ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-21-15221-2021Formation and evolution of secondary organic aerosols derived from urban-lifestyle sources: vehicle exhaust and cooking emissionsSecondary organic aerosols from urban-lifestyle sourcesZhangZiruiZhuWenfeiHuMinminhu@pku.edu.cnLiuKefanWangHuiTangRongzhiShenRuizheYuYingTanRuiSongKaiLiYuanjuZhangWenbinZhangZhouXuHongmingShuaiShijinLiShuangdeChenYunfaLiJiayunWangYuesiGuoSonghttps://orcid.org/0000-0002-9661-2313State Key Joint Laboratory of Environmental Simulation and Pollution
Control, International Joint Laboratory for Regional Pollution Control,
Ministry of Education (IJRC), College of Environmental Sciences and
Engineering, Peking University, Beijing 100871, ChinaCollaborative Innovation Center of Atmospheric Environment and
Equipment Technology, Nanjing University of Information Science &
Technology, Nanjing 210044, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua
University, Beijing 100084, ChinaState Key Laboratory of Multiphase Complex Systems, Institute of
Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaBeijing Innovation Center for Engineering Sciences and Advanced
Technology, Peking University, Beijing 100871, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing 100029, China
Vehicle exhaust and cooking emissions are closely related to the daily life
of city dwellers. Here, we defined the secondary organic aerosols (SOAs)
derived from vehicle exhaust and cooking emissions as “urban-lifestyle SOAs”
and simulated their formation using a Gothenburg potential aerosol mass
reactor (Go:PAM). The vehicle exhaust and cooking emissions were separately
simulated, and their samples were defined as “vehicle group” and “cooking
group”, respectively. After samples had been aged under 0.3–5.5 d of
equivalent photochemical age, these two urban-lifestyle SOAs showed markedly
distinct features in the SOA mass growth potential, oxidation pathways, and
mass spectra. The SOA/POA (primary organic aerosol) mass ratios of vehicle
groups (107) were 44 times larger than those of cooking groups (2.38) at
about 2 d of equivalent photochemical age, according to the measurement
of scanning mobility particle sizer (SMPS). A high-resolution time-of-flight
aerosol mass spectrometer was used to perform a deeper analysis. It revealed
that organics from the vehicle may undergo the alcohol and/or peroxide and
carboxylic acid oxidation pathway to produce abundant less and more oxidized
oxygenated OAs (LO-OOAs and MO-OOAs), and only a few primary hydrocarbon-like
organic aerosols (HOAs) remain unaged. In contrast, organics from cooking may
undergo the alcohol and/or peroxide oxidation pathway to produce moderate LO-OOAs,
and comparable primary cooking organic aerosols (COAs) remain unaged. Our
findings provide an insight into atmospheric contributions and chemical
evolutions for urban-lifestyle SOAs, which could greatly influence the air
quality and health risk assessments in urban areas.
Introduction
Organic aerosols (OAs) contribute 20 %–90 % of submicron aerosols in mass
(Jimenez et al., 2009; Zhang et al., 2011), and their fraction in urban
areas is higher than that in suburban or background areas (Zhou et al., 2020).
The OAs can be divided into primary organic aerosols (POAs) and
secondary organic aerosols (SOAs). There are many potential sources of POAs,
such as coal combustion, biomass burning, vehicle exhaust, cooking
procedure, and so forth (Jimenez et al., 2009; Zhang et al., 2011; Zhou et
al., 2020). SOAs are formed via the oxidation of gas-phase organics and the
distribution between the gas and particle phase (Donahue et al.,
2009). Significant SOA formation has been observed in several urban areas,
but models typically fail to simulate this phenomenon accurately (Matsui
et al., 2009; Kleinman et al., 2008; Volkamer et al., 2006; de Gouw et al.,
2008). This discrepancy may be attributed to the limited knowledge of the
sources and characteristics of urban SOAs.
Over the past decades, megacities have already become widespread in developed
regions, and rapid urbanization has been sweeping across the globe
especially in developing areas (Zhang et al.,
2015). An increasing number of people tend to live in urban areas for their
livelihood, where they suffer from serious air pollution typically simultaneously involving vehicle and cooking fumes (An
et al., 2019; Zhang et al., 2015; Chan and Yao, 2008; Guo et al., 2014, 2020). For instance, polycyclic aromatic hydrocarbons (PAHs) are
important carcinogens coming from vehicles and cooking, which can cause
severe lung cancer (Seow et al., 2000; Kim et al., 2015; Zhong et al.,
1999). After PAHs are emitted into ambient air, they can be oxidized,
be distributed into the particle phase, and finally become part of POAs or SOAs, thus
adding unknown deviations to health risk assessments
(Masuda et al., 2020).
Vehicle and cooking emissions are important sources of OAs in urban areas
(Rogge et al., 1991, 1993; Hu et al., 2015; Hallquist et al.,
2016; Crippa et al., 2013; Mohr et al., 2012; Guo et al., 2013,
2012). Take the megacity (defined as a total metro area population of more than 3 million) for example, in London, where these two lifestyle sources contribute 50 % of
OAs on average (Allan et al., 2010). In addition, vehicles themselves could
even contribute 62 % of OA mass in the rush hour of New York City (Sun
et al., 2012). As for OA source appointments in Paris, vehicle and cooking
contribute a maximum of 46 %–50 % of OAs (Crippa et al., 2013). According to
seasonal observations in Beijing, at least 30 % of OAs there come
from vehicle and cooking emissions (Hu et al., 2017). Briefly, these
two urban-lifestyle sources are closely related to the daily life of city
residents and could account for 20 %–60 % of ambient OA mass in urban areas
when only considering their contributions to POAs (Allan et al., 2010; Sun
et al., 2011; Ge et al., 2012; Sun et al., 2012; Lee et al., 2015; Hu et al.,
2017). Furthermore, it has been speculated that vehicle and cooking emissions
might even contribute over 90 % of SOAs in downtown Los Angeles by applying
hypothetical model parameters with a certain degree of uncertainty (Hayes et al., 2015). Therefore, vehicle
and cooking are momentous sources of both POAs and SOAs in urban areas and
could be defined as “urban-lifestyle sources of OAs”.
As is well-known, large quantities of volatile, semi-volatile, and
intermediate-volatility organic compounds (VOCs, SVOCs, and IVOCs,
respectively) are emitted from vehicle and cooking sources, leading to
large potential SOA production (Klein et al., 2016; Katragadda et al.,
2010; Liu et al., 2017c; Tang et al., 2019; Zhao et al., 2015; Esmaeilirad and
Hosseini, 2018; Zhao et al., 2017; Yu et al., 2020). Laboratory studies have
investigated the formation of vehicle or cooking SOAs using a smog chamber or
an oxidation flow reactor (OFR). On the one hand, some laboratory
experiments have investigated the vehicle SOAs based on variables such as
fuel types, engine types, operating conditions, and so on (Deng et al.,
2020; Suarez-Bertoa et al., 2015; Zhao et al., 2015; Du et al., 2018). Several
smog chamber studies have found that the mass loading of SOAs exceeds that of POAs when
the equivalent photochemical age is more than 1 d (Gordon et al.,
2013; Chirico et al., 2010; Nordin et al., 2013). Besides, an OFR could simulate
a higher OH exposure, and the peak SOA production occurs after 2–3 d of
equivalent atmospheric oxidation (Tkacik et al., 2014; Zhao et al.,
2018; Timonen et al., 2017; Watne et al., 2018; Alanen et al., 2017). The mass
spectra of vehicle SOAs has shown both semi-volatile and low-volatility
oxygenated organic aerosol (SV-OOA and LV-OOA) features along with the
growth of oxidation degree (Tkacik et al., 2014). NOx levels may
greatly influence the chemical evolution of vehicle SOAs, and their
NOx and VOC values are often strongly dependent on the sampling time and
place in urban areas (Zhan et al., 2021; Wei et al., 2014). It has been found
that the photochemical ages for maximum SOA production under high-NOx
levels are lower than those under low-NOx levels among OFR simulations
(Liao et al., 2021). On the other hand, only a few laboratory experiments
have investigated the cooking SOAs based on simplified ingredients or a
single cooking method, involving heated cooking oils (Liu et al.,
2017a, 2018), stir-frying spices (Liu et al., 2017b),
charbroiled meat (Kaltsonoudis et al., 2017), and Chinese cuisines
(Z. Zhang et al., 2020). These laboratory experiments have indicated that
the characteristics of SOAs are influenced by multiple factors, such as
cooking methods, fuels, cookers, or ingredients. The mass ratios of POAs and
SOAs derived from cooking are comparable, and the mass spectra of SOAs show
many more similarities with the ambient semi-volatile oxygenated OA (SV-OOA)
factors (Liu et al., 2018). Although these laboratory studies have
provided important insights into the secondary formation of vehicle and
cooking SOAs, significant uncertainties still exist. Nobody has compared the
different natures generated from these two urban-lifestyle sources in
detail, let alone pointed out their potentially different roles in the real
atmosphere.
In this work, we have designed our vehicle and cooking laboratory
experiments according to daily basis situations in urban areas of China. For
vehicle exhaust simulation, China Phase V gasoline and three common
operating conditions were chosen. For cooking emissions simulation, four
prevalent Chinese domestic cooking types were evaluated. A Gothenburg
potential aerosol mass reactor (Go:PAM) was used as the oxidation system.
All the fresh or aged OAs were characterized in terms of the mass growth
potential, elemental ratios, oxidation pathways, and mass spectra. The aged
OAs could be divided into POAs and SOAs. The latter was defined as “urban-lifestyle SOAs” whose mass spectra would be compared with those of ambient
SOAs, like less oxidized oxygenated OAs (LO-OOAs) and more oxidized oxygenated
OAs (MO-OOAs) measured in urban areas of China. These findings aim to support
the estimation of these two urban-lifestyle SOAs in ambient air, informing
the policy formulation of pollution source control and the health risk
assessment of exposure to vehicle and cooking fumes.
Materials and methodExperimental setup
The vehicle experiment was conducted from July to October in 2019 at the
Department of Automotive Engineering, Tsinghua University. The cooking
experiment was conducted from November 2019 to January 2020 at Langfang
Branch, Institute of Process Engineering, Chinese Academy of Sciences. The
laboratory simulations of the two urban-lifestyle SOAs were conducted with the
same oxidation and measurement system. Tables 1–2 contain information on
vehicle and cooking experiment conditions. The vehicle exhaust was emitted
from a gasoline direct injection (GDI) engine with China V gasoline (similar to Euro 5) under three speeds (20, 40, 60 km h-1), which represented the urban road
conditions in China (Zhang et al., 2020a). The commercial China Phase V
gasoline was used as the fuel, which has an equivalent octane number 92 level
(RON 92), 10 ppm (v/v, max) sulfur, 25 % (v/v, max) olefin, about 40 %
(v/v, max) aromatics, 2 mg L-1 of Mn, and no oxygenates (Yinhui et al.,
2016). More information about the GDI engine can be found in Tables S2–S3 in the Supplement.
For all experiments, the GDI engine ran in a single room; its exhaust was
drawn into the pipeline and then entered Go:PAM at a 30-fold dilution
where aerosols and gases reacted at a stable temperature and relative
humidity. On the other hand, four kinds of domestic cuisines were cooked
with liquefied petroleum gas (LPG) in an iron wok, including deep-frying
chicken; shallow-frying tofu; stir-frying cabbage; and Kung Pao chicken
composed of cucumbers, peanuts, and chicken. The cooking time and oil
temperature were different due to the inherent features of the ingredients.
For all experiments, the closed kitchen was full of fumes where the vision
was blurred and the air was choky after the long time taken for the cooking process.
Subsequently, the cooking fumes were drawn into a pipeline from the kitchen to a
lab and then entered Go:PAM at an 8-fold dilution where aerosols and
gases reacted at a stable temperature and relative humidity. Both vehicle
and cooking fumes were diluted at a constant ratio by a Dekati diluter
(eDiluter, Dekati Ltd.). Vehicle exhaust from a tailpipe was first diluted by
a gradient-heated dilution system (6-fold) and then diluted by an unheated
dilution system (5-fold). The temperature of sample flow was near the indoor
temperature (20–25 ∘C) after secondary dilution systems. The cooking
fumes were collected through the kitchen ventilator, where the temperature
was similar to that of indoor air. Go:PAM was able to produce high OH
exposures using an ultraviolet lamp (λ=254 nm) in the presence of
ozone and water vapor to simulate the photochemical oxidation in the
atmosphere (J. Li et al., 2019; Watne et al., 2018). The internal structure
of Go:PAM can be found in Fig. S1. Blank experiments were separately
designed in the presence of boiling water or dilution air under the same
condition. The OA concentrations of blank groups were far below those of
experimental groups, which indicated the background values were minor (Table S1 in the Supplement). All the sampling tubes were made of silanized stainless steel which is
appropriate for simultaneous gas and particle sampling (Deming et al.,
2019; Wiedensohler et al., 2012). More details about the experimental design and
instruments can be found in the Supplement.
Descriptions of vehicle exhaust and sampling procedures.
ExperimentRevolvingTorqueSamplingParallelsParticleFuelSampling linespeedtimedensitytemperatureGDI 20 km h-11500 Hz16 N m60 min3–51.1–1.2 g cm-3Gasoline (China V, similar to Euro 5)20–25 ∘CGDI 40 km h-12000 Hz16 N m70 min3–6GDI 60 km h-11750 Hz32 N m60 min3–5
Descriptions of cooking emissions and sampling procedures.
ExperimentCookingOilTotal cookingNumber ofSamplingParallelsParticleFuel andKitchenSampling linematerialtemperaturetimedishestimedensitycookwarevolumetemperatureDeep-fried meat170 g chicken, 500 mL corn oil, and a few condiments145–155 ∘C66 min590 min3–81.11 ± 0.02 g cm-3Liquefied petroleum gas (LPG) and iron wok78 m3 (5.6 m × 4 m × 3.5 m)20–25 ∘CShallow-fried tofu500 g tofu, 200 mL corn oil, and a few condiments100–110 ∘C64 min560 min3–51.04 ± 0.03 g cm-3Stir-fried cabbage300 g cabbage, 40 mL corn oil, and a few condiments95–105 ∘C47 min558 min3–51.16 ± 0.03 g cm-3Kung Pao chicken150 g chicken, 50 g peanut, 50 g cucumber, 40 mL corn oil, and a few condimentsUnmeasured*40 min560 min3–51.07 ± 0.02 g cm-3
* It needed to be stirred constantly, so the oil temperature was unstable.
Measurements of the gas and particle phase
Figure 1 presents the design of this laboratory simulation. The gases and
aerosols were emitted from the GDI room or kitchen and then reacted and sampled
in a lab. The chemical compositions of OAs were measured by a high-resolution
time-of-flight aerosol mass spectrometer (HR-ToF-AMS, Aerodyne Research
Inc.), in which the non-refractory particles including organics, sulfate,
nitrate, ammonium, and chloride were instantly vaporized by a 600 ∘C tungsten filament. Next, the vaporized compounds were ionized by electron impact
(EI) ionization with 70 eV. Finally, the fragment ions were pulsed to a
time-of-flight mass spectrometry (MS) chamber and detected by the multi-channel plate detector
(MCP). More information about the HR-ToF-AMS is described in detail elsewhere
(Nash et al., 2006; DeCarlo et al., 2006). In this study, its time
resolution was 2 min (precisely, 1 min for a mass-sensitive V mode and 1 min for a high-mass-resolution W mode). As for the HR-ToF-AMS, the aged OAs were
those measured under certain OH exposure. Two sets of scanning mobility
particle sizers (SMPS-1, differential mobility analyzer, Electrostatic
Classifier model 3080; condensation particle counter model 3778; SMPS-2,
differential mobility analyzer, Electrostatic Classifier model 3082;
condensation particle counter model 3772; TSI Inc.) scanned individually every 2 min
before and after Go:PAM to identify the size distribution and
number concentration of particles. The SMPS-1 determined the mass
concentration of POAs, while the SMPS-2 determined the mass concentration of
aged OAs, and their mass difference could be regarded as the SOAs. A SO2
analyzer (model 43i, Thermo Electron Corp.) was used to measure the decay of
SO2 in offline adjustment. The measured CO2 concentrations (model 410i, Thermo Electron Corp.) were used to conduct CO2 correction for
AMS data to reduce the CO2 interference on organic fragments in mass
spectra of the HR-ToF-AMS. The particle densities were measured through the
determination of the DMA–CPMA–CPC system (DMA – differential mobility
analyzer, Electrostatic Classifier model 3080, TSI Inc.; CPMA – centrifugal
particle mass analyzer, version 1.53, Cambustion Ltd.; CPC – condensation
particle counter, model 3778, TSI Inc.). To
prevent freshly warm gas from condensing on the pipe wall, sampling pipes
were equipped with heat insulation cotton and a temperature controller.
Silicon tubes were used to dry the emissions before they entered measuring
instruments. Before each experiment, all pipelines and the Go:PAM chamber
were continuously flushed with purified dry air, until the concentrations
were minimal (just like blank groups in Table S1) when the UV was on or off.
The SOAs formed in each experiment represented the upper limit due to the
presence of background concentration.
Schematic of experiment system.
Data analysisHR-ToF-AMS data
The SQUIRREL 1.57 and PIKA 1.16 packages written in Igor (WaveMetrics Incorporation,
USA) were used to analyze the HR-ToF-AMS data including mass concentrations,
elemental ratios, ion fragments, and mass spectra. The ionization efficiency
(IE), relative ionization efficiency (RIE), and collection efficiency (CE)
were determined individually before data processing. The 300 nm ammonium
nitrate particles were applied for converting the instrument signals to
actual mass concentrations (Jayne et al., 2000; Drewnick et al., 2005).
Before the formal experiment, the IE and RIESO4 were calculated by
the comparison of the HR-ToF-AMS and SMPSs, when the sampling flow was generated
by 300 nm ammonium nitrate and 300 nm ammonium sulfate, respectively, with
an aerosol generator (DMT Inc.). The CE was a fluctuant value influenced by
the emission condition, so it was estimated by the comparison of the HR-ToF-AMS
(sampling after Go:PAM) and SMPS-2 (sampling after Go:PAM) during the
formal experiment. The CE and RIEOrg were theoretically different in
every emission or oxidation condition, so we directly used the SMPS
measurements to determine the aged OA mass concentration. As for the cooking
experiment, the IE value was 7.77×10-8, the RIESO4 was 1.4, the
RIEOrg was 1.4 (default value, the fluctuation in RIEOrg was included in
the CE), and the average CE was about 0.55 (ranged from 0.3 to 0.7). As for the
vehicle experiment, the IE value was 7.69×10-8, the RIESO4 was
1.3, the RIEOrg was 1.4 (default value, the fluctuation in RIEOrg was
included in the CE), and the average CE was about 0.6 (ranged from 0.4 to 0.7). For
some of the experimental groups, the mass spectra were resolved by positive
matrix factorization (PMF) analysis to perform deeper analyses (Ulbrich et
al., 2009).
Determination and evaluation of oxidation conditions in Go:PAM
The Go:PAM conditions for vehicle and cooking experiments could be seen in
Tables 3 and 4, respectively. Their experiment conditions (such as
residence time and RH) were not completely the same because of the inherent
difference between the two sources and their experimental design, whereas some
comparisons could still be analyzed under a similar OH exposure, and their RH
conditions were both low where photochemical oxidations instead of
aqueous-phase processing dominated the chemical evolution process (Xu et
al., 2017). The OH exposures and corresponding photochemical ages in Go:PAM
were calculated through an offline adjustment based on the decay of SO2
(Lambe et al., 2011). As shown in Eq. (1), KOH-SO2 is the reaction rate constant of the OH radical and SO2 (9.0×10-13 molec.-1 cm3 s-1). SO2,f and SO2,i are the SO2 concentrations
(ppb) under the conditions of the UV lamp being on or off, respectively. The
photochemical age (days) can be calculated in Eq. (2) when assuming the
OH concentration is 1.5×106 molecules cm-3 in the atmosphere (Mao et al.,
2009).
1OH exposure=-1KOH-SO2×lnSO2,fSO2,i2Photochemical age=OH exposure24×3600×1.5×106
Except for in the offline calibration based on the decay of SO2, a flow
reactor exposure estimator was also used in this study (Peng et
al., 2016). The OH exposures calculated by these two methods showed a good
correlation (Figs. S2 and S3). This estimator could also evaluate the
potential non-OH reactions in the flow reactor such as the photolysis of
VOCs and the reactions with O(1D), O(3P), and O3. The flow
reactor exposure estimator showed that OH reactions played a dominant role
in our experiments. It was found that the heterogeneous reaction of ozone
with oleic acid aerosol particles was influenced by the humidity and reaction
time in an aerosol flow reactor (Vesna et al., 2009).
Therefore, non-OH reactions which were not included in specific designs
in our experiment, such as the ozonolysis of unsaturated fatty
acids, may also be important in forming SOAs.
The Go:PAM conditions for vehicle experiments.
ExperimentO3 concentration (ppbV)OH exposurea (×1010 molecules cm-3 s)Photochemical age (days,[OH] = 1.5 ×106 molecules cm-3)External OH reactivity ofSO2 during offline calibration (s-1)External OH reactivity of VOCs during experiment (s-1)Ratio of OH Exposurecalculated by an estimatorb to that calculated by the decay of SO2aTemperature and RH in Go:PAMBasic description of Go:PAMWall LossGDI 20 km h-16247.790.615.810.4119 %Temp: 19–22 ∘C. RH: 44 %–49 %Volume: 7.9 L. Flowrate: 4 L min-1for sample air and 1 L min-1 for sheathgas. Residence time: 110 s.The wall loss of particles was adjusted in each size bin measured by two synchronous SMPSs (two SMPSs run before and after Go:PAM, respectively). The wall loss of the gas phase is minor according to previous research.236721.41.7443337.42.9653353.84.2805065.65.1870170.65.5GDI 40 km h-1The same as the 20 km h-1 experiments 20.283 %GDI 60 km h-1The same as the 20 km h-1 experiments 16.794 %
a OH exposure was calculated based on the decay of SO2. b OH exposure for each ingredient was calculated based on the OFR estimator.
The Go:PAM conditions for cooking experiments.
ExperimentO3 concentration (ppbV)OH exposurea (×1010 molecules cm-3 s)Photochemical age (days, [OH] = 1.5 × 106 molecules cm-3)External OH reactivity ofSO2 during offline calibration (s-1)External OH reactivity of VOCs during experiment (s-1)Ratio of OH exposurecalculated by an estimatorb to that calculatedby the decay of SO2aTemperature and RH in Go:PAMBasic description of Go:PAMWall lossDeep-fried chicken–00.024.025.797 %Temp: 16–19 ∘C. RH: 18 %–23 %Volume: 7.9 L. Flow rate: 7 L min-1 for sample air and 3 L min-1 for sheath gas. Residence time: 55 s.The wall loss of particles was adjusted in each size bin measured by two synchronous SMPSs (two SMPSs run before and after Go:PAM, respectively). The wall loss of the gas phase is minor according to previous research.3104.30.311839.60.7221714.41.1326721.41.7402527.12.1Shallow-fried tofuThe same as the meat experiments 21.7111 %Stir-fried cabbageThe same as the meat experiments 23.3104 %Kung Pao chickenThe same as the meat experiments 23.6103 %
a OH exposure was calculated based on the decay of SO2. b OH exposure for each ingredient was calculated based on the OFR estimator.
Furthermore, both the external OH reactivity and the OH exposure were influenced
by external OH reactants, such as NOx and VOCs, during experiments. The
NOx concentration was measured by a NO–NO2–NOx analyzer (model 42i,
Thermo Electron Corp., USA). As for VOCs, we have divided them into five
types including alkane, alkene, aromatic, O-VOCs (oxidized VOCs, mainly
including aldehyde and ketone), and X-VOCs (halogenated VOCs) using the
measurement of GC-MS (gas chromatography–mass spectrometry, GC 7890,
MS 5977, Agilent Technologies Inc.). The compounds with relatively high
proportions were regarded as surrogate species for each type of VOC. The
total concentrations of VOCs were determined by a portable TVOC analyzer
(PGM-7340, RAE Systems). The external OH reactivities for different vehicle
experiments (10.4–20.2 s-1) were all comparable to those
of offline calibration results (15.8 s-1), and the external OH
reactivities for different cooking experiments (21.7–25.7 s-1) were also comparable to that of offline calibration results (24.0 s-1). Besides, the ratio of OH exposure calculated by the estimator to
that calculated by the decay of SO2 ranged from 83 % to 119 % for
vehicle experiments and 97 % to 111 % for cooking experiments, which
means that our offline OH exposure could be a representative value for all
experiments. Detailed tests about mixing conditions and the wall loss of Go:PAM have been conducted in the previous work of J. Li et
al. (2019) and Watne et al. (2018) and can be found in
Fig. S4. In this study, we still corrected the wall loss of particles in
each size bin measured by two synchronous SMPSs (two SMPSs run before and
after Go:PAM, respectively). More details about Go:PAM can be found in the Supplement.
Results and discussionSecondary formation potential of the urban-lifestyle OAs
The simulated SOAs could be generated by the photochemical oxidation from
gaseous precursors and the heterogeneous oxidation from POAs. As Fig. 2
shows, the mass growth potential of the two urban-lifestyle OAs was quite
different. The mass growth potential was represented by SOA/POA mass
ratios. The SMPS-1 determined the mass concentration of POAs, while the
SMPS-2 determined the mass concentration of aged OAs, and their mass
difference could be regarded as the SOAs. Both their SOA/POA mass ratios
increased gradually and finally reached the peak after 2–3 d of
equivalent photochemical age, and the overall SOA mass growth potential of
vehicle SOAs was far larger than that of cooking SOAs. When the equivalent
photochemical age was near 2 d (1.7 d), the mass growth potential of
vehicle SOAs ranged from 83 to 150. In contrast, the mass growth potential
of cooking SOAs only ranged from 1.8 to 3.2 at about 2.1 d. Even though there
was still a slight growth trend for cooking SOAs at the highest OH exposure,
it exhibited a much weaker mass growth potential on the whole
compared with that of vehicle SOAs. This significant distinction indicated
that the vehicle exhaust may contribute abundant SOAs and relatively fewer
POAs, while cooking emissions may produce moderate POAs and SOAs in the
atmosphere, which could be attributed to their different types of gaseous
precursors. Interestingly, a similar phenomenon has been observed from an
OFR simulation at the urban roadside of Hong Kong, where potential SOAs from
motor vehicle exhaust were much larger than primary hydrocarbon-like organic aerosols (HOAs), while potential SOAs
from cooking emissions was comparable to primary cooking organic aerosols (COAs) (Liu et al., 2019).
Secondary mass growth potential for two urban-lifestyle SOAs. The
SMPS-1 determined the mass concentration of POAs, while the SMPS-2 determined
the mass concentration of aged OAs, and their mass difference could be
regarded as the SOAs. The average data and standard deviation bars are shown
in the figure.
Secondary formation pathway of the urban-lifestyle OAs
As Fig. 3 shows, the evolution of O:C molar ratios (O/C) of the two urban-lifestyle OAs was quite different. Although their oxidation degrees both
increased gradually and finally reached the peak after 2–3 d of
equivalent photochemical age, the O/C values of aged vehicle OAs were far
larger than those of aged cooking OAs. When the equivalent photochemical age
was 0.6 d, the O/C of aged vehicle OAs was 0.4–0.5, resembling a kind of
LO-OOA in ambient air. When the equivalent photochemical age was near 2 d
(1.7 d), the O/C of aged vehicle OAs could reach 0.6, which was almost
like a type of MO-OOA in the atmosphere. In contrast, the O/C of aged
cooking OAs only rose to 0.4 at 2.1 d, similarly to a kind of LO-OOA. These
distinct features of O/C suggested that aged vehicle OAs were divided into
LO-OOAs and MO-OOAs under different oxidation conditions, while the aged
cooking OAs were composed of only LO-OOAs. This difference was probably related
to their precursors.
Evolution of O:C molar ratio for two urban-lifestyle OAs. The O:C
molar ratios are determined by an HR-ToF-AMS. The average data and standard
deviation bars at each gradient are shown in the figure.
Figure 4 illustrates diverse oxidation pathways of various sources of OA in
a Van Krevelen diagram (Heald et al., 2010; Ng et al., 2011; Presto et al.,
2014). The cooking groups fell along a line with a slope of -0.10 implying
an alcohol and/or peroxide pathway in forming SOAs, while the vehicle groups fell
along a line with a slope of -0.55 implying an oxidation pathway between
alcohol and/or peroxide and carboxylic acid reaction. Additionally, these two
secondary evolution properties are both different from those of biomass
burning OAs (slope ≈-0.6) (Lim et al., 2019) and ambient OAs
(slope ≈-1 to -0.5) (Heald et al., 2010; Hu et al., 2017; Ng et
al., 2011), indicating that these two urban-lifestyles SOAs may undergo
distinct oxidation pathways.
Van Krevelen diagram of OAs from various sources. O:C and H:C are determined by an HR-ToF-AMS. The average data at each gradient are shown in
the figure.
Characteristics in mass spectra of the urban-lifestyle OAs
As shown in Fig. 5, the signal fraction of organic fragments at m/z 43
(f43) and m/z 44 (f44) has been widely adopted to represent the
oxidation process of OAs (Ng et al., 2010; Hennigan et al., 2011).
Generally, f43 and f44 derive from oxygen-containing
fragments, the former comes from less oxidized components while the latter
comes from more oxidized ones. The datasets of vehicle and cooking groups
fell in different regions and showed different variations in the plot.
Almost all aged cooking OAs displayed relatively lower f44 and higher
f43, and both their f43 and their f44 increased slightly with
the growing OH exposure, eventually becoming distributed in the LO-OOA region. In
contrast, all aged vehicle OAs displayed moderate f43 and abundant
f44, and only their f44 showed an obvious souring with
the growing OH exposure, initially being distributed in the LO-OOA region but
finally spreading near the MO-OOA region. These distinct evolutions of
oxygen-containing fragments for two urban-lifestyle OAs implied their
intrinsic oxidation pathways and precursors.
Fractions of entire organic signals at m/z 43 (f43) vs. m/z 44 (f44) from various sources as well as an Ng triangle plot. The
f43 and f44 are determined by an HR-ToF-AMS. The average data at
each gradient are shown in the figure.
Figure 6 and Table 5 depict mass spectra and prominent peaks of aged OAs from
two urban-lifestyle sources which could be used to deduce their inherent
properties (Zhang et al., 2005; Kaltsonoudis et al., 2017; Liu et al.,
2018; Chirico et al., 2010; Nordin et al., 2013; Z. Zhang et al., 2020). The
maximum SOA mass growth potential of aged cooking OAs only ranged from
1.9–3.2, implying a mixture of POAs and SOAs, so their mass spectra needed to be
comprehensively resolved by PMF to separate the POAs and SOAs (precisely, a kind of
LO-OOA). Generally, there is at least one POA and one SOA (factor 1 – POA,
factor 2 – SOA). When three or more factors were set, it was found that
elemental ratios or mass spectra of additional OA factors are quite similar
to those of factor 1 or factor 2, which means that it was hard to find another new OA
factor. Therefore, two OA factors were finally set, one for POAs and another
for SOAs. As Figs. S5–S8 show, the SOA factors present a larger fraction of
oxygen-containing fragments (especially in m/z 28, m/z 29, m/z 43, m/z 44) and higher
O/C, which is significantly different from the POA factors., whereas the
mass growth potential of aged vehicle OAs was extremely high, suggesting
that they were fully oxidized and almost composed of SOAs. According to the O/C
ratios, the vehicle SOAs under 0.6 d of photochemical age were defined as
vehicle LO-OOAs, while those under 2.9 d were regarded as vehicle MO-OOAs.
Average mass spectra of OAs from two urban-lifestyle sources. The
numbered symbols represent the m/z values with relatively large fractions.
The gray symbols represent the fragments that mainly come from
hydrocarbon-like fragments, and the green symbols represent those that mainly come
from oxygen-containing fragments. The mass spectra are determined by an
HR-ToF-AMS. The average data are shown in the figure.
A summary of elemental ratios and dominant peaks among various SOAs.
TypeO/CH/Cf28f29f41f43f44f55f57Dominant peaks (m/zin descending order)GDI LO-OOA0.461.800.0660.0760.0510.1330.0770.0430.02943, 44, 29, 28, 41, 55GDI MO-OOA0.911.570.1340.0710.0260.1170.1460.0240.01344, 28, 43, 29, 45, 27Cooking LO-OOA0.361.920.0530.0650.0580.0970.0650.0560.04643, 44, 29, 41, 55, 28Heated oil SOA (Liu et al., 2018)0.381.530.0700.0870.0670.0780.0670.0530.02329, 43, 28, 44, 41, 55Meat charbroiling SOA0.241.830.0390.0610.0770.0750.0520.0740.03541, 43, 55, 29, 27, 44(Kaltsonoudis et al., 2017)Gasoline SOA (Nordin et al., 2013)0.401.380.1220.0320.0310.0940.1290.0190.00844, 28, 39, 27, 29, 41Diesel SOA (Chirico et al., 2010)0.371.570.0690.0920.0620.1120.0730.0450.02243, 29, 44, 28, 41, 27
For average vehicle LO-OOA mass spectra, the prominent peaks were m/z 43
(f43=0.133 ± 0.003), m/z 44 (f44=0.077 ± 0.001), m/z 29
(f29=0.076 ± 0.003), m/z 28 (f28=0.066 ± 0.001), 41
(f41=0.051 ± 0.005), and m/z 55 (f55=0.043 ± 0.004)
dominated by C2H3O+, C3H7+, CO2+, CHO+, C2H5+, CO+,
C3H5+, C3H3O+, and C4H7+, while the prominent peaks of average vehicle MO-OOAs were m/z 44 (f44=0.146 ± 0.060), m/z 28 (f28=0.134 ± 0.062),
m/z 43 (f43=0.117 ± 0.033), m/z 29 (f29=0.071 ± 0.014),
m/z 45 (f45=0.032 ± 0.007), and m/z 27 (f27=0.030 ± 0.009) dominated by CO2+, CO+, C2H3O+,
CHO+, C2H5+, CHO2+, C2H5O+, and
C2H3+. Compared with vehicle SOA mass spectra
from other studies (Table 5), our average GDI SOAs (LO-OOAs and MO-OOAs)
illustrated more abundances of oxygen-containing ions than those of gasoline
SOAs and diesel SOAs simulated by a smog chamber with lower OH exposures
(Chirico et al., 2010; Nordin et al., 2013).
For average cooking LO-OOAs, they was less oxidized than those from vehicle
groups, whose prominent peaks were m/z 43 (f43=0.097 ± 0.008),
m/z 44 (f44=0.065 ± 0.010), m/z 29 (f29=0.065 ± 0.013),
m/z 41 (f41=0.058 ± 0.008), m/z 55 (f55=0.056 ± 0.006),
and m/z 28 (f28=0.053 ± 0.011) dominated by C2H3O+,
C3H7+, CO2+, CHO+, C2H5+,
C3H5+, C3H3O+, C4H7+, and
CO+. Compared with other cooking SOA mass spectra (Table 5), our average cooking LO-OOAs had similar peaks to heated oil SOAs but were
different from the meat charbroiling SOAs which displayed much more
hydrocarbon-like features (Liu et al., 2018; Kaltsonoudis et al., 2017).
Potential chemical evolution of urban-lifestyle OAs in the atmosphere
The AMS mass spectra indicated that the chemical evolution of urban-lifestyle OAs in Go:PAM might provide new insights into and references for those of ambient OAs observed in the atmosphere. Figure 7 plots the
correlation coefficients between the aged laboratory OA and ambient PMF OA
factors with growing photochemical ages (Li et al., 2020a). The
field study was deployed at the Institute of Atmospheric Physics (IAP),
Chinese Academy of Sciences (39∘58′ N, 116∘22′ E), in autumn and winter (autumn, 1 October–15 November 2018; winter, 5–31 January 2019) (Li et al., 2020a).
The sample site is located on the south of Beitucheng West Road and west of
Beijing–Chengde Expressway in Beijing, which is a typical urban site
affected by local emissions (Li et al., 2020b). Table 6 exhibits
correlations of mass spectra between laboratory results and ambient PMF
factors, where the aged laboratory cooking OAs were divided into POAs and
LO-OOAs, while the laboratory vehicle OAs were divided into LO-OOAs and MO-OOAs.
Correlation coefficients (Pearson r) between the aged laboratory
OA and published ambient PMF OA factors with growing photochemical ages.
Ambient PMF OA factors are the average results from two field studies in
Beijing (measured at a typical urban site during autumn and winter; autumn,
1 October–15 November 2018; winter, 5–31 January 2019). The unit mass resolution mass spectra are determined by an HR-ToF-AMS.
Pearson correlations between laboratory OA and ambient OA mass
spectra.
For the aged GDI OAs in Fig. 7, their average mass spectra retained some
ambient HOA features (Pearson r=0.80) under a low photochemical age of 0.6 d with moderate hydrocarbon-like ions such as m/z 41 and m/z 55, but they had
already reached the same oxidation degree of ambient LO-OOAs (Pearson
r=0.81) with high O/C (0.46) and f43 (0.133). After aging in
Go:PAM, the aged OAs might finally become a kind of ambient MO-OOA (Pearson
r=0.97) at 5.1 d of photochemical age. This evolution of GDI OAs (from
HOAs to LO-OOAs to MO-OOAs) was similar to the result of a previous vehicle OA
simulation (from HOAs to SV-OOAs to LV-OOAs) (Tkacik et al., 2014).
For the aged cooking OAs in Fig. 7, although their correlations with ambient
LO-OOAs increased gradually from 0.56 to 0.73 along with the growing
photochemical ages, their correlations with ambient COAs kept at a high level all
the time (Pearson r>0.81), implying a mixture of POAs and SOAs due
to some hardly oxidized compounds emitted from the cooking process.
Therefore, it is necessary to resolve aged cooking OA mass spectra comprehensively by
PMF (Figs. S4–S11) and then compared their laboratory PMF results with
ambient PMF factors. As Table 6 shows, the laboratory cooking POAs were
similar to ambient COAs (Pearson r=0.86) but less like LO-OOAs (Pearson
r=0.46) or MO-OOAs (Pearson r=0.39). By contrast, the laboratory cooking
LO-OOAs displayed many more ambient LO-OOA features (Pearson r=0.76) and
relatively fewer ambient COA characteristics than laboratory cooking POAs
did. In short, these comparisons between laboratory and ambient results
revealed that organics from these two urban-lifestyle sources might
eventually form different SOA types in the real atmosphere.
Conclusions
In the present work, we define two urban-lifestyle SOAs in detail and
investigate their mass growth potential, formation pathways, mass spectra,
and chemical evolutions comprehensively. At about 2 d of equivalent
photochemical age, the SOA/POA mass ratios of vehicle groups (107) were 44
times larger than those of cooking groups (2.38), and the O:C molar ratios
of vehicle groups (0.66) were about 2 times as large as those of cooking groups
(0.34). Besides, both vehicle and cooking groups may undergo an
alcohol and/or peroxide pathway to form LO-OOAs, and the vehicle groups additionally
undergo a carboxylic acid pathway to form a part of MO-OOAs. Furthermore, the
characteristic mass spectra of these two urban-lifestyle SOAs could provide
necessary references to estimate their mass fractions in ambient air,
through a multilinear engine model (ME-2) (Canonaco et al., 2013; Qin et
al., 2017). This application would reduce the large gaps of total
atmospheric contributions and relevant environment effects for urban SOAs,
although several uncertainties in SOA mass spectra remain due to missing
complex mixture conditions in Go:PAM.
There are some uncertainties in our Go:PAM simulation. We focused more on
the photochemical oxidation of SOAs under low RH levels, but aqueous-phase
processing at high RH levels may also have impacts on SOA production. In the
future, it will be better to strictly control the RH, high and low NOx or
SO2, additional inorganic seeds, and so forth to thoroughly investigate
how the aerosols age as a function of equivalent days of atmospheric
oxidation. SVOCs and IVOCs may play important roles in forming SOAs but were partly lost in pipelines, and their sampling and quantification are extremely challenging, needing a more sophisticated experimental design.
Moreover, the contribution of ozonolysis to SOA formation should be
individually studied in further research. Furthermore, the relative strengths
of the photochemical oxidation from gaseous precursors and the heterogeneous
oxidation from POAs were not distinguished in depth in this work. Besides, it
is recommended to add humidity to the carrier gas and turn on the lights
during the OFR clean-out stage, in order to minimize the background
concentration in Go:PAM.
Although strict policies have been implemented to reduce primary particulate
matter (PM) in urban areas, secondary PM, especially for the
abundant and complicated SOAs, is difficult to restrict (Wu et al.,
2017; Li et al., 2018). According to our results, on the one hand, vehicle
SOAs might be a mixture of both LO-OOAs and MO-OOAs with high secondary
formation potential, so it would be better to not only filter out the exhaust
PM with a gasoline particulate filter (GPF) but also reduce the gaseous
precursors to restrict the secondary formation. On the other hand, cooking
SOAs might comprise a kind of LO-OOA with relatively low secondary formation
potential, so it could be enough to remove the gas and particle emissions at
the same level. In the future, these two urban-lifestyle SOAs may present
increasing contributions in urban areas especially in megacities with
growing atmospheric oxidants (K. Li et al., 2019; Wang et al., 2017; Li et
al., 2020a, b), but investigation of them and further
management are far from sufficient, making it possible for this area to become a very
meaningful research focus.
This work is an initial attempt to explore a series of studies on urban-lifestyle SOAs. In another companion publication currently in preparation (Song et al., 2021), gas- and particle-phase VOCs, SVOCs, and IVOCs from four
typical Chinese domestic cuisines are quantified. It is found that 26 %–78 %
of cooking SOAs could be explained from the oxidation of VOCs, SVOCs, and IVOCs.
Moreover, oxygenated compounds, including acids, furans, amides, and esters, were the most abundant in the particle phase. In contrast, significant
differences were found in the gas phase among four cuisines; for example, Kung
Pao chicken and shallow-frying tofu showed a larger proportion of aromatics.
Furthermore, we have attempted to apply the laboratory mass spectra from
this work to the ambient air. The contributions of vehicle SOAs and cooking
SOAs to OAs were estimated by an ME-2 model in urban Beijing (Zhang et al., 2021). It was found that cooking SOAs (27 %–42 %
of OAs) and vehicle SOAs (58 %–73 % of OAs) presented different diurnal
patterns, implying their different formation pathways. Similar features of
urban-lifestyle SOAs were found between laboratory and field results.
Data availability
The data provided in this paper can be obtained from the author upon request
(minhu@pku.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-15221-2021-supplement.
Author contributions
ZiZ contributed in terms of investigation, data curation, methodology, formal analysis,
writing of the original draft, and writing – review and editing. WenfZ contributed in terms of
investigation, data curation, methodology, formal analysis, and writing – review
and editing. MH contributed in terms of project administration, supervision, funding
acquisition, and writing – review and editing. KL contributed in terms of investigation, data
curation, and formal analysis. HW, RoT, RS, YY, RuT, KS, YL, WenbZ, and ZhZ contributed in terms of investigation and data curation. HX, SS, SL, YC, JL, and YW contributed in terms of data curation. SG contributed in terms of project
administration, funding acquisition, and writing – review and editing.
Competing interests
The authors declare that they have no known competing financial interests
or personal relationships that could have influenced the work reported in this paper.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Thanks to all the researchers from PKU who had directly participated in the main
laboratory simulation. Thanks to all the researchers from THU and CAS who
provided the necessary experiment sites, instruments, and data support.
Financial support
The research has been supported by the National Key R&D Program of China
(2016YFC0202000), National Natural Science Foundation of China
(51636003, 91844301, 41977179, and 21677002), Beijing Municipal Science and
Technology Commission (Z201100008220011), Open Research Fund of State Key
Laboratory of Multiphase Complex Systems (MPCS-2019-D-09), and China
Postdoctoral Science Foundation (2020M680242).
Review statement
This paper was edited by James Allan and reviewed by three anonymous referees.
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