ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-6943-2015On the competition among aerosol number, size and composition in predicting
CCN variability: a multi-annual field study in an urbanized desertCrosbieE.YounJ.-S.BalchB.WonaschützA.ShinglerT.WangZ.ConantW. C.BettertonE. A.SorooshianA.armin@email.arizona.eduhttps://orcid.org/0000-0002-2243-2264Department of Atmospheric Sciences, University of Arizona, Tucson, AZ, USAMel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USADepartment of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USAUniversity of Vienna, Faculty of Physics, Vienna, AustriaA. Sorooshian (armin@email.arizona.edu)25June201515126943695810December201410February201510June201511June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/6943/2015/acp-15-6943-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/6943/2015/acp-15-6943-2015.pdf
A 2-year data set of measured CCN (cloud condensation nuclei) concentrations
at 0.2 % supersaturation is combined with aerosol size distribution and
aerosol composition data to probe the effects of aerosol number
concentrations, size distribution and composition on CCN patterns. Data were
collected over a period of 2 years (2012–2014) in central Tucson, Arizona: a
significant urban area surrounded by a sparsely populated desert. Average CCN
concentrations are typically lowest in spring (233 cm-3), highest in
winter (430 cm-3) and have a secondary peak during the North American
monsoon season (July to September; 372 cm-3). There is significant
variability outside of seasonal patterns, with extreme concentrations (1 and
99 % levels) ranging from 56 to 1945 cm-3 as measured during the
winter, the season with highest variability.
Modeled CCN concentrations based on fixed chemical composition achieve better
closure in winter, with size and number alone able to predict 82 % of the
variance in CCN concentration. Changes in aerosol chemical composition are
typically aligned with changes in size and aerosol number, such that
hygroscopicity can be parameterized even though it is still variable. In
summer, models based on fixed chemical composition explain at best only
41 % (pre-monsoon) and 36 % (monsoon) of the variance. This is
attributed to the effects of secondary organic aerosol (SOA) production, the
competition between new particle formation and condensational growth, the
complex interaction of meteorology, regional and local emissions and
multi-phase chemistry during the North American monsoon. Chemical composition
is found to be an important factor for improving predictability in spring and
on longer timescales in winter.
Parameterized models typically exhibit improved predictive skill when there
are strong relationships between CCN concentrations and the prevailing
meteorology and dominant aerosol physicochemical processes, suggesting that
similar findings could be possible in other locations with comparable
climates and geography.
Introduction
The influence of atmospheric aerosol particles on cloud properties and the
consequential changes in radiative forcing carry the largest source of
uncertainty in climate change prediction (IPCC, 2013). Cloud condensation
nuclei (CCN) are the subset of aerosol particles that activate into droplets
at a given supersaturation and their concentration therefore contributes to
governing the microphysical and optical properties of clouds (Twomey, 1977;
Albrecht, 1989). The global, spatial and temporal variability of CCN
concentrations consequently hold significant weight in predicting the droplet
distribution in clouds and the ensuing microphysical and radiative properties
(McFiggans et al., 2006; Andreae and Rosenfeld, 2008). Ultimately, CCN have
been found to be a major factor in modulating cloud dynamics in both clean
and polluted environments, with direct consequences on the hydrological cycle
(Andreae et al., 2004; Altaratz et al., 2008; Stevens and Feingold, 2009).
While laboratory experiments involving the activation of single salt species
(e.g., ammonium sulfate) or simple mixtures of organic compounds have offered
satisfactory experimental validation (e.g., Brechtel and Kreidenweis, 2000)
of the original underlying physical theory of droplet activation
(Köhler, 1936), the extension to ambient atmospheric aerosol has proven
more elusive (Covert et al., 1998; Chuang et al., 2000; Roberts et al.,
2002; McFiggans et al., 2006; Ervens et al., 2010). Recent field studies
(e.g., Broekhuizen et al., 2006; Dusek et al., 2006; Ervens et al., 2007;
Hudson, 2007; Cubison et al., 2008; Quinn et al., 2008; Ervens et al., 2010;
Burkart et al., 2011), spanning a range of aerosol scenarios, have not yet
provided a comprehensive agreement on the relative importance of factors
which affect CCN and the cloud droplet number, namely the following: the
aerosol number, size distribution, composition, supersaturation and aerosol
mixing state (Lance et al., 2004; Rissman et al., 2004; McFiggans et al.,
2006; Andreae and Rosenfeld, 2008; Partridge et al., 2012).
During cloud formation, the supersaturation is driven by a combination of
aerosol-related properties and dynamics (i.e., the updraft velocity) and
therefore a complete description of the cloud system involves a two-way
coupling of aerosol microphysics with circulation dynamics (Feingold, 2003).
Modeling studies have shown that typically, the supersaturation adjusts to
large changes in aerosol properties (i.e., number, size and composition) to
dampen the resulting variability observed in cloud droplet number
concentration (Feingold, 2003); however, it has also been found that the
distribution of CCN can have a significant impact on the cloud microphysics
by affecting the droplet distribution (Feingold et al., 1999; McFiggans et
al., 2006). The dynamics of initial droplet growth is affected by CCN
properties (Feingold and Chuang, 2002; Raymond and Pandis, 2002, 2003;
Chuang, 2003) and interstitial gas chemistry (Nenes et al., 2002; Lim et al.,
2005), affecting gas-particle partitioning through cloud processing.
Excluding the environmental factors that regulate supersaturation and droplet
growth kinetics and focusing only on aerosol-related properties that drive
the initial activation, yields important information relating to
hygroscopicity. CCN closure studies typically attempt to model the CCN
concentration from measured aerosol number, size and composition and then
compare the modeled CCN to direct measurements under a controlled set of
supersaturated conditions (e.g., Dusek et al., 2006; Ervens et al., 2007;
Cubison et al., 2008; Bougiatioti et al., 2009; Lance et al., 2009; Ervens et
al., 2010; Jurányi et al., 2011; Martin et al., 2011; Levin et al., 2012;
Moore et al., 2012; Lathem et al., 2013; Wu et al., 2013; Almeida et al.,
2014). The respective importance of composition and size distribution on CCN
activation remains an outstanding question. Closure studies have generally
been successful for background and remote sites (e.g., Jurányi et al.,
2010), but less so in urban areas (e.g., Burkart et al., 2012). The
complexity of the aerosol composition and variability in the aerosol mixing
state are often the explanation for unsatisfactory closure, under assumptions
of bulk hygroscopic properties (Cubison et al., 2008; Ervens et al., 2010).
The single hygroscopicity parameter κ-Köhler theory (Petters and
Kreidenweis, 2007, 2008) provides a theoretical framework to derive bulk
hygroscopicity for internal mixtures, based on a volume-weighted mixing rule.
While this simplicity is advantageous for closure models, this approach may
not be suitable for particles with complex morphology (e.g., Dusek et al.,
2011; Hersey et al., 2013).
Physical aging processes such as coagulation and condensational growth tend
to shift the aerosol population towards a more uniform mixing state, when
compared to fresh emissions (Covert and Heintzenberg, 1993; Ervens et al.,
2010). While condensational growth processes increase CCN concentration by
growing ultrafine particles into the critical range for droplet activation,
coagulation may result in either increasing or decreasing CCN concentration
since increased size comes at the expense of aerosol number (Riipinen et al.,
2011). Uncertainties in nucleation rates and primary emissions have been
shown to have significant impacts on global estimates of CCN concentration
(Pierce and Adams, 2009).
The study of CCN activation within an urban environment offers unique
opportunities to address the challenges associated with the inhomogeneity of
sources and aerosol aging, which gives rise to difficulties in predicting
water uptake behavior. Field studies purporting to quantify the influences of
aerosol number, size and compositional factors on CCN activity are often
carried out over a limited, but intense period and hence offer a worthy
characterization of the duration of the study but perhaps lack climatological
context, even related to sub-seasonal variability. The current study
addresses the two aforementioned issues by reporting on long-term
measurements of CCN, submicron size distributions and composition taken
jointly over multiple years in an urban area, specifically Tucson, Arizona.
Tucson is located in the heart of the Sonoran Desert in the semi-arid
southwestern United States. This location offers some unique opportunities
for the study of CCN activation, primarily since there have been
comparatively fewer documented measurements of CCN in arid regions. In
addition, southern Arizona is situated in the region affected by the North
American monsoon (NAM) and as a result, the highest monthly rainfall occurs
during July and August and is accompanied by a strong influx of tropical
moisture. The onset of the NAM in late June or early July leads to a rapid
change from very hot and dry pre-monsoon conditions to the humid conditions
associated with the monsoon and leads to changes in the aerosol properties
(Sorooshian et al., 2011; Youn et al., 2013). Aside from the NAM, southern
Arizona is situated in a relatively stable synoptic weather pattern, which
gives rise to generally clear skies and light surface winds. The strong
insolation produces a deep convective boundary layer in the afternoon, and
clear conditions lead to significant nocturnal cooling, which together
produce a significant but predictable diurnal cycle in temperature, humidity
and convective boundary-layer mixing.
The paper is subdivided as follows: (i) experimental methods and data
collection are provided in Sect. 2; (ii) an overview of the
“climatological” results is given in Sect. 3; (iii) the influence of size
distribution and its relationship with composition is discussed in Sect. 4;
(iv) CCN closure analysis is presented in Sect. 5 and (v) conclusions are
presented in Sect. 6.
Data and methodsTucson Aerosol Characterization Observatory (TACO)
The study site is located at a rooftop location (approximately 30 m above
ground) on the University of Arizona campus (32.2299∘ N,
110.9538∘ W; 720 m a.s.l.) in central Tucson (metro population
∼ 1 million; U.S. Census Bureau, 2011). The sample inlet was located at
rooftop level, approximately at the same height as nearby buildings, and
2 km northeast of downtown Tucson. The study period spanned more than 2
years (April 2012–August 2014) and comprised long-term continuous
measurements of CCN and related quantities, with a constant experimental
setup.
Aerosol instrumentation
Bulk CCN concentrations were measured using a CCN counter at fixed 0.2 %
supersaturation (CCN-100 Droplet Measurement Technologies; Roberts and Nenes,
2005). Particle size-resolved number concentrations were obtained using a
scanning mobility particle sizer (SMPS 3080, TSI Inc.) coupled to a
condensation particle counter (CPC 3772, TSI Inc.). The SMPS operated at
10 : 1 sheath-to-sample flow ratio and with a mobility diameter range from
13 to 748 nm. The integration of the size-resolved data over the entire
range provided a measure of total condensation nuclei (CN). The CCN counter
was calibrated twice during the study period using the method described in
Rose et al. (2008) and exhibited a supersaturation of
0.192 ± 0.005 % at the nominal 0.2 % set-point value. A
semi-continuous OC / EC analyzer (Sunset Laboratories Inc.) measured
hourly organic carbon (OC) and elemental carbon (EC) concentrations in
PM2.5. Limits of detection were 0.2 and 1.0 µg m-3 for
EC and OC, respectively. Water-soluble organic carbon (WSOC) was measured in
PM2.5 using a particle-into-liquid sampler (PILS, Brechtel Manufacturing
Inc.) coupled to a total organic carbon analyzer (TOC; Sievers model 800)
(Sullivan et al., 2006; Duong et al., 2011; Wonaschütz et al., 2011). The
overall measurement uncertainty associated with the reported WSOC
concentrations is estimated to be approximately 10 % with a limit of
detection of 0.1 µg m-3.
Local meteorology
Collocated measurements of basic meteorological variables (including
temperature, pressure, humidity, wind speed, wind direction and rainfall)
were obtained at 5 s time resolution and archived as 1 min and hourly
averages. In addition, 1 min direct normal irradiance (DNI) was obtained
from the NREL Observed Atmospheric and Solar Information System (OASIS;
http://www.nrel.gov/midc/ua_oasis/) site on an adjacent building on the
university campus. SuomiNet GPS-derived precipitable water vapor (PW) (Ware
et al., 2000) data were obtained from the University of Arizona SA46 site
(32.2298∘ N, 110.9539∘ W, 762 m a.s.l.) resolved to
30 min mean estimates. Finally, radiosonde data from the nearby National
Weather Service were obtained from twice-daily balloon launches at 04:00 and
16:00, local time.
EPA IMPROVE
PM2.5 aerosol composition measurements were obtained from two sites in
the Interagency Monitoring of Protected Visual Environments (IMPROVE) network
of filter samples (Malm et al., 1994). The Saguaro National Monument site
(32.1742∘ N, 110.7372∘ W; 933 m a.s.l.) is located within
the foothills of the Rincon Mountains at the eastern extent of the Tucson
metropolitan area and approximately 21 km east of TACO. The Saguaro West
site (32.2486∘ N, 111.2178∘ W; 718 m a.s.l.) is located
on the western side of the topographically less prominent Tucson Mountains,
approximately 25 km west of TACO. At each site, 24 h filter samples are
collected every 3 days. Data were obtained to coincide with as much of the
study period as possible and were available up to December 2013 at the time
of writing. Filter samples were analyzed for ions, metal and non-metal
elements, and carbon (elemental and organic). Details on the extraction and
analysis methodology are provided extensively elsewhere
(http://vista.cira.colostate.edu/improve/Publications/IMPROVE_SOPs.htm).
In addition to direct measurement, the IMPROVE network reports empirically
derived concentrations relevant to atmospheric aerosol including fine soil,
sea salt, ammonium sulfate and ammonium nitrate (Malm et al., 1994).
Seasonal mean and extreme CN and CCN concentrations from hourly
averaged data. Seasons are defined as follows: winter (W, DJF), spring (S,
MA), pre-monsoon (PM, MJ), monsoon (M, JAS) and fall (F, ON).
ConcentrationWSPMMF(cm-3)CNMean51894853387242005200Max (99 %)1440613799108691160613682Min (1 %)7496868071070853CCNSS=0.2%Mean430233301372303Max (99 %)1945809667741951Min (1 %)565910110081Data organization and quality control
All TACO data (CCN, SMPS, OC / EC and meteorology) are time-synchronized
and archived as averages at hourly increments. Sub-hourly variability in both
the CCN concentration and the aerosol size distribution is highly influenced
by localized intermittent sources, atmospheric turbulence and
measurement-related lags and noise. Since many of the metrics used in the
interpretation of CCN variability involve ratios (or other non-linear
functions) combining CCN and SMPS data, pre-filtering data to 1 h reduces
extraneous influences caused by sub-hourly covariance. All meteorological
fields (except PW and radiosonde data) were additionally archived at 1 min
resolution. SMPS data from May and June 2013 are removed owing to sub-optimal
data quality, resulting from an instrument malfunction.
Climatological resultsMonthly and seasonal statistics
Monthly statistics of CN and CCN concentrations (henceforth referred to as CN
and CCN) illustrate different trends as CN reveals a more stable annual cycle
with minor reduction towards a minimum in June (Fig. 1). CCN is more variable
annually, and has two distinct peaks with a primary peak in December and a
secondary peak in August. April has the lowest average CCN and also the
lowest variability, as indicated by the interquartile range in Fig. 1 for
both CN and CCN. Conversely the interquartile range in CN for April is one of
the highest, although in general, CN exhibits significant sub-monthly
variability when compared to the mean annual trends. OC and EC mass
concentrations (Fig. 1c) exhibit similar annual cycles, which suggests that
aerosol related to urban combustion sources are ubiquitous; however, in
summer the contribution is diluted by higher mixing heights (Fig. 1f).
Seasonal temperature (T; Fig. 1d), relative humidity (RH; Fig. 1e) and
direct normal irradiance (DNI; Fig. 1f) illustrate the impact of the NAM on
local meteorology, where strong increases in moisture are accompanied by
slight temperature reductions and increased cloud cover.
Monthly statistics of (a) CN, (b) CCN (0.2 %),
(c) OC and EC, (d) temperature, (e) RH and
(f) direct normal irradiance (DNI). Circles, diamonds and the lines
connecting them represent monthly averages. For (a) CN and
(b) CCN, bars represent median and interquartile range of
sub-monthly variability of the 1 h averaged data. For (d)
temperature and (e) relative humidity, bars represent monthly
extremes, as measured by 5 and 95 % levels of the 1 min average data.
DNI is presented using 24 h averages so that it includes the effect of the
changing length of day with season, and peak mixing depth is calculated using
the 16:00 radiosonde data.
Henceforth, data are grouped seasonally rather than monthly to analyze the
annual cycle. Five seasons are defined to reflect the significant difference
in meteorology between the pre-monsoon summer and the onset of the NAM. These
are winter (W, DJF), spring (S, MA), pre-monsoon (PM, MJ), monsoon (M, JAS)
and fall (F, ON). Table 1 provides a summary of seasonal CN and CCN
statistics and includes only periods when both measurements are available.
Winter and fall have the highest mean CN concentrations
(∼ 5200 cm-3), while pre-monsoon has the lowest with a mean just
below 3900 cm-3. Extremes are quantified by 1 and 99 % statistics
and range between 749 and 14406 cm-3, with winter showing the highest
variability. Average CCN concentrations are typically lowest in spring
(233 cm-3), highest in winter (430 cm-3) and have a secondary
peak during the monsoon (372 cm-3). Extremes in CCN range between 56
and 1945 cm-3 and winter variability far exceeds that of any other
season.
Seasonal PM2.5 speciation from the averaged Saguaro National
Park and Saguaro West IMPROVE sites. Six major groupings comprising the
PM2.5 mass are shown: FS (fine soil), OA (organic aerosol), EC
(elemental carbon), AS (ammonium sulfate), AN (ammonium nitrate) and SS (sea
salt).
Fine-mode aerosol composition may help to explain the seasonal patterns in
CCN and are illustrated using the IMPROVE data (Fig. 2). Data are presented
as an average of the two sites to the east and west of Tucson and can be
interpreted as a suburban/semi-rural background reflecting regional-scale
aerosol composition onto which local urban sources are superimposed. Aerosol
loading is highest during the pre-monsoon season, mainly due to the combined
increase in the fine soil fraction, from windblown dust which occurs mainly
in the spring and pre-monsoon seasons, and from the increase in sulfate
during the pre-monsoon and monsoon (Sorooshian et al., 2013). Regional
wildfire emissions are also most significant during the pre-monsoon
(Sorooshian et al., 2013). While dust particles may themselves act as CCN,
they can also enhance the removal of CN and CCN by coalescence, while
contributions from regional wildfire smoke may periodically enhance CN and
CCN concentrations. Nitrate is more abundant in winter (∼ 14 %)
compared to other seasons and may be a factor in the observed winter maximum
in CCN concentrations. Sea salt contributes a modest fraction
(∼ 4.5 %) of pre-monsoon aerosol when mid-tropospheric air
originates mainly from the subtropical Pacific. The sum of the constituents
presented in Fig. 2 constitute between 93 and 101 % of the seasonal
average PM2.5, as reported by gravimetric analysis.
Hourly trends of (a) CN and (b) CCN (0.2 %). Bars indicate
median and interquartile range of the variability within each hour. Mean CN
and CCN concentrations are shown for both weekdays (red) and weekends
(blue). Hourly trends of CCN are shown in (c) for each season. Mean EC
(solid) and OC (dashed) concentrations (d) are shown for weekdays (red) and
weekends (blue).
The strong influence of urban sources on the fine-mode carbonaceous aerosol
in central Tucson is demonstrated by the elevated seasonal mean OC and EC
mass concentrations at TACO versus the IMPROVE data (Table 2). This result
is consistent with comparisons made by Sorooshian et al. (2011) for urban
and rural sites in Arizona, which showed that carbonaceous mass
concentrations varied strongly between urban and rural sites, whereas
sulfate was more regionally homogenous.
Seasonal mean OC and EC concentrations, and associated standard
deviations, at the TACO and IMPROVE sites.
Hourly trends of activation-related properties, OC : EC ratio, and
WSOC : OC ratio for weekdays (red) and weekends (blue). Note the
applicability of the OC : EC ratio starts to become less well defined on
weekends above 25, since EC concentrations are typically below
limit of detection (LOD).
Diurnal and weekly cycles
The diurnal cycle of CN illustrates a clear pattern involving a complex
interaction of sources and sinks (Fig. 3a). During weekdays, early mornings
(07:00 to 09:00) are characterized by traffic emissions, which increase the
CN and EC concentrations (Fig. 3d) indicative of fresh fossil combustion
aerosol. Mean CN concentrations at 08:00 on weekdays (7925 cm-3) are
more than 160 % of the equivalent weekend concentrations
(4887 cm-3). During the late morning, the convective boundary layer
develops and dilutes the surface layer with relatively clean air from the
free troposphere and/or residual layer leading to a marked drop in EC, OC
(Fig. 3d) and CN. Through the middle of the day, the convective boundary
layer is still growing; however, a subtle reduction in the rate of decrease
in CN (12:00 to 14:00) is suggestive of nucleation and growth of new
particles which contribute as a source of CN. This is supported by the
following: (i) concurrent enhancement in WSOC : OC ratios (Fig. 4c), which
can be used as a proxy for secondary organic aerosol (SOA) away from
biomass-burning sources (Miyazaki et al., 2006; Kondo et al., 2007; Weber et
al., 2007); (ii) increasing OC : EC ratios (Fig. 4c) and (iii) a second dip
in the mean aerosol diameter (Fig. 4b). The latter two results are
particularly clear on weekends when the morning traffic signature is
suppressed.
By mid-afternoon (14:00 to 16:00), the convective boundary layer reaches its
peak depth and photochemical processes begin to slow down, leaving transport
(vertical and horizontal) and coagulation as the dominant mechanisms,
producing a net reduction in CN concentrations (Fig. 3a) and increase in
mean diameter (Fig. 4b) while integrated aerosol volume concentration
(used as a proxy for relative trends in PM1) remains flat (Fig. 4b).
By late afternoon (16:00 to 18:00) the convective boundary layer decouples
from the surface and aerosol number and mass concentrations build again in
the surface layer due to the evening peak in traffic emissions, with
accompanying increases in EC and OC and reductions in mean diameter. During
this time, secondary aerosol may still be influential once the boundary
layer is decoupled, since residual ozone concentrations near the surface may
still be sufficient to drive SOA production in the now thin surface layer.
The annualized diurnal cycle of CCN (Fig. 3b) is less pronounced than that
of CN mainly since CCN are typically unaffected by contributions from
ultrafine particles with diameters less than 50 nm, which are highly
variable. There is an increase in CCN during the evening, reaching a daily
maximum at 22:00 and, interestingly, concentrations on weekends (429 cm-3) are higher than on weekdays (380 cm-3). There is a large
range of CCN variability observed within each hour when compared to the
hourly composite mean trend which is partially explained by the seasonal
differences in the CCN diurnal cycle (Fig. 3c). During winter, there is a
significant diurnal cycle in CCN, while in other seasons the diurnal pattern
is relatively flat. Due to reduced winter temperatures, semi-volatile
organics are more likely to partition to the particle phase, which may
incrementally shift the size distribution of freshly emitted particles
associated with morning traffic towards larger sizes. In addition, nitrate
also forms a larger component of the regional aerosol than in other seasons,
which helps to increase the hygroscopicity and to reduce the diameter
required for droplet activation. Both factors likely work in tandem with the
diurnal emissions cycle, which results in a CCN pattern which more closely
follows CN than other seasons. The other notable feature is that the peak
CCN concentration occurs during the night in winter while it occurs during
the afternoon in summer. In addition to partitioning of semi-volatiles,
emissions from domestic wood burning are another potential contributor to
CCN in the winter, while in summer it is likely SOA production, driven by
photochemistry and moisture during the day (Youn et al., 2013).
A bulk hygroscopicity parameter (κ) is derived using the method of
Petters and Kreidenweis (2007) and by assuming total activation above a
critical activation diameter, such that the CCN concentration exactly
matches the concentration of particles exceeding this critical diameter
(Furutani et al., 2008; Burkart et al., 2011; Wonaschütz et al., 2013).
Hygroscopicity decreases concurrently with the morning traffic signature
(Fig. 4a) and then rebounds through the day to produce a peak between 14:00 and 16:00 matching expectations of organic aging and condensational growth by
photochemically oxidized organics and sulfate. As expected, the morning
minimum is less extreme on weekends (κ=0.15) compared to
weekdays (κ=0.10) due to reduced traffic and this trend remains
through the day with weekend maxima (κ=0.21) exceeding weekday
values (κ=0.19). During the evening and night, the offset is far
smaller (Δκ≈0.005). The κ parameter
tracks the diurnal pattern of activation ratio (Fig. 4a), defined as the
ratio of CCN to CN, which on first glance, together with the rather modest
changes in mean aerosol diameter (Fig. 4b), would indicate that chemical
composition is driving the CCN variability at least on diurnal scales.
However, two corollaries should be highlighted: (a) the mean aerosol diameter
is a rather simplistic representation of changes in the size distribution,
and (b) as mentioned earlier, the majority of the CCN variability is not
described by composite mean hourly trends, at least in an annual sense, and
thus, as will be examined in the forthcoming section, a more rigorous
treatment of the size distribution is needed to better explain overall CCN
variability.
Size distribution
Several studies (e.g., Conant et al., 2004; Dusek et al., 2006; Ervens et
al., 2007) have suggested that the size distribution alone can explain CCN
variability; however there are other examples (e.g., Hudson, 2007; Burkart et
al., 2011), which refute this, particularly in cases where the aerosol is
externally mixed. If the physical and chemical processes which govern size
and composition changes are intrinsically tied to a single governing
mechanism, a parameterization involving one component may suitably capture
the variability in the other, at least when considering a fixed
supersaturation. Furutani et al. (2008) reported the activation diameter to
be well correlated with activation ratio during a shipborne study in the
eastern North Pacific, suggesting compositional changes as a result of aging
(where size also increases) to be the major driver for CCN variability. In
contrast, Burkart et al. (2011) examined the same relationship but found poor
correlation between activation ratio and activation diameter in Vienna,
Austria, suggesting a more complex relationship between size and composition.
Size distribution cluster centroids, as derived by the “K-means”
algorithm, and the hourly distribution of cluster associations, separated by
season. Clusters are assigned the following identifiers: nucleation (N;
blue), fresh fossil (FF; red), winter/nocturnal (WN; green) and
condensation/coagulation (CC; black).
Seasonally derived mean cluster properties and associated
environmental conditions (AR is activation ratio). Meteorological variables
(T, RH and direct normal irradiance, DNI) are presented as anomalies, based
on departure from hourly means for each month. Entries in parentheses
indicate that the cluster occurs less than 15 % of the time in that
season. An asterisk (*) next to EC denotes a case when the concentration is
below LOD. O3 data are obtained from a surface pollutant monitoring site
(∼ 9 km from TACO) operated by the Pima County Department of
Environmental Quality (Children's Park station).
The shape of the size distribution can be used to interpret physical
processes (e.g., condensation, evaporation, nucleation, coagulation), while
relative changes in CN concentration, combined with changes in shape, offer
insight into atmospheric processes (e.g., advection and diffusion) and
emissions. The well-established “K-means” clustering algorithm (Hartigan
and Wong, 1979; Lloyd, 1982) was used here as a statistical tool to group
size distributions by shape. The method was implemented with four clusters
and the resulting four cluster centroids denoted archetypal size distribution
shapes (Fig. 5), to which the observations were assigned, according to their
degree of association. The selection of four clusters struck a balance
between capturing the salient patterns, while maintaining simplicity;
however, we do not claim that this choice was optimal for all applications.
Cluster associations were “fuzzy”, and therefore an observation could be
partially assigned to multiple clusters to reflect the continuity of
transitions between clusters in the data set. This provides the added
advantage that smooth transitions in cluster properties can be represented
without the additional complexity of defining intermediate clusters. A full
description of the clustering method and the method by which associations are
made is provided in the Supplement. The mean diurnal cycle of cluster associations
(Fig. 5) and their mean properties (Table 3) provide a physical description
of the clusters and are hereafter given the following identifiers, which are
indicative of the physical process or “regime”, that is, suggested by the
cluster properties: nucleation (N), fresh fossil (FF), winter/nocturnal (WN),
and coagulation/condensation (CC).
Winter (W) and summer (PM and M) exhibit substantially different patterns in
cluster associations on diurnal scales, while the transition seasons (S and
F) contain features of both winter and summer and are therefore more mixed in
terms of the driving mechanisms. During winter (W), large swings in the size
distribution shape are uncommon; however, with activation at 0.2 %
supersaturation occurring at diameters as low as 100 nm, the growth that
accompanies a shift from FF to WN is sufficient to significantly increase the
activation ratio. Unlike other seasons, it is likely that the main driver for
size distribution changes occurring during winter is the equilibrium
partitioning of semi-volatile species between gas and particle phase (e.g.,
nitrate). An additional contributor may result from the offset in emissions
patterns between traffic (day) and domestic wood burning (night). Anomalously
colder or more humid conditions tend to result in larger and more hygroscopic
particle distributions and are typically also associated with more stable
near-surface conditions, leading to suppressed mixing and higher aerosol
loading as seen in the WN CN, EC and OC concentrations (Table 3). In the
extreme, the infrequent winter occurrence of the CC cluster is merely an
extension of this trend occurring during the coldest winter nights, where
average hygroscopicity reaches κ=0.23 and average CCN concentrations
are 811 cm-3. The fact that number, size and hygroscopicity tend to act
in association is perhaps the reason why CCN variability is highest in winter
on both synoptic and diurnal scales.
Conversely, in summer (PM and M) the shape of the size distribution is very
variable and exhibits large swings between N and CC clusters (Fig. 5). After
primary emissions associated with the morning traffic peak (FF cluster) have
been diluted through boundary-layer mixing, competition between the N and CC
cluster takes over. Unlike winter, there is no monotonic relationship between
meteorology and size. Instead, hotter conditions with higher solar exposure
tend to bifurcate the size distribution more between N and CC clusters, with
cooler and cloudy conditions favoring the retention of the intermediate FF or
WN clusters. This suggests that the N and CC clusters are partially driven by
photochemically produced secondary aerosol. Higher temperature and stronger
direct normal irradiance (DNI) are likely coupled with higher hydroxyl
concentrations, and ozone concentrations are typically 30–40 % higher
for N and CC clusters (Table 3), which accelerates the production of reduced
volatility oxidized organic vapors from precursor volatile organic compounds
(VOCs). The partitioning of these vapors between condensation on existing
particles and nucleation of new particles is likely a function of the aerosol
surface area and the production rate of the low-volatility organics.
Anomalously dry conditions are a feature of the N cluster, suggestive of
reduced aerosol water, reducing the available surface area. Another possible
mechanism affecting the N cluster during the summer (PM and M) is the
evaporation, or lack of condensation, of semi-volatile organic compounds
associated with traffic emissions (Robinson et al., 2007) such that the FF
cluster takes on some of the features of the N cluster. This mechanism would
be supported by the anomalous contribution of EC to the N cluster during the
PM and M seasons. Further analysis of the aerosol and gas-phase composition
is needed, before and during the monsoon, in order to fully understand the
balance of regional and local processes in driving the preference of N and CC
clusters.
Tucson is often under the influence of very light mean surface winds and so
during the day, the predominant mechanism for ventilation of urban aerosol is
through vertical mixing of the convective boundary layer, which is supported
by measurements at a nearby mountain site (Shaw, 2007). Furthermore, the
climatological mesoscale surface wind pattern, particularly in summer, is
light southeasterly winds during the night and morning, followed by
northwesterlies in the afternoon and evening, induced by regional topography
(Philippin and Betterton, 1997). It is therefore possible for urban aerosol
particles and precursor gases to be recycled over the site during the course
of the day, through both these mechanisms. Processes which control the
cluster associations may be also dependent on regional (e.g., nucleation of
biogenic SOA) as well as local effects (e.g., recycling of urban emissions),
which happened at an earlier time. The complex influences of this “memory
effect”, together with the interaction of meteorology and emissions, may be
one of the contributing factors which cause evening and overnight CCN
concentrations to be higher on weekends (Fig. 3b).
CCN closure
Studies aimed at achieving a predictive model of CCN concentrations from
measured number, size and composition (i.e., CCN closure) have shown mixed
ability to predict CCN concentrations across a range of aerosol scenarios. To
examine these dependencies, in the context of the present study, we consider
the effect that simplifying assumptions have on the ability to predict CCN.
Traditionally, closure studies aim to predict the hygroscopic properties from
measured composition or subsaturated growth factors, which are then combined
with size distribution measurements to predict CCN (e.g., Ervens et al.,
2010). With this method, the intercomparison of various scenarios, and the
resulting degree to which CCN concentrations are predicted, is affected by
both the model assumptions and the accuracy by which aerosol physicochemical
properties are measured. Our focus here is to study the degree of CCN
variability explained by incremental simplifications in a predictive model
considered across a range of timescales. One major simplification is the
limitation of the treatment of hygroscopicity to a bulk measurement, which is
permitted to vary temporally but does not isolate size-dependent changes in
hygroscopicity nor the hygroscopicity distribution, which may be an important
component in relation to external mixing. These aspects are beyond the scope
of these parameterizations and are likely to contribute to model shortfalls.
Forthcoming work will separately study the degree of correspondence of
hygroscopicity between the sub- and supersaturated regimes, size-dependent
hygroscopicity and composition, and the closure of hygroscopicity from
composition measurements.
Seven, highly simplified, predictive models are used to estimate CCN over the
entire study period: (i) constant CCN (baseline); (ii) constant activation
ratio (assesses the effect of number only); (iii) constant hygroscopicity
(effect of number and size distribution); (iv) constant size distribution
(effect of number and hygroscopicity); (v) measured number with size
distribution shape and hygroscopicity, derived from cluster associations;
(vi) measured size and number with cluster-derived hygroscopicity and (vii)
all parameters (a reconstruction, for reference only). The inclusion of
models (v) and (vi) assesses whether the predictive skill can be improved by
the use of a reduced-order representation of the size distribution and
hygroscopicity parameter (κ). Models (v) and (vi) can be considered
an incremental refinement to models (ii) and (iii) where the assumption is
that there is prior knowledge of expected cluster properties and
associations.
Closure model performance as quantified by variance explained (top)
and normalized mean error (bottom). Models (i)–(iv) include holding constant
either CCN, activation ratio (AR), κ, or size distribution (SD).
Model (v) uses the cluster properties and associations (see Fig. 5 and
Table 3), model (vi) uses the same assumptions as model (iii) except that
κ is determined from cluster associations, and model (vii) is a
reconstruction for reference only. A dash (–) indicates that the result is
not available or performed so poorly it cannot be quantified by the metric.
Predicted CCN concentrations are compared to those measured and two
performance metrics are evaluated: (i) “percentage variance explained” (VE)
metric, which is the variance in the measured CCN explained by the model as
determined by mean square residuals; and (ii) a “normalized mean error”
(NME) metric, defined as the root mean square residual between modeled and
measured CCN concentrations expressed as a percentage of the mean measured
CCN concentration for the epoch. While both these metrics are connected, the
VE is a better descriptor of the specific performance of the model, whereas
the NME puts the model in the context of overall predictability. Models are
first tested using (i) the cumulative data set and (ii) for the five
predefined seasons with model parameters set using seasonal best-fit values.
The models (except v and vi) are then tested, using the same methodology, on
data that have been filtered using a 24 h running average and 7-day average,
with the underlying motivation to determine if environmental factors which
control CCN predictability differ between diurnally and synoptically driven
timescales.
The results (Table 4) show that when all seasons are considered, a constant
hygroscopicity assumption explains more of the measured variance
(∼ 63 % VE) than a constant size distribution (∼ 44 % VE)
suggesting that overall, the size distribution is generally a more important
driver for CCN variability than composition. However, the goodness of fit
(VE) is far lower than that presented by Dusek et al. (2006) and is probably
associated with the complexity of the aerosol mixing state and spatiotemporal
variability in composition, due to the proximity of the TACO site to fresh
emission sources as compared to the Dusek et al. (2006) study site. To put
the TACO results in more context, fresh pollution aerosol in other urban
areas such as Riverside and Houston could not be fully represented without
knowledge of size-resolved composition (Cubison et al., 2008; Ervens et al.,
2010). A number of other studies have shown that mixing state can help
improve predictive capability of CCN behavior (Wex et al., 2010), including
Atlanta (Padró et al., 2012) and during early morning rush hour near
Mexico City (Lance et al., 2013); but studies also report that hydrophobic
particles emitted in urban areas quickly (∼ few hours) become internal
mixtures via condensation of secondary hygroscopic species (e.g., Wang et
al., 2010; Mei et al., 2013).
In the daily and weekly filtered cases, the relative balance between size
and composition is also similar. Using the submicron number concentration as
a predictive model for CCN (i.e., a constant activation ratio assumption)
performs poorly in all annual cases (and all seasonal cases except winter)
since it is strongly affected by variability in nucleation and small Aitken-mode particles from fresh emissions that do not contribute to CCN at the
supersaturation levels considered here.
Compared to other seasons, the simplified predictive models perform the best
in winter in terms of VE; however, this season also has far higher
variability in CCN than any other season across the three timescales
considered. Winter is also the only season where a constant activation ratio
assumption offers any skill in CCN predictability suggesting that the
modulation of CCN is more tied to bulk aerosol sources and sinks than
compositional or size-dependent changes or that these processes are strongly
interlinked. Winter aerosol is mainly controlled by an interplay of urban
emissions balanced by transport and mixing such that there is a strong
correlation between the diurnal cycle of CN and EC, which serves as a
combustion tracer. Strong nocturnal surface inversions, in conjunction with a
lack of surface wind-induced mixing, trap urban emissions close to the
surface before the convective boundary layer develops, which happens later in
the day than other seasons. Intermittent synoptic-scale influences, such as
frontal passages, affect aerosol sinks directly through wet scavenging,
although this effect is presumably much weaker than less arid regions, and
drive regional transport in the lower troposphere, which ventilates the urban
plume. Synoptic systems affect column stability, which indirectly affects
aerosol loading by regulating the extent of diurnally driven vertical mixing.
Chemical aging processes and photochemically driven secondary aerosol
formation are suppressed in winter compared to other seasons simplifying the
diurnal changes in hygroscopicity and size distribution, although size and
hygroscopicity appear to be tied to the diurnal cycle through temperature
changes. Both size-simplified (constant κ, model iii) and
hygroscopicity-simplified (constant size distribution, model iv) models
explain 82 and 73 % of the CCN variance, respectively, reiterating that
size and hygroscopicity changes are strongly coupled. The weekly filtered
data indicate that hygroscopicity becomes marginally more influential than
size changes over longer timescales and is perhaps a consequence of regional
sources associated with long-range transport competing with local emissions.
Regional-scale transport is also an important feature of spring, which is a
transition season where mid-latitude meteorology still affects the region,
boundary-layer mixing becomes more vigorous and surface winds are strongest
on average. Dust loading is highest and temperature changes on diurnal and
synoptic scales are also greatest which affects the partitioning of
semi-volatile species (e.g., nitrate). The complex mixing state and highly
variable aerosol composition makes CCN prediction difficult as reflected in
the poor performance of the simplified models. The modeled predictability
indicates that composition is far more important than size during spring and
in fact, the daily filtered data suggest that using the size distribution
(model iii) to predict CCN is worse than assuming a constant seasonal average
concentration, indicative of complex aerosol mixing states, morphology and
scale-dependent mechanisms.
The pre-monsoon summer reveals a steady improvement in the model performance
towards longer timescales (i.e., weekly) and the increasing relative
importance of hygroscopicity. Intense solar radiation during this season
increases the importance of VOC and SO2 chemistry to form secondary
aerosol species. Aerosol number may be strongly influenced by nucleation and
therefore knowledge of the size distribution becomes essential on sub-diurnal
scales. Over longer timescales, all simplified approximations become
reasonable, suggesting a more stable meteorological pattern which is typical
of this season: as the jet migrates northward, synoptic steering becomes
lighter and the circulation pattern becomes more driven by mesoscale
circulations. The increased importance of hygroscopicity on timescales longer
than a week is perhaps indicative of the influence of wildfire smoke and
intermittent regional dust transport which periodically affect southern
Arizona during this season.
The monsoon season exhibits the poorest performance of the simplified models
out of all seasons, which is perhaps expected given the very complex
meteorological pattern and the interplay between secondary aerosol production
at the regional (e.g., biogenic SOA and sulfate) and local scale (e.g., urban
SOA). Knowledge of the size distribution is essential since it is highly
variable across all scales driven by both meteorological influences, in the
form of monsoon thunderstorms, and secondary aerosol processes. Even
considering size variability alone does not yield very satisfactory results
implying that aerosol composition is very closely tied to changes in size
distribution during the monsoon season. However, CCN variability is also
lowest of all seasons, while the mean CCN concentration is relatively high,
implying partial cancellation in the effects caused by changes in size,
number and composition. The consequence is that the NME metric is actually
lowest in monsoon when a constant hygroscopicity model is used, which is the
opposite of the situation during winter. Fall shows the opposite pattern to
spring and pre-monsoon in that hygroscopicity has decreasing influence over
longer timescales, and for the weekly filtered case, the constant
hygroscopicity model provides a very satisfactory model of CCN variability.
The inclusion of the cluster associations to estimate κ (model vi)
provides an incremental improvement in the predictive skill (+3 to
+15 % additional %VE) when compared to a seasonally constant κ (model iii), with the exception of the pre-monsoon summer season, where a
reduction in %VE was observed (∼-7 %). Annually, the increase
was approximately +5 % on %VE. The comparison between the
cluster-derived activation ratio (model v) and a constant activation ratio
(model ii) was far more significant with an annual increase of +59 % on
%VE suggesting that a low-order representation of the size distribution
shape, where other data are unavailable (e.g., from remote sensing methods),
may offer a worthwhile improvement to the estimation of CCN concentration.
Conclusions
This study investigates the respective importance of aerosol number
concentration, size distribution and composition in driving CCN variability
in Tucson, Arizona. In doing so, a long-term characterization of the
seasonal, weekly and diurnal patterns in aerosol number concentration, size
distribution and selected particle speciation has been achieved. Seasonally,
the average CN concentration exhibits a moderate trend towards a minimum
during summer, while CCN concentrations exhibit significant winter and
summer peaks. Weekday and weekend CN concentrations track the respective
diurnal weekday and weekend EC and OC mass concentrations, indicating a
strong influence of local combustion aerosol, predominantly from vehicle
emissions but also, in winter, from domestic biomass burning. Activation
ratio and hygroscopicity, as estimated by κ, track the morning peak
in fossil fuel emissions, by concurrently showing a marked reduction,
particularly on weekdays. This helps to support the notion that CCN
concentrations are not significantly enhanced by fresh fossil emissions. The
effects of local emissions are typically offset by those of boundary layer
mixing; however, during the warmer and more photochemically active seasons,
secondary aerosol processes become more influential.
During winter, the interplay between chemistry and dynamics is such that
increasing size is accompanied by increasing hygroscopicity. This occurs most
commonly at night and during anomalously cold periods, when boundary layer
mixing is suppressed and aerosol loading is high, thus increasing CCN
concentrations. Conversely, during the day and particularly during
anomalously warm and dry periods, there is sufficient convective mixing to
dilute the aerosol, evaporate hygroscopic semi-volatile species and generally
promote the abundance of smaller particles, reducing CCN concentrations. The
combined result of these effects is to increase the variability in CCN, since
each of these contributing factors act together to enhance or suppress CCN
concentrations. The added consequence is that simplified models offer
substantial predictive skill for CCN variability, even though the observed
changes in the size distribution are relatively subtle.
The summer is divided by the arrival of the North American monsoon (July–September), which rapidly increases the abundance of moisture compared to
the very hot and dry months that precede it (May–June). Secondary
production of sulfate and organics becomes more influential during both
summer seasons, and photochemically produced aerosol appears to be the
mechanism responsible for an afternoon maximum in CCN concentration,
compared to a nocturnal maximum in winter. The diurnal cycle of the boundary
layer follows a similar pattern to other seasons, except that mixing heights
are generally higher and nocturnal surface inversions are less pronounced,
especially during the monsoon. While CN concentrations drop off during the
day similar to other seasons, CCN concentrations remain relatively more
stable indicating that condensed SOA and sulfate play a significant role in
offsetting the loss in CCN caused by dilution.
Another important feature of the summer is the bifurcation in the size
distribution shape, where the pattern swings back and forth from (i) an
abundance of ultrafine particles that are potentially tied to a nucleation
event to (ii) a deficiency of Aitken-mode particles, and a growth in the
number of particles larger than 100 nm that are more in line with a
background aerosol population. While the meteorological conditions favoring
both regimes are similar and likely explained by SOA and sulfate production,
the mechanisms responsible for the bifurcation are still unclear. Possible
mechanisms include aerosol water uptake, leading to increased aerosol
surface area for condensation, which is supported by lower humidity on days
when ultrafine particles are present, particularly before the monsoon.
During the monsoon, regional biogenic SOA produced as a result of increased
vegetation may explain the periodic import of small SOA particles into the
urban plume. Finally, the role of the monsoon thunderstorms may also be
responsible for erratic changes to the size distribution simply through the
sporadic disruption of the local and regional circulation pattern.
The sensitivities of CCN concentration to changes in aerosol number, size and
composition can be well represented in a theoretical framework as described
by Köhler theory and its various refinements. However, the extent to
which these driving components vary, and the mechanisms through which they
interact, is the primary limitation in consolidating parametric
representations suitable for predictive models. Achieving satisfactory CCN
closure using measurements of chemical composition and size has generally
been most successful with background aerosol where substantial changes in
composition are dampened by aging processes. However, the results of this
study suggest that in certain regimes (e.g., during winter), where
composition, size and number concentration have a more deterministic
relationship, there are still opportunities for parametric simplifications to
be successful even when chemical processes are relatively complex. Since the
relationship can be explained by somewhat broad environmental mechanisms not
entirely specific to Tucson, similar conclusions can be drawn for other urban
areas with comparable geographical and climatological settings.
The methods employed in this study also have implications for studies in
other regions, specifically in the use of clustering and reduced models for
CCN closure. While this study has considered model performance with respect
to temporal scales of variability at one site, there is an opportunity to
extend this methodology to assess spatial patterns across multiple sites, and
to include the development of a generalized clustering method that
categorizes spatial and temporal variability. The ultimate goal of such an
effort would be to estimate the global performance (by areal coverage) of
reduced-order CCN closure approximations, a result which has substantial
importance in constraining aerosol–cloud interactions for modeling future
climate scenarios. Future work using the TACO data set will focus on the
predictability of κ using measurements of composition, patterns in
the environmental conditions (e.g., emissions, meteorology and other
auxiliary measures) and subsaturated aerosol hygroscopicity, with the primary
goal being to determine if a single-parameter representation of CCN
activation is suitable for this environment. In addition, we will focus on
addressing the factors which control the summertime size distribution
bifurcations and the extent to which they are influenced by biogenic and
anthropogenic SOA production pathways.
The Supplement related to this article is available online at doi:10.5194/acp-15-6943-2015-supplement.
Acknowledgements
This research was supported in part by grant 2 P42 ES04940–11 from the
National Institute of Environmental Health Sciences (NIEHS) Superfund
Research Program, NIH, the University of Arizona Foundation, the Institute of
the Environment at the University of Arizona, and the Center for
Environmentally Sustainable Mining through TRIF Water Sustainability Program
funding. Funding is also acknowledged from NASA grants NNX14AK79H, NNX12AC10G
and NNX14AP75G. Glenn Shaw is acknowledged for helpful suggestions during the
preparation of the manuscript. We acknowledge the sponsors of the IMPROVE
network, the Pima County Department of Environmental Quality, NREL, UCAR and
SuomiNet for data used in this study. Edited
by: V.-M. Kerminen
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