To improve air quality, the Beijing government has employed several air
pollution control measures since the 2008 Olympics. In order to investigate
organic aerosol sources after the implementation of these measures, ambient
fine particulate matter was collected at a regional site in Changping (CP) and
an urban site at the Peking University Atmosphere Environment Monitoring Station (PKUERS)
during the “Photochemical Smog in China” field campaign in summer
2016. Chemical mass balance (CMB) modeling and the tracer yield method
were used to apportion primary and secondary organic sources. Our results
showed that the particle concentration decreased significantly during the
last few years. The apportioned primary and secondary sources explained
62.8
Beijing is the capital and a major metropolis of China. With rapid
economic growth and urbanization, Beijing is experiencing serious air
pollution problems and has become one of the hotspots of PM
Several studies regarding the source apportionment of fine particles in
Beijing have been conducted using multifarious methods during the last few
years (Yu et al., 2013; Gao et al., 2014; X. X. Zheng et al., 2016; Tan et
al., 2014; Wang et al., 2009; Guo et al., 2013). The receptor model is a commonly
used method to apportion particle sources (Zhang et al., 2013, 2017; Zhou
et al., 2017; Song et al., 2006; Zheng et al., 2005). Elemental tracers were
previously used to apportion particulate matter sources (Yu et al., 2013; Gao
et al., 2014; X. X. Zheng et al., 2016). However, the elemental-tracer-based
method was unable to distinguish sources that mostly emit organic compounds
instead of specific elements such as diesel and gasoline engines. Among all the
apportionment methods, the chemical mass balance (CMB) model was one of the most
commonly used methods to apportion the primary organic sources of fine
particulate matter (Zhang et al., 2017; Hu et al., 2015; Schauer et al.,
1996). Organic tracers have been successfully used in several studies that
aimed to quantify the main sources in Beijing (Liu et al., 2016; Guo et al.,
2013; Wang et al., 2009). Wang et al. (2009) assessed the
source contributions of carbonaceous aerosol during 2005 to 2007. Guo et al. (2013) and Liu et al. (2016) apportioned organic
aerosol sources using the CMB model in summer 2008 and a severe haze event in
winter of 2013. Both studies found that vehicle emissions and coal combustion
were the dominant primary sources of fine organic particles. The tracer yield
method has been considered as a useful tool to semi-quantify SOA derived from
specific VOC precursors (Guo et al., 2012; Zhu et al., 2016, 2017; Tao et
al., 2017; Hu et al., 2008). However, only a few studies have estimated
secondary organic aerosol in Beijing. Yang et al. (2016) estimated the
biogenic SOC to OC during the CAREBEIJING 2007 field campaign and found that
biogenic SOC accounted for 3.1 % of the measured OC. Guo et al. (2012)
illustrated the SOA contributions in 2008 and found that secondary organic
carbon could contribute a great portion (32.5
In this study, we quantified 144 kinds of particulate organic species, including primary and secondary organic tracers, at a regional site and an urban site in Beijing. A CMB modeling and the tracer yield method were used to apportion the primary and secondary sources of organic aerosols in the 2016 summer in Beijing. The results were compared with previous studies to evaluate the effectiveness of control measures on primary and secondary organic aerosols. Moreover, source apportionment results from different air mass origins according to back-trajectory clustering analysis were shown to investigate the influences of air mass from different directions on fine organic particle sources. Influencing factors of SOA formation, i.e., temperature, oxidant concentration, aerosol water content, and particle acidity, were also discussed in this study to improve our understanding of SOA formation in a polluted environment.
The measurements were conducted simultaneously at an urban site at the Peking
University Atmosphere Environment Monitoring Station (PKUERS;
39
Four-channel samplers (TH-16A; Tianhong, China) consisting of three quartz
filter channels and one Teflon filter channel were employed to collect 12 h
aerosol samples at PKUERS and CP, respectively. The sampling flow rate was
16.7 L min
Authentic standards were used to identify and quantify the organic compounds.
The analytical methods used in this study referred to previous work (Song
et al., 2014). Briefly, the samples were first spiked with a mixture of
internal standard, including ketopinic acid (KPA), 20 kinds of deuterated
compounds, and one carbon isotope
A chemical mass balance modeling developed by the US Environmental
Protection Agency (EPA CMB version 8.2) was applied to determine the
apportion of the primary contribution of OC (Schauer et al., 1996). This
receptor model solved a set of linear equations using ambient concentrations
and chemical source profiles. The CMB approach depends strongly on the
representativeness of the source profile. In this study, five primary source
profiles including vegetative detritus (Rogge et al., 1993), coal combustion
(Zheng et al., 2005), gasoline engines (Lough et al., 2007), diesel engines
(Lough et al., 2007), and biomass burning (Sheesley et al., 2007) were
input into the model. Fitting species included EC,
The tracer yield method was used to estimate the contributions of biogenic
and anthropogenic secondary organic aerosols using the fixed-tracers-to-SOC ratio
(
Estimations based on boundary values were generally acknowledged to have the
largest source of uncertainties in the models, so those results could be used
to determine the possible limit of the estimations. Also, Kleindienst et
al. (2007)
carried out a boundary analysis using data from Research Triangle Park North Carolina to
measure the range of estimated SOA contributions. Results revealed that the
possible contributions of isoprene,
Mixing ratios of gaseous pollutants and meteorological conditions during the
observation period are shown in Fig. S2 and Table S1 in the Supplement.
Compared with the results in summer 2010 (J. Zheng et al., 2016), the
gaseous mixing ratios of SO
During the campaign, the average wind speed was low, showing average values
of 2.3
Summer PM
To explore the influence of air masses from different directions on fine
particle loading and sources, back-trajectory analysis was performed using
the
National Oceanic and Atmospheric Administration (NOAA) Hybrid Single Particle
Lagrangian Integrated Trajectory (HYSPLIT) model. We calculated 36 h air
mass back trajectories arriving at two sampling sites during the observation
period using the HYSPIT4 model with a 1
In this study, daily PM
Figure S4 in the Supplement shows the chemical composition of PM
Carbonaceous aerosols, i.e., organic carbon (OC) and elemental carbon (EC),
were also great contributors to PM
To evaluate the influences of air masses from different directions on the
PM
The organic species (except secondary organic tracers) were divided into 12 categories. Their concentrations in different directions according to the back-trajectory clustering are shown in Fig. S6 in the Supplement. Detailed information for each class at the two sites can be found in the Supplement (Fig. S7). Cluster South showed a higher particulate organic matter concentration, followed by cluster Near WN and Far NW, indicating more severe aerosol pollution from the south. Our result are consistent with previous studies showing that more pollution emissions are from the south area of Beijing than from the north (Wu et al., 2011; Q. Zhang et al., 2009).
For all the species, a histogram shows the daily average concentrations
with error bars representing 1 standard deviation. Dicarboxylic acid was
the most abundant species among all the components, demonstrating the great
contribution of secondary formation to organic aerosols in the summer
in Beijing (Guo et al., 2010). A series of
Table S3 in the Supplement compares the SOA tracers measured in this work with those in other regions in the world and those observed in Beijing 2008. The sites for comparison include an urban background site at the Indian Institute of Technology Bombay, Mumbai, India (IITB; Fu et al., 2016), an outflow region of Asian aerosols and precursors in Cape Hedo, Okinawa, Japan (CH; Zhu et al., 2016), a residential site in Yuen Long, Hong Kong (YL; Hu et al., 2008), three industrial sites in Cleveland Ohio (CL; data were averaged among the three sites), and a suburban site in Research Triangle Park (RTP) North Carolina. Detailed information about these sites is summarized in the Supplement.
Three isoprene SOA tracers, i.e., two 2-methyltetrols (2-methyltheitol and
2-methylerythritol) and 2-methylglyceric acid, were detected. The summed
concentration of the isoprene SOA tracers ranged from 3.7 to
62.3 ng m
Nine
2,3-Dihydroxy-4-oxopentanoic acid is deemed as a tracer for toluene SOA. Our
results showed that the 2,3-dihydroxy-4-oxopentanoic acid concentration was
9.7
Concentrations of organic carbon from primary and secondary organic
sources at
A CMB model and the tracer yield method were used to quantify the
contributions of primary and secondary sources to the ambient organic carbon
(see Fig. 1). On average, the primary sources accounted for 42.6
The secondary organic sources accounted for 20.2
Stone et al. (2009) discovered that primary and secondary sources accounted
for 83
Due to the drastic emission control measures taken by the Beijing government,
primary and secondary sources in Beijing may change greatly. Figure 2
displays the comparison of sources between 2008 and 2016 at the same
site: PKUERS. We compared the average contributions by percentage rather than
the mass concentration. In general, primary sources contributed
50.4
Comparison of the sources at PKUERS between 2016 and 2008.
Particle sources from different air mass origins.
Correlations between SOC and different influencing factors:
However, the contribution of toluene SOC was the highest among the
apportioned SOC, which was different from the results of the most developed
countries in the world. Compared with previous studies, except
The regional sources and transport of air pollutants exert profound impacts on air quality in Beijing. To better understand the regional impacts on primary and secondary aerosol sources in Beijing, source apportionment results for air mass from different origins are shown in Fig. 3. Vehicular emissions, i.e., gasoline and diesel exhaust, showed identical contributions from different air mass origins (31.0 % from South vs. 31.3 % from Near WN vs. 31.7 % from Far NW) at PKUERS, demonstrating that vehicular pollution could mostly be attributed to vehicular emissions at the local site. However, the contribution of vehicular emissions at CP showed a significant difference from different air mass origins, with the lowest contribution when the air mass was from the far northwest. This might be explained by regional transport from different directions. Comparable contributions of coal combustion and biomass burning were found at CP and PKUERS from different air mass origins, implying regional pollution in Beijing. Similarly, biogenic SOC showed similar contributions from different air mass origins both at the regional site and the urban site. From all the directions, the toluene SOC (anthropogenic source) was the largest contributor to apportioned SOC, with higher concentrations at the urban site PKUERS. On the whole, most of the sources showed a comparable contribution from different air mass origins, implying that pollution in Beijing was regional.
Laboratory experiments have revealed that several factors can influence SOA formation, e.g., oxidants (OH radical, ozone, etc.), temperature, humidity, particle water content, and acidity. We found that the correlations between SOC and ozone–temperature are different for daytime and nighttime samples. However, it is not significant for water content and hydrogen ion concentration. Therefore, we separate the data between day and night as well as between SOC and ozone–temperature and use all data for the analysis of particle water and acidity. In this work, the relationships between estimated SOA and these factors were investigated to better understand SOA formation in Beijing.
Ozone is considered as an important oxidant for SOA formation. Figure 4a and b
show the correlation of the ozone mixing ratio and SOC. It is clear that SOC
increased significantly with an increasing ozone mixing ratio, which is
consistent with previous studies in Beijing (Guo et al. 2012). Different
correlations were found between day and night samples, with better
correlation for the daytime samples at both sites, implying that SOA may have
formation mechanisms at night other than ozonolysis. At CP, the growth
rate of SOC with O
Temperature was considered as a great influencing factor on SOA formation. On the one hand, higher temperatures promoted the evaporation of semi-volatile SOA. On the other hand, high-temperature conditions would favor oxidation, which would accelerate SOA formation (Saathoff et al., 2009). Figure 4c and d show the variation in SOC concentrations with temperature. In this study, SOC concentrations showed a positive correlation with temperature at CP and PKUERS, demonstrating that temperature favors SOA formation in the summer in Beijing. Moreover, different correlations of the day and the night samples imply different pathways of SOA formation. However, poor relations could be found between SOC and RH.
Aerosol water and acidity have been considered to have a great impact on
aqueous-phase SOA formation (Cheng et al., 2016). To determine the
influences of water content and aerosol acidity on aqueous-phase
reactions, the ISORROPIA-II thermodynamic equilibrium model was used (Surratt et
al., 2007). The model was set at forward mode based on the concentrations
of particle-phase Na
Results showed that the average aerosol water content at CP was
3.87
In this study, modeled H
High concentrations of fine particles were observed during the “Campaign on
Photochemical Smog in China”, with average mass concentrations of
45.48
Relevant data have been included in the paper in the form of a table. Most of the raw data are available from the corresponding author upon request.
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
This research is supported by the National Key R&D Program of China (2016YFC0202000, Task 3), the National Natural Science Foundation of China (21677002), and the framework research program on “Photochemical Smog in China” financed by the Swedish Research Council (639-2013-6917). Edited by: Renyi Zhang Reviewed by: two anonymous referees