Characteristics of atmospheric organic and elemental carbon aerosols in urban Beijing, China

Atmospheric Environment 125 (2016) 293e306

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Characteristics of atmospheric organic and elemental carbon aerosols in urban Beijing, China Dongsheng Ji a, Junke Zhang a, Jun He b, Xiaoju Wang c, Bo Pang a, Zirui Liu a, Lili Wang a, **, Yuesi Wang a, * a

State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, China c Beijing Municipal Environmental Monitoring Center, Beijing, China b

h i g h l i g h t s
Semi-continuous measurements of carbonaceous aerosols were obtained in Beijing.
Seasonal, weekly and diurnal variations of OC and EC are reported.
Clean energy strategies resulted in an effective reduction of OC and EC.
High SOC concentrations were observed in autumn and winter.
Biomass burning emissions accounted for 18.4% of OC.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 February 2015 Received in revised form 6 November 2015 Accepted 9 November 2015 Available online 14 November 2015

Organic carbon (OC) and elemental carbon (EC) in PM2.5 were measured hourly with a semicontinuous thermal-optical analyzer in urban Beijing, China, from Mar 1, 2013 to Feb 28, 2014. The annual mean concentrations of OC and EC in Beijing were 14.0 ± 11.7 and 4.1 ± 3.2 mg/m3, respectively. The concentrations observed in this study were lower than those of other reports over the past ten years; however, the concentrations were higher than those reported from most of the megacities in North America and Europe. These findings suggest that OC and EC remained at high levels despite the implementation of strict control measures to improve air quality. The OC and EC concentrations exhibited strong seasonality, with high values in the autumn and winter but low values in the spring and summer in Beijing. The diurnal OC and EC cycles were characterized by higher values at night and in the morning because of primary emissions, accumulations and low boundary-layer heights. Due to increasing photochemical activity, a well-defined OC peak was observed at approximately noon. The OC and EC concentrations followed typical lognormal patterns in which more than 75% of the OC samples had concentrations between 0.9 and 18.0 mg/m3 and 75% of the EC samples had concentrations between 0.4 and 5.6 mg/m3. An EC tracer method and combined EC tracer and Kþ mass balance methods were used to estimate the contributions from secondary formation and biomass burning, respectively. High secondary organic carbon (SOC) concentrations were found in the autumn and winter due to low temperatures, which are favorable for the absorption and condensation of semi-volatile organic compounds on existing particles. High correlations were found between the estimated SOC in PM2.5 and the observed OOA (oxidized organic aerosol) in PM1; thus, the method proved to be effective and reliable. The annual average OCBiomass burning (OCbb) contribution to the total OC concentration was 18.4%, suggesting that biomass burning is a substantial pollution factor in Beijing. © 2015 Elsevier Ltd. All rights reserved.

Keywords: OC EC PM2.5 Secondary organic carbon Biomass burning Beijing

1. Introduction * Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (L. Wang), [email protected] (Y. Wang). http://dx.doi.org/10.1016/j.atmosenv.2015.11.020 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

Carbonaceous aerosols are of increasing concern because of their complex impacts on human health, the environment and

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climate change (IPCC AR5, 2013; World Health Organization, 2000; Duan et al., 2005; Cao et al., 2003). Despite the importance of carbonaceous aerosols in atmospheric chemistry, physics and climate change, it is challenging to characterize the complex nature of carbonaceous aerosols based on measurements (Hand et al., 2013). Carbonaceous aerosols, containing different and unidentified compounds, are typically divided into OC (organic carbon) and EC (elemental carbon) (Turpin et al., 2000). The classification of OC and EC is empirical and highly method-dependent, and their differentiation is still not clear. Among the commonly accepted OC/EC determination methods, the thermal-optical analysis (TOA) method is one of the most well-known methods (Zhang et al., 2012). The widely used TOA methods include the National Institute of Occupational Safety and Health (NIOSH) thermal-optical transmission (TOT) (Birch and Cary, 1996) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermaloptical reflectance (TOR) (Chow et al., 1993) protocols. In using the thermal-optical transmission method, a semi-continuous TOA analyzer is advantageous compared with an off-line one because the former provides a high sampling resolution. More importantly, the semi-continuous TOA analyzer makes it possible to capture detailed fluctuations in OC and EC emissions, understand the sources and processes affecting the evolution of OC and EC, assess the impact of human activity on atmospheric environment and recognize their atmospheric transport/transformation mechanisms. The Beijing-Tianjin-Hebei (BTH) region is one of the most developed city clusters in China. It is located in northern China and is heavily influenced by anthropogenic emission. A series of studies on carbonaceous aerosol was carried out, and high concentrations were typically recorded in the BTH region, particularly in Beijing (Ji et al., 2014; Cheng et al., 2013; Andersson et al., 2015). However, most of the studies on OC and EC were carried out over short periods or during air pollution episodes, which do not fully reflect the long-term temporal characteristics of OC and EC. Moreover, most of the previous studies were based on filter sampling with a low time resolution and were therefore susceptible to filter-sampling artifacts, and few previous studies have been carried out with high temporal resolutions and one-year study periods (Lin et al., 2009; Zhao et al., 2013). Therefore, high time-resolved, continuous, in situ measurements are required to study aerosol evolution and transport, understand the sources and processes affecting the evolution of carbonaceous aerosol components and explore primary emissions and secondary formation. In addition, a quantitative understanding of the variations in Beijing is vital for constraining the role of carbonaceous aerosols in global climate models because Beijing is located in a large source area in northern China (Han et al., 2009; Bond et al., 2013). The Strategic Priority Research Program of Formation, Mechanism and Control Strategies of Haze in China was initiated by the Chinese Academy of Sciences in Dec 2012, and both OC and EC were observed by the semi-continuous Sunset OC/EC instrument from Mar 1, 2013 to Feb 28, 2014. It was the first time that hourly averaged time-resolution concentrations of OC and EC in Beijing were measured for one year after the 2008 Olympic Games. Energy structure and policy changed substantially in Beijing (http://www. bjstats.gov.cn/sjfb/bssj/tjnj/) since the 2008 Olympic Games. In this study, the OC and EC levels in PM2.5 were observed at an urban site in Beijing, which represents a typical urban city in the BTH region. The OC and EC pollution characteristics and their seasonal and diurnal variations are presented. Frequency and probability distributions of OC and EC are studied. The OC and EC relationship is explored. In addition, this study also reports the sources of carbonaceous aerosols based on an EC tracer method and a combined EC tracer with Kþ mass balance method. The results would distinguish

and quantify the contributions of primary, secondary and biomass burning sources to carbonaceous aerosol. 2. Description of experiment 2.1. Measurement site The sampling site (39 580 2800 N, 116 2201600 E) was located between North 3rd Ring Road and North 4th Ring Road (Fig. 1). The site is approximately 1 km from the 3rd Ring Road, 200 m west of the G6 Highway (which runs north-south) and 50 m south of Beitucheng West Road (which runs east-west). The annual average vehicular speeds in the morning and evening traffic peaks were 27.4 and 24.3 km/h, respectively. No industrial sources were located in the vicinity of the sampling site. The experimental campaign was launched from Mar 1, 2013 to Feb 28, 2014. 2.2. Instrumentation The OC and EC levels in PM2.5 were measured with a thermal optical transmission OC/EC analyzer (RT-4, Sunset Laboratory Inc. Oregon, USA). An inline parallel carbon denuder that removes volatile organic gases was installed on the analyzer. Aerosol particles were collected on a round 16-mm quartz filter with a sampling flow rate of 8 L/m. After 30 min of collection, the oven of the instrument was purged with helium and the temperature was increased in multiple programmed steps based on the selected thermal protocol. Particulate organic carbon was then thermally volatilized and oxidized to carbon dioxide (CO2), which was quantified using a non-dispersive infrared (NDIR) detector. The oven was cooled prior to the second part of the analysis, when the oven was purged with a mixture of 5% oxygen in helium; the sample was again heated incrementally. During this stage, all of the remaining carbon on the filter, including elemental carbon, was oxidized to CO2, which was detected using the NDIR. Each analytical process was performed in approximately 15 min. For charring correction a HeeNe laser beam monitored the sample transmittance throughout the heating process. When the laser signal returned to its initial value, the split point between OC and EC was determined (Birch and Cary, 1996). The standard procedure for calibrating was conducted as recommended by Sunset Laboratory Inc. The quartz fiber filters used for sample collection were changed every 5 days, always before the laser correction factor dropped below 0.90. Calibration with an instrument blank was conducted every 5 days. The analyzer was automatically calibrated at the end of every analysis by injecting an internal standard CH4 mixture (5.0%; ultra-high purity He balance). A further off-line calibration was conducted at the beginning and the end of each campaign with an external source of sucrose standard (86 mg). The uncertainty of the TC measurement was estimated to be approximately 7% as determined by the standard error of the sucrose calibration and the routine methane calibration (Han et al., 2009). Uncertainties of the EC-to-OC split, which may depend on the temperature protocols, can lead to additional errors (Boparai et al., 2008). PM2.5 was determined with a synchronized hybrid ambient realtime particulate monitor (SHARP 5030, Thermo-Fisher Scientific, MA, USA), which is a US EPA Federal Equivalent Method analyzer. The accuracy of the measurements was ±5% for 24 h. Calibration and glass fiber filter tape changes were performed every 6 months. Organic aerosols were measured with an Aerodyne highresolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, DeCarlo et al., 2006). At the beginning, middle and end of the field campaign, the ionization efficiency, inlet flow and particle sizing of HR-ToF-AMS were calibrated following standard protocols (Jimenez et al., 2003; Drewnick et al., 2005). Positive matrix factorization

D. Ji et al. / Atmospheric Environment 125 (2016) 293e306

Fig. 1. The location of the sampling site and its surrounding areas. (The symbol

(PMF) was used to deconvolve the high-resolution mass spectra of organic aerosol (Paatero and Tapper, 1994; Ulbrich et al., 2009) into hydrocarbon-like organic aerosol (HOA), cooking-related organic aerosol (COA) and oxygenated organic aerosol (OOA). Potassium, sodium and calcium levels in the PM2.5 were observed every hour using a rapid collector of particles-ion chromatography system (RCFP-IC). Detailed information on RCFP-IC can be found in Wen et al. (2006). In addition, a parallel experiment was carried out in which the OC and EC concentrations were observed and analyzed by TOR and TOT protocols at the same time in four seasons from 2013 to 2014. In this experiment, a Partisol 2025i Sequential Air Sampler (Thermo Fisher Scientific, MA, USA) equipped with multiple cassettes was used to collect samples on Quartz fiber filters (47-mm diameter; Pall Life Sciences, MI, USA). The OC and EC values were determined using a TOR protocol with a DRI model 2001 carbon analyzer (Atmoslytic, Inc., CA, USA). More detailed information on the samples and the analysis of OC and EC can be found in Ji et al. (2014).

2.3. Data compiling and merging The OC and EC original data produced in this study and cited from other reports (He et al., 2011; Zhao et al., 2013) were obtained by either TOT or TOR protocol. To improve the comparability of these datasets, comparative factors including ROC TOR/TOT (the ratio of the OC concentrations recorded by TOR protocol to the OC concentrations recorded by TOT protocol) and REC TOR/TOT (the ratio of the EC concentrations recorded by TOR protocol to the EC concentrations recorded by TOT protocol) in Table S1 were obtained based on the parallel experiment in which the OC and EC concentrations were observed and analyzed by TOR and TOT protocols at the same time in four seasons from 2013 to 2014. ROC TOR/TOT and REC TOR/TOT were 0.86 and 1.35 during the whole parallel experiment. The results were consistent with the results cited in Table S1. The comparative factors will be used to normalize the OC and EC original data produced in this study and cited from other reports. That is to say, the results observed by TOR protocol divided by the comparative factors will be used to compare with the results observed by TOT protocol. Considering that carbonate carbon may bias the results, the sampling events (Tab. S2) apparently affected by blowing dust, floating dust and dust storm were excluded from analysis in this study. The contribution of carbonate to OC/EC on the remaining days is estimated in the Supplementary Materials.

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indicates the sampling site).

3. Results and discussion 3.1. Characteristics of OC and EC in Beijing 3.1.1. Levels of OC and EC The statistics for the PM2.5 mass, OC and EC at the urban sampling site in Beijing are presented in Table 1. The hourly OC concentrations ranged from 0.8 to 98.5 mg/m3 (average of 14.0 ± 11.7 mg/m3), and the EC concentrations ranged from 0.4 to 24.7 mg/m3 (average of 4.1 ± 3.2 mg/m3). The average contributions of OC and EC to the total measured PM2.5 mass were 15.0% and 4.4%, respectively. OC accounted for 77.3 ± 7.6% of the total carbon (TC), which was calculated as the sum of OC and EC, making it the predominant carbon contributor. These results are consistent with
nchez de findings from other European sites (Lonati et al., 2007; Sa la Campa et al., 2009). However, the contribution of TC to PM2.5 in this study, i.e., 24.5 ± 13.1%, was slightly higher than that reported in Beijing's 2012 Environmental Status (e.g., approximately 21.6%, http://www.bjepb.gov.cn/bjepb/323474/331443/331937/333896/ 396191/index.html). The total carbonaceous aerosol (CA) levels were calculated by the sum of organic matter (multiplying the measured OC by a factor of 1.4) and EC (Russell, 2003); the levels accounted for 31.3 ± 15.7% of the observed PM2.5, suggesting that carbonaceous fraction could contribute significantly to PM2.5. In addition, an interesting result, shown in Fig. S1, was that the ratios of OC/PM2.5, EC/PM2.5 and CA/PM2.5 declined with the enhancement of air pollution levels (air-quality level classification can be found in Qiao et al. (2015)). From this result, it can be inferred that inorganic components contributed relatively more than did OC and EC to the formation of PM2.5, although their absolute occurrence levels remained high. Fig. 2 shows the temporal variations in the normalized annually averaged OC and EC concentrations over the past ten years based on previous results (He et al., 2011; Zhao et al., 2013) and the observations in this study. The OC loading observed in this study is lower than those of previous years after comparative factors are used to normalize the annual OC and EC concentrations. As shown in

Table 1 Concentrations of OC and EC associated with PM2.5 particle fractions (mg/m3). N ¼ 8710

Mean

SD

Median

Min

Max

PM2.5 OC EC OC/EC

93.8 14.0 4.1 3.4

92.0 11.7 3.2 1.7

63.3 10.6 3.3 3.3

6 0.8 0.4 0.4

621.9 98.5 24.7 23.5

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Table S3, this decrease was a result of efforts made by replacing coal fired boilers and family stoves with natural gas facilities, further restricting the emissions standards of vehicles, adopting clean fuel technology and new traffic planning strategies and banning straw burning over the past 10 years. The NO2 and SO2 levels exhibited a similar inter-annual variation as OC. Their reductions were mainly attributed to the more stringent control of local anthropogenic emissions, i.e., industrial emissions, coal combustion and traffic emissions. The annual average EC concentration (4.1 mg/m3) was lower in this study compared to previous reports (He et al., 2011; Zhao et al., 2013). In contrast to OC, EC is formed through the incomplete combustion of carbonaceous fuel. As reported by Bond et al. (2013), residential fuel (i.e. coal and biomass) is a significant source of EC in China. Beijing has used more natural gas to reduce air pollution that was largely caused by coal and oil usage for energy. As shown in Table S3, natural gas consumption increased rapidly in Beijing from 2004 to 2012, whereas the reduction policy of high-emission vehicles with yellow labels was phased in and coal consumption declined rapidly since 2007. All of these measures led to a downward trend in the EC concentrations. Fire spot counts in the BTH region observed from the MODIS Fire Information for Resource Management System (Giglio, 2013) appeared to be inconsistent with the EC variations in Beijing after 2007. It is postulated that fire spot counts estimated from MODIS data may be biased due to the non-detection of biomass burning used for home cooking/heating in rural areas and due to inaccurate counts caused by MODIS daily coverage time gaps and the masking effects of clouds. In addition, a full ban on straw burning under a joint prevention action was not strictly executed in some areas of BTH. It could result in a non-uniform distribution of fire spots in this region. A pronounced decrease in fire spot counts was found in Beijing that consequently lead to improved air quality, whereas higher numbers of fire spots were observed in southern Hebei Province (Wang et al., 2015), which are also included in the total

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Fig. 2. Inter-annual variations in the normalized annually mean OC and EC concentrations in PM2.5 from 2004 to 2013 in Beijing.

counts of fire spots in Fig. 2. In addition, the more stringent and effective control of fossil fuel combustion emissions achieved by replacing coal with natural gas and raising standards for traffic emissions may have significantly counteracted some of the EC emissions arising from open biomass burning in the BTH region such that the EC concentrations in the Beijing area have generally declined since 2008. Table 2 summarizes recently published results for OC and EC levels in megacities around the globe showing that the PM2.5associated OC and EC concentrations in the main megacities of mainland China ranged from 8.6 to 26.4 mg/m3 and from 4.1 to 10.4 mg/m3, respectively. The OC and EC levels in Beijing were similar to those in Chengdu, Chongqing, Ji'nan and Tianjin; however, the concentrations were lower than those in Shijiazhuang and higher than those in Shanghai, Guangzhou and Hong Kong. Compared with megacities outside of China, the OC and EC concentrations in PM2.5 in Beijing were comparable to those observed in Seoul and Sao Paulo but were substantially higher than those in Tokyo and the primary megacities of Europe and North America and lower than those in Mumbai and Delhi. These differences indicate that OC and EC remained at higher levels in Beijing compared with most of the megacities around the world despite that a series of measures to control air pollution here have been carried out which seem to take longer time to show more effects. Moreover, to meet the new national air quality standard of PM2.5, more synergetic air pollution abatement practices of carbonaceous aerosols and volatile organic compounds (VOCs) emissions need to be phased in and better implementation should be executed in this region. 3.1.2. Seasonal characteristics of OC and EC Fig. 3 depicts the seasonal variations in OC and EC during the study period. The seasonality of OC and EC in PM2.5 are influenced by seasonal variations in emission intensities and meteorological factors. The observed OC concentrations exhibited strong seasonality. High concentrations were found in the autumn and winter, whereas lower occurrence levels were observed during the spring and summer in Beijing. The high OC concentrations were likely a result of emissions arising from fuel combustion for heating and secondary aerosol formation by condensation processes fostered by the low temperatures and low boundary layer heights in the winter. The partitioning of semi-volatile gaseous precursors is enhanced by low temperatures, and temperature inversions limit the dilution of precursors and allow for accumulation of semi-volatile OCs (SVOCs) in the gas phase which could subsequently condense on existing aerosol surface once their accumulated occurrence levels exceed their temperature-dependent saturation concentrations in such an atmospheric environment (Pandis et al., 1992; Odum et al., 1996). In addition, the adsorption of SVOCs onto existing solid particles and the dissolution of soluble gases that undergo reactions in particles contribute to the OC concentrations (Pandis et al., 1992; Odum et al., 1996). Moreover, vehicular emissions of carbonaceous particles and VOCs are significantly higher in the winter due to the cold start of vehicles (Singer et al., 1999). In contrast, high temperatures could promote the partitioning of SVOCs into the gaseous phase in the summer (Han et al., 2009). The average OC concentration of 18.3 ± 15.1 mg/m3 in the winter were lower than those reported by Lin et al. (2009) in Beijing, which were observed using the semicontinuous OC/EC analyzer before the 2008 Olympic Games. The average OC concentration in the winter was approximately 1.7 times higher than that in the summer. The difference in OC between in the winter and in the summer was lower than that observed previously (Lin et al., 2009; Zhao et al., 2013). This attributed to that coal combustion for heating remarkably declined in the winter in Beijing. The Beijing municipal government has

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Table 2 Mean OC and EC mass concentrations measured in the primary megacities of the world cited from the literature and observed in this study (mg/m3). Sampling location

Sampling period

Method

OC (PM2.5)

EC (PM2.5)

Reference

Bangkok Beijing Chengdu Chongqing Delhi Guangzhou Hong Kong Ji'nan London Los Angeles Mexico city Mumbai Nanjing New York Paris Sao Paulo Seoul Shanghai Shijiazhuang Tianjin Tokyo Toronto

2007e2008 Mar 2013eFeb 2014 May 2012eApr 2013 May 2012eApr 2013 Nov 2010 to Feb 2011 Four seasons (2009e2010) Four seasons (2011e2012) Four seasons (2010) 2009 2013 March 2006 2007e2008 Jun 2007eMay 2008 2013 Sep 2009eSep 2010 Winter 2008 Dec 27, 2010eJan 20, 2011 Jun 2010eMay 2011 Four seasons (2009e2010) Four seasons (2009e2010) Summer 2008 and winter 2009 Dec. 01, 2010eNov. 30, 2011

TOR TOT TOT TOT TOT TOR TOT TOR TOR TOT TOT TOR TOR TOT TOT TOR TOT TOR TOR TOR TOR TOR

7.7 14 19 15.2 54.1 9 3 17.7 2.8 2.8 8.7 27.3 15.7 3 3 10 9.6 8.6 26.4 18.8 ~3.5 3.4c

3.6 4.1 4.6 4.2 10.4 6 1.7 5.5 1 0.8 3.8 7.3 10.4 0.7 1.4 6.6 2.7 2.4 9.7 6.9 ~2.1 0.5c

Sahu et al. (2011) This study Chen et al. (2014) Chen et al. (2014) Tiwari et al. (2013) Tao et al. (2014) Zhou et al. (2014) Gu et al. (2014) Defra (2009) b US EPA a Stone et al (2010) Joseph et al. (2012) Chen et al. (2010) US EPA a Bressi et al. (2013) Souza et al. (2014) Park et al. (2012) Zhang et al. (2014) Zhao et al. (2013) Zhao et al. (2013) Minoura et al. (2012) Sofowote et al. (2014)

a b c

http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html. http://uk-air.defra.gov.uk/data/particle-data. Median value.

made huge efforts to replace coal with natural gases and electricitypowered facilities. A large number of small stoves and household honeycomb-briquette burning were strictly banned during the heating periods in recent years. This is consistent with variations in the volume of residential coal consumption recorded in the China Energy Statistical Yearbook (www.stats.gov.cn/english/). Less fossil fuel for heating was consumed in the other three seasons compared to the winter. As a result, the OC concentrations were lower in the spring, summer and autumn. In addition to residential heating, other energy consumption used in transport and industry were also important sources of OC. The emission intensities of these sources have few distinct seasonal changes in the spring, summer and autumn, and the differences in OC among these three seasons can be ascribed to the influences of weather conditions. The average wind speed in the autumn was 1.9 m/s, which was lowest speed observed during the four seasons. The average pressure was 1016 Pa, and the atmosphere in northern China was frequently controlled by uniform pressure system in the autumn. Those conditions were favorable for the accumulation of air pollutants and thus resulted in high OC concentrations. The highest wind speeds were observed in the spring and were favorable for air pollutant dispersion and transport, whereas the high frequency of

precipitation could scavenge air pollutants in the summer. Therefore, these meteorological conditions may have led to the low OC concentrations observed in the spring and summer. Slightly different from the seasonal patterns observed in OC, the highest EC concentrations in PM2.5 were observed in the autumn, ranging from 0.4 to 22.7 mg/m3 with an average of 4.7 ± 3.6 mg/m3, which was lower than observed previously in the autumn (Lin et al., 2009; Zhao et al., 2013). This observation also suggests that governmental efforts to improve air quality in Beijing were effective in reducing EC concentrations to some extent despite the high increase in the number of vehicles within the municipality. The average EC concentration in the autumn was approximately 1.3 times higher than that in the summer. Lower concentrations (average of 3.6 ± 2.1 mg/m3) in the summer were believed to be mainly caused by favorable meteorological conditions (washout by rainfall and increased dispersion) and the absence of heating sources. The recirculation of air masses over the BTH region played an important role in the higher EC and OC concentrations observed in the autumn and winter (Ji et al., 2012). OC and EC displayed strong cyclic variations in which their concentrations progressively increased over several days before rapidly decreasing in association with the cold front.

Fig. 3. Seasonal variations in OC and EC during the study period.

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are known to have high EC emissions and low OC emissions whereas low diesel vehicles (LDVs) have low EC emissions and high OC emissions. The evening rush hour emissions from LDVs produced excess OC relative to EC, leading to the observed OC peaks, whereas the relatively low OC and high EC emissions from HDVs and HDDTs, which are only permitted to enter the Beijing urban area at night, resulted in the observed EC peak late at night and in the early morning. The OC diurnal cycles exhibited some similarity in the spring, summer and autumn. Compared with the other three seasons, very stable meteorological conditions (i.e., temperature inversion, narrow mixing layer and weak winds) prevailed at night in the winter (Lin et al., 2009). Thus, the poor dispersion that accompanied domestic residential heating led to the continuous increase in OC at night in the winter. Fig. 4 also shows that the daytime OC peaks in the spring and summer were larger than those in the autumn and winter, which could be associated with the formation of secondary organic carbon through photochemical oxidation. The diurnal variations in OC and EC during weekdays and weekends are shown in Fig. 5. The concentrations of OC and EC were slightly higher (by 4 and 3%, respectively) during weekends relative to weekdays. In particular, the concentrations of OC and EC were 9.0% and 2.0% higher, respectively, on Saturday than on the other days. A plausible explanation for these differences is that LDVs are not banned from the greater Beijing area on weekends; these vehicles increase the traffic intensity and consequent emission intensity on weekends compared to weekdays (no-driving days based on the number of vehicle license plates were enforced from Monday to Friday, and approximately one-fifth of total vehicle population was banned from operating every day during weekdays.

3.1.3. Diurnal characteristics of OC and EC Fig. 4 depicts the diurnal variations in the OC and EC concentrations for the four seasons. Pronounced diurnal variations were observed for OC and EC. The diurnal variations in EC in Beijing were different from those in other megacities because of differences in traffic policies. The EC concentrations maintained high levels at night and in the early morning because of emissions from both heavy-duty vehicles (HDV) and heavy-duty diesel trucks (HDDT) and the decay of the planetary boundary layer (PBL) (Beijing traffic regulations allow HDV and HDDT to enter the urban area inside the 5th Ring Road from 22:00 to 06:00 LT; Beijing Traffic Management Bureau, http://www.bjjtgl.gov.cn/Inquiries/roadControl.asp), whereas the EC concentrations were low because of the elevation of the PBL and low emissions at other times. In addition, the EC peaks coincided with the rush hours, which indicated that these spikes were primarily attributed to traffic emissions. Large-amplitude variations in the average EC concentrations were observed in the autumn. The peak concentrations were 1.7, 1.6, 2.2 and 2.1 times higher than those observed trough concentrations during the afternoon in the spring, summer, autumn and winter, respectively. OC exhibited slightly different diurnal patterns compared with EC. As shown in Fig. 4, pronounced double-peak diurnal patterns occurred in all four seasons for OC. One peak occurred between 9:00 and 14:00, which could have resulted from the combination of vehicular emissions in the morning and SOC formation caused by stronger solar radiation during the mid-day time. The other peak occurred during the evening traffic rush hour, which was mainly attributed to traffic emissions and the decay of the planetary boundary layer. Note that differences between the nighttime OC and EC peaks were caused by the Beijing traffic regulations. HDVs

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Fig. 4. Diurnal variations in EC and OC in all four seasons.

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Fig. 5. Diurnal variations in OC and EC on weekdays and weekends.

In addition, non-local passenger vehicles are forbidden to enter the capital from 09:00 to 17:00 LT on weekdays.). In addition, regional anthropogenic activities (e.g., construction, industrial production and agricultural activity) do not decrease on weekends. Thus, higher OC and EC concentrations were recorded on weekends, which is inconsistent with variations observed in other areas. Note that the nighttime and weekend emissions, in combination with unfavorable meteorological conditions, may be related to multi-day successive accumulations of air pollutants, which can result in or aggravate severe air pollution episodes. Due to the increase in vehicular emissions and the maintenance of regional anthropogenic activities, more stringent control of air pollutant emissions may also be required during weekends and nighttime. 3.1.4. Frequency and probability distributions of OC and EC The frequency distributions of the OC and EC concentrations are shown in Fig. 6. The results suggest that the OC and EC concentrations followed typical lognormal patterns in all four seasons. EC concentrations from 0.4 to 6.0 mg/m3 were dominant, accounting for more than 78.2% of the total sample data for the entire year and for approximately 77.1, 91.5, 74.7 and 77.6% of the total sample data in the spring, summer, autumn and winter, respectively. The maximum frequencies, i.e., 18.5, 21.7, 14.9 and 16.4%, occurred for concentrations of 2 to 3, 2 to 3, 1 to 2 and 1 to 2 mg/m3 in the spring, summer, autumn and winter, respectively. OC concentrations from 0.8 to 20 mg/m3 were dominant, accounting for more than 79.5% of the total sample data for the entire year and for approximately 85.3, 95.8, 72.2 and 65.1% of the total sample data in the spring, summer, autumn and winter, respectively. The maximum frequencies, i.e., 25.3, 35.8, 20.2 and 21.7%, occurred for concentrations of 8 to 12, 8 to 12, 6 to 10 and 2 to 6 mg/m3 in the spring, summer, autumn and winter, respectively. The OC and EC concentrations showed quasisymmetric distributions in the summer, which suggests that a

few extreme values existed because emission sources varied little and the scavenge processes caused by the activity of cold air masses declined except for rainfall. The OC and EC concentrations showed positively skewed distributions in the other three seasons. The OC and EC concentrations, diurnal cycles, and frequencies exhibited seasonal variations. In addition to emissions and precipitation, shifts in the prevailing wind direction in different seasons may also influence the OC and EC levels. As shown in Fig. 7, the OC and EC concentrations were low in the presence of northerly winds and high for southerly winds. In the autumn and winter, higher OC and EC concentrations were also associated with northeasterly and north-northeasterly winds, implying that air masses from these directions may be highly polluted. These results are consistent with the high coal consumption in the industrial areas in northeastern China. The backward trajectory analysis shown in Fig. 8 suggests that the long-distance airflows that affected Beijing were predominantly from the north and northwest during the study period. Air masses from these two directions were relatively clean, leading to lower OC and EC concentrations in Beijing. High EC and OC levels were primarily influenced by local and regional emissions with short-range transport from the south. 3.2. The relationship between OC and EC concentrations The ratio of particulate OC to EC is an important index that reflects the source type and strength (Blando and Turpin, 2000). Fig. 9 shows the regression between the OC and EC concentrations for all PM2.5 samples during the study period. Strong correlations (R2 ¼ 0.70) were observed throughout the study period. This finding indicates that carbonaceous particles in Beijing derived from common emission sources, such as vehicular exhaust and coal combustion, experienced a similar atmospheric dispersion process. In general, the OC/EC ratios ranged from 2.0 to 3.0 for

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Fig. 6. The lognormal patterns of OC and EC in all four seasons.

urban sites (Turpin and Huntzicker, 1995). The average OC/EC ratios observed in this study are comparable to those observed at other urban sites. Variations in the regression slopes (i.e., 2.26e3.91, see Fig. 9) may have been caused by seasonal variability in the emission sources and SOC contributions. Although particulate OC/EC ratios exceeding 2.0 have been widely used to identify SOC formation (Chow et al., 1993), determining the presence of SOC from the absolute OC/EC ratios alone is difficult. Both the primary OC and newly formed SOC can result in high OC/EC ratios in PM2.5. In addition, emissions from biomass burning and cooking also correspond to high OC/EC ratios (Samara et al., 2014). Thus, a quantitative estimate of SOC using OC/EC ratios should be applied only after the local sources of OC and EC are carefully selected. A detailed estimate of SOC levels will be discussed in Section 3.3. The correlation coefficients for the spring, summer, autumn and winter were 0.77, 0.39, 0.67 and 0.87, respectively. In the summer, a weak correlation was found between OC and EC, implying that the OC and EC fractions in urban Beijing were emitted by different sources, including long-range or local SOC formation in the summer. A relatively strong correlation and large slope were observed in the winter, which may have been caused by the synchronized effects of domestic heating and vehicular emissions. This result is consistent with the observed OC/EC ratio of 4.0 presented by Watson et al. (2001), who attributed the value to fossil fuel combustion.

3.3. Estimating SOC Despite its importance, the formation processes of SOC are highly uncertain, and the lack of a quantitative understanding of secondary organic formation processes in the atmosphere is a major weakness when determining atmospheric aerosols and their climate effects (Hallquist et al., 2009). Direct measurements of SOC are not possible because SOC is derived from various physical and chemical transformation processes; therefore, estimations of SOC concentrations are largely dependent on methodological conditions and emission fluctuations. The fractions of organic aerosols from primary and secondary sources are not generally known despite recent modeling and observational efforts. Because EC solely originates from primary emissions and is inert in the atmosphere, it is often used as a tracer of primary OC. Ambient ratios of OC/EC that are greater than the primary aerosol OC/EC ratios indicate secondary formation. However, OC/EC ratios are strongly dependent on particular sources and exhibit substantial variability (Watson et al., 2001). A commonly used approach for indirect evaluations of SOC in atmospheric particles is the EC tracer method, which is based on minimum OC/EC ratios ((OC/EC)min). The values represent samples that exclusively contain primary carbonaceous aerosols from fossil fuel combustion (Turpin and Huntzicker, 1995; Castro et al., 1999). In this approach, the SOC concentration can be estimated from the

Fig. 7. Wind roses of OC and EC in all four seasons.

D. Ji et al. / Atmospheric Environment 125 (2016) 293e306

301

Fig. 8. Backward trajectories of EC and OC in all four seasons.

following equation:

OCsec ¼ OCtotal  ðOC=ECÞminimum  EC

(1)

Because the primary emission ratio can vary as an integrated effect of seasonal and diurnal variations in primary emissions under different meteorological conditions (Plaza et al., 2011), the corresponding seasonal (OC/EC)min ratios were used to obtain the SOC estimates. As shown in Fig. 10, (OC/EC)min was based on the lowest 5% of the measured OC/EC values in a given season (Pio et al., 2011). The seasonal (OC/EC)min values and resulting secondary contributions to organic aerosol and particle mass in all four seasons are presented in Table 3. The SOC concentrations were 3.7 ± 3.4, 3.7 ± 2.9, 6.1 ± 6.2 and 7.2 ± 5.2 mg/m3 in all four seasons, respectively. The SOC fractions in the OC concentrations ranged from 30.1 to 40.8% and averaged 35.8 ± 4.8%, which is higher than the results based on filter samples (Zhao et al., 2013). This difference may be because (OC/EC)min was based on filter samples, which cannot

capture detailed fluctuations in OC and EC emissions, and may have been overestimated. The SOC concentrations in the winter and autumn were approximately 1.8 times higher than those in the spring and summer (Table 3). As previously reported by Pandis et al. (1992), SOC is formed from VOCs via two processes: condensable organic compounds through oxidation reaction and the nucleation and condensation of vapors. Photochemical activity and atmospheric temperature play important roles in SOC formation. An investigation into the effects of atmospheric temperature on SOC formation showed that a 10  C increase in the temperature resulted in an 18% decrease in the SOC concentrations. Lower temperatures are favorable for absorption and condensation of semi-volatile organic compounds on existing particles (Pandis et al., 1992; Odum et al., 1996). However, the mechanism of SOA formation under wintertime conditions is still arguable. For example, Huang et al. (2014) addressed that in details photochemical reaction resulting in SOA formation is significant in winter; Lim et al. (2010) evidenced that water-soluble organic products through gas phase

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Fig. 9. Scatterplots of OC and EC throughout the study period. The solid line is the linear regression line determined using bivariate regression analysis.

photochemistry dissolving into the aqueous phase can react further to form low volatility products which are largely partitioned in the particle phase; but Zheng et al. (2015) found that gaseous oxidant concentrations decreased significantly, suggesting a reduced production of secondary aerosols through gas-phase reactions in winter. Based on meteorological conditions (Table S4) and VOC emissions (Wang et al., 2012), the SOA formation mechanism and pathway proposed by Atkinson and Arey (2003), Huang et al. (2014) and Zheng et al. (2015), it might be reasonable to infer that high average SOC concentrations in autumn and winter were observed as a result of lower temperatures and high VOC emissions. However, it is still difficult to conclude which of the chemical processes (OH chemistry, aqueous-phase chemistry or NO3 chemistry) is the dominant pathway. The results are consistent with previous studies that found atmospheric temperature and VOC emissions to have a pronounced effect on SOC concentrations (Harrison and Yin, 2008; Hallquist et al., 2009). A comparison between the SOC in PM2.5 and OOA in PM1 (a proxy for SOC) inferred from the EC tracer method and the HR-ToFAMS PMF method was carried out based on the observed data for Nov. 2013. As shown in Fig. 11, the SOC concentrations were consistent with those of OOA (R2 ¼ 0.69). The results demonstrate

that the OOA data obtained from HR-ToF-AMS were conceptually similar to the results of the empirical EC tracer method, which indicates that the empirical EC tracer method is a viable and reliable option for estimating SOC levels because it is simple to use. As shown in Fig. S2, the ratios of SOC/OC increased with the enhancement of air pollution levels, which is almost consistent with the ratios of OOA/OA. These results suggest that the increasing contribution of SOC(SOA) to air pollution episodes occurred along with a gradual deterioration of air quality. In addition, the consistency between the SOC/OC and SOA/OA ratios indicates that the estimate of SOC based on the above method is reliable. Regarding the SOC comparison, several factors may have contributed to the observed discrepancy in the values obtained from the HR-ToF-AMS and Sunset instruments. The first factor is the size difference between the HR-ToF-AMS (1 mm) and the Sunset instrument (2.5 mm); this difference may have contributed to differences in the total organic matter mass loadings. The second factor is related to the differences between SOC, referring to the carbon mass within SOA excluding the associated oxygen and hydrogen content and SOA, which is a broad term representing carbon-containing compounds that contain hydrogen and oxygen. The third factor is the different temporal resolutions of the results. The HR-ToF-AMS results

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303

Fig. 10. Linear fit between the hourly OC and EC concentrations and linear regression of the lowest 5% of the hourly OC/EC ratios in all four seasons.

provided more complete statistics compared with the Sunset results.

Kbb ðK=ECÞbb

(3)

3.4. Estimate of the biomass burning contribution to OC

Kbb ¼ Ktotal  Ksoil  Kss

(4)

Biomass burning is an important source of OC, plays a critical role in regional air pollution and global climate change, and adversely affects human health (Andreae, 1991; Koe et al., 2001). Levoglucosan and Kþ are widely used to identify and quantify the biomass OC fraction. Because measurements of levoglucosan are typically conducted offline and the analytical processes are complicated, the contribution of biomass burning to OC cannot be continuously calculated. In addition, PMF was not suitable for determining the sources based on the OC/EC data because of the limited number of chemical components that can be measured online. Thus, to quantify the biomass OC fraction, the Kþ/EC ratio method was employed in this study. In this approach the OCbb concentrations can be estimated from the following equations (Pio et al., 2011; Chen et al., 2014):

i h . Ksoil ¼ K þ Ca2þ

(5)

OCbb ¼ ðOC=ECÞbb  ECbb

(2)

ECbb ¼

soil

i h  non  sea salt Ca2þ

h i h i h i non  sea salt Ca2þ ¼ Ca2þ  0:0373 Naþ

(6)

i h Ksea salt ¼ 0:0355 Naþ

(7)

60 50

R2 = 0.69

SOC OOA

40 30 20

Table 3 The seasonal (OC/EC)min values and resulting secondary contributions to organic aerosol and particle mass in all four seasons.

Spring Summer Autumn Winter

(OC/EC)min

SOC (mg/m3)

2.1 2.0 1.9 2.8

3.7 3.7 6.1 7.2

± ± ± ±

3.4 2.9 6.2 5.2

SOC/OC (%)

SOC/PM2.5 (%)

30.1% 33.8% 40.8% 38.8%

4.1% 5.5% 4.9% 7.1%

10 0 2013-11-1 3 11 1

2013 2013-11-11 11 11

2013 2013-11-21 11 21

2013 2013-12-1

Date (yyyy-mm-dd) Fig. 11. Comparison of the HR-ToF-AMS OOA (PM1) with the Sunset OC (PM2.5) during Nov 2013.

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OCbb (μg/m3)

8 6 4 2 0

Date (mmm-yy) Fig. 12. The contribution of biomass burning to the total OC.

(OC/EC)bb and (K/EC)bb in Eqs. (2) and (3) are representative of biomass burning emissions in Beijing. Herein, the biomass burning source profile was selected from previous results (Cheng et al., 2013; Duan et al., 2004). The average (OC/EC)bb and (K/EC)bb were 5.22 and 1.22, respectively. The sources of Kþ are known to be complex such that sea salt and crustal materials, in addition to biomass burning, contribute to airborne Kþ. In addition, the contribution of fireworks to Kþ is not negligible during the Chinese Spring Festival. Kbb in Eq. (4) represents the fraction of potassium associated with biomass burning in PM2.5 and can be estimated using the relationship between K and a species (M) that shares similar sources (except for biomass burning). The definition of Kbb in Eq. (3) assumes that biomass burning is the only source of Kbb in the region. Kþbb has been found to be closely correlated with levoglucosan, indicating that corrected water soluble potassium is acceptable as a biomass burning tracer (Cheng et al., 2013). The contribution of biomass burning to the total OC may be estimated on a monthly basis by applying Eqs. (2)e(7). As shown in Fig. 12, this approach overestimates the origin of OCbb because it cannot exclude the contribution of fireworks to Kþ in Feb 2014, which is the month of the Chinese Spring Festival. The estimated OCbb concentrations varied from 3.9 mg/m3 in May to 0.9 mg/m3 in the summer months excluding Feb. The annual average OCbb contribution to the total OC concentration was 18.4% and ranged from approximately 23.2% in the winter to 14.5% in the summer. This result suggests that biomass burning is a substantial pollution factor in Beijing, and it is consistent with the PMF-modeled results in which the ratio of OCbb/OC was approximately 18% based on further calculation (Zhang et al., 2013). The combined EC tracer and Kþ mass balance methods prove to be promising and viable because the corresponding time series of biomass-burning contribution are consistent with the observation (Andersson et al., 2015) that biomass burning, e.g., agricultural crop residue burning, has an important influence on the North China Plain. Moreover, monthly variations in OCbb are consistent with the expected seasonal variations in straw burning and domestic heating (Cheng et al., 2013; Zhang et al., 2013). Note that summer conditions in Beijing are favorable for dispersion and scavenging and lead to lower OCbb concentrations. 4. Conclusions A comprehensive analysis of OC and EC was performed at a data resolution of 1 h in urban Beijing from Mar 1, 2013, to Feb 28, 2014. The following conclusions were made. (1) Carbonaceous aerosols were significant contributors to the observed PM2.5 mass in Beijing. Clean energy policies

resulted in declines in the inter-annual OC and EC concentrations. However, OC and EC remained at higher levels in Beijing compared with most of the megacities around the world despite the implementation of a series of measures to control air pollution. To attain the PM2.5 standard, further requirements for effectively controlling regional carbonaceous aerosols and VOC emissions should be established. (2) The observed OC concentrations exhibited strong seasonality. High (low) concentrations were found in the autumn and winter (spring and summer) in Beijing. Unlike the pattern observed with OC, the highest EC concentrations in PM2.5 were observed in the autumn. The ratio of the average EC concentration in the winter to that in the summer was lower than that previously reported, indicating that coal combustion for heating in the winter was remarkably reduced in Beijing. Different diurnal cycles for OC and EC were most likely caused by SOC formation and traffic policies in Beijing in the four seasons. The nighttime and weekend emissions, combined with unfavorable meteorological conditions, may result in multi-day successive accumulations of OC and OC and further aggravate air pollution episodes. (3) OC and EC concentrations followed typical lognormal patterns. High OC and EC concentrations occurred in the autumn and winter when the winds were from the southeast, implying that air masses from these directions were highly polluted. (4) High time-resolved SOC estimation indicated that the results of previous reports underestimated the SOC levels. High SOC concentrations were recorded in the autumn and winter, which were related to low temperatures and stable atmosphere that are favorable for the absorption and condensation of semi-volatile organic compounds on existing particles. The annual average OCbb contribution to the total OC concentration was 18.4%. This result suggests that biomass burning is a substantial pollution factor in Beijing and cannot be neglected. Acknowledgments This work was supported by the Beijing Natural Science Foundation (8142034) and the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (Grant XDB05020501 and Grant XDB05030207). The authors would like to thank all of the members of the LAPC/CERN of IAP, CAS, for maintaining the instruments used herein. We would also like to thank NOAA for providing the HYSPLIT model. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2015.11.020. References € 2015. €ld, M., Gustafsson, O., Andersson, A., Deng, J., Du, K., Zheng, M., Yan, C., Sko Regionally-varying combustion sources of the January 2013 severe haze events over eastern China. Environ. Sci. Technol. 49 (4), 2038e2043. Andreae, M.O., 1991. Biomass burning: its history, use and distribution and its impact on environmental quality and global climate. In: Levine, J.S. (Ed.), Global Biomass Burning: Atmospheric, Climatic and Biospheric Implications. MIT Press, Cambridge, MA, pp. 3e21. Atkinson, R., Arey, J., 2003. Atmospheric degradation of volatile organic compounds. Chem. Rev. 103, 4605e4638. Birch, M., Cary, R., 1996. Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Sci. Technol. 25, 221e241. Blando, J., Turpin, B., 2000. Secondary organic aerosol formation in cloud and fog droplets: a literature evaluation of plausibility. Atmos. Environ. 34 (10),

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