Ubiquity and dominance of oxygenated species in organic aerosols in
anthropogenically-influenced Northern Hemisphere midlatitudes
Q. Zhang,
1
J. L. Jimenez,
2
M. R. Canagaratna,
3
J. D. Allan,
4
H. Coe,
4
I. Ulbrich,
2
M. R. Alfarra,
5
A. Takami,
6
A. M. Middlebrook,
7
Y. L. Sun,
1
K. Dzepina,
2
E. Dunlea,
2
K. Docherty,
2
P. F. DeCarlo,
2
D. Salcedo,
8
T. Onasch,
3
J. T. Jayne,
3
T. Miyoshi,
6
A. Shimono,
9
S. Hatakeyama,
6
N. Takegawa,
10
Y. Kondo,
10
J. Schneider,
11
F. Drewnick,
11
S. Borrmann,
11
S. Weimer,
1
K. Demerjian,
1
P. Williams,
4
K. Bower,
4
R. Bahreini,
2,7
L. Cottrell,
12
R. J. Griffin,
12
J. Rautiainen,
13
J. Y. Sun,
14
Y. M. Zhang,
14
and D. R. Worsnop
3
Received 12 March 2007; revised 24 April 2007; accepted 25 May 2007; published 7 July 2007.
[1] Organic aerosol (OA) data acquired by the Aerosol
Mass Spectrometer (AMS) in 37 field campaigns were
deconvolved into hydrocarbon-like OA (HOA) and several
types of oxygenated OA (OOA) components. HOA has
been linked to primary combustion emissions (mainly from
fossil fuel) and other primary sources such as meat cooking.
OOA is ubiquitous in various atmospheric environments, on
average accounting for 64%, 83% and 95% of the total OA
in urban, urban downwind, and rural/remote s ites,
respectively. A case study analysis of a rural site shows
that the OOA concentration is much greater than the
advected HOA, indicating that HOA oxidation is not an
important source of OOA, and that OOA increases are
mainly due to SOA. Most global models lack an explicit
representation of SOA which may lead to significant biases
in the magnitude, spatial and temporal distributions of OA,
and in aerosol hygroscopic properties.
Citation: Zhang, Q.,
et al. (2007), Ubiquity and dominance of oxygenated species in
organic aerosols in anthropog enically-influenced Northern
Hemisphere midlatitudes, Geophys. Res. Lett., 34,L13801,
doi:10.1029/2007GL029979.
1. Introduction
[2] Submicron aerosols have important effects on region-
al to global climate, visibility, human health, and ecological
integrity. Organic species represent a significant and some-
times major (20–90%) mass fraction of the submicron
aerosol [Kanakidou et al., 2005]. Quantification and char-
acterization of the sources and properties of submicron
organic aerosols (OA) have been hampered by analytical
difficulties [Turpin et al., 2000; Kanakidou et al., 2005], in
particular the discrepancies between different thermal-optical
organic carb on (OC) quantification and artifact removal
techniques, and the minor fraction (10%) of the OA mass
that can typically be speciated by conventional techniques
such as GC-MS [e.g., Schauer et al., 1996]. Several new
methods can be used to gain quantitative data on the types
of OA present [Fuzzi et al., 2001; Russell, 2003; Zhang et
al., 2005a, 2005b]. The review by Kanakidou et al. [2005]
outlined the homogenization of OA observations and the
improvement of the characterization of OA composition and
aging as two major research priorities.
[
3] Recent results have shown that secondary organic
aerosols (SOA), formed by chemical transformation and
condensation of volatile and semivolatile species, are under-
estimated by an order of magnitude or more by current
models when applied in and downwind of urban areas/
polluted regions [Volkamer et al., 2006, and references
therein]. Updated budgets of organic species in the atmo-
sphere also suggest underestimation of SOA [Goldstein and
Galbally, 2007]. Also primary organic aerosol (POA)
formed by fossil fuel combustion can be overestimated by
the elemental carbon (EC) tracer data analysis technique due
to the difficulty of estimating a representative OC/EC ratio
for primary emissions from ambient measurements [Zhang
et al., 2005a]. Most of these recent findings have resulted
from the application of the Aerosol Mass Spectrometer
(AMS) [ Jayne et al., 2000; Jimenez et al., 2003; Allan et
al., 2004] that can determine quantitatively OA with high
time and size resolution. The custom principal component
analysis (CPCA) technique developed by Zhang et al.
[2005a, 2005b] has enabled the separate quantification of
GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L13801, doi:10.1029/2007GL029979, 2007
1
Atmospheric Sciences Research Center, University at Albany, State
University of New York, New York, USA.
2
Cooperative Institute of Research in Environment Sciences and
Department of Chemistry and Biochemistry, University of Colorado,
Boulder, Colorado, USA.
3
Aerodyne Research Inc., Billerica, Massachusetts, USA.
4
School of Earth, Atmospheric and Environmental Science, University
of Manchester, Manchester, UK.
5
Laboratory for Atmospheric Chemistry, Paul Scherrer Institute,
Villigen, Switzerland.
6
Chemical Reaction Section, Atmospheric Environment Divisio n,
National Institute for Environmental Studies, Tsukuba, Japan.
7
Chemical Sciences Division, NOAA Earth System Research Labora-
tory, Boulder, Colorado, USA.
Copyright 2007 by the American Geophysical Union.
0094-8276/07/2007GL029979
L13801
8
Centro de Investigaciones Quimicas, Universidad Auto´noma del
Estado de Morelos, Cuernavaca, Mexico.
9
Sanyu Plant Service Co., Ltd., Sagamihara, Japan.
10
Research Center for Advanced Science and Technology, University of
Tokyo, Tokyo, Japan.
11
Departme nt of Pa rtic le Chemi stry, Max P lanck Institute f or
Chemistry, Mainz, Germany.
12
Climate Change Research Center, University of New Hampshire,
Durham, New Hampshire, USA.
13
Department of Physics, University of Kuopio, Kuopio, Finland.
14
Center for Atmosphere Watch and Services, Chinese Academy of
Meteorological Sciences, Beijing, China.
1of6
several types of OA from AMS mass spectra into hydro-
carbon-like organic aerosol (HOA), which in urban areas
shows correspondence with fossil fuel POA and potentially
include other primary sources such as meat cooking, and
oxygenated organic aerosol (OOA).
[
4] In several ambient case studies OOA has shown
direct correspondence with SOA [Zhang et al., 2005a; de
Gouw et al., 2005; Volkamer et al., 2006; Takegawa et al.,
2006; Johnson et al., 2006] although in some cases OOA
may also include contributions from biomass burning OA
(BBOA) [Salcedo et al., 2006; M. R. Canagaratna et al.,
Identification of organic aerosol sources in Houston during
the TEXAQS 2000 air quality study, manuscript in prepa-
ration, 2007, hereinafter referred to as Canagaratna et al.,
manuscript in preparation, 2007]. SOA produced in the
laboratory has resulted in a range of OOA spectra [Bahreini
et al., 2005; Alfarra et al., 2006], but recent results with
SOA formed from diluted diesel exhaust yield a spectrum
very similar to the most common ambient OOA [Robinson
et al., 2007]. OOA was also found to strongly correlate with
water-soluble OC in Tokyo [Kondo et al., 2007], which had
been linked to SOA in a previous study [Sullivan et al.,
2004]. Lanz et al. [2007] have recently applied the positive
matrix factorization (PMF) model to an urban dataset in
Zurich, Switzerland, and concluded that two types of OOA
contribute about 2/3 of the submicron OA mass, with the
rest accounted by primary sources (mainly combustion and
cooking).
[
5] In this paper we apply an expanded version of the
CPCA—a multip le component analysis (MCA) technique—
to 37 multiple week-long datasets obtained during different
seasons at surface sites in 11 urban areas, 5 regions
downwind of urban areas, and 11 rural/remote locations in
the Northern Hemisphere midlatitudes. The MCA method,
which uses a different algorithm to solve the same mathe-
matical problem as the PMF technique applied by Lanz et
al. [2007], derives multiple components, including several
OOA components that show different fragmentation pat-
terns and oxygen to carbon ratios in their mass spectra. The
MCA results are in general agreement with results from
PMF [Ulbrich et al., 2006; Canagaratna et al., manuscript in
preparation, 2007]. We interpret the results in terms of the
importance of different sources and processes for the organic
aerosol in these regions, and compare to the representation
of OA in current global models.
2. Methods
[6] The AMS and its quantification of OA have been
described in detail elsewhere [Canagaratna et al., 2007, and
references therein]. The list of locations, times, and previous
publications (almost all reporting only total OA, and not
HOA/OOA) for each of these studies are given in Figure 1
and in the auxiliary material.
1
OA spectra were analyzed
with the MCA method, modified from that of Zhang et al.
[2005b] to allow the identification of m ore than two
components [Zhang et al., 2006]. The time series of mass
concentrations and the mass spectra of the components were
obtained and classified as HOA, several types of OOA, and
some other small components. Urban areas typically pro-
duce a very sharp contrast in time and size distribution
between HOA and OOA, due to the more local nature of
HOA and more regional nature of OOA, and to the differ-
ences in the diurnal cycles of these components [Zhang et
al., 2005a; Volkamer et al., 2006].
[
7] At almost all urban downwind and rural/remote sites,
and in some urban locations, more than one type of OOA
component is observed. We frequently identified a highly
oxygenated OOA component that has a mas s spectrum
resembling that of fulvic acid (a model compound that has
previously been proposed as representative for highly pro-
cessed/oxidized organics present in the environment, some-
times known as humic-like substances [Fuzzi et al., 2001]),
and a less oxygenated component whose spectrum i s
dominated by ions that are mainly associated with carbonyls
and alcohols. The highly oxygenated OOA component is
more prevalent at downwind sites influenc ed by urban
transport, while the less oxygenated shows correlation to
biogenic chamber OA at some locations. In a few of the
datasets shown here, e.g., Houston (Can agaratna et al.,
manuscript in preparation, 2007) and Mexico City [Salcedo
et al., 2006], biomass burning events account for a signif-
icant fraction of the total OOA. Biomass burning aerosols
are estimated to make a major contribution to OA globally,
although with a larger contribution further south from the
midlatitude region covered here [Kanakidou et al., 2005].
Their effect at a specific ground site tends to be highly
episodic depending on the proximity and intensity of major
fires, and they can often be readily identified by tracers such
as gas-phase acetonitrile and particle-phase levoglucosan
and K
+
(e.g., Canagaratna et al., manuscript in preparation,
2007).
[
8] The detailed quantitative apportionment of the vari-
ous types of OOA to their sources using AMS spectra is a
topic of intense current research. For clarity, here all of the
OOA components are grouped together. In addition, typi-
cally only one HOA component was identified in each
study, except for one case (TORCH I) where two HOA
factors were extracted and grouped together. The mass
spectra of three most representative OOA components are
presented in the supporting material. Note that the total
concentrations of the OOA components are robus t and
independent to the exact multiple comp onent analysis
method used [e.g., Ulbrich et al., 2006; Canagaratna et al.,
manuscript in preparation, 2007].
3. Results and Discussion
[9] OA comprises a major fraction (18–70%; average =
45%) of the non-refractory submicron parti cle mass at the
various locations studied here (Figure 1), while sulfate (10–
67%; avg = 32%), nitrate (1.2 –28%; avg = 10%), ammo-
nium (6.9–19%; avg = 13%) and chloride (<D.L.-4.8%;
avg = 0.6%) account for the rest of the particle mass. Figure 2
shows the results of the MCA analysis. Note that fewer
datasets were collected during winter (see Table S1 in the
auxiliary material), and a few (e.g., Beijing and Mexico
City) were acquired in highly polluted megacities. The OA
(= HOA + OOA) concentrations in Beijing and Mexico City
are approximately 4 –5 times higher than the average OA
concentration of the other 9 cities.
1
Auxiliary material data sets are available at ftp://ftp.agu.org/apend/gl/
2007gl029979. Other auxiliary material files are in the HTML.
L13801 ZHANG ET AL.: UBIQUITY AND DOMINANCE OF OXYGENATED OA L13801
2of6
Figure 1. Location of the AMS datasets analyzed here (data shown in Table S1 in the auxiliary material). Colors for the
study labels indicate the type of sampling location: urban areas (blue), <100 miles downwind of major cites (black), and
rural/remote areas >100 miles downwind (pink). Pie charts show the average mass concentration and chemical
composition: organics (green), sulfate (red), nitrate (blue), ammonium (orange), and chloride (purple), of NR-PM
1
.
Figure 2. Average mass concentrations of HOA and total OOA (sum of several OOA types) at sites in the Northern
Hemisphere (data shown in Table S1 in the auxiliary material). The winter data of the three urban winter/summer pairs are
placed to the right of the summer data and are shown in a lighter shade. Within each category, sites are ordered from left to
right as Asia, North America, and Europe. Areas of the pie charts are scaled by the average concentrations of total organics
(HOA + OOA).
L13801 ZHANG ET AL.: UBIQUITY AND DOMINANCE OF OXYGENATED OA L13801
3of6
[10] Several patterns regarding HOA and OOA distribu-
tions appear in Figure 2: (1) the average mass concentra-
tions of OOA are generally of the same order in urban,
downwind, and rural/remote areas; (2) urban OOA concen-
trations are highest in two polluted and photochemically
active megacities (Beijing and Mexico City); (3) the fraction
of OOA is comparable to but higher than that of HOA in the
majority of urban areas studied; (4) the urban downwind
and especially rural/remote locations are almost completely
dominated by OOA; (5) HOA levels are similar in all the
cities studied, except in Mexico City and Beijing; and (6)
the HOA concentr ations are lower in urban downwind and
very small in rural/remote areas. In general, cities act as
sources of HOA that quickly loses importance downwind,
while OOA maintai ns similar or higher levels in the urban
and downwind regions and its contribution to total OA
generally increases further downwind.
[
11] The results of the MCA analysis for winter/summer
city pairs are highlighted in Figure 2 using a lighter shade
for the winter data. In all three cities, OOA is larger in the
summer, which is consistent with increased emissions of
SOA precursors and enhanced SOA formation rates by
increased solar radiation. Conversely, HOA is larger in the
winter, which is consistent with increased emissions from
heating, reduced HOA evaporation, and reduced dilution in
winter, due to the relatively suppressed vertical mixing
associated with lower surface temperatures.
[
12] The possible sources for the OOA observed in these
studies include SOA (from either anthropogenic or biogenic
precursors), the oxidation of HOA, and/or BBO A. The
relatively low variability in OOA levels across our studies
and analyses based on BB tracers (e.g., AMS m/z 60,
acetonitrile, and potassium) are consistent with the conclu-
sion that biomass burning events, which are usually epi-
sodic, are not a major contributor to OOA in most of the
studies presented here, although they do play an important
role in Mexico City [Salcedo et al., 2006] and Houston
(Canagaratna et al., manuscript in preparation, 2007) during
some time periods. There is strong evidence linking OOA to
SOA for many of the studies summarized here in or
downwind of urban areas: Pittsburgh [Zhang et al.,
2005a], Mexico City [Volkamer et al., 2006], Tokyo
[Takegawa et al., 2006], downwind of London [Johns on
et al. , 2006], and downwind of the US East Coast [de Gouw
et al., 2005]. Other studies have also found a large contri-
bution of SOA in the free troposphere downwind of East
Asia [Heald et al., 2005] where the OA was completely
dominated by OOA [ Bahreini et al., 2003], which suggest
that SOA is also an important con tributor to the OOA
observed in th e AMS studies p rese nte d her e ove r the
western Pacific Ocean.
[
13] An important observation is the very limited impor-
tance of urban/fossil fuel combustion HOA as a contributor
to organic aerosol mass on regional scales. Although POA
compounds can be oxidized in the particl e-phase [e.g.,
Morris et al., 2002] and could in principle become OOA,
the time scale of this oxidation is at least several days
[Schauer et al., 1996; Molina et al., 2004; Zhang et al.,
2005a; Volkamer et al., 2006]. How ever, the lack of a
significant contribution of HOA to urban downwind and
rural/remote locations can be largely explained by atmo-
spheric dilution of relatively high urban concentrations of
HOA, which occur over a small footprint, into an OOA-
dominated regional environment with a much larger atmo-
spheric volume.
[
14] Figure 3 supports this point using data collected at
the Chebogue Point, Nova Scotia field site during the 2004
ICARTT campaign, where BBOA is known to ma ke a
minor contribution based on low aceton itrile [Holzinger et
al., 2007]. The concentration of HOA derived from the
MCA analysis is similar to or smaller than the POA that
would be predicted using either CO or black carbon (BC) as
tracers. The Pearson’s R between HOA and BC is 0.5.
Lower measured than predicted HOA during some periods
indicates partial oxidation of this component and/or possi-
bly additional evaporation of HOA upon dilution, although
some of the deviations may be due to the difficulty of
precisely retrieving a small fraction of HOA from spectra
dominated by much larger fractions of OOA. Nevertheless,
oxygenated OA accounts for almost all of the total OA
Figure 3. Time series of the mass concentrations of HOA and total OOA at Chebogue Point, Nova Scotia, in summer
2004 during ICARTT. Also shown are the time series of HOA estimated based on the average emission ratios of HOA/CO
(4.3 ng m
3
ppb
1
) and HOA/BC (1.2 mg mgC
1
) determined in the northeast U.S. [Zhang et al., 2005a] and the scatter
plot of HOA vs. HOA
BC
.
L13801 ZHANG ET AL.: UBIQUITY AND DOMINANCE OF OXYGENATED OA L13801
4of6
(average = 91%; Figure 2 and Table S2) and is much larger
than the measured HOA or predicted POA, consistent with a
previous study in this area [de Gouw et al., 2005]. Note that
even though the contribution of HOA to particle mass in
urban downwind regions is very small, the contribution of
primary emissions to particle number concentrations (onto
which secondary species condense) can still be very impor-
tant [e.g., Spracklen et al., 2006]. The fact that the OOA
concentration decreases little from urban to downwind
regions as the air mass is diluted is indicative of an
extended SOA source (either from urban emissions or
from regional biogenic emissions), consistent with recent
studies [Goldstein and Galbally, 2007; Robinson et al.,
2007], although large amounts of OOA can also be pro-
duced in the urban environment [Volkamer et al., 2006].
Finally, Robinson et al. [2007] reported that the semivolatile
and intermediate volatility primary emissions (SVOCs and
IVOCs respectively), which are co-emitted with POA/HOA
but in abou t an order of magnitude higher concentration,
may be a significant source of SOA/OOA over regional
scales. Our results concerning the limited contribution of
HOA to OA mass refer to the HOA present in the particle-
phase in the urban background [e.g., Zhang et al., 2005a]
when evaporation of SVOCs has already taken place. Our
results are potentially consistent with, but are not sufficient
to prove (or disprove), a large contribution to regional OOA
by anthropogenic SVOC and IVOC oxidation.
4. Implications for Global Models
[15] A global distribution of OA in time, space, state-
of-mixing, particle size, and water-uptake and optical prop-
erties, which can only be estimated from global models, is
needed to evaluate the effects of aerosols on climate. It is of
great interest to compare our results for the composition and
distribution of OA with the current state-of-the-art global
modeling. A recent study [Textor et al., 2006] from the
Aer oCom initiative, which compares aerosol simulat ion
across global models, reports the following features for
the representation of organic aerosols in 16 models:
(1) most OA in the models is POA; (2) most models age
the POA by oxidation only with a fixed time-scale of 1 –
2 days; (3) SOA is explicitly simulated in only 1 out of
16 models; (4) most models include biogenic SOA in the
POA emissions; (5) anthropogenic SOA is neglected by
most models; and (6) in some models all SOA is completely
neglected.
[
16] Comparing these model features with the measure-
ment results from the previous section, it appears that the
importance of POA with respect to aerosol mass is over-
estimated while SOA is underestimated by most models.
The dominant mechanism of OA evolution in the atmo-
sphere is likely condensation of inorganic species and SOA
onto the POA, rather than oxidation of POA [Zhang et al.,
2005a; Volkamer et al., 2006; Petters et al., 2006], and the
time scale is determined by the availability of precursors
and atmospheric oxidants, rather than being a fixed value.
SOA is modeled based on parameterized results from
chamber studies conducted under unrepresentative condi-
tions. Finally, the inclusion of SOA as a fraction of POA
emissions may produce biases in the spatial, temporal, and
mixing state distribution of POA and SOA and in the water
uptake properties of simulated SOA. However, before
global models can move towards a more explicit represen-
tation of SOA, new SOA process models built on a sound
understanding of the underlying physical and chemical
processes need to be developed.
5. Conclusions
[17] Analyses of 37 field studies in urban and anthro-
pogenically influenced rural/remote areas indicate the ubiq-
uity and dominance of oxygenated species in organic
aerosols, of which a major fraction is likely to be secondary
in nature. While hydrocarbon-like organic aerosol (HOA)
makes a significant contribution to aerosol mass in urban
boundary layers, its importance i n rural/remote areas is
small compared to the oxygenated fraction due to dilution.
Atmospheric oxidation of HOA which may be occurring,
cannot result in enough mass to explain even a minor
fraction of the observed OOA. In light of our results, the
representation of OA in global models appears to have an
overemphasis on POA and a lack of explicit representation
of SOA. This likely leads to significant biases in the amount
as well as the spatial and tempor al concentrations and
properties of OA.
[
18] Acknowledgments. This work was s upported by EPA
RD832161010, SUNY-Albany Startup funds, NSF-ATM0449815, and
NASA-NNG04GA67G. The support of individual studies is given in the
auxiliary material. This work has not been subjected to agency review and
does not necessarily reflect the views of the agencies. We thank the AMS
Community for helpful discussions.
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![Figure 3. Time series of the mass concentrations of HOA and total OOA at Chebogue Point, Nova Scotia, in summer 2004 during ICARTT. Also shown are the time series of HOA estimated based on the average emission ratios of HOA/CO (4.3 ng m 3 ppb 1) and HOA/BC (1.2 mg mgC 1) determined in the northeast U.S. [Zhang et al., 2005a] and the scatter plot of HOA vs. HOABC.](/figures/figure-3-time-series-of-the-mass-concentrations-of-hoa-and-2bz0ulvp.webp)