Introduction
Ammonia (NH3) is the most abundant basic gas in the troposphere and
plays an important role in many atmospheric processes. It is a major
neutralizer of atmospheric acidic species, reacting readily with sulfuric
acid (H2SO4) and nitric acid (HNO3) to form ammonium sulfate
and nitrate salts (e.g., (NH4)2SO4, and other forms such as
NH4HSO4, (NH4)3H(SO4)2, and
NH4NO3), which are often the main inorganic components of
atmospheric aerosols. The formation of particle-phase ammonium sulfate and
nitrate salts in the aerosol phase depends on the thermodynamic states of
their precursors and the environmental conditions, which can consequently
affect aerosol pH. For example, Guo et al. (2017b) showed that for
southeastern
US summertime conditions, as aerosol pH increases, the relative fractions
of SO42- and HSO4- increase and decrease,
respectively. Wet and dry deposition are the principle NH3 sinks
(Dentener and Crutzen, 1994). NH3 is spatially heterogeneous,
with the highest concentrations typically found near emission sources
(Seinfeld and Pandis, 2016). The dominant NH3 sources in
rural areas are agricultural in nature, and include the application of
fertilizers and volatilization of livestock waste (Reis et al., 2009;
Ellis et al., 2013; Van Damme et al., 2014). Biomass burning, either from
wildfires or from controlled burning during land-clearing operations, is
also a significant source of NH3 in rural environments. The primary
source of NH3 in urban areas is industrial emissions (e.g., NH3
synthesis, manufacture of ammonium nitrate and urea, fluid, and thermal
catalytic cracking processes in petroleum refineries), though vehicular
emissions can be a significant NH3 source in some heavily populated
cities (Reis et al., 2009; Lamarque et al., 2010; Yao et al., 2013; Sun
et al., 2017). Vehicular NH3 emissions are thought to be produced
primarily from the reaction of nitrogen oxide with hydrogen in the presence
of carbon monoxide in three-way catalysts of gasoline light-duty vehicles
(Barbier Jr. and Duprez, 1994; Whittington et al., 1995; Livingston et
al., 2009; Suarez-Bertoa et al., 2014).
In the US, implementation of stringent emission controls on traditional
anthropogenic air pollutants, such as sulfur dioxide (SO2), nitrogen
oxides (NOx), and carbon monoxide (CO), have led to steady decreases in
their emissions, and consequently their concentrations (Blanchard et al.,
2013b; Xing et al., 2013). In contrast, NH3 emissions are largely
unregulated and are projected to increase due to increased agricultural
operations to feed a growing world population (Reis et al., 2009; Ellis
et al., 2013). Satellite observations showed that gas-phase NH3
concentrations have increased substantially in US agricultural areas from
2002 to 2014 (Warner et al., 2017). More wildfires from a changing
climate, or from controlled burning for land clearing for agricultural use,
may also lead to increased NH3 emissions (Reis et al., 2009; Pechony
and Shindell, 2010; Warner et al., 2016). These trends suggest that NH3
could play an increasingly important role in atmospheric chemistry.
Previous laboratory studies have shown that NH3 can influence secondary
organic aerosol (SOA) formation and processing. For example, NH3
increases SOA mass yields in the α-pinene ozonolysis system and is
hypothesized to be due to the formation of ammonium salts from the reaction
of NH3 with organic acids (Na et al., 2007). The
heterogeneous uptake of NH3 by SOA can also lead to the formation of
particulate organonitrogen compounds, a class of brown carbon species that
can reduce visibility and impact climate (J. Laskin et al., 2010; Updyke et
al., 2012; Lee et al., 2013; A. Laskin et al., 2015).
The southeastern US is a natural outdoor laboratory for studying the
effects of biogenic–anthropogenic interactions on atmospheric aerosol
formation and processing. Subtropical vegetation composed mainly of mixed
conifer and deciduous forests emits large quantities of biogenic volatile
organic compounds (BVOCs) that can act as precursors for SOA formation
(Blanchard et al., 2011, 2013a; Guenther et al., 2012).
Large urban centers and small towns are surrounded by large expanses of
forests and widespread rural areas with agricultural activities. Scattered
within the southeastern US are also coal-burning power plants and
industrial facilities. Anthropogenic activities in this region emit large
concentrations of VOCs, SO2, NOx, CO, NH3, and aerosols
(Blanchard et al., 2013c). Similar to other parts of the US,
SO2, CO, and NOx concentrations have decreased steadily in the
southeastern US due to the implementation of emission controls
(Blanchard et al., 2013b). In contrast, gas-phase NH3
concentrations have increased in the southeastern US over the same time
period (Saylor et al., 2015). These factors make the southeastern US an
intriguing place to study the influence of NH3 on atmospheric aerosol
chemistry.
We performed aerosol and gas measurements during a field study conducted in
Yorkville, Georgia, US, in the fall of 2016, with the goal of
understanding how NH3 affects aerosol acidity and SOA formation. The
field site is surrounded by forest and agricultural land, affording an
opportunity to make ambient observations in an area impacted by local
emissions of BVOCs and NH3. In this paper, we present gas and aerosol
composition measurements that include a suite of organic acids. The
thermodynamic equilibrium model, ISORROPIA II, is used to calculate particle
water and pH based on measured inorganic aerosol and gas composition
(Nenes et al., 1998; Fountoukis and Nenes, 2007), and these predictions
are compared to observed gas–particle partitioning of NH3,
HNO3,
and organic acids. Together, these measurements are used to determine how
aerosol acidity affects the mass concentration of particle-phase organic
acids at this site.
Methods
Field site
Aerosol and gas measurements were conducted at the Yorkville, Georgia
(33.929∘ N, 85.046∘ W) SouthEastern Aerosol Research and Characterization
(SEARCH) field site from mid-August to mid-October 2016. This is one of the
sampling sites for the Southeastern Center for Air Pollution and
Epidemiology (SCAPE) study in which aerosol characterization measurements were
conducted in the summer and winter of 2012 (Xu et al., 2015a, b). A detailed description of the field site can be found in Hansen et
al. (2003). This rural site is situated in a mixed
forest–agriculture area approximately 55 km northwest and generally upwind
of Atlanta. The immediate surrounding area is used for cattle grazing and
poultry concentrated animal feeding operations (CAFOs) (Fig. S1 in the Supplement). There are
no major roads near the field site and nearby traffic emissions were
negligible. A large coal-fired power plant (Plant Bowen) is situated
approximately 25 km north of the site. Hence, the field site is impacted
mainly by BVOC and NH3 emissions, with occasional spikes in SO2
and minimal influence from urban anthropogenic pollutants such as HNO3,
O3, NOx, and CO (Fig. S2). The sampling period was characterized by
moderate temperatures (24.0 ∘C average, 32.6 ∘C maximum,
9.5 ∘C minimum) and high relative humidities (RHs) (68.9 % RH average,
100 % RH maximum, 21.6 % RH minimum). Meteorological data are shown in Fig. S3.
Data reported are displayed in eastern daylight time (EDT).
Instrumentation
Instruments were housed in a temperature-controlled (∼20 ∘C)
trailer during the field study. Gas-phase HNO3, SO2,
and organic acids (formic, acetic, oxalic, butyric, glycolic, propionic,
valeric, malonic, and succinic acids) were measured with a custom-built
chemical ionization mass spectrometer (CIMS) using sulfur hexafluoride ions
(SF6-) as reagent ions. SO2 and HNO3 were detected as
fluoride adducts (F2SO2- and NO3-⚫HF,
respectively) while the organic acids (HX) were detected primarily as
conjugated anions (X-) using the quadrupole mass spectrometer (Huey et
al., 1995, 2004; Nah et al., 2018). This CIMS is referred to
hereafter as the SF6-CIMS. Gas-phase NH3 was measured with an
additional custom-built CIMS using protonated ethanol clusters
((C2H5OH)n+) as reagent ions. NH3 was detected
primarily as NH4+ ions with the quadrupole mass spectrometer
(Nowak et al., 2002; Yu and Lee, 2012; You et al., 2014a). This CIMS is
referred to hereafter as the NH3-CIMS.
Since HNO3, NH3, and organic acids may condense on surfaces, both
SF6-CIMS and NH3-CIMS used inlet configurations that minimized
wall interactions (Huey et al., 2004; Nowak et al., 2006). Each CIMS was
connected to an inlet (a 7.6 cm ID aluminum pipe) that protruded beyond the
trailer's wall by ∼40 cm into the ambient air. Both inlets
were ∼2 m above the ground. A donut-shaped ring was attached
to the ambient sampling port of each pipe to curtail the influence of
crosswinds on the pipe's flow dynamics. Both rings were wrapped with a fine
wire mesh to prevent ingestion of insects. A flow of ∼2800 Lmin-1
was maintained in each pipe using regenerative blowers (Ametek
Windjammer 116637-03). Part of this flow (7 Lmin-1 for the
SF6-CIMS and 4.6 Lmin-1 for the NH3-CIMS) was sampled
through a custom-made three-way PFA Teflon valve, which connected the pipe's
center to the CIMS sampling orifice and could be switched automatically
between ambient and background measurements.
Background measurements were performed every 25 min for 4 min for both the
SF6-CIMS and NH3-CIMS. During each background measurement, the
sampled air flow was passed through an activated charcoal scrubber (Sigma
Aldrich) that removed SO2, HNO3, and organic acids prior to
delivery into the SF6-CIMS, and through a silicon phosphate scrubber
(Perma Pure LLC) that removed NH3 prior to delivery into the
NH3-CIMS. More than 99 % of the targeted species were removed
during background measurements for both the SF6-CIMS and NH3-CIMS.
Standard addition calibrations were performed every 5 h for the
SF6-CIMS using the outputs of a 1.12 ppm 34SO2 gas cylinder
(Scott Marrin Inc.) and a formic or acetic acid permeation device (VICI
Metronics). Calibrations for the other gases measured by the SF6-CIMS
were performed in post-field laboratory work, details of which can be found
in Nah et al. (2018) and Supplement Sect. S1. Standard addition calibrations were
performed hourly for the NH3-CIMS using the output of a NH3
permeation device (KIN-TEK). The outputs of the formic and acetic acid
permeation devices were measured periodically by scrubbing the output of the
permeation tube in deionized water, followed by ion chromatography analysis
for formate and acetate. The emission rate of the NH3 permeation device
was measured using UV optical absorption (Neuman et al.,
2003).
The detection limits for species measured by the SF6-CIMS and
NH3-CIMS were approximated from 3 times the standard deviation values
(3σ) of the ion signals measured during background mode. The
detection limits for HNO3, SO2, and the various organic acids
measured by the SF6-CIMS ranged from 1 to 60 ppt for
2.5 min
integration periods, which corresponded to the length of a background
measurement with a ∼4 % duty cycle for each m/z (Table S1 in the Supplement).
Measurement uncertainties for the concentrations of HNO3, SO2, and
the various organic acids originate mainly from calibration measurements
and were between 12 % and 25 % (Table S1). The detection limit for NH3
measured by the NH3-CIMS was 1 ppb for 2.3 min integration periods,
which corresponded to the length of a background measurement with a
∼29 % duty cycle for the NH4+ ion. Measurement
uncertainties for NH3 concentrations were 13 %.
A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS,
Aerodyne Research Inc.) was used to measure the elemental composition of
ambient non-refractory PM1 (particles with aerodynamic diameters
<1 µm). Ambient air was sampled at 16.7 Lmin-1 through
a URG PM1 cyclone and then through a Nafion dryer prior to delivery
into the HR-ToF-AMS. Aerosols were dried to RH <20 % to
eliminate the influence of RH on the HR-ToF-AMS's particle collection
efficiency. A detailed description of the HR-ToF-AMS can be found in the
literature (DeCarlo et al., 2006; Canagaratna et al., 2007, 2015). Briefly, the aerodynamic lens of the HR-ToF-AMS focused the
dried submicron aerosols into a narrow beam. The aerosols were then impacted
onto a heated tungsten surface (∼600 ∘C) on which
they were flash vaporized. The resulting vapors were ionized by electron
impact ionization (70 eV), and the ions were detected by a time-of-flight
mass spectrometer. Gas-phase interferences were accounted for by subtracting
the signals obtained during daily measurements of filtered, particle-free
sampling air. Ionization efficiency calibrations were performed weekly using
300 nm ammonium nitrate and ammonium sulfate particles.
Composition-dependent collection efficiency (CDCE) values of 0.44 to 0.55
were determined using the procedure detailed by Middlebrook et al. (2012),
in which CDCE values are derived based largely on aerosol inorganic species
concentrations and the RH in the sampling line. In
addition, a constant collection efficiency (CE) value of 0.9 was determined
from the comparison of raw HR-ToF-AMS SO42- data with other
particulate SO42- measurements performed during the study.
Comparisons of aerosol mass concentrations obtained from the application of
CDCE values (i.e., 0.44 to 0.55) vs. a constant CE value (i.e., 0.9) to the
raw HR-ToF-AMS data are discussed in Sect. 3.2. Uncertainties in
HR-ToF-AMS measurements were estimated to be approximately 25 %
(Canagaratna et al., 2007).
Particle-phase water-soluble organic acids and inorganic cations and anions
were measured using two particle-into-liquid sampler (PILS) systems coupled
to ion chromatographs (ICs) (Orsini et al., 2003). Each
PILS sampled ambient air at nominally 16.7 Lmin-1 through a URG
PM1 cyclone. Before PILS1, which was used to measure water-soluble
inorganic cation and anions, two long (24 cm) URG glass annular denuders
coated with sodium carbonate and phosphorous acid were used to remove acidic
and basic gases. Before PILS2, which measured water-soluble organic acids, a
28 cm parallel plate carbon denuder (Sunset Lab) was used to remove organic
gases (Eatough et al., 1993). In each PILS, aerosols were
mixed with water vapor at ∼100 ∘C generated from
heated ultrapure deionized water (Weber et al., 2001; Orsini et al.,
2003). The resulting droplets were impacted onto a plate, with the resulting
liquid sample analyzed using the ICs. Each IC system was calibrated at the
beginning and end of the study using five multi-compound standards in order
to create calibration curves. Periodically, a HEPA filter (Pall Life
Sciences) was placed on the inlet to determine the background in near
real time. The measurement uncertainty for each IC system was about 10 %.
PILS1 was connected to two Dionex ICS-1500 ICs (Thermo Fisher Scientific) to
measure the water-soluble inorganic ions. These two IC systems include an
isocratic pump, self-regenerating anion or cation suppressor, and
conductivity detector. This system will be referred to hereafter as the
PILS–IC. Anions were separated using a Dionex IonPac AS15 guard and
analytical column (4×250 mm, Thermo Fisher Scientific) employing an eluent
of 38 mM sodium hydroxide at a flow rate of 1.5 mLmin-1. Cations were
separated using a Dionex IonPac CS12A guard and analytical column (4×250 mm, Thermo Fisher Scientific) employing an eluent of 18 mM methanesulfonic
acid at a flow rate of 1 mLmin-1. A new chromatogram was obtained
every 30 min with a sample loop fill time (i.e., ambient sample integration
time) of 20 min. The limit of detection for the various anions and cations
was approximately 0.01 µgm-3.
PILS2 was coupled to a Dionex ICS-4000 capillary high-pressure ion
chromatography (HPIC) system to measure the water-soluble organic acids. The
HPIC includes an eluent generator, isocratic pump, degausser, suppressor,
carbonate removal device, and conductivity detector. This system will be
referred to hereafter as the PILS–HPIC. The organic acids were separated using
a Dionex AS11-HC-4 µm capillary guard and analytical column (0.4×250 mm, Thermo Fisher Scientific), which used a potassium hydroxide gradient
separation method at a flow rate of 0.015 mLmin-1. A new chromatogram
was obtained every 60 min with a sample loop fill time of 2 min. The limit
of detection for the various organic acids was approximately 0.001 µgm-3.
Particle- and gas-phase water-soluble organic carbon (WSOCp and
WSOCg, respectively) were measured using two Sievers 900 series total
organic carbon (TOC) analyzers (GE Analytical Instruments), as described by
Sullivan et al. (2004). For WSOCp measurements, ambient
air was sampled at 15.2 Lmin-1 through a URG PM1 cyclone and a
parallel plate carbon denuder into a PILS coupled to the first TOC analyzer.
For WSOCg measurements, ambient air was sampled at 20 Lmin-1
through a Teflon filter (45 mm diameter, 2.0 µm pore size, Pall Life
Sciences) to remove particles in the air stream. This filter was changed
every 3 to 4 days. The particle-free air was then directed to a MIST chamber
filled with ultrapure deionized water, which scrubbed the soluble gases at
an air flow rate of 20 Lmin-1. Soluble gases with Henry's law
constants greater than 103 moleL-1atm-1 were
scrubbed into deionized water in the MIST chamber (Spaulding et
al., 2002). The resulting MIST chamber liquid sample was analyzed by the
second TOC analyzer. The TOC analyzers converted the organic carbon in the
liquid samples to carbon dioxide using UV radiation and chemical oxidation.
The carbon dioxide formed was then measured by conductivity. The amount of
organic carbon in the liquid samples is proportional to the measured
increase in conductivity of the dissolved carbon dioxide. Each WSOCp
and WSOCg measurement lasted 4 min. Background WSOCp and
WSOCg measurements were performed for 45 min every 12 h by stopping the
sample air flow and rinsing the system with deionized water. Both TOC
analyzers were calibrated at the beginning and end of the study using
varying concentrations of sucrose solutions to create calibration curves (as
specified by the instrument manual). The limit of detections for WSOCp
and WSOCg were 0.2 and 0.4 µgCm-3, respectively. The
measurement uncertainties for WSOCp and WSOCg were estimated to be
10 % based on uncertainties in the TOC analyzer, sample air, and liquid
flows.
A suite of instruments operated by the SEARCH network provided supporting
gas and aerosol measurements (Hansen et al., 2003; Edgerton et al., 2005,
2006, 2007). O3 was measured with a UV absorption instrument (Thermo Fisher
Scientific) with a temporal resolution of 1 min. NO and NOx were
measured using a chemiluminescence instrument (Thermo Fisher Scientific) with a
temporal resolution of 1 min. NO2 was obtained from the difference
between NO and NOx. CO was measured using a nondispersive infrared
absorption instrument (Thermo Fisher Scientific) with a temporal resolution
of 1 min. NH3 was measured by a denuder-based instrument (ARA) with a
temporal resolution of 5 min. Comparisons of measurements by the
NH3-CIMS and denuder-based instrument will be presented in Sect. 3.1.
A filter-based particle composition monitor (ARA) provided 24 h-integrated
PM2.5 measurements of particle mass and major inorganic ions measured
offline by ion chromatography. Organic carbon (OC) and elemental carbon (EC)
in PM2.5 were measured by an OC–EC analyzer (Sunset Labs) with a temporal
resolution of 1 h. This analyzer determined OC by thermal optical
transmittance. VOCs were measured by a gas chromatography–flame ionization
detector (GC-FID, Agilent Technologies) with a temporal resolution of 1 h.
Particle pH and water calculation
The thermodynamic equilibrium model ISORROPIA II was used to determine the
phase state and composition of an
NH4+–SO42-–NO3-–Cl-–Na+–Ca2+–K+–Mg2+–water
inorganic aerosol in equilibrium with its corresponding gas-phase species
(Fountoukis and Nenes, 2007; Nenes et al., 1998). This approach was used
in previous studies to determine particle water and pH in different parts of
the world (Guo et al., 2015, 2016, 2017a, c; Bougiatioti et al., 2016;
Weber et al., 2016; Shi et al., 2017).
The pH of an aqueous solution is defined as the negative logarithm of the
hydronium ion (H3O+) activity on a molality basis (http://www.goldbook.iupac.org/html/P/P04524.html, last access: 6 July 2018):
pH=-log10a(H+)=-log10m(H+)γm(H+)/mθ,
where a(H+) is the hydronium ion activity in an aqueous solution,
m(H+) is the hydronium ion molality, γm(H+) is the
molality-based hydronium ion activity coefficient, and mθ is the
standard molality (1 molkg-1). For simplicity, H3O+ is
denoted here as H+ even though we recognize that the un-hydrated
hydrogen ion is rare in aqueous solutions. Since most thermodynamic
equilibrium models (e.g., ISORROPIA II, E-AIM) do not report liquid
concentrations, but instead report species in terms of concentration per
volume of air (e.g., µgm-3, µmolm-3), we have
calculated the particle pH by
pH=-log10γH+Haq+=-log101000γH+Hair+Wi+Wo≅-log101000γH+Hair+Wi,
where γH+ is the hydronium ion activity coefficient (assumed
to be 1), Haq+ is the concentration of hydronium ions in
particle water in moles per liter (i.e., the density of water is assumed to be
1000 kgm-3, and so pH is calculated in terms of molality),
Hair+ (µgm-3) is the hydronium ion concentration per
volume of air, and Wi and Wo (µgm-3) are the bulk
particle water concentrations associated with inorganic and organic species
per volume of air, respectively. In Eq. (1b), the molecular weight of
H+ is taken as 1 gmole-1, and 1000 is the factor needed for unit
conversion of grams per liter to micrograms per cubic meter. Hair+ and Wi
are outputs of the ISORROPIA II model. Previous studies have shown that
particle pH values predicted using only Wi are reasonably accurate
since the sensitivity of particle pH to the effects of Wo is small
(Guo et al., 2015). For the southeastern US,
Guo et al. (2015) reported that particle pH values predicted using only
Wi were systematically 0.15 to 0.23 units lower than those predicted
using Wi+Wo during the 2013 Southern Oxidant Aerosol Study
(SOAS) and SCAPE campaigns. Given this small deviation and that organic
aerosol hygroscopicity was not measured in this field study, we report
particle pH only considering Wi.
ISORROPIA II was run in “forward” mode, which assumes that aerosols are
“metastable” with no solid precipitates, to predict particle pH and the
partitioning of semi-volatile compounds. In forward mode, the model
calculates the gas–particle equilibrium partitioning concentrations based on
the input of the total concentration of a species (i.e., gas + particle).
In reverse mode, the model calculates the gas–particle equilibrium
partitioning concentrations based on the input of only the particle-phase
concentration of a species. We used forward mode because the reverse
mode is sensitive to measurement errors, which often result in large model
biases in the predicted particle pH (Hennigan et al., 2015).
The measured particle-phase inorganic NH4+, SO42-, and
NO3- concentrations and gas-phase HNO3 and NH3
concentrations were used as model inputs. The metastable assumption is
reasonable since the high RH (average RH 68.9 %) observed during the
study indicated that the aerosols had likely deliquesced. We excluded data
for periods when the RH was above 95 % since the exponential growth in
particle liquid water with RH introduces large pH uncertainties (Malm and
Day, 2001; Guo et al., 2015).
In using ISORROPIA II to predict particle pH and the partitioning of
semi-volatile compounds, we also assumed that the aerosols are internally
mixed and that the particle pH does not change with particle size (i.e., the
overall particle pH is characterized by the particle's bulk properties). As
long as some small fraction of sulfate is mixed with various aerosol
components, (e.g., non-volatile cations), the assumption that aerosols are
completely internally mixed has a small effect on the predicted pH
(Guo et al., 2017b). However, the presence of multiple
organic and inorganic species in ambient aerosols may lead to multiple
phases within the particle (i.e., phase separation). Consequently, this may
result in the unequal distribution of inorganic species among different
phases, each with its own water activity and inorganic concentration.
Previous studies have shown that liquid–liquid and solid–liquid phase
separations may occur for mixed organic and inorganic aerosols at low RH and
organic aerosol oxygen-to-carbon atomic ratios (O/C) (Bertram et al.,
2011; Song et al., 2012; You et al., 2013, 2014b; You and
Bertram, 2015). Phase separations were always observed at O/C≤0.5,
while no phase separation was observed at O/C≥0.8. The probability
for the occurrence of phase separation decreased at higher RH for 0.5<O/C<0.8. The average O/C for this field study is 0.69±0.06. Organic acids were not included in the calculation of particle
pH. This is reasonable since their total mass concentration was small
compared to the total inorganic mass concentration. The average ratio of the
organic acid mass concentration to the inorganic mass concentration is 0.25.
Furthermore, Song et al. (2018) showed that
including organic acid mass concentrations in thermodynamic model
calculations had minor effects on particle pH if the system is in
equilibrium. The validity of these assumptions and the resulting
thermodynamic model predictions will be evaluated by comparing the predicted
gas–particle partitioning ratios of semi-volatile inorganic compounds with
measured values in Sect. 3.3.
Measurements by the NH3-CIMS during the second half of the
study. (a) Time series of NH3 concentration. The data are displayed as
1 h averages. (b) Diurnal profiles of NH3 concentration (mean and
median) and temperature. Error bars shown are the standard errors. Dates and
times displayed are local time. All the concentrations represent averages in
1 h intervals and the standard errors are plotted as error bars.
(c) Average NH3 concentration normalized to wind speed (i.e., NH3
concentration (ppb) × wind speed (ms-1)) in each 10∘ bin (red
line). The average normalized NH3 concentration is shown as a grey
line.
Results and discussion
NH3 observations
Continuous measurements of NH3 were made using the NH3-CIMS from
13 September to 12 October. Figure 1a and b show the time series and
average diurnal profile of NH3, respectively. NH3 concentrations
ranged from 0.7 to 39.0 ppb (0.5 to 28.5 µgm-3) and exhibited
consistent diurnal cycles. NH3 was generally higher in the late
mornings and early afternoons. Concentrations started to increase at 07:30,
which coincided with an increase in temperature at sunrise (Fig. S3).
Possible reasons for the morning increase include volatilization of
particulate ammonium and animal waste, entrainment from the residual layer
where NH3 may not have been depleted, evaporation of dew or fog that
contained dissolved NH3, and emission from plant stomata
(Ellis et al., 2011). NH3 decreased at 14:30,
approximately 1 h before temperature decreased, and may be due to changes
in the boundary layer height. However, this hypothesis cannot be tested
since the boundary layer height was not measured during the study. The
diurnal plot does not account for dilution as the boundary layer expanded
and only indicates that if emissions were solely from the surface and lower
concentrations aloft, these NH3 sources were of significant magnitude.
The average NH3 concentration measured by the NH3-CIMS is 8.1±5.2 ppb. This is approximately 2 times higher than the average
NH3 concentration (3.8±2.9 ppb) measured by the denuder-based
instrument operated by the SEARCH network over the same time period (Fig. S4).
Differences in NH3 concentrations measured by the two instruments
may be due to positive and negative sampling artifacts caused by differences
in sampling inlets (e.g., inlet length and location), frequency of
calibration and background measurements, and (in the case of the
denuder-based instrument) possible sample contamination during chemical
analysis. Discussions on how differences in measured NH3 concentrations
affect PM1 pH predictions will be presented in Sect. 3.3.
Nevertheless, there is a record of NH3 concentrations measured by the
denuder-based instrument at this site since 2008. Just prior to and during
this study, NH3 concentrations are generally the highest observed since
2011 (Fig. S5). These elevated NH3 concentrations may be due to
sporadic biomass burning episodes caused by elevated temperatures and
widespread drought across the southeastern US in 2016 (Park Williams et
al., 2017; Case and Zavodsky, 2018).
The NH3-CIMS measurements are examined with the meteorological data to
gain insights into the primary NH3 sources during the sampling period. To
account for wind speed, the 1 h averaged NH3 concentrations are
first multiplied by their corresponding 1 h averaged wind speeds. These
normalized NH3 concentrations are then used to construct a wind
direction polar plot showing the average normalized NH3 concentration
per 10∘ bin (Fig. 1c). The wind direction polar plot shows that the
normalized NH3 is approximately 2 times greater than the average when
air masses are transported from the southeast, the general direction of the
poultry CAFOs located approximately 2 km from the field site (Fig. S1),
which are known for having high NH3 emissions. This conclusion is reaffirmed
by NH3 measurements from the SEARCH network's denuder-based instrument.
NH3 concentrations measured by the two instruments in this study are
substantially higher than those measured in three recent field studies
conducted in the continental US: the 2010 California Nexus (CalNex) study,
2013 Southeast Nexus (SENEX) study, and 2013 SOAS study (see Table 1). The
differences in NH3 may be attributed to differences in land use,
proximity to CAFOs, and meteorological conditions. The high NH3
concentrations in this study allow us to make ambient observations of the
effect of NH3 on particle acidity and the gas–particle partitioning of
semi-volatile inorganic and organic compounds, and compare them with
previous studies.
PM1 composition
The aerosol inorganic chemical composition was measured by several
instruments during this study. The HR-ToF-AMS, PILS–IC and PILS–HPIC
measured the composition of PM1, while a filter-based particle
composition monitor measured the composition of PM2.5. Comparisons of
aerosol SO42-, NO3-, and NH4+ mass
concentrations obtained from the application of CDCE values to the raw
HR-ToF-AMS data are compared to those measured by the other three
instruments in Fig. S6. NH4+ measurements by the PILS–IC are not
available for comparison due to denuder breakthrough that occurred during
the study.
SO42- measurements by the various instruments are generally well
correlated with each other, with R2 values ranging from 0.64 to 0.92.
Although PM1 SO42- measurements by the two PILS systems show
good agreement with each other, HR-ToF-AMS CDCE-applied SO42-
measurements are approximately 2 times higher than the PILS and filter
measurements. Similar systematic differences are also observed for
NO3- and NH4+ measurements. NO3- and
NH4+ measurements from the four instruments are moderately
correlated (R2=0.54 to 0.79 and R2=0.94, respectively).
NO3- measurements from the PILS and filter systems are mostly
similar; however, HR-ToF-AMS CDCE-applied PM1 NO3- and
NH4+ measurements are approximately 3 times and 2 times
higher than the PILS and filter measurements. One possible reason is that
the calculated CDCE is lower due to organics dominating the aerosol
composition during the study (average of 74.2±7.9 % of the
non-refractory PM1 mass concentration). Lee et al. (2015) suggested
that a high organic mass fraction may impede the complete efflorescence of
aerosols when they are passed through the drier prior to delivery into the
HR-ToF-AMS, thus reducing the particle bounce and increasing the CE
value. Hence, we estimated HR-ToF-AMS PM1 mass
concentrations that would be consistent with PILS and filter measurements by
multiplying all the raw HR-ToF-AMS data by a constant CE value of 0.9, which
was obtained from comparisons of the raw HR-ToF-AMS SO42-
data with PILS–IC and PILS–HPIC SO42- measurements. The
constant CE-applied HR-ToF-AMS data are used in all our subsequent analyses.
(a) Time series and (b) diurnal profiles of non-refractory
PM1 species measured by the AMS. Error bars shown in (b) are the
standard errors. Dates and times displayed are local time.
Figure 2 shows the time series and average diurnal profiles of
non-refractory PM1 species. The average non-refractory PM1
organics, SO42-, NO3-, and NH4+ mass
concentrations are 5.0±2.3, 1.6±0.4, 0.2±0.1, and 0.4±0.2 µgm-3, respectively. Organics are the dominant
non-refractory PM1 species, accounting for 74.2±7.9 % of the
non-refractory PM1 mass concentration during the field study. Organic
aerosol mass concentration was slightly higher at night, which is likely
caused by changes in the boundary layer height, emission sources, and SOA
formation processes (Xu et al., 2015b). Previous studies have
shown that nighttime SOA production in the southeastern US is largely
attributed to nitrate radical oxidation and ozonolysis of monoterpenes,
which are abundant at night (Pye et al., 2015; Xu et al., 2015a, b; Lee et al., 2016; Zhang et al., 2018). Specifically, the nitrate
radical oxidation of some monoterpenes (e.g., β-pinene) could form
low-volatility organic nitrates that are condensable and could contribute
substantially to the nocturnal organic aerosol mass (Boyd et al., 2015, 2017; Ng et al., 2017). Apportionment of organic aerosol
sources will be discussed in an upcoming publication. SO42- is the
second most abundant non-refractory PM1 species (16.3±5.7 %
mass fraction), followed by NH4+ (5.9±2 % mass
fraction) and NO3- (3.6±2.2 % mass fraction).
SO42- mass concentration peaked in the afternoon due to enhanced
SO2 photooxidation (Weber et al., 2003). The NO3- mass
concentration measured by the HR-ToF-AMS is the nitrate functional group
(-ONO2) present on organic and inorganic nitrates. Hence, the diurnal
profile of the NO3- mass concentration in Fig. 2 has contributions
from both organic and inorganic nitrates. The mass concentrations of organic
and inorganic nitrates increased after sunset and peaked at sunrise (Fig. S7),
likely due to the formation of organic nitrates from nighttime NO3
chemistry and increased gas-to-particle partitioning of organic and
inorganic nitrates as temperature decreased (Xu et al., 2015a,
b). Quantification and characterization of organic nitrates based on
HR-ToF-AMS and PILS–IC PM1 NO3- measurements will be
discussed in a future publication. NH4+ mass concentration has
moderate diurnal variations with marginally higher concentrations in the
afternoon, likely due to the contrasting day–night phases of ammonium
sulfate and ammonium nitrate formation. SO42-, NO3-, and
NH4+ molar concentrations indicated that NH4+ is mainly
associated with SO42- in PM1.
(a) Time series and (b) diurnal profiles of ISORROPIA-predicted
PM1 pH and Wi. The diurnal profiles of RH and ISORROPIA-predicted
Hair+ are also shown in (b). Dates and times
displayed are local time. All the data shown here represent averages in 1 h intervals.
Error bars shown in (b) are the standard errors.
PM1 pH predictions
CIMS HNO3 and NH3 data, HR-ToF-AMS PM1 SO42- and
NH4+ data, PILS–IC PM1 NO3- and non-volatile cation
(Cl-, Na+, Ca2+, K+, and Mg2+) data, measured
temperature, and RH are used as ISORROPIA II model inputs to predict PM1
Wi and pH from 13 September to 6 October. Figure 3 shows the time
series and average diurnal profiles of ISORROPIA-predicted PM1 Wi
and pH. PM1 is highly acidic with pH values ranging from 0.9 to 3.8
and an average pH of 2.2±0.6. The average PM1 pH is 2.5±0.6 during periods when the NH3 concentration is higher than
13.3 ppb (i.e., average NH3 concentration +1 standard deviation = 8.1+5.2=13.3 ppb).
The PM1 pH values in this study are
generally similar to those reported by Guo et al. (2015) at the same field
site during winter 2012. Our observation that PM1 is acidic despite
the high NH3 concentrations in this study is consistent with previous
studies showing that particle pH has weak sensitivities to wide NH3 and
SO42- mass concentration ranges due to pH buffering caused by the
partitioning of NH3 between the gas and particle phases (Weber et
al., 2016; Guo et al., 2017c). This weak particle pH sensitivity also
explains the small changes in PM1 pH values (about 10 % lower,
Fig. S8) when NH3 measurements by the SEARCH network denuder-based
instrument are used in ISORROPIA II calculations (instead of NH3-CIMS
measurements).
Comparisons among different field campaigns for particle pH,
major inorganic ions and gases, and meteorological conditions. All pH values
were calculated using ISORROPIA II run in forward mode. These statistics
were previously compiled by Guo et al. (2017a). Campaign acronyms used here
stand for the California Research at the Nexus of Air Quality and Climate
Change (CalNex), Southern Oxidant and Aerosol Study (SOAS), and Southeastern
Nexus of Air Quality and Climate (SENEX).
Campaign
CalNex
SOAS
SENEX
This study
Type
Ground
Ground
Aircraft
Ground
PM cut size
PM1
PM2.5a
PM1 & PM2.5b
PM1
PM1
Year
2010
2013
2013
2016
Season
(early summer)
Summer
Summer
Fall
Region/location
SW US
SE US
SE US
SE US
SO42-, µgm-3
2.86±1.70
1.88±0.69
1.73±1.21
2.05±0.80
1.6±0.4
NO3-, µgm-3
3.58±3.65
3.74±1.53
0.08±0.08
0.28±0.09
0.20±0.10
HNO3, µgm-3
6.65±7.03
4.45±3.59
0.36±0.14
1.35±0.66
0.50±0.26
ε(NO3-)
39±16 %
51±18 %
22±16 %
18±6 %
26±15 %
Total NO3-, µgm-3
10.22±9.74
8.19±3.89
0.45±0.26
1.63±0.70
0.70±0.28
NH4+, µgm-3
2.06±1.67
1.79±0.65
0.46±0.34
1.06±0.25
0.40±0.20
NH3, µgm-3
1.37±0.90
0.75±0.61
0.39±0.25
0.12±0.19
5.79±3.67
ε(NH4+)
55±25 %
71±19 %
50±25 %
92±11 %
7±5 %
Total NH4+, µgm-3
3.44±1.81
2.54±0.89
0.78±0.50
1.17±0.81
6.19±3.68
Na+, µgm-3
NA
0.77±0.39
0.03±0.07
NA
NA
Cl-, µgm-3
NA
0.64±0.48
0.02±0.03
NA
0.01±0.01
RH, %
79±17
87±9
74±16
72±9
69±18
T, ∘C
18±4
18±3
25±3
22±3
24±4
Wi, µgm-3
13.9±18.1
29.8±20.7
5.1±3.8
3.2±2.8
1.6±1.7
pH
1.9±0.5
2.7±0.3
0.9±0.6
1.1±0.4
2.2±0.6
Reference
Guo et al. (2017a)
Guo et al. (2015)
Xu et al. (2016)
This study
a Only during the last week of CalNex.b PM2.5 was sampled in the first half and PM1 sampled in the
second half of the study. Various parameters were similar in both cases.
Crustal components were higher but are overall generally at low
concentrations so the differences had minor effects. For example, PM2.5
Na+ was 0.06±0.09 µgm-3 and PM1 Na+ was
0.01±0.01 µgm-3.NA = not available.
PM1 pH varied by approximately 1.4 units throughout the day. Wi
has an average value of 1.6±1.7 µgm-3. PM1 Wi
and pH showed similar diurnal profiles, with both peaking in the midmorning
and reaching their minima in the midafternoon. These diurnal trends are
consistent with those previously reported by Guo et al. (2015) for PM1
measured during the summer and winter in different parts of the southeastern
US. Also shown in Fig. 3b is the diurnal profile of Hair+, which
peaked in the midafternoon. The Wi and Hair+ maximum / minimum
ratios are comparable (6.5 and 5.3, respectively), thus indicating that the
diurnal variation in particle pH is driven by both Wi and
Hair+.
The average PM1 pH for this study is about 1 unit higher than that for
the SENEX and SOAS campaigns (Table 1) and is likely due to the much higher
abundance of NH3 in this study. The average NH3 mass concentration
in this study is approximately 49 times and 15 times higher than that in
the SENEX and SOAS campaigns, respectively. The average PM1 pH for this
study is similar to that for the CalNex campaign even though the average
NH3 mass concentration in this study is only approximately 4 times
higher than that in the CalNex campaign (Guo et al., 2017a). This may be
due, in part, to PM1 SO42- and NO3- mass
concentrations at CalNex being approximately 2 times and 18 times larger
than those of this study, respectively. Aerosol inorganic SO42-
and NO3- species are hygroscopic species. The much higher
NO3- mass concentrations in the CalNex campaign (due, in part, to
high NOx emissions) increased particle Wi substantially, which
diluted H+ and raised particle pH, resulting in more gas-to-particle
partitioning of NO3-, and eventually leading to pH levels similar
to those observed in this study. This type of feedback does not happen in
the southeastern US, where non-volatile SO42- dominates the
uptake of particle water. It is also possible that the higher RH and lower
temperatures during the CalNex campaign (relative to this study) contributed
to high particle Wi, which diluted H+ and raised particle pH
levels similar to those observed in this study.
The validity of this study's thermodynamic model predictions is evaluated by
comparing the predicted gas–particle partitioning ratios of semi-volatile
inorganic compounds (i.e., NO3- and NH4+) with measured
values (Fig. S9). CIMS HNO3 and NH3 data, PILS–IC
NO3-,
and HR-ToF-AMS NH4+ data are used in this comparison. ε(NO3-) and ε(NH4+) are defined as the
particle-phase molar concentration divided by the total molar concentration
(gas + particle), i.e., ε(NO3-)=NO3-/(HNO3+NO3-) and ε(NH4+)=NH4+/(NH3+NH4+). Predicted NH3,
NH4+, and ε(NH4+) values are generally
within 10 % of and are highly correlated (R2=0.96 to 0.99) with
measured values (Fig. S9). While predicted HNO3 values generally agreed
with measurements, substantial scatter can be seen between the predicted and
measured values for NO3- and ε(NO3-). This
scatter can be attributed, at least in part, to uncertainties brought about
by the low PM1 NO3- mass concentrations and effects of coarse-mode cations (e.g., Na+, Ca2+, K+, and Mg2+) on fine-mode
HNO3–NO3- gas–particle equilibrium (i.e., HNO3 can
partition to both fine and coarse modes, thereby affecting fine-mode
NO3- concentrations; no such effect occurs for
NH3–NH4+ gas–particle equilibrium). In general, the overall
good agreement between model predictions and measurements indicated that our
assumptions that aerosols are metastable (i.e., aerosols are supersaturated
aqueous droplets) with no phase separation for the thermodynamic
calculations are reasonable for the conditions of this study and do not
affect model predictions.
Analytically calculated S curves of ε(NH4+)
and ε(NO3-) and ambient data plotted against
ISORROPIA-predicted particle pH for this study, SENEX, SOAS, and CalNex. For
the ambient data sets, a narrow range of Wi (1 to 4 µgm-3)
and temperature (15 to 25 ∘C) is selected to be close to the
analytical calculation input (i.e., Wi=2.5 µgm-3 and
temperature = 20 ∘C). Similar to Guo et al. (2017a), γNH4+=1 and γH+-NO3-=γH+γNO3-=0.28 are used for the analytically
calculated S curves.
The molar fractions of NO3- and NH4+ in the particle
phase (i.e., ε(NO3-) and ε(NH4+)) measured in this study are compared with those measured
during the CalNex, SENEX, and SOAS campaigns. Figure 4 shows the measured
ε(NO3-) and ε(NH4+) values as
a function of their ISORROPIA-predicted particle pH for the various field
studies. For each field study, only a subset of the data are chosen for this
comparison (1≤Wi≤4 µgm-3 and 15 ∘C ≤ temperature ≤ 25 ∘C) to reduce the effects of
variability in Wi and temperature on gas–particle partitioning for
comparison with the calculated S (or sigmoidal) curves, which are calculated
based on Wi=2.5 µgm-3 and temperature = 20 ∘C.
The S curves for HNO3–NO3- and NH3–NH4+
partitioning as a function of particle pH are also plotted as solid lines.
The S curves are calculated based on the solubility and dissociation of
NO3- and NH4+ species in water:
εNO3-=HHNO3∗RTWi×0.987×10-14γH+γNO3-10-pH+HHNO3∗RTWi×0.987×10-14,εNH4+=γH+10-pHγNH4+HNH3∗RTWi×0.987×10-141+γH+10-pHγNH4+HNH3∗RTWi×0.987×10-14,
where HHNO3∗ and HNH3∗ (mole2kg-2atm-1)
are equilibrium constants and are the products of
the Henry's law constant and the dissociation constant of HNO3 and
NH3, respectively; R is the gas constant
(8.314 m3PaK-1mol-1); T is temperature (K); and γi's are activity
coefficients. HHNO3∗ and HNH3∗ values at
20 ∘C are calculated using equations found in Clegg and
Brimblecombe (1990) and Clegg et al. (1998), respectively.
Activity coefficients predicted by ISORROPIA II are γH+-NO3-=γH+γNO3-=0.28, γH+=1 and γNH4+=1.
Derivations of the analytically calculated S curves for ε(NO3-) and ε(NH4+) in Eqs. (2) and
(3)
can be found in Guo et al. (2017a). As shown in Fig. 4, the measured
ε(NO3-) and ε(NH4+) values for
the four field studies all generally converged on the calculated S curves.
The higher particle pH values in this study and the CalNex campaign relative
to those for the SENEX and SOAS campaigns resulted in less NH3 and more
HNO3 partitioned to the particle phase, as predicted by these simple
analytical expressions. A similar analysis will be performed for the organic
acids in Sect. 3.5.
Particle- and gas-phase measurements of (a) formic, (b) acetic,
(c) oxalic, (d) malonic, (e) succinic, (f) glutaric, and (g) maleic acids.
Particle-phase measurements are shown on the left y axes, while gas-phase
measurements are shown on the right y axes. Dates and times displayed are
local time. Gas-phase measurements of glutaric and maleic acids are not
available.
Diurnal profiles of particle- and gas-phase (a) formic,
(b) acetic, (c) oxalic, (d) malonic, (e) succinic, (f) glutaric, and (g) maleic
acids. Particle-phase measurements are shown on the left y axes, while
gas-phase measurements are shown on the right y axes. All the data shown
here represent averages in 1 h intervals. Error bars shown are the
standard errors.
WSOC and water-soluble organic acids
The time series and average diurnal profiles of WSOCg and WSOCp
are shown in Fig. S10. The average WSOCg mass concentration (3.6±2.7 µgCm-3) is roughly 4 times higher than that of
WSOCp (1.0±0.6 µgCm-3). The diurnal profile of
WSOCp is somewhat flat, likely due to various organic aerosol sources
having different water solubility and diurnal cycles and compensating for each
other throughout the day (Xu et al., 2015b, 2017). In
contrast, WSOCg displayed strong diurnal variations. WSOCg
increased at 07:30, which coincided with the sharp increase in solar
irradiance (Fig. S3). WSOCg decreased at 21:30, approximately
2 h
after sunset. Also shown in Fig. S10 are the time series and average diurnal
profile of the mass fraction of total WSOC in the particle phase, i.e.,
Fp=WSOCp/(WSOCp+WSOCg). The peak Fp
coincided with the minima of WSOCg at 07:30.
The average WSOCg and WSOCp (3.6±2.7 µgCm-3 and
1.0±0.6 µgCm-3) are slightly lower than those measured
during the SOAS campaign (SOAS WSOCg=4.9 µgCm-3 and WSOCp=1.7 µgCm-3) (Xu et al., 2017). While the diurnal
profiles of WSOCp in both studies are flat, the diurnal profiles of
WSOCg measured in the two studies are different. WSOCg measured in
the SOAS study decreased at sunset, while WSOCg measured in this study
decreased 2 h after sunset. Differences in WSOCg diurnal profiles
in the two studies are likely due to differences in emission sources as a
result of different sampling periods (SOAS was in early summer and this
study was in early fall), land use, and/or land cover. The ratio of
WSOCp to OC for this study was estimated at 30 %, but this
comparison is imprecise because WSOCp was PM1 and OC was
PM2.5 (refer to Fig. S11 and Supplement Sect. S2).
Figure 5 shows the time series of particle- and gas-phase concentrations of
formic, acetic, oxalic, malonic, succinic, glutaric, and maleic acids. Their
diurnal profiles are shown in Fig. 6. Gas-phase measurements of glutaric and
maleic acids are not available. Gas-phase measurements of butyric, glycolic,
propionic, and valeric acids were also measured during the study and have
been presented in Nah et al. (2018), but will
not be discussed here since their particle-phase measurements are not
available.
Assuming that all the measured organic acids are completely water soluble,
30 % of the WSOCg is comprised of these organic acids
(Nah et al., 2018). Formic and acetic acids are the most abundant gas-phase organic acids, with averages of 2.2±1.6 and 1.9±1.3 µgm-3, respectively. The average carbon
mass fraction of WSOCg comprised of formic and acetic acids is 7 % and
13 %, respectively. All the gas-phase organic acids displayed strong and
consistent diurnal cycles, with higher concentrations being measured during
warm and sunny days. Their concentrations start to increase at sunrise (at
07:30), building to a peak between 15:30 and 19:30, then decrease overnight.
Nah et al. (2018) previously showed that the measured gas-phase organic
acids during the study, including oxalic acid, likely have the same or
similar sources. Poor correlations between gas-phase organic acid
concentrations and those of anthropogenic pollutants (HNO3, SO2,
CO, and O3) indicated that these organic acids are not due to
anthropogenic emissions and are likely biogenic in nature. Biogenic
emissions of gas-phase organic acids and/or their BVOC precursors are
elevated at high temperatures, resulting in higher organic acid
concentrations during warm and sunny days. For example, isoprene, which is
the dominant BVOC in Yorkville, has a somewhat similar diurnal profile to
the organic acids. In addition, the concentration of isoprene is moderately
correlated with those of formic and acetic acids (Fig. S10 of Nah et al.,
2018), which are known products of isoprene photooxidation. Some of these
gas-phase organic acids may also be formed in the particle phase during
organic aerosol photochemical aging, with subsequent volatilization into the
gas phase. The gas–particle partitioning of organic acids likely depends on
thermodynamic conditions, which are controlled by particle pH and Wi
and meteorological conditions, as will be shown in Sect. 3.5.
The measured particle-phase water-soluble organic acids contributed on
average 6 % to the HR-ToF-AMS-measured organic aerosol mass
concentration. The average carbon mass fraction of WSOCp comprised of
these organic acids is 4 %. Previous studies have shown that
particle-phase organic acids found in rural environments are oxidation
products of gas-phase aliphatic monocarboxylic acids, which are formed in
the photochemical oxidation of biogenic unsaturated fatty acids and other
BVOC precursors (Kawamura and Gagosian, 1987; Kawamura and Ikushima,
1993; Kerminen et al., 2000; Kawamura and Bikkina, 2016). These
particle-phase organic acids can also be produced during the multiphase
photochemical aging of ambient organic aerosols (Ervens et al., 2004; Lim
et al., 2005; Sorooshian et al., 2007, 2010).
Oxalate is the most abundant measured particle-phase water-soluble organic
acid anion (contributing on average 26 % to the total particle-phase
organic acid mass concentration), with mass concentrations ranging from 0.01
to 0.34 µgm-3 and an average of 0.07±0.05 µgm-3.
Acetate (average of 0.06±0.03 µgm-3) and
formate (average of 0.05±0.03 µgm-3) are the second and
third most abundant measured particle-phase water-soluble organic acid
anions, respectively. Particle-phase formate, acetate, and maleate showed
weak diurnal variations, and may be due, in part, to various emission
sources having different diurnal cycles and compensating for each other
throughout the day. Particle-phase oxalate, malonate, and succinate peaked in
the middle to late afternoon, while glutarate generally peaked in the
midmorning. This suggests that while the production of these organic acids
is photochemically driven, they may have different BVOC precursors and/or
different photochemical production pathways. In addition, since oxalic
(C2), malonic (C3), succinic (C4), and glutaric (C5)
acids belong to the same homologous series of organic diacids, it is
possible that the photochemical aging of particle-phase glutaric acid
resulted in the formation of its successive homologues via the cleavage of
C–C bonds. Hence, organic aerosol photochemical aging may also have
contributed to the diurnal profiles of particle-phase oxalate, malonate,
succinate, and glutarate.
Gas–particle partitioning of organic acids
The online and simultaneous measurements of gas- and particle-phase organic
acid mass concentrations provided the opportunity to study gas–particle
partitioning behavior of semi-volatile organic compounds with respect to
particle pH, as is more commonly done with semi-volatile inorganic species
(see Sect. 3.3). Since formic, acetic, and oxalic acids are the three most
abundant measured organic acids present in the gas and particle phases, we
focus on the gas–particle partitioning behaviors of these three organic
acids. The average molar fractions (±1 standard deviation) of formic,
acetic, and oxalic acid in the particle phase (i.e., ε(HCOO-), ε(CH3CO2-), and ε(C2O42-)) are 3.6±3.6 %, 5.8±5.0 %, and
73.7±9.8 %, respectively. The uncertainties of these ratios for
formic, acetic, and oxalic acids are 16 %, 16 %, and 17 %, respectively, which
are obtained from the propagation of their SF6-CIMS and PILS–HPIC
measurement uncertainties.
Oxalic acid
To investigate the factors affecting oxalic acid gas–particle partitioning,
the equation for the S curve describing the dependence of oxalic acid
gas–particle partitioning (i.e., ε(C2O42-)=C2O42-/(C2H2O4+C2O42-))
on particle pH is derived. As shown in Supplement Sect. S3, the analytically
calculated S curve for ε(C2O42-) can be
simplified to
εC2O42-≅HC2H2O4WiRTγH+γC2HO4-γC2H2O410-pH+Ka1×0.987×10-14γH+γC2HO4-10-pH+HC2H2O4WiRTγH+γC2HO4-γC2H2O410-pH+Ka1×0.987×10-14,
where HC2H2O4 (moleL-1atm-1) is the
Henry's law constant for oxalic acid, Ka1 (moleL-1) is the
first acid dissociation constant for oxalic acid, R is the gas constant
(8.314 m3PaK-1mol-1), T is temperature (K), and γi's are activity coefficients. We used the web version of AIOMFAC
(http://www.aiomfac.caltech.edu, last access: 6 December 2017)
(Zuend et al., 2008, 2011, 2012) to compute an average γC2H2O4 value of
0.0492. Since AIOMFAC does not predict γH+γC2HO4-, we assumed that γH+γC2HO4-=γH+γNO3- and used
the ISORROPIA-predicted γH+γNO3- value of
0.07. We used the average of HC2H2O4 values provided by Clegg
et al. (1996), Compernolle and Müller (2014), and Saxena and Hildemann
(1996) (6.11×108 moleL-1atm-1 at 25 ∘C)
and accounted for the effect of temperature using Eq. (19) in Sander (2015). Although Ka1 also depends on temperature,
we used the Ka1 value at 25 ∘C (5.62×10-2 moleL-1, Haynes, 2014) for all the oxalic acid S curve
calculations since equations that compute Ka1 values for pure aqueous
oxalic acid solutions at different temperatures are not available in the
literature. In addition, the temperatures observed in this study were close
to 25 ∘C (study-average temperature = 23.4±4.0 ∘C).
Different S curves for ε(C2O42-) are calculated
using 1 h average values obtained from the diurnal profiles of
temperature and Wi (specifically at 00:30, 06:30, and 12:30). The shape
of the S curve changes with the time of day due to the diurnal variations in
temperature and Wi (Fig. S12 and Supplement Sect. S3). The S curves for
ε(C2O42-) are very different from those of other
acids, such as ε(NO3-) (shown in Fig. 4b). From the S
curves for ε(C2O42-), which are calculated using
conditions in this study, some molar fraction of oxalic acid is always
expected to be present in the particle phase, even at low particle pH (i.e.,
the S curve does not go to zero at low pH). In contrast, HNO3 is
expected to be present primarily in the gas phase at low particle pH (i.e.,
pH<1) under similar temperature and Wi conditions. This is
due primarily to differences in the Henry's law constants for the two acids.
HHNO3 (2.57×105 moleL-1atm-1) at 23.4 ∘C
is 3 orders of magnitude smaller than
HC2H2O4 (7.27×108 moleL-1atm-1)
(Clegg and Brimblecombe, 1990; Sander, 2015). This means that some
undissociated form of oxalate can be found in the particle phase at any pH,
and the molar fraction of this form increases with particle Wi (see
Fig. S12). Oxalic acid's very high Henry's law constant combined with the
Wi conditions in this study ensures that some fraction of the organic
acid will be in the particle phase regardless of the particle pH.
Analytically calculated S curve of ε(C2O42-) and ambient data from 13 September to 6 October 2016
plotted against ISORROPIA-predicted particle pH. For the ambient data,
a range in Wi (0.5 to 4 µgm-3) and temperature (15 to 31 ∘C)
is chosen to be close to the analytically calculated
outputs. For the analytically calculated S curves, we used γC2H2O4=0.0492 (AIOMFAC predicted). We also assumed that
γH+γC2HO4-=γH+γNO3- and used the ISORROPIA-predicted γH+-NO3-=γH+γNO3-=0.265. The black line is the S curve calculated using the selected time
period's average temperature (23.4±4.0 ∘C) and Wi
(1.6±1.7 µgm-3). The grey lines are S curves calculated
using 1 standard deviation from the average temperature and Wi (i.e.,
temperature = 27.4 ∘C and Wi=0.5 µgm-3 for
dotted grey line; temperature = 19.4 ∘C and Wi=3.3 µgm-3 for solid grey line).
Figure 7 compares the measured ε(C2O42-) vs. ISORROPIA-predicted PM1 pH to the analytically calculated S curve(s).
The S curve is calculated based on the average temperature and Wi from
13 September to 6 October (23.4±4.0 ∘C and 1.6±1.7 µgm-3, respectively). We also calculated the “upper” and
“lower” bounds of this S curve based on 1 standard deviation from the
average temperature and average Wi. A temperature = 27.4 ∘C
and Wi=0.5 µgm-3 are used for calculations of the
lower bound, while a temperature = 19.4 ∘C and Wi=3.3 µgm-3 are used for calculations of the upper bound. For
the ambient data, a range in Wi (0.5 to 4 µgm-3) and
temperature (15 to 31 ∘C) is chosen to be close to the analytical
calculation. As shown in Fig. 7, the measured ε(C2O42-) generally converged around the S curve calculated
using the average temperature and Wi values. Although there is some
scatter, the measured ratios are mostly within the upper and lower
bounds of the S curve.
Since the measured ε(C2O42-) values are in general
agreement with the analytically calculated S curve (Fig. 7), we can use the
S curve to understand qualitatively how high NH3 events at the site
affect oxalic acid gas–particle partitioning. Here we define high NH3
events as periods when the NH3 concentration was higher than
13.3 ppb
(which is the average NH3 concentration +1 standard deviation). As
discussed in Sect. 3.3, the PM1 pH during high NH3 events is 2.5±0.6, which is slightly higher than the average PM1 pH of 2.2±0.6. Based on the S curve calculated using the average temperature
and Wi values, ε(C2O42-) increases from
81 % to 89 % when particle pH increases from 2.2 to 2.5. While this
result indicates that high NH3 concentrations can raise the particle pH
sufficiently such that it can promote gas-to-particle partitioning of oxalic
acid, this is not always the case. Specifically, increasing the particle pH
from -2 (or lower) to 1 will not result in a significant increase in
ε(C2O42-). Therefore, whether or not particle
pH, and consequently oxalic acid gas–particle partitioning, is sensitive to
NH3 concentration depends strongly on particle pH values.
We also examined how well the analytically calculated S curve for
ε(C2O42-) captures diurnal variations in the
measured ε(C2O42-). The ambient data are divided
into two 12 h sets (08:00 to 19:59 and 20:00 to 07:59) based on the
diurnal profile of solar irradiance. Two S curves and their corresponding
upper and lower bounds are calculated based on the average
temperature and Wi of the two data sets and are subsequently compared
to the ambient data. As shown in Fig. S13, the measured ε(C2O42-) values in both data sets are generally consistent with
predicted values.
A number of inferences can be drawn from the overall good agreement between
the predicted and measured molar fractions of oxalic acid in the particle
phase in Figs. 7 and S13. Our assumptions regarding the activity
coefficients, Henry's law constant, and acid dissociation constants used in
the S curve calculations of ε(C2O42-) are
reasonable for the conditions of this study (or are at least
self-consistent). Analytically calculated S curves are a simple way of
exploring how the gas–particle partitioning of semi-volatile inorganic and
organic compounds in the atmosphere are affected by the compound's
physicochemical properties (e.g., Henry's law constants and acid
dissociation constants), temperature, Wi, and pH. Overall, these results
indicate that particle-phase oxalate is in equilibrium with gas-phase oxalic
acid and that particle pH can influence particle-phase oxalate
concentrations. It also showed that particle-phase oxalate can be found over
a broad pH range and that the presence of oxalate does not necessarily
provide insights into the particle pH. Because of its high Henry's law
constant, particle-phase oxalate can be found in aerosols even at extremely
low pH values (i.e., the flat region in Fig. 7), although our data cannot be
used to test this since ambient particle pH values in this study are too
high.
Formic and acetic acids
Similar comparisons between the predicted and measured ε(HCOO-) and ε(CH3CO2-) can also be made.
Derivation of the equations for S curves describing the dependence of formic
and acetic acid gas–particle partitioning (i.e., ε(HCOO-)=HCOO-/(HCOOH+HCOO-) and ε(CH3CO2-)=CH3CO2-/(CH3CO2H+CH3CO2-), respectively) on particle pH are similar to
that of HNO3 since they are monoprotic acids:
εHCOO-=HHCOOHWiRTγH+γHCOO-γHCOOH10-pH+Ka1×0.987×10-14γH+γHCOO-10-pH+HHCOOHWiRTγH+γHCOO-γHCOOH10-pH+Ka1×0.987×10-14,εCH3CO2-=HCH3CO2HWiRTγH+γCH3CO2-γCH3CO2H10-pH+Ka1×0.987×10-14γH+γCH3CO2-10-pH+HCH3CO2HWiRTγH+γCH3CO2-γCH3CO2H10-pH+Ka1×0.987×10-14,
where HHCOOH and HCH3CO2H (moleL-1atm-1)
are the Henry's law constants for formic and acetic acid,
Ka1's (moleL-1) are the first acid dissociation constants,
R is the gas constant (8.314 m3PaK-1mol-1), T is temperature
(K), and γi's are activity coefficients. We used the web version
of AIOMFAC (http://www.aiomfac.caltech.edu, last access: 6 December 2017) (Zuend et al., 2008,
2011, 2012) to compute average γHCOOH and γCH3COOH values of 0.334 and 2.150, respectively. Similar to the
case of oxalic acid, we assumed that γH+γHCOO-=γH+γCH3COO-=γH+γNO3- and used the ISORROPIA-predicted γH+γNO3- value of 0.07. Temperature-dependent HHCOOH and
HCH3CO2H values are obtained from Sander (2015) using the same
methodology employed to determine temperature-dependent
HC2H2O4 values. We used Ka1 values at 25 ∘C (1.78×10-4 moleL-1 for formic acid, and
1.75×10-5 moleL-1 for acetic acid; Haynes, 2014)
for the S curve calculations.
Analytically calculated S curves of ε(HCOO-) and
ε(CH3CO2-) (solid black lines) and ambient data
from 13 September to 6 October 2016 plotted against ISORROPIA-predicted
particle pH (shown in a and b, respectively). For the ambient
data, a narrow range in Wi (0.5 to 4 µgm-3) and RH (20 % to
90 %) is chosen to be close to the analytically calculated outputs. For the
analytically calculated S curves, we used γHCOOH=0.334 and
γCH3COOH=2.150 (AIOMFAC predicted). We also assumed that
γH+γHCOO-=γH+γCH3COO-=γH+γNO3- and used
the ISORROPIA-predicted γH+-NO3-=γH+γNO3-=0.265. The black lines are S curves
calculated using the selected time period's average temperature (23.4±4.0 ∘C) and Wi (1.6±1.7 µgm-3).
The grey lines are S curves calculated using 1 standard deviation from the
average temperature and Wi (i.e., temperature = 27.4 ∘C
and Wi=0.5 µgm-3 for dotted grey line; temperature = 19.4 ∘C
and Wi=3.3 µgm-3 for solid grey
line).
S curves for ε(HCOO-) and ε(CH3CO2-) calculated based on temperature = 23.4 ∘C and
Wi=1.6 µgm-3 can be seen in Fig. 8.
Practically no formic or acetic acids are predicted to partition to the
particle phase (relative to oxalic acid) for the range of PM1 pH
calculated in this study. This is due to significant differences in the
Henry's law constants and acid dissociation constants for the three organic
acids. HHCOOH and HCH3CO2H (9540 and 5370 moleL-1atm-1, respectively) at 23.4 ∘C are
substantially smaller than HC2H2O4 (7.27×108 moleL-1atm-1) (Sander, 2015). The Ka1
values for formic and acetic acids (1.78×10-4 and 1.75×10-5 moleL-1,
respectively) are also considerably smaller than the
Ka1 value for oxalic acid (5.62×10-2 moleL-1)
(Haynes, 2014). Note that HHNO3 is between that of
HC2H2O4 and those of HHCOOH and HCH3CO2H
(compare Fig. 4b with Figs. 7 and 8).
As shown in Fig. 8, higher-than-expected levels of formate and acetate are
observed in the particle phase. This has also been reported in previous
studies (Liu et al., 2012). Laboratory tests showed that the
disagreement cannot be explained by positive biases in the particle-phase
formate and acetate PILS–HPIC measurements resulting from less than 100 %
gas removal by the carbon denuder. The measured denuder efficiency for
formic acid was ≥99.97 % (Supplement Sect. S4). The possibility that
formic and acetic acid dimers in the aqueous phase (Schrier et al., 1964;
Gilson et al., 1997; Chen et al., 2008) may result in higher-than-predicted
molar fractions of formate and acetate in the particle phase was explored
but also could not explain the observed gas–particle partitioning of these
acids (Supplement Sect. S5). The disagreement could be due to incorrect Henry's
law constants for formic and acetic acids. However, the Henry's law
constants for formic and acetic acid would have to be ∼104 times and ∼3×105 times larger than their
literature values, respectively, in order for their S curves to match our
measured molar fractions of formic and acetic acid in the particle phase. In
addition, formic and acetic acids may not be internally mixed with most of
the other PM1 aerosol components (e.g., SO42-,
NO3-, NH4+, C2O42-) and thus are not
associated with acidic aerosols, as assumed above. They may instead be
associated with aerosols largely composed of non-volatile cations and have a
pH closer to neutral. More research is needed to explain this disagreement.
Summary
Gas- and particle-phase measurements were conducted in Yorkville, Georgia (a
rural field site), during fall 2016. The goal of the field study was to
understand how NH3 affects particle acidity and consequently SOA
formation through the gas–particle partitioning of semi-volatile inorganic
and organic compounds. Since it is a rural site surrounded by forest,
agricultural land, and CAFOs, this study provided an opportunity for ambient
observations in an area impacted by high local emissions of BVOCs and
NH3.
NH3 concentrations measured by the NH3-CIMS ranged from 0.7 to
39.0 ppb (average 8.1±5.2 ppb), which were substantially higher than
typical levels in the southeastern US. PM1 inorganic chemical
composition, gas-phase HNO3 and NH3 concentrations,
temperature,
and RH were used as model inputs in the ISORROPIA II thermodynamic model to
calculate PM1 pH and Wi. PM1 pH ranged from 0.9 to 3.8, with
an average pH of 2.2±0.6. The measured and predicted
HNO3–NO3- and NH3–NH4+ gas–particle
partitioning ratios were in good agreement. The measured gas-phase organic
acids were estimated to contribute 30 % of the overall WSOCg on a
carbon mass basis, whereas measured particle-phase organic acids comprised 6 %
of the total organic aerosol mass concentration and 4 % of the
overall WSOCp on a carbon mass basis. Formic and acetic acids were the
most abundant gas-phase organic acids, with averages of 2.2±1.6 and
1.9±1.3 µgm-3, respectively. Oxalate was the most
abundant particle-phase water-soluble organic acid anion, with an average of
0.07±0.05 µgm-3. Measured oxalic acid gas–particle
partitioning ratios generally agreed with analytical predictions, which were
based on oxalic acid's physicochemical properties (specifically, its Henry's
law constants, acid dissociation constants, and activity coefficients),
temperature, Wi, and particle pH. The partitioning of oxalic acid to the
particle phase is highly sensitive to temperature and Wi. In contrast,
the partitioning of formic and acetic acids to the particle phase was
higher than predicted for reasons currently unknown.
Although past air regulations have resulted in decreased sulfate, nitrate,
and ammonium aerosol mass concentrations across the US, our study suggests
that the current limited regulation of NH3 emissions may result in some
increase in the organic aerosol mass concentration due to increased
gas-to-particle partitioning of some organic acids. However, in this study,
the effect was small since the organic acids comprised a small fraction of
the overall organic aerosol mass.