Effects of combustion emissions from the Eurasian continent in winter on seasonal δ13C of elemental carbon in aerosols in Japan

Atmospheric Environment 46 (2012) 568e579

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Effects of combustion emissions from the Eurasian continent in winter on seasonal d13C of elemental carbon in aerosols in Japan Hiroto Kawashima*, Yuya Haneishi Department of Management Science and Engineering, Faculty of Systems Science & Technology, Akita Prefectural University, Akita Prefecture, Japan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 February 2011 Received in revised form 25 April 2011 Accepted 3 May 2011

We investigated suspended particulate matter (SPM, particles with a 100% cut-off aerodynamic diameter of 10 mm) and PM2.5 (particles with a 50% cut-off aerodynamic diameter of 2.5 mm) concentrations in aerosols sampled in Akita Prefecture, Japan, from April 2008 to January 2010, and the carbon isotope ratios (d13C) of elemental carbon (EC) in both SPM and PM2.5 and in samples from possible sources. We also determined the ion contents of SPM and estimated the back trajectories of air masses arriving at Akita Prefecture during the study period. The SPM concentration was very low (annual average, 15.2 mg m
3), and it tended to be higher in spring and lower in winter. We attributed the higher SPM in spring to dust storms brought from the Asian continent. The average annual PM2.5 concentration was 8.6 mg m
3. d13C of source samples (gasoline and diesel vehicle exhaust, fireplace soot, open biomass burning emissions, street dust, soil, charcoal, and coal) ranged from
34.7& to
1.8&. d13C values of soot from gasoline light-duty (
24.4  0.7&) and passenger vehicles (
24.1  0.6&) were very similar to that of soot from all diesel vehicles (
24.3  0.3&). d13C was enriched in SPM in winter compared with summer values, moreover, only a slight seasonal trend was detected in d13C in PM2.5. From these data and the source results, we hypothesized that the enrichment of d13C of SPM and PM2.5 in winter was a long-range effect of overseas combustion processes such as coal combustion. In addition, d13C of SPM was correlated with Cl
and Mg2þ contents in SPM, suggesting the influence of sea salt. We verified this hypothesis by back trajectory analyses. The results indicated a continental influence effects on EC of SPM and PM2.5 in winter. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Stable carbon isotope ratio IRMS Particulate matter Elemental carbon SPM PM2.5 Source apportionment

1. Introduction Particulate matter (PM) can have adverse effects on human health (e.g., Pope et al., 1991, 1992). PM is partitionable on the basis of particle size differences (e.g., Wilson and Suh, 1997). Here, suspended particulate matter (SPM) is defined as particles with a 100% cut-off aerodynamic diameter of 10 mm. In 1973, the Government of Japan established as an environmental air standard for SPM a maximum daily mean hourly concentration of 100 mg m
3 (Japan Ministry of the Environment Japan, 2010). SPM concentrations in Japan have decreased dramatically since the 1980s. In 2008, the Japanese air quality standard was met at 99.6% of sampling sites whereas in 2000 and 2001, only 66.6% and 52.5%, respectively, of sites met the standard (MOE, 2009a). Hayasaki et al. (2007) suggested that SPM should be investigated over large areas and with high resolution to determine the causes of high SPM concentrations in such years. Moreover, the Harvard Six Cities Study reported that

* Corresponding author. Tel.: þ81 184 27 2166; fax: þ81 184 27 2189. E-mail address: [email protected] (H. Kawashima). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.05.015

variations in the daily mean concentration of PM in aerosols are correlated with fluctuations in the daily death rate (Dockery et al., 1993); in particular, the death rate is positively correlated with the PM2.5 concentration, defined as particles with a 50% cut-off aerodynamic diameter of 2.5 mm (e.g., Pope et al., 2002). In 1997, the U.S. Environmental Protection Agency (USEPA) established a daily mean environmental air standard for PM2.5 of 35 mg m
3 and an annual mean standard of 15 mg m
3 (e.g., USEPA, 2005, 2006). On 9 September 2009, the Government of Japan also established environmental air standards for PM2.5 (MOE, 2009b, 2010) at the same levels as the USEPA standards. However, this standard might be difficult to attain in some metropolitan areas. For example, in Yamanashi Prefecture, daily mean PM2.5 concentrations ranged from 17 to 25 mg m
3 during 1996 (Kyotani and Iwatsuki, 2002), and in Tokyo during 1998e1999 the daily mean concentration was 27.9 mg m
3 (Saitoh et al., 2002). In Shizuoka Prefecture during 1999e2000, they ranged from 14 to 97 mg m
3 (Ohura et al., 2004). Moreover, Ma et al. (2004) reported that the PM2.5 concentration in winter (December) 2002 in Osaka Prefecture averaged 21.3 mg m
3, and Yonemochi et al. (2007) reported that the mean PM2.5 concentration in Saitama Prefecture, near Tokyo, ranged

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

from 20.4 to 24.9 mg m
3 during September 2000 to December 2005, with an annual mean of approximately 22 mg m
3. Because the mechanisms of PM formation and behavior in the atmosphere are very complicated, it is difficult to determine relationships between potential source and receptor (e.g., Chow et al., 2003). In general, PM is composed primarily of elemental carbon (EC) and metals and secondarily of organic carbon and ionic components. In Japan, a large proportion (e.g., 22.7% in fine particles and 13.5% in coarse particles in Chiba Prefecture) of PM is EC (Fukagawa et al., 2006). The main sources of EC are diesel vehicle emissions, coal combustion, and open biomass burning. To reduce PM concentrations effectively and attain environmental standards, sourcee receptor relationships must be clearly understood; in particular, the sources of EC in PM need to be determined. It has recently become possible to determine stable isotope ratios in small sample volumes with high accuracy and precision by using an isotope ratio mass spectrometer (IRMS) coupled with an elemental analyzer (EA) (e.g., Sharp, 2007). Stable isotope ratios can be used to detect and distinguish primary materials, chemical processes, and sources. In environmental science, especially research on aerosols, stable isotope ratios are expected to constitute a powerful tool for source identification. Studies of the stable carbon isotope ratios (d13C) of particulate matter are limited, however (Court et al., 1981; Cachier et al., 1985, 1986; Tanner and Miguel, 1989; Widory et al., 2004; Gorka and Jedrysek, 2004; Szidat et al., 2004; Cao et al., 2004, 2005, 2008; Wang et al., 2005; Huang et al., 2006; Ho et al., 2006; Grassi et al., 2007; Liu et al., 2007; Narukawa et al., 2008; Takahashi et al., 2008; Fisseha et al., 2009; Gorka et al., 2009; Marley et al., 2009; LopezVeneroni, 2009; Huang et al., 2010; Wang et al., 2010). For example, in 2002, Widory et al. (2004) measured d13C of total carbon (TC) in PM2.5 and PM10 in exhaust soot of diesel and gasoline vehicles (both regular and unleaded gasoline), as well as in coal, fuel oil, and natural gas sources in Paris. They found that d13C of soot from diesel vehicles (
26.5&) differed from that of soot from vehicles using regular (
24.5&) or unleaded (
24.2&) gasoline. They also measured d13C of TC in PM10 of ambient air, and inferred that PM10 contained contributions from diesel vehicles. Takahashi et al. (2008) measured d13C of EC in total suspended particles (TSP) in Beijing in 1998 (annual mean,
24.6  0.7&) and in PM2.1 in Tokyo in 2004 (annual mean,
25.1  0.1&) and reported that, although in Beijing, d13C of TSP in winter

569

(mean,
24.0  0.3&) was higher, they observed no clear seasonal difference in d13C of PM2.1 in Tokyo. They attributed the higher winter values in Beijing to coal combustion in winter (d13C of Chinese coal,
23.4  1.2&). Lopez-Veneroni (2009) measured d13C of TC in PM2.5 (
25.4  1.2&) and PM10 (
25.5  1.3&) at three sites in Mexico (Xalostoc, Merced, and Pedregal) in 2000, as well as in 21 sources: PM2.5 and PM10 from gasoline (2) and diesel (2) vehicles, car tailpipe soot (1) and truck tailpipe (1), soils (9) and fossil (6). They found that soil on the on hand diesel and gasoline on the other vehicles accounted about equally (54% and 46%, respectively) to d13C of PM2.5. Likewise, d13C of PM10 was affected by soil (73%) and particle emissions from diesel and gasoline vehicles (27%). In this study, we measured d13C of EC of PM (both PM2.5 and SPM) in Akita Prefecture, Japan, with high accuracy and precision, after separating EC and organic carbon (OC). We also measured d13C of many sources (soot from diesel, gasoline vehicles, and construction machine exhaust; fireplace soot; open biomass burning; soils; street dust; charcoal; and coal) and compared d13C values between ambient EC and the sources. We also analyzed the ionic contents of SPM and estimated seasonal differences in longrange contributions by air mass back trajectory analysis. 2. Methods 2.1. SPM and PM2.5 sampling schedule and methods SPM and PM2.5 were sampled at Akita Prefectural University, Yurihonjo City, Japan (latitude 39.2 N, longitude 140.4 E) from April 2008 to January 2010 (Fig. 1). In this study, we consider summer to be from June to August and winter to be from December to February. In 2009, the annual average daily temperature at this site was 12.0  C (maximum 16.3  C; minimum 7.9  C), the annual average precipitation was 1826 mm, and the yearly snow accumulation was 210 cm (Japan Meteorological Agency, 2010). This site was selected as a typical Japanese rural site with no industrial emission and heavy snow in winter. We also selected this site as appropriate for distinguishing effects from the Eurasian continent from local Japanese effects, because in winter and spring the prevailing wind direction is from the Eurasian continent. SPM was sampled with a High-Volume Air Sampler (HVS-1000, Sibata Scientific Technology Ltd., Saitama, Japan) at a flow rate of

Fig. 1. Sampling location at Akita Prefectural University in Yurihonjo City, Akita Prefecture, Japan (top left: Japan; bottom left: Yurihonjo City, Akita Prefecture; right: Akita Prefectural University, latitude: 39.2 N, longitude: 140.4 E).

570

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

1000 L min
1, and PM2.5 was sampled with a Mini-pump Air Sampler (MP-S300, Sibata Scientific Technology) and impactor (NWPS-35H, Sibata Scientific Technology) at a flow rate of 2.5 L min
1. SPM was sampled over intervals of approximately 10 days, and PM2.5 over intervals of approximately 14 days, from April 2008 to January 2010. In all, 58 SPM samples and 39 PM2.5 samples were obtained. From April 2008 to April 2009, SPM was sampled using glass fiber filters (GB-100R, Advantec Toyo Kaisha, Ltd, Tokyo, Japan), and from May 2009 to November 2009, it was sampled with quartz-fiber filters (Pallflex 2500QAT-UP, Nihon Pall Ltd., Tokyo, Japan). PM2.5 was sampled with quartz-fiber filters (Pallflex 2500QAT-UP) during the entire study period. The quartz-fiber filters were conditioned in a ceramic combustion furnace at 900  C for at least 3 h and then stored in a desiccator until used for sampling to prevent contamination by moisture or organics. After the sampling, the filters were brought to the laboratory, placed in a desiccator for at least 1 day, and then weighed carefully. Then, the filter was stored at
31  C until the ion and isotopic analyses. 2.2. Potential sources Gasoline and diesel vehicle exhaust soot, fireplace soot, and open biomass burning emissions, as well as street dust, soils, charcoal, coal, were sampled as possible sources of PM. Gasoline vehicles in this study included light-duty vehicles (9 vehicles), passenger vehicles (14), and sports-type vehicles (3). Diesel vehicles (25 buses; 16 in August 2009 and 9 in January 2010), passenger vehicles (4), and a construction machine (1). Soot was collected from buses in both summer and winter because the diesel fuel composition can differ seasonally owing to the addition of antiknock components in winter (JIS, 2007). Soot deposits for analysis were scraped directly from the tailpipes of the gasoline and diesel vehicles (e.g., Widory et al., 2004). Gasoline and diesel vehicle types were determined according to information from the Akita Transport Branch Office (2010). Fireplace soot (from house heating with wood fires) was scraped directly from chimney pipes at two sites in Yurihonjo City. Another possible source of PM is open burning of agricultural fields and plant wastes and forest fires (Marley et al., 2009). Therefore, we collected and burned as biomass samples of rice plants, grass, and dry leaves, because Akita Prefecture is a rice farming area. All these plant materials were collected in Yurihonjo City, and the rice plants from two sites. All plant materials were brought to the laboratory at Akita Prefectural University, where they were dried for 3 days and then ignited with a lighter and burned in a glass petri dish. In addition, corn, cucumber, potherb mustard, montbretia, sunflower, rosemary, chamomile, soybean, and watermelon plants were grown from seeds in the same environment at Akita Prefectural University from April to November 2009, and then dried and burned in the same way as the collected plant materials. Therefore, we examined open biomass burning samples from a total of 13 plant samples, including both C4 (corn and grass) and C3 (all others) plants, in this study. Street dust and soil were collected at Akita Prefectural University. For street dust, we collected asphalt, concrete, and curbstone. Soil was collected from three sites. After collection, the street dust and soil samples were crushed with a mortar and pestle. Charcoal was purchased from a market in Yurihonjo City. Coal samples were obtained from coal that had been imported from China. The charcoal and coal samples were also crushed in a mortar. 2.3. Separation of EC and OC First, carbonate carbon in all SPM and PM2.5 samples and source samples was removed by exposing the samples to gaseous

hydrochloric acid (1 M) in a glass desiccator for approximately 24 h as described in the literature (e.g., Cachier et al., 1985; Jankowski et al., 2008). Following Takahashi et al. (2008), we separated EC and OC using the Interagency Monitor of Protected Visual Environments (IMPROVE) method developed by the Desert Research Institute (DRI) (e.g., DRI Operating Procedure, 2005; Chow et al., 1993, 2001, 2007). In detail, the samples were heated in a quartz tube in a ceramic combustion furnace (Asahirika Co. Ltd., Chiba, Japan) at 550  C for 15 min under ultrahigh purity helium (99.9999%), flow rate 50 mL min
1, to remove OC. Then the sample was cooled under ultrahigh purity helium flow (50 mL min
1) and stored in a freezer at
31  C until d13C measurement. 2.4. Analytical method for stable carbon isotope ratio (d13C) Each sample (sample size, approximately 0.5e20 mg) was packed in a tin cup and analyzed with an elemental analyzer (Flash EA 1112, Thermo Fisher Scientific Inc., Bremen, Germany) and an isotope ratio mass spectrometer (MAT253, Thermo Fisher Scientific) (EA/IRMS). The contents of the tin cup were combusted instantaneously in the EA, and then the carbon content of the sample in the tin cup was converted to carbon dioxide using an oxidation catalyst and reduction tube in the EA. The oxidation and reduction tubes in the EA was maintained at 1000  C and 750  C, respectively. The flow rate of ultrahigh purity helium during the analysis was 100 mL min
1. The CO2 from the EA was ionized and its d13C value measured by IRMS using the provided ISODAT NT 2.0 software (Thermo Fisher Scientific). The stable isotope composition of the carbon, expressed in delta (d) notation in permil (&) units, was calculated as follows:

d13 C ¼



13

ð13 C=12 CÞ sample

ð13 C=12

CÞ standard


1  1000 &

12

where C/ Csample and 13C/12Cstd. are the atomic ratio of 13C to 12C in the sample and in the Pee Dee Belemnite (PDB) standard, respectively. The working standard (for validation) and the international standard (for calculation of d13C) used were histidine (L-histidine, Shoko Co., Ltd, Tokyo, Japan;
9.8&) and NBS-19 (RM8544, National Institute of Standards and Technology, Gaithersburg, Maryland, USA; þ1.95&), respectively. The working and international standards were measured after every 6 or every 30 samples, respectively. All samples were measured in triplicate. The analytical uncertainty (standard deviation) of the reported d13C values for SPM (53 samples) and PM2.5 (35 samples) in this study was within 0.57& (mean, 0.14&) and 0.47& (mean, 0.14&), respectively. 2.5. Verification of the analytical method To verify the precision and accuracy of the separation method (Section 2.3) we used the method of Hayashi et al. (1985). We used graphite powder for EC and adipic acid, benzoic acid, cellulose, and glucose for OC. We purchased these five reagents from Wako Pure Chemical Industries, Ltd. (Osaka, Japan): EC, highest purity graphite powder (Code No. 072-03845); OC, highest purity adipic acid (Code No. 017-00935); highest purity benzoic acid (Code No. 024-00985); microcrystalline cellulose (Code No. 557-70535); and glucose (Code No. 522-04255). First, d13C of each reagent alone was determined by EA/IRMS. Then, mixtures of 50/50 (EC/OC) as carbon contents of graphite powder and each of the four types of OC were prepared. After combusting the four mixtures according to the separation method described in Section 2.3, d13C of each mixture was determined by EA/IRMS. Three samples of each mixture were analyzed (separation combustion to EA/IRMS analysis).

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

The mean (SD) d13C values of graphite powder, adipic acid, benzoic acid, cellulose, and glucose, analyzed individually, were
19.4& (0.1&),
26.0& (0.1&),
29.0& (0.2&),
24.4& (0.2&), and
10.0& (0.3&), respectively. d13C values of the reagent mixtures, graphite powder þ adipic acid, graphite powder þ benzoic acid, graphite powder þ cellulose, and graphite powder þ glucose, after heating, ranged from
19.7& to
19.3& (Table 1). The replication error (SD) was within  0.2&. Therefore, the EC/OC separation method in this study yielded analytical results with high accuracy and precision. In addition, blank filters were analyzed in this method, and not detected by IRMS. 2.6. SPM ion contents

571

(METEX) program (Center for Global Environmental Research, National Institute for Environmental Studies) developed by Zeng et al. (2003) and available from the METEX website (Zeng, 2009). For the calculation, we used meteorological data from the U.S. National Centers for Environmental Prediction and a kinematic model. To investigate seasonal differences, we calculated back trajectories during four periods: spring (April and May 2008), summer (June to August 2008), autumn (September to November 2008), and winter (December to February 2008). Back trajectories from Akita Prefectural University covering the previous 240 h were calculated beginning every 24 h. The altitude above ground level was set to 1 m, which is the height of the SPM and PM2.5 samplers. 3. Results and discussion

We applied back trajectory analysis to estimate seasonal differences related to long-distance transport of PM. We calculated the back trajectories using the Meteorological Data Explorer Table 1 Precision and accuracy of EC/OC separation and the d13C analytical method used in this study. d13C values of the reagent mixtures, graphite powder þ adipic acid, graphite powder þ benzoic acid, graphite powder þ cellulose, and graphite powder þ glucose, after heating. Sample no.

Graphite powder þAdipic acid

Graphite powder þ Benzoic acid

Graphite powder þ Cellulose

Graphite powder þ Glucose

1 2 3


19.3  0.0&
19.6  0.2&
19.4  0.0&


19.4  0.1&
19.5  0.1&
19.4  0.0&


19.4  0.1&
19.7  0.0&
19.6  0.1&


19.5  0.2&
19.4  0.0&
19.3  0.0&

The SPM and PM2.5 results are summarized in Fig. 2 and Table 2. Monthly SPM concentrations ranged from 7.6 to 38.0 mg m
3 (annual average, 15.2 mg m
3). SPM is used in Japan for coarse particulate matter. All measured SPM concentrations are lower than the Japan Environmental Standard (annual standard, 100 mg m
3; hourly average standard, 200 mg m
3) (MOE, 2010), and they are also lower than values for Japan overall (2008 all cities annual average, 22 mg m
3; MOE, 2009a). We compared our SPM results with PM10 concentrations measured at non-Japanese sites. SPM concentrations in Akita Prefecture are lower than PM10 concentrations in European (e.g., Querol et al., 2004) and Chinese cities (e.g., Xiaohui et al., 2007). SPM in Akita Prefecture tended to be higher in spring (average, 22.7 mg m
3; 14 April to 26 May 2008, 1 March to 21 May 2009) and lower in winter (average, 11.0 mg m
3; 15 December to 18 February 2008, 7 December to 18 January 2009). The apparent reason for the higher springtime SPM was

SPM conc. (

g/m3)

a

40

30

20

10 0

A M J J A S O N D J F M A M J J A S O N D J F 2008

b

40

g/m3)

2.7. Meteorological information and back trajectory analysis

3.1. SPM and PM2.5 concentrations and yearly trends

30

PM2.5 conc. (

Water-soluble ions in SPM were measured by punching out a 3.5-cm-diameter circle from each filter. The ion contents of the circle were extracted for 60 min in deionized water in an ultrasonic bath. The extracts were then filtered through a 0.45-mm pore size membrane filter, Mini Sart RC15 (Sartorius Stedim Japan K.K., Tokyo, Japan), and the ions were analyzed by ion chromatography (ICS-1000, Nippon Dionex K.K., Osaka, Japan). Concentrations of þ 2þ 2þ cations (Naþ, NHþ 4 , K , Mg , Ca ) were determined with a cationexchange analytical column (IonPac CS12A, 4  250 mm) and guard column (IonPac CG12A, 4  50 mm) (Nippon Dionex), using 20 mM methanesulfonic acid (Wako Pure Chemical Industries) as the
2

eluent. Anions (Cl
, NO
2 , Br , NO3 , SO4 ) were determined with an anion-exchange analytical column (IonPac AS12A column, 4  250 mm) and guard column (IonPac AG12A, 4  50 mm) (Nippon Dionex), using 0.27 mM Na2CO3 þ 0.03 mM NaHCO3 (Wako Pure Chemical Industries) as the eluent. Cations and anions were eluted isocratically at a flow rate of 1.0 and 1.5 mL min
1, respectively. A calibration curve was constructed by analyzing five dilutions of standard mixtures, Multication standard solution III (Wako Pure
Chemical Industries) for anions (Cl
, 10 ppm; NO
2 , 50 ppm; Br , 2
, 50 ppb; SO , 100 ppm) and Multianion standard 50 ppm; NO
3 4 solution III (Wako Pure Chemical Industries) for cations (Liþ, 5 ppm; þ 2þ 2þ Naþ, 20 ppm; NHþ 4 , 25 ppm; K , 50 ppm; Mg , 30 ppm; Ca , 50 ppb). In addition, the quantification limit was determined using the diluted solution with the lowest concentration, which was tested five times. The results showed that for every measurement the coefficient of determination was >0.999; the detection limits
obtained were 8 ppb for Cl
, 11 ppb for NO
2 , 8 ppb for Br , 10 ppb 2
þ þ for NO
3 , 13 ppb for SO4 , 29 ppb for Na , 26 ppb for NH4 , 27 ppb for Kþ, 10 ppb for Mg2þ, and 13 ppb for Ca2þ. These values are in reasonable agreement to reported values (e.g., Shen et al., 2009).

2009

2010

20

10 0

A M J J A S O N D J F M A M J J A S O N D J F 2008

2009

2010

Fig. 2. Time series of (a) SPM and (b) PM2.5 concentrations from April 2008 to January 2010 in Akita Prefecture.

572

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

Table 2 Summary of annual and seasonal results for SPM and PM2.5 concentrations (mg m
3), d13C (&) of SPM and PM2.5, and ion concentrations (mg m
3) in SPM, Akita Prefecture, Japan, from 2008 to 2009.a n

Annual

2008 Spring

2009 Summer

Autumn

Winter

Spring

Summer

Autumn

Winter

Concentration (mg m
3)

SPM PM2.5

58 39

15.2  6.4 8.6  3.9

24.6  8.6 10.3  2.8

13.2  2.6 10.8  4.8

14.1  4.4 6.9  3.8

11.2  3.2 8.9  6.9

21.1  6.9 10.2  3.6

14.1  4.4 8.0  1.4

13.1  5.6 6.5  2.5

11.7  3.0 8.9  3.3

d13C (&)

SPM PM2.5

53 35


24.6  0.7
24.3  0.4


25.1  0.2
24.3  0.1


25.2  0.5
24.6  0.4


24.8  0.4
24.4  0.4


23.8  0.6
24.1  0.1


24.6  0.8
24.1  0.2


24.9  0.3
25.0  0.0


25.0  0.4
24.5  0.2


23.6  0.5
24.1  0.5

Ion concentrations (mg m
3)

Naþ NHþ 4 Kþ Mg2þ Ca2þ Cl
NO3
SO2
4

58 57 58 58 58 58 58 58

2.0  0.4 0.4  0.3 0.2  0.1 0.1  0.0 0.3  0.1 0.8  0.8 0.9  0.4 3.8  1.7

2.4  0.3 0.5  0.3 0.2  0.1 0.1  0.0 0.3  0.1 0.4  0.3 1.1  0.4 5.3  1.8

2.1  0.4 0.4  0.4 0.2  0.0 0.1  0.0 0.2  0.1 0.2  0.2 0.6  0.2 4.3  2.0

2.0  0.4 0.3  0.3 0.2  0.1 0.1  0.1 0.2  0.1 0.7  0.7 0.7  0.3 2.9  1.1

2.2  0.8 0.2  0.1 0.2  0.0 0.2  0.1 0.3  0.2 1.8  2.0 0.8  0.3 2.8  0.2

2.0  0.3 0.6  0.5 0.2  0.1 0.2  0.0 0.4  0.1 0.4  0.5 1.3  0.9 5.0  2.5

1.9  0.3 0.3  0.3 0.2  0.1 0.1  0.0 0.3  0.1 0.5  0.4 0.9  0.2 3.7  1.9

1.8  0.2 0.3  0.2 0.2  0.1 0.1  0.0 0.2  0.1 0.9  0.3 0.8  0.2 3.0  0.9

2.2  0.4 0.4  0.2 0.2  0.0 0.2  0.1 0.2  0.1 1.7  1.0 0.8  0.2 3.5  0.7

a

Standard deviation after plus or minus.

contributions from dust storms transported by monsoon winds from the Eurasian continent. In China, PM10 concentrations are higher in spring than in other seasons (Wang et al., 2006, 2009; Hayasaki et al., 2007). On 15 April 2008, when the maximum SPM concentration was observed in this study, a huge dust storm originating in China occurred in northern Japan (MOE, 2009c). Monthly PM2.5 concentrations ranged from 1.5 to 18.2 mg m
3 (annual average, 8.6 mg m
3). These values are also lower than the Japan Environmental Standard (annual standard, 15 mg m
3; 24-h average, 35 mg m
3; MOE, 2009a). The PM2.5 concentrations observed in this study are lower than those reported from European (e.g., Querol et al., 2004), Korean (e.g., Kim et al., 2007), Chinese cities (e.g., Meng et al., 2007) as well as those reported for other parts of Japan. No distinct seasonal trend in the PM2.5 concentration was detected in this study. In particular, dust storms seemed to have little effect on PM2.5 in Akita Prefecture. PM2.5 might not be affected even by north winds blowing from the continent because PM2.5 particles are smaller than the smallest collected dust storm particles; for example, dust storm particles collected in Yamanashi Prefecture ranged from 5 to 7 mm (Kyotani and Koshimizu, 2001). In fact, Sugiyama et al. (2008) reported that measured PM2.5 concentrations in Okayama, Japan, are not affected by dust storms at any time during the year, because of particle size differences of dust storms.

regular (
24.5  0.7&) and unleaded (
24.2  0.6&) gasoline were heavier than d13C of soot from diesel vehicles (
26.5  0.5&). As in this study, these samples were also scraped from tailpipes. Widory et al. (2004) concluded that the difference between gasoline and diesel vehicles was at least 2&, and that d13C could therefore be used to distinguish between these sources. In addition, Widory (2006) investigated d13C values of diesel and unleaded gasoline and of particles from unleaded gasoline and diesel vehicle exhaust. They found that d13C values of unleaded gasoline (
28.1  0.1&) and diesel fuel (
28.3  0.1&) were similar, but those of particles from unleaded gasoline (
24.8  0.1&) and diesel (
27.1  0.2&) vehicle exhaust were clearly different. More recently, Lopez-Veneroni (2009) reported that in Mexico City, d13C

Table 3 d13C (&) of all sources measured in this study: vehicle exhaust, non-vehicular fuel sources, soils, street dust (derived from asphalt, concrete, or curbstone), and C4 and C3 plants. All measurements were made in triplicate.

d13C (&)

Gasoline vehicle

Light-duty vehicle Passenger vehicle Sports-type vehicle

9 14 3


24.4  0.7
24.1  0.6
20.6  1.5

Diesel vehicle

Vehicle, buses and construction machine Vehicle and buses (summer) Buses (winter) Construction machine

30


24.3  0.3

20 9 1


24.4  0.4
24.2  0.2
24.9

3.2. Stable carbon isotope ratio differences among sources 3.2.1. Soot from gasoline and diesel vehicles d13C values of EC in source samples (soot from gasoline and diesel vehicle exhaust, fireplace soot, open biomass burning emissions, street dust, soils, charcoal, coal combustion emissions), summarized in Table 3. d13C values of soot from gasoline-powered light-duty vehicles (
24.4  0.7&) and passenger vehicles (
24.1  0.6&) were similar to the average value of all diesel vehicles (
24.3  0.3&), and to d13C of soot from diesel buses in both summer and winter (
24.4  0.4& and
24.2  0.2&, respectively). Although we anticipated that d13C of diesel vehicles would differ between summer and winter owing to the seasonal diesel fuel difference (JIS, 2007), we did not observe any seasonal difference in their d13C values. d13C of sports-type vehicle was
20.6  1.5&, heavier than values of soot from both other gasoline and diesel vehicles, possibly because of their different engine type. Further research is needed to clarify the reason for this heavy value. Widory et al. (2004) reported that d13C values of TC of soot of vehicles in Paris, France, using

n

Non-vehicle fuels

Coal Fireplace soot Charcoal

1 2 3


23.3
26.5  0.1
27.4  1.7

Soils

Soils

3


18.8  3.4

Street dust

Asphalt Concrete Curbstone

1 1 1


1.8
18.4
16.4

C4 plants

Corn Grass

1 1


16.1
19.3

C3 plants

Rice plant Dry leaf Cucumber Potherb mustard Montbretia Sunflower Rosemary Chamomile Soybean Watermelon

2 1 1 1 1 1 1 1 1 1


28.0  0.1
29.4
29.2
32.1
32.3
30.7
31.7
34.7
28.8
29.4

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

3.2.5. Plant combustion sources We analyzed many samples of combusted plants. d13C values obtained from C3 plants by open biomass burning (
34.7& to
28.0&) were lighter than those obtained from C4 plants (
19.3& and
16.1&). Many reports of d13C values of plants are available, but not of open burning emissions. In general, d13C values of C3 plants are lighter than those of C4 plants (e.g., O’Leary, 1981), so it is not surprising that in our data, d13C values from open burning of C3 plants were lighter than those of C4 plants.

a

-22

SPM SP

13EC

(‰)

-23 -24 -25 -26 -27

b (‰)

3.2.3. Street dust sources d13C varied greatly depending on the origin of the street dust: d13C of asphalt, concrete, and curbstone were
1.8&,
18.4&, and
16.4&, respectively, with only that of asphalt being very heavy, suggesting that d13C of asphalt is enriched by distillation. Lopez-Veneroni (2009), who sampled particulates with a MiniVol portable particle sampler, reported d13C of PM10 and PM2.5 fractions of street dust in Mexico City to be
17.0  0.1& and
21.0  0.1&, respectively. Thus, d13C values of concrete and curbstone in this study were similar to that of the PM10 fraction, but heavier than that of the PM2.5 fraction, of street dust in Mexico City. This difference between d13C of concrete and curbstone in this study and that of the PM2.5 fraction of street dust in Mexico City can be attributed to differences in the collecting sites and sampling

3.2.4. Soil sources Mean (SD) d13C of soils in this study was
18.8  3.4&. Many other soil values are reported in the literature. Lopez-Veneroni (2009), who used a MiniVol portable particle sampler, reported that d13C of the PM10 and PM2.5 fractions of agricultural soils range from
24.5& to
23.5& and from
24.5& to
24.2&, respectively (d13C value read off his figure). Lopez-Veneroni (2009) also sampled rural soils directly, obtaining a d13C value (
20.7  1.5&) similar to the values for soils obtained in this study. d13C values obtained in this study, however, were heavier than those of the PM10 and PM2.5 fractions of agricultural soils in Mexico. We conclude that the values of the soils determined in this study and those of rural soils in Mexico are the most reliable, because the PM might include not only soil particulates but also particulates from various other sources.

A M J J A S O N D J F M A M J J A S O N D J F 2008 2009 2010

-22 -23

13EC

3.2.2. Non-vehicular fuel sources In this study, d13C of charcoal (
27.4  1.7&) was similar to that of fireplace soot (
26.5  0.1&) but lighter than that of coal (
23.3&). Mori et al. (1999) reported that d13C of Chinese coal is
23.4  1.2&, and Widory et al. (2004) reported that d13C of domestic fuel oil used in Paris is
26.0  0.5&, and coal values range from
24.4& to
23.4&. Thus, d13C of coal in this study is similar to that of coal in China and Paris, and d13C values of charcoal and of fireplace soot are similar to that of domestic fuel oil in France. In addition, Court et al. (1981) reported that d13C values of coal and crude oil are heavier than that of petroleum fuel. Thus, carbon isotopic fractionation may occur during distillation of petroleum fuel from coal or crude oil.

methods. Lopez-Veneroni (2009) collected PM10 and PM2.5 fractions of street dust from the roadside, although roadside particulate matter includes particles from vehicle emissions, especially in the PM2.5 fraction. Moreover, in general, EC in PM2.5 contains more vehicle soot than that in PM10. Thus, the d13C value of the PM2.5 fraction of Mexico City street dust reported by Lopez-Veneroni (2009),
21.0&, is relatively closer to that of all vehicle soots (
24.2&) in this study.

-24

PM2.5

values of PM10 and PM2.5 from regular vehicle emissions ranged from
25.5& to
24.4& and from
25.5& to
25.0&, respectively, and those from diesel vehicle emissions ranged from
24.8& to
24.5& and from
25.4& to
24.6&, respectively. These emission samples were collected with a MiniVol portable particle sampler. Lopez-Veneroni (2009) also collected soot from gasoline (
23.6& to
22.2&) and diesel (
25.3& to
24.7&) vehicle exhaust directly onto a filter, rather than by scraping soot from the tailpipes. Although the gasoline and diesel vehicle PM10 and PM2.5 results in Mexico were similar, d13C from soot of gasoline vehicle exhaust was very slightly heavier than the diesel vehicle value. To summarize, d13C values of vehicles in this study, excluding the sports-type vehicle results, were similar to those of soot from regular and unleaded gasoline vehicles in Paris, PM10 and PM2.5 values from regular and diesel vehicle emissions in Mexico City, and soot from diesel vehicles in Mexico City. They were heavier, however, than those of soot from diesel vehicles in Paris, particles from vehicle exhaust in Vancouver, and soot from gasoline and diesel vehicle exhaust in Mexico City, whereas they were lighter than soot from gasoline vehicle exhaust in Mexico City. Our measured d13C values of soot from sports-type vehicles were heavier than those from all other vehicle types both in this study and in other studies. Although other studies reported that d13C values of gasoline and diesel vehicle sources could be distinguished (Widory et al., 2004; Lopez-Veneroni, 2009), in this study d13C values from gasoline and diesel vehicles were very similar and did not allow these two source types to be distinguished. The reasons for this discrepancy are unclear. In this regard, we note that, in sampling soot from vehicle exhaust, it is possible to obtain soot from approximately one in 10 gasoline vehicles, whereas soot can be collected easily from most diesel vehicles that do not have a diesel particulate filter, suggesting that soot in air is derived primarily from diesel vehicles. Therefore, in this study, we consider “vehicle sources” to include both gasoline and diesel vehicles when evaluating contributions from the Eurasian continent.

573

-25 -26 -27

A M J J A S O N D J F M A M J J A S O N D J F 2008

2009 13

2010

Fig. 3. Time series of d C of (a) SPM and (b) PM2.5 from April 2008 to January 2010 in Akita Prefecture, Japan.

574

Table 4 Annual and seasonal mean d13C (&) of EC in SPM in this study, compared with published results of other studies. Location

Type of site

Period

d13C (&), mean  standard deviation (minimum, maximum) Average

Spring

Summer

Autumn

Winter

SPM/EC

Akita, Japan

Rural

Apr. ’08eJan. ’10


24.6  0.7


24.7  0.7 (
25.2,
23.2)


25.0  0.4 (
25.9,
24.4)


25.9  0.4 (
25.6,
23.9)


23.6  0.5 (
24.5,
23.1)

This study

TSP/TC

Lhasa, Tibet

Suburban

Aug. ’06eJul. ’07


25.8


25.5 (
25.9,
25.1)


26.0 (
26.3,
25.7)

Cieplice, Poland

Urban


26.3

Gorka et al. (2009)

TSP/TC

Czerniawa, Poland

Suburban


26.2


26.6,
26.4


27.4,
25.6

Gorka et al. (2009)

TSP/TC

Kathmandu, Nepal

Urban

Jul. ’06, Feb. ’07 Jul. ’07, Feb. ’08 Jul. ’06, Feb. ’07 Jul. ’07, Feb. ’08 Dec. ’07eJan. ’08


26.1 (
26.4,
25.8)
25.6,
25.5

Huang et al. (2010)

TSP/TC


25.6 (
25.9,
25.2)
26.9,
27.1

Shakya et al. (2010)

PM10/TC PM10/TC PM10/TC

Baoji, China Baoji, China Arctic region

Urban Suburban e

Feb., Apr. ’08 Feb., Apr. ’08 Feb., Apr. ’00


23.9
23.2
24.7

TSP/EC PM10/OC

Beijing, China Zurich, Switzerland

Urban Urban


24.6  0.7
26.7  0.5

PM10/EC

Zurich, Switzerland

Urban

PM/OC

Rio de Janeiro, Brazil

Urban

Jan. ’98eNov. ’99 Aug., Sep. ’02 Feb., Mar. ’03 Aug., Sep. ’02 Feb., Mar. ’03 Jun.eJul. ’85


24.7

PM/EC

Rio de Janeiro, Brazil

Urban

Jun.eJul. ’85


23.8


25.7  0.2 (
26.0,
25.5)
23.4  0.4
22.5  0.2
25.7  0.7 (
26.0,
25.6)
24.0  0.3
26.3  0.3 (
26.7,
26.0)
26.5  0.4 (
26.9,
26.2)
24.7  0.17 (
24.9,
24.4)
23.8  0.14 (
24.1,
23.7)

PM/OC

Rio de Janeiro, Brazil

Tunnel

Apr. ’85


25.4

PM/EC

Rio de Janeiro, Brazil

Tunnel

Apr. ’85


24.8

PM10/TC

Mexico City, Mexico

Nov., Mar., Dec. ’01

PM/TC

Zurich, Switzerland

Metropolitan area Urban


25.0  0.5 (
26.3,
24.3)
26.6

PM/EC

Zurich, Switzerland

Urban

PM10/TC

Paris, France

Urban

Aug., Sep. ’02 Mar. ’03 Aug., Sep. ’02 Mar. ’03 May., Sep. ’02

PM10/TC PM/OC

Tuscany, Italy Wroclaw, Poland

Various Industrial area

Autumn ’05eSpring ’06 Nov. ’03eMar. ’04

a

d13C value read off the published figure by H.K.


25.7  0.2


26.6  0.5


26.6
26.5 (
26.7,a
25.8a) (
27.5,a
23.0a) (
27.78,
25.04)


24.4  0.5
23.9  0.7
23.7  0.8 (
24.7,
23.0)
25.1  0.6
27.0  0.4 (
27.4,
26.5)
26.7  0.9 (
27.3,
26.1)


25.4  0.15 (
25.6,
25.3)
24.8  1.0 (
25.5,
24.0)

Wang et al. (2010) Wang et al. (2010) Narukawa et al. (2008) Takahashi et al. (2008) Szidat et al. (2004) Szidat et al. (2004) Tanner and Miguel (1989) Tanner and Miguel (1989) Tanner and Miguel (1989) Tanner and Miguel (1989) Lopez-Veneroni (2009)


26.8a  0.8a


26.5a  0.8a

Fisseha et al. (2009)

a

a

Fisseha et al. (2009)

a


26.8  2.0


26.5  0.8

a

Widory et al. (2004) Grassi et al. (2007) Gorka and Jedrysek (2004)

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

Tested substance

Table 5 Annual and seasonal mean d13C (&) of EC in PM2.5 in this study, compared with published results of other studies. Location

Type of site

Time

d13C (&), mean  standard deviation (minimum, maximum) Average

Spring

Summer

Autumn

Winter

PM2.5/EC

Akita, Japan

Rural

Apr. ’08eJan. ’10


24.3  0.4


24.2  0.2 (
24.4,
24.0)


24.7  0.4 (
25.0,
24.1)


24.4  0.3 (
24.8,
23.8)


24.1  0.4 (
24.6,
23.4)

PM2.1/EC PM2.5/OC

Tokyo, Japan Hong Kong, China

Urban Roadside


25.1  0.1
26.9

PM2.5/EC

Hong Kong, China

Roadside

PM2.5/OC

Hangzhou, China

Residential area

Apr. ’04eJun. ’05 Nov.’00eFeb. ’01 Jun.eAug. ’01 Nov. ’00eFeb. ’01 Jun.eAug. ’01 Apr. ’04eMar. ’05

PM2.5/EC

Hangzhou, China

Residential area

Apr. ’04eMar. ’05


26.5

PM2.5/TC

Mexico City, Mexico

Metropolitan area

Nov., Mar., Dec. ’01

PM2.5/EC

British Columbia, Canada British Columbia, Canada British Columbia, Canada British Columbia, Canada Paris, France

Urban

Aug. ’01


25.1  0.6 (
26.3,
24.1)
26.7b


26.7b

Huang et al. (2006)

Urban

Aug. ’01


26.3


26.3

Huang et al. (2006)

Tunnel

Aug. ’01


27.0


27.0

Huang et al. (2006)

Tunnel

Aug. ’01


27.1


27.1

Huang et al. (2006)

Urban

May.eSep. ’02

Widory et al. (2004)

Semirural area


26.5 (
26.6,a
26.5a)
27.7  3.3

Semirural area


27.0  3.1

Kelly et al. (2005)

PM2.5/OC PM2.5/EC PM2.5/OC PM2.5/TC PM < 1 mm/OC PM > 1 mm/OC a b

Three sites, United Kingdom Three sites, United Kingdom


25.6  0.1 (
25.9,
25.4)
50.9


26.9  0.5 (
28.1,
26.3)


26.9  0.6 (
27.7,
26.4)

This study Takahashi et al. (2008) Ho et al. (2006) Ho et al. (2006)


52.8 (
85.0,
40.7)
26.4 (
29.2,
3.1)


48.1 (
73.3,
35.6)
26.9 (
30.3,
23.0)


50.0 (
61.4,
39.0)
26.7 (
30.5,
24.1)


52.5 (
67.9,
40.2)
25.9 (
28.6,
23.8)

Liu et al. (2007) Liu et al. (2007) Lopez-Veneroni (2009)

Kelly et al. (2005)

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

Tested substance

d13C value read off the published figure by H.K. d13C value calculated by H.K.

575

576

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

3.3. Stable carbon isotope ratios of EC in SPM and PM2.5

3.4. Ion contents in SPM

3.3.1. SPM d13C of EC in SPM ranged from
25.9& to
23.1& (average,
24.6&). Although SPM concentrations tended to be higher in spring, d13C in SPM was clearly more enriched in winter (avg.
23.6&; 1 December to 18 February 2008 and 7 December 2009 to 7 January 2010) than in summer (avg.
25.0&; 10 June to 11 August 2008 and 3 June to 31 August 2009) (Fig. 3a, Table 2). The average d13C value and the seasonal trend found in this study are in agreement with those in Beijing (Takahashi et al., 2008); in particular, the seasonal average values are the same within analytical precision. Before sampling, we predicted that the specific wintertime source of EC in SPM might not be in Akita Prefecture, but the data indicate that the source of EC in PM was similar or the same between Beijing and Akita Prefecture. Although the values shown in Table 4 differ as to whether d13C of total carbon, EC, or OC was measured, the average value of EC in SPM in this study was similar to those of EC in PM in Rio de Janeiro (Tanner and Miguel, 1989) and of TC in PM10 in Mexico City (Lopez-Veneroni, 2009) and in Baoji, China (Wang et al., 2010), but heavier than values of EC and OC in PM10 in Zurich(Szidat et al., 2004; Fisseha et al., 2009), of TC in PM10 in Paris (Widory et al., 2004), and of TC in TSP in Lhasa, Tibet (Huang et al., 2010). The seasonal trend of d13C in this study was similar to that in Zurich (Szidat et al., 2004; Fisseha et al., 2009) and Baoji (Wang et al., 2010) as well as that in Beijing (Takahashi et al., 2008), with values differing between summer and winter by approximately from 1.0& to 1.5&.

þ The concentrations of water-soluble cations (Naþ, NHþ 4, K , 2
, SO ) in SPM are summarized Mg2þ, Ca2þ) and anions (Cl
, NO2
3 4 in Table 2. In total, ions accounted for an average of 57.7% of the SPM mass concentration: contributions of the major cations were Naþ, 2þ 13.5%; NHþ 4 , 2.5%; Ca , 1.3%, and those of the major anions were 2
2
SO4 , 25.1%; NO3 , 5.7%; Cl
, 5.1%. The inorganic ion balance (total anions/total cations) was 0.84 (R2 ¼ 0.88) under molar unit. The concentration of SO2
4 in SPM was highest among the ions in this study: average, 3.8  1.7 mg m
3 (range, 1.1e10.2 mg m
3). In is derived from combustion of coal and from general, SO2
4 seawater. Matsumoto et al. (2003) reported that the concentration in TSP was 2.48 mg m
3 (range, 0.36e9.22 mg m
3) on of SO2
4 Rishiri Island, Hokkaido, Japan, a value close that of this study. In Sapporo, Japan, Aggarwal and Kawamura (2009) reported that the
3 mass concentration of SO2
4 in TSP ranged from 1.4 to 9.7 mg m (average, 5.2  2.2 mg m
3). This average is intermediate between that of this study and that reported by Matsumoto et al. (2003), suggesting that the SO2
4 concentration obtained in this study is valid. Correlation coefficients between d13C and ion contents ranged from 0.04 to 0.47, with the higher value, 0.47 and 0.44 obtained for Cl and Mg2þ, respectively (Table 6). Cl
and Mg2þ contents in particulates from seawater are high, 40.0% and 4.8%, respectively (Shareef and Bravo, 1988). d13C seemed to be affected from sea, in other words continental effect.

3.3.2. PM2.5 d13C of EC in PM2.5 ranged from
25.0& to
23.4& (average,
24.3&) (Fig. 3b). Thus, PM2.5 values varied within a narrower range than SPM values. Although it was difficult to detect any trend in the PM2.5 concentration, similar to d13C of SPM, d13C in PM2.5 was slightly more enriched in winter (
24.1&; 1 December 2008 to 18 February 2009 and 11 December 2009 to 13 January 2010) than in summer (
24.7&; 10 June to 21 August 2008 and 31 July to 31 August 2009). Although the values shown in Table 5 differ as to whether d13C of total carbon, EC, or OC was measured, the annual average d13C of EC in PM2.5 in this study was heavier than values reported by all of the other studies. We found a seasonal trend similar to that in Hangzhou (Liu et al., 2007), but the differences between summer and winter were small compared with those in SPM and PM10, þ0.6& (this study) and þ1.0& (Liu et al., 2007). The seasonal difference in d13C of EC in PM2.5 in this study and in Tokyo may reflect a source difference. d13C of PM2.5 varied within a more narrow range than that of SPM.

Seasonal back trajectory analysis results are shown in Fig. 4. In spring, the air masses came primarily from the Pacific Ocean; only five back trajectories in spring indicated an air mass origin over the Gobi and Taklamakan Deserts. We concluded that the five air mass trajectories in spring indicated that dust was advected from these deserts during dust storms. As in spring, most air masses in summer came from the Pacific Ocean. In autumn and winter, air masses came mainly from northeastern China and Siberia. Air masses in autumn and winter thus advected coal combustion emissions from northeastern China and Siberia.

Table 6 Correlation coefficients (R) between ion contents in SPM and d13C of SPM. SPM SPM Naþ NHþ 4 Kþ Mg2þ Ca2þ Cl
NO3
SO2
4 d13C

Naþ

NHþ 4

Kþ

Mg2þ

Ca2þ

Cl


NO3


SO2
4

d13C

0.27

0.64 0.16

0.57 0.34 0.79

0.25 0.68 0.25 0.53

0.64 0.31 0.54 0.62 0.47


0.31 0.49
0.30
0.02 0.68
0.11

0.42 0.29 0.52 0.63 0.37 0.59
0.06

0.65 0.36 0.89 0.71 0.32 0.61
0.31 0.51


0.08 0.27 0.17 0.27 0.44 0.30 0.47 0.26 0.09

3.5. Back trajectory analysis

3.6. Source apportionment in EC of SPM and PM2.5 based on source data and ion contents Here, we consider the sources affecting EC in SPM and PM2.5. First, we compared d13C of EC of SPM and PM2.5 with values of the various sources. d13C of SPM (
24.6&) and PM2.5 (
24.3&) in this study were very similar to those of vehicles (light-duty vehicles,
24.4&; passenger vehicles,
24.1&; diesel,
24.3&), but lighter than those of coal (
23.3&), soils (
18.8&), street dust (asphalt,
1.8&; concrete,
18.4&; curbstone,
16.4&), or C4 plant combustion emissions (corn,
16.1&; grass,
19.3&), and heavier than values of fireplace soot (
26.5&), charcoal (
27.4&), or C3 plant combustion emissions (e.g., rice plant,
28.0&; range,
34.7& to
28.0&). These results, consistent with those of other studies, suggest that baseline values of d13C of SPM and PM2.5 in this study primarily reflected contributions from all vehicle emissions (about
24.2&). d13C of SPM in winter (heavy) and summer (light) showed clear seasonal differences, but seasonal differences in the PM2.5 data were less clear (Section 3.3). These results suggest that coal, soils, street dust, and C4 plant combustion may affect d13C of SPM and PM2.5 in winter. As there is no reason why soils and street dust should increase during winter, we concluded that the increased d13C in winter reflects coal combustion or biomass burning, probably for residential heating. The

H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

577

Fig. 4. Back trajectories of air masses during (a) spring (April to May 2008), (b) summer (June to August 2008), (c) autumn (September to November 2008), and (d) winter (December 2008 to February 2009) from Yurihonjo City, Akita Prefecture, Japan (latitude 39.2 N, longitude 140.4 E). (Produced using METEX: http://db.cger.nies.go.jp/metex/index.html).

concentration of water-soluble Kþ in particulates from biomass burning is high, making Kþ an indicator of biomass burning (Wang et al., 2007). In this study, however, the correlation between Kþ and d13C was not very high (R ¼ 0.27), so the increased d13C in winter is probably attributable to coal combustion. Coal is not used for household heating in winter in Japan: the proportion of households heating with coal was 35% in 1965 but 0% in 2008 (Japan Agency for Natural Resources and Energy, 2010). Therefore, we hypothesized that the increase of d13C of particulates in winter resulted from the long-distance transport of coal combustion emissions across the sea from the Eurasian continent. Ma et al. (2004) indicated that fine particles (PM2.5) may be transported long distances in winter. The polycyclic aromatic hydrocarbon concentration in aerosols in Japan was higher in winter than in summer, apparently owing to the long-range transport of coal or biomass combustion products from the Eurasian continent, especially eastern China, from 2004 to 2005 (Yang et al., 2007) and from 2005 to 2006 (Sato et al., 2007). Sulfur isotope ratios of non-sea salt in aerosols in Japan also suggest longrange transport of fossil fuel combustion emissions from China (Akata and Yanagisawa, 2002). China has the highest coal consumption in the world, generating more than 75% of its energy from coal (Chen et al., 2005). Moreover, black carbon emissions in China are 1499.2 Gg (543.9 Gg [36%] industrial, and 817.6 Gg [55%] residential). Most residential emission is from domestic coal combustion in rural areas (487.1 Gg; 32% of total emission) and emissions increase in winter (Cao et al., 2006). Therefore, seasonal differences in d13C in SPM and PM2.5 may reflect seasonal differences in residential coal combustion in China. In addition, as described above, the correlation of d13C of SPM with Cl
and Mg2þ was relatively high, possibly reflecting a contribution of sea salt superimposed on the contribution from China. The back trajectory analysis results in winter indicated an effect across the sea from the Eurasian continent. In conclusion, on the basis of (1) the very similar d13C values between SPM and PM2.5 and coal combustion emissions; (2) the relatively high correlation between d13C of SPM and Cl
and Mg2þ, which are mainly from sea salt; and (3) the back trajectory model results, we infer that the annual baseline values of d13C of SPM and PM2.5 reflect vehicle sources, and that the increase in d13C in winter reflects emissions from coal combustion for heating in eastern China.

4. Summary To understand the seasonal trends and to apportion sources, we investigated SPM and PM2.5 concentrations and carbon isotope ratios (d13C) of EC in SPM (defined as particles with a 100% cut-off aerodynamic diameter of 10 mm), EC in PM2.5, and source samples, along with ion contents of SPM in Akita Prefecture, Japan, from April 2008 to January 2010, and we performed a back trajectory analysis. The main conclusions from this work are:  The SPM concentration ranged from 7.6 to 38.0 mg m
3 (average, 15.2 mg m
3). SPM was higher in spring and lower in winter, apparently because monsoonal winds brought dust storms from the continent in spring. The PM2.5 concentration ranged from 1.5 to 18.2 mg m
3 (average, 8.6 mg m
3). Both SPM and PM2.5 concentrations were lower than reported values of other studies. No distinct seasonal trend in PM2.5 was detected, suggesting that PM2.5 was little influenced by dust storms.  d13C of source samples (soot from gasoline and diesel vehicles, fireplace soot, open biomass burning emissions, street dust, soils, charcoal, and coal combustion emissions) ranged widely, from
34.7& to
1.8&. d13C values of soot of gasoline lightduty vehicles (
24.4  0.7&) and passenger vehicles (
24.1  0.6&) were similar to that for all diesel vehicles (
24.3  0.3&). d13C of charcoal (
27.4  1.7&) was similar to that of fireplace soot (
26.5  0.1&) and lighter than that of coal (
23.3&). d13C of street dust was
12.2  9.0&, with a very wide range. Street dust comprised dust from asphalt, concrete, and curbstone, with d13C of
1.8&,
18.4&, and
16.4&, respectively.  d13C values of EC in SPM and PM2.5 were similar, from
25.9& to
23.1& (average,
24.6&) and from
25.0& to
23.4& (average,
24.3&), respectively. d13C in SPM was more enriched in winter than in summer, but the seasonal trend of d13C in PM2.5 was less clear. These data combined with the source results suggest that the enrichment of d13C of SPM and PM2.5 in winter was a long-range effect of coal combustion emissions transported overseas. In addition, d13C of SPM was highly correlated with Cl
and Mg2þ, which are derived from sea salt. We performed a back trajectory analysis to verify these results. The results indicated that d13C of EC in SPM and PM2.5 in winter were likely affected from the continent.

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H. Kawashima, Y. Haneishi / Atmospheric Environment 46 (2012) 568e579

 In the future, these isotope data should be validated and used in the source profiles of receptor models such as Chemical Mass Balance, Positive Matrix Factorization, and Unmix models, which will make it possible to unravel complicated sources for better quantitative understanding of aerosol sources. Acknowledgments We greatly appreciate the support from the Nippon Life Insurance Foundation, the Tostem Foundation for Construction Materials Industry Promotion, the Japan Securities Scholarship Foundation and Grant-in-Aid for Scientific Research on Innovative Areas, No. 23120704, from the Ministry of Education, Culture, Sports, Science and Technology, Japan. We are grateful to Akita Transport Branch Office by presenting information distinguishing gasoline vehicle and diesel vehicle. In addition, Dr. Takashi Niioka, Executive Vice President of Akita Prefectural University, provided the research environment and encouraged our efforts. Dr. Eiji Kikuchi and Dr. Ruilu Liang, of the Faculty of Systems Science and Technology, Akita Prefectural University, kindly provided facilities for the analyses and offered us strong encouragement every day. We are extremely grateful to them. Furthermore, Takahiro Kurahashi and Nami Kikuchi, graduate students at Akita Prefectural University, contributed greatly, for example, by participating in discussions relating to this research, and Mr. Kurahashi also conducted sampling at Akita Prefectural University from 2008. We thank Yumi Sone of Thermo Fisher Scientific Inc., Japan, for their help with our isotope analysis by IRMS. Ms. Sone offered many helpful suggestions. We offer our sincere thanks to all of these people. References Aggarwal, S.G., Kawamura, K., 2009. Carbonaceous and inorganic composition in long-range transported aerosols over northern Japan: implication for aging of water-soluble organic fraction. Atmospheric Environment 43, 2532e2540. Akata, N., Yanagisawa, F., 2002. Sulfur isotope ratio of non-sea salt sulfate in bulk deposition and aerosol. Earozoru Kenkyu 17, 247e251 (in Japanese with English abstract). Akita Transport Branch Office, 2010. Personal communication. Cachier, H., Buat-Menard, P., Fontugne, M., Rancher, J., 1985. Source terms and source strengths of the carbonaceous aerosol in the tropics. Journal of Atmospheric Chemistry 3, 469e489. Cachier, H., Buat-Menard, P., FontugneFontugne, M., Chesselet, R., 1986. Long-range transport of continentally-derived particulate carbon in the marine atmosphere: evidence from stable carbon isotope studies. Tellus 38B, 161e177. Cao, G., Zhang, X., Zheng, F., 2006. Inventory of black carbon and organic carbon emissions from China. Atmospheric Environment 40, 6516e6527. Cao, J.J., Lee, S.C., Zhang, X.Y., Chow, J.C., An, Z.S., Ho, K.F., Watson, J.G., Fung, K., Wang, Y.Q., Shen, Z.X., 2005. Characterization of airborne carbonate over a site on Asian dust source regions during spring 2002 and its climatic and environmental significance. Journal of Geophysical Research 110, D03203. doi:10.1029/2004JD005244. Cao, J.J., Wang, Y.Q., Zhang, X.Y., Lee, S.C., Ho, K.F., Cao, Y.N., 2004. Analysis of carbon isotope in airborne carbonate: implication for aeolian sources. Chinese Science Bulletin 49, 1637e1641. Cao, J.J., Zhu, C.S., Chow, J.C., Liu, W.G., Han, Y.M., Watson, J.G., 2008. Stable carbon and oxygen isotopic composition of carbonate in fugitive dust in the Chinese Loess Plateau. Atmospheric Environment 42, 9118e9122. Chen, Y.J., Sheng, G.Y., Bi, X.B., Feng, Y.L., Mai, B.X., Fu, J.M., 2005. Emission factors for carbonaceous particles and polycyclic aromatic hydrocarbons from residential coal combustion in China. Environmental Science and Technology 39, 1861e1867. Chow, J.C., Watson, J.G., Ashbaugh, L.L., Magliano, K.L., 2003. Similarities and differences in PM10 chemical source profiles for geological dust from the San Joaquin Valley, California. Atmospheric Environment 37, 1317e1340. Chow, J.C., Watson, J.G., Chen, L.-W.A., Chang, M.C.O., Robinson, N.F., Trimble, D., Kohl, S., 2007. The IMPROVE_A temperature protocol for thermal/optical carbon analysis: maintaining consistency with a long-term database. Journal of the Air & Waste Management Association 57, 1014e1023. Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H., Merrifield, T., 2001. Comparison of IMPROVE and NIOSH carbon measurements. Aerosol Science & Technology 34, 23e34. Chow, J.C., Watson, J.G., Pritchett, L.C., Pierson, W.R., Frazier, C.A., Purcell, R.G., 1993. The DRI thermal optical reflectance carbon analysis system-description,

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