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Characterizing and sourcing ambient PM2.5 over key emission regions in China II: Organic molecular markers and CMB modeling
Atmospheric Environment 163 (2017) 57e64
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Characterizing and sourcing ambient PM2.5 over key emission regions in China II: Organic molecular markers and CMB modeling Jiabin Zhou a, b, Ying Xiong b, Zhenyu Xing a, Junjun Deng c, Ke Du a, * a
Department of Mechanical and Manufacturing Engineering, University of Calgary, Alberta, T2N 1N4, Canada School of Resources and Environmental Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China c Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China b
h i g h l i g h t s Molecular marker-CMB model was applied to estimate primary contributors to PM2.5 OC. Main contributors to PM2.5 were sulfate, vehicle emission, nitrate, coal combustion. Vehicle emission had the largest impact on PM2.5 mass in PRD region. PM2.5 level was affected by local emissions and regional enriched sources across the sites.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 3 January 2017 Received in revised form 16 May 2017 Accepted 18 May 2017 Available online 20 May 2017
From November 2012 to July 2013, a sampling campaign was completed for comprehensive characterization of PM2.5 over four key emission regions in China: Beijing-Tianjin-Hebei (BTH), Yangzi River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SB). A multi-method approach, adopting different analytical and receptor modeling methods, was employed to determine the relative abundances of region-specific air pollution constituents and contributions of emission sources. This paper is focused on organic molecular marker based source apportionment using chemical mass balance (CMB) receptor modeling. Analyses of the organic molecular markers revealed that vehicle emission, coal combustion, biomass burning, meat cooking and natural gas combustion were the major contributors to organic carbon (OC) in PM2.5. The vehicle emission dominated the sources contributing to OC in spring at four sampling sites. During wintertime, the coal combustion had highest contribution to OC at BTH site, while the major source contributing to OC at YRD and PRD sites was vehicle emission. In addition, the relative contributions of different emission sources to PM2.5 mass at a specific location site and in a specific season revealed seasonal and spatial variations across all four sampling locations. The largest contributor to PM2.5 mass was secondary sulfate (14e17%) in winter at the four sites. The vehicle emission was found to be the major source (14e21%) for PM2.5 mass at PRD site. The secondary ammonium has minor variation (4e5%) across the sites, confirming the influences of regional emission sources on these sites. The distinct patterns of seasonal and spatial variations of source apportionment observed in this study were consistent with the findings in our previous paper based upon water-soluble ions and carbonaceous fractions. This makes it essential for the local government to make season- and region-specific mitigation strategies for abating PM2.5 pollution in China. © 2017 Elsevier Ltd. All rights reserved.
Keywords: PM2.5 Organic aerosol Source apportionment Receptor model
1. Introduction Over the past decade, China has been faced with increasingly severe air pollution that has not only caught world-wide attention,
* Corresponding author. E-mail address: [email protected] (K. Du). http://dx.doi.org/10.1016/j.atmosenv.2017.05.033 1352-2310/© 2017 Elsevier Ltd. All rights reserved.
but prompted extensive research on atmospheric aerosols, the essential air pollutants. Ambient atmospheric aerosols have long been shown to have pronounced effect on human health, visibility reduction and global climate (Charlson et al., 1992; Seinfeld and Pandis, 1998). As significant constituents of aerosol particles, organic aerosols comprise of thousands of compounds that display highly distinctive physical and chemical properties (Saxena and
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Hildemann, 1996). The complicated characteristics of organic compounds, along with the un-predictivity of their exact composition associated with the unique emission patterns of specific locations, make it extremely challenging to conduct an exhaustive study, at the chemical species level, of all the organic compounds in atmospheric aerosols (Nolte et al., 2002; Schauer et al., 2002). Although considerable effort was made to measure the detailed chemical composition of carbonaceous aerosol, approximately less than 20% of the organic compounds in aerosols can be identified as individual organic species. Interestingly, some of these compounds have a high degree of source specificity and have been used as molecular markers in source apportionment models to estimate various source contributions to the aerosol particles (Park et al., 2006; Schauer and Cass, 2000; Schauer et al., 1996). Research on China air pollution has generated abundant datasets, indicating that Beijing-Tianjin-Hebei area (BTH), Yangtze River Delta area (YRD), Pearl River Delta area (PRD) and Sichuan Basin area (SB) are the four key emission areas in China (Cao et al., 2006; Xing et al., 2014; Zhou et al., 2016). Therefore, in this study, sampling sites were selected in these four main emission areas. The exact sampling locations, serving as receptor sites where air samples were collected downwind of their corresponding megacities Beijing, Shanghai, Guangzhou, Chengdu, were determined by performing the meteorological reanalysis of the prevailing wind in the four areas. A sampling campaign between 2012 and 2013 was completed for comprehensive characterizing the sourcing of PM2.5 over these four areas using a multi-method approach. The results are presented in a series of three papers. Part I paper presents findings on ionic and carbonaceous components (Zhou et al., 2016) in the aerosols. Part III, still under preparation, is around source apportionment based upon isotope analysis of black carbon in the aerosols. This paper (Part II of the series) compares the compositions of the individual organic compounds in the aerosols, and evaluates the source contributions to OC and PM2.5 using organic tracerchemical mass balance (CMB) receptor modeling approach. To date, only very limited information is available on the composition of individual organic chemicals within the ambient aerosol collected from the four emission areas in China. Therefore, results presented in this paper provide a unique perspective for policymakers to understand air pollution problems and thus formulate pollution control strategies. 2. Experiments and methods 2.1. Sample collection Sample collection was reported in part І (Zhou et al., 2016) and summarized here. The aerosol PM2.5 samples were collected in four satellite cities of their corresponding megacities Beijing, Shanghai, Guangzhou, Chengdu in China. These sites are Wuqing (39 390 N, 117 390 E) in BTH area, Haining (30 510 N, 120 690 E) in YRD area, Zhongshan (22 560 N, 113 330 E) in PRD area, and Deyang (31100 N, 104 390 E) in SB area, respectively. PM2.5 samples were collected simultaneously at four sites in November 2012, January 2013, April 2013 and July 2013, which represented fall, winter, spring and summer, respectively. After sampling, the PM2.5 quartz fiber filter samples were analyzed for PM2.5 mass using gravimetric method. The detailed description of the sampling sites and sampling method can be found in our previous paper (Xing et al., 2014). 2.2. Chemical analysis The concentrations of organic carbon (OC) and elemental carbon (EC) were measured by a Sunset Carbon Aerosol Analyzer (Sunset
Laboratory Inc., USA) following the NIOSH TOT protocol. More details about the analytical procedure can be found in our series paper І (Zhou et al., 2016). To determine individual organic compound, the collected aerosol PM2.5 filters were cut into small pieces and then extracted twice with dichloromethane (TEDIA) and twice with methanol (ACROS) using an ultrasonic bath, followed by a rotary evaporator to concentrate the extracts to about 5 mL. The mixture of isotopically labeled compounds was spiked into each sample prior to extraction. The extracts were further blown down to reduce the volume to 0.5 mL under high-purity nitrogen (99.99%). Each extract was split into two fractions, and a half of the extracts were derivatized by BSTFA/TMCS 99:1 (Regis Technologies, Inc. USA) in sealed vials at 70 C for 2 h to convert organic acids to their silylated analogues. Organic speciation was conducted using gas chromatography-mass spectrometry (Agilent Technologies GC6890, MS-5973). One microliter of derivatized extracts was injected into GC-MS for identification and quantification of organic species. The GC was fitted with a fused silica capillary column (DB-5, 30 m, 0.25 mm i.d., 0.25 mm film thickness) and its condition was: initially, the specimen was isothermally held at 60 C for 5 min, then increasing the temperature at a ramp of 5 C/min to 300 C, and finally was isothermally held at 300 C for 30 min. The pure helium (99.999% purity) was used as a carrier gas with a flow rate of 1.0 mL/min. The interface temperature of GC-MS was maintained at 300 C. Organic compounds were ionized by electron impact (70 eV) and scanned from 50 amu to 550 amu in the MS. The GC-MS data were acquired and analyzed by an Agilent Chemstation. According to the GC retention times, MS spectrum and prepared authentic standards, individual organic compounds were identified and quantified, including alkanes, organic acids, polycyclic aromatic hydrocarbons (PAHs), and some molecular tracer compounds such as levoglucosan, hopanes, and steranes. Blanks (including blank filters, field blanks, and solvent) and duplicate sample analyses were performed (10% of all samples) according to the standard operating procedures. Recovery (%) of organic species was calculated from the extraction recovery of the surrogate organic standards spiked. The spike recoveries of authentic standards including alkanes, polycyclic aromatic hydrocarbons, alkanoic acids were estimated to be in the range of 85%e 118%. Results of the organic analysis were blank corrected, and uncertainties were determined based on standard deviation of field blanks and the detection limit of the instruments. In addition, the concentrations of water-soluble ions including 2þ þ þ 2þ five cations (NHþ and four anions 4 ; Na ; K ; Ca ; Mg ) (F ; Cl ; SO2 ; NO ) were also determined using an ion chroma4 3 tography (Metrohm, Switzerland) coupled with a conductivity detector (Zhou et al., 2016). The collected PM2.5 filter was ultrasonically extracted with 20 mL Milli-Q water (18.2 MUcm) for 40 min, followed by filtering through 0.45 mm PTFE syringe filter (Pall Co. Ltd, USA). Quality assurance and quality control tests including field blank, laboratory blank, method detection limit and recovery efficiency were conducted. Multi-points calibrations using standard solutions were performed and the result of correlation coefficient was higher than 0.999. All the reported ions concentrations have been corrected by field blanks. 2.3. Source apportionment model As a receptor model, chemical mass balance (CMB) model expresses concentrations of different chemical species measured at a monitoring site as a linear sum of products of source profile abundances and source contribution estimates. In this study, using molecular marker-based chemical mass balance (MM-CMB) approach, the specific source contributions to ambient OC and
J. Zhou et al. / Atmospheric Environment 163 (2017) 57e64
PM2.5 mass concentrations were determined by calculating linear combination of the product of source effluents and its concentrations of a set of chemical species in ambient aerosol (Schauer et al., 1996; Watson et al., 1984). For successfully using the MM-CMB model, several assumptions need to be met. First, the organic compounds selected as tracers in the model should be chemically stable and conserved during transport from sources to receptor sampling sites. Second, all main sources of chemical species should be included in the model (Schauer et al., 1996). The species selected for use in the present study were based upon the recommendations of previous studies (Lough et al., 2007; Schauer and Cass, 2000). Twenty-one fitting species were included in the model (Table S1), which consist of a continuous series of n-alkanes from C25-C33, levoglucosan, abb-20R-C27-cholestane, abb-20R-C29-sitostane, 17a(H)-21b(H)-hopane, 17a(H)-22,29,30-trinorhopane, 17a(H)21b(H)-30- norhopane, benzo(e)pyrene, benzo(b)fluoranthene, benzo(k)fluranthene, indeno (1,2,3-cd)pyrene, benzo(ghi)perylene and picene. The picene, specific to coal combustion (Oros and Simoneit, 2000), was selected to infer whether coal combustion is a major contributor to ambient OC (Stone et al., 2008). The series of n-alkanes from C25-C33 were used to distinguish the biogenic and anthropogenic contributors according to carbon preference index (CPI). The particulate PAHs, hopanes, and steranes were included to estimate the contributions from the combustion of fossil fuels (Manchester-Neesvig et al., 2003). The source profiles used in this study were mainly obtained from previous studies conducted in China. The coal combustion profile was obtained from measurement of particulate carbon emissions from real-world Chinese coal combustion experiment (Zhang et al., 2008). The vehicle emission profile was collected from a tunnel experiment in the western urban area of Guangzhou (He et al., 2008). The biomass burning profile was the measured data from dilution chamber measurement of three kinds of cereal straw from the main grain producing regions in China (Zhang et al., 2007). The source profiles of meat cooking and natural gas combustion were obtained from USA (Rogge et al., 1991, 1993b). In addition, the criteria for acceptable CMB results included the square regression coefficient R2 > 0.85, the sum of residual square value CHI2<4, the degree of the freedom DF > 5, and the percentage of explained mass to total mass ranging from 80 to 120%. 3. Results and discussions 3.1. Organic species and source identification The annual average OC concentration varied between 7.0 and 14.1 mg m3 at these sampling sites, while the EC concentrations were observed to be 1.2e1.6 mg m3. More than one hundred of organic compounds including alkanes, PAHs, n-alkanoic acids, aliphatic and aromatic dicarboxylic acids were identified and quantified in each sample. Among them, some molecular markers or indicator compounds were well documented as the tracers to the source of organic matter (Simoneit et al., 1999). In this work, twelve selected organic molecular markers in PM2.5 including hopanes, picene, high-molecular-weight (HMW) PAHs, levoglucosan, cholesterol, dicarboxylic acids are presented in Fig. 1. Hopanes and petroleum biomarkers are commonly utilized as tracers for motor vehicle exhaust in the atmosphere (Rogge et al., 1993a; Schauer and Cass, 2000). As shown in Fig. 1a, the hopanes, including 17a(H)-22,29,30-trisnorhopane, 17a(H)-21b(H)-30norhopane, and 17a(H)- 21b(H)-hopane, contributed to PM2.5 mass with an annual average concentration (±standard deviation) of 2.3 ± 1.8 ng m3. The maximum values at these sites were observed in fall and winter, which were mainly due to the impact from regional enriched sources as well as unfavorable
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meteorological conditions such as low wind speed and boundary height during the cold seasons. And the minimum values were in spring, except that the highest level occurred at Deyang site during the summer period. Such extremely high levels of the hopanes at Deyang site was ascribed to a polluted episode in summer. The hopanes in the aerosol confirmed the input sources from fossil fuel utilization and engine lubricating oil at these sites. The concentrations of picene, a tracer for coal combustion, was found to have the highest value in winter and lowest in summer with an annual average of 0.2 ± 0.1 ng m3 (Fig. 1b). Wuqing site exhibited much higher picene level than that at Zhongshan site, revealing the difference of energy consumptions between northern and southern China. The maximum value occurred in winter at Wuqing (0.4 ng m3), which might be due to coal combustion for residential heating in the cold season. The selected polycyclic aromatic hydrocarbons (PAH) were detected in the samples and the results are shown in Fig. 1c. The annual average concentration of the PAHs was 7.4 ± 7.1 ng m3. These PAHs (indeno(1,2,3-cd) pyrene, benzo(ghi)perylene, coronene) with high molecular weight (MW > 228) are common product of incomplete combustion and usually associated with vehicle emission particulate matter. The highest concentration was measured in winter and lowest in summer. The seasonal trend of these tracers indicated that the meteorological factors have a substantial impact on increased PAHs levels during the winter period. Such high-molecular-weight PAHs are stable enough to resist chemical reaction and evaporation (Schauer et al., 1996), and are preferentially accumulated on the fine particulate matters once emitted from motor vehicle exhaust when radiation inversion occurs in winter. It is interesting to note that much higher concentrations of high-molecular-weight PAHs, picene, and the hopanes were observed simultaneously at Deyang site during summer period. The extremely high levels of these tracers were mainly related to a polluted episode in Deyang (Fig. S1). On July 13th, 2013, the levels of picene, indeno(1,2,3-cd)pyrene, benzo(g,h,i)perylene, 17a(H),21b(H)-hopane, 17a(H),21b(H)-30-norhopane, and 17a(H)22,29,30- trisnorhopane enhanced dramatically, increasing by a factor of 7.3, 20.4, 13.5, 9.7, 10.0 and 2.6, respectively. Such dramatic and consistent variations of these species suggested the prevailing contributions from fossil fuels combustion (e.g., vehicle emission, coal combustion) at Deyang site during the episode. Levoglucosan, which is considered as a cellulose pyrolysis tracer (Simoneit et al., 1999), was observed higher in samples during fall and winter periods and lower level in summer with an annual average concentration of 452.7 ± 337.6 ng m3 (Fig. 1d). The measured concentrations of levoglucosan are much higher than reported values at some US sites (Lough et al., 2006). It is worthy to note that the seasonal average concentration of levoglucosan observed at Zhongshan site increased dramatically with the largest concentration (averaging 1055.2 ng/m3, wintertime) nearly 25-fold of the lowest one (averaging 41.6 ng/m3, summertime). In addition, a much higher ratio of levoglucosan to OC (0.079) was also found at this site during the winter, and the related ratio for Wuqing, Haining and Deyang was 0.025, 0.021, and 0.039, respectively. The difference of levoglucosan to OC ratio across the sites could be resulted from local biomass burning activity. It was reported that the high OC/EC ratio (about 8) and Kþ/EC ratio (>1.0) could be used as immediate indicators for biomass burning pollution (Chen and Xie, 2014). In this study, the respective ratio for OC/EC and Kþ/EC was 6.7 and 1.6 at Zhongshan site during wintertime, confirming the intensive biomass burning activities in the PRD region during cold season. Meanwhile, cholesterol is considered as a tracer for meat charbroiling smoke (Rogge et al., 1991). As shown in Fig. 1e, the cholesterol exhibited slightly higher level in winter than that in
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Fig. 1. Seasonal variation of organic molecular markers at the sites in China. Wuqing (WQ), Haining (HN), Zhongshan (ZS), Deyang (DY).
summer with an annual average concentration of 1.4 ± 2.5 ng m3. Interestingly, the maximum value of 10.7 ng m3 was observed at Deyang site. Compared to other sites, such a sharp increase of cholesterol seemed to be impacted by local sources, i.e. meat cooking emissions. Aliphatic and aromatic diacids are commonly used as secondary organic aerosol tracers (Schauer et al., 2002; Zheng et al., 2002). The annual average of the collective concentration of these acids was 432.4 ± 412.5 ng m3. The measured dicarboxylic acids (terephthalic acid, phthalic acid, isophthalic acid) had almost no clear seasonal trends and varied slightly across the sites except the samples at Haining site during the fall period (Fig. 1f). Extremely high concentrations of dicarboxylic acids in Haining during the fall period could be ascribed to the local industrial emission sources
such as the leather production processes etc. Otherwise, the general trends of dicarboxylic acids in this study were similar to that in Baghdad, Iraq (Hamad et al., 2015). And the potential reasons behind this seasonal variability might be the impact of regional sources and also some meteorological factors. 3.2. Source apportionment of PM2.5 OC The primary source contributions to organic carbon in PM2.5 was estimated by molecular marker based CMB modeling method with the source profiles discussed previously as well as the concentration of fitting species determined from the ambient PM2.5 samples (Schauer et al., 1996; Turpin et al., 2000). All the CMB model output results were presented in Table 1, including R2, CHI2, % Mass
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Table 1 Seasonal source contributions to OC in PM2.5 at four sites of China (mean ± SD in mg m3). Site Season
Meat cooking
WQ HN ZS DY WQ HN ZS DY WQ HN ZS DY WQ HN ZS DY
2.05 0.67 0.59 0.56 2.64 1.10 2.66 —a 1.88 0.89 0.72 4.85 0.73 0.59 0.32 0.28
a
Fall Fall Fall Fall Winter Winter Winter Winter Spring Spring Spring Spring Summer Summer Summer Summer
± ± ± ± ± ± ±
0.58 0.25 0.17 0.16 0.74 0.30 0.75
± ± ± ± ± ± ± ±
0.53 0.25 0.20 1.37 0.21 0.17 0.09 0.15
Natural gas combustion 0.12 0.68 0.16 0.14 1.25 2.55 0.52 0.97 0.63 0.40 0.11 0.55 0.40 0.04 0.08 —a
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.76 0.23 0.05 0.04 0.54 0.46 0.15 0.23 0.15 0.12 0.04 0.12 0.09 0.02 0.02
Biomass burning 5.49 4.66 1.98 2.58 5.01 2.97 —a 9.17 2.13 2.25 1.06 4.14 1.52 0.42 0.28 1.53
± ± ± ± ± ±
1.76 1.33 0.55 0.82 1.60 0.83
± ± ± ± ± ± ± ± ±
2.87 0.60 0.73 0.34 1.15 0.48 0.13 0.08 0.45
Vehicle emission 2.40 5.68 1.96 0.91 2.69 2.69 5.10 —a 5.03 2.21 3.14 9.56 1.16 3.67 1.71 1.23
± ± ± ± ± ± ±
0.59 1.15 0.78 0.15 0.56 0.64 1.03
± ± ± ± ± ± ± ±
1.65 0.69 0.57 2.76 0.22 0.65 0.52 0.34
Coal combustion 4.42 2.00 0.46 0.23 5.35 1.32 1.26 1.64 1.15 1.28 0.35 0.65 0.69 0.29 0.65 2.13
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.60 0.38 0.08 0.07 0.82 0.25 0.21 0.25 0.21 0.21 0.07 0.14 0.15 0.06 0.11 0.26
Sum of identified sources
Measured OC
R2
CHI2 %Mass explained
14.5 ± 2.1 13.7 ± 1.8 5.1 ± 0.8 4.4 ± 1.1 16.9 ± 3.1 10.6 ± 2.0 9.5 ± 1.1 11.8 ± 3.0 10.8 ± 1.4 7.0 ± 1.2 5.4 ± 1.1 19.8 ± 2.6 4.5 ± 0.6 5.0 ± 0.8 3.1 ± 0.6 5.2 ± 1.0
14.9 ± 3.0 10.7 ± 2.1 5.0 ± 1.0 4.8 ± 1.0 27.3 ± 3.5 10.8 ± 2.2 13.3 ± 2.1 31.2 ± 4.2 11.4 ± 2.3 7.6 ± 1.5 6.2 ± 1.2 16.9 ± 3.4 6.8 ± 1.4 7.0 ± 1.4 6.5 ± 1.3 6.1 ± 1.2
0.97 0.97 0.97 0.97 0.97 0.98 0.96 0.96 0.95 0.94 0.97 0.95 0.95 0.97 0.97 0.97
0.80 0.91 0.70 0.98 0.81 0.43 0.97 0.85 1.47 1.73 0.98 1.59 1.34 0.92 0.79 0.85
97.2 128.0 102.8 91.6 62.1 98.4 71.7 37.8 94.8 92.1 86.6 116.9 66.2 71.8 46.8 84.5
Source contribution to OC is insignificant different from zero.
explained, and source contribution estimates (±standard deviation). The CMB results in this study are statistically significant with the average R2, CHI2 and the percent mass explained as 0.96, 1.01, and 84%, respectively. The other organic carbon category represents the difference between the measured total organic carbon concentration and the summed concentration of the primary source contributions (which were normalized by the total OC concentration) quantified from the receptor model. Five sources were identified that contribute to the OC in fine particles (i.e., PM2.5), a.k.a. fine OC, in this study, which varied in site and season. On an annual average, the vehicle emission, coal combustion, and biomass burning were found to be the most significant contributors to fine OC at these sites. Other identified primary sources include meat cooking and natural gas combustion. The contribution to fine OC from these primary sources showed a distinct seasonal pattern (Fig. S2). In spring, the vehicle emission was the largest contributor, which account for on average 47% of measured OC. The contributions to fine OC from this source were 5.03 ± 1.65 mg m3, 2.21 ± 0.69 mg m3, 3.14 ± 0.57 mg m3, 9.56 ± 2.76 mg m3 at Wuqing, Haining, Zhongshan, and Deyang site, respectively. Vehicle emission is also the dominating source contributing to the OC in summer, explaining 31% of OC on average. The lowest percentage of explained organic carbon to measured organic carbon was in summer at all sites (67% average), possibly reflecting an increase in secondary organic aerosol formation in the hot season. A trend toward a higher contribution to the OC from biomass burning was found in fall, accounting for on average 41% of the measured OC. Similar results were found at the urban sites in China and U.S (Zheng et al., 2005, 2007). Such dramatic increase of biomass aerosol contribution is possibly due to straw burning widely present around China at the time of the year. Interestingly, meat cooking exhibited the highest seasonal contribution in spring, with an average PM2.5 OC contribution of 19% at these sites and the largest contribution of 29% at Deyang site. In contrast, contributions from coal combustion increased greatly during cold months, accounting for 20% and 12% of OC in fall and winter, respectively. Model results revealed that the primary sources contributing to the PM2.5 OC varied significantly across the sites in this study. At Wuqing and Deyang site, the largest contributor to OC was biomass burning, explaining 23% and 28% of measured OC, respectively. While the vehicle emission had the most important impact on OC at Haining and Zhongshan site, with an annual average of 37% and 38%
contributing to OC, respectively. It is expected that vehicle emission is the predominant contributor to OC at Haining and Zhongshan site, since these two sampling sites are close to commercial centers and consequently experiences high level of traffic activities. The differences of vehicle emission contributions between sites also reflected the fast growth of economy in YRD and PRD regions. 3.3. Source apportionment of PM2.5 mass Since the ratio of the emissions of fine organic carbon to fine particle mass can be found in published studies for each of the sources in the source profiles, source contributions to fine particle mass concentrations also can be calculated. The following factors are used to convert fine organic carbon to fine particulate mass: 0.304 for vehicle emission, 0.336 for coal combustion, 0.588 for biomass burning, 0.849 for natural gas combustion, and 0.556 for meat cooking (Fine et al., 2004; He et al., 2008; Rogge et al., 1991; Schauer and Cass, 2000; Zhang et al., 2008; Zheng et al., 2005). “Other mass” refers to the difference between PM2.5 mass and the sum of identified sources. The other organic matter (other OM) was calculated by multiplying the other organic carbon by 1.6 (Turpin and Huntzicker, 1995). The OM/OC ratio of 1.4 was generally used to estimate the organic matter mass (Zheng et al., 2005); however, the ratio of 1.6 ± 0.2 was recommended for urban areas and 2.1 ± 0.2 for rural and suburban areas (Turpin and Lim, 2001). Therefore, we used an OM/OC ratio of 1.6 for the four sampling sites. The fine particle mass contributions from the primary sources plus the other organic matter, EC as well as secondary sulfate, nitrate, and ammonium ion concentrations were shown in Fig. 2 and Table S2. The sum of identified primary sources and secondary aerosol formation accounted for between 39% and 74% of the measured PM2.5 mass during different seasons. The major contributors to PM2.5 mass in BTH region were calculated as secondary sulfate (19.3 ± 6.5 mg m3), secondary nitrate (16.7 ± 6.2 mg m3), vehicle emission (9.3 ± 5.3 mg m3), coal combustion (8.6 ± 6.9 mg m3), secondary ammonium (6.3 ± 4.5 mg m3), biomass burning (6.0 ± 3.4 mg m3), other organic matter (5.5 ± 7.6 mg m3), meat cooking (3.3 ± 1.4 mg m3), EC (1.7 ± 0.1 mg m3) and natural gas combustion (0.7 ± 0.6 mg m3) on annual average. Secondary sulfate was also the largest contributor to PM2.5 mass in YRD, PRD and SB regions, averaging 15.1 ± 7.4 mg m3, 9.8 ± 6.3 mg m3, and 20.1 ± 14.3 mg m3 of total mass, respectively. Vehicle emission was the next major contributor
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Fig. 2. Source contributions to PM2.5 mass at the sites in China. Wuqing (WQ), Haining (HN), Zhongshan (ZS), Deyang (DY).
to PM2.5 in PRD and SB regions, accounting for 16.6 ± 8.6% and 7.6 ± 11.6% of PM2.5 mass. Nevertheless, secondary nitrate surpassed vehicle emission to become the second largest contributor to PM2.5 mass in YRD and BTH regions, contributing an average of 12.6 ± 8.8% and 12.4 ± 4.6% of PM2.5 mass. It is not surprising that higher contributions of secondary nitrate were observed in YRD and BTH regions in contrast to PRD and SB regions given the ammonium nitrate is favorable to form during the cold months. From the source apportionment results, the secondary inorganic ions including sulfate, nitrate and ammonium were found the major components in PM2.5. Meanwhile, the other organic matter varied greatly between the seasons. It contributed on average 11% of the sum of identified sources in summer. On one hand, a high portion of “other OM” in summer is possibly associated with secondary organic aerosol formed when volatile organic precursor compound and oxidant species exist in warm months of the year. On the other hand, this seasonal variability may be also related with the lowest total mass explained in summertime. Additionally, high concentrations of “other OM” were found at Wuqing and Deyang sites during winter, which were due to the influence of regional enriched sources. The 72 h backward trajectories of air masses analysis (Fig. S3) revealed that air masses passed through highly polluted BTH region (69%) and SB region (27%) before arriving at Wuqing and Deyang site, respectively. It is interesting to note that secondary sulfate, secondary nitrate and secondary ammonium exhibited consistently much lower relative standard deviations across the four sites during wintertime, with the value of 16.3%e 33.5%, indicating the regional enriched sources had amplified impact on these sites in winter. Previous study had also confirmed that regional transported source contributed to the formation and evolution of haze pollution in BTH region during the same study period (Sun et al., 2014). Overall, the annual average source contribution to PM2.5 mass at four sites of China was shown in Fig. 3. The highest percentage of identified source contribution to PM2.5 was found at Zhongshan site, accounting for 66% of total mass, while the lowest at Deyang site explained only 54% of the mass. At Wuqing and Haining site, the major contributors to PM2.5 mass were secondary sulfate (14e15%), secondary nitrate (13%) and vehicle emission (7e11%). In
contrast, secondary sulfate (16.6%) and vehicle emission (16.6%) have the highest impact on PM2.5 mass at Zhongshan site. The secondary sulfate was found to the highest contributor, accounting for 16% of PM2.5 mass at Deyang site, followed by vehicle emission (8%) and other OM (7%). The secondary ammonium showed little change across the sites, contributing 4e5% of the PM2.5 mass. Meanwhile, biomass burning accounted for 2e6% of fine particles, and it has a higher impact on Deyang compared to other sites. The coal combustion has relatively higher contributing to PM2.5 mass at Wuqing site (6%), whereas the other sites were found to be around 3e4%. It is not surprising that the coal combustion is widely used for residential heating at Wuqing during the cold months. However, there is no central heating in winter at southern cities of China. We note that the present results were confirmed by the findings of Andersson et al. using dual carbon isotope constrained (D14C and d13C) source apportionment technology during the same study period (Andersson et al., 2015). It was found that higher coal combustion is contributing to EC in North China Plain (NCP) region and more liquid fossil in YRD and PRD region. In addition, small amounts of PM2.5 mass are contributed by other OM (1e7%), meat cooking (1e3%), EC (1e2%), and natural gas combustion (0.4e1%). However, it may also have other small sources contributing to PM2.5, such as industrial sources, mineral dust and sea salt. These sources contributions are not quantified in this study owing to the unavailable concentrations of elements in the collected PM2.5 samples. The lowest percentage of explained PM2.5 mass to total measured mass was shown in summer at all sites (47% average). It was possibly related to the missing of mineral dust which is favored by dry summer conditions. Besides, the water contained in the fine particles could also explain part of the unaccounted mass (Ohta et al., 1998). 4. Conclusions To investigate the organic constituents of ambient fine PM and quantify the source contributions of PM2.5 mass, a sampling campaign was conducted at four receptor sites in China from November 2012 to July 2013. A molecular marker-chemical mass balance (CMB) receptor model was applied to apportion the seasonal source contributions to OC and PM2.5 mass at these sites. The CMB results revealed that the major primary sources of PM2.5 OC are vehicle emission, biomass burning, coal combustion, meat cooking, and natural gas combustion. These primary sources collectively accounted for 84 ± 24% of the measured OC over the year with a relatively lower contribution in summer (67%), which could be associated with secondary formation processes. The vehicle emission dominated the sources contributing to OC in spring at four sampling sites. During wintertime, the coal combustion had highest contribution to OC in BTH region, while the major source contributing to OC at YRD and PRD sites was vehicle emission. The sum of identified primary sources and secondary aerosol formation accounted for between 54% and 66% of the measured PM2.5 mass on average. Source contribution showed distinct seasonality with higher contribution from biomass burning in fall and more coal combustion emission in winter. The secondary ammonium have minor variations across the sites, especially during wintertime, which was possibly due to the impact from regional emission sources on these sites. Local source contributors to the fine particulate matter at these sites were also observed, including the highest vehicle emission at PRD site (17%), the largest coal combustion at BTH site (7%), and the highest contribution from biomass burning at SB site (6%). Therefore, the source apportionment results based on molecular marker CMB receptor model in this study can assist the local government to make season- and region-specific mitigation strategies for abating fine particulate
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Fig. 3. Annual average source contribution to PM2.5 mass at four sites of China. Wuqing (WQ), Haining (HN), Zhongshan (ZS), Deyang (DY).
matter pollution in China. Acknowledgements The authors thanks to NSERC Discovery grant (RGPIN/067842015), Queen Elizabeth II Scholarships, and the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB05060200) for financial support of this research. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2017.05.033. 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
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Stone, E.A., Snyder, D.C., Sheesley, R.J., Sullivan, A., Weber, R., Schauer, J., 2008. Source apportionment of fine organic aerosol in Mexico City during the MILAGRO experiment 2006. Atmos. Chem. Phys. 8, 1249e1259. Sun, Y., Jiang, Q., Wang, Z., Fu, P., Li, J., Yang, T., Yin, Y., 2014. Investigation of the sources and evolution processes of severe haze pollution in Beijing in january 2013. J. Geophys. Res. Atmos. 119, 4380e4398. Turpin, B.J., Huntzicker, J.J., 1995. Identification of secondary organic aerosol episodes and quantitation of primary and secondary organic aerosol concentrations during SCAQS. Atmos. Environ. 29, 3527e3544. Turpin, B.J., Lim, H.-J., 2001. Species contributions to PM2. 5 mass concentrations: revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol. 35, 602e610. Turpin, B.J., Saxena, P., Andrews, E., 2000. Measuring and simulating particulate organics in the atmosphere: problems and prospects. Atmos. Environ. 34, 2983e3013. Watson, J.G., Cooper, J.A., Huntzicker, J.J., 1984. The effective variance weighting for least squares calculations applied to the mass balance receptor model. Atmos. Environ. 18, 1347e1355 (1967). Xing, Z., Deng, J., Mu, C., Wang, Y., Du, K., 2014. Seasonal variation of mass absorption efficiency of elemental carbon in the four major emission areas in China. Aerosol Air Qual. Res. 14, 1897e1905. Zhang, Y., Schauer, J.J., Zhang, Y., Zeng, L., Wei, Y., Liu, Y., Shao, M., 2008. Characteristics of particulate carbon emissions from real-world Chinese coal combustion. Environ. Sci. Technol. 42, 5068e5073. Zhang, Y.X., Min, S., Zhang, Y.H., Zeng, L.M., Ling-Yan, H.E., Zhu, B., Wei, Y.J., Zhu, X.L., 2007. Source profiles of particulate organic matters emitted from cereal straw burnings. J. Environ. Sci. 19, 167e175. Zheng, M., Cass, G.R., Ke, L., Wang, F., Schauer, J.J., Edgerton, E.S., Russell, A.G., 2007. Source apportionment of daily fine particulate matter at Jefferson Street, Atlanta, GA, during summer and winter. J. Air & Waste Manag. Assoc. 57, 228. Zheng, M., Cass, G.R., Schauer, J.J., Edgerton, E.S., 2002. Source apportionment of PM2.5 in the Southeastern United States using solvent-extractable organic compounds as tracers. Environ. Sci. Technol. 36, 2361e2371. Zheng, M., Salmon, L.G., Schauer, J.J., Zeng, L., Kiang, C.S., Zhang, Y., Cass, G.R., 2005. Seasonal trends in PM2.5 source contributions in Beijing, China. Atmos. Environ. 39, 3967e3976. Zhou, J., Xing, Z., Deng, J., Du, K., 2016. Characterizing and sourcing ambient PM 2.5 over key emission regions in China I: water-soluble ions and carbonaceous fractions. Atmos. Environ. 135, 20e30.
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Characterizing and sourcing ambient PM2.5 over key emission regions in China II: Organic molecular markers and CMB modeling Jiabin Zhou a, b, Ying Xiong b, Zhenyu Xing a, Junjun Deng c, Ke Du a, * a
Department of Mechanical and Manufacturing Engineering, University of Calgary, Alberta, T2N 1N4, Canada School of Resources and Environmental Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China c Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China b
h i g h l i g h t s Molecular marker-CMB model was applied to estimate primary contributors to PM2.5 OC. Main contributors to PM2.5 were sulfate, vehicle emission, nitrate, coal combustion. Vehicle emission had the largest impact on PM2.5 mass in PRD region. PM2.5 level was affected by local emissions and regional enriched sources across the sites.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 3 January 2017 Received in revised form 16 May 2017 Accepted 18 May 2017 Available online 20 May 2017
From November 2012 to July 2013, a sampling campaign was completed for comprehensive characterization of PM2.5 over four key emission regions in China: Beijing-Tianjin-Hebei (BTH), Yangzi River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SB). A multi-method approach, adopting different analytical and receptor modeling methods, was employed to determine the relative abundances of region-specific air pollution constituents and contributions of emission sources. This paper is focused on organic molecular marker based source apportionment using chemical mass balance (CMB) receptor modeling. Analyses of the organic molecular markers revealed that vehicle emission, coal combustion, biomass burning, meat cooking and natural gas combustion were the major contributors to organic carbon (OC) in PM2.5. The vehicle emission dominated the sources contributing to OC in spring at four sampling sites. During wintertime, the coal combustion had highest contribution to OC at BTH site, while the major source contributing to OC at YRD and PRD sites was vehicle emission. In addition, the relative contributions of different emission sources to PM2.5 mass at a specific location site and in a specific season revealed seasonal and spatial variations across all four sampling locations. The largest contributor to PM2.5 mass was secondary sulfate (14e17%) in winter at the four sites. The vehicle emission was found to be the major source (14e21%) for PM2.5 mass at PRD site. The secondary ammonium has minor variation (4e5%) across the sites, confirming the influences of regional emission sources on these sites. The distinct patterns of seasonal and spatial variations of source apportionment observed in this study were consistent with the findings in our previous paper based upon water-soluble ions and carbonaceous fractions. This makes it essential for the local government to make season- and region-specific mitigation strategies for abating PM2.5 pollution in China. © 2017 Elsevier Ltd. All rights reserved.
Keywords: PM2.5 Organic aerosol Source apportionment Receptor model
1. Introduction Over the past decade, China has been faced with increasingly severe air pollution that has not only caught world-wide attention,
* Corresponding author. E-mail address: [email protected] (K. Du). http://dx.doi.org/10.1016/j.atmosenv.2017.05.033 1352-2310/© 2017 Elsevier Ltd. All rights reserved.
but prompted extensive research on atmospheric aerosols, the essential air pollutants. Ambient atmospheric aerosols have long been shown to have pronounced effect on human health, visibility reduction and global climate (Charlson et al., 1992; Seinfeld and Pandis, 1998). As significant constituents of aerosol particles, organic aerosols comprise of thousands of compounds that display highly distinctive physical and chemical properties (Saxena and
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Hildemann, 1996). The complicated characteristics of organic compounds, along with the un-predictivity of their exact composition associated with the unique emission patterns of specific locations, make it extremely challenging to conduct an exhaustive study, at the chemical species level, of all the organic compounds in atmospheric aerosols (Nolte et al., 2002; Schauer et al., 2002). Although considerable effort was made to measure the detailed chemical composition of carbonaceous aerosol, approximately less than 20% of the organic compounds in aerosols can be identified as individual organic species. Interestingly, some of these compounds have a high degree of source specificity and have been used as molecular markers in source apportionment models to estimate various source contributions to the aerosol particles (Park et al., 2006; Schauer and Cass, 2000; Schauer et al., 1996). Research on China air pollution has generated abundant datasets, indicating that Beijing-Tianjin-Hebei area (BTH), Yangtze River Delta area (YRD), Pearl River Delta area (PRD) and Sichuan Basin area (SB) are the four key emission areas in China (Cao et al., 2006; Xing et al., 2014; Zhou et al., 2016). Therefore, in this study, sampling sites were selected in these four main emission areas. The exact sampling locations, serving as receptor sites where air samples were collected downwind of their corresponding megacities Beijing, Shanghai, Guangzhou, Chengdu, were determined by performing the meteorological reanalysis of the prevailing wind in the four areas. A sampling campaign between 2012 and 2013 was completed for comprehensive characterizing the sourcing of PM2.5 over these four areas using a multi-method approach. The results are presented in a series of three papers. Part I paper presents findings on ionic and carbonaceous components (Zhou et al., 2016) in the aerosols. Part III, still under preparation, is around source apportionment based upon isotope analysis of black carbon in the aerosols. This paper (Part II of the series) compares the compositions of the individual organic compounds in the aerosols, and evaluates the source contributions to OC and PM2.5 using organic tracerchemical mass balance (CMB) receptor modeling approach. To date, only very limited information is available on the composition of individual organic chemicals within the ambient aerosol collected from the four emission areas in China. Therefore, results presented in this paper provide a unique perspective for policymakers to understand air pollution problems and thus formulate pollution control strategies. 2. Experiments and methods 2.1. Sample collection Sample collection was reported in part І (Zhou et al., 2016) and summarized here. The aerosol PM2.5 samples were collected in four satellite cities of their corresponding megacities Beijing, Shanghai, Guangzhou, Chengdu in China. These sites are Wuqing (39 390 N, 117 390 E) in BTH area, Haining (30 510 N, 120 690 E) in YRD area, Zhongshan (22 560 N, 113 330 E) in PRD area, and Deyang (31100 N, 104 390 E) in SB area, respectively. PM2.5 samples were collected simultaneously at four sites in November 2012, January 2013, April 2013 and July 2013, which represented fall, winter, spring and summer, respectively. After sampling, the PM2.5 quartz fiber filter samples were analyzed for PM2.5 mass using gravimetric method. The detailed description of the sampling sites and sampling method can be found in our previous paper (Xing et al., 2014). 2.2. Chemical analysis The concentrations of organic carbon (OC) and elemental carbon (EC) were measured by a Sunset Carbon Aerosol Analyzer (Sunset
Laboratory Inc., USA) following the NIOSH TOT protocol. More details about the analytical procedure can be found in our series paper І (Zhou et al., 2016). To determine individual organic compound, the collected aerosol PM2.5 filters were cut into small pieces and then extracted twice with dichloromethane (TEDIA) and twice with methanol (ACROS) using an ultrasonic bath, followed by a rotary evaporator to concentrate the extracts to about 5 mL. The mixture of isotopically labeled compounds was spiked into each sample prior to extraction. The extracts were further blown down to reduce the volume to 0.5 mL under high-purity nitrogen (99.99%). Each extract was split into two fractions, and a half of the extracts were derivatized by BSTFA/TMCS 99:1 (Regis Technologies, Inc. USA) in sealed vials at 70 C for 2 h to convert organic acids to their silylated analogues. Organic speciation was conducted using gas chromatography-mass spectrometry (Agilent Technologies GC6890, MS-5973). One microliter of derivatized extracts was injected into GC-MS for identification and quantification of organic species. The GC was fitted with a fused silica capillary column (DB-5, 30 m, 0.25 mm i.d., 0.25 mm film thickness) and its condition was: initially, the specimen was isothermally held at 60 C for 5 min, then increasing the temperature at a ramp of 5 C/min to 300 C, and finally was isothermally held at 300 C for 30 min. The pure helium (99.999% purity) was used as a carrier gas with a flow rate of 1.0 mL/min. The interface temperature of GC-MS was maintained at 300 C. Organic compounds were ionized by electron impact (70 eV) and scanned from 50 amu to 550 amu in the MS. The GC-MS data were acquired and analyzed by an Agilent Chemstation. According to the GC retention times, MS spectrum and prepared authentic standards, individual organic compounds were identified and quantified, including alkanes, organic acids, polycyclic aromatic hydrocarbons (PAHs), and some molecular tracer compounds such as levoglucosan, hopanes, and steranes. Blanks (including blank filters, field blanks, and solvent) and duplicate sample analyses were performed (10% of all samples) according to the standard operating procedures. Recovery (%) of organic species was calculated from the extraction recovery of the surrogate organic standards spiked. The spike recoveries of authentic standards including alkanes, polycyclic aromatic hydrocarbons, alkanoic acids were estimated to be in the range of 85%e 118%. Results of the organic analysis were blank corrected, and uncertainties were determined based on standard deviation of field blanks and the detection limit of the instruments. In addition, the concentrations of water-soluble ions including 2þ þ þ 2þ five cations (NHþ and four anions 4 ; Na ; K ; Ca ; Mg ) (F ; Cl ; SO2 ; NO ) were also determined using an ion chroma4 3 tography (Metrohm, Switzerland) coupled with a conductivity detector (Zhou et al., 2016). The collected PM2.5 filter was ultrasonically extracted with 20 mL Milli-Q water (18.2 MUcm) for 40 min, followed by filtering through 0.45 mm PTFE syringe filter (Pall Co. Ltd, USA). Quality assurance and quality control tests including field blank, laboratory blank, method detection limit and recovery efficiency were conducted. Multi-points calibrations using standard solutions were performed and the result of correlation coefficient was higher than 0.999. All the reported ions concentrations have been corrected by field blanks. 2.3. Source apportionment model As a receptor model, chemical mass balance (CMB) model expresses concentrations of different chemical species measured at a monitoring site as a linear sum of products of source profile abundances and source contribution estimates. In this study, using molecular marker-based chemical mass balance (MM-CMB) approach, the specific source contributions to ambient OC and
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PM2.5 mass concentrations were determined by calculating linear combination of the product of source effluents and its concentrations of a set of chemical species in ambient aerosol (Schauer et al., 1996; Watson et al., 1984). For successfully using the MM-CMB model, several assumptions need to be met. First, the organic compounds selected as tracers in the model should be chemically stable and conserved during transport from sources to receptor sampling sites. Second, all main sources of chemical species should be included in the model (Schauer et al., 1996). The species selected for use in the present study were based upon the recommendations of previous studies (Lough et al., 2007; Schauer and Cass, 2000). Twenty-one fitting species were included in the model (Table S1), which consist of a continuous series of n-alkanes from C25-C33, levoglucosan, abb-20R-C27-cholestane, abb-20R-C29-sitostane, 17a(H)-21b(H)-hopane, 17a(H)-22,29,30-trinorhopane, 17a(H)21b(H)-30- norhopane, benzo(e)pyrene, benzo(b)fluoranthene, benzo(k)fluranthene, indeno (1,2,3-cd)pyrene, benzo(ghi)perylene and picene. The picene, specific to coal combustion (Oros and Simoneit, 2000), was selected to infer whether coal combustion is a major contributor to ambient OC (Stone et al., 2008). The series of n-alkanes from C25-C33 were used to distinguish the biogenic and anthropogenic contributors according to carbon preference index (CPI). The particulate PAHs, hopanes, and steranes were included to estimate the contributions from the combustion of fossil fuels (Manchester-Neesvig et al., 2003). The source profiles used in this study were mainly obtained from previous studies conducted in China. The coal combustion profile was obtained from measurement of particulate carbon emissions from real-world Chinese coal combustion experiment (Zhang et al., 2008). The vehicle emission profile was collected from a tunnel experiment in the western urban area of Guangzhou (He et al., 2008). The biomass burning profile was the measured data from dilution chamber measurement of three kinds of cereal straw from the main grain producing regions in China (Zhang et al., 2007). The source profiles of meat cooking and natural gas combustion were obtained from USA (Rogge et al., 1991, 1993b). In addition, the criteria for acceptable CMB results included the square regression coefficient R2 > 0.85, the sum of residual square value CHI2<4, the degree of the freedom DF > 5, and the percentage of explained mass to total mass ranging from 80 to 120%. 3. Results and discussions 3.1. Organic species and source identification The annual average OC concentration varied between 7.0 and 14.1 mg m3 at these sampling sites, while the EC concentrations were observed to be 1.2e1.6 mg m3. More than one hundred of organic compounds including alkanes, PAHs, n-alkanoic acids, aliphatic and aromatic dicarboxylic acids were identified and quantified in each sample. Among them, some molecular markers or indicator compounds were well documented as the tracers to the source of organic matter (Simoneit et al., 1999). In this work, twelve selected organic molecular markers in PM2.5 including hopanes, picene, high-molecular-weight (HMW) PAHs, levoglucosan, cholesterol, dicarboxylic acids are presented in Fig. 1. Hopanes and petroleum biomarkers are commonly utilized as tracers for motor vehicle exhaust in the atmosphere (Rogge et al., 1993a; Schauer and Cass, 2000). As shown in Fig. 1a, the hopanes, including 17a(H)-22,29,30-trisnorhopane, 17a(H)-21b(H)-30norhopane, and 17a(H)- 21b(H)-hopane, contributed to PM2.5 mass with an annual average concentration (±standard deviation) of 2.3 ± 1.8 ng m3. The maximum values at these sites were observed in fall and winter, which were mainly due to the impact from regional enriched sources as well as unfavorable
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meteorological conditions such as low wind speed and boundary height during the cold seasons. And the minimum values were in spring, except that the highest level occurred at Deyang site during the summer period. Such extremely high levels of the hopanes at Deyang site was ascribed to a polluted episode in summer. The hopanes in the aerosol confirmed the input sources from fossil fuel utilization and engine lubricating oil at these sites. The concentrations of picene, a tracer for coal combustion, was found to have the highest value in winter and lowest in summer with an annual average of 0.2 ± 0.1 ng m3 (Fig. 1b). Wuqing site exhibited much higher picene level than that at Zhongshan site, revealing the difference of energy consumptions between northern and southern China. The maximum value occurred in winter at Wuqing (0.4 ng m3), which might be due to coal combustion for residential heating in the cold season. The selected polycyclic aromatic hydrocarbons (PAH) were detected in the samples and the results are shown in Fig. 1c. The annual average concentration of the PAHs was 7.4 ± 7.1 ng m3. These PAHs (indeno(1,2,3-cd) pyrene, benzo(ghi)perylene, coronene) with high molecular weight (MW > 228) are common product of incomplete combustion and usually associated with vehicle emission particulate matter. The highest concentration was measured in winter and lowest in summer. The seasonal trend of these tracers indicated that the meteorological factors have a substantial impact on increased PAHs levels during the winter period. Such high-molecular-weight PAHs are stable enough to resist chemical reaction and evaporation (Schauer et al., 1996), and are preferentially accumulated on the fine particulate matters once emitted from motor vehicle exhaust when radiation inversion occurs in winter. It is interesting to note that much higher concentrations of high-molecular-weight PAHs, picene, and the hopanes were observed simultaneously at Deyang site during summer period. The extremely high levels of these tracers were mainly related to a polluted episode in Deyang (Fig. S1). On July 13th, 2013, the levels of picene, indeno(1,2,3-cd)pyrene, benzo(g,h,i)perylene, 17a(H),21b(H)-hopane, 17a(H),21b(H)-30-norhopane, and 17a(H)22,29,30- trisnorhopane enhanced dramatically, increasing by a factor of 7.3, 20.4, 13.5, 9.7, 10.0 and 2.6, respectively. Such dramatic and consistent variations of these species suggested the prevailing contributions from fossil fuels combustion (e.g., vehicle emission, coal combustion) at Deyang site during the episode. Levoglucosan, which is considered as a cellulose pyrolysis tracer (Simoneit et al., 1999), was observed higher in samples during fall and winter periods and lower level in summer with an annual average concentration of 452.7 ± 337.6 ng m3 (Fig. 1d). The measured concentrations of levoglucosan are much higher than reported values at some US sites (Lough et al., 2006). It is worthy to note that the seasonal average concentration of levoglucosan observed at Zhongshan site increased dramatically with the largest concentration (averaging 1055.2 ng/m3, wintertime) nearly 25-fold of the lowest one (averaging 41.6 ng/m3, summertime). In addition, a much higher ratio of levoglucosan to OC (0.079) was also found at this site during the winter, and the related ratio for Wuqing, Haining and Deyang was 0.025, 0.021, and 0.039, respectively. The difference of levoglucosan to OC ratio across the sites could be resulted from local biomass burning activity. It was reported that the high OC/EC ratio (about 8) and Kþ/EC ratio (>1.0) could be used as immediate indicators for biomass burning pollution (Chen and Xie, 2014). In this study, the respective ratio for OC/EC and Kþ/EC was 6.7 and 1.6 at Zhongshan site during wintertime, confirming the intensive biomass burning activities in the PRD region during cold season. Meanwhile, cholesterol is considered as a tracer for meat charbroiling smoke (Rogge et al., 1991). As shown in Fig. 1e, the cholesterol exhibited slightly higher level in winter than that in
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Fig. 1. Seasonal variation of organic molecular markers at the sites in China. Wuqing (WQ), Haining (HN), Zhongshan (ZS), Deyang (DY).
summer with an annual average concentration of 1.4 ± 2.5 ng m3. Interestingly, the maximum value of 10.7 ng m3 was observed at Deyang site. Compared to other sites, such a sharp increase of cholesterol seemed to be impacted by local sources, i.e. meat cooking emissions. Aliphatic and aromatic diacids are commonly used as secondary organic aerosol tracers (Schauer et al., 2002; Zheng et al., 2002). The annual average of the collective concentration of these acids was 432.4 ± 412.5 ng m3. The measured dicarboxylic acids (terephthalic acid, phthalic acid, isophthalic acid) had almost no clear seasonal trends and varied slightly across the sites except the samples at Haining site during the fall period (Fig. 1f). Extremely high concentrations of dicarboxylic acids in Haining during the fall period could be ascribed to the local industrial emission sources
such as the leather production processes etc. Otherwise, the general trends of dicarboxylic acids in this study were similar to that in Baghdad, Iraq (Hamad et al., 2015). And the potential reasons behind this seasonal variability might be the impact of regional sources and also some meteorological factors. 3.2. Source apportionment of PM2.5 OC The primary source contributions to organic carbon in PM2.5 was estimated by molecular marker based CMB modeling method with the source profiles discussed previously as well as the concentration of fitting species determined from the ambient PM2.5 samples (Schauer et al., 1996; Turpin et al., 2000). All the CMB model output results were presented in Table 1, including R2, CHI2, % Mass
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Table 1 Seasonal source contributions to OC in PM2.5 at four sites of China (mean ± SD in mg m3). Site Season
Meat cooking
WQ HN ZS DY WQ HN ZS DY WQ HN ZS DY WQ HN ZS DY
2.05 0.67 0.59 0.56 2.64 1.10 2.66 —a 1.88 0.89 0.72 4.85 0.73 0.59 0.32 0.28
a
Fall Fall Fall Fall Winter Winter Winter Winter Spring Spring Spring Spring Summer Summer Summer Summer
± ± ± ± ± ± ±
0.58 0.25 0.17 0.16 0.74 0.30 0.75
± ± ± ± ± ± ± ±
0.53 0.25 0.20 1.37 0.21 0.17 0.09 0.15
Natural gas combustion 0.12 0.68 0.16 0.14 1.25 2.55 0.52 0.97 0.63 0.40 0.11 0.55 0.40 0.04 0.08 —a
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.76 0.23 0.05 0.04 0.54 0.46 0.15 0.23 0.15 0.12 0.04 0.12 0.09 0.02 0.02
Biomass burning 5.49 4.66 1.98 2.58 5.01 2.97 —a 9.17 2.13 2.25 1.06 4.14 1.52 0.42 0.28 1.53
± ± ± ± ± ±
1.76 1.33 0.55 0.82 1.60 0.83
± ± ± ± ± ± ± ± ±
2.87 0.60 0.73 0.34 1.15 0.48 0.13 0.08 0.45
Vehicle emission 2.40 5.68 1.96 0.91 2.69 2.69 5.10 —a 5.03 2.21 3.14 9.56 1.16 3.67 1.71 1.23
± ± ± ± ± ± ±
0.59 1.15 0.78 0.15 0.56 0.64 1.03
± ± ± ± ± ± ± ±
1.65 0.69 0.57 2.76 0.22 0.65 0.52 0.34
Coal combustion 4.42 2.00 0.46 0.23 5.35 1.32 1.26 1.64 1.15 1.28 0.35 0.65 0.69 0.29 0.65 2.13
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.60 0.38 0.08 0.07 0.82 0.25 0.21 0.25 0.21 0.21 0.07 0.14 0.15 0.06 0.11 0.26
Sum of identified sources
Measured OC
R2
CHI2 %Mass explained
14.5 ± 2.1 13.7 ± 1.8 5.1 ± 0.8 4.4 ± 1.1 16.9 ± 3.1 10.6 ± 2.0 9.5 ± 1.1 11.8 ± 3.0 10.8 ± 1.4 7.0 ± 1.2 5.4 ± 1.1 19.8 ± 2.6 4.5 ± 0.6 5.0 ± 0.8 3.1 ± 0.6 5.2 ± 1.0
14.9 ± 3.0 10.7 ± 2.1 5.0 ± 1.0 4.8 ± 1.0 27.3 ± 3.5 10.8 ± 2.2 13.3 ± 2.1 31.2 ± 4.2 11.4 ± 2.3 7.6 ± 1.5 6.2 ± 1.2 16.9 ± 3.4 6.8 ± 1.4 7.0 ± 1.4 6.5 ± 1.3 6.1 ± 1.2
0.97 0.97 0.97 0.97 0.97 0.98 0.96 0.96 0.95 0.94 0.97 0.95 0.95 0.97 0.97 0.97
0.80 0.91 0.70 0.98 0.81 0.43 0.97 0.85 1.47 1.73 0.98 1.59 1.34 0.92 0.79 0.85
97.2 128.0 102.8 91.6 62.1 98.4 71.7 37.8 94.8 92.1 86.6 116.9 66.2 71.8 46.8 84.5
Source contribution to OC is insignificant different from zero.
explained, and source contribution estimates (±standard deviation). The CMB results in this study are statistically significant with the average R2, CHI2 and the percent mass explained as 0.96, 1.01, and 84%, respectively. The other organic carbon category represents the difference between the measured total organic carbon concentration and the summed concentration of the primary source contributions (which were normalized by the total OC concentration) quantified from the receptor model. Five sources were identified that contribute to the OC in fine particles (i.e., PM2.5), a.k.a. fine OC, in this study, which varied in site and season. On an annual average, the vehicle emission, coal combustion, and biomass burning were found to be the most significant contributors to fine OC at these sites. Other identified primary sources include meat cooking and natural gas combustion. The contribution to fine OC from these primary sources showed a distinct seasonal pattern (Fig. S2). In spring, the vehicle emission was the largest contributor, which account for on average 47% of measured OC. The contributions to fine OC from this source were 5.03 ± 1.65 mg m3, 2.21 ± 0.69 mg m3, 3.14 ± 0.57 mg m3, 9.56 ± 2.76 mg m3 at Wuqing, Haining, Zhongshan, and Deyang site, respectively. Vehicle emission is also the dominating source contributing to the OC in summer, explaining 31% of OC on average. The lowest percentage of explained organic carbon to measured organic carbon was in summer at all sites (67% average), possibly reflecting an increase in secondary organic aerosol formation in the hot season. A trend toward a higher contribution to the OC from biomass burning was found in fall, accounting for on average 41% of the measured OC. Similar results were found at the urban sites in China and U.S (Zheng et al., 2005, 2007). Such dramatic increase of biomass aerosol contribution is possibly due to straw burning widely present around China at the time of the year. Interestingly, meat cooking exhibited the highest seasonal contribution in spring, with an average PM2.5 OC contribution of 19% at these sites and the largest contribution of 29% at Deyang site. In contrast, contributions from coal combustion increased greatly during cold months, accounting for 20% and 12% of OC in fall and winter, respectively. Model results revealed that the primary sources contributing to the PM2.5 OC varied significantly across the sites in this study. At Wuqing and Deyang site, the largest contributor to OC was biomass burning, explaining 23% and 28% of measured OC, respectively. While the vehicle emission had the most important impact on OC at Haining and Zhongshan site, with an annual average of 37% and 38%
contributing to OC, respectively. It is expected that vehicle emission is the predominant contributor to OC at Haining and Zhongshan site, since these two sampling sites are close to commercial centers and consequently experiences high level of traffic activities. The differences of vehicle emission contributions between sites also reflected the fast growth of economy in YRD and PRD regions. 3.3. Source apportionment of PM2.5 mass Since the ratio of the emissions of fine organic carbon to fine particle mass can be found in published studies for each of the sources in the source profiles, source contributions to fine particle mass concentrations also can be calculated. The following factors are used to convert fine organic carbon to fine particulate mass: 0.304 for vehicle emission, 0.336 for coal combustion, 0.588 for biomass burning, 0.849 for natural gas combustion, and 0.556 for meat cooking (Fine et al., 2004; He et al., 2008; Rogge et al., 1991; Schauer and Cass, 2000; Zhang et al., 2008; Zheng et al., 2005). “Other mass” refers to the difference between PM2.5 mass and the sum of identified sources. The other organic matter (other OM) was calculated by multiplying the other organic carbon by 1.6 (Turpin and Huntzicker, 1995). The OM/OC ratio of 1.4 was generally used to estimate the organic matter mass (Zheng et al., 2005); however, the ratio of 1.6 ± 0.2 was recommended for urban areas and 2.1 ± 0.2 for rural and suburban areas (Turpin and Lim, 2001). Therefore, we used an OM/OC ratio of 1.6 for the four sampling sites. The fine particle mass contributions from the primary sources plus the other organic matter, EC as well as secondary sulfate, nitrate, and ammonium ion concentrations were shown in Fig. 2 and Table S2. The sum of identified primary sources and secondary aerosol formation accounted for between 39% and 74% of the measured PM2.5 mass during different seasons. The major contributors to PM2.5 mass in BTH region were calculated as secondary sulfate (19.3 ± 6.5 mg m3), secondary nitrate (16.7 ± 6.2 mg m3), vehicle emission (9.3 ± 5.3 mg m3), coal combustion (8.6 ± 6.9 mg m3), secondary ammonium (6.3 ± 4.5 mg m3), biomass burning (6.0 ± 3.4 mg m3), other organic matter (5.5 ± 7.6 mg m3), meat cooking (3.3 ± 1.4 mg m3), EC (1.7 ± 0.1 mg m3) and natural gas combustion (0.7 ± 0.6 mg m3) on annual average. Secondary sulfate was also the largest contributor to PM2.5 mass in YRD, PRD and SB regions, averaging 15.1 ± 7.4 mg m3, 9.8 ± 6.3 mg m3, and 20.1 ± 14.3 mg m3 of total mass, respectively. Vehicle emission was the next major contributor
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Fig. 2. Source contributions to PM2.5 mass at the sites in China. Wuqing (WQ), Haining (HN), Zhongshan (ZS), Deyang (DY).
to PM2.5 in PRD and SB regions, accounting for 16.6 ± 8.6% and 7.6 ± 11.6% of PM2.5 mass. Nevertheless, secondary nitrate surpassed vehicle emission to become the second largest contributor to PM2.5 mass in YRD and BTH regions, contributing an average of 12.6 ± 8.8% and 12.4 ± 4.6% of PM2.5 mass. It is not surprising that higher contributions of secondary nitrate were observed in YRD and BTH regions in contrast to PRD and SB regions given the ammonium nitrate is favorable to form during the cold months. From the source apportionment results, the secondary inorganic ions including sulfate, nitrate and ammonium were found the major components in PM2.5. Meanwhile, the other organic matter varied greatly between the seasons. It contributed on average 11% of the sum of identified sources in summer. On one hand, a high portion of “other OM” in summer is possibly associated with secondary organic aerosol formed when volatile organic precursor compound and oxidant species exist in warm months of the year. On the other hand, this seasonal variability may be also related with the lowest total mass explained in summertime. Additionally, high concentrations of “other OM” were found at Wuqing and Deyang sites during winter, which were due to the influence of regional enriched sources. The 72 h backward trajectories of air masses analysis (Fig. S3) revealed that air masses passed through highly polluted BTH region (69%) and SB region (27%) before arriving at Wuqing and Deyang site, respectively. It is interesting to note that secondary sulfate, secondary nitrate and secondary ammonium exhibited consistently much lower relative standard deviations across the four sites during wintertime, with the value of 16.3%e 33.5%, indicating the regional enriched sources had amplified impact on these sites in winter. Previous study had also confirmed that regional transported source contributed to the formation and evolution of haze pollution in BTH region during the same study period (Sun et al., 2014). Overall, the annual average source contribution to PM2.5 mass at four sites of China was shown in Fig. 3. The highest percentage of identified source contribution to PM2.5 was found at Zhongshan site, accounting for 66% of total mass, while the lowest at Deyang site explained only 54% of the mass. At Wuqing and Haining site, the major contributors to PM2.5 mass were secondary sulfate (14e15%), secondary nitrate (13%) and vehicle emission (7e11%). In
contrast, secondary sulfate (16.6%) and vehicle emission (16.6%) have the highest impact on PM2.5 mass at Zhongshan site. The secondary sulfate was found to the highest contributor, accounting for 16% of PM2.5 mass at Deyang site, followed by vehicle emission (8%) and other OM (7%). The secondary ammonium showed little change across the sites, contributing 4e5% of the PM2.5 mass. Meanwhile, biomass burning accounted for 2e6% of fine particles, and it has a higher impact on Deyang compared to other sites. The coal combustion has relatively higher contributing to PM2.5 mass at Wuqing site (6%), whereas the other sites were found to be around 3e4%. It is not surprising that the coal combustion is widely used for residential heating at Wuqing during the cold months. However, there is no central heating in winter at southern cities of China. We note that the present results were confirmed by the findings of Andersson et al. using dual carbon isotope constrained (D14C and d13C) source apportionment technology during the same study period (Andersson et al., 2015). It was found that higher coal combustion is contributing to EC in North China Plain (NCP) region and more liquid fossil in YRD and PRD region. In addition, small amounts of PM2.5 mass are contributed by other OM (1e7%), meat cooking (1e3%), EC (1e2%), and natural gas combustion (0.4e1%). However, it may also have other small sources contributing to PM2.5, such as industrial sources, mineral dust and sea salt. These sources contributions are not quantified in this study owing to the unavailable concentrations of elements in the collected PM2.5 samples. The lowest percentage of explained PM2.5 mass to total measured mass was shown in summer at all sites (47% average). It was possibly related to the missing of mineral dust which is favored by dry summer conditions. Besides, the water contained in the fine particles could also explain part of the unaccounted mass (Ohta et al., 1998). 4. Conclusions To investigate the organic constituents of ambient fine PM and quantify the source contributions of PM2.5 mass, a sampling campaign was conducted at four receptor sites in China from November 2012 to July 2013. A molecular marker-chemical mass balance (CMB) receptor model was applied to apportion the seasonal source contributions to OC and PM2.5 mass at these sites. The CMB results revealed that the major primary sources of PM2.5 OC are vehicle emission, biomass burning, coal combustion, meat cooking, and natural gas combustion. These primary sources collectively accounted for 84 ± 24% of the measured OC over the year with a relatively lower contribution in summer (67%), which could be associated with secondary formation processes. The vehicle emission dominated the sources contributing to OC in spring at four sampling sites. During wintertime, the coal combustion had highest contribution to OC in BTH region, while the major source contributing to OC at YRD and PRD sites was vehicle emission. The sum of identified primary sources and secondary aerosol formation accounted for between 54% and 66% of the measured PM2.5 mass on average. Source contribution showed distinct seasonality with higher contribution from biomass burning in fall and more coal combustion emission in winter. The secondary ammonium have minor variations across the sites, especially during wintertime, which was possibly due to the impact from regional emission sources on these sites. Local source contributors to the fine particulate matter at these sites were also observed, including the highest vehicle emission at PRD site (17%), the largest coal combustion at BTH site (7%), and the highest contribution from biomass burning at SB site (6%). Therefore, the source apportionment results based on molecular marker CMB receptor model in this study can assist the local government to make season- and region-specific mitigation strategies for abating fine particulate
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Fig. 3. Annual average source contribution to PM2.5 mass at four sites of China. Wuqing (WQ), Haining (HN), Zhongshan (ZS), Deyang (DY).
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