VOC characteristics, emissions and contributions to SOA formation during hazy episodes

Accepted Manuscript VOC characteristics, emissions and contributions to SOA formation during hazy episodes Jie Sun, Fangkun Wu, Bo Hu, Guiqian Tang, Yuesi Wang PII:

S1352-2310(16)30493-9

DOI:

10.1016/j.atmosenv.2016.06.060

Reference:

AEA 14710

To appear in:

Atmospheric Environment

Received Date: 22 December 2015 Revised Date:

15 June 2016

Accepted Date: 22 June 2016

Please cite this article as: Sun, J., Wu, F., Hu, B., Tang, G., Wang, Y., VOC characteristics, emissions and contributions to SOA formation during hazy episodes, Atmospheric Environment (2016), doi: 10.1016/j.atmosenv.2016.06.060. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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VOC characteristics, emissions and contributions to SOA formation during hazy episodes Jie Sun, Fangkun Wu, Bo Hu, Guiqian Tang*, Yuesi Wang LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, China Abstract Volatile organic compounds (VOC) are important precursors of secondary organic aerosols (SOA). The pollution processes in Beijing were investigated from 18th October to 6th November 2013 to study the characteristics, SOA formation potential and contributing factors of VOC during hazy episodes. The mean concentrations of VOC were 67.4±33.3µg·m−3 on clear days and have 5-7-fold increase in polluted periods. VOC concentrations rapidly increased at a visibility range of 4-5 km with the rate of 25%/km in alkanes, alkenes and halocarbons and the rate of 45%/km in aromatics. Analysis of the mixing layer height (MLH); wind speed and ratios of benzene/toluene (B/T), ethylbenzene/m,p-xylene (E/X), and isopentane/n-pentane (i/n) under different visibility conditions revealed that the MLH and wind speed were the 2 major factors affecting the variability of VOC during clear days and that local emissions and photochemical reactions were main causes of VOC variation on polluted days. Combined with the fractional aerosol coefficient (FAC) method, the SOA formation potentials of alkanes, alkenes and aromatics were 0.3±0.2 µg·m−3, 1.1±1.0 µg·m−3 and 6.5±6.4 µg·m−3, respectively. As the visibility deteriorated, the SOA formation potential increased from 2.1 µg·m−3 to 13.2 µg·m−3, and the fraction of SOA-forming aromatics rapidly increased from 56.3% to 90.1%. Initial sources were resolved by a positive matrix factorization (PMF) model. Vehicle-related emissions were an important source of VOC at all visibility ranges, accounting for 23%-32%. As visibility declined, emissions from solvents and the chemical industry increased from 13.2% and 6.3% to 34.2% and 23.0%, respectively. Solvents had the greatest SOA formation ability, accounting for 52.5% on average on hazy days, followed by vehicle-related emissions (20.7%). Key words: VOC; haze; SOA formation potential; initial emission source 1 Introduction Volatile organic compounds (VOC) are a very important class of atmospheric pollutants, and many VOC are classified as toxins or hazardous air pollutants by the U.S. EPA due to their harmful human health effects (Pérez-Rial et al., 2010). Their reaction with OH, O3, and nitrogen oxides (NOx) generates secondary organic aerosols (SOA) and aggravates groundlevel ozone pollution in the presence of sunlight, thus directly affecting regional air quality (Atkinson, 2000; Kroll and Seinfeld, 2008). Recently, high concentrations of VOC have been frequently observed in Beijing (39.8°N, 116.5°E), the political and cultural capital of China with rapid economic development and an intense increase in the number of vehicles in recent decades (Chan and Yao, 2008; Li et al., 2010; Mao et al., 2008; Wang et al., 2006). For example, VOC emissions increased at an average rate of 10.6%/a from 1980 to 2005 in Beijing (Bo et al., 2008). An average total VOC concentration of 132.6 µg·m−3 was measured by Liu et al. (2005) during 2002-2003 at six sites in Beijing. Several studies have apportioned VOC sources in Beijing using receptor models. Gasoline-related emissions (vehicle exhaust and gasoline vapour) were identified

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*Corresponding author. Tel.: +861082080530; fax: +861082028726; email: tgq@ [email protected].

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with a chemical mass balance (CMB) model as the main sources, contributing approximately 70% of the ambient VOCs in summer (Liu et al., 2005). Song et al. (2007) reported that gasoline-related emissions, petrochemicals, and liquefied petroleum gas (LPG) contributed 52, 20 and 11%, respectively, of the total ambient VOC using a positive matrix factorization (PMF) method. In these studies, observed concentrations were used as the input parameters of the model calculations. However, the results are often unsatisfactory because the chemical losses of some reactive VOC compounds, which are key compounds for SOA formation, from emission sources to receptor sites are not considered. In recent years, heavy pollution events occur frequently in Beijing (Chan et al., 2006a; Pang et al., 2009; Sun et al., 2006). Chan and Yao (2008) reported concentrations of ambient PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) that ranged between 96.5 µg·m−3 and 154.3 µg·m−3, 6-10 times as high as the limit recommended by the U.S. EPA (annual average of 15 µg·m−3). Huang et al. (2014) also found that severe hazy pollution events were driven largely by secondary aerosol formation in China, contributing 30–77% of PM2.5 and 44–71% of organic aerosols. Although increasing numbers of studies are concerned with the SOA formation potential, many studies have focused on estimating SOA in specific sources, such as vehicle emissions and biogenic emissions (Huang et al., 2015; Kroll et al., 2006; Liao et al., 2007; Zhao et al., 2014). Studies estimating the ability of VOC to form SOA under different pollution conditions are limited. To address the aforementioned deficiencies, we measured 72 VOC using online instruments in a hazy episode at an urban site in Beijing from 18th October to 6th November 2013. The hourly concentrations and chemical compositions of ambient VOCs during the hazy process were investigated. The SOA formation potentials and initial mixing ratios of VOC were estimated using fractional aerosol coefficient (FAC) methods, and the visibility was used as an index for their variation characteristics under different pollution conditions. A PMF model was used to extract the initial VOC sources and was combined with the FAC method to calculate the VOC source contribution and the SOA formation potential of each VOC source. The SOA yields of emission sources during different periods of haze were estimated to determine the contributions of the emission sources to air pollution. These results strengthen our knowledge of pollution formation and development in hazy episodes and provide a scientific basis for pollution control in Beijing. 2 Experimental methods 2.1 Monitoring site The fieldwork was conducted at the Institute of Atmospheric Physics (IAP) site near a meteorological tower (39°54′N, 116°23′E) that is 325 m high and located west of the Olympic Sports Center in northwestern Beijing. The sampling site, located between the 3rd ring road and 4th ring road of the city and approximately 1 km away from a highway, is an urban area approximately 7 km from the centre of Beijing surrounded by a park to the southeast and west; residential areas are located approximately 300 m to the north and south. No direct industrial sources of atmospheric pollutants are located near the site. Therefore, this sampling site is a suitable representative of the urban ambient atmosphere of Beijing. 2.2 Sampling and analysis 2.2.1 VOC data (GC-MS/FID) Continuous hourly measurements of VOC with C2–C12 carbon backbones were

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ACCEPTED MANUSCRIPT conducted during hazy periods (from 18th Oct. to 6th Nov.). A total of 435 valid samples were collected in 22 days. All air samples were automatically measured using a three-stage preconcentration system (7100A concentrator, Entech Inc., USA), followed by gas chromatographic (GC) analysis (Shimadzu 2012SE). In this system, dual columns coupled to a mass spectrometer (MS) and flame ionization detector (FID) were configured. The FID was used for the quantification of ethane, ethylene, acetylene, propylene and propane on a GasPro column (30 m × 0.32 mm, Agilent, USA). The MS was used to quantify VOC with a C4–C12 carbon backbone on a DB-1 column (60 m × 0.32 mm × 1 µm, Agilent, USA). VOC (500 mL aliquots) were sampled every hour through the pre-concentration system at a flow rate of 100 ml/min, which removed the water, carbon dioxide, nitrogen and oxygen and pre-concentrated the sample on the cold top of the capillary column. Subsequently, the sample was desorbed at >60°C and injected into the GC with high purity helium (99.99%) as the carrier gas. The following multi-ramp temperature programme was used: 30°C (10 min, hold); 3.8°C min−1 to 90°C; 15°C min−1 to 130°C; and 8.5°C min−1 to 200°C (17 min, hold). The total running time was 48 minutes. The FID was maintained at 250°C and supplied with high purity hydrogen and compressed air. The MS ionization source temperature was 250°C, and full scan mode was used for quantitative determinations with a mass–to-charge ratio range, m/z, of 35 to 200 amu and a scan rate of 4.2 scans per second. The analytical system was calibrated using prepared standards, comprising a minimum of five concentrations and a blank. System stability was verified before each use. Internal standards (benzene-d6, 2-bromo-1,1,1trifluoroethane and chlorobenzene-d5) were added to the sample stream prior to the trap. 2.2.2 Other data Meteorological parameters in the atmosphere at ground level were observed by an automatic meteorological observation instrument (Milos520, Vaisala, Finland) located at a level of 8 m at the IAP site. Detailed information can be found elsewhere (Xin et al., 2015). The atmospheric mixing layer heights (MLH) were measured by a ceilometer located at the IAP site (Tang et al., 2015). The observed visibility data were from the Wyoming Engineering University Network (http://weather.uwyo.edu). Organic aerosol (OA) in PM1 was measured by aerosol mass spectrometers (AMS). The detail analysis method was described by Zhang et al. (2014).

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2.3 Data analysis 2.3.1 Estimates of the initial VOC and SOA formation potentials Initial mixing ratio of VOC is characterized by a photochemical age (Mckeen et al., 1996) : [VOC]0=[VOC]t / EXP(-k[OH] △t) (1) Equation (1) is based on the following assumptions: 1)The removal of VOCs was governed by the reaction with OH radicals; 2)The photochemical age of the sampled air masses can be described by the ratio of two VOC species from the same emission sources but possessing different reaction constants with OH (Jimenez et al., 2009; Roberts et al., 1984).‐ In this study, we selected ethylbenzene (E) and m,p-xylenes (X) as the photochemical age clock.

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∆t ⋅ [OH ] =

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1 × (kX − kE )

 [X ]     − ln[X ] ln   [E ]   [E ]t = 0 

(2)

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Here, △t is the photochemical age (h), [OH] is the average OH radical concentration (molecules·cm-3), and kE and kx are the OH rate constants of ethylbenzene and m,p-xylenes. [X]/[E]t and [X]/[E]0 are the initial emission ratio and the measured ratio of ethylbenzene to m,p-xylenes at the sampling site. The limitations of using these ratios to calculate the photochemical age have been discussed in many studies (Mukund et al., 1996). Although there is substantial uncertainty in assessing the photochemical age for the mixing of fresh emissions with aged air masses, it still provides a useful method to estimate photochemical processing in the atmosphere (Parrish et al., 2007). FAC were developed by Grosjean and Seinfeld (1989) and defined by Grosjean (1992) as the fraction of SOA that would result from the reactions of a particular gas-phase VOC: FAC = [SOA] / [VOC]0 (3) Generally, FAC are assumed to be constant and the implicit assumption is that the fraction of the emitted VOC that reacts and the yield of SOA from the reactions is constant. While this is clearly an oversimplification, the use of FAC provides an order of magnitude estimate of SOA formation rates from individual precursors and an indication of the relative importance of different precursors in SOA formation. 2.3.2 PMF model analysis The US PMF 3.0 (Paatero, 1997; Paatero and Tapper, 1994) was applied to identify the initial emission sources of VOCs. The detailed calculation of replacing uncertainty values for missing and below detection limit (DL) data in this analysis were drawn from previous studies (Buzcu and Fraser, 2006; Poirot et al., 2001). Data below the DL were substituted with DL/2, and missing data were substituted with mean concentrations. Because the analytical uncertainties were not available, the uncertainties of the normal data were substituted with 10% of the measured concentration. The uncertainties of data below the DL were set to 5/6 DL, and the uncertainties of missing data were substituted with 4 times the mean concentrations. In this analysis, the robust mode was used to smooth the influence of outliers. Between 3 and 7 factor intervals were explored, with 6 factors to obtain the most reasonable results. Many different starting locations were explored using the seed parameter, and no multiple solutions were found. More than 96% of the residuals were between -3 and 3 for all compounds, showing a good fit of the modelled results (Paatero et al., 2005). 3 Results and discussion 3.1 VOC concentration characteristics Fig. 1 presents the time series of VOC measured by GC-MS/FID, the atmospheric visibility and the MLH from 18th October to 6th November 2013. The observation period lasted for 22 days, and three hazy events were observed, each of which lasted for 4.1 days on average. A hazy event is defined by conditions with visibility <10 km and RH > 90%, according to the definition by the Meteorological Administration of the People’s Republic of China (CMA, 2010). During the 3 hazy events, the VOC concentrations were higher than 600 µg·m−3, suggesting that the high VOC concentration was a main cause of the hazy events. During each pollution episode, the VOC concentrations were characterized by “slowly increasing and rapidly declining”, which is consistent with the variations in fine particles (Jia et al., 2008). For example, the VOC concentrations gradually increased from 50 µg·m−3 to 580 µg·m−3 over 4 days (from 25th to 28th Oct.); on the 29th Oct, the VOC concentrations decreased to an average of 25.6 µg·m−3, and the visibility increased to approximately 40 km.

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A total of 72 VOC (C2-C10) were measured, including alkanes, alkenes, aromatics and halocarbons, with average concentrations of 3671.8±156.2µg·m−3, in the range of 13.1712.4µg·m−3. The peak concentrations of VOC during hazy periods were 5-7 times higher than the average concentrations (67.4±33.3µg·m−3) on clear days. Toluene was the dominant compound of the measured VOC species, with an average mixing ratio of 21.3 µg·m−3, followed by propane (14.9 µg·m−3), isopentane (11.7 µg·m−3) and acetylene (9.1 µg·m−3). Dichloromethane was the most abundant compound in the measured halogenated hydrocarbon species, with an average mixing ratio of 5.3 µg·m−3. All the high mixing ratio compounds measured during the campaign can be found in local emissions, i.e., toluene and isopentane are main ingredients of exhaust and solvents (Barletta et al., 2002; Jobson et al., 2004); propane and acetylene are released from the fossil chemical industry (Wei et al., 2014); and dichloromethane is an important medium in the pharmaceutical industry (Freitas dos Santos and Lo Biundo, 1999). Therefore, the variation and effect factors of VOC during pollution processes must be analysed. 3.2 Concentration variation during different pollution stages To investigate the growth of VOC during the hazy process, visibility was used to indicate the degree of atmospheric pollution. The statistical analysis of VOC in the atmospheric mixing layer and the variation features of alkanes, alkenes, aromatics and halocarbons under different visibility conditions are shown in Fig. 2. On clear days with atmospheric visibility >10 km, the concentrations of alkanes, alkenes, aromatics and halocarbons increased from 12.1, 3.5, 8.6 and 5.6 µg·m−3 to 89.6, 28.0, 85.5 and 22.4 µg·m−3, respectively, when the visibility decreased from 50 to 10 km. When the atmospheric visibility declined to a range of 5 to 10 km, clear days transformed to slight hazy days. Compared with clear days, the concentrations of each species in the atmosphere maintained a high concentration, and an approximate 10%/km increase was observed. As the visibility continued to decline, hazy pollution occurred. Compared with slight hazy pollution conditions, the VOC concentrations rapidly increased at a visibility range of 4-5 km. The increases in alkanes, alkenes and halocarbons were at similar rates of approximately 25%/km, whereas aromatics increased at a rate of 45%/km. At a visibility below 4 km, VOC remained at a high level, with values of 136.1, 37.5, 149.6 and 48.2 µg·m−3, respectively, for alkanes, alkenes, aromatics and halocarbons. These results indicate that the average growth rate of VOC was greater at the beginning of the hazy process (from 4 to 5 km visibility), which is similar to other pollutants, such as PM2.5, SO2 and NOx, as reported by Han et al. (2014). Moreover, the aromatics accumulated faster than that of alkanes and alkenes. The top ten species with high growth rates at this stage included eight aromatics (styrene, ethylbenzene, m,p-xylene, benzene, o-xylene, toluene and isopropyl benzene) and three alkanes (3methylheptane, methyl cyclohexane and ethane). The composition of VOC at the stages of different pollution was also investigated as shown in Fig. 2. When the visibility ranged from 50 to 20 km, the VOC composition was very stable, with alkanes, alkenes, aromatics and halocarbons accounting for 43.7%, 13.3%, 30.9% and 13.1%, respectively. As the visibility decreased to ≤10 km, the mass fraction of aromatics increased to 39.8%, followed by alkanes (36.5%), halocarbons (12.5%) and alkenes (11.4%), and exceeded alkanes gradually became the most abundant species, reaching a maximum average fraction of 42.0% at a visibility of approximately 3 km. Aromatics are widely used as industrial solvents and in paints (Leuchner

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which △t [OH] varied from 0 to1.2×1011 cm-3·s, with an average of 4.9 ×1010 cm-3·s. The air

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during hazy days had more short age (1.5 ×1010 cm-3·s) than that during clear days (2.9 ×1010 cm-3·s). The statistical analysis of △t [OH] at different visibility stages (Fig.5) showed the average △t [OH] continued to decrease with the pollution aggravated, and the lowest △t [OH] values was 1.5 ×1010 cm-3·s at a visibility of approximately 5 km. As the atmospheric visibility further declined, heavy hazy pollution occurred, and the desired decreased in the △t [OH] values were not observed.

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and Rappenglück, 2010) and are the major species in exhaust from gasoline vehicles, accounting for approximately 50% of the total VOC (Huang et al., 2015). Therefore, the increase in the aromatic fraction may indicate a greater contribution from vehicles and solvent emissions during the pollution period. 3.3 Initial mixing ratio of VOC and SOA formation potential 3.3.1 Initial mixing ratio of VOC A linear fit was applied to the m,p-xylene and ethylbenzene data measured in the early morning (0:00–5:00 A.M. local time) during the Beijing campaign to estimate the initial emission ratio of m,p-xylenes to ethylbenzene ([X]/[E]0). At this time, the photochemical reactions of VOC are considered to be the weakest, and the concentrations of VOC are most reflective of the initial emissions (Yuan et al., 2012). The initial emission ratio of m,pxylenes to ethylbenzene was determined to be 1.8 (w/w), which is close to the slope of the upper edge in the scatterplot (Fig. 3). This initial emission ratio is comparable to the ratios in various source profiles of China (Liu et al., 2008a) and close to the ratios of biomass burning (1.54) and chemical processes (1.4-2) and is lower than that obtained from studies in Beijing (2.0) during the Olympics (Min et al., 2011) and in studies in Changdao (2.2) (Yuan et al., 2013). This result indicates that the air was under the control of an ageing air mass during the Beijing campaign, as proven by the higher background of VOC (15 µg·m−3). A total of 32 VOC, including alkanes, alkenes and aromatics, with an SOA formation potential that represents 89% of the measured VOC was used to calculate the SOA (Grosjean, 1992). Fig. 4 summarizes the observed and initial mixing ratios of each VOC chemical group at different visibility ranges. The initial mixing ratios of alkanes, alkenes and aromatics are 0.2, 1.5 and 0.7 times greater than the observed concentrations. Moreover, alkenes increased by approximately 3.2-fold on clear days due to the high mixing ratio of certain reactive species such as isoprene on clear days, particularly on days when the visibility was greater than 30 km. Similar results were obtained by Li et al. (2015), who determined that isoprene accounted for most of the reactivity when the mixing ratio of the total VOC was less than 30 ppbv. Compared with the measured results, the average contributions of alkenes to the initial VOC increased to 31.9% on clear days, and the average contributions of aromatics increased to 43.8% on polluted days. Correspondingly, the proportion of alkanes and halocarbons in the initial VOC mixing ratio decreased to approximately 25.5% and 6.7% on clear days and 33.2% and 10.4% on hazy days, respectively, compared with the measured results. 3.3.2 SOA formation potential The determined △t [OH] values were 0–6.4×1010 cm-3·s during the campaign, with an average of 2.3×1010 cm-3·s. The air measured in this campaign had experienced less ageing processes than that measured by Yuan et al. (2012), in August–September 2010, during

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The SOA concentration ranged from 0.3 to 28.5 µg·m−3, with an average SOA formation potential of 4.3±3.1 and 12.3±6.9 µg·m−3 on clear and hazy days, respectively, during the observation period. The SOA concentration calculated here is consistent with previously reported results in Beijing during a hazy event in 2006 (Han et al., 2015) (non-hazy day: 5.1±2.4 µg·m−3 and hazy day: 8.9±4.1 µg·m−3), for which SOA were estimated by the minimum OC/EC ratios of the ambient aerosols (Castro et al., 1999). In addition, our value is lower than other previously reported results in Beijing (1.4-30.8 µg·m−3), for which SOA were also estimated by the minimum OC/EC ratios (Duan et al., 2005). Though, literature results were the total amount of SOA in the atmosphere and the SOA we calculated just come from the conversion of VOC, we needed to point out that some compounds with lower concentrations but high SOA formation potentials, such as carbonyls, terpenes and pinene (Hartz et al., 2005; Utembe et al., 2009), were not measured, and the results are therefore an underestimate. In order to confirm the validity of our assessment method, the SOA concentrations calculated here were also compared with the organic aerosol (OA) in PM1 measured by AMS during the same period. Since the organic aerosols are mainly in fine particles (Ladji et al., 2014; Yu and Luo, 2009) , so the comparison is feasible. The average OA concentration was 12.6 µg·m−3 on clear days and 48.2 µg·m−3 on hazy days, as shown in Fig.6. The time series of SOA was consistent with OA, and accounted for 25.5-31.7% of OA. The results are similar to the previous research in Beijing which showed low-volatile SOA accounted for 21-34% of OA (Zhang et al., 2014). These comparison results showed it is feasible to estimate SOA use FAC method. Aromatics were the dominant contributors to SOA formation of the measured VOC species, with an average concentration of 6.5±6.4 µg·m−3, followed by alkenes (1.1±1.0 µg·m−3) and alkanes (0.3±0.2 µg·m−3). Aromatics accounted for 78.5% and 91.9% of the total SOA formation by the measured VOC on clear and hazy days, respectively. The most abundant SOA-forming species was toluene, accounting for more than 16% of the total SOA, followed by styrene (15%) and ethylbenzene (9.5%). The SOA from alkanes represented only 4.7%, of which 45-55% was undecane and dodecane, although alkanes represented more than 40% of the mixing concentration. Only two of the top 15 most abundant compounds were not aromatics: undecane and dodecane. This result is similar to the work conducted by Derwent et al. (2010) and Yuan et al. (2013). Isoprene was an important SOA-forming compound among the alkenes, accounting for 40% on hazy days and approximately 78% during clear days, particularly on days when the visibility was greater than 20 km. Since the formation of SOA results in visibility declined, the SOA formation potential was calculated at different pollution levels, as shown in Fig. 5. The average SOA concentration was 2.1 µg·m−3 on clear days (visibility above 50 km), and the value continuously increased as the pollution deteriorated to reach a value of 13.2 µg·m−3 with the visibility declined to 1- 3 km. The variations of the SOA from aromatics and alkanes with the decline of atmospheric visibility showed the same trends as those of the VOC mass concentration. The SOA formation potentials of aromatics and alkanes accumulated in the beginning of the hazy process, with an average accumulation rate of approximately 6%/km, and maintained their high formation potentials during the slight haze period. An approximate 10%/km increase was observed during the polluted period with visibility in the range of 1-5 km. Different alkenes were observed; SOA-forming alkenes decreased as the visibility

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3.4 Regional transport and local contribution Although VOC rapidly increased on clear days, the composition of VOC was stable, and the slow growth of the VOC concentration corresponded to an increase in the fraction of aromatics on polluted days. On clear days, alkenes were the important contributors to SOA formation, accounting for 30%, whereas on polluted days, more than 90% of the SOA contribution resulted from aromatics. These results show that the factors influencing the variation of VOC were different between clear and polluted days. To estimate the relative contribution of regional transport and the local contribution, the MLH and wind speed were used as diffusion indexes because they affect the vertical diffusion capability of atmospheric pollutants (Stull, 2009). VOC×MLH was calculated under different visibility ranges to eliminate the effect of the MLH; therefore, the variation of VOC×MLH was mainly affected by wind speed. To further eliminate the effect of wind speed, VOC×MLH×WS was also calculated. As shown in Fig. 7, the value of VOC×MLH increased as the visibility declined, but the increasing trend was significantly slower than the VOC trend itself, suggesting that the decrease in the MLH height strongly affected the VOC concentrations, especially at visibility ranges of 1 to 5 km. The value of VOC×MLH×WS was nearly constant at visibilities of 50-20 km suggesting that the meteorological factors (MLH and wind speed) were the 2 major factors affecting the variability of VOC during clear days. The values of VOC×MLH×WS at visibilities under 10 km were not constant, but rather strongly increased, suggesting that weaker transport/diffusion conditions were not the only reason for the increasing VOC on polluted days. To verify this result, back trajectory analysis was performed on clear days and hazy days using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model (Fig. 8). The 72-h back trajectories staring at 500 m above ground level in Beijing (39.8, 116.5) were calculated every 6 h (at 0, 6, 12 and 18:00 LT, local time) during the entire campaign. As a result, the air masses are transmitted long distances on clear days, and the air originates more from the clean area north of Beijing. On hazy days, air masses move shorter distances and primarily pass over the southern region with documented high emissions (Cao et al., 2006; Guo et al., 2009; Zhang et al., 2009). A similar result was reported in studies of PM2.5 (Song et al., 2014; Tang et al., 2015) that showed air mass movement of less than 50 km·d−1 during polluted periods and indicated a negligible impact from regional VOC transport. To further understand the influencing factors during periods of haze, the ratios of benzene/toluene (B/T), ethylbenzene/m,p-xylene (E/X), and isopentane/n-pentane (i/n) were

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calculated under the different visibility conditions shown in Fig. 9. The E/X ratio is often used as an indicator for photochemical reactivity because these chemicals usually have common sources but the reaction rate constant of m,p-xylene with OH radical is approximately three times higher than that of ethylbenzene (Atkinson, 1990; Nelson and Quigley, 1983; Vardoulakis et al., 2011). The E/X ratio was 1.56 to 1.69 on hazy days, which is much higher than that on clear days. High E/X ratio means more m,p-xylene was consumed by photochemical reaction. Due to the air mass experienced less ageing on hazy days this ratio increase indicated that the oxidation capacity of atmosphere increased with pollution aggravated. A B/T ratio of approximately 0.5 (wt/wt) is widely used as an indicator for vehicle exhaust in urban areas (Baldasano et al., 1998; Song et al., 2007; Sweet and Vermette, 1992), and a high i/n ratio is characteristic of gasoline evaporation. The i/n ratio rapidly increased with the decrease in visibility, suggesting that more gasoline evaporated into the atmosphere in lower visibility conditions. The B/T ratio decreased with the decrease in visibility and approached a value of 0.5 at a visibility of 1 to 10 km, suggesting that vehicle exhaust emissions dominated in haze periods. Both vehicle exhaust and gasoline evaporation represent local emission characteristics, demonstrating that local VOC emissions are a key factor affecting VOC concentrations during polluted periods. 3.5 Apportionment and the contribution of initial VOC sources to SOA 3.5.1 Identification of initial sources In previous studies, observed VOC data were regularly applied to PMF (Austin et al., 2001; Liu et al., 2005) because PMF models assume that no chemical reaction occurs during transport from the sources to the measurement site. However, this assumption is commonly illogical because VOC are lost at different rates through reactions with OH, NOx and ozone from the time that they are emitted into the atmosphere. In this study, the initial mixing ratio of alkanes, alkenes and aromatics calculated by Equations (1) and (2) was input into PMF to correct for the influences of photochemistry on the source profiles. The measured mixing ratios of halohydrocarbons were used in PMF because of their long chemical lifetime. All the data used in PMF were assumed to represent the initial emissions during the campaign, and the goal was to profile the initial emission sources and analyse the source contributions to SOA. Six sources were identified using PMF for the initial mixing ratio of VOC: five can be ascribed to anthropogenic sources and one is a biogenic emissions source. The profiles of the resolved factors are shown in Fig. 10. Factor 1 was a biomass emissions source containing most of the alkenes, with a particularly large content of isoprene. High percentages of aromatics were obtained in Factor 2, especially BTEX (benzene, toluene, ethylbenzene, m,pxylene and o-xylene). BTEX is often used as a solvent in paints, coatings, synthetic fragrances, adhesives, inks, and cleaning agents (Borbon et al., 2002; Chan et al., 2006b; Liu et al., 2008b). This source was therefore assigned to solvent use. Factor 3 was identified by a high content of isopentane and pentane with a high i/n-pentane ratio and higher C5-C6 alkanes that originated from unburned liquid gasoline. The compounds 3-methylpentane, nhexane and 2-methylhexane are good tracers of gasoline exhaust (Watson et al., 2001), and the mean toluene/benzene ratio was 0.55 (wt/wt), which is close to a previous study conducted in a tunnel (0.6±0.2 wt/wt) (Jobson et al., 2004). For the above reasons, this factor was named vehicle-related. Factor 4 was associated with approximately 55% acetylene and

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35% propylene in the initial mixing ratio, which are the major species emitted from combustion processes (Liu et al., 2008a). This factor was also characterized by significant amount of ethane, propane, some C3-C4 alkenes and some aromatics. Ethane is a tracer of natural gas, and coal combustion can release aromatics into the atmosphere (Klimont et al., 2002). Therefore, this factor was named stationary combustion. The main species in the profile of factor 5 were n-hexane, isopentane and benzene, which are the main species emitted from oil refineries; in particular, n-hexane accounted for approximately 10% (V/V) (Wei et al., 2014). This factor also included some halohydrocarbons, such as chloroform, dichloroethane and methylene chloride, which are widely used as feedstock in organic synthesis (U.S. EPA, 1994). This fifth factor was identified as the chemical industry. The last factor was rich in CFC, such as F-11, F-113, and tetrachlorocarbon, and was named CFC. 3.5.2 Estimation of source contributions The concentration contributions and compositions of each VOC source under different visibility ranges are shown in Fig. 11. The reconstructed concentrations of all the sources increased, except the biomass source, with declining visibility. The contribution of the biomass source was high during clear days because isoprene accounted for more than 70% of this source and the mixing ratio of isoprene decreased with the declining visibility mentioned above. Vehicle-related emissions were an important source of VOC at all visibility ranges, accounting for 23%-32%. On days with visibility ≥ 20 km, the most abundant source, representing 29.4% of the VOC on average, was vehicle-related emissions. The second most abundant source was the biomass source, accounting for 27.2%, followed by solvents (13.2%), CFC (13.1%), stationary combustion (10.9%) and the chemical industry (6.3%). As the visibility decreased, solvents gradually became the largest contributor, accounting for an average of 34.2% at a visibility of 1-3 km. The second most abundant source at this visibility level was vehicle-related emissions, representing an average of 27.8% of the VOC. Another source with a contribution that continuously increased on polluted days was the chemical industry, accounting for an average of 23.0% at a visibility of 1-3 km. The contributions of the biomass source and CFC decreased continuously over time. CFC can be regarded as a regional transport tracer due to their long chemical lifetime in the atmosphere. The chemical lifetime of CFC ranges from approximately few months to a few years depending on the concentration of OH (Seinfeld and Pandis, 1998). The biomass source and CFC explained 37% of the VOC concentration on clear days, which indicates that regional transport and vegetation play important roles in good air quality. Vehicle-related emissions, the chemical industry and solvents together accounted for 80% of the VOC mixing ratios during the polluted period. This sources contribution behaviour suggests that reducing vehicle-related and industry-related (chemical industry and solvents) emissions are an effective means to improve poor air quality. The SOA formation potential of each emissions source was calculated. Solvents represented the majority of the SOA formation potential of VOC (2.4 µg·m−3), accounting for 37.0% on average, followed by vehicle-related emissions (1.8 µg·m−3, 27.2%), the chemical industry (0.9 µg·m−3, 13.1%), the biomass source (0.7 µg·m−3, 11.1%), stationary combustion (0.7 µg·m−3, 10.6%), and CFC (0.1 µg·m−3, 1.0%). The variation trend of SOA at different visibility levels was consistent with the corresponding proportion of VOC emissions. On clear days, biogenic, solvent and vehicle-related emissions apportioned more than 80% (2.8

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µg·m−3) of the SOA contributors, accounting for 26.5%, 26.2% and 28.6%, respectively. On polluted days, vehicle-related and solvent emissions were the main contributors of the SOA formation potential of VOC, accounting for 20.7% and 52.5%. Solvent sources demonstrated a prominent ability to form SOA because they included more relatively reactive and high SOA-forming species. The SOA contribution of the solvent source was more than 50% of the total SOA in heavy pollution periods, whereas the fraction of the solvent source was only 35% in the initial mixing concentration. Analysis of the SOA formation potential of each emission source revealed that vehicle-related emissions and the solvent source contributed more than 70% of the SOA that originate from VOC during polluted periods. Therefore, controls on vehicle-related and solvent emissions are the most important measures to reduce secondary aerosol pollution. 4 Conclusions To comprehend the evolution of pollution and provide reliable insights for new emergency pollution policies in Beijing, it is necessary to detail the source and SOA formation ability of VOC. For this purpose, continuous hourly measurements of VOC were collected during a hazy episode from 18th October to 6th November 2013 in Beijing to investigate the behaviour, emission characteristics and SOA formation potential of VOC during hazy conditions. The average concentrations of VOC were 3671.8±156.2µg·m−3, in the range of 13.1712.4µg·m−3. More than 25%/km average growth speed of VOC was found at the beginning of the haze process which was drive by aromatic with an increase rate of 45%/km. The initial mixing ratios of alkanes, alkenes and aromatics were 0.2, 1.5 and 0.7 times higher than the ambient concentrations, and their SOA formation potentials were 0.3±0.2µg·m−3, 1.1±1.0 µg·m−3 and 6.5±6.4 µg·m−3, respectively. When the visibility declined from 50 to 1 km, the SOA increased from 2.1 µg·m−3 to 13.2 µg·m−3. Aromatics were the dominant contributors to SOA formation of the measured VOC species, accounting for 56.3% on clear days and up to 85.7% when visibility declined to 20 km. Analysis of the effect of the MLH and wind speed under different visibility conditions revealed that these meteorological parameters were the major factors affecting the variability of VOC during clear days. The ratios of E/X, B/T and i/n showed the air masses during hazy days have high oxidation capacity and gasoline evaporation and exhaust emissions dominated during polluted periods. To correct for the influences of photochemistry on the source profiles, the initial mixing ratio of VOCs was input into PMF, and six sources (vehicle-related emissions, a biogenic source, solvents, stationary combustion, the chemical industry and CFC) were resolved. Vehicle-related emissions were an important source of VOC under any pollution conditions, accounting for 23%-32%. As visibility declined, emissions from solvents and the chemical industry increased from 13.2% and 6.3% to 34.2% and 23.0%, respectively. Solvents represented the majority of the SOA formation potential (2.4 µg·m−3), accounting for an average of 37.0%. As visibility declined the contribution of solvents increased to 52.5% followed by vehicle-related emissions (20.7%). Our study indicates that releases from vehicles, the chemical industry and solvents are the main driving forces behind rising VOC during hazy episodes. The photochemical reactions of VOC, particularly the promotion of SOA formation by the reaction of aromatics with OH, are an important factor in the hazy formation process. This finding is important in

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understanding the causes of urban smog-hazy formation and can also serve as reference information for the concerned parties who establish statutes and policies. Acknowledgements This work was supported by the CAS Strategic Priority Research Program Grant (no. XDB05020000 & XDA05100100) and the National Natural Science Foundation of China (nos. 41230642).

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m,p-Xylene (µg·m-3)

40

30

20

10

0 0

10 20 Ethylbenzene (µg·m-3)

30

ACCEPTED MANUSCRIPT Fig. 3. Scatterplot of m,p-xylenes with ethylbenzene. The black line indicates the estimated initial emission ratio of m,p-xylenes/ethylbenzene (1.8).

80

Alkanes ini

250

Alkenes ini

70

Aromatics ini 200

60

150

50 100

150

40 100

30 50

RI PT

Concentration (µg·m-3)

200

20

50

10 0 50 30 10 8 6 4 Visibility (km)

2

0

2

50 30 10 8 6 4 Visibility (km)

2

SC

0 50 30 10 8 6 4 Visibility (km)

15 10 5

0 1.0 0.8 0.6 0.4 0.2 0.0 50

40

30

20

10

9

8

7

6

5

4

3

2

5 4 3 2 1 0

△t [O H ]×10 10 (cm -3 · s)

Alkanes Alkenes Aromatics

TE D

P roportion (% )

S O A (µg·m -3 )

20

M AN U

Fig. 4. The initial mixing ratios of each chemical VOC groups at different visibility ranges

1

EP

Visibility (km)

AC C

Fig. 5. Calculation results of SOA formation potential and photochemical age during the campaign.

160

120

30

80

-3

SOA(µg·m-3)

40

20 40 10 0

0 2013/10/18

2013/10/22

2013/10/26

2013/10/30

2013/11/3

OA(µg·m )

SOA OA

50

2013/11/7

ACCEPTED MANUSCRIPT Fig. 6. Time series of SOA formation potential and OA measured by AMS during the campaign.

400

VOC VOC×MLH VOC×MLH×WS

300

200

100

0 50 40 30 20 10 9

8

7

6

5

4

3

2

1

SC

Visibility (km)

RI PT

VOC (µg·m-3), VOC×MLH(µg·m-2), VOC×MLH×WS(µg·m-1·s-1)

500

TE D

M AN U

Fig. 7. The avariations of VOC, VOC×MLH and VOC×MLH×WS under the different visibility ranges.

AC C

EP

Fig. 8. Back trajectories for different atmospheric conditions, ( a) clear days, (b) haze days.

Fig. 10. Source profiles of the resolved factors from PMF model.

EP

0.5

0.0 1.0

0.5

0.0 1.0

0.0 1.0

0.0 1.0

0.0 1.0

0.5

0.0 chemical industry

0.5 stationary combusion

0.5 vehicle-related

0.5 solvent

biomass

M AN U

1.0

TE D

Ratio 0.9

B/T 14

0.8

0.5

50 30 10 8 6 4 Visibility (km) 2 8

i/n

0.7

0.6

13 1.7

12 1.6

11 1.5

10 1.4

9 1.3

50 30 10 8 6 4 Visibility (km)

CFC

2 1.2

RI PT

Fig. 9. The variations of the ratios of benzene/toluene (B/T), ethylbenzene/m,p-xylene (E/X), and isopentant/n-pentant (i/n) under the different visibility conditions

SC

0.4

Ethane propane Isobutane Butane Butane, 2-methylPentane Butane, 2,2-dimethylButane, 2,3-dimethylButane, 2,3-dimethyl-1 Pentane, 3-methylHexane Pentane, 2,4-dimethylCyclopentane, methylHexane, 2,3,5-trimethylPentane, 2,3-dimethylHexane, 3-methylHexane, 2,2-dimethylHeptane Cyclohexane, methylPentane, 2,3,4-trimethylHeptane, 2-methylHeptane, 3-methylNonane Decane Undecane Dodecane Acetylene Propylene 1-butene 1-Pentene 2-Pentene, (E)Isoprene 2-Pentene, (Z)1-Hexene Benzene Toluene Ethylbenzene m/p-Xylene Styrene o-Xylene Benzene, IsopropylBenzene, propylm-Ethyltoluene p-Ethyltoluene Benzene, 1,2,3-trimethylo-Ethyltoluene Benzene, 1,3,5-trimethylBenzene, 1,2,4-trimethylBenzene, 1,2-diethylBenzene, 1,4-diethylF-11 Ethene, 1,1-dichloroF-113 Methylene Chloride 1,1-Dichloroethane cis-1,2-Dichloroethylene Chloroform Tetranchlorocarbon 1,2-Dichloroethane Trichloroethylene cis-1,3-dichloropropene 1,1,2-Trichloroethane Tetrachloroethylene Ethane, 1,1,2,2-tetrachloro p-Dichlorobenzene

AC C Factor faction (%)

ACCEPTED MANUSCRIPT 1.8

E/X

50 30 10 8 6 4 Visibility (km)

2

ACCEPTED MANUSCRIPT

VOC (µg·m -3 )

500 400

20

vehicle-related CFC chemical industry solvent biomass stationary combusion

15

SOA (µg·m -3)

600

300 200

10

vehicle-related CFC chemical industry solvent biomass stationary combusion

5

0 1.0

0.8

0.8

0.4 0.2 0.0

0.6 0.4 0.2 0.0

50 40 30 20 10 9

8

7

6

5

4

3

2

1

Visibility (km)

SC

0.6

RI PT

0 1.0

SOA proportion (%)

VOC proportion (%)

100

50 40 30 20 10 9

8

7

6

5

4

3

2

1

Visibility (km)

AC C

EP

TE D

M AN U

Fig. 11. The contributions and compositions of VOCs initial source and the ability of formation SOA under the different visibility conditions.

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

1. The variation characteristics of VOCs during haze process were investigated. 2. Initial concentration and SOA potentials of VOCs were estimated at different pollution stages. 3. Initial emission source were resolved during different pollution conditions. 4. 52.5% SOA come from solvents source on pollution days.