Introduction
On the global scale, biomass burning (BB) is the main source of primary
organic carbon (OC) (Bond et al., 2004; Huang et al., 2015), black carbon
(BC) (Bond et al., 2013; Cheng et al., 2016), and brown carbon (BrC) (Laskin
et al., 2015). It is also the second largest source of non-methane organic
gases (NMOGs) in the atmosphere (Yokelson et al., 2008; Stockwell et al.,
2014). In addition, atmospheric aging of biomass burning plumes produces
substantial secondary pollutants. The increase in tropospheric ozone
(O3) in aged biomass burning plumes could last for days and even months
(Thompson et al., 2001; Duncan et al., 2003; Real et al., 2007) with complex
atmospheric chemistry (Arnold et al., 2015; Müller et al., 2016).
Moreover, biomass and biofuel burning could contribute up to 70 % of the
global secondary organic aerosol (SOA) burden (Shrivastava et al., 2015) and
hence influence the seasonal variation of global SOA (Tsigaridis et al.,
2014). Since it produces large amounts of primary and secondary pollutants,
it is essential to characterize primary emissions and photochemical evolution
of biomass burning in order to better understand its impacts on air quality
(Huang et al., 2014), human health (Alves et al., 2015) and climate change
(Andreae et al., 2004; Koren et al., 2004; Laskin et al., 2015; X. Huang et al.,
2016).
Open burning of agricultural residues, a convenient and inexpensive way to
prepare for the next crop planting, could induce severe regional haze events
(Cheng et al., 2013; Tariq et al., 2016). Among all the biomass burning
types, agricultural residue burning in the field is estimated to contribute
∼ 10 % of the total mass burned globally (Andreae and Merlet,
2001), and its relative contribution is even larger in Asia
(∼ 34 %), and especially in China (> 60 %) (Streets et al.,
2003), where > 600 million people live in the countryside (NBSPRC, 2015).
Agricultural residues burned in China were estimated to be up to 160 million
ton in 2012, accounting for ∼ 40 % of the global agricultural
residues burned (J. Li et al., 2016). As estimated by Tian et al. (2011),
agricultural residue burning contributed 70–80 % of non-methane
hydrocarbons (NMHCs) and particulate matter (PM) emitted by biomass burning
in China during 2000–2007. A better understanding of the role agricultural
residual burning plays in air pollution in China and elsewhere requires
better characterization of primary emission and atmospheric aging of emitted
trace gases and particles for different types of agricultural residues under
different burning conditions.
In the past 2 decades, there have been increasing numbers of
characterizations of biomass burning emissions. Andreae and Merlet (2001)
summarized emission factors (EFs) for both gaseous and particulate compounds
from seven types of biomass burning. Akagi et al. (2011) updated the emission
data for 14 types of biomass burning, and newly identified species were
included. Since biomass types and combustion conditions may differ in
different studies, reported emission factors are highly variable, especially
for agricultural residue burning (Li et al., 2007, 2009, 2017; Cao et al.,
2008; Zhang et al., 2008; Yokelson et al., 2011; Brassard et al., 2014;
Sanchis et al., 2014; Wang et al., 2014; Ni et al., 2015; Kim Oanh et al.,
2015; Stockwell et al., 2016; Bruns et al., 2017; Tkacik et al., 2017). Moreover,
previous studies on agricultural residue burning were mostly carried out near
fire spots or in chambers with low dilution ratios. Since biomass burning
organic aerosols (BBOAs) are typically semi-volatile (Grieshop et al., 2009b;
May et al., 2013), it is expected that measured BBOA emission factors would
be affected by dilution processes (Lipsky and Robinson, 2006), and BBOA emission factors
under ambient dilution conditions are still unclear. Furthermore, knowledge
on NMOGs emitted from agricultural residue burning is very limited. As
reported by Stockwell et al. (2015), ∼ 21 % (in weight) of NMOGs in
biomass burning plumes have not been identified yet. Therefore, comprehensive
measurement and characterization of gaseous and particulate species emitted
by agricultural residue burning under ambient dilution conditions are
urgently needed.
Great attention has been drawn to SOA formation and transformation in biomass
burning plumes recently, since a significant increase in mass and apparent
change in physicochemical characteristics of aerosols have been observed
during atmospheric aging of biomass burning plumes in both field and
laboratory studies (Grieshop et al., 2009a, b; Hennigan et al., 2011; Heringa
et al., 2011; Lambe et al., 2011; Jolleys et al., 2012; Giordano et al.,
2013; Martin et al., 2013; Ortega et al., 2013; Ding et al., 2016a, b, 2017).
For agricultural residue burning, evolution processes have not been well
characterized yet. To our knowledge, up to now there has only been a chamber
study (Li et al., 2015) which has investigated the evolution of aerosol
particles emitted by wheat straw burning under dark conditions. Although
field studies (Adler et al., 2011; X. X. Liu et al., 2016) witnessed the evolution
in mass concentrations, size distribution, oxidation state and optical
properties of aerosol particles emitted by agricultural residue burning,
these changes could be also influenced by other emission sources and
meteorological conditions as well. Since NMOGs emitted by agricultural
residue burning are not fully quantified, it is still challenging to predict
the concentration and physicochemical properties of SOA that resulted from
biomass burning (Spracklen et al., 2011; Jathar et al., 2014; Shrivastava et
al., 2015; Hatch et al., 2017). Bruns et al. (2016) suggested that the 22
major NMOGs identified in residential wood combustion could explain the
majority of observed SOA, but it remains unclear whether identified NMOGs
emitted by agricultural residue burning could fully (or at least largely)
explain the SOA formed. In addition, aerosol mass spectrometry (AMS) has been
widely used to characterize sources and evolution of ambient OA (Q. Zhang et
al., 2011). Although agricultural residue burning is an important type of
biomass burning in Asia and especially in China, the lack of AMS spectra for
primary and aged OA from agricultural residue burning significantly limits
further application of AMS in BBOA research.
In this study, plumes from agricultural residue open burning were
directly introduced into a large indoor chamber to firstly characterize
primary emissions and then investigate their photochemical evolution under
∼ 25 ∘C and ∼ 50 % relative humidity. Corn, rice and
wheat straws, which account for more than 90 % of the crop residues
burned in China (FAO, 2017), were chosen. A suite of advanced online and
offline techniques were utilized to measure gaseous and particulate species,
enabling comprehensive measurements of emission factors of gaseous and
particulate compounds for burning of each type of straw under ambient
dilution conditions. In addition, the corresponding formation and
transformation of SOA during photochemical aging were investigated using a
large indoor smog chamber. This work would help improve our understanding of
primary emission, SOA formation and thus environmental impacts of
agricultural residue burning.
Materials and methods
Experimental setup
Photochemical aging was investigated in a smog chamber at the Guangzhou
Institute of Geochemistry, Chinese Academy of Sciences (GIG-CAS). The GIG-CAS
smog chamber is a ∼ 30 m3 fluorinated ethylene propylene (FEP)
reactor housed in a temperature-controlled room. Details of the chamber setup
and associated facilities are provided elsewhere (Wang et al., 2014; Liu et
al., 2015; Deng et al., 2017). Briefly, 135 black lamps (1.2 m long,
60 W Philips, Royal Dutch Philips Electronics Ltd, the Netherlands) are used
as light sources, giving a NO2 photolysis rate of approximately
0.25 min-1. Two Teflon-coated fans are installed inside the reactor to
ensure introduced gaseous and particulate species mixed well within 2 min.
Prior to each experiment, the reactor was flushed with the purified dry air
at a rate of 100 L min-1 for at least 48 h. The compressed indoor air
is forced through an air dryer (FXe1; Atlas Copco; Sweden) and a series of
gas scrubbers containing activated carbon, Purafil, Hopcalite and allochroic
silica gel, followed by a PTFE filter to provide the source of the purified
air. The purified dry air contains < 1 ppb NOx, O3 and carbonyl
compounds, < 5 ppb NMHCs and no detectable particles with relative
humidity < 5 %.
Corn, rice and wheat straws were collected from Henan, Hunan and Guangdong
provinces, respectively. Since moisture content in straws would affect
emission factors of atmospheric pollutants (Sanchis et al., 2014; Ni et al.,
2015), all the agricultural residues used in this study were dried in a stove
at 80 ∘C for 24 h before being burned. After baking, the water
content in the crop residues was less than 1 %. The water content of crop
residues was measured by using the method recommended by Liao et
al. (2004). Straws were
weighed before and after baking in a stove at 105∘ for 24 h, and the
difference in weights was calculated to be the weight of the water in the
crop residues. Water content was the quotient of the water weight and the
whole weight of the straws. In each experiment, ∼ 300 g straws were
burned and the burning typically lasted for 3–5 min. Straws were ignited by
a butane-fueled lighter and burned under open field burning conditions. The
resulting smoke was collected by an inverted funnel and introduced into the
chamber using an oil-free pump (Gast Manufacturing, Inc, USA) at a flow rate
of ∼ 15 L min-1 through a 5.5 m long copper tube (inner
diameter: 3/8 inch), and the residence time in the tube was estimated to be
< 2 s. Before each experiment, the transfer tube was pre-flushed for
15 min with ambient air and 2 min with smokes (not introduced into the
chamber reactor). During the whole process, the tube was heated at
80 ∘C to reduce the losses of organic vapors. Based on the volumes
of the smoke introduced and the chamber reactor, the dilution ratios were
estimated to be 1300–4000, falling into the typical range (1000–10000)
under ambient dilution conditions (Robinson et al., 2007). After being
characterized in the dark for > 20 min, black lamps were turned on and
the diluted smokes were photochemically aged for 5 h. At the end, the black
lamps were switched off and the aged aerosols were characterized in the next
1 h to determine the particle wall loss. The particle size evolved through
the course of photo-oxidation, and the differences in particle wall-loss
rates during photoreaction and after the lamps were off brought about by the
size evolving are estimated to be within ±9 % (Fig. S1 in the
Supplement).
In total 20 experiments were conducted (9 for rice straw, 6 for corn straw
and 5 for wheat straw), among which 14 experiments were conducted only in the
dark to measure primary emissions and 6 experiments were carried out both in
the dark and under irradiation to investigate photochemical evolution of open
straw burning emissions. Tables 1 and 2 summarize important experimental
conditions and key results for all the experiments.
Instrumentation
Commercial instruments were used for online monitoring of NOx (EC9841T,
Ecotech, Australia), NH3 (Model 911-0016, Los Gatos Research, USA) and
SO2 (Model 43i, Thermo Scientific, USA). CH4 and CO were analyzed
offline using gas chromatography (Agilent 6980GC, USA) coupled with a flame
ionization detector and a packed column (5A molecular sieve 60/80 mesh,
3 m × 1/8 inch) (Zhang et al., 2012), and CO2 was analyzed
using a HP 4890D gas chromatograph (Yi et al., 2007). The detection limits
were all less than 30 ppbv for CH4, CO and CO2. The relative
standard deviations (RSDs) of CO and CO2 measurements were both less
than 3 % based on seven duplicate injection of 1.0 ppmv standards
(Spectra Gases Inc, USA). Volatile organic compounds (VOCs) were continuously
measured using a proton-transfer-reaction time-of-flight mass spectrometer
(PTR-TOF-MS; Model 2000, Ionicon Analytik GmbH, Austria). Calibration of the
PTR-TOF-MS was performed every few weeks using a certified custom-made
standard mixture of VOCs (Ionicon Analytik Gmbh, Austria) that were
dynamically diluted to six levels (2, 5, 10, 20, 50 and 100 ppbv). Methanol,
acetonitrile, acetaldehyde, acrolein, acetone, isoprene, crotonaldehyde,
2-butanone, benzene, toluene, o-xylene, chlorobenzene and α-pinene
were included in the calibration mixture. Their sensitivities, indicated by
the ratio of the normalized counts per second to the concentration levels of
the VOCs in ppbv, were used to convert the raw PTR-TOF-MS signal to
concentration (Z. Huang et al., 2016). Quantification of the compounds that were
not included in the mixture was performed by using calculated mass-dependent
sensitivities based on the measured sensitivities (Stockwell et al., 2015).
Mass-dependent sensitivities were linearly fitted for oxygen-containing
compounds and the remaining compounds separately. The decay of toluene
measured by PTR-TOF-MS was used to derive the OH radical concentrations for
every 2 min during each experiment, and the OH exposure was calculated as
the product of the OH concentration and the time interval. Continuous
monitoring of 20 SOA precursors (including 9 NMHCs and 11 oxygen-containing
VOCs) from PTR-TOF-MS provided us with data to do the SOA prediction
discussed in the Sects. 2.3.5 and 3.3.2. Air samples were also collected from
the chamber reactor using 2 L electro-polished stainless-steel canisters
before and after smoke injection. In total 67 C2–C12 NMHCs were
measured (Table S1 in the Supplement) using an Agilent 5973N gas
chromatography mass-selective detector/flame ionization detector (GC-MSD/FID;
Agilent Technologies, USA) coupled to a Preconcentrator (Model 7100, Entech
Instruments Inc., USA), and analytical procedures have been detailed
elsewhere (Wang and Wu, 2008; Zhang et al., 2010, 2012). Results from
GC-MSD/FID were used to quantify the emission factors of 67 NMHCs discussed
in the Sect. 3.1.
Particle number/volume concentrations and size distribution were measured
with a scanning mobility particle sizer (SMPS; Classifier model 3080, CPC
model 3775, TSI Incorporated, USA). The SMPS was operated with a sheath flow
of 3.0 L min-1 and a sampling flow of 0.3 L min-1, allowing for
a size scanning range of 14 to 760 nm within 255 s. A high-resolution
time-of-flight aerosol mass spectrometer (HR-TOF-AMS; Aerodyne Research
Incorporated, USA) was used to measure chemical compositions of
non-refractory aerosols (DeCarlo et al., 2006). The HR-ToF-AMS was operated
by alternating every other minute between the high
sensitivity V mode and the high-resolution W mode. Toolkit Squirrel 1.57I
was used to obtain
real-time concentration variations of sulfate, nitrate, ammonium, chloride
and organics, and toolkit Pika 1.16I was used to determine the detailed
compositions of OA (Aiken et al., 2007, 2008; Canagaratna et al., 2015). The
AMS signal at m/z 44 was corrected for the contribution from gaseous
CO2. The ionization efficiency of the AMS was calibrated routinely by
measuring 300 nm monodisperse ammonium nitrate aerosols. Considering the
underestimation of particulate matter by the AMS, aerosol mass measured by
AMS was corrected with the data from the SMPS and the aethalometer.
Conductive silicon tubes were used for aerosol sampling to reduce
electrostatic losses of particles.
BC was measured with a seven-channel aethalometer (Model AE-31, Magee
Scientific, USA). Cheng et al. (2016) measured the mass absorption efficiency
(MAE) of BC from biomass burning at wavelengths of 532 and 1047 nm,
respectively, and the absorption Ängström exponents (AAEs) were
estimated to be in the range of 0.9–1.1. Based on the relationship between
MAE and wavelength, a MAE value of 4.7 m2 g-1 was calculated for
880 nm by assuming the AAE to be 1.0. The MAE value was then applied to
convert absorption data in 880 nm to BC mass concentrations. Aethalometer
attenuation measurements were corrected for particle loading effects and the
scattering of filter fibers using the method developed by Kirchstetter and
Novakov (2007) and Schmid et al. (2006).
Data analysis
Particle effective density
Assuming that particles are spherical and non-porous, the effective density
(ρeff) can be estimated by Eq. (1) (DeCarlo et al., 2004; Schmid
et al., 2007):
ρeff=ρ0⋅dvadm,
where ρ0 is the standard density (1.0 g cm-3), and
dva and dm are the AMS-measured vacuum aerodynamic
diameter and SMPS-measured mobility diameter. The input diameters to this
equation were determined by comparing distributions of vacuum aerodynamic and
electric mobility diameters, using the AMS and SMPS, respectively. Derived
ρeff was used to convert volume concentrations of aerosol
particles measured by the SMPS to mass concentrations.
Emission factors and modified combustion efficiency
The carbon mass balance approach (Ward et al., 1992; Andreae and Merlet,
2001) was used to calculate fuel-based emission factors (EFs) for each
compound (g kg-1 dry fuel). The emission factor for the ith species,
EFi, is calculated by Eq. (2):
EFi=mi⋅EFCΔ[CO2]+Δ[CO]+Δ[PMC]+Δ[HC],
where mi is the concentration (g m-3) of the ith species;
Δ[CO2], Δ[CO], and Δ[HC] are the
background-corrected carbon mass concentration (g C m-3) of the
CO2, CO, and speciated hydrocarbons, respectively; Δ[PMC] is the background-corrected carbon in the particle phase
(g C m-3); and EFC is the emission factor of carbon into the
air determined by elemental and gravitational analyses, given by Eq. (3):
Primary emission factors measured for agricultural residue burning.
All the units are g kg-1, except that the unit for particle number (PN)
is 1015 particle kg-1. MCE: modified combustion efficiency;
NMHCs: non-methane hydrocarbons; POA: primary organic aerosol; POC primary
organic carbon; BC: black carbon.
Rice
Corn
Wheat
Species
This study (n=9)
Others
This study (n=6)
Others
This study (n=5)
Others
MCE
0.926 ± 0.049
0.953 ± 0.019
0.949 ± 0.035
CO2
1262 ± 81
1477 ± 28
1423 ± 60
CO
63.5 ± 41.4
46.1 ± 19.2
48.6 ± 33.0
NOx
1.47 ± 0.61
3.51 ± 0.38a
5.00 ± 3.94
4.3 ± 1.8b
3.08 ± 0.93
3.3 ± 1.7b;
2.27 ± 0.04a
NH3
0.45 ± 0.15
0.95 ± 0.65a;
0.63 ± 0.30
0.68 ± 0.52b
0.22 ± 0.19
0.37 ± 0.14b;
4.10 ± 1.24c
0.21 ± 0.14a
SO2
0.07 ± 0.07
0.18 ± 0.31d;
0.99 ± 1.53
0.04 ± 0.04d
0.72 ± 0.34
0.04 ± 0.04d;
0.37 ± 0.27e;
0.73 ± 0.15a
1.27 ± 0.35a
NMHCs
5.04 ± 2.04
1.25f
2.47 ± 2.11
1.59 ± 0.43g
3.08 ± 2.43
1.69 ± 0.58g;
0.90f
PM
3.73 ± 3.28
8.5 ± 6.7h;
5.44 ± 3.43
12.2 ± 5.4h;
6.36 ± 2.98
11.4 ± 4.9h;
8.3 ± 2.2e;
11.7 ± 1.0b;
7.6 ± 4.1b;
13.2 ± 1.44i;
5.36 ± 0.55i
5.30 ± 0.30i
4.2c
PN
2.94 ± 0.91
0.018 ± 0.001j
7.29 ± 4.17
0.017 ± 0.001j
5.87 ± 2.89
0.010 ± 0.001j
POA
2.99 ± 1.00
3.99 ± 2.68
5.96 ± 0.19
POC
2.05 ± 0.72
3.3 ± 2.8h;
2.52 ± 1.66
6.3 ± 3.6h;
4.11 ± 0.29
5.1 ± 3.0h;
6.02 ± 0.60i
3.9 ± 1.7b;
2.7 ± 1.0b;
2.06 ± 0.34i
2.42 ± 0.13i
BC
0.22 ± 0.11
0.21 ± 0.13h
0.24 ± 0.09
0.28 ± 0.09h;
0.27 ± 0.07
0.24 ± 0.12h;
0.35 ± 0.10b
0.49 ± 0.12b
a Stockwell et al. (2015); b Li et
al. (2007), PM corresponds to PM2.5; c Christian et
al. (2010); d Cao et al. (2008);
e Kim Oanh et al. (2015), PM corresponds to PM2.5;
f Wang et al. (2014), 56 NMHCs species summarized; g Li
et al. (2009), 52 NMHCs species summarized; h Ni et al. (2015), PM
corresponds to PM2.5; i Li et al. (2017), PM corresponds to
PM1; j Zhang et al. (2008).
EFC=mfuel⋅ωfuel-mash⋅ωashmfuel,
where ωfuel and ωash are mass fractions of
carbon in the dry fuel and its ash, and mfuel and
mash are the mass of dry fuel and its ash. The modified
combustion efficiency (MCE) is defined by Eq. (4) (Heringa et al., 2011;
Hennigan et al., 2011; Ni et al., 2015):
MCE=Δ[CO2]Δ[CO2]+Δ[CO].
(a–c) Non-methane hydrocarbon (NMHC) compositions and
(d–f) their relative contribution to ozone formation potential
(OFP) for open burning of rice, corn and wheat straw.
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Ozone formation potential
The ozone formation potential (OFP) of the speciated NMHCs was calculated
from the emission factor and maximum incremental reactivity (MIR) of each
individual NMHC, using Eq. (5):
OFP=∑i=1n(EFi⋅MIRi),
where OFP is the ozone formation potential of NMHCs emitted per unit of
biomass (unit: g kg-1), and MIRi is the MIR of the ith NMHC
(unit: g O3 g NHMC-1) (Carter, 2008).
Wall-loss corrections
Due to the loss of particles and vapors to chamber walls, measured data in
chamber studies need to be corrected for wall loss. For this purpose, in our
study 1 h dark decay of aged aerosols was undertaken after photochemical
aging was terminated. The loss of particles on the chamber wall is a
first-order process (McMurry and Grosjean, 1985). The wall-loss rates of
AMS-measured organics, sulfate, nitrate, chloride and ammonium were
determined using the dark decay data and were applied to wall-loss correction
for the entire experiment. By assuming that the condensed materials on the
wall remain completely in equilibrium with the gas phase, we used the ω=1 case to correct the OA mass, where ω is a proportionality factor
of organic vapor partitioning to chamber walls and suspended particles
(Weitkamp et al., 2007; Henry et al., 2012). For SMPS measurements, the
number concentration in each size channel (110 channels in total) was
corrected for wall loss separately, since wall-loss rates of aerosol
particles are size-dependent (Takekawa et al., 2003).
OA production prediction
In this study, 20 NMOGs which have been used to estimate SOA yields by
previous work (Ng et al., 2007; Chan et al., 2009, 2010; Hildebrandt et al.,
2009; Gómez Alvarez et al., 2009; Shakya and Griffin, 2010; Chhabra et
al., 2011; Nakao et al., 2011; Borras and Tortajada-Genaro, 2012; Yee et al.,
2013; Lim et al., 2013) were quantified using PTR-TOF-MS, and the applied SOA
yields are summarized in Table S2. The mass concentration of SOA
([SOA]predicted, µg m-3) formed from these 20
precursors can be estimated using Eq. (6):
[SOA]predicted=∑i(Δ[Xi]⋅Yi),
where Δ[Xi] (µg m-3) is the reacted amount of the
ith gas-phase precursor and Yi is the corresponding SOA yield.
Assuming that primary OA (POA) levels stayed constant during aging processes,
the mass concentration of SOA formed could be estimated as the difference in
OA mass concentrations before and after photochemical aging. It should be
noted that POA would decrease during aging processes (Tiitta et al., 2016),
probably leading to the underestimation of the formed SOA. In papers where
those SOA yields were borrowed from, no organic vapor wall loss was accounted
for when calculating the mass concentration of the formed SOA, so the same
wall-loss correction method was used when comparing the predicted SOA and the
formed SOA.
Results and discussion
Emissions of gaseous pollutants
Table 1 compares emission factors of gaseous and particulate species measured
in our and previous studies. In our study, the emission factors of NOx
were 1.47 ± 0.61, 5.00 ± 3.94, and
3.08 ± 0.93 g kg-1 for rice, corn, and wheat straw, and NO
accounted for 84 ± 11 % of NOx primary emission for all
experiments. Emission factors of NH3 were measured to be
0.45 ± 0.15, 0.63 ± 0.30 and 0.22 ± 0.19 g kg-1 for
rice, corn and wheat straw. Our measured emission factors of reactive
nitrogen species were comparable to those reported by previous studies (Li et
al., 2007; Tian et al., 2011). Emission factors of SO2 were
0.07 ± 0.07, 0.99 ± 1.53 and 0.72 ± 0.34 g kg-1 for
rice, corn and wheat straw. Our measured emission factors of SO2 were
lower than those reported by Cao et al. (2008) and Kim Oanh et al. (2015) for
rice straw, but higher than those reported by Cao et al. (2008) for corn and
wheat straw. Due to low sulfur contents in crop straws, the SO2 emission
factors for open burning of crop residues were much lower than those for
domestic coal combustion, which were determined to be
2.43–5.36 g kg-1 for raw bituminous coal (Du et al., 2016).
Emission factors (EFs) of NMHCs for straw burning of rice, corn and
wheat. Only species with emission factors > 0.01 g kg-1 are shown.
The order of NMHC species is the same as Table S1, in which a comprehensive
dataset of emission factors measured in this work is included.
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Emission factors of the total speciated NMHCs analyzed by the GC-MSD/FID system were 5.04 ± 2.04,
2.47 ± 2.11 and 3.08 ± 2.43 g kg-1 for rice, corn and
wheat straw, respectively (Table 1). Our results were higher than those
reported by previous studies (Li et al., 2009; Wang et al., 2014), partly due
to the fact that more NMHCs were analyzed in our study (67 species in total).
As shown in Fig. 1a–c, olefins and acetylene accounted for 56–58 % of
the total speciated NMHCs, followed by alkanes (22–28 %) and aromatic
hydrocarbons (16–21 %). Table S1 and Fig. 2 show the emission factors of
each NMHC for open burning of different straws. Emission factors of
unsaturated hydrocarbons ranged from 1.37 (corn) to 2.91 g kg-1
(rice), with the majority being ethene, acetylene and propene. Emission
factors of alkanes ranged from 0.69 (corn) to 1.09 g kg-1 (rice), with
ethane and propane being the two most abundant compounds. The emission
factors of aromatic hydrocarbons were in the range of 0.42 (corn) to 1.04
(rice), and benzene and toluene are dominant species. It is worth noting that
major compounds in the three groups (alkanes, alkenes and aromatic
hydrocarbons) were all negatively correlated with the modified combustion
efficiency (Fig. S2), suggesting that more efficient combustion would reduce
their emissions.
Based on their emission factors, we calculated the ozone formation potential
for each NMHC. The summed ozone formation potentials were 22.5 ± 10.1,
13.7 ± 12.4 and 16.3 ± 13.5 g kg-1 for open burning of
rice, corn and wheat straw, respectively. As shown in Fig. 1d–e, the
relative contributions of olefins to the total ozone formation potential
could reach > 80 %. Ethene was the largest ozone precursor
(35–42 %), followed by propene (16–28 %), and these two compounds
contributed 58–64 % of the total ozone formation potential. Although the
emission factors of aromatic hydrocarbons were lower than those of alkanes,
their ozone formation potential was dominant over those of alkanes, with
toluene being the largest contributor among all the aromatic hydrocarbons.
The contribution of alkanes to the total ozone formation potential was minor
(2–3 %). It is noted that oxygen-containing organic vapors in
agricultural residue burning plumes could also have large ozone formation
potentials. For example, the OFPs of formaldehyde and acetaldehyde for all
experiments were 0.57–2.46 times those of the 67 speciated NMHCs.
Emission of particulate matter
The emission factors of particulate matter were 3.73 ± 3.28,
5.44 ± 3.43 and 6.36 ± 2.98 g kg-1 for rice, corn and
wheat straw, lower than those reported in the previous studies (Table 1). As
suggested by Robinson et al. (2007), the POA emission factors would decrease
with increasing dilution ratios, due to evaporation of semi-volatile organic
compounds. In this study, the dilution ratios ranged from 1300 to 4000, which
were within the typical range of ambient dilution ratios (1000–10 000)
(Robinson et al., 2007). Therefore, it can be expected that emission factors
of primary organic carbon (POC) measured in our study
(2.05–4.11 gC kg-1) were lower than those measured by previous work
with dilution ratios of 5–20 (Li et al., 2007; Ni et al., 2015). Moreover,
it has been shown that the modified combustion efficiency could affect
emission factors (Heringa et al., 2011; Stockwell et al., 2015). Figure S3
shows negative correlations of the modified combustion efficiency with
emission factors of PM and POC (p < 0.05 for both cases), indicating
that enhancement of combustion efficiency could reduce the emissions of PM
and POC. In our study, all straws were pre-baked to reduce the moisture
content to < 1 %, and this treatment could increase the modified
combustion efficiency and thus reduce emission factors of particulate matters
(Ni et al., 2015). In addition, the amount of straw burned each time in our
experiments was much less than that in the fields, which is expected to avoid
oxygen deficit during burning to some extent and thus increase the modified
combustion efficiency as well.
While POA emission factors showed large variability for different types of
straw, BC emission factors were relatively constant
(0.22–0.27 gC kg-1). Since BC is a mixture of non-volatile compounds
in particulate matters, as expected, its emission factors measured in our
work were comparable to those reported under lower dilution conditions (Li et
al., 2007; Ni et al., 2015). The Δ[POA] / Δ[CO] ratios
ranged from 0.022 to 0.133 in our study, larger than those (0.001–0.067)
measured in chamber studies for hard- and soft-wood fires (Grieshop et al.,
2009b) and vegetation commonly burned in North American wildfires (Heringa et
al., 2011), but lower than those (0.051–0.329) obtained in field campaigns
(Jolleys et al., 2012).
For particle numbers, the emission factors were
(2.94 ± 0.91) × 1015,
(7.29 ± 4.17) × 1015, and (5.87 ± 2.89) ×
1015 particle kg-1 for rice, corn, and wheat straw, respectively
(Table 1). Our results were comparable to that
(1 × 1015 particle kg-1) for crop residue burning
(Andreae and Merlet, 2001) and those
(3.2 × 1015–10.9 × 1015 particle kg-1)
for wood burning (Hosseini et al., 2013), but 2 magnitudes larger than those
for crop residue burning in a sealed stove (Zhang et al., 2008).
Evolution of particles
Growth of particle size
Figure 3 shows the evolution of particle size distribution after
photochemical aging of 0, 0.5, 2.5 and 5 h. Aerosol particles emitted from
open straw burning peaked at 50–90 nm under ambient dilution conditions.
The geometric mean diameters for primarily emitted particles in this study
were smaller than those (100–150 nm) reported for crop residual burning
under low dilution conditions (H. Zhang et al., 2011; Li et al., 2015), probably
due to evaporation of organic vapors under the high dilution conditions
(Lipsky et al., 2006) and coagulation of fine particles under the low
dilution conditions (Hossain et al., 2012).
Particle size distributions in different burnings.
(a) Burn 2: rice straw; (b) Burn 3: corn straw;
(c) Burn 5: wheat straw.
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After switching on black lamps, apparent growth of particle size was
observed. In all the aging experiments, growth rates of particle diameters in
the first 0.5 h were 10 times larger than those afterwards, and after 5 h
aging the geometric mean diameters peaked at 60–120 nm. For instance, in
the photochemical aging experiment for wheat straw burning (Fig. 3c), the
growth rate of particles was 18 nm h-1 in the first 0.5 h and
decreased to ∼ 1 nm h-1 during the following 4.5 h. The size
distribution of aged aerosol particles in our study is similar to those of
ambient particles under the severe biomass burning impact during haze events
(Betha et al., 2014; Niu et al., 2016).
Particle mass enhancement
Figure 4 shows the chemical evolution of aerosol particles during the 5 h
photochemical aging of wheat straw burning. During the whole process, OA kept
increasing and was dominant over inorganic species. After 3 h of
photochemical aging, the levels of all the inorganic species were constant,
and nitrate was the second most abundant component, with a mass fraction of
7 %, followed by chloride (2 %), ammonium (1 %) and sulfate
(< 1 %). Figure 4b depicts [OA] evolution as a function of OH
exposure. OA increased slowly in the first ∼ 0.2 h, and then increased
rapidly with OH exposure.
(a) The evolution of particulate matter components
(Burn 2). (b) OA mass growth as a function of OH exposure (Burn 5).
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The OA enhancement ratio, defined as the mass ratio of aged OA at the end of
each aging experiment to POA, was calculated. In the six aging experiments,
the OH exposure and OA enhancement ratios ranged from
(1.87–4.97) × 1010 molecule cm-3 s to 2.4–7.6,
respectively. Assuming an average OH concentration of
1.5 × 106 molecule cm-3 in the ambient air (Hayes et
al., 2013), this means that rapid SOA formation would occur in 3.5–9.2 h
during the daytime after straw burning. The OA enhancement ratios determined
in our study were higher than those (0.7–2.9) for the combustion of
vegetation commonly burned in North American wildfires (Hennigan et al.,
2011), and comparable to those (0.7–6.9) for wood burning (Grieshop et al.,
2009b; Heringa et al., 2011).
Recently, Bruns et al. (2016) found that 22 NMOGs emitted from residential
wood burning could explain the majority of the formed SOA. In our study, 20
of the 22 NMOGs were detected and quantified with the PTR-TOF-MS.
Concentration differences of each compound before and after photo-oxidation
were calculated to estimate the SOA formed from these precursors. Since SOA
formation highly depends on oxidation conditions, SOA yields for a certain
precursor vary with VOC / NOx ratios. In our work, we chose a set of
SOA yields for these NMOGs based on the observed VOC / NOx ratio in
the chamber experiments. More specifically, if the observed
VOC / NOx ratio for a certain precursor in the chamber was within
the VOC / NOx range reported in the literature, the mean value of
the highest and lowest yields within the VOC / NOx range in the
literature was used to estimate the SOA formed from the precursor in the
chamber; if the observed VOC / NOx ratio for a certain precursor was
higher than the maximum VOC / NOx ratio reported in the literature,
we chose the yield reported at the maximum VOC / NOx ratio; if the
observed VOC / NOx ratio was lower than the minimum
VOC / NOx ratio reported in the literature, we chose the yield
reported at the minimum VOC / NOx ratio.
Figure 5a shows the time series of POA, SOApredicted and
unexplained SOA in a typical aging experiment. The contribution of
SOApredicted by the 20 NMOGs was minor, and large fractions of
observed SOA could not be explained. In all the experiments, only
5.0–27.3 % of the observed SOA mass could be explained by the 20 NMOGs
(Fig. 5b). Even if the highest SOA yield for each precursor reported in the
literature were used, 60–90 % of the observed SOA mass could still not
be explained. It has been suggested that aqueous-phase oxidation of alkenes
could produce substantial SOA (Ervens et al., 2011). Considering large
emissions of olefins from straw burning (Fig. 1a–c), we also estimated the
SOA formed from the three most abundant alkenes (ethene, acetylene, and
propene) with their newly developed SOA yields (Ge et al., 2016, 2017; Jia
and Xu, 2016), and their total contribution to the observed SOA was found to
be negligible (< 0.5 %). It is noted that although over 80 VOCs
species were quantified by the GC-MSD/FID and the PTR-TOF-MS in this study,
only 20 species among them were taken into the SOA prediction because of the
lack of published data for SOA yields. The unaccounted VOC species might be a
reason for the discrepancy. On the other hand, as indicated by Deng et
al. (2017), SOA yields obtained from chamber studies in purified air matrix
might be lower than that in a real ambient air matrix. Consequently, using
SOA yields from studies in a purified air matrix might also underpredict SOA
yields in the complex biomass burning plume matrix. Moreover, oxidation of
particulate organic matter (POM), like semi-volatile organic compounds
(SVOCs) and intermediate volatility organic compounds (IVOCs), would also
contribute substantially to SOA formation (Presto et al., 2009; Zhao et al.,
2014), yet this is not accounted for in our prediction. Above all, there are
still unknown precursors and/or physicochemical processes contributing the
majority of SOA formed from open straw burning.
(a) Time series plots of concentrations of POA, secondary
organic aerosol that can be explained by the reacted precursors
(SOApredicted), the difference between the formed SOA and the
predicted SOA (unexplained SOA) in Burn 6. (b) Contribution of
20 NMOGs to the formed SOA at the end of photoreactions. Error bars
correspond to the range of contributions when the lowest/highest SOA yields
in references were used for all precursors.
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OA mass spectrum evolution
In the high-resolution W mode of AMS, ions generated from particles could be
identified by their exact mass–charge ratio (m/z) and then grouped into
the CHON, CHO, CHN and CH families. Figure 6 presents the evolution of OA
mass spectra. For POA (Fig. 6a), the CH-family was the major component, with
a mass fraction of 68 %, followed by CHO (23 %), CHN (6 %), and
CHON (2 %). The ions at m/z 43, 41 and 55 were the dominant peaks in
the POA mass spectrum. The major ions at m/z 27, 39, 41, 55, 57, 67 and 69
belonged to the CH-family and could be the fragments of hydrocarbons (Weimer
et al., 2008). The peaks at m/z 28, 29, 43, 44 and 55 contained
considerable CHO ions, and the corresponding ions (CO+, CHO+,
C2H3O+, CO2+ and C3H3O+) could be the
fragments of aldehydes, ketones and carboxylic acid (Ng et al., 2011a). The
peak at m/z 91 was mainly attributed to C7H7+, possibly
originating from aromatic compounds.
(a) Mass spectrum of POA; (b) mass spectrum of
aged OA; (c) difference in mass spectra between aged OA and POA. The
data were all taken from Burn 5.
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The mass spectra of aged OA were quite different from those of POA
(Fig. 6b–c). The mass fraction of the CH-family decreased to 46 % and
was comparable to that of the CHO-family, while the contribution of
N-containing OA (CHN and CHON) increased to ∼ 11 %. The ions at
m/z 44 and 43, mainly coming from the CHO-family, became the dominant
peaks for the aged OA. The fractions of two major masses at m/z 44
(f44) and m/z 43 (f43) in OA can be used to generate an f44
vs. f43 triangular space, in which oxygenated organic aerosol (OOA)
moves towards the apex during the aging process (Ng et al., 2010). In
addition, f44 in the ambient air was suggested to be 0.07 ± 0.04
for semi-volatile OOA (SV-OOA) and 0.17 ± 0.04 for low-volatility OOA
(LV-OOA), respectively (Ng et al., 2010). Figure 7a plots f44 and
f43 of the POA and the aged OA in all six experiments. Most of the data
are within the f44 vs. f43 triangular space and close to the left
margin. Photochemical aging led to an increase in f44 for all the
experiments, suggesting transformation of OA from SV-OOA to LV-OOA. For
comparison, the f43 did not change significantly in all the experiments.
The main ions at m/z 43 were C2H3O+ and C3H7+.
It can be observed in Fig. 6c that the increased contribution of
C2H3O+ and the decreased contribution of C3H7+
were comparable during photoreaction.
(a) Comparison of f44 vs. f43 determined in our
work with those for the ambient BBOA data sets (Ng et al., 2011b) and the
ambient OOA range (Ng et al., 2010). The typical f44 ranges of ambient
SV-OOA and LV-OOA are indicated with the vertical arrows.
(b) Comparison of f44 vs. f60 for straw burning OA with
those for other types of biomass burning OA (Alfarra et al., 2007; Hennigan
et al., 2011; Cubison et al., 2011; Brito et al., 2014; May et al., 2015).
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Overview of important experimental conditions and key results in the
photochemical oxidation experiments. The unit for OH exposure is
1010 molecule cm-3 s. NA: data were not available because no
data were recorded in the W-mode.
NO.
Straw type
Temp (∘C)
RH (%)
OH
POA
Aged OA
OA
exposure
O / C
H / C
OSc
O / C
H / C
OSc
ER
Burn 1
Rice
25.0 ± 0.4
48.9 ± 1.4
3.80
NA
NA
NA
NA
NA
NA
2.7
Burn 2
Rice
25.1 ± 0.4
55.0 ± 2.3
4.97
0.25
1.74
-1.25
0.50
1.65
-0.65
7.6
Burn 3
Corn
25.5 ± 0.4
53.0 ± 2.9
4.16
0.38
1.66
-0.89
0.60
1.66
-0.46
3.6
Burn 4
Corn
26.1 ± 0.4
48.4 ± 2.2
4.16
0.30
1.58
-0.97
0.65
1.57
-0.26
4.6
Burn 5
Wheat
25.3 ± 0.5
52.8 ± 2.2
3.20
0.20
1.66
-1.25
0.50
1.56
-0.55
2.4
Burn 6
Wheat
25.2 ± 0.4
55.1 ± 2.7
1.87
0.26
1.71
-1.20
0.53
1.66
-0.61
6.6
The ion at m/z 60, mainly consisting of C2H4O2+, is
regarded as a BBOA marker, and the mass fraction of this ion in OA, f60,
is widely used to probe the evolution of BBOA (Brito et al., 2014; May et
al., 2015). Figure 7b plots the evolution of f44 and f60 in all the
experiments conducted in this study, in order to compare them with
measurements in aging biomass burning plumes (Cubison et al., 2011) and those
in the POA from different types of biomass burning (Alfarra et al., 2007;
Brito et al., 2014; May et al., 2015). Photo-oxidation caused an increase in
f44 and a decrease in f60, and this is consistent with the general
evolution of OA in ambient biomass burning plumes (Cubison et al., 2011).
However, our measured f60, 0.003–0.006 in the POA from open straw
burning and 0.002–0004 in aged OA, were all lower than those from other
field campaigns and quite near the background f60 level of 0.003 for
ambient OA (Cubison et al., 2011; Fig. 7b). Low values of f60
(0.005–0.02) were also reported by Hennigan et al. (2011) in a chamber study
for fuels commonly burned in wildfires. In their study, biomass burning took
place in a 3000 m3 combustion chamber, and the smokes were then
injected into another chamber for aging experiments with a dilution ratio of
∼ 25. Previous studies have demonstrated that levoglucosan is a
semi-volatile compound with a saturation concentration of
∼ 8 µg m-3 at 293 K (Grieshop et al., 2009b; Huffman
et al., 2009; Hennigan et al., 2011). As a result, the high dilution
conditions used in our study would cause levoglucosan to evaporate, and this
may at least partly explain the low f60 observed in the POA from straw
burning. From previous studies, the levoglucosan / OC ratios of straw
burning ranging from 4.92 to 16.8 % (4 types of vegetation summarized;
Dhammapala et al., 2007; Kim Oanh et al., 2011; Hall et al., 2012) were not
significantly (two-sample t-test, p > 0.05) lower than those of
prescribed fuel burning, wildfire and wood burning ranging from 1.46 to
13.5 % (20 types of vegetation summarized; Hosseini et al., 2013; Shahid
et al., 2015). So the difference in fuel type cannot explain the lower
f60 observed in our study.
Elemental ratio and oxidation state of OA
In this study, the O / C and H / C ratios in the POA from different
straws burning were in the ranges of 0.20–0.38 and 1.58–1.74, respectively.
After 5 h aging, O / C increased and H/C decreased (Table 2). Kroll et
al. (2011) proposed a metric, the average carbon oxidation state
(OSc), to describe the degree of oxidation of atmospheric organic
species. OSc could be calculated from the elemental composition of
OA measured by AMS, given by Eq. (7):
OSc=2×O/C-H/C.
In this study, the OSc values for the fresh POA from open straw
burning ranged from -1.25 to -0.89, consistent with those suggested for
BBOA (-1 to -0.7) (Kroll et al., 2011). During photochemical aging, the
OSc values increased linearly (p < 0.001) with OH exposure
(Fig. 8), and the slopes were quite near each other even for different types
of straws, implying AMS measured OSc might be a good indicator of
OH exposure and thereby of photochemical aging.
The growth of the OA carbon oxidation state with OH exposure for
burning corn (Burns 3 and 4) and wheat (Burns 5 and 6) straws. Data for
burning rice straws were not included since in Burn 1 AMS was then not run in
W-mode.
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Figure 9 shows the Van Krevelen diagram of OA. In this study, the slopes of
linear correlations between H / C and O / C range from -0.49 to
-0.24 for the five experiments. Slopes of -1, 0.5 and 0 in the Van
Krevelen diagrams indicate addition of carboxylic acids without
fragmentation, addition of carboxylic acids with fragmentation, and addition
of alcohols/peroxides, respectively (Heald et al., 2010; Ng et al., 2011a).
Therefore, the slopes determined in our study suggest that open straw burning
OA aging resulted in net changes in chemical composition equivalent to
addition of carboxylic acid groups with C–C bond breakage and addition of
alcohol/peroxide functional groups.
Van Krevelen diagram for the OA. Each slope corresponds to the
addition of a specific functional group to an aliphatic carbon.
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Conclusion
In this study, primary emissions of open burning of rice, corn and wheat
straw and their photochemistry were investigated using a large indoor
chamber. Emission factors of NOx, NH3, SO2, 67 NMHCs, PM and
particle number were measured under dilution ratios ranging from 1300 to
4000. Emission factors of PM (3.73–6.36 g kg-1) and POC
(2.05–4.11 gC kg-1) were lower than those reported in previous
studies conducted at lower dilution ratios, probably due to the evaporation
of semi-volatile organic compounds. Emission factors of POC, PM and major
NMHC compounds were all negatively correlated with the modified combustion
efficiency, suggesting that incomplete burning of agricultural residues could
lead to larger primary emission.
Both agricultural residue burning and domestic coal combustion have been
recognized as contributing substantially to the deteriorating regional air
quality, especially in rural areas of China (Pan et al., 2015; J. Liu et al.,
2016; Zhu et al., 2016). The emission factors of the speciated NMHCs, PM,
NOx, CO and SO2 from combustion of raw bituminous coal, which is currently prevalent
for cooking and heating in rural areas, have been reported to be 0.56–5.40,
25.49 ± 2.30, 0.97 ± 0.03, 208 ± 5 and
2.43–5.36 g kg-1, respectively (Du et al., 2016; Li et al., 2016; Liu
et al., 2017). Annually burned crop residues and domestic coals were
estimated to be 160 Tg (Q. Li et al., 2016) and 99.6 Tg (NBSPRC, 2014) in
China. Therefore, with the emission factors of the speciated NMHCs
(2.47–5.04 g kg-1), PM (3.73–6.36 g kg-1), NOx
(1.47–5.00 g kg-1), CO (46.1–63.5 g kg-1) and SO2
(0.07–0.99 g kg-1) measured for agricultural residue burning in this
study, agricultural residue burning might emit more NMHCs and NOx but
less primary PM, CO and SO2 than domestic coal burning on a national
scale.
Photochemical aging of primary emissions was investigated with OH exposure
equal to 3.2–9.2 h under typical ambient conditions, and at the end of
experiments the OA mass concentrations increased by a factor of 2.4–7.6,
suggesting that SOA could be rapidly produced within several hours. Our
estimation suggests that phenols are the most important identified SOA
precursors, and more than 70 % of the formed OA still cannot be explained
by the oxidation of known precursors. Measurements using HR-TOF-AMS reveal
that after photochemical aging, signals for oxygen- and nitrogen-containing
compounds were largely increased, with OSc increased in a highly
significant linear way with OH exposure.