New particle formation (NPF) events are defined as a
sudden burst of aerosols followed by growth and can impact climate by
growing to larger sizes and under proper conditions, potentially forming
cloud condensation nuclei (CCN). Field measurements relating NPF and CCN are
crucial in expanding regional understanding of how aerosols impact
climate. To quantify the possible impact of NPF on CCN formation, it is
important to not only maintain consistency when classifying NPF events but
also consider the proper timeframe for particle growth to CCN-relevant
sizes. Here, we analyze 15 years of direct measurements of both aerosol size
distributions and CCN concentrations and combine them with novel methods to
quantify the impact of NPF on CCN formation at Storm Peak Laboratory (SPL),
a remote, mountaintop observatory in Colorado. Using the new automatic
method to classify NPF, we find that NPF occurs on 50 % of all days
considered in the study from 2006 to 2021, demonstrating consistency with
previous work at SPL. NPF significantly enhances CCN during the winter by a
factor of 1.36 and during the spring by a factor of 1.54, which, when combined with
previous work at SPL, suggests the enhancement of CCN by NPF occurs on a
regional scale. We confirm that events with persistent growth are common in
the spring and winter, while burst events are more common in the summer and
fall. A visual validation of the automatic method was performed in the
study. For the first time, results clearly demonstrate the significant
impact of NPF on CCN in montane North American regions and the potential for
widespread impact of NPF on CCN.
Introduction
Atmospheric aerosols, which originate from primary emissions or through
secondary gas-to-particle conversions, are a large source of climatic
uncertainty (Stocker et al., 2014). Aerosols can affect Earth's radiative
balance directly by interacting with incoming radiation and indirectly
through their role as cloud condensation nuclei (CCN) (Twomey, 1974; Twomey
et al., 1984; Albrecht, 1989; Charlson et al., 1992). New particle formation
(NPF) is a source of atmospheric aerosols that involves the formation of
particles less than 3 nm in diameter and the subsequent growth of these
freshly nucleated particles to larger sizes (Yu and Luo, 2009; Spracklen et
al., 2010). These secondary aerosols originating from NPF can indirectly
impact climate by acting as CCN (Kerminen et al., 2012; Gordon et al., 2017;
Kerminen et al., 2018). Previous modeling studies estimate that the
contribution of secondary aerosols from NPF to CCN is significant in the
free troposphere, with some estimates predicting that 35 % of CCN, at a
supersaturation of 0.2 %, originate from secondary aerosols (Merikanto et
al., 2009). Gordon et al. (2017) estimate that at a supersaturation of
0.2 %, 67 % of CCN at low-level cloud heights in the pre-industrial
atmosphere are attributed to NPF, compared to 54 % in the current
atmosphere (Gordon et al., 2017). Understanding the contribution of NPF to
CCN in clean, remote environments will not only expand the regional
understanding of how NPF can impact CCN but also allow for potential
estimates of CCN concentrations in the pre-industrial atmosphere, providing
a baseline for the comparison of current, anthropogenic-influenced climate
trends to those of the pre-industrial atmosphere (Carslaw et al., 2017).
Mountaintop studies evaluating the relationship between NPF and CCN are
important in understanding the impact that NPF can have on CCN in remote
regions, including the free troposphere (Hallar et al., 2011,
2016; Rose et al., 2017; Sellegri et al., 2019). A review by Zhu et al. (2021) analyzes data from multiple campaigns at Mt. Tai in China, showing
that NPF does contribute to CCN at the site, but decreases in anthropogenic
sulfur dioxide (SO2) over time have contributed to the lower production of
CCN number concentrations from NPF events (Zhu et al., 2021). At the Mt.
Chacaltaya Observatory, Rose et al. (2017) find that 61 % of NPF events
during a 2012 study grew to CCN-relevant sizes, reaching a minimum diameter
of 50–150 nm, and that these events are highly likely to enhance CCN
concentrations, especially in the free troposphere (Rose et al., 2017).
Because a CCN counter was not available at Mt. Chacaltaya, the study
utilizes a methodology to identify times in which NPF contributes to CCN
concentrations, starting from when aerosol number concentrations at 50,
80, and 100 nm begin to increase and ending when the maximum number
concentration is observed at the respective size bin (Rose et al., 2017).
Recent observations from a remote site in the western Himalayas estimated
the survival probability to show that a majority of secondary aerosols grew
to CCN-relevant sizes during observations; there was an 82 % probability
that a particle would grow to 50 nm and a 53 % probability that a particle
would grow to 100 nm (Sebastian et al., 2021; Pierce and Adams, 2007).
Findings from these mountaintop studies not only show the potential of secondary
aerosols to activate as CCN at clean mountaintop sites but also highlight
the importance of long-term studies at different remote locations to
increase regional understanding of how NPF impacts CCN in different
environments, since the ability of secondary aerosols to impact CCN is
highly dependent on the regional characteristics of a given observatory.
Observational studies that consider the contribution of NPF to CCN formation
must classify NPF events and determine the time at which CCN is enhanced by
NPF. Historically, NPF is classified visually by scientists looking for a
particle burst and subsequent growth over multiple hours, forming a new
nucleation mode (Dal Maso et al., 2005). However, visual classification can
lead to potential human biases and brings into question the accuracy of
comparisons between studies (Joutsensaari et al., 2018). An automatic
methodology to identify NPF, such as the one used in this study, can
minimize human bias during long-term studies at different locations. Since
the time period in which CCN is enhanced by NPF is highly dependent on
particle growth, the time to reach CCN-relevant sizes can range from a few
hours in polluted environments with high growth rates to over a day in
remote environments with lower growth rates (Kerminen et al., 2012).
In an effort to increase the number of observational studies relating NPF to
CCN, previous studies, both with and without a cloud condensation nuclei counter (CCNC), have developed various
methodologies to determine the time period in which observed CCN
concentrations can be attributed to the occurrence of an NPF event
(Kalivitis et al., 2015; Kalkavouras et al., 2017; Dameto de España et
al., 2017; Rose et al., 2017; Kalkavouras et al., 2019; Kecorius et al.,
2019; Rejano et al., 2021; Ren et al., 2021). Similar to the methodology of
Rose et al. (2017) detailed above, Kalkavouras et al. (2017) estimate
CCN by finding particle concentrations above the minimum size required for
aerosols to activate as CCN and then consider the environmental
supersaturation when estimating how many aerosols in a given distribution
could act as CCN. This approach further calculates the droplet number and
considers how supersaturation, chemical composition and updraft velocity
may impact the cloud droplet number (Kalkavouras et al., 2017). An evolution
of this approach by Kalkavouras et al. (2019) calculates the relative
dispersion of CCN at different supersaturations and considers CCN times when
the relative dispersion is higher than initial conditions before a CCN event
(Kalkavouras et al., 2019). This method was further employed at 35 different
sites around the globe, both urban and remote, to determine the impact NPF
has on CCN concentrations (Ren et al., 2021). Kecorius et al. (2019) utilize
a CCNC in the Arctic to analyze CCN enhancements by fitting a slope to CCN
measurements, starting when aerosol formation rates increased and ending when
an air mass shift occurred (Kecorius et al., 2019). In another study
utilizing a CCNC in Vienna, Austria, Dameto de España et al. (2017)
consider CCN number concentrations for a time period that occurs after particles reach CCN-relevant sizes (Dameto de España et al., 2017). This time period is the same duration as the time between NPF initiation and when
particles reach CCN-relevant sizes. When
it comes to determining the time period that NPF may impact CCN for long-term datasets, the methodology not only should efficiently and independently
(without using CCN observations) ensure that aerosols are growing to CCN
sizes but also needs to consider the growth patterns of individual NPF
events to accurately determine when NPF stops contributing to CCN.
In this study, we present 15 years of aerosol and CCN data from Storm Peak
Laboratory (SPL), a remote, mountaintop observatory in Steamboat Springs, CO,
USA, and quantify the impact of NPF events on CCN concentrations. Datasets
of this length are rare and provide a unique opportunity to quantify
long-term trends that have enough data to make statistically significant
conclusions. NPF occurs frequently at SPL allowing for the seasonal
comparison of the relationship between NPF and CCN (Hallar et al., 2011). To
identify the occurrence of NPF and when to consider CCN concentrations, we
present two new, statistical-based methods: one to classify NPF events and
another to determine the period in which CCN number concentrations can be
attributed to NPF.
MethodologyStorm Peak Laboratory
Storm Peak Laboratory (SPL) is a remote, mountaintop observatory (3210 m a.s.l., 40.455∘ N, 106.745∘ W) located in Steamboat
Springs, CO. SPL is one of the only sites in North America with long-term
measurements of aerosol size distributions and CCN number concentrations
(Lowenthal et al., 2002; Borys and Wetzel, 1997; Hallar et al., 2017). The
laboratory is commonly in-cloud during storms and sees frequent NPF events
(Hallar et al., 2011; Borys and Wetzel, 1997; Lowenthal et al., 2019). The
primary wind direction at the laboratory is westerly, which allows for the
potential transport of SO2 and the formation of sulfuric acid
(H2SO4), an NPF precursor, from multiple powerplants 50–250 km
upwind of SPL (Hallar et al., 2016; Obrist et al., 2008). SPL is located
above a mixed forest allowing for the emission of a variety of different
biogenic volatile organic compounds (BVOCs) that can impact aerosol
formation and growth (Amin et al., 2012). Given the remote, mountaintop
location of SPL, clean atmospheric conditions are common at the laboratory
(Obrist et al., 2008).
To measure aerosols at SPL, we use a TSI Inc. (Shoreview, MN) scanning
mobility particle sizer (SMPS) 3936 (with a TSI 3010 condensation particle
counter (CPC)) for particles with diameters between 8 and 340 nm that scans
every 5 min. Data are collected on a log-normal scale with particle
diameter on a log scale and time on a normal scale. The instrument is
periodically shipped back to TSI Inc. for routine maintenance and
calibrations. The sheath and sample flow rates are 10 and 1 L min-1, respectively, for the SMPS. Multiple charge corrections and
diffusion corrections are applied to all SMPS data used in the analysis.
SMPS data from SPL are now available on the European Monitoring and Evaluation Programme (EBAS) database, including level 1 data, which
maintain 5 min time resolution while removing invalid values and
calibrations, as well as level 2 data which present hourly averages and
quantify atmospheric variability. Level 1 SMPS data are used in this study.
The goal of EBAS data is to store long-term atmospheric science datasets and
provide standards for quality assurance; thus rigorous standards for data
quality are implemented to any data admitted to EBAS (Norwegian Institute
for Air Research). SPL consistently runs a single-column droplet measurement
technology (DMT; Boulder, CO) CCNC that
collects number concentrations of CCN every second (Lance et al., 2006;
Roberts and Nenes, 2005). We consider instrument measurements at
supersaturation levels between 0.2 % and 0.4 % in our study.
An automatic method to classify new particle formation
A crux of studying atmospheric NPF is the identification of NPF events. The
identification process historically utilized three-dimensional plots of
log-normal size distributions and visual inspection aimed at identifying a
burst of particles below 20 nm, followed by growth over the course of
multiple hours that forms a new nucleation mode (Dal Maso et al., 2005;
Kulmala et al., 2012). By visually inspecting these plots, the viewer sorts
days into the following broad categories based on the observed growth
patterns: event, non-event, or undefined. In an effort to improve the visual
classification process proposed by Dal Maso et al. (2005), studies split
events into subcategories to provide more specific classifications detailing
whether particle growth is sustained during a given day or if the given day
exhibits a burst of particles (Hirsikko et al., 2007; Kulmala et al., 2012;
Boy et al., 2008; Svenningsson et al., 2008; Dal Maso et al., 2005). The
visual classification of NPF can present problems, since human biases can
influence classification leading to issues with the reproducibility and
comparability of studies (Joutsensaari et al., 2018). To minimize the
potential biases that influence visual classification, we present a
statistically based, automatic sorting technique that evaluates particle
burst and growth patterns to classify days into one of the following
categories: type 1a event, type 1b event, class II event, undefined, or
non-event (Hirsikko et al., 2007; Tröstl et al., 2016). The logic of the
automatic classification technique is shown in a flowchart (Fig. 1) and
described below.
Flowchart illustrating the step-by-step process of the
automatic NPF classification method. PGR: particle growth rate.
The first step of the automatic classification method is to ensure the
availability of SMPS level 1 data. Although NPF events can span multiple
days, we consider daily data (00:00–23:59 MST) as well as the first 12 h (00:00–12:00 MST) of the next day to ensure the consideration of an
NPF event does not prematurely end if growth continues overnight. The 5 min
SMPS data are only considered if the first 24 h period meets the following
conditions: there are at least 16 h of data present and the period
between 10:00–23:00 MST (the times in which NPF is most common at SPL)
has less than 1 h of data missing.
The days that successfully undergo quality control are then considered by
the automatic classification method. For data to be classified as an event,
two general conditions must be met: a burst of particles in the nucleation
mode and growth that spans multiple hours contributing to the formation of
a new nucleation mode. To first address the presence of a burst and identify
days that are non-events, we compute the percentiles of all particle
concentrations in our dataset below 25 nm from 10:00–23:00 MST. Days below the
10th percentile were automatically categorized as non-events, since they
are automatically assumed to not have high enough nucleation mode number
concentrations for an NPF event to have occurred. For days where the average
particle concentration below 25 nm is above the 10th percentile of all
data considered, the maximum of the Gaussians is calculated at each size
bin. The normal distributions were fit by solving for the non-linear
least-squares estimates using the R programming language (Eq. 1), which
considers the particle size distribution at each diameter to return the time
that corresponds to the maximum concentration at that given diameter (Bates
and Watts, 1988). In the equation, “k” is the maximum aerosol number
concentration, “t” is the time index where the normalized maximum at
Dp occurs, “μ” is the mean aerosol concentration, and “σ” is the corresponding standard deviation. This equation is used for the
calculation of individual maximum Gaussians at each size bin:
ft|k,μ,σ=ke-(t-μ)22σ2,k=maxdNdlogDp.
The derived time index represents the time at which the maximum of the peak-fitted particle size distributions occurs for each value of Dp. For
data where at least five different Gaussian maximum points are calculated, a
linear regression is fit to these maxima allowing for further analysis of
growth over the course of an event (Lehtinen and Kulmala, 2003). R2
values for the linear regression (one below 20 nm and another from 20 to
about 70 nm), as well as the time differences between the maxima, are also
considered to ensure growth. For days to be defined as an event, the time
difference between bin maxima must be positive and non-zero for at least
40 % of occurrences, the largest r2 value must be at least 0.6, there
must be at least five maxima considered in the fit, and the largest size bin
with a calculated Gaussian maximum must be above 25 nm for type 1a event
classification and 15 nm for type 1b event classification. While 15 nm may
seem like a low threshold for NPF growth, the growth of a given event often
reaches sizes exceeding the diameter where the last Gaussian is calculated.
Figure 2 is an example of a day that is calculated as an event because the
threshold is lowered to 15 nm. Days that do not meet the above
statistic-based criteria are initially classified as undefined but can be
classified as a class II event later in the method.
An example of a day classified as a type 1b event.
Setting 15 nm as the diameter that the growth Gaussian maxima must reach
allows for this day to be classified as an event demonstrating why the
threshold is set at 15 nm. Gaussian maxima (black points) are outlined by
the first-order derivative of the fitted distribution at each size (black
line). The vertical red lines denote the initiation and end times of a given
event as assigned by the automated methodology.
For days that are defined as an event, the growth rate and event start and
stop times are calculated. To find the growth rate, a linear regression is
fit to the maximum Gaussians which are time dependent. The growth rate is
determined by the following equation:
GR=ddtDp=ΔDpΔdt.
Because the slope of the linear regression fit of the maximum Gaussians
represents particle growth over time during NPF events, this value is used
when determining the growth rate. This method is most similar to the
log-normal function fitting method of calculating growth rate but finds the
growth rate by fitting a linear regression to the maximum Gaussians.
Derivatives of the linear regressions are used to determine the start and
end time of events, where the start time of the event is defined by the time
of the first maximum of the first-order derivative and the end time of the
event is defined by the time of the last first-order derivative minimum.
Figure 3 illustrates an example of an NPF event and a day classified as a
non-event.
Strong NPF event (a) with midpoint size bin maxima
(black points) outlined by the first-order derivative of the fitted
distribution at each size (black sloped lines). The vertical red lines
denote the initiation and end times of a given event as assigned by the
automatic methodology. A non-event (b) is added for comparison. The
vertical black lines represent the time period when CCN is considered
(CCNstart through CCNend) which is determined for each
individual event day, while the seasonal average of this period is used for
comparing CCN during non-event days.
Days that are not classified as a type 1a or type 1b event are further
considered to determine whether the given day is a class II event or
undefined. Class II events are different than type 1a and type 1b events due
to the presence of a particle burst which resembles an “apple” shape
rather than persistent growth (Dal Maso et al., 2005; Junninen et al.,
2008). Because the methodology detailed thus far considers growth patterns,
significant class II particle bursts are initially classified as undefined
due to weak growth (undefined stats fail) or Gaussian stacking in which
greater than 75 % of the calculated Gaussian maxima occur at the same time
(undefined burst). To address class II events, we apply an additional set of
threshold tests to determine if days initially classified as undefined
should be classified as a class II event.
Days that were classified as “undefined burst” or “undefined stats fail”
are eligible for reclassification as class II events based on multiple
thresholds. Class II events exhibit Gaussian maxima that occur at elevated
number concentrations, exhibit particles bursting to a larger size, and
originate in the nucleation mode; however, there is often a minimal
difference in the temporal location of the calculated Gaussian maxima. Thus,
the focus of this analysis is the identification of a significant particle
burst. To confirm that the burst originates with smaller particles and
exceeds the sizes required for class II events, the lowest size bin of a
calculated Gaussian for a given day must be below 15 nm and the highest size
bin of a calculated Gaussian must be above 15 nm. To ensure that the given
day exhibits a strong enough burst for consideration as a class II event, at
least 50 % of calculated Gaussians must have dn/dlogDp values above the
95th percentile of all values in a given day. In addition, the
diameters of consecutively calculated Gaussian maxima for days initially
classified as an undefined burst cannot differ by more than 20 nm. The
reason this threshold is not applied to days initially defined as
undefined stats fail is because there is some growth observed, thus
removing days with large Gaussian maxima differences could lead to the
accidental removal of a class II NPF event that exhibits weak growth in
addition to a significant burst.
Formation rates (J8) and condensation sink (CS) values
The aerosol formation rate (J8) and condensation sink (CS) values are
calculated as part of the automatic classification method. J8 values
are calculated for type 1a and type 1b events. CS values are calculated for
the comparison of values between type 1a and type 1b events and non-events.
The J8 value for an event is defined by the formation rate equation
(Kulmala et al., 2004, 2012):
J8=ΔN8,DmaxΔt+CoagSdp⋅Ndp+GRΔdp⋅Ndp,
where ΔN8,Dmax is the change in the number concentration of
particles across the considered size distribution from about 8 to 25 nm
during Δt which is the time difference from the defined start of an
event to the defined end of an event. When calculating the initial and final
number concentrations, we utilize the average number concentration observed
between 4 and 1 h prior to NPF initiation as the initial number
concentration. The final number concentration is the average number
concentration from all 5 min scans taken during an event. Doing so allows
for the comparison of the initial conditions of an NPF event and aerosol
formation across the entirety of a given event. The additional loss terms in
the equation represent loss to the coagulation sink and loss due to growth
out of the size range (Kulmala et al., 2012). The entire size distribution
measured by the SMPS is used when calculating the coagulation sink loss term
(Casquero-Vera et al., 2020).
CS values are calculated for the entire size distribution using the
following equation (Pirjola et al., 1999; Kulmala et al., 2001):
CS=2πD∫0∞dpβmdpndpddp=4πD∑iβiriNi.
In the equation, ri is the radius of a given size bin (cm), and Ni
is the number concentration (cm-3) of the given size bin. D is the
diffusion coefficient of vapor, which is assumed to be 0.13 cm2 s-1
for H2SO4 at SPL based on calculations using representative
pressure and temperature at the site (Hanson and Eisele, 2000; Welty et al.,
2020; Tuovinen et al., 2021). βm is calculated following the
protocols of Kulmala et al. (2012) and Tuovinen et al. (2020) where the mass
accommodation coefficient in these calculations is assumed to be unity
(Kulmala et al., 2001; Nishita et al., 2008; Hallar et al., 2011; Kulmala et
al., 2012; Tuovinen et al., 2020).
Determining when to consider cloud condensation nuclei
concentrations
Determining whether an NPF event is impacting CCN concentrations is crucial
in understanding the exact contribution of aerosols to cloud formation and,
thus, understanding the potential climatic impacts. While environmental
supersaturation and particle hygroscopicity are both crucial factors for CCN
activation, aerosols from NPF must grow to CCN-relevant sizes before
activating as CCN. Therefore, it is important to consider CCN enhancements
due to NPF at times when particles reach CCN sizes. In this study, we
propose and apply a statistical method to determine the time in which to
consider the contribution of NPF to CCN concentrations. Our method sets a
start and end time for CCN concentrations based only on aerosol
concentration measurements that consider growth patterns of aerosols over
and around the time of NPF.
For days classified as type 1a events and type 1b events, the start time of
CCN consideration (CCNstart) is the first time after the start of an
NPF event that 25 % of all particles in a given scan (ranging from 8 to
about 340 nm) are above 40 nm. Utilizing a percentile-based threshold method
to determine CCNstart allows for newly formed particles to grow to CCN
sizes and is an effective metric when dealing with multi-year datasets.
CCNstart for non-event days is calculated by using the average of
CCNstart calculated for each season during events. We consider CCN
concentrations during non-events to determine if NPF events result in an
enhancement of CCN. Sunlight is generally necessary for NPF and growth;
therefore, it is important to consider the variations in the seasonal
diurnal cycle and obtain one unique value of CCNstart for each season
that accurately represents the time that NPF impacts the site
during each season (Hallar et al., 2011).
The end time of CCN consideration (CCNend) is determined by finding the
time at which particle growth from an event tapers off. To do so, we
estimate the bin that corresponds to the normalized maximum aerosol
concentration at each timestamp from the start of the NPF event to 17:00 MST
the next day. This ensures that consecutive events are not erroneously
considered. The maximum bin diameter at each timestamp is determined in a
similar way to the NPF classification method (Eq. 1), but when
considering CCN, we find the maximum of fitted Gaussians at each timestamp.
Because the formation of CCN from nucleated particles can exceed the time
period of NPF, especially in remote environments, our method allows for the
evolution of particle growth over a time period long enough to ensure
particles originating from NPF can grow to CCN sizes.
Once each time has a corresponding diameter maximum, we evaluate the overall
growth pattern by fitting a polynomial curve to the Gaussian bin maxima
over time. Once the curve is fit, the time at which the last inflection
point occurs (in which the fitted line transitions from positive slopes to
negative slopes) is selected as CCNend. The last inflection point of
the curve serves as an indicator of growth tapering off, and, therefore, we
assume that the enhancement of CCN from NPF has concluded. For non-events,
CCNend is determined by adding the average duration of CCN
consideration (CCNend- CCNstart) to the previously averaged
CCNstart. Four different values of CCNend, one for each season,
are determined when finding CCNend values for non-events. An example
NPF day including labels of CCNstart and CCNend illustrating the
time at which we assume CCN is enhanced by NPF is included in Fig. 3. To
compare the impact NPF events have on CCN, CCN number concentrations
directly measured are considered during the time period spanning from
CCNstart to CCNend during valid events and non-events. An average
CCN number concentration for supersaturation levels between 0.2 % and
0.4 % is calculated for each individual time period. These values are then
averaged separately each season between events and non-events. The goal is
to determine whether CCN concentrations are enhanced by NPF events. During
long-term studies, especially at clean, remote locations like SPL, directly
comparing events and non-events will result in the relative enhancement of
CCN due to events at a given location. By removing the subjectivity of
selecting idealized cases, we provide a more robust methodology to evaluate
long-term datasets. The methodology within this paper carefully considers
similar timeframes within the diel pattern with and without NPF, to look at
the relative change induced by NPF. At other high-altitude mountaintop sites
around the globe, this approach could have sources of error, since NPF can be
associated with the transport of both condensable vapors and pre-existing
aerosol that could become CCN (Sellegri et al., 2019). However, SPL seems to
be an exception to this rule, since previous observations of NPF show an
association with lower existing particle surface areas which allows for a
more direct comparison of events and non-events (Hallar et al., 2011;
Sellegri et al., 2019). By further comparing events to non-events through a
seasonal lens, we ensure that days with similar meteorological conditions
are compared.
ResultsFifteen years of new particle formation at Storm Peak Laboratory
Over the course of 15 years (2006–2021), we consider 835 d that pass
basic quality control protocols and have both aerosol and CCN data available
for analysis. The automatic method to determine NPF classification splits
the data into one of the following five categories: type 1a event, type 1b
event, class II event, undefined, or non-event (Hirsikko et al., 2007;
Tröstl et al., 2016). Of the 835 d considered, 95 d are classified
as a type 1a event, 80 d are classified as a type 1b event, 244 d are
classified as a class II (burst) event, 269 d are classified as
undefined, and 147 d are classified as a non-event. When considering the
overall NPF event frequency, which includes type 1a events, type 1b events,
or class II events, the overall event frequency calculated by the automatic
method is 50 %, which compares well to the 52 % overall event frequency
observed at SPL by Hallar et al. (2011).
Evaluating NPF from a seasonal lens at SPL creates a better understanding of
how important variables, such as temperature, SO2 concentrations, and
the presence of organics, affect NPF (Hallar et al., 2016,
2013). Table 1 details the frequency of NPF events across all seasons. The
summer and fall display the highest frequency of events with either a type
1a event, type 1b event, or class II event occurring on 56 % of days in
the summer and 59 % of days in the fall. The spring (53 %) and winter
(41 %) display similar but slightly lower event frequencies than the
summer and fall at SPL. An analysis focusing on the frequency of different
event types is conducted to determine which seasons may be conducive to the
occurrence of type 1a and type 1b events in which persistent growth occurs.
We find that type 1a events and type 1b events are more likely to occur in
the winter (62 % of all NPF events) and spring (51 % of all NPF events)
than in the summer (17 % of all NPF events) and fall (32 % of all NPF
events) where burst events are more common partially due to higher
temperatures (Yu et al., 2015).
A summary of variables related to the frequency of NPF
split by season. The total event frequency considers the percentage of days
that a type 1a event, type 1b event, or class II event occurs compared to an
undefined or non-event day. The “Frequency of type 1a/1b events” row
considers the percentage of all events in a given season that are persistent
growth events (type 1a event or type 1b event).
VariablesSpringSummerFallWinterTotal days considered170178179308Type 1a events29102036Type 1b events1771442Class II events44827147Undefined events564541127Non-events24343356Total event frequency53 %56 %59 %41 %Frequency of type 1a and 1b events51 %17 %32 %62 %
When analyzing the impact of NPF on CCN concentrations, it is important to
focus on days that exhibit a prolonged period of consistent particle growth,
allowing for aerosols from NPF to reach CCN-relevant sizes. While type 1a
events, type 1b events, and class II events are all considered NPF events,
class II events do not exhibit strong, consistent growth, making it difficult
to calculate growth statistics (Dal Maso et al., 2005). From this point
forward, we focus on comparing type 1a events and type 1b events against
non-events to better understand how aerosols from NPF affect CCN. Figure 4
compares the average number of particles of a given size produced during
type 1a and type 1b events against non-events. We find that NPF days at SPL
produce significantly more particles than non-event days up to diameters of
82.0 nm, which is larger than the critical diameter, theorized to be as low
as 30 nm at SPL, required for aerosols to activate as CCN (Lowenthal et al.,
2002). The significant increase in particles between 30.0 and 82.0 nm
during type 1a and type 1b NPF events, providing an average enhancement of
2.78 (cm-3) times more particles during events, demonstrates an
important influx of particles from NPF that reach sizes relevant to CCN
formation at SPL. Above 82 nm, days with NPF events do not indicate more
particles than non-events, which suggests any enhancements in CCN due to NPF
events are likely due to particles below 82 nm. Previous work at SPL has
shown that during NPF events, particles as low as 5 nm are observed
alongside events demonstrating that particles observed during NPF originate
from nucleation (Hallar et al., 2016).
Average number of particles produced at each particle
diameter for NPF events (blue) and non-events (red). NPF events produce
significantly more particles at aerosol diameters below the vertical line
(82.0 nm) as determined by a two-sample t test (p<0.05 indicates
significance).
Enhancements of cloud condensation nuclei due to new particle
formation
To better understand the extent that aerosols from NPF events affect CCN
concentrations, additional quality control is conducted to determine days
when NPF events grow to CCN-relevant sizes and days with available CCN data
taken at supersaturation levels between 0.2 % and 0.4 %. If there are
errors in the CCN data or the difference between CCNstart and
CCNend is less than an hour, the day is discarded from CCN
consideration. We compare 139 type 1a and 1b events that exhibit growth to
CCN sizes against 111 non-events.
Figure 5 illustrates comparative CCN number concentrations following type 1a and 1b events and non-events. We find that NPF enhances CCN concentrations by
a factor of 1.54 in the spring and 1.36 in the winter. Higher CCN
concentrations during NPF events than non-events are statistically
significant in both the winter (p=0.020) and spring (p=0.025).
However, CCN concentrations between events and non-events during the summer
(p=0.889) and fall (p=0.432) are not statistically significant.
Average number concentrations of CCN are higher during NPF events in the
spring (event: 146.47 cm-3, non-event: 94.92 cm-3),
winter (event: 98.60 cm-3, non-event: 72.67 cm-3), and
fall (event: 258.84 cm-3, non-event: 245.61 cm-3) but
lower during NPF events in the summer (event: 306.63 cm-3,
non-event: 388.05 cm-3).
Seasonal comparisons of average CCN number concentrations
(cm-3) during NPF events (blue) and non-events (red). CCN concentrations during events are considered starting at the CCNstart
time and ending at the CCNend time of a given day. CCN is considered
during non-events starting at the seasonal average of CCNstart and
ending at the seasonal average of CCNend. Displayed p values represent
the results of a two-sample t test with a one-sided hypothesis that NPF days
would exhibit greater CCN concentrations than non-events. We interpret
values below p=0.05 to be statistically significant. p values show that
the spring and winter display statistically significant enhancements of CCN
due to NPF, a trend that is absent during the summer and fall.
Discussion
NPF significantly enhances CCN concentrations in the spring and winter, the
two seasons with the highest frequency of type 1a and type 1b events.
Previous work at SPL indicates that the increased prevalence of
anthropogenic H2SO4 precursors and cooler temperatures are two
potential reasons that can lead to conditions that are conducive to NPF
during the spring and winter seasons (Hallar et al., 2016; Yu and Hallar,
2014). While previous laboratory studies suggest that multiple gases
including ammonia, amines, and organic compounds all influence NPF,
H2SO4 is important for initiating particle nucleation due to its
low volatility under atmospheric-relevant conditions (Yu et al., 2015;
Sipila et al., 2010). SO2, which is a precursor of H2SO4, is
emitted from coal-fired powerplants upwind of SPL allowing for the transport
of SO2 which has been previously observed at SPL and can help explain
the high frequency of NPF events (Hallar et al., 2016). In addition to
H2SO4, lower temperatures are another important factor that can
aid the enhancement of particle nucleation by lowering the thermodynamic
energy barrier required for nucleation to occur (Yu, 2010; Bianchi et al.,
2016; Duplissy et al., 2016; Lee et al., 2019). The combination of prevalent
H2SO4 precursors and lower temperatures are two possible factors
that can allow for the occurrence of persistent NPF on a regional scale
during the spring and winter (Yu and Hallar, 2014). These results from
modeling work suggest the significant enhancement of CCN due to NPF events
during the winter and spring at SPL may be applicable on a regional scale in
remote regions of North America downwind of power plants providing insight
into the processes that drive CCN formation.
NPF does not significantly enhance CCN concentrations in the summer and fall
seasons, compared to the spring and winter seasons (Fig. 5). One factor
that could help explain this phenomenon is higher temperatures observed in
the summer and fall compared to the spring and winter. Higher
temperatures in the summer and fall, the seasons where NPF is not
significant for forming CCN, can be a barrier to nucleation, since higher
temperatures lead to lower supersaturation ratios of H2SO4 (Yu
et al., 2015). In addition to higher temperatures, previous work shows that
SO2 concentrations at SPL are slightly lower in the summer and fall
than in the spring and winter, suggesting that H2SO4 could be less
likely to form due to the combination of higher temperatures and lower
available SO2 (Hallar et al., 2016; Yu et al., 2015). SO2 is not
available for the entirety of the dataset, hindering the direct connection
between H2SO4 precursors to the occurrence of NPF at SPL.
The CS and environmental conditions are two additional factors that can
potentially explain the presence of higher aerosol concentrations during the
summer and fall, despite the lack of a CCN enhancement due to NPF. The CS is
a parameter that indicates how fast aerosols will condense onto pre-existing
particles while also indicating how many pre-existing particles are present
(Kulmala et al., 2001; Pirjola et al., 1999). Table 2 shows that CS values
are highest in the summer, followed by the fall at SPL, indicating there is
more pre-existing aerosol in the summer and fall than in the spring and
winter. Data from the Whistler Aerosol and Cloud Study, which also takes
place in a montane setting in western North America, also find that
particles are more likely to grow to CCN-relevant sizes when the CS is lower,
since there are fewer particles to react with condensable gases, a trend
that is also observed in this work (Pierce et al., 2012). Because the CS is
calculated before NPF initiation, these trends further suggest that aerosol
transport to the site is not affecting the background particle
concentrations during events. More work to analyze the relationship between
CS and particle transport is required, since the role the CS has on NPF is
highly dependent on the conditions of a given site. Environmental conditions
in the intermountain US, such as wildfires, are another factor that could
help explain the higher CCN concentrations present in the summer and
fall during both events and non-events, since aged smoke has been observed to
enhance CCN concentrations at sizes above 80 nm in the western US (Twohy et
al., 2021). With wildfires becoming more frequent in the western US, CCN
from wildfire emissions is expected to be a contributor to total CCN during
the summer and fall months at SPL (Hallar et al., 2017). More work is needed
to better understand the role that the CS and wildfires play on CCN at SPL
during the summer and fall.
Seasonal summary of variables calculated for type 1a and 1b events. Values are presented as the mean of the variable ± 1 standard deviation.
The lack of a significant CCN enhancement by NPF at SPL during the summer
suggests that one potential phenomenon influencing NPF, and eventually CCN
concentrations, is that lower temperatures are lowering the energy barrier
required for H2SO4 formation in the winter and spring (Yu et al.,
2015). This suggests that an anthropogenic source of SO2, similar to
the powerplants upwind of SPL, is one important aspect for the occurrence of
NPF events that can enhance CCN observed in the spring and winter at SPL
(Hallar et al., 2016). Other mountaintop studies that report NPF events
enhancing CCN are near an anthropogenic emission source. For example, the
Mt. Chacaltaya Observatory, where previous studies report 61 % of events
grow to CCN sizes, is located 15 km away from the city of La Paz, Bolivia
(Rose et al., 2017). Mt. Tai, a mountaintop observatory in Shandong, China,
on the transport path of the Asian continental outflow, reports a decreased
frequency of NPF events that grow to CCN sizes because of decreases in
SO2 concentrations over time, demonstrating the importance that
H2SO4 precursors have on growing aerosols from NPF to CCN sizes
(Zhu et al., 2021; Fu et al., 2008). The results from this work can be
compared to other results from studies that report an enhancement of CCN due
to NPF (Table 3).
Details of multiple studies that find the enhancement of
CCN by NPF using observational data. For a study to be included on this
list, an enhancement percentage or factor of CCN due to NPF must be
calculated.
SiteAuthorsEnvironmentTime periodNPF frequencyContribution of NPF to CCNStorm Peak Laboratory, Steamboat Springs, CO, USAThis workMountaintop2006–202150 %1.36 enhancement in winter, 1.54 enhancement in springMt. Chacaltaya Observatory, BoliviaRose et al. (2017)Mountaintop2012Boundary layer: 48 % Free tropos- phere: 39 %Boundary layer: 67 % of events enhance CCN Free troposphere: 53 % of events enhance CCNVienna, AustriaDameto de España et al. (2017)Urban2014–201513 %14 d display 1.43 enhancementUniversity of Crete at Finokalia, Crete, GreeceKalkavouras et al. (2019)Coastal2008–2015162 episodes1.29–1.77 enhancementRV Polarstern near Svalbard, NorwayKecorius et al. (2019)Polar20174 events analyzedEnhancement factor 2–5Iberian Peninsula, SpainRejano et al. (2021)One urban site, one mountaintop site2018–2019Urban: NA Mountaintop: NAUrban: NA Mountaintop: 1.7535 sites worldwideRen et al. (2021)Multiple sites, urban and remoteVariedNAUrban: 3.6 enhancement Remote: 1.8 enhancement
NA – not available.
Verifying the automatic methodology to classify new particle
formation
To verify the automatic classifications of NPF events, we visually classify
NPF events and then compare the agreement. Figure 6 contains the total
number of days classified into the four classification schemes: event
(includes type 1a and type 1b events), class II event, non-event, and
undefined. The agreement rate of the four classification schemes between
visual and automatic classification is 51 %. The automatic method
classifies more days as undefined (32.2 %) compared to
the visual classification method (14.6 %), leading to this
poor agreement rate. However, this agreement rate increases to 79 % when
considering the binary classification of events (type 1a event, type 1b
event, class II event) and non-events (undefined, non-event).
Comparisons detailing the number of days considered a
given classification category between the automatic classification method
(red) and visual classification (blue). The event category includes type 1a
and type 1b events.
A large source of the days classified as undefined by the automatic method
are days in which five Gaussian maxima are not able to be calculated. These are
days that are classified as events, undefined, and non-events during visual
classification. Future work to improve the visual classification method
should consider why specific days may not have Gaussian maxima fitted and
thus could be incorrectly classified as undefined days. The automatic method
has an 85 % agreement with visual classification at identifying events
when these undefined days, due to a lack of Gaussians, are removed,
demonstrating a generally good overall agreement. Because the automatic
method analyzes the number concentrations with different metrics, while
visual classification looks at patterns in a colored size distribution, the
automatic method may be more sensitive to small perturbations in the data.
More studies utilizing automatic methodology and comparing automatic
classification to visual classification will help to determine aspects where
automatic classification can be improved.
Because the particle growth rate and J8 values are based on
calculations implemented in the automatic methodology, analysis of these
variables can allow for further verification of the automatic method (Table 2). Average growth rates are the highest in the summer (10.61 nm h-1)
and lowest in the winter (4.98 nm h-1). The spring and fall seasons
display similar growth rates (spring: 6.51 nm h-1, fall: 5.83 nm h-1), which could indicate that the growth rate displays a seasonal
cycle at SPL. Compared to Hallar et al. (2011) which utilizes visual
classification methods, growth rates determined by the automatic
classification are similar, albeit lower by 13.2 % in the spring, higher
by 16.5 % in the summer, and lower by 14.1 % in the winter (Hallar et
al., 2011). These results affirm that the automatic method is calculating
growth rates well, since there is an expected difference due to different
dates considered in each study. J8 values are also calculated at SPL
for all seasons (Table 2). Average seasonal J8 values range from 1.76 to 11.07 cm-3 s-1, which are higher
than the average seasonal values observed at SPL in 2011 ranging from 0.37 to 1.19 cm-3 s-1 (Hallar et al.,
2011). Because this study uses the methodology of Kulmala et al. (2012), and
Hallar et al. (2011) uses methodology from Kulmala et al. (2004), differences
between the two studies are expected since loss terms are not considered in
the simplified equation used in Hallar et al. (2011; Kulmala et al., 2004, 2012).
While the J8 values are higher than calculated in Hallar et al. (2011), the seasonal variation in the J8 values, with the highest
observed values in the summer, indicates that our method aligns with
previous work. Observations of summer NPF at SPL indicate that short bursts
of particles are common in the summer which would lead to higher J8
values (Yu and Hallar, 2014). The higher growth rates observed in the summer
accompanied by slightly shorter event durations further supports that NPF in
the summer is likely due to significant bursts rather than prolonged growth
(Table 2). Since 83 % of events classified by the method in the summer are
class II events, the automatic method is successfully identifying these
burst days and calculates variables that are consistent with these
observations.
While a comparison with the automatic methods that use deep-learning-based
convolution neural networks (CNNs) (Joutsensaari et al., 2018; Su et al.,
2022) would provide an important comparison, training the CNN would require
the removal of the data used in training from consideration. For example, Su
et al. (2022) require 358 annotated days to train and only classifies class I (banana-shaped) events, while our method can also identify class II days.
Joutsensaari et al. (2018) present another option of automatic
classification using deep learning but recommend 150 d per class to
properly train the method for each site. The big advantage of our method
compared to other automatic methods is that aspects of the statistical
method can be altered to fit individual sites without having to train the
method. Assuming there are enough data available, future studies focusing on
using automatic methodology should attempt to use both the statistical
method detailed here and CNN-based automatic methods.
Conclusions
This work at SPL marks the first long-term, direct observations of
aerosols and CCN that are analyzed in North America to quantify the impact NPF
events have on CCN concentrations. Findings show that NPF events
significantly enhance CCN concentrations in the spring by a factor of 1.54
and in the winter by a factor of 1.36, while there is no significant
enhancement observed in the summer or fall. Type 1a and type 1b NPF events,
characterized by persistent growth, are more common in the spring and winter
while class II burst events are more common in the summer and fall. Lower
temperatures which decrease the barrier for nucleation in the spring and
winter alongside higher levels of SO2 (an important H2SO4
precursor) in these seasons are likely factors that contribute to the
occurrence of NPF events that eventually enhance CCN concentrations (Hallar
et al., 2016; Yu et al., 2015).
An innovative aspect of this research is the implementation of two new
automatic methods: one to classify NPF and another to determine the times
when CCN concentrations are impacted by NPF. The automatic method to
identify NPF produces an overall event frequency of 50 % which compares
well to event frequencies calculated by previous studies using visual
classification. A comparison of the automatic classification method to
visual classification produces close to an 80 % agreement showing the
promise of automatic methodology to be applied in future studies. A
threshold method to determine CCNstart and a growth-based method to
determine CCNend ensure that CCN concentrations are considered during
times that particles from a given NPF event could activate as CCN. These
methods are easily applicable to larger datasets, making it possible to
increase efficiency when comparing the effect of NPF on CCN at multiple
sites.
At SPL, the presence of an anthropogenic SO2 plume from upwind
coal-fired powerplants during the spring and winter appears to be an
important factor allowing for particles from NPF to eventually activate as
CCN. Similar enhancements of CCN in remote, continental regions, such as
SPL, may require an anthropogenic source of NPF precursors to grow to sizes
relevant to CCN formation. These results are in contrast to previous
modeling studies that find NPF reduces CCN, thus providing a new perspective on
the significant extent NPF enhances CCN concentrations in remote regions
with close proximity to an anthropogenic source of H2SO4
precursors (Sullivan et al., 2018). More studies connecting NPF to CCN in
different regions across the globe will add important information and
increase understanding of the climate-relevant relationship between NPF and
CCN.
Code and data availability
All aerosol and CCN data will be available on the EBAS dataset. Please feel
free to reach out to the corresponding author for access to relevant data
and/or code.
Author contributions
The manuscript was written by NSH with contributions from all authors.
Experiment design, data analysis, and methodology development were performed
by NSH, LMZ, CR, GCC, and AGH. IM, FY,
and AGH further contributed materials and analysis tools necessary for
the study.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors would like to acknowledge the University of Utah's Global Change and Sustainability
Center and Douglas Lowenthal, DRI, for their support. Thank you to Maria Garcia and Dan Gilchrist for
their hard work maintaining instruments at Storm Peak Laboratory.
Financial support
This research has been supported by the Division of Atmospheric and Geospace Sciences (grant no. 1951632).
Review statement
This paper was edited by Lynn M. Russell and reviewed by two anonymous referees.
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