2.1 Sampling and chemical analysis

The measurements were conducted simultaneously at an urban site at the Peking University Atmosphere Environment Monitoring Station (PKUERS; 39^∘59^′21^∘ N, 116^∘18^′25^∘ E) and a regional site in Changping (CP; 40^∘8^′24^∘ N, 116^∘6^′36^∘ E) 40 km north of the PKUERS site during the “Photochemical Smog in China” campaign from 16 May to 5 June 2016 (see Fig. S1 in the Supplement; Hallquist et al., 2016). The PKUERS site is set on the roof of an academic building on the campus of Peking University in the northwest of Beijing. The CP site is located on the fourth floor of a building on the Peking University Changping campus.

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⁻¹. Teflon filters were weighed by a microbalance (Toledo AX105DR, USA) after a 24 h balance in an environmentally controlled room (temperature 20 ± 1 ^∘C, relative humidity 40 ± 3 %) for gravimetric analysis. Teflon-based samples were extracted by deionized water to measure water-soluble inorganic compounds (WSICs), namely Na⁺, NH${}_{\mathrm{4}}^{+}$, K⁺, Mg²⁺, Ca²⁺, NO${}_{\mathrm{3}}^{-}$, SO${}_{\mathrm{4}}^{\mathrm{2}-}$, and Cl⁻ by using a DIONEX ICS-2500 and ICS-2000 ion chromatograph. One punch (1.45 m²) of quartz-based sample was then cut off to analyze the EC and OC via a thermal–optical method using a Sunset Laboratory instrument (NIOSH protocol; TOT). The other two quartz filters were then extracted and analyzed for chemical composition and particulate organic matter. Some daytime and nighttime samples were combined to ensure the detection of most organic compounds. To better understand the chemical speciation, daytime samples were separated from nighttime samples.

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 ¹³C-substituted compound. The quartz filters were then ultrasonically extracted with methanol : dichloromethane (v:v = 1 : 3) solvent in a water bath (temperature < 30 ^∘C) three times. Each time was 20 min. The extracts were filtered and then concentrated using a rotary vacuum evaporator. An ultrapure nitrogen flow was used to further concentrate the extracts into 0.5–1 mL. Each extracted solution was divided into two portions, to one of which BSTFA (BSTFA ∕ TMCS = 99:1; Supelco) was added to convert polar organic compounds into trimethylsilanized derivatives. Afterwards, the derivatized and the untreated samples were analyzed by an Agilent 6890 GC-MS System (MSD GC-5973N) equipped with an Agilent DB-5MS GC column (30 m × 0.25 mm × 0.5 µm).

2.2 Source apportionment

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, n-alkanes, levoglucosan, 17β(H)-21α(H)-norhopane, 17α(H)-21β(H)-hopane, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[e]pyrene, benzo[ghi]perylene, and indeno[1,2,3-cd]pyrene. The criteria for acceptable fitting results included the square regression coefficient of the regression equation R² > 0.85 and the sum of square residual chi-square value χ² < 4.

The tracer yield method was used to estimate the contributions of biogenic and anthropogenic secondary organic aerosols using the fixed-tracers-to-SOC ratio (f_SOC) based on laboratory experiments, which was 0.155 ± 0.039 for isoprene, 0.231 ± 0.111 for α-pinene, 0.0230 ± 0.0046 for β-caryophyllene, and 0.0079 ± 0.0026 for toluene (Kleindienst et al., 2007). The mass fraction depends on the degree of oxidation. The uncertainty also depends on the selection of molecular tracers and the simplified procedures by using single-valued mass fractions (Yttri et al., 2011; El Haddad et al., 2011; Song et al., 2014; Guo et al., 2014b, c). Previous studies showed that SOA estimated by the tracer yield method and POA apportioned by the CMB model could fully account for the OA in the atmosphere (Lewandowski et al., 2008; Kleindienst et al., 2010). Researchers also found that the total estimated SOC derived from the tracer yield method was in accordance with that from the EC-tracer method during summer (Ding et al., 2012; Kleindienst et al., 2010; Turpin and Huntzicker, 1995). Comparable results were also found between the tracer yield method and positive matrix factorization model (Hu et al., 2010; Y. X. Zhang et al., 2009). All these results firmly demonstrated that the tracer yield method is a valuable and convincing method to estimate SOA contributions (Ding et al., 2014).

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, α-pinene, β-caryophyllene, and toluene were within the scope of 70–130, 50–220, 70–120, and 60–160 %, respectively. The above results were assumed to be in the acceptable range for PM_2.5 source apportionment. In addition, the standard deviations of the tracer-to-SOC ratios were suitable as a source profile uncertainty (Kleindienst et al., 2007). Despite the uncertainties above, the tracer yield method represented a unique approach to estimate the SOA contributions using individual hydrocarbon precursors (Offenberg et al., 2007).

3.1 Gaseous pollutants and meteorological conditions of the observation period

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 SO₂ and CO were lower than before owing to the desulfurization efforts made by the government. Higher concentrations of NO and NO₂ were caused by the increasing number of vehicles. The increment of ozone indicated the importance of secondary pollution. Clearly, the ozone concentration at CP was higher than that of PKUERS, while other pollutants were lower.

During the campaign, the average wind speed was low, showing average values of 2.3 ± 1.4 and 2.4 ± 1.5 m s⁻¹ at CP and PKUERS, respectively. The diurnal variations in wind directions and speeds are exhibited in Fig. S2 in the Supplement. The prevailing wind was from the south, with a higher wind speed during the daytime.

Table 1Summer PM_2.5 mass concentrations in Beijing from 2008–2016; average ± standard deviation (µg m⁻³).

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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^∘ × 1^∘ latitude–longitude grid and the final meteorological database. The model was run with starting times of 00:00, 04:00, 08:00, 12:00, 16:00, and 20:00 UTC. The arrival level was set at 200 m above ground level. The method used in trajectory clustering was based on the GIS-based software TrajStat (http://www.meteothinker.com/TrajStatProduct.aspx). Back trajectories of 36 h staring at 200 m above ground level at CP and PKUERS were calculated every 4 h during the entire campaign and then clustered according to their similarity in spatial distribution using the HYSPLIT4 software. A three-cluster solution was adopted as shown in Fig. S3 in the Supplement. The three clusters were defined as Far North West (Cluster 1, Far NW), Near West North (Cluster 2, Near WN), and South (Cluster 3). The South cluster was found to be the most frequent one, accounting for 52 % at CP and 64 % at PKUERS. Clusters Far NW and Near NW accounted for 17 and 31 %, 17, and 19 % at CP and PKUERS, respectively.

3.2 Overview of PM_2.5 chemical composition

In this study, daily PM_2.5 concentrations fluctuated dramatically from 6.7 to 80.3 µg m⁻³ at CP and from 9.6 to 82.5 µg m⁻³ at PKUERS, respectively. A paired t test was used to compare the mass concentrations at two sites. The results indicate that the mass concentrations showed a statistically nonsignificant difference, suggesting regional particle pollution in Beijing. PM_2.5 mass concentrations during the summer of 2008 to 2016 in Beijing are summarized in Table 1. Guo et al. (2013) reported average PM_2.5 concentrations during the summers of 2000 to 2008, which showed a distinct decreasing tendency during 2000–2006 and then slightly increased in 2007 due to unfavorable meteorological conditions. To better understand the variation tendency of the PM_2.5 in the summer in Beijing, we compared the fine particle matter data since 2008. Compared with 2008, the PM_2.5 concentrations decreased from 92.3 ± 44.7 to 88.2 µg m⁻³ in 2009 and 62.7 µg m⁻³ in 2010. The mass concentration continued falling to 45.5 µg m⁻³ in 2016. This decrease is attributed to the drastic emission control measures implemented by the Beijing government since 2012. In spite of the prominent decrease in the PM_2.5 mass concentrations, the aerosol loading in Beijing was still much higher than that in developed countries (Tai et al., 2010; Barmpadimos et al., 2012; Park and Cho, 2011).

Figure S4 in the Supplement shows the chemical composition of PM_2.5. In general, organic particulate matter (OM, OC × 1.6) and sulfate were the two dominant components, accounting for more than 50 % of the PM_2.5 mass concentration during the field campaign. The average concentration of total WSICs for CP was 17.4 ± 11.5 µg m⁻³, higher than that of PKUERS (12.2 ± 8.5 µg m⁻³). Among the WSICs, secondary inorganic ions (sulfate, nitrate, and ammonium) were the most abundant compounds, indicating that secondary particles played great roles in the summer in Beijing. The higher sulfate proportion could be explained by the increased photochemical conversion of sulfur dioxide to sulfate aerosol (Xiang et al., 2017). Relevant data on the main WSICs (sulfate, nitrate, and ammonia) during 2008 to 2016 are also included in Table 1 to better elucidate the drastic decrease in fine particulate matter in recent years. Results showed that the daily average concentration of WSICs decreased from 2008 to 2016, with sulfate decreasing from 35.6 to 4.7 µg m⁻³, nitrate decreasing from 7.9 to 2.4 µg m⁻³, and ammonia decreasing from 15.2 to 3.5 µg m⁻³. The significant decrease in WSICs confirmed the effectiveness of the control measures taken by the government.

Carbonaceous aerosols, i.e., organic carbon (OC) and elemental carbon (EC), were also great contributors to PM_2.5 concentrations. Higher proportions of OC and EC at PKUERS demonstrated severe carbonaceous pollution in urban Beijing, which might have close a correlation with the higher traffic flow, coal and/or wood combustion by residents, and industrial emissions (Wang et al., 2006; Dan et al., 2004; Cao et al., 2004). A comparison of the OC and EC concentrations from 2008 to 2016 is also listed in Table 1. Unlike PM_2.5, the OC concentration at PKUERS showed a higher OC concentration (11.0 ± 3.7 µg m⁻³) compared with that in 2008 (9.2 ± 3.3 µg m⁻³), suggesting that organic aerosol pollution is becoming more and more important. The EC concentration decreased dramatically to 0.7 ± 0.5 µg m⁻³ at CP and 1.8 ± 1.0 µg m⁻³ at PKUERS, which showed the lowest value since 2000. This could be attributed to the implementation of an air pollution prevention and control action plan enacted by the state council since 2013. Therefore, we could draw the conclusion that the drastic decrease in fine particulate matter in Beijing was mainly due to the well-controlled EC and WSICs, with a negligible contribution of OC.

To evaluate the influences of air masses from different directions on the PM_2.5 loadings during the campaign, three categories were identified according to the back-trajectory clustering analysis (see Fig. S5 in the Supplement). In general, cluster South represented the most polluted air mass origin, followed by clusters Near WN and Far NW, which was in accordance with previous studies demonstrating severe aerosol pollution in southerly airflow in summer in Beijing (Huang et al., 2010; Sun et al., 2010).

3.3 Concentration of particulate organic species from different air mass origins

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 n-alkanes ranging from C12 to C36 were analyzed. Their distribution during the observation period is shown in Fig. S7a in the Supplement. The maximum alkane concentration species (C_max) were C27 and C29. The odd carbon preference was indicative of biogenic sources (vegetative matter and biomass burning; Huang et al., 2006; Rogge et al., 1993). In this study, total PAHs were much lower than previous studies in summer in Beijing, suggesting the effectiveness of the control strategies since 2013 (Wang et al., 2009). According to Fig. S7c in the Supplement, five-ring PAHs were the dominant species among all the species, followed by four-ring and six-ring PAHs. In total, four- to six-ring PAHs had a higher abundance, accounting for more than 60 % of the total PAHs. The result was similar to previous studies showing that the distribution of PAHs was impacted by the volatility of PAHs and the temperature (Wang et al., 2009; Guo et al., 2013). Saccharide was considered to originate from biomass burning (Simoneit et al., 1999). In this study, we quantified three sugar compounds including levoglucosan, mannosan, and galactosan, and levoglucosan was considered as a good tracer for biomass burning. The average daily mass concentrations of levoglucosan at CP and PKUERS were 53.03 ± 39.26 and 59.87 ± 38.93 ng m⁻³, respectively. It is worth mentioning that the levoglucosan concentration was the lowest in recent years (Cheng et al., 2013; Guo et al., 2013). Hopanes have been considered as markers for oil combustion (Lambe et al., 2009), vehicles (i.e., gasoline-powered and diesel-powered engines; Cass, 1998; Lough et al., 2007), and coal combustion (Oros and Simoneit, 2000). Nevertheless, contributions of coal combustion to hopanes were much less than those of vehicle exhaust. Concentrations of quantified hopanes including 17α(H)-22,29,30-trishopane, 17β(H)-21α(H)-norhopane, and 17α(H)-21β(H)-hopane at CP and PKUERS are shown in Fig. S7d in the Supplement. The total average concentrations of hopanes were 3.05 ± 1.53 ng m⁻³ for CP and 3.90 ± 2.06 ng m⁻³ for PKUERS. The daily averaged hopane concentrations at the urban site PKUERS were much higher than those of CP, which could probably be explained by the heavier vehicle emissions in the urban area. The concentrations of primary organic tracers used in CMB model are listed in Table S2 in the Supplement.

3.4 Biogenic and anthropogenic SOA tracers

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⁻³ at CP and 8.6 to 46.5 ng m⁻³ at PKUERS. The concentration was higher than that of IITB and CH. Compared with the isoprene SOA tracers in 2008, the concentrations in 2016 were lower.

Nine α-pinene tracers were identified. The sum of the tracers ranged from 20.9 to 282.3 ng m⁻³ at CP and 50.0 to 161.4 ng m⁻³ at PKUERS, which had a similar distribution pattern to that measured in 2008 in Beijing and YL. The total α-pinene tracer concentrations were lower than those in 2008, while still much higher than the concentrations in other regions of the world.

β-Caryophyllinic acid is one of the oxidation products of β-caryophyllene that is considered as a tracer for β-caryophyllene SOA. In this study, β-caryophyllinic acid concentrations ranged from 1.4 to 16.7 ng m⁻³ at CP and 0.9 to 12.0 ng m⁻³ at PKUERS, with average daily average concentrations of 6.1 ± 3.5 and 6.0 ± 2.8 ng m⁻³ for CP and PKUERS, respectively. The values were lower than those at YL and RPT, but higher than those measured at Yufa and PKUERS in 2008.

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 ± 7.3 ng m⁻³ at CP and 11.0 ± 3.7 ng m⁻³ at PKUERS. Compared with other regions of the world, the concentrations of 2,3-dihydroxy-4-oxopentanoic acid were much higher, implying higher contributions of anthropogenic sources at Beijing. However, the 2,3-dihydroxy-4-oxopentanoic acid concentrations at CP were lower than those of PKUERS.

Figure 1Concentrations of organic carbon from primary and secondary organic sources at (a) CP and (b) PKUERS as well as their contributions to the measured organic carbon at (c) CP and (d) PKUERS (%).

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4.1 Contributions of primary and secondary organic aerosols

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 ± 15.4 and 50.4 ± 19.1 % of the measured OC at CP and PKUERS, respectively. Vehicle emissions were the dominant primary source, with contributions of 28.8 ± 14.8 and 37.6 ± 19.3 % at PKUERS and CP, respectively, implying the urgency to control vehicular exhaust in urban areas. Despite the lower contribution of the gasoline exhaust at PKUERS, the mass concentration of the gasoline exhaust was higher compared with that of CP given the higher OC loading at PKUERS. The contributions of biomass burning were 3.9 ± 2.6 and 5.0 ± 2.2 % at CP and PKUERS, respectively, showing higher concentrations at night. The drastic change in the biomass burning contribution at CP at night was due to occasional burning activities at night. Coal combustion contributed 5.8 ± 5.5 and 4.6 ± 2.6 % of the measured OC at CP and PKUERS. The higher contribution at CP was due to more burning activities in the suburban areas.

The secondary organic sources accounted for 20.2 ± 6.7 % of the organic carbon at CP, with 1.6 ± 0.4 % from isoprene, 4.4 ± 1.5 % from α-pinene, 2.7 ± 1.0 % from β-caryophyllene, and 12.5 ± 3.4 % from toluene. As for PKUERS, the secondary organic sources took up 30.5 ± 12.0 % of the measured OC, in which isoprene was responsible for 2.3 ± 0.9 %, α-pinene for 5.6 ± 1.9 %, β-caryophyllene for 3.6 ± 2.6 %, and toluene for 19.0 ± 8.2 %. Haque et al. (2016) used a tracer-based method to apportion organic carbon and the results showed that biogenic SOC was responsible for 21.3 % of the total OC, with isoprene SOC contributing 17.4 %, α- and β-pinene SOC contributing 2.5 %, and β-caryophyllene SOC contributing 1.4 % in the summer in Alaska, implying significant contributions of biogenic SOA to the loading of organic aerosol. Our results showed that the biogenic SOA concentration was comparable or even high than that at some forest sites in other places of the world (Miyazaki et al., 2012; Stone et al., 2012; Claeys et al., 2004; Kourtchev et al., 2008). The SOA formation mechanism is complicated. A possible reason is the high oxidation capacity in China. The higher oxidation capacity in China may hasten the chemical lifetime of reactive gases and accelerate the aerosol aging process, which leads to an increase in biogenic SOA (Ghirardo et al., 2016). Another possible reason is the complicated emissions of anthropogenic VOCs, which can lead to an enhancement of secondary organic aerosol formation from biogenic precursors (Hoyle et al., 2011). We also compare the isoprene concentration with the forest site according to some literature. Wang et al. (2010) discovered that the mean isoprene concentration was 0.24 ppbv at PKUERS in June 2008. Lappalainen et al. (2009) measured the isoprene concentration of a boreal forest in Hyytiälä and found that the mean concentration of isoprene was 0.15 ppbv. This comparable or even higher concentration of isoprene may be due to different sources of biogenic VOCs. More work is still needed to investigate the SOA formation mechanism under the “air pollution complex” in China.

Stone et al. (2009) discovered that primary and secondary sources accounted for 83 ± 8 % of the measured organic carbon, with primary sources accounting for 37 ± 2 % and SOC contributing 46 ± 6 %, with 16 ± 2 % from biogenic gas-phase precursors and 30 ± 4 % from toluene, using the CMB model and tracer-based method in Cleveland with heavy industries, implying that anthropogenic sources played great roles in the formation of SOA. Our results showed a similar trend as the results published by Stone et al. (2009), in which anthropogenic sources, i.e., toluene-derived SOC, dominated the apportioned SOC. Our research revealed an important point that controlling SOA seems feasible in developing countries like China. It is difficult to control SOA in developed countries, since biogenic SOA is dominant. However, deducting anthropogenic precursors may be an efficient way to reduce SOA pollution in regions where anthropogenic SOA is dominant. On average, 62.8 ± 18.3 and 80.9 ± 27.2 % of the measured OC was apportioned at CP and PKUERS, respectively. About 36.3 ± 18.1 and 29.3 ± 15.6 % of the OC sources remained unknown, which were probably composed of uncharacterized primary or secondary sources. Further research is needed to explain the unapportioned sources of OC.

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 ± 19.1 % of the measured OC in 2016, closely correlated with the increasing contribution of vehicular emissions. Gasoline engines accounted for 18 % of the measured OC, showing an enhancement of 80 % with respect to 2008. This might be related to the rising number of the vehicles in Beijing. In comparison, diesel exhaust decreased by 12.5 % due to the strict control measures made by the government. A 28.5 and 20 % reduction of coal combustion and biomass burning could also be found due to the drastic measures made by the government. Compared with 2008, contributions of secondary organic aerosol decreased by 29.4 %, in which biogenic SOC served as the biggest contributor to the decrease. The formation of biogenic SOA is complicated. Several factors can affect biogenic SOC formation, among which the precursor concentration is one of the crucial factors. Biogenic VOCs, i.e., isoprene, α-pinene, etc., are predominantly emitted from plant foliage in a constitutive manner. The emission rate of biogenic VOCs depends on various factors, e.g., radiation, temperature, humidity, meteorological conditions, and seasonality (Ghirardo et al., 2016). Two or more of them will act synergistically to have an effect on the concentration of isoprene SOC. The changes in the vegetation in a certain area may also play a part in the change in the SOC concentration. Considering its comprehensive synergistic effect, it is difficult to determine the main reason responsible for the isoprene SOC decrease.

Figure 2Comparison of the sources at PKUERS between 2016 and 2008.

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Figure 3Particle sources from different air mass origins.

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Figure 4Correlations between SOC and different influencing factors: (a–b) ozone, (c–d) temperature, (e–f) water, (g–h) H⁺ concentration, and (i–j) sulfate concentration.

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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 β-caryophyllene SOC, vegetative detritus, and gasoline exhaust, the contributions of all other sources decreased, e.g., isoprene SOC, α-pinene SOC, toluene SOC, biomass burning, diesel exhaust, and coal combustion. However, the increases in β-caryophyllene SOC, vegetative detritus, and gasoline exhaust could not compensate for the decreases in other sources. This can be attributed to the larger portion of uncharacterized sources compared with 2008. The uncharacterized sources may mainly contain cooking emissions, mineral and road dust, industrial pollution, and other uncharacterized secondary sources (Tian et al., 2016; Liu et al., 2016). In summary, the contributions of most POA decreased in recent years, except for gasoline exhaust, indicating that more efforts should be made to control gasoline emissions. The apportioned SOC also decreased, with toluene SOC serving as the dominant source. Our results revealed that deducting anthropogenic precursors may be an efficient way to control SOA pollution in China.

4.2 Organic aerosol sources from different air mass origins

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.

4.3 Influencing factors for secondary organic aerosol formation in the summer in Beijing

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.