Introduction
Particulate matter (PM) that is suspended in the atmosphere as atmospheric
aerosol plays a crucial role in the regional and global climate system
(Ramanathan et al., 2001; Kaufman et al., 2002), air pollution (Sun et al.,
2013), ambient visibility reduction (Watson, 2002), and human health (Ge et
al., 2011). Significant amounts of PM can be generated from human
activities. In particular, biomass-burning (BB) activities, e.g., forest
fires, wildfire, and agricultural fires, can become the main sources of fine
particulate matter (PM2.5, particulates ≤2.5 µm in
aerodynamic diameter) and/or submicron particulate matter (PM1,
particulates ≤1 µm in aerodynamic diameter) (Andreae and Merlet,
2001, Aiken et al., 2010; DeCarlo et al., 2010; Lee et al., 2010;
Cubison et al., 2011; Reche et al., 2012; Bougiatioti et al., 2014).
Agricultural residues burning is one of the most serious sources leading to
severe air quality problems during harvest seasons in China (Li et al.,
2007; Wang et al., 2009a; Du et al., 2011; Cheng et al., 2013; Ding et al.,
2013). Moreover, China is an agricultural country which has 1.8 billion
cultivated fields with a large amount of agricultural crop residue (Zhang et
al., 2008). Recently, the use of agricultural residues as fuel in China
declined. During harvest seasons, farmers usually harvest the crop in the
daytime and then burn agricultural residues directly in their fields, which
results in BB emissions. The investigation of the compositions, sources, and
processes of atmospheric aerosol particles during harvest seasons is
urgently needed to better understand the impact of aerosol particles from BB
sources on air quality.
Organic aerosol (OA) composes a large fraction of atmospheric aerosol
particles (Zhang et al., 2007). Combination of positive matrix factorization
(PMF; Paatero, 1997) and a PMF evaluation tool (PET; Ulbrich et al.,
2009) has been well used to identify and apportion the sources of OA in
recent studies (e.g., Lanz et al., 2007; Ulbrich et al., 2009; Allan et al.,
2010; Zhang et al., 2005a, 2011; Crippa et al., 2013, 2014; Sun et al.,
2013). In addition, an IGOR™-based source finder (SoFi; Canonaco et al.,
2013) with a multilinear engine algorithm (ME-2; Paatero, 1999) can also
resolve the emission sources of OA. The current PMF and ME-2 method can only
be employed to analyze OA data sets a posteriori (Sun et al., 2012; Zhang et
al., 2011; Canonaco et al., 2013), but cannot be easily utilized in the
real-time online estimation of atmospheric OA sources. To identify the
sources of atmospheric OA online, an algorithm based solely on organic mass
fragments, namely, m/z (mass-to-charge ratio) 57 (mostly C4H9+) and m/z 44 (mostly
CO2+), was developed to estimate hydrocarbon-like OA (HOA) and
oxygenated OA (OOA), respectively (Zhang et al., 2005a, b; Ng et al.,
2011c). Mohr et al. (2012) also identified cooking-related OA (COA) in ambient
data sets based on the fractions of COA tracers at m/z 55 (mostly
C4H7+) and m/z 57 organic mass fragments. Biomass-burning organic
aerosol (BBOA) is one of the major atmospheric OA species during BB periods
(Aiken et al., 2010; Allan et al., 2010). However, limited information on
developing the tracer-based method a posteriori is available for estimating
the source apportionment of BBOA.
The evolution processes of atmospheric OA, e.g., aging and/or oxidation, can
significantly influence the physicochemical properties of OA (Aiken et al.,
2008; Jimenez et al., 2009; Sun et al., 2011b). In the presence of BB
source, various volatile and semi-volatile organic precursors can be emitted
from the field burning of agricultural wastes, and secondary organic aerosol (SOA) can be formed from
these precursors rapidly (Jimenez et al. 2009; Grieshop et al., 2009;
Heringa et al., 2011; Kawamura et al., 2013). Furthermore, BB plumes can be
mixed with urban and regional pollutants during aging processes (DeCarlo et
al., 2010; Cubison et al., 2011). In addition, the secondary formation,
atmospheric transport, and diffusion, as well as the mass loadings and
oxidation state of ambient OA, can also be affected by the aging processes of
OA (Jimenez et al., 2009; Cubison et al., 2011; Sun et al., 2011b). Thus, it
is important to investigate
the evolution of OA and the evolution process effects in order to better understand the nature of atmospheric OA.
This study investigates the characteristics of PM1 species using an
Aerodyne Aerosol Chemical Speciation Monitor (ACSM), and OA mass spectra are
analyzed with the PMF model during summer and autumn harvests in the Yangtze River
delta (YRD) region;
the evolution of OA and the effects of the evolution process on PM burden
were also investigated. Combination of back trajectory analysis and local
wind meteorology was used to investigate the origins.
Results and discussion
Meteorological factors and PM1 components
Time series of meteorological factors and PM1 components
Figure 1 shows the time series of NR-PM1 species and BC in the presence
of different meteorological conditions during the harvest seasons in urban
Nanjing, i.e., WS, WD, RH, T, and precipitation. During the summer harvest,
the average values were 70.7 ± 15.3 %, 3.7 ± 1.7 m s-1,
and 23.4 ± 4.1 ∘C for the ambient RH, WS, and T,
respectively. In the autumn harvest, the average values were 54.3 ± 13.7 %,
2.6 ± 1.4 m s-1, and 18.1 ± 3.6 ∘C
for the ambient RH, WS, and T, respectively. The frequency distribution of
hourly averaged WD and WS throughout the summer and autumn
harvests are shown in Fig. S2a–b.
Mean mass concentrations (µg m-3) and standard deviation (SD) of
PM1 (NR-PM1+ BC) species, OA components and meteorological
factors (i.e., WS, RH, and T) during the harvest seasons.
Species
Summer harvest
Autumn harvest
Mean
SD
Mean
SD
Aerosol species
NO3
9.0
7.1
9.2
6.2
SO4
5.0
2.4
4.7
2.5
NH4
7.0
3.5
6.4
3.5
Chl
0.4
0.9
0.7
0.8
OA
15.4
12.8
22.3
17.5
BC
3.2
2.2
6.0
3.8
PM1
38.5
24.3
46.4
27.0
OA components
HOA + COA
2.2
2.4
5.7
7.6
BBOA
1.1
1.0
1.5
1.6
OOA - BB
4.1
4.6
6.5
7.3
OOA
7.1
3.6
6.6
3.2
Meteorological factors
WS (m s-1)
3.5
1.7
2.6
1.4
RH (%)
70.7
15.3
54.3
13.7
T (∘C)
24.1
4.1
18.1
3.5
As shown in Fig. S3, there is a strong correlation between the Met One
PM1 measured by Met One BAM-1020 and the PM1 (i.e., NR-PM1 + BC)
mass concentrations (r2=0.88, slope = 1.11), indicating that
the ambient submicron aerosols consisted mainly of the NR-PM1 and BC.
Note that the mass concentration of BC in the PM1 may be overestimated
due to the fact that the mass concentration of BC was measured by the
7-wavelength aethalometer for PM2.5. An overestimation was previously
suggested by Huang et al. (2011). The average PM1 mass for the summer
harvest is 38.5 µg m-3 with an hourly average ranging from 3.6 to
270.6 µg m-3, which is similar to that observed in the autumn
harvest (42.3 µg m-3) with an hourly average ranging 8.1 to
191.5 µg m-3. Indeed, PM1 consisted of OA (39 %), nitrate
(23 %), ammonium (16 %), sulfate (12 %), BC (8 %), and chloride
(1 %) during the summer harvest. During the autumn harvest, PM1 was
composed of OA (41 %), nitrate (20 %), ammonium (14 %), sulfate
(11 %), BC (13 %), and chloride (1 %). Table 1 presents a comparison
of the average composition of PM1 between the summer harvest and autumn
harvest periods. The average bulk composition of PM1 during the summer
harvest shows a similar dominance of OA to the PM pollution during the autumn
harvest, but lower mass fractions for other species except nitrate. Overall,
those species with the exception of BC also show a similar contribution
between the summer and autumn harvest to the PM1 mass, suggesting that
the PM pollution could be affected by similar pollution sources for the two
harvests.
As shown in Fig. 1, all aerosol species exhibited very dynamic variations
in mass concentrations due to the changes of source emissions, meteorology
factors (such as WD, RH, T, and planetary boundary layer height),
photochemical reactions, and regional transport (e.g., the BB plumes). For
example, the aerosol species dramatically reduced because of the quick
removal processes associated with heavy wet scavenging (e.g., 6–8 June)
during the summer harvest. However, the wet scavenging plays a minor role in
changing aerosol loadings with little precipitation during the autumn
harvest. OA shows a significant dynamic variation in mass concentrations
during the harvest seasons (Fig. 1c), likely due to the changes of source
emissions (such as cooking, traffic and/or BB emissions). There are three
sharp peaks during the summer harvest (case 1) and autumn harvest (case 2
and case 3). The relationships between the PM pollution, meteorology, and
chemical composition are presented in three case events (Table S1). The case
1, at 21:00–22:00 LT on 10 June, with the highest PM1 mass (253.1 µg m-3)
during the summer harvest is characterized by high loadings of
K+, BBOA, OOA-BB, chloride, and BC, indicating the significant impacts
of agricultural burning from the northwest of Nanjing (Fig. 1). Apart from
the high loadings of BB-related components, such as BBOA, OOA-BB, and
K+ seen in the case 2 and case 3 periods, local source-related
components, e.g., HOA + COA and BC, also present high concentrations. This
suggests that both local primary source emissions and regional BB plumes
dominate the PM pollution during the case 2 and case 3 periods. Therefore,
those findings indicate that indeed BB contributes significantly in the area
during the specific time period.
Diurnal variation patterns of meteorological factors (i.e., RH,
T, and WS) and PM1 species including organic aerosol (OA), nitrate
(NO3), sulfate (SO4), ammonium (NH4), chloride (Chl), and
black carbon (BC) during the harvest seasons.
Diurnal variations of meteorological factors and PM1
components
Figure 2 depicts the diurnal variations of the meteorological factors, i.e.,
RH, T, and WS, and PM1 species (including OA, nitrate, sulfate,
ammonium, chloride, and BC). Generally, the diurnal variations of the
meteorological parameters and PM1 species are similar during the summer
and autumn harvest. However, the ambient RH and T during summer harvest were
higher than those during autumn harvest. OA obviously exhibits three peaks
occurring between 06:00–08:00, 11:00–14:00, and 19:00–22:00 LT, which is
in agreement with the contributions of pollution sources, e.g., traffic,
cooking and/or BB (Allan et al., 2010; Huang et al., 2012; Sun et al., 2012;
Crippa et al., 2013). More details of the diurnal variations of the OA
components will be presented in Sect. 3.2.
Sulfate does not show any significant diurnal trend during both summer and
autumn harvest, and shows a similar concentration during the two harvests.
This means the non-volatile character and regional pollution of sulfate in
the YRD region during the summer and autumn harvest are analogous. A similar diurnal trend
of sulfate was also found by Huang et al. (2012) in the eastern YRD region.
Nitrate presents in a higher fraction of the total PM1 compared with
sulfate, yet with lower concentrations in the afternoon and higher
concentrations in the evening during the harvests. Similarly, nitrate also
shows a similar concentration for the two harvests during the whole day; in
addition, chloride shows a similar diurnal cycle with nitrate during the two
harvest seasons. This is in accordance with the volatile and gas-particle
partitioning properties of ammonium nitrate and ammonium chloride dependent
on ambient T and RH (Lanz et al., 2007; Sun et al., 2011b, 2012). This also
reflects that the photochemical production of HNO3 cannot compensate
for the evaporative loss at the relatively high T conditions during the two
harvests, which is similar to previous results observed by Huang et al. (2012)
in the eastern YRD region and Sun et al. (2012) in Beijing.
Furthermore, the higher boundary layer may dilute their loadings during the
daytime, and then influence their diurnal cycles (Sun et al., 2012).
Chloride is mainly ammonium chloride (NH4Cl) and/or organic
chlorine-containing species (Huffman et al., 2009; Huang et al., 2012; Sun
et al., 2012). During the harvest seasons, the evening high values of
nitrate and chloride might be affected by the BB emissions and/or formed via
gas-phase and aqueous-phase oxidations.
BC shows a classic diurnal variation with higher loadings appearing in early
morning and during nighttime, which is consistent with traffic rush hours in
early morning (07:00–08:00 LT) and during nighttime (20:00–21:00 LT). As in
previous studies, atmospheric BC is strongly associated with combustion
emissions (including traffic and BB source emissions), particular for BB
periods (Sandradewi et al., 2008; Liu et al., 2011, 2014; Crippa et al.,
2013). Therefore, the reason for the peak values of BC during the nighttime
may be also caused by the BB emissions during the harvest seasons, apart
from the effect of traffic source on the BC loadings. The lower
concentrations of BC in the afternoon can be associated with the dilution
effects of higher planetary boundary layer and reduced traffic emissions.
Mass spectra profiles (left) and diurnal variations (right) of
four OA factors, i.e., hydrocarbon-like and cooking-emissions-related OA (HOA
+ COA), fresh biomass-burning (BB) OA (BBOA), oxygenated BB-influenced OA
(OOA-BB), and highly oxygenated OA (OOA). Note that reference mass spectra
(MS) are obtained from the results by Crippa et al. (2013), and oxygenated
BBOA components have been resolved (OOA2-BBOA).
Organic source apportionment
Four OA factors (i.e., HOA + COA, BBOA, OOA-BB, and OOA) were identified,
as illustrated in Figs. 3 and 4. The mean mass concentrations of HOA + COA, BBOA, OOA-BB, and OOA during the harvest seasons are presented in
Table 1. HOA + COA, BBOA, OOA-BB, and OOA accounted on average for 15 %
(28 %), 7 % (7 %), 29 % (33 %) and 49 % (32 %) of the total OA
mass concentrations during the summer (autumn) harvest, respectively.
Hydrocarbon-like and cooking-emissions-related OA (HOA + COA)
The prominent hydrocarbon ion series of CnH2n+1+ and
CnH2n-1+ (e.g., 27, 29, 41, 43, 55, 57) obtained from the
mass spectrum were characterized as components of HOA (Zhang et al., 2005a,
2011; Mohr et al. 2009; Allan et al., 2010). As reported in previous
studies, m/z 57 (C3H5O+ and/or C4H9+) and m/z 55
(C3H3O+ and/or C4H7+) are commonly considered
as tracers for the primary organic emissions of combustion sources in urban
areas, including COA and HOA (Zhang et al., 2005a; Ng et al., 2010, 2011b;
He et al., 2010; Sun et al., 2012, 2013; Hu et al., 2013). It is found that
there is no significant difference in the mass spectrum between the summer
harvest and the autumn harvest (Fig. 3a). Compared with traffic-like OA (Liu
et al., 2011; Crippa et al., 2013), the mass spectrum obtained in the
present study shows a higher m/z 55/57 ratio. Previous studies indicated that
high m/z 55/57 together with a unique diurnal variation can be used as a
diagnostics for the presence of COA (Mohr et al., 2009; Allan et al., 2010;
Sun et al., 2012). The mass spectrum of HOA in this study is characterized
by more abundant ions, i.e., m/z 41 (mainly C3H5+), m/z 55 (mainly
C4H7+), and m/z 57 (Fig. 3a), which is similar to the
characteristics of COA mass spectrum measured by He et al. (2010). As shown
in Fig. 3, the diurnal variation of HOA + COA shows two pronounced peaks
corresponding to noon (a weak peak) and evening traffic/cooking activities
(a strong peak). Hence, HOA + COA in this study refers to the sum of
traffic-related HOA and COA. Similarly, Sun et al. (2010) and Sun et al. (2012)
also found that HOA species in urban ambient were influenced by both
traffic and cooking-like emissions.
Fresh biomass-burning OA (BBOA)
As shown in Fig. 3b, the mass spectrum of BBOA extracted in this study
shows a prominent peak of m/z 60 which is a well-known tracer ion for BB
emissions (Alfarra et al., 2007; Aiken et al., 2009; Cubison et al.,
2011; Huang et al., 2011; Liu et al., 2011). Levoglucosan was shown to
contribute to m/z 60 and was found in large amounts in urban, suburban, and
rural background atmosphere during BB periods (Maenhaut et al., 2012). In
addition, the BBOA is also characterized by higher peaks at m/z 27, 29,
41, 43, 55, 57, 77, and 91 that are indicative of freshly emitted organic
aerosol because fresh m/z 43–m/z 57 can also be from BB-related
emissions (Aiken et al., 2009; Heringa et al., 2011; Bougiatioti et al.,
2014). For example, primary BBOA (P-BBOA) has a significant contribution
from a non-oxygenated ion C3H7+ at m/z 43, but not from an
oxygenated ion C2H3O+ (m/z 43) in smog chamber experiments by
Heringa et al. (2011). The BBOA spectrum profiles with the lack of m/z 44
signal (CO2+) during the summer and autumn harvest show high
correlation (r2=0.82 and r2=0.87) with a result in Paris
(Crippa et al., 2013). Moreover, the spectrum of BBOA in this study is
qualitatively similar to published BB spectra from the fresh BB smoke in a
smog chamber (Grieshop et al., 2009). These findings suggest that this
factor can be related to BBOA with low atmospheric oxidants, and thus this
factor might be associated with fresh/primary BBOA during
the harvests.
Using soluble K+ as a tracer for BB has also been reported by previous
analyses of BB campaign data (Gilardoni et al., 2009; Aiken et al., 2010; Du
et al., 2011; Crippa et al., 2013). The time series of BBOA along with
K+ measured by MARGA is shown in Fig. 4b. BBOA is strongly correlated
with K+ (r2=0.95 and r2=0.78) during the summer and
autumn harvest, respectively (Fig. 5a), suggesting that BBOA and K+ were from the same source. In addition, the diurnal variation of BBOA
shows a pronounced peak at nighttime (Fig. 3), which is consistent with
the effects of the BB emissions (Fig. 3). This means that BBOA contributes
to primary organic aerosol (POA) mainly during the nighttime. This finding
is also consistent with the habit of the farmers in the YRD region, i.e.,
they usually harvest wheat or rice in the daytime and burn off straw in
the nighttime during the harvest seasons each year. In addition, chloride
correlates well with BBOA (r2=0.61 and r2=0.66) and K+
(r2=0.60 and r2=0.64) during the harvest seasons (Fig. S5). This suggests that chloride was mainly from the BB emissions and might
be in the form of KCl during BB periods.
Oxygenated OA (OOA) and oxidized BB-influenced OA (OOA-BB)
The mass spectrum of both OOA components (Fig. 3c and d) was characterized
by the prominent CxHyOz+ fragments, which has been
denoted as being previously found in many AMS studies (Zhang et al., 2005a; Lanz et
al., 2007; Sun et al., 2010; Crippa et al., 2013). The mass spectra of OOA
by the prominent peak of m/z 44 (mainly CO2+) (22.9 and 25.5 %
of the total OOA signal) during the summer and autumn harvest
are strongly consistent with more oxidized OOA component determined
(r2=0.91 and r2=0.89, Fig. 3d) during a BB period in Paris (Crippa
et al., 2013) and OOA components resolved at other urban sites (Lanz et al.,
2007; Ulbrich et al., 2009).
Time series of OA factors (left) and relevant tracer species
(right): (a) HOA + COA; (b) BBOA and a surrogate of
levoglucosan (m/z 60) and potassium ion (K+); (c) OOA-BB, nitrate
and chloride; (d) OOA and SO4. Note that different BBOA mass
concentrations for a low biomass-burning period (L-BB), medium biomass-burning
period (M-BB), and high biomass-burning period (H-BB).
In Figure 4d the time series of OOA is compared with the sulfate mass
loadings. A correlation was observed between time series of OOA and sulfate
mass loadings (r2=0.60 and r2=0.46; Fig. 6) during the
summer and autumn harvests, respectively. Previous studies performed at
various sites also showed that these two species were secondary with
low volatility properties in the atmosphere (Zhang et al., 2005a; Lanz et al.,
2007; Ulbrich et al., 2009; Sun et al., 2011a; Huang et al., 2012). Overall,
the diurnal pattern of OOA shows a relatively stable trend throughout the
whole day (Fig. 3). OOA often remains at a high concentration across several
days until a change of air mass occurs, which shows a regional production
(Sun et al., 2012, 2013). This may be the main reason causing the relatively
stable trend through the whole day in this study. Nevertheless, OOA shows a
slight increase at around 12:00–15:00, suggesting that more oxidized OOA
might be formed by photochemical processing. OOA also exhibits higher
loadings during the nighttime, probably caused by the aging of BB plumes, in
which BB emissions will be further oxidized and begin to transition into OOA
(Jimenez et al., 2009; DeCarlo et al., 2010). The uniform distribution of
its concentrations is almost in association with an abundance of WD during the
summer and autumn harvest (Fig. S4). This is strong proof
for explaining the regional pollution of OOA in the YRD region during the
harvest seasons.
Additionally, an oxygenated factor with the high degree of oxygenation
during the summer and autumn harvest (m/z 44, 18.2, and 14.5 % of the
total factor signal) in its mass spectrum has been resolved and
identified as oxidized BB-influenced OA (OOA-BB, Fig. 3c). The mass spectra
of OOA-BB are characterized by both the oxidized ions (m/z 18, 29, 43, and 44)
and the typical marker of BB (m/z 60) during the summer and autumn harvest,
which correlates well with those of BB-emission-related OOA (OOA2-BBOA)
(r2=0.85 and r2=0.86) during BB periods at an urban site in
Paris (Crippa et al., 2013). It is also highly similar to the mass spectrum
of the aged BBOA identified by DeCarlo et al. (2010) for airborne
measurements during the MILAGRO (Megacity Initiative: Local and Global Research Observations) campaign, and very much in agreement with the
aged BBOA from a BB experiment in a chamber study by Heringa et al. (2011).
In addition, the mass spectrum of OOA-BB shows more oxygenated degree,
compared to mass spectrum of fresh/primary BBOA from PMF analysis in the
atmosphere, from laboratory open wood burning (Aiken et al., 2009) and
from BBOA in this study. The OOA-BB spectrum in this study is also very
similar to the spectrum of the aged OA produced from aged biomass smoke in a
smog chamber (Grieshop et al., 2009). OOA-BB presents a pronounced diurnal
cycle with the highest concentration in the evening and early morning during
the harvests (Fig. 3), which is very consistent with the diurnal variations
of BBOA. This means that the OOA-BB production from open BB is rapid in
short timescales with the high RH and low T conditions in the nighttime.
OOA-BB also shows relatively low loadings in the daytime, due to the
dilution effects by enhanced mixing in the planetary boundary layer and the
evaporative loss of semi-volatile components.
As shown in Fig. 4c, the OOA-BB time series strongly correlates with
K+ and Δm/z 60 (i.e., m/z 60–0.26 % × OA, in
which the
applied metric of background f60=0.26 % of OA will be discussed
in Sect. 3.4) during the summer and autumn harvests, supporting the BB
influence. In addition, the sum of BBOA and OOA-BB also shows high
correlation with K+ and Δm/z 60 for the two harvests (Fig. 5a–b).
This suggests that OOA-BB represents an atmospheric mixture of BBOA and
OOA, which is similar to a recent HR-ToF-AMS (high-resolution time-of-flight aerosol mass spectrometer) study by Crippa et al. (2013).
It is interesting that OOA-BB correlates well with nitrate (r2=0.30 and r2=0.54), yet shows lower correlation with sulfate
(r2=0.16 and r2=0.30) for the summer and autumn harvest,
respectively (Fig. 6). Also, the time series of OOA-BB shows a similar trend
as chloride during the two harvest seasons (Fig. 4c). This implies an
indication of the semi-volatile character of OOA-BB, which is consistent
with the results from a recent field study in the eastern Mediterranean
(Bougiatioti et al., 2014) and some laboratory chamber studies (Lipsky and
Robinson, 2006; Robinson et al., 2007; Yee et al., 2013). Particularly, this
also means that aged biomass-burning OA (OOA-BB) may be significantly mixed
with nitrate in the BB plumes. Healy et al. (2013) also found a similar
result in Paris using single-particle mass spectrometer (SP-AMS) and
HR-ToF-AMS measurements.
Comparison of biomass-burning-related PMF factors (BBOA and
OOA-BB) and biomass-related species: (a) K+ and (b) the
ACSM m/z 60 minus 0.26 % × OA (applied metric of background
f60=0.26 % of OA is discussed in the Sect. 3.4 of the text)
during the summer and autumn harvest.
Effects of Chemical components on PM pollution
Figure 7 presents the average contributions of PM1 species and OA
components during the summer and autumn harvest, respectively. It is also
compared with other sites, including megacities (Mexico City, Paris,
Beijing, and Shanghai), and suburban/remote areas (Crete, Jiaxing, and Pearl
River delta) (Aiken et al., 2009; Crippa et al., 2013; Huang et al., 2012,
2013; Sun et al., 2012; Bougiatioti et al., 2014). Using the relative
contribution of the sum of BBOA and OOA-BB to OA, the harvest season was
separated into three time periods, i.e., a low BB (L-BB, 28 and 29 %)
period, medium BB (M-BB, 49 and 38 %) period, and high BB (H-BB,
93 and 50 %) period, during the summer and autumn harvest. We also include averages of some meteorological parameters
(i.e., RH, T, WS, and WD) for the reference, and these averages are shown in
Table S2. Compared with other sites including Mexico City, Paris, Crete,
Jiaxing, and Pearl River delta (Fig. 7), the BB source shows the largest
contribution to aerosol pollution during the H-BB both in the summer and
autumn harvest in urban Nanjing. In addition, in the absence of BB source
the sum of OOA and OOA-BB shows a higher fraction of total OA mass during
the harvests in urban Nanjing in comparison with Beijing and Shanghai in
China (Fig. 7). This means that the BB source significantly contributes to
the SOA pollution in urban Nanjing during harvest seasons.
Comparison of two kinds of oxygenated OA (OOA-BB and OOA) and
two kinds of secondary inorganic species, i.e., nitrate (NO3) and
sulfate (SO4), during the harvest seasons. Colored by the f60 as a
biomass-burning marker. The three dashed lines in the plot refer to 2:1, 1:1, and 1:2 lines, respectively.
As shown in Fig. 7, OA is important in PM pollution in the summer and
autumn harvest (39 and 41 %). Furthermore, the average fraction of
BBOA to OA during the summer harvest (7 %) is highly consistent with that
in the autumn (7 %), while BC shows a higher fraction during the autumn
harvest (12 %) than that in the summer harvest (8 %). This is also
corresponding to the fraction of HOA + COA, which shows a higher
contribution during the autumn harvest (28 %) than that in the summer
harvest (15 %). The different boundary layer height and primary sources
emission influences on primary pollutants (including BC, HOA and COA) may be
all potential causes of such seasonal differences. On average, the total
oxidized fraction of OA (including OOA and OOA-BB) accounts for more than
60 % (78 % for summer and 65 % for autumn harvest), which indicates
that regional OOA plays an important role in PM pollution in urban Nanjing
during the harvest seasons. As a comparison, OOA-BB shows a higher fraction
to OA in the H-BB period than in the L-BB period. The fraction of OOA-BB to OA is
higher than the fraction of BBOA during the harvest seasons, even in the
H-BB period. These findings indicate that aged BBOA plays a more
significant role in PM pollution than BBOA in the BB plumes, particularly in
the H-BB period. This is consistent with recent studies (Grieshop et al.,
2009; Heringa et al., 2011; Lathem et al., 2013; Yee et al., 2013;
Bougiatioti et al., 2014) indicating that the fresh BB-emission OA can be
rapidly surpassed by SOA formation within a few hours after its emission.
Average relative contributions of PM1 species and OA
components for a low biomass-burning period (L-BB), medium biomass-burning
period (M-BB), and high biomass-burning period (H-BB), as well as entire
period during the harvest seasons and other sites including megacities
(Mexico City, Paris, Beijing, and Shanghai), suburban area (Jiaxing), remote
background site (Crete), and PRD (Pearl River delta, China). Note that OOA
in this plot includes OOA2-BBOA in Paris.
The secondary inorganic aerosols (including sulfate, nitrate, and ammonium)
can be seen in lower fraction in the H-BB period than in the L-BB period.
However, the mass concentrations of sulfate, nitrate, and ammonium are higher
in the H-BB period than in the L-BB period (Table S2).
Therefore, these findings indicate that the BB source contributes more to OA
than secondary inorganic components. It is interesting that the contribution
of nitrate to PM1 is higher than the contribution of sulfate in the
H-BB periods during the two harvest seasons. For example, the average
contribution of nitrate to PM1 is ∼ 18 % in the H-BB
periods, which is almost two times higher than that of sulfate. However, the
contribution of nitrate to PM1 is very similar to the sulfate
contribution in the L-BB periods. This all indicates that BB is a much
more important source of nitrate, compared to sulfate. Similar results have
been observed by Crippa et al. (2013), Healy et al. (2013), and Bougiatioti
et al. (2014) during open BB periods.
The mass fractions of PM1 species and OA components as a
function of PM1 mass loadings (left), and probability density of
PM1 mass loadings (right, with the white lines in the plots) during the
summer (a) and autumn (b) harvest, respectively.
Figure 8 presents the mass fractions of PM1 species and OA components
as a function of total PM1 mass loadings, as well as the probability
density of total PM1 mass loadings during the summer and autumn harvest. Overall, the total OA fraction increases from about
∼ 15 to 40 % and from ∼ 30 to 45 %
as a function of the PM1 loadings during summer and autumn,
respectively. Indeed, OOA-BB and BBOA show a significant increase as a
function of the PM1 loadings during the harvest seasons.
The contribution of OOA-BB to PM1 increases from ∼ 3
(∼ 5 %) to 33 % (26 %) during the summer (autumn)
harvest. And the contribution of BBOA increases from ∼ 2
(∼ 4 %) to 8 % (8 %). The results highlight the
contribution of OOA-BB arising from BB emissions to PM pollution in the
harvests. During the summer harvest, the HOA + COA and BC mass fractions
display a slight decrease, suggesting that local primary sources play an
important in the low PM pollution period. In addition, the nitrate and
sulfate contributions show a slight increase and decrease, indicating additional production of nitrate mass during high
PM episodes. Note that the mass fraction of OOA shows a slight decrease with
the increasing of total PM1 loadings during the autumn harvest. This
suggests that OOA mainly contributes to the low PM pollution, and OOA-BB
mainly contributes to the high PM pollution. However, the contribution of
HOA + COA, BC, and the secondary inorganic species to the total PM1
loadings did not show clear PM mass loading dependency, which indicates that
the high PM pollution during the autumn harvest may be caused by the
synergistic effects of all pollutants.
Summary plots showing f44 vs. f60 for measurements
with little or negligible biomass-burning influence. Colored by the summer
harvest (blue), autumn harvest (orange), and a little/negligible biomass-burning influence period (gray, 1 to 8 July 2013). Also shown
is the average background level of f60 (∼ 0.3 %, red
dashed line) in other studies from Aiken et al. (2009) and Cubison et al. (2011) for references.
The (BBOA + OOA-BB) / ΔCO ratio as a function of
f44 during the summer and autumn harvest. Colored by the HOA + COA
mass concentrations for the summer and autumn harvest.
Estimation of BBOA directly from a tracer (Δm/z 60)
The BBOA mass loadings during the harvest seasons were estimated a
posteriori using a simple method. As described in previous studies, the
parameter f60, fraction of m/z 60 in total OA, is considered as a marker of
fresh/primary BBOA (Alfarra et al., 2007; DeCarlo et al., 2008; Aiken et
al., 2009; Cubison et al., 2011). To estimate the real value of the BBOA
loadings, the background fraction of f60 (0.26 ± 0.1 %) during
little/negligible BB-influenced periods (non-BB periods) was determined (Fig. 9).
Aiken et al. (2009) and Cubison et al. (2011) also obtained a similar
background level of f60 (0.3 ± 0.06 %) for an urban city in
Mexico. Therefore, the levoglucosan-like species in ambient BB plumes was
estimated with Δm/z 60 (Δm/z 60 = m/z 60 – background value of
f60 × OA). As shown in Fig. 5b, the strong correlations
(r2=0.95, r2=0.98, and r2=0.97) between the
BBOA and Δm/z 60 with the similar slopes, i.e., 16.3 for summer, 14.6
for autumn, and 15.1 for the total harvest seasons, were observed. The
OOA-BB mass loadings also show the high correlations with Δm/z 60
(r2=0.95 and r2=0.97), but with very different slopes
(74.8 and 64.4) during the summer and autumn harvest, respectively (Fig. 5b).
Aiken et al. (2009) also found that BBOA strongly correlated with Δ
m/z 60 mass loadings (r2=0.91, slope = 34) during the BB/wood-smoke
periods in Mexico City. Furthermore, Lee et al. (2010) obtained a strong
relationship between BBOA and m/z 60 mass loadings (r2=0.92, slope = 34.5)
through a wildland fuels fire experiment in the lab. Thus, we reconstructed
the time series of BBOA to compare the relationship between the extracted
BBOA by the PMF model (PMF BBOA) and the estimated BBOA. As shown in Fig. S16,
an excellent agreement is observed between the identified and reconstructed
BBOA concentrations during the total harvest seasons (r2=0.97).
Therefore, the BBOA component during the BB periods in urban Nanjing of the
YRD region can be estimated with the equation of BBOA = 15.1 × (m/z 60–0.26 % × OA) for the harvest seasons.
Summary plots showing (a) triangle plot (f44 vs. f43); SV-OOA and LV-OOA indicate semi-volatile OOA and low volatility
OOA, respectively. The dots are colored by f60 as a biomass-burning
marker; (b) f44 as a function of f60 (f44 vs. f60),
colored by the PM1 mass concentration and sized by the OA loadings.
Evaluation of OA
To further investigate the probable importance of the aging and/or mixing
processes of BB plumes, the total BB-related OA (BBOA + OOA-BB) to ΔCO ratio as a function of the f44 during the summer and autumn harvest
is shown in Fig. 10. The CO background is determined as
14.9 µg m-3 for summer harvest and 17.9 µg m-3 for autumn
harvest based on an average of the lowest 5 % CO during two
plumes (Takegawa et al., 2006). The ratio of BBOA + OOA-BB to ΔCO
can remove the effect of dilution in the atmosphere (de Gouw 2005; Dunlea et
al., 2009; DeCarlo et al., 2008, 2010). As discussed in de Gouw et al. (2005), Aiken et al. (2008), Jimenez et al. (2009), and Ng et al. (2010),
the f44 can be considered as indicator of atmospheric aging due to
photochemical aging processes leading to the increasing of f44 in the
atmosphere. Overall, the (BBOA + OOA-BB) / ΔCO ratio shows an
obvious reduction with increasing f44 values during the summer and
autumn harvest in the absence of traffic and cooking-like
plumes. This is likely due to a synergistic effect of the rapid formation of
OOA from BB plumes and the mixing of BBOA with regional OOA and/or CO.
Similar results have been found by DeCarlo et al. (2010), from aircraft
measurements during MILAGRO in Mexico City and the central Mexican plateau.
Average composition of PM1 (pie charts) and OA factors
(bar charts) for each cluster. The four clusters are (a)
northeasterly (NE) back trajectories (BTs), easterly marine (EM) BTs,
southeasterly marine (SEM) BTs, and westerly continental (WC) during the
summer harvest; and (b) northerly continental (NC) BTs, northeasterly
marine (NEM) BTs, easterly marine (EM) BTs, and southerly continental (SC)
during the autumn harvest. The markers on the trajectories indicate 6 h interval.
Figure 11a depicts the evolution process of OA with the f44 vs. f43 space during two harvest seasons. The BBOA and HOA + COA show
similar low oxidative properties with varying f43, which are located at
the bottom left of the triangular region during the summer and autumn
harvest. With the aging process in the atmosphere, OA clusters
within a well-defined triangular region and shows more similar oxidative
properties to OOA-BB and/or OOA (Fig. 11a). This implies that OOA-BB and/or
BBOA might be further oxidized, and might be transformed into highly
oxidized OOA.
Furthermore, the formation and transformation of primary and secondary BBOA
during BB periods can be described by f44 vs. f60 plot (Cubison et
al., 2011). In the f44 vs. f60 space of Fig. 11b, OA shows a trend
toward higher f44 and lower f60 values with the aging of BB plumes,
appearing into the low volatility OOA (LV-OOA) range. This is very
consistent with previous reports in aircraft and laboratory studies (Cubison
et al., 2011) with a similar trend. In a smog chamber experiment, Grieshop
et al. (2009) also found that the relative contribution at m/z 44 and m/z 60
rapidly increases and decreases, respectively, during the aging process, which
presents the characteristics of fresh and aged BBOA.
Impacts of various source regions on the PM pollution
Figure 12 presents the BTs clusters of air masses at 500 m arrival height
above ground level at intervals of 2 h (00:00, 02:00, 04:00, etc.) starting at CST using the HYSPLIT model (Draxler and
Rolph, 2003) in Nanjing (118∘46′ N, 32∘05′ E). The
corresponding BTs can be broadly classified into four principal clusters of
air masses based on the spatial distributions during the summer and autumn
harvests, i.e., northeasterly (NE) BTs,
easterly marine (EM) BTs, southeasterly marine (SEM) BTs, and southwesterly
continental (SWC) for the summer harvest; northerly continental (NC) BTs,
northeasterly marine (NEM) BTs, easterly marine (EM) BTs and southerly
continental (SC) for the autumn harvest. The air masses in Nanjing in this
study were mainly from the SEM BTs (accounting for 57.4 % of all the BTs)
during the summer harvest, while predominantly from the NC and EM BTs (at
frequencies of 43.8 and 24 %, respectively) during the autumn harvest
(Fig. 12 and Table S3).
The average PM1 loadings are the highest (71.3 µg m-3) for
the westerly continental (WC) BTs, which is almost two times higher than that of the lowest (24.4 µg m-3) for the EM BTs during the summer harvest. This suggests that
the long-range transported pollutants from southwestern areas can cause the
high PM pollution in the YRD region during the summer harvest. Similarly,
the highest average concentration of PM1 (80.9 µg m-3) is
associated with the continental-related air masses during the autumn
harvest. Therefore, source regions related to the fire locations (Fig. S1)
are of utmost importance to the high air pollution in the YRD region during
the harvest seasons.
The PM1 chemical compositions show also significantly different
fraction among the four clusters during the summer and autumn harvest, which might be associated with the different source regions of
air pollution. The lowest PM1 loadings are associated with the EM BTs,
but with the high contribution of HOA + COA during the summer harvest
(Fig. 12a). This suggests that the local sources play a key role in controlling
aerosol pollution during relatively clean periods in the summer harvest. For
the NE BTs, the OOA, nitrate, and sulfate provide high fractions of the
total PM1 mass, suggesting that regional pollution plays a key role in
controlling PM pollution. With regards to the marine-related air masses,
PM1 loadings associated with the NEM and NE BTs are higher in the
autumn harvest than in the summer harvest (Fig. 12b), which may be due to
the fact that high local POA contributes to PM pollution. This suggests
that local sources play a more important role in the aerosol pollution in
the autumn harvest than in the summer harvest. Compared with other clusters
during the autumn harvest, the BB-related components (e.g., BBOA, OOA-BB, and
chloride) contribute the highest fractions to the PM1 mass in air
masses originating from the SC BTs, indicating that BB plumes can contribute
to the highest, heaviest PM pollution during the autumn harvest. Apart from
the high contributions of nitrate and OOA, HOA + COA also accounted for a
higher fraction to PM1 mass in the NEM and EM BTs than in other
clusters during the autumn harvest. In addition, the PM1 components
show the lowest concentrations for the NC BTs, compared to the other
clusters during the autumn harvest. When removing the mass concentrations of
BB-related OA (BBOA and OOA-BB), the mean concentration of PM1
(31.6 µg m-3) for the NC BTs is corresponding to a result
(28.7 µg m-3) for a similar cluster during a non-BB period (Huang et al., 2012).
Conclusions
The characteristics, sources, and evolution of atmospheric PM1 species
in urban Nanjing, the YRD region of China, were investigated using an
Aerodyne ACSM during the two harvest seasons, namely, the summer wheat
harvest (June 1 to 15, 2013) and the autumn rice harvest (October 15 to 30,
2013). The PM1 species show a similar contribution, which on average
account for 39 % (41 %) OA, 23 % (20 %) nitrate, 16 % (14 %)
ammonium, 12 % (11 %) sulfate, 8 % (13 %) BC, and 1 % (1 %)
chloride during the summer (autumn) harvest. Secondary inorganic species,
i.e., nitrate, sulfate, and ammonium, show highly similar diurnal patterns
between the summer and autumn harvest, namely, similar chemical
processing and physical processes (e.g., gas-particle partitioning). In
particular, OA, chloride, and BC present higher concentrations in the diurnal
cycles during the autumn harvest than during the summer harvest, due to
larger impacts of BB and/or local primary emissions during the autumn
harvest.
PMF analysis was performed on the ACSM OA mass spectra to investigate
organic source apportionment during the two harvests. Four OA components
were resolved including two POA factors associated with traffic and cooking
(HOA + COA) and biomass-burning OA (BBOA) emissions and two secondary
factors associated with regional and highly oxidized OOA and less oxidized
BB-like OA (OOA-BB). Apart from HOA + COA, BBOA, and OOA-BB also present
pronounced diurnal cycles during the harvests, with the highest
concentrations occurring at night due to the nighttime BB plumes over urban
Nanjing. This suggests that BBOA components may be quickly oxidized to
OOA-BB during the nighttime with the high RH and low T conditions. The
diurnal profiles of OOA are similar to that of sulfate with relatively flat
variations, reflecting their regional origin. OA was dominated by secondary
organics (OOA and OOA-BB) with the fraction more than 60 % to total OA
mass. POA shows a lower contribution to OA during the summer (autumn)
harvest, traffic and cooking 15 % (28 %), and BB 7 % (7 %) emissions.
The background level of f60 (0.26 ± 0.1 %) was determined using
the f44 vs. f60 space during the non-BB periods (in July). Thus, we
suggest a simpler method for estimating the fresh BBOA loadings based on the
equation of BBOA = 15.1 × (m/z 60–0.26 % × OA) during
the harvests. The (BBOA + OOA-BB) / ΔCO ratios decrease with the
increasing of the f44, suggesting that BBOA components may
be oxidized to more aged and less volatile OOA, e.g., LV-OOA during the
aging process. Air mass trajectory analysis indicates that the high PM
pollution is mainly contributed by nitrate, BBOA, and OOA-BB, which is
associated with air masses originating from the western (summer harvest) and
southern (autumn harvest) areas.