Special Issue on 2022 Asian Aerosol Conference (AAC 2022) (VII)

Yeonjung Lee1+, Soo Ran Won1+, Hye Jung Shin2, Dae Gon Kim2, Ji Yi Lee  This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 Department of Environmental Engineering and Science, Ewha Womans University, Seoul 03760, Korea
2 Air Quality Research Division, National Institute of Environmental Research, Incheon 22689, Korea
+ These authors contributed equally to this work

Received: November 30, 2022
Revised: May 26, 2023
Accepted: June 2, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.220429  

  • Download: PDF

Cite this article:

Lee, Y., Won, S.R., Shin, H.J., Kim, D.G., Lee, J.Y. (2023). Seasonal Characteristics of Volatile Organic Compounds in Seoul, Korea: Major Sources and Contribution to Secondary Organic Aerosol Formation. Aerosol Air Qual. Res. 23, 220429. https://doi.org/10.4209/aaqr.220429


  • 34 VOCs were measured during the winter and summer in Seoul.
  • Benzene, toluene, ethylbenzene, and xylenes were the majority of 34 VOCs.
  • Main sources in each season were solvent usage and industry/burning of fossil fuels.
  • Aromatic VOCs were the most significant SOA formation contributors.


Volatile organic compounds (VOCs) are major pollutants that cause air pollution and are precursors that react in the air to produce secondary organic aerosol (SOA). This study attempted to elucidate the distribution characteristics of VOCs in the atmosphere of Seoul by measuring 34 types of VOCs in real time in the winter of 2020 and summer of 2021. The objectives of this research are as follows: (1) understand the characteristics of VOCs in Seoul and the difference between winter and summer compositions, (2) identify the main sources of VOCs in winter and summer, and (3) estimate the contribution of VOCs to the SOA formation potential in Seoul.

Total VOC concentrations were found to be higher in summer (7.61 ± 4.22 ppb) than in winter (6.28 ± 4.11 ppb). To further specify the cause of the difference in major VOC components in winter and summer, a cause analysis was performed using the ratio between marker components, and an emission source analysis of VOCs was performed by applying the positive matrix factorization model (PMF). The source distribution of VOCs in Seoul was attributed to five factors: solvent usage, vehicle exhaust, industry/burning of fossil fuels, petrochemical industry, and road emission (winter)/gasoline-related (summer). The contribution of VOCs to SOA formation was estimated using the secondary organic aerosol formation potential. The results showed that toluene was the primary contributor to SOA formation in both winter and summer. In the summer, solvent usage containing high proportion of ethylbenzene and xylenes contributed more than twice as much to SOA formation compared to the winter.

Keywords: Seoul, Volatile organic compounds, Seasonal variation, Source apportionment, Secondary organic aerosol formation potential


Volatile organic compounds (VOCs) generally refer to all organic compounds with a boiling point below 50°C–260°C at standard atmospheric pressure and a melting point below room temperature, including alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, and halogenated VOCs (Zhang et al., 2017). VOCs are ubiquitous in the atmosphere and are emitted from various natural and anthropogenic sources such as vehicle exhaust, oil refineries, paint/solvent use, construction materials, tobacco smoke, various trees or forests, and wildfires (Bari and Kindzierski, 2018; Na et al., 2005). Additionally, ambient VOCs chemically interact with sunlight and nitrogen oxides to form ground-level ozone and are a representative precursor of secondary organic aerosol (SOA). VOCs may adversely affect human health by causing breathing difficulties, headaches, and carcinogenesis (He et al., 2015; Keller and Burtscher, 2017; Srivastava et al., 2018; Wang et al., 2013Zielinska et al., 1996; Zhang et al., 2017). For example, in 1986, among aromatic VOCs, benzene was designated by the United States Environmental Protection Agency (U.S. EPA) Integrated Risk Information System as a human carcinogen (Group A) that caused leukemia (U.S. EPA, 2000). Considering these dangers, VOCs are classified and managed as hazardous air pollutants (HAPs) (Bari and Kindzierski, 2017). Among HAPs, benzene is the only species that is managed according to air environment standards in Korea.

Research on VOCs, particularly with regard to indoor air quality, is being actively conducted both in Korea and abroad; moreover, most studies have been conducted in various indoor environments such as shopping malls, houses, and offices (Edwards et al., 2001; Goodman et al., 2017; Guo, 2011; Won et al., 2021b). Overseas, studies have been conducted on the measurement of ambient VOC source apportionment and risk assessment (Al-Naiema et al., 2018; Bari and Kindzierski, 2017; Liao et al., 2015; Su et al., 2019; Zhang et al., 2017; Zheng et al., 2019). In Korea, many studies focusing on monitoring VOCs and their source apportionment and risk assessment have been conducted (Baek et al., 2015; Choi et al., 2011; Kwon et al., 2021; Na et al., 2005). As a measurement method used in Korea, gas chromatography coupled with flameionization detection (GC-FID) or gas chromatography-mass spectrometry (GC-MS) analysis is performed using a Tedlar bag, canister, and adsorption tube as suggested by the U.S. EPA (Son et al., 2018). Although high-resolution instruments such as SIFT-MS and PTR-MS, which measure VOCs without sampling tools, have recently been developed, they are yet to be utilized in Korea.

Seoul is the capital of the Republic of Korea and one of the smaller areas (650 km2) among the country’s 17 administrative districts (KOSIS, 2022a). Nevertheless, it has the highest population (9.6 million people) (KOSIS, 2022b) and the highest VOC emissions per unit area among all districts. The annual VOC emissions showed a steady increase from the 1999 survey to 2019. Solvent use, a source of VOC emissions, accounted for approximately half of the emissions, followed by industrial processes, biomass burning, transport sources, and waste disposal (NAIR, 2022). Kim and Lee (2018) reported the severity of air pollution in the Seoul metropolitan area (SMA) and suggested that identification of the source of VOCs, precursors of ozone and aerosol, should be prioritized for policy implementation. Therefore, the present study aimed to elucidate the characteristics of VOCs in Seoul to enable their efficient management. The hourly and seasonal characteristics and sources of VOCs generated in Seoul were identified using a high-resolution VOC instrument (30 min) and the effect of VOCs on SOA confirmed their contribution.


2.1 Measurements

The measurements were conducted on the roof of the Seoul Metropolitan Air Environment Research Institute (37.61°N, 126.93°E), which is located in northwestern Seoul, as shown in Fig. 1. Residential, commercial, and green were distributed around the sampling site. Measurements were performed for 32 days (December 15, 2020–January 15, 2021) during winter and for 30 days (June 1, 2021–June 30, 2021) during summer.

 Fig. 1. The location of sampling site in Seoul, South Korea.Fig. 1. The location of sampling site in Seoul, South Korea.

For measurement, VOCs corresponding to C6–C12 in the atmosphere were observed at 30-min intervals using a real-time ozone precursor analyzer GC955-611 (Synspec, Netherlands). When the sample collected from GC955-611 was concentrated in an internal pre-concentration tube, it was separated from column SY-1 (30 m in length, 0.32 mm in internal diameter; Synspec, Netherlands) and analyzed using a photoionization detector. A total of 34 compounds (34 VOCs) were analyzed using GC955-611, including 16 aromatic VOCs, such as benzene and toluene; 16 alkanes, including n-hexane and n-octane; and two alkenes, cyclohexane and methylcyclohexane. During the winter and summer measurement periods, the 34 VOCs were calibrated using standard mixed gas (Rigas, Korea) before sampling. Most of the correlation coefficients for the standard gas containing 34 VOCs were greater than 0.99.

2.2 PMF Model

The positive matrix factorization (PMF) model, first introduced by Paatero and Tapper (1994), is a statistical analysis that excludes the negative value of the factor load by considering the uncertainty and standard deviation of the measured data value and computes it as a positive value to enable quantitative estimation. The ultimate goal of PMF analysis is to quantify the effects of specific sources on receptors, and it is widely used to identify multiple sources of pollutants such as aerosols and VOCs (Su et al., 2019; Zheng et al., 2019).

The EPA PMF 5.0 user guide was followed for data preprocessing, 24 and 27 of the 34 measured compounds in winter and summer, respectively, were used, excluding ten species in winter and seven species in summer owing to their low signal-to-noise ratio. The method detection limit for the instrument was not measured; lower detection limit (LDL) values were used instead. For the input data, the missing value was replaced with the median value of the component, and a concentration corresponding to one-fourth of LDL was entered for a value smaller than LDL. If the uncertainty was ≤ LDL, LDL was multiplied by 5/6. If a concentration value exceeded LDL, the uncertainty was calculated using Eq. (1) (Norris and Duvall, 2014):


The error fraction can be estimated from the precision of replicate measurements, but when the results for the precision are not available, it can be used the relative standard deviation or 5 to 20% of photochemical activity or VOC concentration (Abeleira et al., 2017; Xie et al., 2021). In this study, 10%, which is the most frequently used value for the error fraction was applied to all species (Huang et al., 2022; Liu et al., 2016; Song et al., 2019; Xie et al., 2021; Yang et al., 2022). The PMF models are described in greater detail by Paatero and Tapper (1994) and Norris and Duvall (2014). Information on the values used in the process of operating the PMF in this study is presented in Table 1.

Table 1. The input data for PMF analysis in this study.

2.3 Contribution of VOCs to SOA

VOCs are among the most important precursors of SOA, which constitute a large proportion of PM2.5 (Zhang et al., 2017). Since the early 1990s, several studies have been conducted to characterize the yield of SOA from VOCs using chamber and chemical models (Ng et al., 2007; Odum et al., 1996; Pandis et al., 1992). In this study, two methods of SOAFP (secondary organic aerosol formation potential, SOAI) and SOAP (secondary organic aerosol potential, SOAII) were compared to investigate SOA formation by VOCs. The first method calculated SOAI using the SOA yield of each compound reported in Shin et al. (2013). In this study, SOAI was obtained by multiplying the VOC concentration value with the SOA yield, using Eq. (2):


where VOCi represents the measured concentration (ppb) of VOC individual compounds i, SOAyield,i (µg m3 ppm1) is the SOA yield produced by i components of VOCs (Table 2).

Table 2. The VOCs compounds with SOAyield,i and SOAyield-to-toluene,i.

The second method involved calculating the SOAII to obtain toluene-based SOA, which was then compared with the trends for the generated secondary aerosols. The concept of SOAII was developed by Derwent et al. (2010) to reflect the tendency of each organic compound to form SOA on the same mass emission basis as toluene. SOAII differs from the existing SOAI as it is based on the toluene SOA yield (100). Toluene was selected as the base compound for SOAII because its emission properties are better known than those of other compounds and it is an important precursor for SOA formation (Hui et al., 2019; Zhao et al., 2023). The method of calculating SOAyield-to-toluene,i is shown in Eq. (3):


here, the SOAyield-to-toluene,i values used the values reported by Derwent et al. (2010). The SOAII for each compound was calculated by multiplying SOAyield-to-toluene,i with the concentration of each VOC species observed in this study as follows Eq. (4):


where the VOCi represents the concentrations (ppb) of VOC each compound i, and the unit of SOAyield-to-toluene,i was not available (Table 2). The SOAI and SOAII values were calculated based on individual compounds for which yield values were available.

Additionally, the secondary organic carbon (SOC) method based on the ratio of organic carbon (OC) and elemental carbon (EC) included in PM2.5 was also compared. The PM2.5, OC, and EC data used in this study were measured simultaneously during the measurement period and location and were provided by the National Institute of Environmental Research, Korea. SOC can be obtained using several methods. In this study, SOC was estimated from the measured OC and EC concentrations (µg m3) according to the method of Yoo et al. (2020) using Eq. (5) by employing the EC trace method:

where (OC/EC)pri was calculated through regression analysis of the OC and EC concentrations with an OC/EC ratio lower than 5%.


3.1 Characteristics of VOCs during Winter and Summer

The concentrations of VOCs measured in Seoul during the winter of 2020 and summer of 2021 and the winter and summer ratios are listed in Table 3. The temperature at the sampling site using the automatic weather system (AWS) data of the Korea Meteorological Administration (KMA) was −5.80 ± 7.16°C in winter and 21.3 ± 3.94°C in summer, showing a large difference, and the seasonal difference was statistically significant (p < 0.05). Insolation in Seoul, where automated synoptic observing system (ASOS) data were measured, was 0.82 ± 0.67 MJ m2 (8:00 AM–6:00 PM) in winter and 1.25 ± 1.05 MJ m2 (6:00 AM–8:00 PM) in summer (KMA, https://data.kma.go.kr). In the case of the 34 VOCs, the arithmetic average and standard deviations were found to be 6.28 ± 4.11 and 7.61 ± 4.22 ppb during winter and summer, respectively, and the concentration in summer was not only 1.2 times higher than that in winter but also statistically significant (p < 0.05). The correlation (r) between the 34 VOCs and temperature and insolation was low at 0.21 and 0.10, respectively, but it was statistically significant (p < 0.05).

Table 3. The arithmetic mean and standard deviation of VOCs concentrations (ppb), SOAI (µg m–3) and SOAII (ppb) of winter and summer measured in Seoul.

Toluene had the highest concentration among the individual compounds in both winter and summer. The ratio of BTEX (benzene, toluene, ethylbenzene, and xylenes) to 34 VOCs was 45.4% (2.85 ppb) and 47.3% (3.60 ppb) during winter and summer, respectively, accounting for the majority of the 34 VOCs. Comparing the results of this study with those of studies conducted in other urban cities revealed that the BTEX concentrations in Seoul was lower than that in Chengdu (5.29 ppb, measured in 2016) in China, but higher than that in Beijing (1.87 ppb, measured in 2018) and New York (3.52 µg m3 ≒ 0.82 ppb under standard condition, measured during 2015–2019) (Li et al., 2022; Song et al., 2018; Paul and Bari, 2022). As suggested by Li et al. (2022), global VOC concentrations have gradually decreased in recent times, and the reduction of VOC emissions in New York has also shown a decreasing trend in recent years, as reported by Paul and Bari (2022). Compared with the VOCs measured in this study (2020–2021), the concentration of BTEX (11.4–13.6 ppb) measured in Seoul during 1997–1999 was approximately five times higher (Na et al., 2005). In a study by Shin et al. (2013), which was performed in Seoul from 2004 to 2008, the concentration of VOCs (40.9 ppb) was approximately 10 times higher than that in this study; however, the proportions of aromatic VOCs to 34 VOCs were similar. The overall concentration has decreased owing to Korea’s air quality policy since 2005. In 2003, the Ministry of Environment (MOE) of Korea regulated a law to improve the air environment in the SMA and manage air pollution sources. As reported by Kim and Yeo (2013), primary pollutants such as SO2 and CO have clearly decreased since the 2000s compared to the 1980s primarily owing to various policies implemented in the early 2000s. Additionally, annual average concentrations of benzene have shown a tendency to rapidly decrease since 2009.

The compound with the highest summer-to-winter (S/W) ratio was 2,3,4-trimethylpentane, followed by ethylbenzene and cyclohexane. 2,3,4-trimethylpentane has been reported to be highly correlated with gasoline vehicle emissions (Chang et al., 2006; Song et al., 2020). The concentration of 2,3,4-trimethylpentane during winter was low, and the S/W ratio was relatively high. Ethylbenzene and cyclohexane are known ozone precursors (Grosjean and Grosjean, 1997; Jia and Xu, 2013), and their potential sources are paint solvent usage and industrial production (Song et al., 2018). Conversely, compounds with high concentrations during winter were benzene and m-diethylbenzene. Benzene is classified as a specified hazardous air pollutant in the Clean Air Conservation Act of the MOE of Korea and an ozone precursor (Ng et al., 2007). Although m-diethylbenzene was reported to be toxic by Hartwig (2019), this was insufficient to classify it as a carcinogen. Similar to benzene, m-diethylbenzene showed a relatively low S/W ratio owing to its low concentration in summer.

Fig. 2 shows the diurnal patterns of the hourly average for 34 VOCs and BTEX during the summer and winter. The 34 VOCs in winter increased between 8:00 AM–11:00 AM during rush hour, and the highest concentration (7.14 ± 3.91 ppb) was measured around noon. This is likely affected by increased human activity and traffic in the morning. Subsequently, the 34 VOCs decreased and showed a minimum concentration of 5.58 ± 3.44 ppb around 7:00 PM. In summer, the 34 VOCs showed an increasing pattern similar to that in winter in the morning; however, they increased steadily after noon till around 5:00 PM and had the highest concentration of 9.60 ± 5.13 ppb. This is presumably due to the stronger insolation and temperature in summer than in winter. By comparing the diurnal patterns of the individual BTEX components, the summer and winter trends of each BTEX component were confirmed to be similar; however, the diurnal patterns between the components were found to be different. Although the concentration of benzene in winter was significantly high compared to summer (p < 0.05), the diurnal patterns between summer and winter were similar unlike other components. The emission sources of benzene are known to be vehicles and heating (Elbir et al. 2007); thus, the concentration increased because of heating in winter.

  Fig. 2. Diurnal pattern of (a) 34 VOCs and (b) BTEX compounds.Fig. 2. Diurnal pattern of (a) 34 VOCs and (b) BTEX compounds.

3.2 Source Apportionment by PMF Model

Source apportionment was performed using PMF, and five factors were identified according to the contributions of the representative markers (Fig. 3). VOC sources in winter and summer were classified into five factors. Four factors (solvent usage, petrochemical industry, industry/burning of fossil fuel, and vehicle exhaust) were common; however, road emission and gasoline-related sources were divided into different factors in winter and summer.

Fig. 3. Source apportionment by PMF in (a) winter and (b) summer.Fig. 3. Source apportionment by PMF in (a) winter and (b) summer.

Factor 1 was found to be highly loaded with ethylbenzene and xylenes in both winter and summer, contributing to 1.27 ppb (20.7%) and 2.44 ppb (28.4%) of the total, respectively. These species are commonly emitted from paints, building coatings, and asphalt (Dehghani et al., 2017; Liu et al., 2008; Won et al., 2021b; Zheng et al., 2019), and factor 1 represents “Solvent usage”. The daily variation of solvent usage in winter and summer was similar to the insolation pattern, increased from the morning, showed the highest concentration around noon (1.63 ± 1.41 and 2.84 ± 1.93 ppb in winter and summer, respectively), and showed a tendency to decrease (Fig. 4). Xylenes have a shorter lifetime (11.8 hour, 19.4 hour, and 20.3 hour in m-xylene, p-xylene, and o-xylene, respectively) compared to ethylbenzene (1.6 days) which is more stable in the atmosphere (Ho et al., 2004). Therefore, the m, p-xylene-to-ethylbenzene (X/E) ratio was used as an indicator to determine whether the air mass was affected by local sources or photochemical aging. If the X/E ratio is less than 3, it can be regarded as aging due to long-range transport or photochemical activity (Hui et al., 2020; Ibragimova et al., 2021) and the ranges of X/E ratios were between 2.5 and 2.9 in urban areas (Hui et al., 2020). The average X/E ratio measured in this study was 2.53 and 0.99 in winter and summer, respectively, indicating that the air mass was generally aging, and that the photochemical reaction. It has been reported that ethylbenzene and xylenes are easily removed by OH radicals, and their concentrations tend to decrease when photochemical activity is strong (Hui et al., 2020). In Fig. 2, ethylbenzene and xylenes were decreased around noon in summer and this was might due to strong photochemical activity.

Factor 2 had a large proportion of toluene, 3-methylpentane, and n-hexane in both winter (1.64 ppb, 26.5%) and summer (2.16 ppb, 25.1%). These compounds are associated with vehicle emissions, gasoline, and diesel fuel evaporation (Chan et al., 2006; Hui et al., 2020; Song et al., 2018). As for the vehicle exhaust source, the increase in rush hour in winter can be clearly observed in Fig. 4. Therefore, this factor was classified as “Vehicle exhaust”. In summer, n-heptane, 2,2,4-trimethypentane, and methylcyclohexane were highly loaded, and these components have also been reported to be affected by vehicle emissions, fuel additives, and diesel evaporation (Liu et al., 2008; McCarthy et al., 2013; Zweldlinger et al., 1990).

Fig. 4. Temporal variation of sources in (a) winter and (b) summer.Fig. 4Temporal variation of sources in (a) winter and (b) summer.

In the case of Factor 3, benzene, n-nonane, n-decane, and C8–C9 aromatic VOCs such as ethyltoluenes and trimethylbenzenes had the highest contribution in both winter (1.93 ppb, 31.3%) and summer (1.92 ppb, 22.3%). C8–C9 aromatics, n-nonane, and n-decane are related to vehicle and industrial production processes (Hui et al., 2020; Song et al., 2018), and benzene is an indicator of fossil fuel combustion emissions. In northern China, the use of fossil fuels for heating has been shown to increase benzene concentrations during winter (Zhang et al., 2015). The meteorological data of the KMA revealed that the winter contribution rate in Seoul may have increased owing to the influence of heating fuel used in northeast China, as the northwest wind mainly blows in winter. Industry/burning of fossil fuel sources gradually increased from around 9:00 AM and peaked in the afternoon (Fig. 4). There was no significant change from the evening to the morning, and the range of variations was larger in summer than in winter. The daily variations of Factor 3 in summer were estimated to indicate emissions during industry operating hours, excluding long-range transport by heating effects in winter. Therefore, Factor 3 was identified as “Industry/burning of fossil fuels”.

The ratio of cyclohexane was highest in factor 4, which is known to be a tracer of petrochemical complexes (Hsu et al., 2018; Hui et al., 2020; Liu et al., 2008). In Fig. 3, the VOCs concentrations in Factor 4 were similar in both winter and summer, as 0.99 ppb and 0.71 ppb, respectively. The petrochemical industry sources should be steady emissions without diurnal variation in both winter and summer, indicating that there was no local impact. Therefore, Factor 4 was classified as the “Petrochemical industry”. The measurement site is not only influenced by a large-scale coal-fired power plant located approximately 50 km away in the southwest (Won et al., 2021a) but it also appears that the winter contribution may have increased because of the impact of air mass transport from northeast China in winter, as noted previously.

The last factor was named “Road emission” (0.34 ppb, 5.54%) and “Gasoline-related” (1.38 ppb, 16.0%) because the winter and summer factors displayed notable differences. In winter, n-octane exhibits a high loading, which is known to be released from asphalt or paint (Hui et al., 2020; Liu et al., 2008; Zheng et al., 2018). At a distance of 150 m from the sampling site, there is a round-trip, six-lane road that is paved with asphalt. In Fig. 4(a), daily variation of the road emission source is not observed, and because the contribution is low, it appears to be a steady emission. This is similar to a study that showed that asphalt sources do not have a large daily change compared to other sources and have the smallest contribution (Zheng et al., 2018). In summer, the contribution of 3-methylhexane and 2,3,4-trimethypentane was high loading; moreover, they have been reported to be emitted from vehicle emissions or industrial sources (Chang et al., 2006; Song et al., 2018; Song et al., 2020; Zweldlinger et al., 1990). According to previous studies, 3-methylhexane and 2,3,4-trimethylpentane showed good correlation with methyl tert-butyl ether, a motor vehicle indicator, and were classified as potential VOC tracers that could distinguish gasoline vehicles (Chang et al., 2006; Song et al., 2020). Unlike the vehicle exhaust of Factor 2, there is no noticeable daily variation (Fig. 4(b)); thus, it appears to be steadily emitted, which is presumed to be related to the presence of three gas stations within a radius of 1 km. For this factor, a detailed discussion through data analysis, such as using the conditional probability function, is apparently necessary in the future.

3.3 SOA Formation from Each VOCs

According to Li et al. (2020), VOCs and PM2.5 show good seasonal correlation, and identification of the precursor VOCs of SOA is critical in controlling PM2.5. In particular, anthropogenically and biologically derived VOCs can produce SOA via photochemical reactions. Although there was no seasonal variation in the concentration of EC (0.88 µg m–3 in winter, 0.74 µg m–3 in summer), the concentration of OC was higher in the summer (3.66 µg m–3) than it was in the winter (3.34 µg m–3), and its daily variation followed a pattern similar to that of PM2.5 in summer (Fig. 5(a)). In other words, the concentration of SOC in OC was higher in summer (2.46 µg m–3) than in winter (1.33 µg m–3) and correlated well with PM2.5, implying that secondary formation was active in summer. The correlations (r) of PM2.5 and SOC were high, at 0.72 and 0.73 in both winter and summer, respectively (p < 0.05). SOAI and SOAII were higher in summer (2.29 µg m–3 in SOAI and 375 ppb in SOAII) than in the winter (2.20 µg m–3 in SOAI and 309 ppb in SOAII), similar to the SOC. In Fig. 5(b), the results confirmed that the trends of the three methods (SOAI, SOAII, and SOC) were similar, and the validity of the SOA yield method was confirmed. The two SOA yield methods showed trends similar to SOC and PM2.5, and the correlation (r) with SOC was high for SOAI and SOAII, at 0.47 and 0.53, respectively (p < 0.05). Toluene is the primary contributor to both SOAI and SOAII (Table 3 and Fig. 6); however, the SOAI method indicates that the absolute concentration of individual compounds when applying SOA yield makes a significant contribution to SOA formation. Conversely, because SOAII sets toluene as 100 and applies a relative value to SOA formation, aromatic VOCs were predominant. The correlation between SOAI and SOAII is strong, at 0.97 and 0.98 in winter and summer, respectively (p < 0.05), which suggests that both methods are reliable for representing SOA formation.

 Fig. 5. Comparison of daily patterns for the three methods (SOAI, SOAII, and SOC) using (a) SOC through OC and EC configured in PM2.5 and (b) SOC, SOAI and SOAII.Fig. 5Comparison of daily patterns for the three methods (SOAI, SOAII, and SOC) using (a) SOC through OC and EC configured in PM2.5 and (b) SOC, SOAI and SOAII.

The compound that formed the most SOA was toluene, and the top 10 compounds (including toluene, benzene, xylenes, ethylbenzene, n-decane, and 1,2,4-trimbenzene) accounted for more than 95% of the SOAI and SOAII (Table 3). In particular, BTEX was found to be involved in the formation of most SOA, accounting for 63% of SOAI and 92% of SOAII. According to the study by Li et al. (2020), it was found that the top 10 contributing species, which included BTEX, were all aromatic VOCs, which accounted for more than 95% of the total SOA. Additionally, Zhang et al. (2017) reported that BTEX contributes to SOA formation by 80% or more. When the diurnal pattern for the top 10 compounds was examined, as shown in Fig. 6. Although, the methods used to estimate SOA differed, the trends of concentrations were similar. The highest SOA production was observed at noon in winter; however, the diurnal variation was not significant. Conversely, during summer, the diurnal pattern rapidly increased, and SOA production was active roughly 9:00 AM–5:00 PM. In Fig. 6(a), it can be observed that for SOAI, toluene and benzene were major contributors to SOA formation during the winter, while toluene and n-decane played significant roles in SOA formation during the summer. In Fig. 6(b), toluene was found to contribute significantly to SOA formation in both winter and summer. Additionally, in summer, ethylbenzene and xylenes appeared to be mostly involved in SOA formation.

Fig. 6. SOA hourly variations in winter and summer by (a) SOAI and (b) SOAII.Fig. 6. SOA hourly variations in winter and summer by (a) SOAI and (b) SOAII.

In this study, the formation of SOA was examined by applying the SOA yield to the concentration of each species obtained through factor profile by PMF modeling (Fig. 7). During winter, the industry/burning of fossil fuels accounted for the highest proportion of SOAI (42.9%, 0.89 µg m–3). However, there was no significant difference in vehicle exhaust (28.6%, 84.4 ppb), industry/burning of fossil fuels (27.0%, 79.8 ppb), and solvent usage (26.9%, 79.5 ppb) for SOAII in winter. It has been shown that the contribution of SOA in winter differs due to the higher SOA yield from benzene, n-nonane, and n-decane contained in the industrial emissions of SOAI compared to SOAII. Conversely, during summer, the SOAI (31.9%, 0.67 µg m–3) and SOAII (55.7%, 194 ppb) by the solvent usage source was the most significant. The benzene contributions from the industry/burning of fossil fuel source decreased rapidly during summer, while the ethylbenzene, and xylenes from solvent usage increased. Among the sources, vehicle exhaust sources showed a constant SOAI (20.3%, 0.42 µg m–3 in winter and 21.9%, 0.46 µg m–3 in summer) and SOAII (28.6%, 84.4 ppb in winter and 27.6%, 96.1 ppb in summer) in winter and summer, implying that a significant amount of SOA is formed from toluene emitted to solvent usage rather than vehicle exhaust. In addition, this implies that VOCs management can also be used to manage SOA and PM2.5 through the derivation of emission source priorities and components to be controlled by SOA formation for each source.

 Fig. 7. SOA contributions by each source in winter and summer of SOAI and SOAII, (a) concentration and (b) proportion.Fig. 7. SOA contributions by each source in winter and summer of SOAI and SOAII, (a) concentration and (b) proportion.


In this study, 34 VOC compounds were investigated using a high-resolution instrument in the winter of 2020 and summer of 2021 to identify the seasonal characteristics of VOCs and the emission sources of VOCs in Seoul and confirm the contribution of VOCs to SOA.

During the monitoring period, the average mixing ratio of VOCs in Seoul was 6.28 ± 4.11 ppb in winter and 7.61 ± 4.22 ppb in summer. Toluene occupied the highest proportion, followed by n-hexane, n-nonane, m, p-xylene, and 3-methylpentane, ethylbenzene. The emission sources in Seoul were divided into five factors following the implementation of the PMF model: solvent usage, vehicle exhaust, industry/burning of fossil fuels, petrochemical industry, and road emission (winter)/gasoline-related (summer). Compared to summer, the contribution of industry/burning of fossil fuel increased in winter. The proportion of solvent usage increased in summer. There was no seasonal difference in vehicle exhaust, but in the daily variation in winter, there was a clear increase and decrease during rush hours. The three methods (SOAI, SOAII, and SOC) of calculating SOA generated by VOCs showed similar results, and greater SOA formation occurred in summer than in winter. The compound that contributed the most to SOA formation was toluene, and the top 10 compounds, accounted for more than 95% of the SOA formed. By each emission source, industry/burning of fossil fuel and solvent usage had a high possibility of SOA formation in winter and summer, respectively. In the diurnal pattern, the production of SOA was significantly influenced by benzene in winter and n-decane, ethylbenzene, and xylenes in the summer. In Seoul, a considerable amount of SOA is understood to be produced by aromatic VOCs. Therefore, managing SOA through aromatic VOCs by a major sources reduction policy is expected to have some impact on reducing PM2.5.


This research was supported by the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) project through the National Research Foundation of Korea funded by the Ministry of Science and ICT (NRF: 2020M3G1A1114537) and by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF: 2022R1I1A1A01066647).


  1. Abeleira, A., Pollack, I.B., Sive, B., Zhou, Y., Fischer, E.V., Farmer, D.K. (2017). Source characterization of volatile organic compounds in the Colorado Northern Front Range Metropolitan Area during spring and summer 2015. J. Geophys. Res. 122, 3595–3613. https://doi.org/10.1002/2016​JD026227

  2. Al-Naiema, I.M., Hettiyadura, A.P.S., Wallace, H.W., Sanchez, N.P., Madler, C.J., Cevik, B.K., Bui, A.A.T., Kettler, J., Griffin, R.J., Stone, E.A. (2018). Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols. Atmos. Chem. Phys. 18, 15601–15622. https://doi.org/10.5194/acp-18-15601-2018

  3. Baek, S.O., Suvarapu, L.N., Seo, Y.K. (2015). Occurrence and concentration of toxic VOCs in the ambient air of Gumi, an electronics-industrial city in Korea. Sensors 15, 19102–19123. https://doi.org/10.3390/s150819102

  4. Bari, M.A., Kindzierski, W.B. (2017). Concentrations, sources and human health risk of inhalation exposure to air toxics in Edmonton, Canada. Chemosphere 173, 160–171. https://doi.org/​10.1016/j.chemosphere.2016.12.157

  5. Bari, M.A., Kindzierski, W.B. (2018). Ambient volatile organic compounds (VOCs) in Calgary, Alberta: Sources and screening health risk assessment. Sci. Total Environ. 631–632, 627–640. https://doi.org/10.1016/j.scitotenv.2018.03.023

  6. Chan, L.Y., Chu, K.W., Zou, S.C., Chan, C.Y., Wang, X.M., Barletta, B., Blake, D.R., Guo, H., Tsai, W.Y. (2006). Characteristics of nonmethane hydrocarbons (NMHCs) in industrial, industrial-urban, and industrial-suburban atmospheres of the Pearl River Delta (PRD) region of south China. J. Geophys. Res. 111, D11304. https://doi.org/10.1029/2005JD006481

  7. Chang, C.C., Wang, J.L., Liu, S.C., Lung, S.C.C. (2006). Assessment of vehicular and non-vehicular contributions to hydrocarbons using exclusive vehicular indicators. Atmos. Environ. 40, 6349–6361. https://doi.org/10.1016/j.atmosenv.2006.05.043

  8. Choi, E., Choi, K., Yi, S.M., (2011). Non-methane hydrocarbons in the atmosphere of a Metropolitan City and a background site in South Korea: Sources and health risk potentials. Atmos. Environ. 45, 7563–7573. https://doi.org/10.1016/j.atmosenv.2010.11.049

  9. Dehghani, M.H., Sanaei, D., Nabizadeh, R., Nazmara, S., Kumar, P. (2017). Source apportionment of BTEX compounds in Tehran, Iran using UNMIX receptor model. Air Qual. Atmos. Health 10, 225–234. https://doi.org/10.1007/s11869-016-0425-0

  10. Derwent, R.G., Jenkin, M.E., Utembe, S.R., Shallcross, D.E., Murrells, T.P., Passant, N.R. (2010). Secondary organic aerosol formation from a large number of reactive man-made organic compounds. Sci. Total Environ. 408, 3374–3381. https://doi.org/10.1016/j.scitotenv.2010.04.013

  11. Edwards, R.D., Jurvelin, J., Koistinen, K., Saarela, K., Jantunen, M. (2001). VOC source identification form personal and residential indoor, outdoor and workplace microenvironment samples in EXPOLIS0Helsinki, Finland. Atmos. Environ. 35, 4829–4841. https://doi.org/10.1016/S1352-2310(01)00271-0

  12. Elbir, T., Cetin, B., Cetin, E., Bayram, A., Odabasi, M. (2007). Characterization of volatile organic compounds (VOCs) and their sources in the air of Izmir, Turkey. Environ. Monit. Assess. 133, 149–160. https://doi.org/10.1007/s10661-006-9568-z

  13. Goodman, N.B., Steinemann, A., Wheeler, A.J., Paevere, P.J., Cheng, M., Brown, S.K. (2017). Volatile organic compounds within indoor environments in Australia. Build. Environ. 122, 116–125. https://doi.org/10.1016/j.buildenv.2017.05.033

  14. Grosjean, E., Grosjean, D. (1997). Gas phase reaction of alkenes with ozone:  Formation yields of primary carbonyls and biradicals. Environ. Sci. Technol. 31, 2421–2427. https://doi.org/​10.1021/es970075b

  15. Guo, H. (2011). Source apportionment of volatile organic compounds in Hong Kong homes. Build. Environ. 46, 2280–2286. https://doi.org/10.1016/j.buildenv.2011.05.008

  16. Hartwig, A. (2019). Diethylbenzene (all isomers) [MAK value documentation, 2018], in: Deutsche Forschungsgemeinschaft, Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area (Eds.), The MAK-Collection for Occupational Health and Safety, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, pp. 1100–1129. https://doi.org/​10.1002/3527600418.mb13501isme6519

  17. He, Q., Yan, Y., Li, H., Zhang, Y., Chen, L., Wang, Y. (2015). Characteristics and reactivity of volatile organic compounds from non-coal emission sources in China. Atmos. Environ. 15, 153–162. https://doi.org/10.1016/j.atmosenv.2015.05.066

  18. Ho, K.F., Lee, S.C., Guo, H., Tsai, W.Y. (2004). Seasonal and diurnal variations of volatile organic compounds (VOCs) in the atmosphere of Hong Kong. Sci. Total Environ. 322, 155–166. https://doi.org/10.1016/j.scitotenv.2003.10.004

  19. Hsu, C.Y., Chiang, H.C., Shie, R.H., Ku, C.H., Lin, T.Y., Chen, M.J., Chen, N.T., Chen, Y.C. (2018). Ambient VOCs in residential areas near a large-scale petrochemical complex: Spatiotemporal variation, source apportionment and health risk. Environ. Pollut. 240, 95–104. https://doi.org/​10.1016/j.envpol.2018.04.076

  20. Huang, A., Yin, S., Yuan, M., Xu, Y., Yu, S., Zhang, D., Lu, X., Zhang, R. (2022). Characteristics, source analysis and chemical reactivity of ambient VOCs in a heavily polluted city of central China. Atmos. Pollut. Res. 13, 101390. https://doi.org/10.1016/j.apr.2022.101390

  21. Hui, L., Liu, X., Tan, Q., Feng, M., An, J., Qu, Y., Zhang, Y., Cheng, N. (2019). VOC characteristics, sources and contributions to SOA formation during haze events in Wuhan, Central China. Sci. Total Environ. 650, 2624–2639. https://doi.org/10.1016/j.scitotenv.2018.10.029

  22. Hui, L., Liu, X., Tan, Q., Feng, M., An, J., Qu, Y., Zhang, Y., Deng, Y., Zhai, R., Wang, Z. (2020). VOC characteristics, chemical reactivity and sources in urban Wuhan, central China. Atmos. Environ. 224, 117340. https://doi.org/10.1016/j.atmosenv.2020.117340

  23. Ibragimova, O.P., Omarova, A., Bukenov. B., Zhakupbekova, A., Baimatova, N. (2021). Seasonal and spatial variation of volatile organic compounds in ambient air of Almaty City, Kazakhstan. Atmosphere 12, 1592. https://doi.org/10.3390/atmos12121592

  24. Jia, L., Xu, Y. (2013). Effects of relative humidity on ozone and secondary organic aerosol formation from the photooxidation of benzene and ethylbenzene. Aerosol Sci. Technol. 48, 1–12. https://doi.org/10.1080/02786826.2013.847269

  25. Keller, A., Burtscher, H. (2017). Characterizing particulate emissions from wood burning appliances including secondary organic aerosol formation potential. J. Aerosol. Sci. 114, 21–30. https://doi.org/10.1016/j.jaerosci.2017.08.014

  26. Kim, Y.P., Yeo, M.J. (2013). The trend of the concentrations of the criteria pollutants over Seoul. J. Korean Soc. Atmos. Environ. 29, 369–377. https://doi.org/10.5572/KOSAE.2013.29.4.369 (in Korean)

  27. Kim, Y.P., Lee, G. (2018). Trend of air quality in Seoul: Policy and science. Aerosol Air Qual. Res. 18, 2141–2156. https://doi.org/10.4209/aaqr.2018.03.0081

  28. Korean Statistical Information Service (KOSIS) (2022a). KOSIS - Land use - Cadastral statistics - Lands by province, Land category (districts/counties). (accessed 11 October 2022).

  29. Korean Statistical Information Service (KOSIS) (2022b). KOSIS - Population - Population by census (2015~) - Population, Households and Housing Units. (accessed 11 October 2022).

  30. Kwon, S., Choi, Y., Park, M., Lee, H., Kim, G., Yoo, S., Cho, S., Shin, J., Shin, Y., Lee, C. (2021). Health risk assessment with source apportionment of ambient volatile organic compounds in Seoul by positive matrix factorization. J. Environ. Health Sci. 47, 384–397. https://doi.org/10.5668/​JEHS.2021.47.5.384 (in Korean)

  31. Li, C., Liu, Y., Cheng, B., Zhang, Y., Liu, X., Qu, Y., An, J., Kong, L., Zhang, Y., Zhang, C., Feng, M. (2022). A comprehensive investigation on volatile organic compounds (VOCs) in 2018 in Beijing, China: Characteristics, sources and behaviours in response to O3 formation. Sci. Total Environ. 806, 150247. https://doi.org/10.1016/j.scitotenv.2021.150247

  32. Li, Q., Su, G., Li, C., Liu, P., Zhao, X., Zhang, C., Sun, X., Mu, Y., Wu, M., Wang, Q., Sun, B. (2020). An investigation into the role of VOCs in SOA and ozone production in Beijing, China. Sci. Total Environ. 720, 137536. https://doi.org/10.1016/j.scitotenv.2020.137536

  33. Liao, H.T., Chou, C.C.K., Chow, J.C., Watson, J.G., Hopke, P.K., Wu, C.F. (2015). Source and risk apportionment of selected VOCs and PM2.5 species using partially constrained receptor models with multiple time resolution data. Environ. Pollut. 205, 121–130. https://doi.org/10.1016/​j.envpol.2015.05.035

  34. Liu, B., Liang, D., Yang, J., Dai, Q., Bi, X., Feng, Y., Yuan, J., Xiao, Z., Zhang, Y., Xu, H., (2016) Characterization and source apportionment of volatile organic compounds based on 1-year of observational data in Tianjin, China. Environ. Pollut. 218, 757–769. https://doi.org/10.1016/j.​envpol.2016.07.072

  35. Liu, Y., Shao, M., Fu, L., Lu, S., Zeng, L., Tang, D. (2008). Source profiles of volatile organic compounds (VOCs) measured in China: Part I. Atmos. Environ. 42, 6248-6260. https://doi.org/​10.1016/j.atmosenv.2008.01.070

  36. McCarthy, M.C., Aklilu, Y.A., Brown, S.G., Lyder, D.A. (2013). Source apportionment of volatile organic compounds measured in Edmonton, Alberta. Atmos. Environ. 81, 504–516. https://doi.org/10.1016/j.atmosenv.2013.09.016

  37. Na, K., Moon, K.C., Kim, Y.P. (2005). Source contribution to aromatic VOC concentration and ozone formation potential in the atmosphere of Seoul. Atmos. Environ. 39, 5517–5524. https://doi.org/10.1016/j.atmosenv.2005.06.005

  38. National Air Emission Inventory and Research Center (NAIR) (2022). National Air Emission Inventory and Research Center – Emission data. (accessed 11 October 2022).

  39. Ng, N.L., Kroll, J.H., Chan, A.W.H., Chhabra, P.S., Flagan, R.C., Seinfeld, J.H. (2007). Secondary organic aerosol formation from m-xylene, toluene, and benzene. Atmos. Chem. Phys. 7, 3909–3922. https://doi.org/10.5194/acp-7-3909-2007

  40. Norris, G., Duvall, R. (2014). EPA positive matrix factorization (PMF) 5.0 fundamentals and user guide, US EPA, Office of Research and Development, Washington, DC 20460, EPA/600/R-14/108. (accessed 11 October 2022).

  41. Odum, J.R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R.C., Seinfeld, J.H. (1996). Gas/particle partitioning and secondary organic aerosol yields. Environ. Sci. Technol. 30, 2580–2585. https://doi.org/10.1021/es950943+

  42. Paatero, P., Tapper, U. (1994). Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111–126. https://doi.org/​10.1002/env.3170050203

  43. Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H. (1992). Secondary organic aerosol formation and transport. Atmos. Environ. Part A 26, 2269–2282. https://doi.org/10.1016/0960-1686(92)​90358-R

  44. Paul, S., Bari, M.A. (2022). Elucidating sources of VOCs in the Capital Region of New York State: Implications to secondary transformation and public health exposure. Chemosphere 299, 134407. https://doi.org/10.1016/j.chemosphere.2022.134407

  45. Shin, H.J., Kim, J.C., Lee, S.J., Kim, Y.P. (2013). Evaluation of the optimum volatile organic compounds control strategy considering the formation of ozone and secondary organic aerosol in Seoul, Korea. Environ. Sci. Pollut. Res. 20, 1468–1481. https://doi.org/10.1007/s11356-012-1108-5

  46. Son, H.D., An, J.G., Ha, S.Y., Kim, G.B., Yim, U.H. (2018). Development of real-time and simultaneous quantification of volatile organic compounds in ambient with SIFR-MS (selected ion flow tube-mass spectrometry). J. Korean Soc. Atmos. Environ. 34, 393–405. https://doi.org/​10.5572/KOSAE.2018.34.3.393 (in Korean)

  47. Song, C., Liu, B., Dai, Q., Li, H., Mao, H. (2019). Temperature dependence and source apportionment of volatile organic compounds (VOCs) at an urban site on the North China Plain. Atmos. Environ., 207, 167–181. https://doi.org/10.1016/j.atmosenv.2019.03.030

  48. Song, C., Liu, Y., Sun, L., Zhang, Q., Mao, H. (2020). Emissions of volatile organic compounds (VOCs) from gasoline- and liquified natural gas (LNG)-fueled vehicles in tunnel studies. Atmos. Environ. 234, 117626. https://doi.org/10.1016/j.atmosenv.2020.117626

  49. Song, M., Tan, Q., Feng, M., Qu, Y., Liu, X., An, J., Zhang, Y. (2018). Source apportionment and secondary transformation of atmospheric nonmethane hydrocarbons in Chengdu, Southwest China. J. Geophys. Res. 123, 9741–9763. https://doi.org/10.1029/2018JD028479

  50. Srivastava, D., Favez, O., Perraudin, E., Villenave, E., Albinet, A. (2018). Comparison of measurement-based methodologies to apportion secondary organic carbon (SOC) in PM2.5: A Review of recent studies. Atmosphere 9, 452. https://doi.org/10.3390/atmos9110452

  51. Su, Y.C., Chen, W.H., Fan, C.L., Tong, Y.H., Weng, T.H., Chen, S.P., Kuo, C.P., Wang, J.L., Chang, J.S. (2019). Source apportionment of volatile organic compounds (VOCs) by positive matrix factorization (PMF) supported by model simulation and source markers-using petrochemical emissions as a showcase. Environ. Pollut. 254, 112848. https://doi.org/10.1016/j.envpol.2019.​07.016

  52. U.S. Environmental Protection Agency (U.S. EPA) (2000). Integrated Risk Information System (IRIS) National Center for Environmental Assessment: Chemical Assessment Summary - Benzene (CASRN. 71-43-2). (accessed 2 October 2022).

  53. Wang, H.L., Chen, C.H., Wang, Q., Huang, C., Su, L.Y., Huang, H.Y., Lou, S.R, Zhou, M., Li, L., Qiao, L.P., Wang, Y.H. (2013). Chemical loss of volatile organic compounds and its impact on the source analysis through a two-year continuous measurement. Atmos. Environ. 80, 488–498. https://doi.org/10.1016/j.atmosenv.2013.08.040

  54. Won, S.R., Shim, I.K., Kim, J., Ji, H.A., Lee, Y., Lee, J., Ghim, Y.S. (2021a). PM2.5 and trace elements in underground shopping districts in the Seoul metropolitan area, Korea. Int. J. Environ. Res. Public Health 18, 297. https://doi.org/10.3390/ijerph18010297

  55. Won, S.R., Ghim, Y.S., Kim, J. Ryu, J., Shim, I.K., Lee, J. (2021b). Volatile organic compounds in underground shopping districts in Korea. Int. J. Environ. Res. Public Health 18, 5508. https://doi.org/10.3390/ijerph18115508

  56. Xie, F., Zhou, X., Wang, H., Gao, J., Hao, F., He, J., Lu, C. (2021). Heating events drive the seasonal patterns of volatile organic compounds in a typical semi-arid city. Sci. Total Environ. 788, 147781. https://doi.org/10.1016/j.scitotenv.2021.147781

  57. Yang, Y., Liu, B., Hua, J., Yang, T., Dai, Q., Wu, J., Feng, Y., Hopke, P.K. (2022). Global review of source apportionment of volatile organic compounds based on highly time-resolved data from 2015 to 2021. Environ. Int. 165, 107330. https://doi.org/10.1016/j.envint.2022.107330

  58. Yoo, H.Y., Kim, K.A., Kim, Y.P., Jung, C.H., Shin, H.J., Moon, K.J., Park, S.M., Lee, J.Y. (2020). Validation for SOC estimation from OC and EC concentration in PM2.5 measured at Seoul. Part. Aerosol Res. 16, 19–30. https://doi.org/10.11629/jpaar.2020.16.1.019 (in Korean)

  59. Zhang, X., Xue, Z., Li, H., Yan, L., Yang, Y., Wang, Y., Duan, J., Li, L., Chai, F., Cheng, M., Zhang, W. (2017). Ambient volatile organic compounds pollution in China. J. Environ. Sci. 55, 69–75. https://doi.org/10.1016/j.jes.2016.05.036

  60. Zhang, Z., Wang, X., Zhang, Y., Lü, S., Huang, Z., Huang, X., Wang, Y. (2015). Ambient air benzene at background sites in China's most developed coastal regions: Exposure levels, source implications and health risks. Sci. Total Environ. 511, 792–800. https://doi.org/10.1016/j.​scitotenv.2015.01.003

  61. Zhao, S., Li, R., Wang, S., Liu, Y., Lu, W., Zhao, Y. (2023). Emission of volatile organic compounds from landfill working surfaces: Formation potential of ozone and secondary organic aerosols. Sci. Total Environ. 886, 163954. https://doi.org/10.1016/j.scitotenv.2023.163954

  62. Zheng, H., Kong, S., Xing, X., Mao, Y., Hu, T., Ding, Y., Li, G., Liu, D., Li, S., Qi, S. (2018). Monitoring of volatile organic compounds (VOCs) from an oil and gas station in northwest China for 1 year. Atmos. Chem. Phys. 18, 4567–4595. https://doi.org/10.5194/acp-18-4567-2018

  63. Zheng, S., Xu, X., Zhang, Y., Wang, L., Yang, Y., Jin, S., Yang, X. (2019). Characteristics and sources of VOCs in urban and suburban environments in Shanghai, China, during the 2016 G20 summit. Atmos. Pollut. Res. 10, 1766–1779. https://doi.org/10.1016/j.apr.2019.07.008

  64. Zielinska, B., Sagebiel, J.C., Harshfield, G., Gertler, A.W., Pierson, W.R. (1996). Volatile organic compounds up to C20 emitted from motor vehicles, measurement methods. Atmos. Environ. 30, 2269–2286. https://doi.org/10.1016/1352-2310(95)00116-6

  65. Zweldlinger, R.B., Stevens, R.K., Lewis, C.W. (1990). Identification of volatile hydrocarbons as mobile source tracers for fine-particulate organics. Environ. Sci. Technol. 24, 538–542. https://doi.org/10.1021/es00074a012

Share this article with your colleagues 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

77st percentile
Powered by
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

The Future Environment and Role of Multiple Air Pollutants

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.