Smoke from laboratory chamber burning of peat fuels from Russia, Siberia, the
USA (Alaska and Florida), and Malaysia representing boreal, temperate,
subtropical, and tropical regions was sampled before and after passing
through a potential-aerosol-mass oxidation flow reactor (PAM-OFR) to
simulate intermediately aged (∼2 d) and well-aged
(∼7 d) source profiles. Species abundances in PM2.5 between aged and fresh profiles varied by several orders of magnitude with
two distinguishable clusters, centered around 0.1 % for reactive and ionic
species and centered around 10 % for carbon.
Organic carbon (OC) accounted for 58 %–85 % of PM2.5 mass in fresh
profiles with low elemental carbon (EC) abundances (0.67 %–4.4 %). OC abundances decreased by
20 %–33 % for well-aged profiles, with reductions of 3 %–14 % for the
volatile OC fractions (e.g., OC1 and OC2, thermally evolved at 140 and 280 ∘C). Ratios of organic matter (OM) to OC abundances increased by
12 %–19 % from intermediately aged to well-aged smoke. Ratios of ammonia (NH3) to
PM2.5 decreased after intermediate aging.
Well-aged NH4+ and NO3- abundances increased to 7 %–8 %
of PM2.5 mass, associated with decreases in NH3, low-temperature
OC, and levoglucosan abundances for Siberia, Alaska, and Everglades
(Florida) peats. Elevated levoglucosan was found for Russian peats,
accounting for 35 %–39 % and 20 %–25 % of PM2.5 mass for fresh and
aged profiles, respectively. The water-soluble organic carbon (WSOC)
fractions of PM2.5 were over 2-fold higher in fresh Russian peat (37.0±2.7 %) than in Malaysian (14.6±0.9 %) peat. While
Russian peat OC emissions were largely water-soluble, Malaysian peat
emissions were mostly water-insoluble, with WSOC / OC ratios of 0.59–0.71 and
0.18–0.40, respectively.
This study shows significant differences between fresh and aged peat
combustion profiles among the four biomes that can be used to establish
speciated emission inventories for atmospheric modeling and receptor model
source apportionment. A sufficient aging time (∼7 d) is
needed to allow gas-to-particle partitioning of semi-volatilized species,
gas-phase oxidation, and particle volatilization to achieve representative
source profiles for regional-scale source apportionment.
Introduction
Receptor-oriented source-apportionment models have played a major role in
establishing the weight of evidence (U.S.EPA, 2007) for
pollution control decisions. These models, particularly the different
solutions (Watson et al., 2016) to the chemical mass balance
(CMB) equations (Hidy and Friedlander, 1971), rely on patterns
of chemical abundances in different source types that can be separated from
each other when superimposed in ambient samples of volatile organic
compounds (VOCs) and suspended particulate matter (PM). These patterns,
termed “source profiles,” have been measured in diluted exhaust emissions
and resuspended mineral dusts for a variety of representative emitters. Many
of these source profiles are compiled in country-specific source profile
data bases (Cao, 2018; CARB, 2019; Liu et al., 2017; Mo et al., 2016;
Pernigotti et al., 2016; U.S.EPA, 2019) and have been widely used for source
apportionment and speciated emission inventories.
Chemical profiles measured at the source have been sufficient to identify
and quantify nearby, and reasonably fresh, source contributions. These
source types include gasoline- and diesel-engine exhaust, biomass burning,
cooking, industrial processes, and fugitive dust. Ambient VOC and PM
concentrations have been reduced as a result of control measures applied to
these sources, and additional reductions have been implemented for toxic
materials such as lead, nickel, vanadium, arsenic, diesel particulate
matter, and several organic compounds. As these fresh emission contributions
in neighborhood- and urban-scale environments (Chow et
al., 2002) decrease, regional-scale contributions that may have aged for
intermediate (∼2 d) or long (∼7 d)
periods prior to arrival at a receptor gain in importance. These profiles
experience augmentation and depletion of chemical abundances owing to
photochemical reactions among their gases and particles, as well as
interactions upon mixing with other source emissions.
Peatland fires produce long-lasting thick smoke that leads to adverse
atmospheric, climate, ecological, and health impacts. Smoke from Indonesian
and Malaysian peatlands is a major concern in the countries of southeastern
Asia (Wiggins et al., 2018) and elsewhere; it is transported over long
distances. Aged peat smoke profiles are likely to differ from fresh
emissions, as well as among the different types of peat in other parts of
the world.
Ground-based, aircraft, shipboard, and laboratory peat combustion
experiments have been carried out to better represent global peat fire
emissions and estimate their environmental impacts (e.g., Akagi et al.,
2011; Iinuma et al., 2007; Nara et al., 2017; Stockwell et al., 2014, 2016).
Most peat fire studies report emission factors (EFs) for pyrogenic gases
(e.g., methane, carbon monoxide, and carbon dioxide) and fine particle
(PM2.5, particles with aerodynamic diameter <2.5µm)
mass, with a few studies reporting EFs for organic and elemental carbon (OC
and EC) (Hu et al., 2018).
Despite this lack of peat-specific fresh and aged source profiles, results
have been published for source apportionment in Indonesia
(See et al., 2007), Malaysia
(Fujii et al., 2017), Singapore
(Budisulistiorini et al., 2018), and Ireland (Dall'Osto et al., 2013;
Kourtchev et al., 2011; Lin et al., 2019). These have involved sampling
under environments dominated by near-source and far-from-source emissions, such as the
2015 Indonesia burning episode, to determine changes in thermally derived
carbon fractions with aging (Tham et al., 2019) and inference of aged
peat burning profiles from positive matrix factorization (PMF) application
to chemically speciated ambient PM samples
(Fujii et al., 2017). Budisulistiorini et
al. (2018) observe that “… atmospheric processing of aerosol
particles in haze from Indonesian wildfires has scarcely been investigated.
This lack of study inhibits a detailed treatment of atmospheric processes in
the models, including aerosol aging and secondary aerosol formation.”
Changes in source profiles have been demonstrated in large smog chambers
(Pratap et al., 2019), wherein gas–particle
mixtures are illuminated with ultraviolet (UV) light for several hours and
their end products are measured. Such chambers are specially constructed and
limited to laboratory testing. A more recent method for simulating such
aging is the oxidation flow reactor (OFR), based on the early studies of
Kang et al. (2007), revised and improved by several
researchers (e.g., Jimenez, 2018; Lambe et al., 2011), and commercially
available from Aerodyne (2019a, b). Although the Aerodyne
potential aerosol mass (PAM)-OFR has many limitations, as explained in the
Supplement (Sect. S1), it is a practical method for
understanding how profiles might change with different degrees of
atmospheric aging. A growing users group (PAMWiki, 2019) provides
increasing knowledge of its characteristics and operations.
Laboratory peat combustion EFs for gaseous carbon and nitrogen species
corresponding with the profiles described here, as well as PM2.5 mass
and major chemical species (e.g., carbon and ions), are reported by
Watson et al. (2019). The PM2.5 speciated
source profiles derive from six peat fuels collected from Odintsovo, Russia;
Pskov, Siberia; northern Alaska and Florida, USA; and Borneo, Malaysia,
representing boreal, temperate, subtropical, and tropical climate regions.
Comparisons between fresh (diluted and unaged) and aged (representing
intermediately aged (∼2 d) and well-aged (∼7 d) laboratory-simulated oxidation with an OFR) PM2.5 speciated
profiles are made to highlight chemical abundance changes with photochemical
aging. The objectives of this study are to (1) evaluate similarities and
differences among the peat source profiles from four biomes; (2) examine the
extent of gas-to-particle oxidation and volatilization between 2 and 7 d
of simulated atmospheric aging; and (3) characterize carbon and nitrogen
properties in peat combustion emissions.
Experiment
The Supplement describes the sampling configuration shown in Fig. S1
and OFR operation. Briefly, peat smoke generated in a laboratory combustion
chamber (Tian et al., 2015) was diluted with
clean air (by factors of 3 to 5) to allow for nucleation and
condensation at ambient temperatures (Watson et al.,
2012). These diluted emissions were then passed through an unmodified
Aerodyne PAM-OFR in the OFR185 mode without ozone (O3) injection.
Hydroxyl radical (OH) production as a function of UV lamp voltage was
estimated by inference from sulfur dioxide (SO2) decay using
well-established rate constants. UV lamps were operated at 2 and 3.5 V
with a flow rate of 10 L min-1 and a plug-flow residence time of
∼80 s in the 13.3 L anodyne-coated reactor, which translates
to OH exposures (OHexp) of ∼2.6×1011 and
∼8.8×1011 molecules s cm-3 at 2 and 3.5 V, respectively.
Transport times between source and receptor of 1 to 10 d are typical of
peat burning plumes, and the two OHexp estimates were selected to
examine intermediate (∼2 d) and long-term (∼7 d) atmospheric aging. Other emissions aging experiments
(e.g., Lambe et al., 2011) cite Mao et al. (2009) for a 24 h average atmospheric OH concentration (OHatm) of
1.5×106 molecules cm-3. This number appears nowhere in the text of
Mao et al. (2009), but it corresponds to the ground-level median value in
Mao's Fig. 8 plot of OH vs. altitude for Asian outflows over the Pacific
Ocean. The individual measurements in the plot range from OHatm
near zero to 5.3×106 molecules cm-3. Altshuller (1989)
concluded that “the literature contains reports of atmospheric OH radical
concentrations measured during daylight hours ranging from 105 to over 108 molecule cm-3, but almost all of the values
reported are below 5×107 molecules cm-3.”
Stone et al. (2012) report atmospheric values ranging
from 1.1×105 molecules cm-3 in polar environments to 1.5×107 molecules cm-3 in a vegetated forest. Uncertainties in OHexp
within the OFR are, therefore, not the controlling uncertainty in estimating
profile aging times. Added to this uncertainty are reactions among emission
constituents that are not embodied in the OFR185 mode that tend to suppress
OHexp with respect to that estimated by the SO2 calibration (Li
et al., 2015; Peng et al., 2015, 2016, 2018; Peng and Jimenez, 2017). The “OFR Exposure Estimator” available from the
PAMWiki (2019) intends to estimate this OHexp, but detailed VOCs
from these experiments are insufficient to apply it. The nominal 2 and
7 d aging times determined by dividing OHexp by Mao's 1.5×106 molecules cm-3 are subject to these uncertainties, which may increase
or decrease the aging time estimates. However, these uncertainties, along
with other uncertainties related to peat sample selection, moisture content,
and laboratory burning conditions, do not negate the value of the
measurements reported here. There are distinct differences in the fresh,
intermediately aged, and well-aged profiles that address the concerns
expressed by Budisulistiorini et al. (2018).
A total of 40 smoldering-dominated peat combustion tests were conducted that
included three to six tests for each type of peat fuel (Table S1). The
following analysis uses time-integrated (∼40–60 min)
gaseous and PM2.5 filter pack samples collected upstream and downstream
of the OFR, representing fresh and aged peat combustion emissions,
respectively.
PM2.5 mass and chemical analyses
Measured chemical abundances included PM2.5 precursor gases (i.e.,
nitric acid (HNO3) and ammonia (NH3)) as well as PM2.5 mass
and major components (e.g., elements, ions, and carbon). Water-soluble
organic carbon (WSOC), carbohydrates, and organic acids that are commonly
used as markers in source apportionment for biomass burning were also
quantified (Chow and Watson, 2013; Watson et al., 2016).
The filter pack sampling configurations for the four upstream and two
downstream channels along with filter types and analytical instrument
specifications are shown in Fig. 1. Multiple sampling channels accommodate
different filter substrates that allow for comprehensive chemical
speciation. Additional upstream Teflon-membrane and quartz-fiber filters
were taken for more specific nitrogen and organic compound analyses that are
not reported here. The limited flow through the OFR precludes additional
downstream sampling.
Teflon-membrane filters (i.e., channels one and five in Fig. 1) were
submitted for (1) gravimetric analysis by microbalance with ±1µg sensitivity before and after sampling to acquire PM2.5 mass
concentrations (Watson et al., 2017); (2) filter light
reflectance and transmittance by an ultraviolet–visible (UV-vis) spectrometer
(200–900 nm) equipped with an integrating sphere that measures
transmitted/reflected light at 1 nm intervals (Johnson, 2015); (3) 51
elements (i.e., sodium, Na, to uranium, U) by energy-dispersive X-ray
fluorescence (XRF) analysis (Watson et al., 1999); and (4) organic functional groups by Fourier transform infrared (FTIR) spectrometry.
Results from UV-vis and FTIR spectrometry will be reported elsewhere.
Half of the quartz-fiber filter (i.e., channels two and six) was analyzed
for (1) four anions (i.e., chloride, Cl-; nitrite, NO2-;
nitrate, NO3-; and sulfate, SO4=), three cations
(i.e., water-soluble sodium, Na+; potassium, K+; and ammonium,
NH4+), and nine organic acids (including four mono- and five
dicarboxylic acids) by ion chromatography (IC) with a conductivity detector
(CD) (Chow and Watson, 2017); (2) 17 carbohydrates including
levoglucosan and its isomers by IC with a pulsed amperometric detector
(PAD); and (3) WSOC by combustion and nondispersive infrared (NDIR)
detection. A portion (0.5 cm2) of the other half quartz-fiber filter
was analyzed for OC, EC, and brown carbon (BrC) by the
IMPROVE_A multiwavelength thermal–optical
reflectance–transmittance method (Chen et al., 2015; Chow et al., 2007,
2015b); the IMPROVE_A protocol (Chow et al., 2007) reports
eight operationally defined thermal fractions (i.e., OC1 to OC4 evolved at
140, 280, 480, and 580 ∘C in helium atmosphere; EC1 to EC3
evolved at 580, 740, and 840 ∘C in helium–oxygen atmosphere; and
pyrolyzed carbon, OP) that further characterize carbon properties under
different combustion and aging conditions. Citric acid- and sodium chloride-impregnated cellulose-fiber filters placed behind the Teflon-membrane and
quartz-fiber filters, respectively, acquired NH3 as NH4+ and
HNO3 as volatilized nitrate, respectively, with analysis by an IC-CD.
Detailed chemical analyses along with quality assurance–quality control
(QA–QC) measures are documented in Chow and Watson (2013). For
each analysis, a minimum of 10 % of the samples were submitted for
replicate analysis to estimate precisions. Precisions associated with each
concentration were calculated based on error propagation (Bevington,
1969) of the analytical and sampling volume precisions
(Watson et al., 2001).
Average fresh and aged peat combustion source profiles (as a percentage of
PM2.5 mass) for six types of peats.
a Analytical uncertainties are used for species below the minimum
detection limit, mostly for carbohydrate species and elements with an
average concentration of 0.00.
b Only one sample was analyzed for elements by X-ray fluorescence with
abundance and measurement uncertainty.
c Peat ID code, detailed operation parameters are reported in Watson et al. (2019).
d Data not available; water-soluble K+ data were contaminated for
aged samples due to the use of potassium iodide denuder downstream of the
oxidation flow reactor.
e WSOC measures from peat sample ID PEAT028 were invalidated due to a crack in the test tube. Therefore, only two measurements are used to
calculate the average and standard deviation.
f Data not available due to the invalidated citric-acid-impregnated
filter sample.
g The carbon analysis follows the IMPROVE_A
thermal–optical reflectance protocol (Chow et al., 2007) that is applied in
long-term US non-urban IMPROVE and urban Chemical Speciation Network.
Organic carbon (OC) is the sum of OC1 + OC2 + OC3 + OC4 plus pyrolyzed
carbon (OP). Elemental carbon (EC) is the sum of EC1 + EC2 + EC3 minus OP.
Total carbon is the sum of OC and EC. Since a large fraction of OP (7 %–13 %) is found in smoldering peat combustion emissions – indicative of
higher-molecular-weight compounds that are likely to char – the resulting EC fractions
are lower than the individual EC fraction after OP correction.
PM2.5 source profiles
Concentrations of two gases (i.e., NH3 and HNO3) and 125 chemical
species acquired from each sample pair (fresh vs. aged) were normalized by
the PM2.5 gravimetric mass to obtain source profiles with
species-specific fractional abundances. The following analyses are based on
the average of 24 paired profiles (shown in Table 1), grouped by upstream
(fresh) and downstream (aged) samples for 2 and 7 d aging (i.e., denoted
as Fresh 2 vs. Aged 2 and Fresh 7 vs. Aged 7) for each of the six peats with
25 % fuel moisture. Composite profiles are calculated based on the
average of individual abundances and the standard deviation of the average
within each group (Chow et al., 2002). Although the standard deviation is
termed the source profile abundance uncertainty, it is really an estimate of
the profile variability for the same fuels and burning conditions, which
exceeds the propagated measurement precision.
To assess changes with fuel moisture content, tests of three sets of Putnam
(FL1) peats at 60 % fuel moisture were conducted with the resulting profiles
shown in Table S2. A few samples were voided due to filter damage or
sampling abnormality, which produced five unpaired (either fresh or aged)
individual profiles (Table S3). These profiles are reported as they might be
useful for future source apportionment studies.
Equivalence measures
The Student's t test is commonly used to estimate the statistical significance
of differences between chemical abundances. Two additional measures are used
to determine the similarities and differences between profiles: (1) the
correlation coefficient (r) between the source profile abundances (Fij,
the fraction of species i in peat j) divided by the source profile
variabilities (σij) that quantifies the strength of association
between profiles, and (2) the distribution of weighted differences (residual
[R]/uncertainty [U] = [Fi1-Fi2] / [σi12+σi22]0.5) for <1σ,1σ–2σ,2σ–3σ, and >3σ. The percent distribution of R/U ratios is used to understand how
many of the chemical species differ by multiples of the uncertainty of the
difference. These measures are also used in the effective variance chemical
mass balance (EV-CMB) receptor model solution that uses the variance
(r2) and the R/U ratio to quantify agreement between measured receptor
concentrations and those produced by the source profiles and source
contribution estimates (Watson, 2004).
Results and discussionSimilarities and differences among peat profiles
The equivalence measures are used to provide guidance in compositing and
comparing the 40 sets of fresh vs. aged profiles. The first comparison is
made between two Florida samples from locations separated by ∼485 km (i.e., Putnam County lake bed, FL1; and Everglades National Park,
FL2), representing different geological areas and land uses. Panel A of
Table S4 shows that the two profiles yield high correlations (r>0.994), but are statistically different (P<0.002), with over 93 % of the chemical abundance differences within ±3σ.
However, when combining both fresh Florida profiles (i.e., all Fresh 2 vs. all Fresh 7 in Panel B), statistical differences are not found, with over 98 % of abundance differences within ±1σ and P>0.5. Notice that statistical differences are found between the two fresh
Florida profiles (i.e., FL1 Fresh 2 vs. FL2 Fresh 2 and FL1 Fresh 7 vs. FL2
Fresh 7 in Panel A) with few (<0.81 % and 5.6 %) R/U ratios
exceeding 3σ; combining the two Florida profiles may cancel out some
of the differences. However, paired comparisons of other combined profiles
show statistical differences with low P values (P<0.002). To further
demonstrate the differences, these two Florida profiles are classified as
Subtropical 1 and Subtropical 2 to compare with other biomes.
Equivalence measuresa for comparison of PM2.5 peat source
profiles.
a For the t test, a cutoff probability level of 5 % is selected; if P<0.05, there is a 95 % probability that the two profiles are different. For correlations, r>0.8 suggests similar profiles, 0.5<r<0.8 indicates a moderate similarity, and r<0.5 denotes little or no similarity. The R/U ratio indicates the percentage of the >93 reported chemical abundances differ by more than an expected number of uncertainty intervals. The normal probability density function of 68 %, 95.5 %, and 99.7 % for ±1σ, ±2σ, and ±3σ, respectively, is used to evaluate the R/U ratios. The two profiles are considered to be similar, within the uncertainties of the chemical abundances when 80 % of the R/U ratios are within ±3σ, with r>0.8 and P>0.05. Species with R/U ratios >3σ are further examined as these may be markers that further allow source contributions to be distinguished by receptor measurements. They may also reflect the sampling and analysis artifacts that are not representative of the larger population of source profiles.
b Unless otherwise noted, boreal represents Russia and Siberia regions, temperate represents the northern Alaska region, subtropical is divided into Subtropical 1 for Putnam (FL1) and Subtropical 2 for Everglades (FL2) peats, and tropical represents the island of Borneo, Malaysia, region. c n1 and n2 denote number of samples in comparison.
d Student's t test P values.
Similarities and differences in peat profiles by biome are summarized in
Table 2. Comparisons are made for (1) paired fresh vs. aged profiles (i.e.,
All Fresh vs. All Aged; Fresh 2 vs. Aged 2; and Fresh 7 vs. Aged 7), (2) different experimental tests (i.e., Fresh 2 vs. Fresh 7), and (3) two aging
times (i.e., aged 2 vs. aged 7). Equivalence measures show that most of
these profiles are highly correlated (r>0.97, mostly
>0.99) but statistically different (P<0.05), with a few
exceptions.
Group comparisons between fresh and aged samples (Panel A of Table 2) show
statistical differences for all but Putnam (FL1) peat (P>0.94).
This is consistent with Watson et al (2019) where atmospheric aging (7 d)
reduced organic carbon EFs (i.e., EFOC) by ∼20 %–33 %
for all but Putnam (FL1) peats (EFOC remained within ±0.5 %).
As OC is a major component of PM2.5, no apparent changes in OC and
carbon fraction abundances may dictate the lack of statistical differences
between the fresh and aged profiles.
Paired comparisons for 2 d aging (Panel B of Table 2) show no statistical
differences between the Fresh 2 vs. Aged 2 Putnam (FL1) and Malaysian
profiles (P>0.30 and 0.95), which may be due to the low number
of samples (n=2) in the comparison; this results in no statistical
differences for combined Putnam (FL1) and Malaysian peat comparison (P>0.62). Similar to the findings of combining both fresh Florida
profiles (i.e., all Fresh2 vs. all Fresh 7 in Table S4), the two fresh
Alaskan profiles (Fresh 2 vs. Fresh 7 in Panel D of Table 2) do not show
statistical differences (P>0.12).
Compositing profiles by averaging each of the measured abundances may
disguise some useful information. For receptor model source apportionment,
region-specific profiles are most accurate for estimating source
contributions.
Student's t tests for the gravimetric PM2.5 mass concentrations (µg m-3) measured upstream and downstream of the OFR (Table S5) show
statistically significant differences (P<0.05) between fresh vs. aged PM2.5 (i.e., Fresh 2 vs. Aged 2 and Fresh 7 vs. Aged 7). Fresh 2 and
Fresh 7 PM2.5 mass concentrations are similar, as expected from
replicate tests for the same conditions. Increases in some species
abundances offset decreases on other abundances, resulting in similar
PM2.5 levels for the “all fresh vs. all aged” comparison.
Ratios of sum of species to PM2.5 mass
The sum of the major PM chemical abundances should be less than unity since
oxygen, hydrogen, and liquid water content are not measured (Chow et al.,
1994, 1996). As shown in Table S6, the sums of elements, ions, and carbon
explain averages of ∼70 %–90 % of PM2.5 mass for fresh
profiles except for Russian peat (62 %–64 %). The “sum of species”
decreased by an average of 6 % and 11 % after 2 and 7 d,
respectively. These differences are consistent with loss of semi-volatile
organic compounds (SVOCs) in the low-temperature carbon fractions, although
they are offset by formation of oxygenated compounds during aging. This is
true for all but Putnam (FL1) peat, for which the sum of species
explains nearly the same fraction of PM2.5 for the fresh and aged
profiles.
Comparison between fresh and aged profile chemical abundances for
each of the six types of peat with 2 and 7 d aging times. Standard
deviations associated with averages for x and y variables are also shown. Vertical
dashed lines (red) at 1 % on the x axis delineate the two
distinguishable clusters: centered around 0.1 % for reactive and ionic species
and centered around 10 % for carbon compounds.
Comparison between fresh and aged profiles
Fresh and aged chemical abundances are compared in Fig. 2. Species
abundances vary by several orders of magnitude but exhibit two
distinguishable clusters: centered around 0.1 % for reactive and
secondary ionic species (e.g., NH4+, NO3-, and
SO4=) and centered around 10 % for carbon compounds (e.g., OC
fractions and WSOC). While most gaseous NH3/PM2.5 ratios exceed 10 %, HNO3/PM2.5 ratios are well below 1 % in fresh emissions. Reactive–ionic
species and carbon components are mostly above and below the 1:1 line,
respectively, implying particle formation and evaporation after atmospheric
aging. Large variabilities are found for individual species as noted by the
standard deviations associated with each average.
Ratios of average aged (A) to fresh (F) chemical species for
2 d (A2/F2) and 7 d (A7/F7) of atmospheric aging for six types of
peats. Error bars represent the standard deviations associated with each
ratio. Note that different scales were used for the two Y axes, with 0.001 to
10 000 on the left axis and 0.1 to 100 on the right axis (species
abbreviations are shown in Table 1; OM is organic mass).
Figure 3 shows the ratio of averages between aged and fresh profiles. Atmospheric aging increased oxalic
acid, NO3-, NH4+, and SO4= abundances (likely
due to conversion of nitrogen and sulfur gases (e.g., NH3, NO,
NO2, and SO2) to particles), but decreased NH3, levoglucosan,
and low-temperature OC1 and OC2 abundances in most cases. Large variations
are found among measured species (left panels in Fig. 3) as ratios range
several orders of magnitude for mineral and ionic species. Consistent with
Fig. 2 where most carbon compounds are close to but below the 1:1 line, the
right panels in Fig. 3 show the reduction of carbonaceous abundances with
aged / fresh ratios between 0.1 and 1. Higher aged / fresh ratios in low-temperature OC1 and OC2 after 7 d aging are consistent with additional
volatilization with longer aging time.
Atmospheric aging should not change the abundances of mineral species (e.g.,
Al, Si, Ca, Ti, and Fe), except to the extent that the PM2.5 mass (to
which all species are normalized) increases or decreases with aging. Large
standard deviations associated with the ratio of averages for mineral
species in the left panels of Fig. 3 illustrate variabilities among
different combustion tests for the less abundant species.
Carbon abundancesOrganic carbon and thermally evolved carbon fractions
Total carbon (TC, sum of OC and EC) constitutes the largest fraction of
PM2.5 (Table 1), accounting for 59 %–87 % and 43 %–77 % of the
PM2.5 mass for the fresh and aged profiles, respectively. OC dominates
TC with low EC abundances (0.67 %–4.4 %), as commonly found in
smoldering-dominated biomass combustion (Chakrabarty et al., 2006; Chen
et al., 2007). The largest OC fractions are high-temperature OC3 (15 %–30 %
of PM2.5), consistent with past studies for biomass burning emissions
(Chen et al., 2007; Chow et al., 2004).
OC abundances decreased with aging time. As shown in Fig. S2, upstream
(Fresh 2 and Fresh 7) OC abundances ranged from 58 % to 85 % and decreased by
4 %–12 % and 20 %–33 % after 2 and 7 d aging, respectively. The
exception is for Putnam (FL1) peat, where the OC abundances were similar
(changed by ∼0.5 to 1.5 %) between fresh and aged profiles.
Part, but not all of this reduction is due to increasing abundances of
non-carbon components, particularly nitrogen-containing species that add to
PM2.5 mass. OC abundance decreases after aging for other profiles may
have contributed to the statistical differences found between fresh and aged
PM2.5 mass (Table S5). With the exception of Putnam (FL1) peat, the
additional 7 %–22 % OC degradation from 2 to 7 d aging implies that much
of the OC changes require about a week of aging time.
The Student's t test for fresh and aged profiles shows statistical differences
(P<0.05) for TC, OC, and low-temperature OC1 and OC2, but
similarities for OC3 and OC4. High-temperature OC3 and OC4 contain more
polar and/or high-molecular-weight organic components (Chen et al., 2007)
that are less likely to photochemically degrade. Large fractions of
pyrolyzed carbon (OP of 7 %–13 %) are also found, indicative of higher-molecular-weight compounds that are likely to char (Chow et al., 2001, 2004, 2018).
Reduction in OC abundances after atmospheric aging is attributed mostly to
decreases in low-temperature OC1 and OC2 abundances in the OFR as shown in
the fresh vs. aged ratios of average abundances (Fig. 3). Figure S3a shows
reductions in OC1 abundances after 2 and 7 d of atmospheric aging are
apparent but at a similar level: ranging from 2 % to 10 % and 3 % to 14 %,
respectively. Additional OC1 reductions from 2 to 7 d are most apparent
for Russia and Everglades (FL2) peats at the 6 %–10 % level. Similar
reductions are found for OC2 (Fig. S3b): ranging from 3 % to 11 % and 3 % to 12 % after the 2 and 7 d of aging, respectively. Prolonged aging times
resulted in additional 4 %–8 % OC2 reduction for all but Russian and Putnam
(FL1) peats. As oxidation of organic compounds with OH radicals is an
efficient chemical aging process (Chim et al., 2018), some of
the VOCs and SVOCs may have been liberated (Smith et al., 2009).
Organic mass (OM) and OM/OC ratios
Reduction of the sum of species and OC abundances from fresh to aged
profiles can be offset by the formation of oxygenated organic compounds as
the profiles age. Different assumptions have been used to transform OC to
organic mass (OM) to account for unmeasured H, O, N, and S in organic
compounds (Cao, 2018; Chow et al., 2015a; Riggio et al., 2018). As single
multipliers for OC cannot capture changes by oxidation in the OFR, OM is
calculated by subtracting mineral components (using the IMPROVE soil formula
by Malm et al., 1994), major ions (i.e., NH4+,
NO3-, and SO4=), and EC from PM2.5 mass to account
for unmeasured mass in organic compounds (Chow et al., 2015a; Frank,
2006). This approach assumes that no major chemical species are unmeasured
and that the remaining mass consists of H, O, N, and S associated with OC in
forming OM.
Table 3 shows that OM / OC ratios ranged from 1.1 to 1.7 and 1.3 to 2.2 for fresh
and aged profiles, respectively. The lower OM / OC ratios in fresh emissions
are consistent with those reported for other types of biomass burning
(Chen et al., 2007; Reid et al., 2005). Figure S4 shows a general upward
trend in OM / OC ratios after atmospheric aging with additional 14 %–21 %
increases from 2 to 7 d for all but Putnam (FL1) peat. The increase in
OM / OC ratios with aging is likely due to an increase in oxygenated
organics. The OM / OC ratio of 1.20 ± 0.05 for fresh Borneo, Malaysia,
peat is consistent with the 1.26 ± 0.04 ratio for fresh peat burning
emissions in Central Kalimantan, Indonesia (Jayarathne et al., 2018), both
located on the island of Borneo.
The highest OM / OC ratios are found for Russian peat, ranging from 1.6 to 1.7 for
fresh profiles and increasing to 2.1–2.2 for aged profiles, consistent with
formation of low-vapor-pressure oxygenated compounds in the OFR.
Watson et al. (2019) report that the Russian peat
fuel contains the lowest carbon (44.20±1.01 %) and highest oxygen
(38.64±0.78 %) contents among the six peats. The low carbon
contents in peat fuel and source profiles are consistent with the lowest
sum of species found in Russian peat, with 62 %–64 % and 50 %–52 % of
PM2.5 mass for the fresh and aged profiles, respectively. After 7 d aging for Siberian peat, the increasing OM / OC ratios from 1.2±0.14
to 1.5±0.18 are similar to the increase from 1.22 to 1.42 reported
by Bhattarai et al. (2018).
Organic carbon diagnostic ratios for different peat samples.
a Uncertainty associated with each ratio is calculated based on the square root of the individual uncertainties multiplied by the ratio (Bevington, 1969). b OM (organic mass) is calculated by subtracting major ions (i.e., sum of NH4+, NO3-, and SO4=), crustal components (2.2 Al + 2.49 Si + 1.63 Ca + 1.94 Ti + 2.42 Fe), and elemental carbon from PM2.5 mass. c WSOC: water-soluble organic carbon. d Levoglucosan/2.25 represents carbon content in levoglucosan, based on the chemical composition C6H10O5. e Oxalic acid/3.75 represents carbon content in oxalic acid based on the chemical composition C2H2O4.
Water-soluble organic carbon (WSOC)
WSOC abundances in PM2.5 were over 2-fold higher in fresh Russian peat
(36 %–37 %) than Malaysian (15 %–17 %) peat. The 15 %–17 % WSOC in
PM2.5 for fresh Borneo, Malaysia, peat (Table 1) is consistent with the
16±11 % from Central Kalimantan, Indonesia, peat (Jayarathne et
al., 2018). However, the WSOC /PM2.5 ratio is not a good indicator of
changes in WSOC abundances during atmospheric aging as PM2.5 also
contains non-water-soluble and non-carbonaceous aerosol. Table S7 shows
large variabilities associated with the differences (i.e., aged minus
fresh), suggesting that no differences exist within ±3 standard
deviations. The only exceptions are for the 7 d Putnam (FL1) peat and
2 d Malaysian peat, where aging resulted in 7 %–8 % increases in WSOC
abundances in PM2.5.
As WSOC is part of the OC, the WSOC / OC ratio is a better indicator of
atmospheric aging. WSOC / OC ratios (Table 3) vary between fresh (0.18–0.64)
and aged (0.31–0.71) profiles. Figure S5 shows a general increase in WSOC / OC
ratios from fresh to aged profiles. Longer aging time from 2 to 7 d
results in 5 %–10 % higher WSOC / OC ratios for all but the two Florida
peats. OC water solubility also varies by peat type. Russian peat OC
emissions are largely water-soluble, whereas Malaysian peat emissions are
mostly water-insoluble, with WSOC / OC ratios of 0.59–0.71 and 0.18–0.40,
respectively.
Carbohydrates
Bates et al. (1991) found that peat from Sumatra,
Indonesia, consisted of 18 %–46 % carbohydrate (mainly levoglucosan)
relative to total carbon based on nuclear magnetic resonance spectroscopy.
Levoglucosan and its isomers (mannosan and galactosan) are saccharide
derivatives formed from incomplete combustion of cellulose and
hemicellulose (Kuo et al., 2008; Louchouarn et al., 2009) and have been
used as markers for biomass burning in receptor model source apportionment
(Bates et al., 1991; Watson et al., 2016). These carbohydrate-derived
pyrolysis products undergo heterogeneous oxidation when exposed to OH
radicals in the OFR (Hennigan et al., 2010; Kessler et al., 2010).
Only five of the 17 carbohydrates (Table 1) were detected, with noticeable
variations (e.g., >2 orders of magnitude) in levoglucosan for
boreal and temperate peats. Levoglucosan abundances account for 35 %–39 %
and 20 %–25 % of PM2.5 mass for fresh and aged Russian profiles,
respectively. On a carbon basis, Table 3 shows that levoglucosan carbon
(with an OM / OC ratio of 2.25) accounts for 42 %–48 % and 30 %–35 % of WSOC
and 27 %–28 % and 21 %–24 % of OC for fresh and aged Russian profiles,
respectively. These levels are less than the 96±3.8 %
levoglucosan or ∼42.7 % of levoglucosan carbon in OC
reported for German and Indonesian peats (Iinuma et al.,
2007). Elevated levoglucosan is also found for Siberian and Alaskan peats,
ranging from 4 % to 18 % in PM2.5. However, the levoglucosan abundances
are low (1 %–4 %) for the subtropical and tropical peats. An aging time of 7 d resulted in an additional 1 %–4 % levoglucosan degradation relative to
2 d aging with the exception of an additional 9 % reduction for Russian
peat.
Abundances of fresh and aged carbon-containing components in
PM2.5. Levoglucosan (C6H10O5) is divided by 2.25 and
oxalic acid (C2H2O4) is divided by 3.75 to obtain the carbon
content. These levels are subtracted from the water-soluble organic carbon
(WSOC) to obtain the remainder, and WSOC is subtracted from organic carbon
(OC) to obtain non-soluble carbon. Elemental carbon (EC) is unaltered.
The extent of levoglucosan degradation depends on organic aerosol
composition, OH exposure in the OFR, and vapor wall losses
(Bertrand et al., 2018a, b; Pratap et al., 2019). Figure 4 shows the
presence of levoglucosan carbon for the Russian and Alaskan peats after 2 and 7 d aging, at the levels of 8 %–11 % and 2 %–9 %, respectively, in
line with a chemical lifetime longer than 2 d. This is consistent with
the estimated 1.2–3.9 d of levoglucosan lifetimes under different
environments reported by Lai et al. (2014). However, other
studies (Hennigan et al., 2010; May et al., 2012; Pratap et al., 2019)
found that levoglucosan experiences rapid gas-phase oxidation, resulting in
∼1–2 d lifetimes at ambient temperatures.
Among the carbohydrates, Jayarathne et al. (2018) reported 4.6±4.0 % of levoglucosan in OC for fresh Indonesian peat. Converting to
levoglucosan carbon in Jayarathne et al. (2018) yields a fraction of 2 %, consistent with findings for Malaysian peat (1.4 %–2.4 %) in this
study.
While the presence of levoglucosan in peat smoke is apparent, its isomer
galactosan was not detectable. Mannosan is detectable in cold climate peats
with 1 %–5 % of PM2.5 for the Russian and Alaskan peats and up to 1.3 % for Siberian peat. Apparent degradations from 3.9 % to 2.5 % and from
5.0 % to 2.1 % in mannosan abundances are found for Russian peat (Table 1)
after 2 and 7 d, respectively. A 2- to 3-fold reduction in mannosan is
also evident after 7 d aging for the Siberian and Alaskan peats. Similar
observations apply to glycerol in Russian peat, ranging from 1.9 % to 3.5 % and
1.3 % to 1.7 % of PM2.5 for fresh and aged profiles, respectively. Other
detectable carbohydrates are galactose and mannitol, typically present at
<5 % of the levoglucosan abundance.
Organic acids
Organic acids have been associated with many anthropogenic sources,
including engine exhaust, biomass burning, meat cooking, bioaerosol, and
biogenic emissions. Past studies show the presence of low-molecular-weight
dicarboxylic acids in biomass burning emissions (e.g., Falkovich et al.,
2005; Veres et al., 2010).
Only four of the 10 measured organic acids (Table 1) (i.e., formic acid,
acetic acid, oxalic acid, and propionic acid) were detected with variable
abundances (<0.02 %–3.9 %). The largest changes between fresh and
aged profiles are found for oxalic acid, ranging from <0.02 % to 0.43 % of PM2.5 for fresh profiles, with an ∼10- to 20-fold
increase after 2 d (0.6 %–1.3 %) and with 1 to 2 orders of
magnitude increases after 7 d (1.1 %–3.9 %). With the exception of
Putnam (FL1) peat (1.1±0.19 %), oxalic acid accounts for
≥2.9 % of PM2.5 mass after 7 d.
Acetic acid abundances are stable between fresh and aged profiles, mostly in
the range of 0.2 %–0.5 % except for a 6-fold increase from 0.23±0.15 % (Fresh 7) to 1.5±2.0 % (Aged 7) for Siberian peat with
large variability among the tests. Formic acid and propionic acid abundances
are low (<0.5 % and <0.02 %, respectively), but increase
with aging. Extending the aging time from 2 to 7 d resulted in a notable
increase in organic acid abundances, consistent with the increases in
WSOC / OC ratios (Table 3). By biome, the highest abundances for organic acids
in PM2.5 are found for aged (Aged 7) Siberian peat, with 3.9±1.4 % oxalic acid, 1.5±2.0 % acetic acid, and 0.44±0.28 % formic acid (Table 1).
Comparison of nitrogen species for (a)NH3 and
NH4+ and (b)HNO3, NO2-, and NO3- between
fresh and aged profiles for six types of peats.
Nitrogen species, sulfate, and chloride abundances
Ammonia normalized to PM2.5 mass is high for fresh profiles, ranging from
17 % to 64 %, except for the low NH3 content in Russian peat (6 %–8 %). These abundances are reduced to 3 %–14 % and 1 %–7 % after 2 and
7 d aging, respectively. As shown in Fig. 5, most of the NH3 rapidly
diminished after 2 d, with increasing particle-phase NH4+ and
NO3- after 7 d. The highest NH3-to-PM2.5 ratios are
found for fresh Everglades (FL2) peat profiles (51 %–64 %), ∼2–8-fold higher than other peats. These high and low NH3/PM2.5
ratios are consistent with the nitrogen contents in peat fuel: 3.93±0.08 % for Everglades and 1.50±0.52 % for Russian peats
(Watson et al., 2019).
Ionic abundances are typically <0.5 %, especially in fresh
profiles. Abundances of NH4+ in PM2.5 are low (0.0005 %–0.13 %) for fresh emissions, but increase to 0.05 %–1.0 % after 2 d and
3.4 %–6.7 % after 7 d, with the exception of Putnam (FL1) peat (1.01±0.05 % NH4+). Extending the aging time from 2 to 7 d results in an additional increase of ∼1 %–7 %
NH4+ abundances, in contrast to NH3 that is largely depleted
after 2 d.
Figure 5b shows increasing NO3- abundances with aging,
0.04 %–0.23 % for fresh profiles, increasing to 0.74 %–2.64 % after 2 d, and to 2.0 %–8.2 % after 7 d with the exception of Putnam (FL1)
peat (1.10±0.18 % NO3-). After 7 d, NH4+
and NO3- account for ∼4 %–7 % and ∼8 % of PM2.5 mass, respectively, for Siberian, Alaskan, and
Everglades (FL2) peats. No specific trend is evident for NO2-,
mostly <0.002 %, with ∼0.2 % for some fresh
Siberian and Alaskan peats. The ratio of gaseous HNO3 to PM2.5 is
low, in the range of 0.2 %–0.5 % without much change between fresh and
aged profiles. HNO3 created through photochemistry is largely
neutralized by the abundant NH3 in the emissions, resulting in the
increasing NH4+ and NO3- to PM2.5 in aged profiles.
The reaction of NH3 with HNO3 to form ammonium nitrate
(NH4NO3) is the main pathway for inorganic aerosol formation,
owing to low sulfur content in the peat fuels
(Watson et al., 2019). SO4= abundances
are low in fresh profiles (0.13 %–1.4 %), but they increase 2–3-fold after
2 d aging except for the Alaskan (0.35 %–0.46 %) and Everglades (FL2)
(1.3 %–1.4 %) profiles. More apparent changes are found for 7 d with the
largest increase in SO4= from 0.13 % to 1.96 % for the Malaysian
peats – indicating formation of ammonium sulfate ((NH4)2SO4).
The ion balance shows more NH4+ than needed to completely
neutralize NO3- and SO4= (Chow et al., 1994). Some
NH4+ may be present as ammonium chloride (NH4Cl); however,
the abundance of chloride (Cl-) is low (<0.3 %). The large
increase in NO3- and SO4= after 7 d implies that a
2 d aging time is not sufficient to allow the full formation of secondary
NH4NO3 and (NH4)2SO4.
Reconstruction of PM2.5 mass with organic mass (OM, see Table 3 for OM / OC ratios), elemental carbon (EC), major ions (i.e., sum of
NH4+, NO3-, and SO4=), and mineral component
(= 2.2 Al + 2.49 Si + 1.63 Ca + 1.94 Ti + 2.42 Fe) for six types of
peat between fresh and aged profiles.
Mass reconstruction
Mass reconstruction is applied to understand the changes in major chemical
composition between the fresh and aged profiles. As shown in Fig. 6, the
largest component of PM2.5 is OM, accounting for 94 %–99 % and 80 %–95 % of PM2.5 mass for fresh and aged profiles, respectively. Although
the 7 d aging time increased the OM / OC ratios (by 12 %–19 %), the
abundances of OM in PM2.5 are reduced (3 %–18 %). This can be
attributed to the combined effects of increased oxygenated organics, SVOC
volatilization
(Smith et al.,
2009), and an increase in ionic species as shown in the average aged / fresh
ratios in Fig. 3. Figure 6 shows increases in ionic species (i.e., sum of
NH4+, NO3-, and SO4=), with low abundances
(0.3 %–1.7 %) in fresh profiles, increasing 3 %–16 % after aging. The
sum of ionic species accounts for 11 %–16 % of PM2.5 mass for the
Siberian, Alaskan, Everglades (FL2), and Malaysian peats after 7 d,
mainly due to the increase in NH4+ and NO3- as shown in
Fig. 5.
Elemental abundances are low (<0.0001 %), mostly below the lower
quantifiable limits. Table 1 only lists 34 of the 51 elements (Na to U)
detected by XRF. Using the IMPROVE soil formula (assuming metal oxides of
major mineral species; Malm et al., 1994) yielded 0.07 %–2.9 % of mineral components. The IMPROVE soil formula has been applied in
many other studies (e.g., Chan et al., 1997; Pant et al., 2015;
Rogula-Kozlowska et al., 2012), which provides an adequate estimate of
geological mineral in reconstructed mass. Since geological minerals are not
a major component of PM2.5, variations in the assumption regarding
metal oxides or multipliers do not contribute to large variations in
reconstructed mass (Chow et al., 2015a).
This study indicates that an aging time of ∼2 d represents
the intermediately aged source profile, whereas ∼7 d represents the profile
with adequate residence time to complete the atmospheric process.
Changes in source profiles by fuel moisture content
The effect of fuel moisture content on source profiles is mostly unknown.
The 25 % fuel moisture content selected for this study intends to better
simulate the conditions of moderate to severe droughts where most peat fires
occur. Increasing fuel moisture content from ∼25 % to 60 %
for the three Putnam (FL1) peat fuels yielded 12 % higher EFs for
CO2 (EFCO2), but 12 %–20 % lower EFs for CO, NO, NO2, and
PM2.5 mass (Watson et al., 2019). Tests of
fuel moisture content on profile changes are available for only 2 d aging.
Equivalence measures (Table S8) show statistical differences (P<0.001) between 25 % and 60 % moisture profiles for either fresh or
aged profiles with high correlations (r>0.997), and over 93 %
of species abundance falls within ±3σ. While OC abundances in
PM2.5 are comparable for the fresh and aged profiles (70 %–72 %) for
25 % fuel moisture, a reduction of 18 % OC in PM2.5 is found for
60 % fuel moisture (from 82 % to 64 %) after aging (Table S2). The
higher fuel moisture content also reduced WSOC by 6 % and levoglucosan by
1.3 % with <1 % increases for NH4+ and organic
acids. After aging, the NH3-to-PM2.5 ratios decreased from 28 %
to 5 % and from 20 % to 8 % for the 25 % and 60 % fuel
moisture, respectively. These results are not conclusive as most
measurements are associated with high variabilities.
Summary and conclusion
Fresh and aged peat fire emission profiles from laboratory combustion
chamber and potential aerosol mass-oxidation flow reactor (PAM-OFR) for six
types of peats representing boreal (Odintsovo, Russia, and Pskov, Siberia),
temperate (northern Alaska, USA), subtropical (Putnam County lake bed and
Everglades National Park, Florida, USA), and tropical (Borneo, Malaysia)
biomes are compared. Analyses are focused on the average of 24 paired
profiles grouped by six peats and by fresh vs. aged profiles for 2 and 7 d of simulated atmospheric aging that represent intermediately aged and
well-aged source profiles, respectively.
Equivalence measures show that these profiles are highly correlated (r>0.97, mostly >0.99) but statistically different
(P<0.05) between different biomes, suggesting that these profiles
should be used independently for receptor model source apportionment studies
in different climate regions.
The sum of chemical species (i.e., elements, ions, and carbon) explains an
average of ∼70 %–90 % of PM2.5 mass for fresh profiles
except for Russian peat (62 %–64 %), confirming that major PM2.5
chemical species are measured. Aging times of 2 and 7 d resulted in an
average mass depletion of 6 % and 11 %, respectively. These
differences are caused by (1) loss of SVOCs with aging, as indicated by
lower abundances of OC1 and OC2 (evolved at 140 and 280 ∘C) in
the aged profiles and (2) replacement of the lost OC mass with unmeasured
oxygen associated with secondary organic aerosol formation in the OFR.
Species abundances in PM2.5 between aged and fresh profiles varied by
several orders of magnitude but exhibited two distinguishable clusters, with
reactive–ionic species (e.g., NH4+, SO4=, oxalic acid,
and HNO3) constituting 0.1 %–1 % and carbon compounds (e.g., OC,
organic carbon fractions (OC1–OC4), and WSOC) constituting >1 % (mostly >10 %) of PM2.5 mass. Most
NH3/PM2.5 ratios are >10 % whereas
HNO3/PM2.5 ratios are <1 % in fresh profiles.
Total carbon (TC, sum of OC and EC) is the largest component, accounting for
59 %–87 % and 43 %–77 % of the PM2.5 mass for the fresh and aged
profiles, respectively. With predominantly smoldering combustion, the majority
of the TC is OC, with low EC abundances (0.67 %–4.4 %). Further degradation
in OC abundances (7 %–22 %) from 2 to 7 d aging implies an incomplete
transformation with short aging time. Different thermal carbon fractions are
used to characterize combustion and aging conditions. While most of the OC
thermally evolved at high temperatures (OC3 at 480 ∘C), losses of
low-temperature OC1 and OC2 are found, indicating a shift of gas–particle
partitioning of SVOC to gas phase, where particle volatilization outweighed
gas-to-particle conversion.
Formation of oxygenated compounds is pronounced after aging, with ratios of organic
mass (OM) to OC increasing by 14 %–21 % from 2 to 7 d aging. The
WSOC abundance in PM2.5 varies 15 %–17 % and 36 %–37 % for fresh
Malaysian and Russian peats, respectively. While levoglucosan accounts for
∼1 %–4 % of PM2.5 mass for fresh subtropical and
tropical peats, elevated levels (6 %–39 %) are found for boreal and
temperate peats. Increasing the atmospheric aging time from 2 to 7 d
results in additional formation of organic acid and ionic species (e.g.,
oxalic acid, NO3-, NH4+, and SO4=), but
enhances losses of NH3, levoglucosan, and low-temperature OC1 and OC2.
Among the four climate regions, Russian peat with the lowest carbon (44 %) and highest oxygen (39 %) content resulted in ∼59 %–71 % of WSOC in OC along with the highest levoglucosan (20 %–39 % of
PM2.5) and lowest NH3/PM2.5 ratios (3 %–8 %). It also
yielded the highest oxygenated compounds after aging with OM / OC ratios of
2.1–2.2. This contrasts with Malaysian peats that are mostly water-insoluble
(WSOC / OC of 0.18–0.40) with low oxygenated compounds after aging (OM / OC
ratios of 1.2–1.5). Large increases are found for oxalic acid abundances
from fresh (<0.02 %–0.43 %) to 7 d aging (1 %–4%).
With the exception of Russian peats, fresh profiles contain high
NH3/PM2.5 ratios (17 %–64 %) with low abundances after aging
(3 %–14 % for 2 d and 1 %–7 % for 7 d). Extending the aging time
from 2 to 7 d results in an increase to ∼7 %–8 %
NH4+ and NO3- abundances. Although the week-long aging
time increased the OM / OC ratios, abundances of OM in PM2.5 were reduced
by 3 %–18 %.
Source profiles can change with aging during transport from source to
receptor. This study shows significant differences between fresh and aged
peat combustion profiles among the four biomes that can be used to establish
speciated emission inventories for air quality modeling. A sufficient aging
time (∼7 d) is needed to allow gas-to-particle
partitioning of semi-volatilized species, gas-phase oxidation, and
volatilization to achieve representative source profiles for
receptor-oriented source apportionment.
Data availability
All required measured data are presented in the article and the supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-5475-2019-supplement.
Author contributions
JCC, JGW, JC, L-WAC, and XW jointly designed the study, performed the data
analyses, and prepared the paper. ACW collected the peat fuels and
provided technical advice. QW, JT, and SSHH carried out the peat combustion
experiments. TBC and SDK assembled the database and performed the similarity
and difference tests between the fresh and aged profiles.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the supporting staff from both the Desert Research
Institute, Reno, NV, USA, and Institute of Earth Environment, Chinese
Academy of Sciences, Xi'an, China.
Financial support
This research has been supported by the U.S. National Science Foundation (grant nos. AGS1464501 and CHE-1214163) and the National Science Foundation of China (grant no. 21661132005), .
Review statement
This paper was edited by Yoshiteru Iinuma and reviewed by two anonymous referees.
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