting the water vapor flux
increases the total NR-PM1flux by less than 1%, a
negligible amount. The water vapor flux would affect
flux calculations at UR 16, 17, and 18 (i.e., nominal
m/z dominated by O+, OH+, and H2O+). However,
as sulphate and organics also contribute to these three
UR signals (Allan et al., 2004; Hogrefe et al., 2004),
interpreting the particulate water flux would require de-
convolution beyond the scope of this study.
CO2is the other likely gas-phase flux interference. CO2
dominantly fragments under EI to UR m/z 44 and 28 (Stein,
retrieved 5 June 2010) and would thus contribute to the ob-
served organic aerosol flux. This can be corrected by sub-
tracting the observed gas-phase CO2flux, which is com-
monly measured during field projects, from the UR m/z 44
flux signal (or the HR CO+
2fragment) taking into account
the efficiency with which the HR-AMS detects gas-phase
CO2, relative to aerosol-derived CO+
2(1.9×10−7during this
campaign). The largest gas-phase CO2flux observed dur-
ing BEARPEX-2007, −58µmolm−2h−1, would thus be ob-
served by the HR-AMS as a flux of −0.07ngm−2s−1. The
gas-phase CO2correction is, on average, −0.4 % for the
aerosol flux at UR m/z 44, a negligible correction for the to-
tal NR-PM1mass flux. While the correction ranges between
−98 and +55 %, the extremes occur rarely, and only when the
observed UR m/z 44 flux is near zero and below its detection
limit.
0.4
0.3
0.2
0.1
0.0
Intensity (m/z 46, arbitrary units)
15x103
1050
200
100
0
-100
-200
Vertical wind speed (cm/s)
15x103
1050 Time (s)
Fig. 1. A complete 30 min flux cycle of vertical wind speed and
the UR m/z 46 signal acquired at 10 Hz between 16:00:–16:30PST,
7 September 2007. The first and final 30 s represent the gas + back-
ground signal, while the intervening 29min represent the aerosol +
gas + background transmitted signal. The black line is the 100 point
(10 s) running mean.
3 Constraints on particle fluxes by HR-AMS
To quantify the ability of the HR-AMS to measure
chemically-resolved aerosol fluxes, we use three approaches:
(i) spectral analysis to demonstrate that the HR-AMS meets
the instrumental requirements for eddy covariance flux mea-
surements (Sect. 3.1); (ii) quantitative constraints on uncer-
tainty for both individual flux measurements and the entire
dataset (Sect. 3.2); and (iii) internal comparisons (Sect. 3.3)
to demonstrate that HR-AMS UMR and HR fragment fluxes
accurately describe the fluxes of given aerosol chemical com-
ponents.
3.1 Instrument time response
As described above, instruments used for eddy covariance
flux measurements must be both fast and sensitive. Further,
the stationarity requirement specifies that concentration mea-
surements must not vary within the time-scale of the anal-
ysis (Kaimal et al., 1994). Figure 1 shows that the fast
time resolution (10Hz) HR-AMS particle signal is clearly
distinguishable over instrument background, evidenced by
comparing the background (first and last 30s for a given
www.atmos-meas-tech.net/4/1275/2011/ Atmos. Meas. Tech., 4, 1275–1289, 2011
1280 D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry
1.0x10-3
0.8
0.6
0.4
0.2
0.0
16.416.216.015.8
100x10-6
80
60
40
20
0
39.439.239.038.8
10-5
10-4
10-3
10-2
Mass concentration (µg m-3)
100806040200 m/z
O+
NH2+
80x10-6
60
40
20
0
43.443.243.042.8
80x10-6
60
40
20
0
55.455.255.054.8
K+
C3H3+
C3H7+
C2H3O+
C3H3O+ C3H7+
30x10-6
25
20
15
10
5
0
48.448.248.047.8
SO+
Fig. 2. The particle and gas phase mass spectrum (NO3-eq. µgm−3, indicating that the data is unadjusted for potential differences in
ionization efficiencies between nitrate and other components, Jimenez et al., 2003) was taken on 7 September 2007 and calculated from a
0.0925s average of ambient data collected in the transmitted (aerosol + gas + AMS background) minus the average gas + AMS background
mass spectra. The insets show the mass spectrum around m/z 16, 39, 43, 48 and 55.
flux period) and transmitted (continuous 29min) time peri-
ods. Composition changes were visible, though rarely oc-
curred over rapid timescales within the 30-min flux mea-
surement periods during the BEARPEX-2007 campaign due
to the site’s remoteness and consistent meteorology. Thus
the eddy covariance requirements for stationarity were typ-
ically met. Further, individual high resolution mass spectra
show clear peaks above the noise (Fig. 2). However, the ob-
servation of mass spectral signal above the noise does not
demonstrate that the HR-AMS measurements are sensitive
enough to measure fluxes over forests. An additional diag-
nostic tool for EC measurements is spectral analysis. Fig-
ure 3 shows a typical frequency-multiplied co-spectrum ob-
tained from the covariance between the vertical velocity (w)
and the HR-AMS signal for a single flux measurement –
in this case, the HR NH+
2fragment taken between 16:00–
16:30PST, 7 September 2007. Both the frequency-binned
average and the entire set of 10Hz observations are in-
cluded. The frequency-binned data exhibit a (frequency)−4/3
response between 0.005 and 2.5Hz. This frequency re-
sponse is characteristic of the inertial sub-range as predicted
from dimensional analysis through the Kolmogorov theory
(Kaimal et al., 1994). The inertial sub-range is an intermedi-
ate range of turbulent scales characterized by energetic equi-
librium; measurements should encompass this sub-range of
Fig. 3. Frequency-multiplied, normalized co-spectrum as a function
of dimensionless frequency of the HR NH+
2fragment for a single
half hour, acquired at 10Hz between 16:00–16:30 PST, 7 Septem-
ber 2007. The data presented are binned averages of the entire
cospectrum, including both positive and negative points. Binned
points that were averaged to be positive are indicated by open cir-
cles, while those averaged to be negative are indicated by filled cir-
cles. As the average NH+
3flux is downwards, negative points domi-
nate the co-spectrum. The dashed black line follows the −4/3 slope
characteristic of the inertial turbulence sub-range.
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D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry 1281
turbulence for accurate eddy covariance fluxes. Deviations
from this frequency response trend towards a steeper slope
at higher frequencies would be evidence of “spectral atten-
uation”, or underestimation of fluxes due to either damping
of high-frequency signals within the sampling lines or slow
instrument response. Such deviations are not observed in
Fig. 3, nor in most daytime BEARPEX-2007 HR-AMS co-
spectra, indicating that the turbulent inlet flow minimized at-
tenuation and that the instrument response is sufficiently fast.
Co-spectra are often used in eddy covariance analysis to
determine whether flux measurements were averaged over
long enough periods of time to capture all flux-carrying
eddies. Figure 3 shows that the low-frequency eddies
(<0.004Hz, corresponding to a spatial scale >750 m for a
wind speed of 3ms−1)may still contribute some flux sig-
nal and that averaging for longer than 29 minutes may cause
a slight increase in the flux. However, as described in Ne-
mitz et al. (2008) for similar Q-AMS co-spectra, this slight
increase in flux would be captured at the expense of longer
averaging times and a potential lack of stationarity.
3.2 Detection limits and uncertainty
Several sources contribute to the uncertainty of a single flux
measurements. Instrument noise causes random errors. At-
tenuation from air flow smearing in the sample tubing and
the distance between the aerosol inlet and sonic anemometer
can cause underestimates of flux, and are thus systematic er-
rors. However, as described by Nemitz et al. (2008), because
a small number of particles are sampled during a 100ms
measurement period, and, unlike for gas-phase molecules,
this number or particles is not necessarily continuous; thus,
particle flux measurements are typically limited by particle
counting statistics (Nemitz et al., 2008; Pryor et al., 2008).
Further, particle size affects the flux measurement, as larger
particles are fewer in number, but carry the majority of the
total particle mass: Jimenez et al. (2003) reported that 2 %
of the particle number represented 50% of the submicron
particle mass for an ambient dataset in Massachusetts, USA.
Such large particles appear as spikes in a fast time series
(e.g., Fig. 1). As they contribute real flux, these large parti-
cles generally should not be removed by the de-spiking rou-
tines commonly used for gas-phase flux measurements (Ne-
mitz et al., 2008). As described in Wienhold et al. (1995),
the uncertainty of a single flux measurement can be derived
from the baseline fluctuation in the cross correlation func-
tion between vertical wind speed and the scalar of interest,
calculated with lag times significantly longer than the delay
time. This provides an alternative empirical measurement
of the detection limit, which should represent a more com-
prehensive definition of uncertainty. We calculated the pre-
cision, and thus detection limit (DL), of a single flux mea-
surement to be 3×σFlag, where σFlag is the standard de-
viation of the fluxes calculated with lagtimes offset by be-
tween 50 and 80s. For example, this metric provided a
relative error (1σ ) of the high resolution NH+
2fragment of
60%, or 0.49 ng m−2s−1, for the single flux measurement
taken between 16:00–16:30PST, 7 September 2007. The
median 1σrelative error for the complete ensemble of HR
NH+
3fluxes from BEARPEX-2007 was 62% (mode 20 %),
corresponding to a median DL of ±0.42ngm−2s−1. Rela-
tive errors for BEARPEX-2007 HR NH+
2fluxes were similar
(median 65%, mode 35%), corresponding to median DL of
±0.43ngm−2s−1. Thus, during this campaign, the typical
single half-hour flux measurement for the ammonium frag-
ments was below the detection limit, and averages of multi-
ple points must be used for scientific interpretation.
In contrast, fluxes of UR m/z fluxes dominated by ni-
trate or sulphate such as UR m/z 46 (mostly NO+
2from
nitrate) and UR m/z 64 (mostly SO+
2from sulphate) have
much smaller relative errors and lower detection limits. For
example, the relative error for a single flux measurement
at UR m/z 46 (12:00–12:30PST, 15 September 2007) is
18%, corresponding to a 3σdetection limit for an indi-
vidual 30-min nitrate flux measurement via the UR 46 sig-
nal of ±0.56ngm−2s−1, smaller in magnitude than the ob-
served flux of −1.04ng m−2s−1. Sulphate fluxes derived
from UR m/z 64 during the BEARPEX-2007 campaign had
median 1σrelative errors of 60% (20 % mode). However,
the DL for UR 64 fluctuated between 0.05–6.4ngm−2s−1,
with a median value of 1.15ng m−2s−1(mode 0.4, mean
1.49ngm−2s−1). The particle flux errors as derived by this
lagged covariance approach increase with the magnitude of
the flux, although they do not result in a constant relative er-
ror. Flux errors increase with wind speed and friction veloc-
ity, and the rate of increase is greater at higher wind speeds
(>2ms−1). The behavior of the particle flux errors suggests
that larger wind speeds, which increase mixing between the
forest canopy and atmosphere increase particle emission and
deposition and its associated uncertainty. Similarly, the DL
is larger during the daytime than nighttime for UR m/z 64.
These findings are consistent with theoretical considerations
that show that during windier/more turbulent conditions, a
concentration measurement needs to be more precise to re-
solve the same flux. For example, Fairall (1984) showed that
the error in Vdincreases with increasing standard deviation
in vertical wind speed (σw). Rowe et al. (2011) demonstrated
that sensor resolution requirements increase with u∗and in-
stability. The error considered here is the error in determin-
ing the correct local co-variance between cand w. Addi-
tional error is introduced in that the local flux detected during
a 29-min period at a single position may not be statistically
representative of the average flux over the surface – i.e., the
assumption of horizontal homogeneity is not met. Even two
“perfect” eddy-covariance flux measurement systems would
therefore not derive the same flux and this error decreases
with increasing turbulence (e.g., Hollinger et al., 2005; Ne-
mitz et al., 2009b and references therein).
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1282 D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Concentration m/z 48 (µg m-3)
0.160.120.080.040.00
Concentration m/z 64 (µg m-3)
y = -0.01 + 0.86 x
r2 = 0.97 -1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Flux m/z 48 (ng m-2s-1)
-1.0 0.0 1.0
Flux m/z 64 (ng m-2s-1)
y = 0.00 + 0.66 x
r2 = 0.34
20
15
10
5
0
-5
-10
-15
Vdep m/z 48 (mm/s)
20100-10 Vdep m/z 64 (mm/s)
y = -0.02 + 0.90 x
r2 = 0.42
1.0
0.8
0.6
0.4
0.2
0.0
NH3+ HR Fragment Mean
(bits x ns/extraction)
1.00.80.60.40.20.0 NH2+ HR Fragment Mean
(bits x ns/extraction)
y = 0.00 + 1.02 x
r2 = 0.79 -20
-15
-10
-5
0
5
10
NH3+ HR Fragment Flux
(bits x ns x m / extraction / s)
-20 -10 0 10
NH2+ HR Fragment Flux (Hz m/s)
(bits x ns x m / extraction / s)
y = 0.00 + 1.21 x
r2 = 0.72 -30
-20
-10
0
10
20
30
NH3+ HR Fragment Vdep (mm/s)
-20 0 20
NH2+ HR Fragment Vdep (mm/s)
y = -0.035 + 1.13 x
r2 = 0.47
abc
def
Fig. 4. Comparisons of mean mass concentration (or signal), flux and deposition velocity for sulphate-dominated UR m/z 48 and 64 (a–c)
and high resolution fragments NH+
2and NH+
3(d–f). Linear regressions are calculated with a weighted robust regression to account for
uncertainties in both x and y directions, with the exception of ammonium deposition velocity (f), for which we use a weighted orthogonal
distance regression.
3.3 Internal comparisons and validation
We use internal comparisons to determine whether UR
m/z particle fluxes are appropriate proxies for a species flux.
UR m/z particle fluxes have the advantage over species fluxes
of being less computationally expensive. Thus, we compare
UR m/z particle fluxes for m/z dominated by nitrate, sulphate,
or organic ions. For example, in terms of the concentration
measurements, UR m/z 48 is dominated by SO+, while UR
m/z 64 is dominated by SO+
2. To determine whether the flux
at these two nominal masses can be used as a proxy for the
sulphate flux, we compare the UR m/z signals, fluxes and de-
position velocities (Fig. 4a–c). Because there are errors asso-
ciated with values for both m/z’s, the linear regression anal-
ysis uses a robust regression based on absolute deviations
on both coordinates. For mass concentrations, we use the
standard deviation of observed mass concentrations within a
given half-hour measurement period as weights for each dat-
apoint in the regression. For the fluxes, we calculated uncer-
tainties for a single measurement with the lagged covariance
approach (Sect. 3.2). Uncertainties for individual deposition
velocity measurements were calculated following error prop-
agation from the flux and concentration uncertainties. The
signals are linearly correlated with a slope depending on the
fragmentation of sulphate in the AMS; Fig. 4a shows that
ambient sulphate fragments to SO+
2in a slightly larger frac-
tion than to SO+. Similarly, the two UR fluxes are linearly
correlated, with a slope representative of the different con-
tributions to the fluxes of the two fragments (Fig. 4b). Note
that removing the two outlying points improves the correla-
tion (r2=0.80), but does not change the slope or intercept.
This correlation is consistent with the signals at UR m/z 48
and 64 being controlled by the same mechanisms, providing
evidence that both UR m/z signals are dominated by sulphate.
While the magnitude of both the signals and fluxes are not
necessarily the same due to fragmentation patterns, the de-
position velocity (Fig. 4c) should represent the overall sul-
phate deposition. In the absence of additional peaks in high
resolution data indicating potential interferences, a non-unity
slope can be interpreted as an upper estimate for the uncer-
tainty in sulphate deposition velocity. Thus, the slope of 0.90
suggests that the mean deposition velocity calculated from a
single sulphate fragment has a potential bias of ∼10%.
Unlike the sulphate-derived SO+and SO+
2signals at UR
m/z 48 and 64, which generally only have much smaller
contributions from organic species, particulate ammonium
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D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry 1283
0.5
0.4
0.3
0.2
0.1
0.0
Mass Loading (µg m-3)
24181260 Time of Day
1.0
0.8
0.6
0.4
0.2
0.0 24181260
15
10
5
0
-5
x10-3
24181260 Time of Day
6
5
4
3
2
1
0
Mass Loading (µg m-3)
24181260
6
5
4
3
2
1
024181260
0.5
0.4
0.3
0.2
0.1
0.0 24181260 Time of Day
25th-75th percentile
median
mean
NR-PM1
Organic SO42-
NO3-Cl-
NH4+
Fig. 5. Diurnal cycles (local time, PDT) for mass concentrations of total NR-PM1and the organic, ammonium, nitrate, sulphate, and chloride
components for the entire BEARPEX-2007 campaign (18 August 2007–2 October 2007). Hourly means and medians are shown in black
diamonds and grey circles, respectively; grey lines indicate the 25th and 75th percentiles.
dominantly fragments to NH+, NH+
2and NH+
3, which over-
lap in the UR mass spectrum with CH+
3, O+and OH+at
m/z 15, 16 and 17, respectively (e.g. inset, Fig. 2). The
O+and OH+fragments are typically much larger than the
ammonium fragments in the mass spectrometer background
(due to residual H2O); both particulate and gas-phase H2O
also contribute to the transmitted signal. The CH+
3aerosol
fragment is of similar magnitude to NH+. Thus quantifica-
tion of ammonium fluxes at unit mass resolution is particu-
larly difficult. In standard UR AMS concentration data, this
is typically dealt with by use of the fragmentation table (Al-
lan et al., 2004), and accepting a higher level of noise for
ammonium than other species (e.g., DeCarlo et al., 2006).
However, because fluxes can have both negative and pos-
itive components, and can change in both magnitude and
direction throughout the day, creation of a separate, robust
fragmentation table for fluxes is difficult. While the calcu-
lation of “species fluxes” through application of the stan-
dard fragmentation table to every 0.1s measurement point
is as valid as its application to routine HR-AMS data anal-
ysis, it is not only computationally expensive, but also can
result in large uncertainties where a flux is calculated from a
UR m/z that is subject to a large gas-phase correction: small
absolute random noise in the air beam signal will induce
large relative noise for the aerosol mass derived from these
peaks. In contrast, the increased resolution of the HR-AMS
allows for the mass spectral separation of these interferences
and creates the potential to measure particulate ammonium
fluxes. Figure 4d–f show the correlation in signal, flux and
deposition velocity for HR NH+
2and NH+
3ions, integrated
between m/z 16.010–16.040 and m/z 17.020–17.050, respec-
tively. The near-unity slope for mean signals (Fig. 4d) in-
dicates that ammonium is fragmented in the instrument to
these ions nearly equally, as typically observed for the AMS
(Allan et al., 2004). The values for r2for fluxes and de-
position velocities between the two HR NH+
xfragments are
0.72 and 0.47, respectively, providing evidence that the con-
centrations and fluxes for the two HR fragments are likely
derived from the same source. This is different from the
correlation between the corresponding UR m/z signals (not
shown): while the signals for m/z 16 and 17 are linearly cor-
related, the fluxes are not correlated (r2=0.09). Thus, while
the signals and fluxes of the HR fragments are specific to the
exact fragment (e.g., the NH+
2ion), we consider the deposi-
tion velocity for either of the HR fragments to be representa-
tive of total particulate-ammonium. The difference between
deposition velocities derived from the two HR fragments is
reflected in the slope of the regression line (Fig. 4f, 1.13),
suggesting an uncertainty in the average ammonium deposi-
tion velocity derived from each fragment of up to 13%, al-
though the uncertainty is much larger (∼65%) for individual
30-min fluxes, as described above.
4 Observations
NR-PM1mass concentrations were, on average, 4 (±2,
s.d.)µgm−3during the BEARPEX-2007 project (Fig. 5).
As expected for a forested field site downwind of urban
sources, organic aerosol dominated NR-PM1at Blodgett
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1284 D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry
5
4
3
2
1
0
NR-PM1 (µg m-3)
24181260
-4
-2
0
2
4
NR-PM1 Flux (ng m-2s-1)
24181260
-2
-1
0
1
2
NR-PM1 Vdep (mm/s)
24181260
Time of Day
a
b
c
Fig. 6. Diurnal cycles (local time, PDT) of mass concentrations,
fluxes and deposition velocities of total NR-PM1mass from flux
data for 13–27 September 2007. Uncertainties are taken as the stan-
dard error of the mean for each time bin.
Forest, contributing 70 % (±10 %, s.d.) of the mass on av-
erage. Diurnal cycles of aerosol components (Fig. 5) were
consistent with the regular pattern in meteorology typically
observed in the region (Murphy et al., 2006). Total NR-PM1
concentration was lowest in the early morning and increased
both in the mid-morning due to arrival of plumes from the
upwind oak forest, and mid-afternoon due to the arrival of
urban-influenced air masses from the Greater Sacramento
Area. Similar diurnal patterns were observed for organic and
nitrate components. Ammonium and sulphate were lowest at
∼11:00PST, consistent with previous VOC and NOxmea-
surements that indicate that morning air was dominated by
biogenic emissions and less influenced by the agricultural or
combustion sources that tend to play a larger role later in the
day (Lamanna et al., 1999; Day et al., 2009).
A detailed presentation and analysis of particle fluxes is
beyond the scope of this manuscript, but diurnal observations
for both total aerosol and ammonium from HR NH+
2are pre-
sented in Figs. 6 and 7, respectively. On average, deposi-
tion of both total NR-PM1and submicron ammonium were
4
2
0
-2
-4
NH4 Vdep (via NH2+
fragment, mm/s)
24181260
Time of Day
-2
-1
0
NH4 Flux (via NH2+
fragment, ng m-2s-1)
24181260
0.4
0.3
0.2
0.1
0.0
NH4 (via HR NH2+
fragment, µg m-3)
24181260
a
b
c
Fig. 7. Diurnal cycles (local time, PDT) of mass concentrations,
fluxes and deposition velocities of particulate ammonium, as cal-
culated from the NH+
2HR fragment from fast, flux data for 13–
27 September 2007. Uncertainties are taken as the standard error of
the mean for each time bin.
observed, with maximum deposition velocities occurring in
the late morning. Note that total NR-PM1fluxes were calcu-
lated as the sum of species fluxes. Deposition velocities for
a given subset of data are derived from the negative slope of
flux as a function of mass concentration. From the slope of
flux versus mass concentration for noon-time data, the mag-
nitude of the NH+
2fragment deposition (1.9±0.7mms−1)
is within the uncertainty of total PM1deposition velocities
(2.05±0.04mms−1).
5 Discussion
5.1 HR-AMS eddy covariance fluxes
In this manuscript, we presented three approaches to defin-
ing fluxes: UR m/z, HR and species fluxes. Each of these
approaches makes assumptions. The UR m/z flux gives a
combined flux signal comprised of individual contributions
from each ion present at the mass, which may have fluxes of
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D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry 1285
y = -0.05 + 0.94 x
r2 = 0.46
y = 0.94 x
r2 = 0.92
ab
Fig. 8. The acid balance for NR-PM1aerosol: comparison of concentrations and fluxes for cations (ammonium) versus anions (sulphate,
nitrate, chloride). The solid lines are the robust regression fits. Note that the regression for cation and anion concentrations forces a zero
intercept because the zero of these components are verified by periods in which ambient air is sampled through a total particle filter, and
should not have an offset with respect to each other.
different magnitudes and signs. The contribution of different
ions to the flux of a given UR m/z is not necessarily equiva-
lent to the contribution of those ions to the signal, or mass,
at that m/z. Comparing fluxes and concentrations for two UR
m/z attributed to the same chemical component provides val-
idation of this approach. HR fluxes rely on the integration
of data points within a defined m/z window, and require ei-
ther that fragments are isolated from a parent peak, as is the
case for NH+
2(inset, Fig. 2), or that the peak can be clearly
distinguished in fast mass spectra by use of a fitting routine.
Species fluxes share the same set of caveats as mass concen-
trations (Canagaratna et al., 2007), along with the additional
uncertainties of correcting for gas-phase contributions. Val-
idation of the fluxes using correlations of fluxes calculated
from several ions or m/z as described here (Fig. 4) provides
additional insight on interpreting fluxes, and is highly recom-
mended for future studies.
It is important to realize that HR-AMS fluxes are subject to
the same interpretation uncertainties as standard AMS mass
concentrations calculated by well-established routines (e.g.,
Canagaratna et al., 2007). In particular, the sulphate, ni-
trate and ammonium fluxes are not necessarily due to pure
inorganic components. Organic sulphates are known to frag-
ment to inorganic HxSO+
yions indistinguishably from inor-
ganic ammonium sulphate (Farmer et al., 2010). Organic ni-
trates fragment to NO+
xions slightly differently from ammo-
nium nitrate, but not so differently as to enable easy quantifi-
cation given variations in the organic nitrate fragmentation
and potential contributions from mineral nitrates and pos-
sibly nitrites. Thus HR-AMS derived sulphate and nitrate
fluxes must be considered the sum of both organic and in-
organic components (Farmer et al., 2010). The CH3SO+
2
HR fragment may help to separate organic sulphate and or-
ganic sulfonic acid contributions from inorganic sulphate.
Additionally, amines and other reduced organic nitrogen
components of aerosol may produce NH+
2and NH+
3frag-
ments (Sun and Zhang, 2011) that may contribute to the ob-
served particulate ammonium fluxes derived from HR frag-
ments.
Further, in interpreting these HR-AMS fluxes, it is impor-
tant to realize that aerosol chemical components (e.g. nitrate)
may be affected by chemistry and changes in the gas/aerosol
partitioning (e.g., photochemistry, uptake on aerosol sur-
faces, evaporation to or condensation from the gas phase).
As a result, the flux observed at the measurement height
will not only represent the surface flux, but will also in-
clude any chemical sources and sinks below the measure-
ment height. In addition, the fluxes are derived from the
aerosol mass within a certain size-range, which may not be
a conserved parameter where the aerosol size changes be-
yond the upper or lower size cut-off during vertical transport.
By integrating over the total accumulation mode mass of the
chemical components, the HR-AMS is relatively insensitive
to changes in size distribution within the instrument’s sub-
micron range, and we can generally consider the HR-AMS
flux measurement to be insensitive to artifacts due to the
small changes in submicron particle size caused by evapora-
tion/condensational growth, which have been found to affect
the measurement of size-segregated particle number fluxes
within individual accumulation mode size bins or fluxes of
total particle number above a specified cutoff (e.g., Nemitz
et al., 2004a, 2009a; Vong et al., 2010). Under some con-
ditions, however, vertical gradients in particle growth in or
out of the AMS-observable size range due to water uptake or
changes in gas/aerosol partitioning with condensable chemi-
cal species could, however, cause an artifact. More detailed
analyses are required to parse out such effects on surface flux
measurements, and will be pursued in future manuscripts.
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1286 D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry
5.2 BEARPEX-2007 and ammonium deposition
We observed deposition of NR-PM1during the BEARPEX-
2007 campaign, consistent with particle number fluxes (Vong
et al., 2010). Ammonium deposited to the forest surface. To
our knowledge, the measurements described here include the
first direct eddy covariance flux measurements of particulate
ammonium. Availability of instrumentation has limited past
studies to indirect flux methods (Nemitz et al., 2004b; Trebs
et al., 2006; Wolff et al., 2010). Ammonium is an unusually
challenging aerosol component for which to interpret fluxes,
as one subset of ammonium is irreversibly tied to sulphate
ions, while another is in equilibrium with gas-phase species:
NH3(g)+HNO3(g)↔NH4NO3(particle)(R1)
The flux of any single species in R1 may be subject to
chemical flux divergence through the canopy. HR-AMS am-
monium deposition velocities for BEARPEX-2007 are con-
sistent with previous measurements at other sites (Nemitz
et al., 2004b), but an order of magnitude less than the to-
tal NH3(g)+ particulate ammonium deposition velocities ob-
served over a spruce forest in Germany (Wolff et al., 2010).
To understand the observed ammonium fluxes and mass con-
centrations, we use the observed balance between NR-PM1
cations and anions, also known as the “ammonium balance”.
This is the comparison between ammonium observed (the
positively charged component, or cations) and ammonium
concentration required to balance the charges of the observed
particulate sulphate, nitrate and chloride (the negatively
charged component, or anions). During the BEARPEX-2007
project, we observed ammonium concentrations that were,
within the uncertainties, equivalent to the calculated amount
needed to neutralize the observed anions (Fig. 8a, robust re-
gression slope=0.94, r2=0.92). The small discrepancy be-
tween the anion and cation balance may be due to ammo-
nium oxalate, which has been observed in a higher eleva-
tion site in the Sierra Nevada (Malm et al., 2005). Except
for a few isolated time periods when nitrate was elevated,
sulphate dominated the total anion charge. Ammonium sul-
phate is effectively non-volatile, and would not be subject to
flux divergence driven by evaporation, while its production
is limited by local H2SO4production. Further, due to the
small contribution of ammonium nitrate to PM1mass dur-
ing BEARPEX-2007, it is unlikely that NH4NO3evapora-
tion would have been significant, and NH3(g)concentrations
are too low at this site (<1–2ppb) (Fischer et al., 2007) to
support substantial NH4NO3production with the warm day-
time temperatures and low humidity present at the site. Sim-
ilar to the concentrations, the cation flux (observed ammo-
nium) was well correlated with the anion flux (Fig. 8b, ro-
bust regression slope= 0.94, intercept = −0.05 neq m−2s−1,
r2=0.46). On average, sulphate and nitrate fluxes balanced
2/3 and 1/3 of the ammonium fluxes, respectively. Ammo-
nium chloride is a minor component at Blodgett, and chloride
fluxes typically contributed <2% of the anion charge flux.
The HR-AMS ammonium deposition velocities can be
compared to particle deposition models. Ruijgrok et
al. (1997) used data collected over the Dutch Speulder Bos
experimental forest to propose a chemically-resolved depo-
sition parameterization that depends on friction velocity and
relative humidity. However, the Ruijgrok parameterization
provides a substantial (∼40%) overestimate of ammonium
fluxes during BEARPEX-2007. This would be consistent
with the measurements used by Ruijgrok et al. (1997) being
enhanced by NH4NO3volatilization during deposition. Am-
monium at Speulder Bos was dominantly bound to nitrate, as
opposed to sulphate during BEARPEX-2007.
6 Conclusions
We have presented a new system for measuring chemically-
resolved aerosol fluxes using the HR-AMS. We have demon-
strated that the HR- AMS can be used with the eddy co-
variance acquisition software alongside a sonic anemome-
ter to measure chemically resolved particle fluxes. Such
chemically-resolved mass fluxes have the potential to pro-
vide different information from to particle number fluxes.
Differences in flux between chemically resolved components
have the potential to provide additional information rele-
vant to regional air quality and global atmospheric chemistry
models. Further, we demonstrate the first direct observations
of particulate ammonium deposition over a forest. The an-
ion/cation balance in both concentrations and fluxes show
that the ammonium flux during BEARPEX-2007 is domi-
nated by ammonium sulphate.
The approach described here for HR-AMS fluxes could be
applied to other TOF mass spectrometers, including chemical
ionization TOFMS instruments for more accurate and pre-
cise flux measurements of VOCs and other trace gases than
are typically available with the more widely used quadrupole
mass spectrometer flux measurements.
Acknowledgements. We thank Alex Guenther and Andrew
Turnipseed from NCAR for lending us a sonic anemometer for
the BEARPEX-2007 study. We also thank BFRS staff for their
logistical support and Sierra Pacific Industries for providing access
to their property. This work was partially supported by NSF
ATM-0449815 and ATM-0919189, and by NASA NNX08AD39G
and by the UK Natural Environment Research Council through the
DIASPORA grant (NE/E007309/1). D. Farmer acknowledges a
NOAA Climate and Global Change Postdoctoral Fellowship.
Edited by: J.-P. Putaud
Atmos. Meas. Tech., 4, 1275–1289, 2011 www.atmos-meas-tech.net/4/1275/2011/
D. K. Farmer et al.: Eddy covariance measurements with high-resolution time-of-flight aerosol mass spectrometry 1287
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