We measured amines in boreal forest air in Finland both in gas and particle
phases with 1 h time resolution using an online ion chromatograph
(instrument for Measuring AeRosols and Gases in Ambient Air – MARGA)
connected to an electrospray ionization quadrupole mass spectrometer (MS).
The developed MARGA-MS method was able to separate and detect seven different
amines: monomethylamine (MMA), dimethylamine (DMA), trimethylamine (TMA),
ethylamine (EA), diethylamine (DEA), propylamine (PA), and butylamine (BA).
The detection limits of the method for amines were low
(0.2–3.1 ng m-3), the accuracy of IC-MS analysis was 11–37 %, and
the precision 10–15 %. The proper measurements in the boreal forest
covered about 8 weeks between March and December 2015. The amines were found
to be an inhomogeneous group of compounds, showing different seasonal and
diurnal variability. Total MMA (MMA(tot)) peaked together with the sum of
ammonia and ammonium ions already in March. In March, monthly means for MMA
were < 2.4 and 6.8 ± 9.1 ng m-3 in gas and aerosol phases,
respectively, and for NH3 and NH4+ these were 52 ± 16 and
425 ± 371 ng m-3, respectively. Monthly medians in March for
MMA(tot), NH3, and NH4+ were < 2.4, 19 and 90 ng m-3,
respectively. DMA(tot) and TMA(tot) had summer maxima indicating biogenic
sources. We observed diurnal variation for DMA(tot) but not for TMA(tot). The
highest concentrations of these compounds were measured in July. Then,
monthly means for DMA were < 3.1 and 8.4 ± 3.1 ng m-3 in gas
and aerosol phases, respectively, and for TMA these were 0.4 ± 0.1 and
1.8 ± 0.5 ng m-3. Monthly medians in July for DMA were below the
detection limit (DL) and 4.9 ng m-3 in gas and aerosol phases,
respectively, and for TMA these were 0.4 and 1.4 ng m-3. When relative
humidity of air was > 90 %, gas-phase DMA correlated well with
1.1–2 nm particle number concentration (R2=0.63) suggesting that it
participates in atmospheric clustering. EA concentrations were low all the
time. Its July means were < 0.36 and 0.4 ± 0.4 ng m-3 in gas
and aerosol phases, respectively, but individual concentration data
correlated well with monoterpene concentrations in July. Monthly means of PA
and BA were below detection limits at all times.
Introduction
In atmospheric chemistry and secondary aerosol production,
bases are crucial since they can neutralize acids and therefore accelerate
several processes, e.g., subsequent growth of newly born aerosol particles
(Lehtipalo et al., 2016). Amines are gaseous bases, whose general formula is
RNH2, R2NH, or R3N. Due to their effective participation in
neutralization, it is hard to detect their real atmospheric concentrations.
Globally, the main known anthropogenic amine emissions are from animal
husbandry, industry, and compost processes, and the natural sources of amines
are assumed to be ocean, biomass burning, vegetation, and soil (Ge et al.,
2011). It has been shown that amines affect hydroxyl radical (OH) reactivity
and therefore all atmospheric chemistry (Hellén et al., 2014; Kieloaho et
al., 2013).
Models based on quantum chemistry data have shown that amines could
participate in new particle formation (NPF) with sulfuric acid even at very
low mixing ratios (Kurtén et al., 2008; Paasonen et al., 2012), and also
laboratory experiments have proved formation of aminium salts when amines
react with nitric or sulfuric acid (Murphy et al., 2007). In addition, the
recent experiments at the CLOUD chamber show that even at minute
concentrations of dimethylamine (DMA) new particles with sulfuric acid are
produced (Almeida et al., 2013; Kürten et al., 2016). Atmospheric
aerosols affect the climate, because they can act as cloud condensation
nuclei (IPCC, 2013). They also scatter and absorb solar radiation.
Ambient concentrations of gas-phase amines have been measured earlier using
different methods: samples have been collected in
phosphoric-acid-impregnated fiberglass filters (Kieloaho et al., 2013),
solid-phase micro-extraction fiber (SPME; Parshintsev et al., 2015), and
ion exchange resin (Dawson et al., 2014), and they have also been percolated
through an acidic solution (Akyüz, 2007). Samples have been analyzed
later in the laboratory with various chromatographic techniques, such as gas
chromatography coupled to mass spectrometry (GC-MS) (Akyüz, 2007;
Parshintsev et al., 2015), ion chromatography (IC) (Dawson et al., 2014), and
high-performance liquid chromatography coupled to mass spectrometry
(HPLC-MS) (Kieloaho et al., 2013). The abovementioned techniques have
various shortcomings: quantitation based on collection onto fibers is
problematic, collecting in filters requires long sampling times (usually
several days), and percolating in acidic solutions requires intensive sample
pretreatment. Dawson et al. (2014) used weak cation exchange resin as a
substrate for collection of gas-phase ammonia and amines. The method
minimizes sample losses on walls during sampling and has quite short
sampling times (less than an hour), but the detection limits remain too high
for the boreal forest environment.
In addition, novel in situ methods for measuring ambient air gas-phase amines
have been developed, usually based on mass-spectrometric detection: chemical
ionization mass spectrometry (CIMS), (Sellegri et al., 2005; You et al.,
2014), ambient pressure proton transfer mass spectrometry (AmPMS) (Hanson et al., 2011;
Freshour et al., 2014), chemical ionization atmospheric pressure
interface time-of-flight mass spectrometry (CI-APi-TOF) (Kulmala et al.,
2013; Sipilä et al., 2015; Kürten et al., 2016), and TOF-CIMS (Zheng et
al., 2015). These in situ techniques have short time resolution and the limits
of detections are small. However, these methods cannot separate amines with
same masses (e.g., DMA and EA) and identification of the measured compounds
remains uncertain. Chang et al. (2003) used high-efficiency planar diffusion
scrubber IC (HEDS-IC) to successfully separate amines with identical masses.
Aerosol-phase amines have been sampled onto filters and analyzed later in
the laboratory with similar techniques: LC-MS (Ruiz-Jiménez et al.,
2012), GC-MS (Huang et al., 2014), and IC (Huang et al., 2014; van Pinxteren et al.,
2015). With these methods, sampling time was long (24–133 h) and biases
may be introduced due to transport and pretreatment of samples. VandenBoer
et al. (2011) measured amine concentrations both in gas and particle phase
with an ambient ion monitor – IC (AIM-IC). This method had 60 min sampling
time and relatively low detection limits (5–9 ng m-3). However, it
could not separate TMA and DEA from each other. Also, because in atmospheric
samples ammonia/ammonium can be present in concentrations several orders of
magnitude higher than amines, in this method they can impede detection of
some amines (e.g., MMA and EA).
These methods have been utilized in short campaigns from a couple of days to
a couple of weeks. Only Kieloaho et al. (2013) measured for a longer period,
but their sampling time was long (24–72 h). Most of the studies discussed
previously were made in urban or suburban areas, and only a few
measurements (Sellegri et al., 2005; Kieloaho et al., 2013; Kulmala et al.,
2013; Sipilä et al., 2015) were made in a boreal forest. In these
studies, the observed alkylamine concentrations ranged from below the detection
limit to ∼ 150 pptv, depending on the sampling time and
the analysis method used.
Here, we present the in situ method developed for atmospheric amine
measurements in this study, using an online ion chromatography instrument
for Measuring AeRosols and Gases in Ambient air, coupled with mass
spectrometer (MARGA-MS). The method was used in the boreal forest, where
amines are expected to affect secondary aerosol formation even at extremely
low concentrations (Kurtén et al., 2008; Paasonen et al., 2012; Almeida
et al., 2013). We report seasonal and diurnal variations of amines in boreal
forest air and their partitioning between gas and aerosol phases. A time
series of diurnal observations and linkages to known boreal biogenic
processes is discussed for several amines. Our investigation is the first
long-term survey of sources and phase distribution of amines at the sub-pptv
level in a remote boreal forest environment. In this study, we use supporting
physical measurements to initiate a better understanding of this entire
class of compounds relative to what we already know about ammonia.
Experimental
We measured amine and ammonia concentrations in 2015 from March to May
(spring), July to August (summer), and November to December (early winter)
with 1 h time resolution. However, due to instrumental problems,
good-quality data were captured for a total of only about 8 weeks.
Measurement site
Measurements were performed in a Scots pine forest at the SMEAR II station
(Station for Measuring Forest Ecosystem-Atmosphere Relations) in
Hyytiälä, southern Finland (61∘ 51′ N,
24∘ 17′ E, 180 m a.s.l.; Hari and Kulmala, 2005,; Fig. S1 in the Supplement).
The largest nearby city is Tampere, situated 60 km southwest from the
station with approximately 222 000 inhabitants in the city itself (although
with 364 000 in the wider metropolitan area). The instrument was located in a
container about 4 m outside the forest in a small opening. In addition
to pines, also small spruces (Picea abies) grow nearby. The forest was
planted about 50 years ago and its current tree height is about 19 m.
Meteorological conditions
Meteorological quantities were obtained from the SmartSmear AVAA portal
(Junninen et al., 2009). SmartSmear is the data portal for visualization and
download of continuous atmospheric, flux, soil, tree, physiological, and
water quality measurements at SMEAR research stations of the University of
Helsinki. Table S1 in the Supplement shows the meteorological conditions during measurement
periods.
Measurement methodsMARGA-MS
We used the MARGA instrument
(Metrohm-Applikon, Schiedam, the Netherlands) (ten Brink et al., 2007) for
sampling and separating amines. MARGA is an online IC connected to a sampling
system. In addition, this system was connected to an electrospray ionization
(ESI) quadrupole MS (Shimadzu LCMS-2020; Shimadzu Corporation, Kyoto, Japan)
to improve sensitivity of amine measurements (see Table S2 for MS settings).
The MARGA instrument earlier used for measuring anions and cations in
Helsinki and Hyytiälä is described in more detailed in earlier papers
(Makkonen et al., 2012, 2014).
Ambient air was taken through a PM10 cyclone (URG 1032, Teflon coated)
and polyethylene tubing (ID 0.5′′, length ∼ 1 m) with a flow rate of
16.7 L min-1. After passing the inlet, sample air entered to a wet
rotating denuder (WRD), where the gases diffused into the absorption solution
(10 ppm hydrogen peroxide). Particles passed through the WRD and entered the
steam jet aerosol collector (SJAC), where they were collected in a
supersaturated environment (in 10 ppm hydrogen peroxide). During each hour,
liquid samples from the WRD and SJAC were collected in the syringes (25 mL),
mixed with the internal standard (LiBr and deuterated diethyl-d10-amine)
and injected to the cation ion chromatograph. The two sets of syringes worked
in tandem, so that when a set of samples was collected, the previous ones
were injected. In the cation chromatograph, 3.2 mmol L-1 oxalic acid
(Merck, Darmstadt, Germany) solution was used as an eluent (constant flow
0.7 mL min-1). To get the detection limits lower, we used a
concentration column (Metrosep C PCC 1 VHC/4.0) before the analytical column
(Metrosep C4-100/4.0, 100 mm × 4.0 mm ID, stationary phase silica gel with carboxyl groups, particle
size 5 µm). After passing the cation column and the conductivity
detector, samples were guided to the ESI needle of the mass spectrometer
without any additional solvent. All solutions used were made with ultrapure
water (Milli-Q, resistivity ≥ 18 MΩ cm)
Detection limits (DLs) for MARGA-MS were calculated from signal-to-noise
ratios (3:1) for most of the compounds and they were similar in gas and
aerosol phases, because their blank values were so small (Table 1 in Sect. 3.1).
However, DLs for DMA and TMA were calculated from blank values (3
times the standard deviations of blank values) and the DLs were different for
gas- and aerosol-phase measurements.
DLs of different amines, ammonia, and ammonium. Conversions from
ng m-3 to pptv have been made using the conversion factor
pptv=c (ng m-3):(0.0409× (MW)) by Finlayson-Pitts
and Pitts (2000), with MW
the molar mass of the amine, ammonia, or ammonium. The precision for IC-MS
analysis was defined by calculating standard deviations of liquid
200 ng m-3 standard measured six times in a row. In the data series,
there were both gas and particle side measurements. The accuracy for IC-MS
analysis was calculated by subtracting the averages of the data series
described earlier from the expected values, dividing those by the expected
values, and multiplying them by 100 %.
AmineDLDLPrecisionAccuracy(ng m-3)(pptv)(%)(%)MMA, both gas and aerosol2.41.91024DMA,3.11.71131(March to August) gas aerosols1.10.20(November to December) gas aerosols0.370.76TMA, gas aerosols0.20.114110.511EA, both gas and aerosol0.360.191116DEA, both gas and aerosol0.240.081537PA, both gas and aerosol0.310.131121BA, both gas and aerosol0.260.091214NH3, gas11.416.4NH4+, aerosol2.9
Deuterated diethyl-d10-amine (DEA10, Sigma-Aldrich:
Isotec™; Sigma-Aldrich, St. Louis, MO, USA) was used as an
internal standard (ISTD) for all amines. DEA10 was used because it
behaved same way in IC separation but had different mass than studied
amines. A total of 50.0 µL of DEA10 was added to the MARGAs ISTD solution
bottle (LiBr). After the ion chromatograph, the ISTD mixed with the sample
entered the MS detection. DEA10 was used to correct for possible losses
to instrumentation and correct changes of MS response. A three-point external
calibration was used for all measured alkyl amines (concentration levels 10,
50, and 300 ng m-3). The system was calibrated every 2 weeks in the
field, by stopping the air flow of the MARGA and directing standard
solutions to the sample syringe pumps, before analysis by IC separation and
MS detection. Ammonia (NH3) and ammonium (NH4+) (the sum of
them referred to as NHx) were also measured with MARGA at the same time
with the method described in Makkonen et al. (2012, 2014), except we used
oxalic acid solution for eluent. For NHx measurements, only a conductivity
detector was used and the internal standard was lithium bromide (Acros
Organics, New Jersey, USA). Instrumental blank values for MARGA-MS were
measured every at least every other month with MARGAs blank mode: the sample
airflow was stopped, and the analysis cycle was running for 6 h without
sampling.
In calculations, the values under DLs were taken into account as 0.5 × DL.
In the figures, we used a moving average for DMA, because every other
measured DMA concentration was a little higher than the in-between one. The
system used different syringes for sample collection every other hour, and
the reasons for differences are expected to be losses or contamination in the
syringes. Further causes for these minor differences were not found.
Aerosol measurements
To study the role of amines in atmospheric particle formation, particle
number concentration measurements were utilized. The particle number size
distribution between 3 and 1000 nm was measured with a twin differential
mobility particle sizer (DMPS) system (Aalto et al., 2001). From these
measurements, the particle concentration between 3 and 25 nm (N3–25 nm), referred to as the nucleation mode, and the total particle
concentration between 3 and 1000 nm (Ntot) were obtained. In addition,
the concentrations of sub-3 nm particles were measured with an Airmodus
particle size magnifier (PSM A11; Vanhanen et al., 2011). The PSM is a
mixing-type condensation particle counter in which particles are first
grown to 90 nm size by condensation of diethylene glycol, after which
butanol is used to grow them to detectable sizes. The cut-off size of the
PSM can be changed by altering the mixing ratios of saturated and sample
flows, which allows the measurement of particle size distribution in the
sub-3 nm size range. In this study, the particle concentration obtained for
the size range between 1.1 and 2.0 nm (N1.1–2 nm) was used. In addition,
the particle concentration between 2 and 3 nm (N2–3 nm) was obtained by
subtracting the total particle concentration measured with the highest
cut-off size of the PSM from the total particle concentration measured with
the DMPS. For more discussion about the particle concentration measurements
and their uncertainties, see Kontkanen et al. (2017), who have published the
data set used in this study.
Regression calculations
Simple linear regressions were calculated to find whether meteorological
conditions affect amine concentrations. The statistical significance of the
slope of the linear regression of the amine concentration y vs. the ambient
condition x, i.e., y=β1x+β0, was estimated.
The null hypothesis, which means that the slope β1 is not
dependent on the ambient condition x (i.e., β1=0), was
examined using test statistics given by the estimate of the slope divided by
its standard error (t=β1/ s.e.). The test statistics were
compared with the Student's t distribution on n-2 (sample size minus the
number of regression coefficients) degrees of freedom. The analysis yields
also the p value of the slope. The lower the p value is, the stronger the
evidence against the null hypothesis. The statistical significance of the
slope can be interpreted so that if p>0.1, there is no evidence
against the null hypothesis, and p values in the ranges 0.05–0.1, 0.01–0.05,
and < 0.01 suggest, respectively, weak, moderate, and strong evidence
against the null hypothesis in favor of the alternative. The regressions
were calculated for amine concentrations vs. air temperature, relative
humidity, wind speed, soil temperature, and soil humidity.
ResultsCharacterization of MARGA-MS
An online method for sampling, separating, and detecting amines from the
ambient air both in the gas and aerosol phases has been developed. With
MARGA-MS, we studied seven different amines: monomethylamine (MMA), dimethylamine
(DMA), trimethylamine (TMA), ethylamine (EA), diethylamine (DEA),
propylamine (PA), and butylamine (BA); see Figs. S2 and S3 for the
chromatogram. The time resolution of measurements was 1 h, and as can
be seen in Table 1, the detection limits were low, and precision
(10–15 %) and accuracy (11–37 %) for the analytical method of MARGA-MS
were moderately good. In addition to improved DLs, MS detection after MARGA
also solved the problem with co-elution of amines with different molecular
masses and inorganic cations (e.g., K+, Mg2+). Verriele et al. (2012) developed also an IC-MS method for amines with offline sampling with
midget impingers. They also noticed that adding MS detection after a
conductivity detector overcomes the co-eluting problem of IC separation.
They had a four-step gradient elution in their method, and suppression before
the conductivity detector. We wanted to keep our method as simple as
possible to make it easy to use in the field, and isocratic elution without
suppression was good in that purpose. Calibration levels (10, 50, and 300 ng m-3) were selected so that the lowest level was a bit higher than
biggest alkyl amine detection limit (DMA, 3.1 ng m-3), and the highest was
too high to exceed in the measurements. When the measurements started, most
of the data were under the lowest calibration point, which increased the
uncertainty.
The whole analysis was conducted in the field, so the method had no biases
from sample transportation. However, the drawback in the analysis was that
DEA and BA, which have the same molecular masses, did not separate
completely. From a technical point of view, one of the drawbacks of the
MARGA-MS was that the system was quite vulnerable. We lost many measuring
days because some part of the system was broken. The MARGA side also needed
∼ 40 L solutions (e.g., eluents, absorbtion solution for
sampling, and internal standard solution) that needed to be changed weekly.
The ESI chamber of the MS needed to be cleaned weekly, because oxalic acid
was crystallizing into it. Theoretical calculations of the efficiency of the
denuder could be found in the Supplement (Fig. S4).
Variability of the concentrations
Monthly box-and-whisker plots of the most abundant amines (tot)
and summed up ammonia and ammonium. The boxes represent the second and third
quartiles and the lines in the boxes the median values. The whiskers show
the highest and the lowest observations.
Figure S5 shows the monthly means and medians of total amine concentrations
(tot, sum of gas and aerosol phases) and Fig. 1 shows the box-and-whisker
plots to describe the distribution of the measured concentrations. Total
amine concentrations were used because we wanted to study how amine sources
and partitioning between aerosol (a) and gas phases (g) depend on
environmental quantities. Even though the average ratios
(gas/(gas+aerosol)) for values above the DL in Table 2 are close to 0.5,
amines were still mainly in the aerosol phase (Tables 2 and S2), which is
shown by the more data points above the DL in the aerosol phase. Table S3
shows the number of data points in each month, as well as the mean and
median values of concentrations of different amines, ammonia, and ammonium.
It can be seen that most concentrations were below the DL especially in the gas
phase, so we can conclude that concentrations of amines in the boreal forest
are low compared to, for example, ammonia or monoterpene concentrations
(Hakola et al., 2012). In Table 3, concentrations in other studies are
compared to our findings. Different seasonal patterns were found for
different amines and they are described below.
Ratio of gas and aerosol phases. N(g) indicates the number of gas-phase data
above the detection limit (DL), N(a) indicates the number of aerosol-phase data above the DL, and
N indicates the number of data above the DL at the same time both in the gas and aerosol
phases; ratio is gas/(gas+aerosol) (when both values were above the
DL).
A spring maximum was observed for MMA(tot) (maximum 50 ng m-3) and the
concentrations correlated with the sum of NH3 and NH4+ (R2=0.52, Fig. 2).
During spring, we observed two occasions when
MMA(tot) and the sum of NH3 and NH4+ concentrations increased
considerably at the same time. The concentration increase in March is
characterized with rain (Fig. 3a) and the later increase in April took place
during night with decreasing wind speed and higher temperature (Fig. 3b).
This increase could be connected to evaporation from melting snow and
ground. Bigg et al. (2001) suggest that water from melting snow penetrates
the soil and leaf litter beneath the snow, displacing gases produced by
decomposition of organic material. These gases are then released to the air,
where they participate in the nucleation process. At humid conditions, this
bubbling of gases would be efficient, whereas the evaporation to air would
be more efficient on warm, sunny days.
Concentrations (ng m-3) of total MMA vs. concentrations of
NH3+NH4+ in March and April 2015.
MMA(tot) concentrations and rainfall measured in Hyytiälä
during spring 2015 in March (a) and MMA(tot) concentrations, wind speed, and
ambient temperature in April (b).
Most of the MMA was in aerosol phase (Tables 3 and S2): monthly mean of
aerosol-phase MMA(a) varied between < 2.4 and 6.8 ng m-3 (Table S3),
while monthly means of gas-phase MMA(g) were below the DL throughout the
measurements. In early winter (late November to early December), MMA was not
detected. NHx showed similar seasonal variation as MMA with the maximum
in March and lower concentrations towards the end of summer. During spring,
NHx was also mainly in aerosol phase.
Comparison of concentrations of MMA, DMA, TMA, and EA in different
sites and seasons, in gas and aerosol phases.
AmineGas (pptv)Aerosol (ng m-3)Site descriptionLocationSeasonYearReferenceMMA< DL–8.8< DL–61.2Rural forestFinlandSpring–early winter2015This studymax. ∼ 2Rural forestAL, USASummer2013You et al. (2014)5Semi-ruralDE, USASummer2012Freshour et al. (2014)4RuralOK, USASpring2013Freshour et al. (2014)4UrbanMN, USAAutumn2012Freshour et al. (2014)0.26dUrbanTurkeySummer2004–2005Akyüz (2007)1.3dUrbanTurkeyWinter2005–2006Akyüz (2007)DMA< DL–4.1< DL–55.5Rural forestFinlandSpring–early winter2015This studymax. ∼ 7aRural forestAL, USASummer2013You et al. (2014)28aSemi-ruralDE, USASummer2012Freshour et al. (2014)20aRuralOK, USASpring2013Freshour et al. (2014)42aUrbanMN, USAAutumn2012Freshour et al. (2014)2.18dUrbanTurkeySummer2004–2005Akyüz (2007)2.96*UrbanTurkeyWinter2005–2006Akyüz (2007)< 2.7< 2.7UrbanCanadaSummer2009VandenBoer et al. (2011)6.5 ± 2.10.1 ± 0.2RuralCanadaAutumn2010VandenBoer et al. (2012)42 ± 30aRural forestFinlandMay–October2011Kieloaho et al. (2013)max. 10UrbanGA, USASummer2009Hanson et al. (2011)9.3-20.5Semi-aridAZ, USAWhole year2012–2013Youn et al. (2015)TMA< DL–6.1Rural forestFinlandSpring–early winter2015This study34–80Rural forestFinlandSpring2002Sellegri et al. (2005)max. ∼ 20bRural forestUSASummer2013You et al. (2014)6bSemi-ruralDE, USASummer2012Freshour et al. (2014)35bRuralOK, USASpring2013Freshour et al. (2014)19bUrbanMN, USAAutumn2012Freshour et al. (2014)15bRural forestAL, USASummer2013You et al. (2014)< 2.7c< 2.7cUrbanCanadaSummer2009VandenBoer et al. (2011)∼ 1c1 ± 0.6cRuralCanadaAutumn2010VandenBoer et al. (2012)21 ± 23Rural forestFinlandMay–October2011Kieloaho et al. (2013)≤6.8×103AgriculturalCA, USAAutumn2013Dawson et al. (2014)max. 9 ± 7WildfireeCanadaSummer2015Place et al. (2017)EA< DL–8.2Rural forestFinlandSpring–early winter2015This study0.35dUrbanTurkeyWinter2005–2006Akyüz (2007)
a Mass 46, i.e., DMA+EA. b Mass 60,
i.e., TMA+PA. c TMA+DEA. d Units
in ng m-3. e Samples are collected in British Columbia during
wildfires.
In earlier studies (Table 3), You et al. (2014) detected gaseous MMA(g) with
CIMS in an Alabama forest in summer, at about the same concentrations as our
measurements (maximum ∼ 2 pptv, approximately 3.8 ng m-3). Also Freshour et al. (2014) measured MMA(g)
with AmPMS in three different sites in the US, and their mean concentrations
were at the same level as ours (4–5 pptv, approximately
5.1–6.4 ng m-3). Akyüz (2007) took urban outdoor air samples in
Turkey during summertime in 2004–2005 and wintertime in 2005–2006, and
analyzed them later with GC-MS. MMA(g) mean results were 0.26 and
1.30 ng m-3, respectively. Values were at similar levels to our
measurements. That is surprising, because in urban areas we expect many MMA
sources (e.g., industry and cars; Ge et al., 2011), so higher mean
concentrations would have been expected.
Trimethylamine
TMA(tot) had higher concentrations in March after which they declined,
before increasing again in July to their maximum concentrations suggesting
biogenic sources (Figs. 1, S5). TMA(tot) concentrations also peaked at
the end of March during rain simultaneously with MMA(tot) and the sum of
NH3 and NH4+ increasing from about 1.5 to 6.0 ng m-3, so
melting snow and ground could also be the sources of TMA as discussed in
Sect. 3.2.1. During summer, TMA(tot) concentrations increased again, concomitant
with the sum of NH3 and NH4+ in July. The share of the gas
phase was roughly half of the aerosol-phase concentration throughout the
measurements (Tables 2 and S2). TMA(tot) did not show a clear diurnal
variation (Fig. 4).
Mean diurnal variation of total DMA (blue), total TMA (green)
concentrations and temperature (yellow) in August 2015.
Kieloaho et al. (2013) collected filter samples of gaseous amines from the
same boreal forest as we did from May to October 2011 and they also measured
low concentrations for the sum of TMA(g) and PA(g) in July. In their
measurements, the concentrations increased during autumn. You et al. (2014)
measured gaseous C3 amines (TMA and PA) with CIMS in a forest in
Alabama from June to early July in 2013 and their highest concentration
(∼ 15 pptv, approximately 36 ng m-3) was ∼ 10
times higher than ours (3.5 ng m-3). Dawson et al. (2014) collected
TMA samples in ion resin cartridges from late August to mid-September
near a cattle farm in Chino, California, and analyzed the samples with IC.
Their results varied from 1.3 to 6.8 ppbv (approximately 3.1–16.4 µg m-3),
so they measured ∼ 1000 times higher concentrations
than we did. This is not surprising, because cattle are a known source of
amines. Sellegri et al. (2014) measured amines in March 2002 with CIMS in
same boreal forest that we did. They found TMA(g) with mixing ratios 34–80 pptv
(approximately 82–193 ng m-3), so their results are ∼ 30
times higher than ours. Ambient conditions were different than ours when
they measured TMA, and this could be one reason for the higher
concentrations they observed.
Dimethylamine
DMA(tot) concentrations also increased from about 3 to 6 ng m-3 during
the MMA episode in April. Moreover, both particulate- and gas-phase DMA had
maximum concentrations in July suggesting a biogenic source (the highest
value was 14.5 ng m-3 in the aerosol phase and 7.5 ng m-3 in
the gas phase). The particle fraction was again generally more abundant than
the gaseous fraction. Because amines can be expected to partition in the
aqueous aerosols (Ge et al., 2010), it is not surprising to find them mostly
in the aerosol phase, considering the high average relative humidity measured
(> 68 %). In August, the concentrations decreased, and they were the
lowest during the early winter. Kieloaho et al. (2013) measured also high
gas-phase concentrations of the sum of DMA and EA in July, reaching a maximum
of ∼ 75 pptv (approximately 138 ng m-3). In their
measurements, the concentration levels decreased in August, similar to our
measurements. High DMA and TMA concentrations in summer could indicate
biogenic sources. However, these amines' concentrations did not correlate
with monoterpene concentrations like EA (see Sect. 3.2.4). VandenBoer et
al. (2011) measured both DMA(g) and DMA(a) with AIM-IC from late June to
early July 2009 in an urban area, with highest concentrations of
2.7 pptv (approximately 4.6 ng m-3) and 2.7 ng m-3 which
were at the same level as our DMA(g) in July (7.5 ng m-3). Hanson et
al. (2011) also measured DMA concentrations with AmPMS in an urban area with
a little higher gas-phase concentrations (maximum of 10 pptv,
approximately 19 ng m-3) than in the studies mentioned earlier. Ge et al. (2010)
gives DMA also urban sources (e.g., tobacco smoke, automobiles), so that can
explain results from Hanson et al. (2011). Youn et al. (2015) measured DMA
aerosols and cloud water, and they noticed that DMA concentrations in
PM1 aerosols peaked in September. We were also expecting high
concentrations in autumn, but due to instrumental problems, we unfortunately
missed the season. In July, we measured from PM10 particles the average
concentration of 8.4 ng m-3, and Youn et al. (2015) measured from
PM1 particles about twice as high concentration. Different measurement
sites could explain the difference. Youn et al., also noticed that DMA(a)
displays a unimodal size distribution with dominant peak between 0.18 and
0.56 µm, and concluded that it indicates aminium salt formation
with sulfate.
In August, DMA(tot) had a diurnal variation with a daytime maximum (Fig. 4),
but during some nights the concentrations also increased slightly. The
DMA(tot) afternoon maxima could be caused by re-emission of DMA that has
earlier deposited on surfaces and evaporates when temperature increases
during the afternoon. The maximum could also be related to direct biogenic
emission. Usually ambient concentrations of biogenic volatile organic
compounds, which have temperature dependent emissions, peak during nighttime
due to weak atmospheric mixing and lack of hydroxyl radical reactions which
only take place during daytime (Hakola et al., 2012). The concentrations of
light-dependent biogenic volatile organic compound (BVOC) emissions such as
isoprene have daytime maxima because they are emitted only during daytime.
Thus, the DMA source could be light dependent. DMA(tot) peaks also at night.
Because the atmospheric mixing in the night is weak and there are no OH
reactions, even small emissions can be trapped in a shallower atmospheric
boundary layer and cause the increase in concentrations.
Ethylamine
EA(tot) and monoterpene concentrations in Hyytiälä in July
2015. The EA(tot) concentration axis starts from 0.36 ng m-3, because
values under that are below the detection limit.
EA(tot) concentrations were low throughout the measurements but showed a
clear diurnal variation in July with a maximum at night (Fig. 5). Monoterpene
concentrations were measured simultaneously at the same site and had a
similar diurnal pattern. This type of diurnal variation is typical for many
reactive compounds having local sources in a boreal forest (Hakola et al.,
2012). Low daytime concentrations are due to strong atmospheric mixing and
reactions with an OH radicals. The rate coefficients of alkyl amines are
slightly lower but comparable to monoterpene reactions with OH radical. The
most common monoterpenes in the boreal forest are α-pinene, 3-carene
and β-pinene (Hakola et al., 2012). Their rate coefficients for
reaction with OH are 53.7×10-12, 88×10-12, and
78.9×10-12 cm3 molecule-1 s-1, respectively
(Atkinson, 1994), whereas MMA, EA, DMA, and TMA rate coefficients with OH are
22.26×10-12, 29.85×10-12, 65.53×10-12, and
69.75×10-12 cm3 molecule-1 s-1, respectively (US
EPA, 2017). Similar diurnal patterns and reactivities indicate that EA has a
biogenic source. Kürten et al. (2016) measured C2 amines (i.e., DMA
and EA) with CI-APi-TOF in Germany near three dairy farms and a forest from
May to June 2014. They did not observe clear diurnal variation for C2
amines. In our measurements, EA and DMA had opposite diurnal variations (see
Sect. 3.2.3). That could be an explanation for the observations of Kürten
et al. (2016), where both C2 amines were measured together.
Akyüz (2007) measured EA(g) 0.35 ng m-3 (mean concentration) in an
urban area in Turkey during the winters of 2005–2006, and the concentrations
were at the same level as ours.
Correlations between meteorological quantities and amines
We noticed that the concentrations of DMA(g) followed, although vaguely, the
variations of both air and soil temperature (Fig. S6), so it was reasonable
to study whether there are any clear relationships between the amine
concentrations and parameters describing ambient conditions. We calculated
linear regressions of amines, ammonia, and ammonium vs. air relative humidity
(RH) and temperature (T) as well as soil temperature (ST) and soil humidity
(SH). The results of the linear regression analyses of the amines, ammonia,
ammonium, and the ambient conditions are presented in Tables S4 and S5 for
the gas and aerosol phases, respectively.
In the gas phase, DMA had the strongest correlation with ambient condition
parameters, suggesting that DMA(g) concentrations increase with increasing
air temperature, soil temperature, and soil humidity but decrease with
increasing atmospheric humidity and wind speed. The scatter plots of DMA(g)
compared vs. these parameters (Fig. 6) show, however, that the relationships are
different in different seasons. The most consistent relationships of DMA(g)
are with air and soil temperature; the slopes of the linear regressions are
positive for all of the data and for summer alone (Fig. 7).
DMA in the gas phase vs. selected ambient condition parameters:
(a) air temperature, (b) relative humidity, (c) wind speed, (d) soil temperature,
and (e) soil humidity in spring, summer, and early winter. The linear
regressions shown in the plots were calculated using the data points of all
seasons.
DMA in the gas phase vs. (a) air temperature and (b) soil temperature in
summer.
In the gas phase, the second strongest correlations – even though they are weak –
are those of TMA against environmental conditions (Table S4). Interestingly,
when looking at all data, TMA(g) concentration seems to decrease with
increasing air and soil temperature (Fig. S7), opposite to the relationship
of DMA and temperature. As already mentioned, TMA concentrations were high in
spring and they are likely to originate partly from melting snow and ground,
whereas DMA might have biogenic sources in summer, which could explain
different correlation behavior. The scatter plot of TMA(g) vs. temperature
(Fig. S7) also reveals that the relationship is not consistent in all
seasons: in summer, it is even a vaguely positive, statistically not
significant positive relationship. The ammonia concentration increased with
air temperature consistent with Makkonen et al. (2014) and decreased with
increasing relative humidity. The latter suggests that at high humidity
surfaces are moist and ammonia gets absorbed onto the water.
All the gas-phase amines except MMA were found to have a positive
correlation with soil water content. The studied amines are water soluble
and therefore negative correlation would be expected if the soil would act
only as a sink. However, our results suggest that soil processes are
producing amines and they may be enhanced with increasing humidity. Forest
soils are a reservoir of the alkyl amines (Kieloaho et al., 2016) and
modeling studies have shown that they can act as a source of alkyl amines
to the atmosphere (Kieloaho et al., 2017). With their model, Kieloaho et al. (2017)
found a positive correlation with soil temperature for soil-to-atmosphere flux of DMA, but correlation with soil water content was opposite
to our observation.
Correlations of amines with nanoparticle concentrations
In addition to the dependency of amine concentrations on ambient conditions,
the relationships between aerosol number and amine concentrations were
studied with a similar regression analysis. The amine concentrations were
compared with the total number concentration integrated from the size
distributions measured with the DMPS (Ntot), with the aerosol number
concentrations in the size ranges 1.1–2 and 2–3 nm, measured with the PSM
(N1.1–2 nm and N2–3 nm, respectively) and with the aerosol particle
and cluster number concentrations between 3 and 25 nm measured with the DMPS
(N3–25 nm). The regression analysis results for the gas-phase amines
and aerosol-phase amines are presented in Tables S6 and S7, respectively.
Cluster-mode aerosol number concentration (N1.1–2 nm) as
a function of DMA concentration in the gas phase and color-coded with
(a) air temperature (t), (b) air
relative humidity (RH), (c) soil temperature (ST), and
(d) soil humidity (SH). In all subplots, the black line shows the
linear regression calculated by using all data, and in panel (b) the
red line shows in addition the linear regression by using only those data
that were measured at RH > 90 %.
Cluster-mode aerosol number concentration (N1.1–2 nm) as a
function of ammonia (NH3) concentration and color-coded with (a) air
temperature (t), (b) air relative humidity (RH), (c) soil temperature (ST), and
(d) soil humidity (SH).
The period during which both the MARGA-MS detected DMA(g) concentrations
above the detection limit and the PSM detected cluster-mode aerosols
simultaneously was short. There were 33 data points for the regression
analysis. There was a weak positive correlation between them (Fig. 8) even
though the correlation was statistically not significant (R2=0.06,
p=0.18, Table S6). The correlation had some dependence on the ambient
conditions: air relative humidity (RH) and temperature (T) as well as soil
temperature (ST) and soil humidity (SH). The correlation was more
significant when both soil and air were humid (RH > 90 %, SH > 0.3 m3 m-3). The linear regression calculated by
using only those data that were measured at RH > 90 % has a higher
correlation coefficient and slope is statistically significant (R2=0.63, p=0.006, Table S6, Fig. 8b), but it has to be noted that there were
only 10 simultaneous data points at the high RH.
There was no correlation between the slightly larger aerosols
(N2–3 nm) and DMA(g) (Table S6), suggesting that DMA(g) took part
in the initial steps of secondary aerosol formation namely clustering. This
is qualitatively in agreement with an experimental CLOUD chamber study where
it has been demonstrated that even very small amounts of DMA greatly enhance
the formation of nanoparticles (Almeida et al., 2013; Lehtipalo et al.,
2016). In the aerosol phase, DMA was the only amine that had a statistically
significant correlation with the cluster-mode number concentrations and
where the gas-phase correlation coefficient was higher at high relative humidity (Table S7). Other ambient
condition quantities apparently did not affect this relationship (Fig. 8).
There were considerably more simultaneous data points of the cluster-mode
aerosol number concentration and ammonia (NH3). The correlation
N1.1–2 nm vs. NH3 was statistically significant (R2=0.13,
p<0.001, Table S6). In addition, this correlation apparently also
depended on the ambient conditions. This was visualized by color-coding the
scatter plot of cluster-mode particle number concentrations vs. ammonia
concentration with air temperature (Fig. 9a), relative humidity (Fig. 9b),
soil temperature (Fig. 9c), and soil humidity (Fig. 9c). The plots show that in
warm (T>15∘C, ST > 14 ∘C) and
dry (RH < 60 %, 495 SH < 0.25 m3 m-3) conditions
the positive correlation was more obvious (Fig. 9). In the aerosol phase,
ammonium (NH4+) did not correlate at all with the cluster-mode
particle number concentration but positively with the total number
concentration (Table S6) as expected. The other amines did not have any
significant correlations with the aerosols in the smallest aerosol size
ranges.
Conclusions
An online method using in situ ion chromatography with mass-spectrometric
detection for measuring amines in low concentrations from the ambient air
both in the gas and aerosol phases was developed. In situ amine and ammonia
measurements were conducted at the SMEAR II station (Hyytiälä, Finland)
from March to December 2015, covering altogether about 8 weeks.
Concentrations of seven different amines and ammonia in aerosol and gas phases
were measured with 1 h time resolution.
The developed MARGA-MS method was suitable for field measurements of amines.
The DLs were low (0.2–11.4 ng m-3), and the accuracy and precision of
IC-MS analysis were moderately good. With the method, amines with same masses
or same retention time were separated; only DEA and BA were incompletely
separated. However, MARGA-MS had some technical drawbacks (e.g., consumption
of ∼ 40 L of solutions per week).
The amines turned out to be a heterogeneous group of compounds; different
amines are likely to have different sources. All amines had higher
concentrations in the aerosol phase than in the gas phase. MMA and TMA
concentrations were the highest in spring, concomitant with ammonia and
ammonium. Melting of snow and ground can be the source of these compounds.
The decomposing litter and organic soil layer beneath snow can release
organic compounds to snow cover and to the atmosphere.
TMA had an additional maximum simultaneously with DMA during summer, and EA
was only detected in July. The summer maxima could indicate biogenic
sources. However, unlike EA, DMA and TMA did not show similar diurnal
variation as monoterpenes. The diurnal variation is determined by the
balance between emissions, reactivity, and mixing in the atmosphere. Usually
ambient concentrations of biogenic volatile organic compounds, which have
temperature dependent emissions, peak during nighttime due to inefficient
mixing and lack of hydroxyl radical reactions which only take place during
daytime. The missing daytime minima of DMA and TMA can be due to
light-dependent biogenic sources, or TMA and DMA might be re-emitted from surfaces
during daytime, when temperature increases.
All amines except MMA correlated positively with soil humidity, which could
indicate a humidity-dependent production mechanism. Gas-phase DMA correlated
positively with small 1.1–2 nm aerosols, when both soil and air were humid.
It did not correlate with slightly larger aerosols at all, suggesting that
gas-phase DMA may be important in new aerosol formation.
Data availability
The data sets can be accessed by contacting the corresponding
author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-6367-2018-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The financial support by the Academy of Finland Centre of Excellence
program (project no. 272041) and Academy Research Fellow program (project no.
275608) are gratefully acknowledged.Edited by: Armin Sorooshian
Reviewed by: three anonymous referees
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