Characteristics and sources of formic, acetic and oxalic acids in PM2.5 and PM10 aerosols in Beijing, China

Atmospheric Research 84 (2007) 169 – 181 www.elsevier.com/locate/atmos

Characteristics and sources of formic, acetic and oxalic acids in PM2.5 and PM10 aerosols in Beijing, China Ying Wang a,b , Guoshun Zhuang a,b,c,⁎, Shuang Chen b , Zhisheng An b,c , Aihua Zheng d b

a Department of Chemistry, Beijing Normal University, Beijing 100875, China Department of Environmental Science and Engineering, Center for Atmospheric Chemistry Study, Fudan University, Shanghai 200433, China c State key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Science, Xi'an 710075, China d The Analytical and Testing Center, Beijing Normal University, Beijing 100875, China

Received 2 May 2006; received in revised form 15 July 2006; accepted 19 July 2006

Abstract Chemistry of formic, acetic and oxalic acids was studied at four sites representing the urban and rural conditions in Beijing from March 2002 to October 2003. The investigation was based on the PM2.5 and PM10 aerosols collected with virtual samplers. The total concentrations of these carboxylic acids averaged at 541 ng m− 3 in PM2.5 and 615 ng m− 3 in PM10, contributing 0.4% and 0.3% to the total mass of the aerosol, respectively. Oxalic acid was the most abundant carboxylic acids in aerosols. Formic and acetic acids displayed different seasonal variations (formic: spring b summer b autumn b winter; acetic: spring N summer N autumn N winter), and the variations of these acids were consistent among different sites in urban area. Formic and oxalic acids had a diurnal variation of nighttime b daytime. Formic and acetic acids had mass both in the fine and in the coarse modes, while oxalic acid predominated in the fine mode. The coarse mode fraction of these acids was elevated in summer. The traffic/dust/ soil/vegetation emissions, coal/waste/biomass burnings, cooking and secondary formation from anthropogenic or natural gas-phase precursors could be the major sources of these acids. Acetic-to-formic acid ratio (A/F) was used to distinguish the primary sources and the secondary sources, and it indicated that the contribution of the primary sources was higher at rural site than at urban sites. A new method was developed to study the contribution of the biomass burning to these acids, which was estimated to be 30–60% for formic and oxalic acids in aerosols. © 2006 Elsevier B.V. All rights reserved. Keywords: Carboxylic acid; PM2.5; PM10; Primary source; Secondary source; Biomass burning

1. Introduction Monocarboxylic acids (MCAs) and dicarboxylic acids (DCAs) are important groups of organic compounds identified in the atmospheric aerosols (Rogge et al., 1993; ⁎ Corresponding author. Center for Atmospheric Chemistry Study, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China. Tel.: +86 21 5566 4579; fax: +86 21 6564 2080. E-mail address: [email protected] (G. Zhuang). 0169-8095/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2006.07.001

Limbeck et al., 2001). Formic and acetic acids, the dominant species of the organic acids in the tropospheric aqueous and gaseous phases, are also ubiquitous in aerosol particles; oxalic acid has been detected as the major fraction of water-soluble organic compounds in urban, rural and even remote background air (Kawamura and Ikushima, 1993; Kawamura and Usukura, 1993; Sempéré and Kawamura, 1994; Limbeck and Puxbaum, 1999; Jacobson et al., 2000; Röhrl and Lammel, 2001, Wang et al., 2002). Since carboxylic acids (CAs) are

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highly water-soluble, they have the potential to modify the hygroscopic properties of atmospheric particles, including their ambient size and cloud condensation nuclei activity (Hara et al., 2002; Yu, 2000). Weak organic acids could contribute ∼ 40% and ∼ 60% to the free acidity in precipitation at urban (Fornaroa and Gutz, 2003) and remote areas (Galloway and Gaudry, 1984; Likens et al., 1987), respectively. They could also affect those atmospheric reactions controlled by pH. Furthermore, there might be a link of Fe with S in the long-range transport of atmospheric particles from the continent to the open ocean (Zhuang et al., 1992, 2003), and the coordination of CAs with Fe and S of different valences can hinder, shield or accelerate the oxidation processes of S (IV) and the solubility of particulate Fe in water (rain water or/and seawater), which may have tremendous influence on the global biologic productivity in the open ocean (Zhuang et al., 1992, 1995). Therefore, the study on CAs in the atmosphere has become an issue of growing interest. CAs have several different sources, including the primary emissions from fossil fuel combustion and biomass burning, homogeneous photochemical oxidation of organic precursors from both anthropogenic and biogenic origin (Kawamura and Ikushima, 1993; Chebbi and Carlier, 1996; Kawamura et al., 1996a,b; Limbeck and Puxbaum, 1999), as well as in-cloud and heterogeneous formations (Kerminen, 1997; Blando and Turpin, 2000). Despite the progress made in elucidating these source types, their relative importance has remained poorly understood. Even less is known about the formation pathways of CAs. CAs in atmospheric aerosols have been investigated by many studies across the world (Krivacsy and Molnar, 1998; Souza et al., 1999; Kubátová et al., 2000; Kerminen et al., 2000; Limbeck et al., 2001; Röhrl and Lammel, 2001, 2002; Limon-Sanchez et al., 2002). However, many of the published studies on China aerosols have focused mainly on the elemental constituents and major inorganic ions (Yao et al., 2002a; Wang et al., 2005 and references therein). In comparison, studies on the CAs in aerosols in China have been scant, with recent documents only of the studies on a few large cities including Beijing (Yao et al., 2003; Yu et al., 2005), Nanjing (Wang et al., 2002; Yang et al., 2005) and Hong Kong (Yao et al., 2002b, 2004). These previous works are not sufficient for a comprehensive understanding of the behavior of CAs. In this paper, the chemistry of formic, acetic and oxalic acids in PM2.5 and PM10 aerosols is examined. The investigation relies on the field measurements conducted at four sites in Beijing in four seasons. The major objectives of this paper are: (1) to improve our general understanding of the relative

roles of primary emissions, long-range transport and secondary formations in the chemistry of CAs in the atmosphere; and (2) to provide insight into how CAs are distributed between the particles of different size and type, and what implications this might have on atmospheric chemistry. This is the first comprehensive study on the characteristics of water-soluble CAs in aerosols in the mega cities in China. 2. Experimental 2.1. Sampling Aerosol samples of PM2.5 and PM10 were collected at three sampling sites in urban Beijing, i.e. (1) a traffic site located in the campus of Beijing Normal University (BNU) between the 2nd and 3rd Ring Roads, (2) an industrial site near the Capital Steel Company (CS), (3) a residential site, Yihai Garden (YH), located near the South 4th Ring Road, and (4) one rural site at Miyun (MY) county, using medium-volume samplers (model, (TSP/PM10/PM2.5) − 2; flow rate, 77.59 l min− 1). The location of sampling sites was shown in Fig.1. Samples were collected on Whatman® 41 filters (Whatman Inc., Maidstone, UK) for ion and element analysis. Sampling was carried out approximately in 12-h intervals. The sampling periods were chosen to represent the different seasons: (1) 12 March–26 April 2002, spring; (2) 18 June–21 July 2002, summer; (3) 1 December 2002–5 January 2003, winter; (4) 5 September–6 October 2003, autumn, respectively. Samples were put in polyethylene plastic bags right after sampling and reserved in a refrigerator (− 18 °C). All of those filters were weighed before and after sampling with an analytical balance (Sartorius 2004 MP, reading precision 10 μg) after stabilizing under constant temperature (20 ± 1 °C) and humidity (40 ± 2%). A total of 316 aerosol samples were collected and used in this study. All the procedures were strictly quality-controlled to avoid any possible contamination of the samples. The sampling periods at each site, as well as the number of samples collected, were summarized in Table 1. 2.2. Meteorological conditions and the sampling artifact The meteorological data, including temperature, atmospheric pressure, wind speed, relative humidity (RH), cloud cover, vapor pressure, etc., was downloaded from National Climate Data Center (http://cdc.cma.gov.cn). The extents of the sampling artifacts of semi-volatile species increase with increasing gas-to-particulate concentration ratio (Pathak and Chan, 2005). Over 90% of

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Fig. 1. Map of the four sampling sites in Beijing. Urban sites: (1) Beijing Normal University (BNU), traffic site; (2) Capital Steel Company (CS), industrial site; (3) Yi-Hai Garden (YH), residential site. Rural site: (4) Mi-Yun (MY).

total formate (gas plus aerosol) and total acetate were present in the gas phase (Khwaja, 1995; Andreae et al., 1988), while oxalic acid was mostly associated with particles, consistent with their vapor pressures (DCAs higher by a factor of at least 104 than MCAs). Therefore, the formic and acetic measurement is known to be associated with sorption (by sampling media or collected particles) and desorption artifacts, but the oxalic measurement is not affected. The amount of artifact is strongly dependent on the aerosol acidity and the relative humidity (RH) of the ambient air (Pathak and Chan, 2005; Souza et al., 1999). Low acidity and high RH might bring more CAs to the particulate phase and, in turn, reduce the sampling artifact. The aerosols in Beijing are known to be alkaline in nature because of the high concentration of NH3 and more basic oxides, such as Fe/Al/Si/Ca oxides, which could adsorb and neutralize acidic gases, such as NOx, SO2, etc., then decrease the acidity. It was reported that the aerosol acidity was only 0.6, 6.4, 4.6 and 1.5 nmol m− 3 in the spring, summer, autumn and winter of 1995, respectively (Zhou et al., 1998). The annual average RH was generally ∼ 60% in Beijing. Consequently, significant concentrations of MCAs are expected to occur in the particulate phase, which might minimize their sampling artifact. Denuder/filter pack systems have been widely accepted as useful tools to reduce the sampling artifact. However, the preparation and subsequent chemical

analyses of the denuders and back filters are tedious and time consuming. In this study, the pH of the water extract of aerosols was strongly anti-correlated with RH (r = − 0.47, n = 316, correlation is significant at the 0.01 level (two-tailed)), indicating that the extent of the sampling artifact might be similar among different samples, which will not affect our discussions on the variations and sources of these acids. Thus, filter pack samplers without denuders were used in this study. However, in the rest of this paper, we should keep in mind that those concentrations may give an overestimate Table 1 The number of samples collected at different sites in four seasons in Beijing Spring

Summer

Autumn

Winter

March 12– April 26, 2002

June 18–July 21, 2002

September 5–October 6, 2003

December 1, 2002– January 5, 2003

PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 BNU 48(0) CS YH MY

18(3) 18(3) 17(4)

17(4) 20(0) 19(2) 17(4) 20(0)

9(9) 7(9) 7(10)

9(9) 7(9) 7(10)

Number in the brackets denote the night samples; outside denote the day samples; blanks indicate no sample collected.

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for the actual particle-phase concentrations of the measured MCAs, because some unknown contributions may come from the corresponding gaseous compounds. 2.3. Chemical analysis 2.3.1. Ion analysis One-fourth of the sample and blank filter was extracted ultrasonically for 40 min by 10 ml deionized water (resistivity of 18 MΩ cm). After passing through microporous membranes (pore size, 0.45 μm; diameter, 25 mm; made by the affiliated plant of Beijing Chemical School), the filtrates were determined for pH with a pH meter (model, Orion 818). Each filtrate was stored at 4 °C in a clean tube for analysis. The ionic concentrations of the aqueous extracts were determined by ion chromatography (Dionex-600) with an electrochemical detector (ED50). An AS11 column (4 mm) with an AG11 guard column and an anion trap column (4 mm) was used for water-soluble anion detection (CH3COO−, HCOO−, C2O42−, SO42−, Cl−, NO2−, MSA, NO3−, F− and PO43−). In this study, the eluent for the anion analysis was 0.1–38.1 mM NaOH (gradient). A CS12 column and an eluent of 20 mM MSA were used for cation detection (K+, NH4+, Ca2+, Mg2+ and Na+). The recoveries of formic, acetic and oxalic acids were 82%, 96% and 105%; the relative standard deviation were 4.58%, 3.04% and 1.92%; the detection limits (S/ N = 3) were 5.7, 32.9 and 14.5 μg l− 1, respectively. The quality assurance was routinely carried out by using Standard Reference Materials (GBW 08606) produced by National Research Center for Certified Reference Materials, China. Blank values were subtracted from sample determinations. The details were given elsewhere (Yuan et al., 2003). 2.3.2. Element analysis Half of the sample and blank filter was digested at 170 °C for 4 h in high-pressure Teflon digestion vessel

with 3 ml concentrated HNO3, 1 ml concentrated HCl and 1 ml concentrated HF. After cooling, the solutions were dried, then added 0.1 ml concentrated HNO3 and diluted to 10 ml with deionized water (resistivity of 18 MΩ cm− 1). A total of 23 elements (Al, Fe, Mn, Mg, Ti, Sc, Na, Eu, Ce, Sr, Ca, Co, Cr, Ni, Cu, Pb, Zn, Cd, V, S, As, Se and Sb) were determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES, model, ULTIMA, made by JOBIN-YVON Company, France). The detailed analytical procedures were given elsewhere (Zhuang et al., 2001; Sun et al., 2004). Al was used as the reference element of crustal source in this study. 3. Results and discussion 3.1. Mass concentrations of PM2.5 and PM10 aerosols The annual average mass concentrations of PM2.5 and PM10 were 147.5 and 207.5 μg m− 3, respectively, with a PM2.5/PM10 mass ratio of 0.71. Compared with the standard values of 65 μg m− 3 for PM2.5 and 150 μg m− 3 for PM10 (US EPA, 1997), almost 80% PM2.5 and 60% PM10 mass concentrations were higher than the standards, showing that particulate pollution, especially the fine part, was very serious in Beijing. The seasonal and spatial variations of PM2.5 and PM10 aerosols in Beijing were shown in Fig. 2. The obvious seasonal variation could be seen from those PM2.5 aerosols collected at BNU. The season-averaged PM2.5 concentrations were in the order of spring (256) N winter (149)N autumn (109) N summer (81 μg m− 3). The high concentration in spring was related to the frequent intrusion of dust from western or northwestern China. It was reported that there were four major dust events that occurred in Beijing in the spring 2002. Accordingly, PM2.5 concentrations were over 500 μg m− 3 during these dust days, with a highest value of 1393 μg m− 3 when a super dust storm attacked Beijing on 20 March 2002. The high

Fig. 2. Seasonal and spatial variations of PM2.5 and PM10 in Beijing. (Data above the column: average concentration; line in the column: standard deviation; open column: PM2.5; shaded column: PM10.)

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concentration in winter was most likely due to the combination of the elevated emissions (from fossil fuel and coal burning) and meteorological conditions (low mixing/ inversion layer which limits the dilution and dispersion of pollutants) in this season. The high mixing layer in autumn and the frequent rain showers or wash out effects in summer might be the main reasons for the low concentrations in these seasons. Spatial variations of PM concentrations were categorized into two groups, urban vs. rural sites (BNU and MY in Fig. 2) and among urban sites (BNU, CS and YH in Fig. 2). The PM2.5 concentrations were similar at BNU (109 μg m− 3) and MY (103 μg m− 3), indicating that PM2.5 was evenly distributed between urban and rural sites, which were contrary to our previous results (Wang et al., 2005). The main reason might be that these samples were collected in autumn, whose meteorological conditions favor the dispersion of particles. No special emission sources in the urban area in autumn might be another reason. The concentration of PM2.5 was a little higher at YH, and the concentration of PM10 was a little higher at CS. The higher concentration of fine particles at YH (residential site) might be related to the meteorological conditions and the location of these sites. The prevailing wind direction in Beijing is northerly. YH is located in the

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southern part of Beijing (see Fig. 1). Therefore, pollutants can be transported from the upwind area to YH, elevating its pollution level. Emissions from residential heating might be another reason. The higher concentration of coarse particles at CS (industrial site) was probably due to the emissions from industrial activities. 3.2. Characteristics of carboxylic acids in aerosols 3.2.1. Concentration level and ionic composition Average concentrations and standard deviations of three CAs were presented in Table 2. For comparison, those ionic concentrations of other sites over the world reported in the literatures were also listed in Table 2. The concentrations of formic, acetic and oxalic acids averaged at 110 ± 81, 78 ± 221 and 353 ± 259 (mean ng m− 3 ± S.D.) in PM2.5, and 154 ± 102, 84 ± 110 and 377 ± 320 in PM10. Formic, acetic and oxalic acids together contributed 0.4% and 0.3% to the mass of PM2.5 and PM10, respectively. Oxalic acid was the most abundant species, while acetic acid had the lowest concentration with the largest variation. Table 2 showed that the concentrations of formic and acetic acids in Beijing were much lower than those in São Paulo and Amazon, but comparable with those in New York, and much higher

Table 2 Carboxylic acid concentrations (ng m− 3) measured at various sampling sites around the world in recent years Site

Sampling period

Size

HCOO−

CH3COO− C2O2− 4

References

Beijing, China São Paulo, Brazil Shanghai, China Hong Kong, China

2002–2003 Winter 1996 1999–2000 Winter 2000 Summer 2001 1999–2000 1989 July–August 1999 November–December 1999 2001 September 1995–February 1996 December 1994–January 1997 2002–2003

PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2 PM1.1 PM10 TSP TSP TSP TSP TSP TSP TSP TSP TSP PM7 PM16 TSP TSP TSP

110 ± 81 480

78 ± 221 430

17.3–107 27.6 17.5 154 ± 102 1100±500 400 ± 200

4.5–92.7 9.3 5.86 84 ± 110 900 ± 700 260 ± 220

This work Souza et al. (1999) Yao et al. (2002a) Yao et al. (2004) Yao et al. (2004) Yao et al. (2002a) Kawamua and Ikushima, 1993 Röhrl and Lammel (2001) Röhrl and Lammel (2001) Yang et al. (2005) Kiss et al. (1997) Matsumoto et al. (1998) This work Talbot et al. (1990) Talbot et al. (1990) Sempéré and Kawamura (1994) Xu et al. (2002) Uchiyama (1996) Limbeck and Puxbaum (1999) Kawamura and Kaplan (1987) Kawamura et al. (1996a,b) Khwaja (1995) Matsumoto et al. (1998) Kiss et al. (1997) Talbot et al. (1988) Gregory et al. (1986) Gregory et al. (1986)

Beijing, China Tokyo, Japan Leipzig, Germany Merseburg, Germany Nanjing, China Lake Balaton, Hungary Hajajima, Northwest Pacific Beijing, China Amazon mixed layer Amazon free troposphere Tokyo, Japan Beijing, China Chiba, Japan Vienna, Australia Los Angeles, USA Alert, Canada (Arctic aerosol) New York, USA Hajajima, Northwest Pacific Lake Balaton, Hungary Virginia, USA Guyana free troposphere Guyana boundary layer

February 1992 September 1999–January 2000 April 1987–March 1993 June 1997 1987–1988 October 1991 1994/12–1997/1 1995/9–1996/2

89–245 117 44.5 27 ± 27 1 b0.5

87–328 42.7 9.3 22 ± 19 2 11

353 ± 259 1140 ± 1200 500 350 ± 140 90 ± 60 300 270 ± 190 229 57 220–299 118.5 35.4 377 ± 320

521–650 444 380(170–790) 340 190 ± 779 14 ± 12 58–360 89.4 130.9

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than those in Nanjing, Virginia, and lake/marine areas. Vehicular emission appeared to be the major primary source of acetic acid, whereas formic acid appeared to be formed through photochemical reactions during the daytime (Souza et al., 1999). The similar levels of CAs in Beijing and New York indicated that the traffic emission sources and the oxidation ability of the atmosphere were similar in both metropolitan cities. The high concentration in São Paulo and Amazon might be related to the strong vegetation/soil emissions and the strong atmospheric oxidation ability, as these sites are located in the equator areas and have large amounts of vegetations. The concentration of oxalic acid was comparable to the levels in Shanghai, Tokyo, Hong Kong, Los Angels, Chiba and Vienna, suggesting that there were similar conditions among these big cities. The concentration of oxalic acid was also comparable to the measurements conducted in Beijing by Xu et al. (2002) and Yao et al. (2002a). The concentration of oxalic acid in Beijing was higher than most of the sites listed in Table 2, suggesting that it was very urgent to study the characteristics and sources of these CAs to better understand their roles in the environment. 3.2.2. Seasonal variation The seasonal variations of CAs in PM2.5 samples collected at urban sites were shown in Fig. 3. The concentrations of formic and acetic acids displayed different seasonal variations (formic: spring summer b autumn b winter; acetic: spring N summer N autumn N winter) at all the three urban sites. This result coincided with the observations made by Andreae et al. (1988) in India. The different seasonal variations of formic acid and acetic acid indicated that these two acids have different sources and different behaviors in the

atmosphere. For formic acid, the high concentration in winter might be due to the enhanced emissions from heating sources, and the shift from the gas phase to the particle phase for the low temperature. The low concentration and mass percentage in spring could likely imply that formic might be mainly from the local sources, either by direct emissions or by secondary formations, and the intrusion of dust in spring could sweep it off the atmosphere. The low concentration in summer might be related to the low emission strength, the washout effect, as well as the partition between gas and particle phase. Acetic acid showed good correlations with Mg2+ and Ca2+ (Mg2+: r = 0.60, p = 0.00; Ca2+: r = 0.32, p = 0.04) during the spring season. This might indicate that part of acetic acid could be carried by those dust particles during the longrange transport, as Mg2+ and Ca2+ were mostly from the mineral aerosol, the major part of the dust. The high concentration of acetic acid in spring might be due to the high dust loading in this season. The concentration and mass fraction of acetic acid were high in summer, but very low in winter, even below the detection limits. This is similar to the results of study in Hungary (Meszaros et al., 1997) and suggests that acetic acid may be mostly from primary emissions of vegetations and soils, which is prominent in warm weather. The seasonal variation of oxalic acid was different among the three urban sites, indicating that its main source was different at different sites. It displayed a relative strong seasonal cycle with spring, winter N summer, autumn at BNU, the traffic site. Oxalic acid may be produced by the photo-oxidation of cyclo-olefins and aliphatic diolefins, emitted from motor vehicles, by ozone (Nolte et al., 1999). As the traffic emissions could be constant all the year round, this seasonal cycle might be related to other emission sources and the meteorological conditions. The high

Fig. 3. Seasonal variations of the mass concentrations (ng m− 3) and percentages (%) of formic, acetic, oxalic acids and the sum of these three species (TCA) in PM2.5 aerosols in Beijing.

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value in winter indicated that coal burning for heating might be a major source of oxalic acid. Its low levels in summer and autumn may be explained either by a weak source of burning or a rainy season that allows efficient scavenging of soluble species in aerosol. Furthermore, summer and autumn are also the favorable seasons for atmospheric diffusion and pollutant dispersion. Seasonal variations of oxalic acid at CS and YH were not obvious. At CS, it might come from the secondary formations of industrial solvents such as acetone, 2-butanone, etc. (Nolte et al., 1999); at YH, it might be formed during the autooxidation process of unsaturated lipids by meat cooking operations (Rogge et al., 1991). These explanations were based on the fact that the processes mentioned above have little seasonal variations. 3.2.3. Spatial variation The variations of formic, acetic and oxalic acids among BNU, CS, YH and MY were presented in Fig. 4. In addition, the correlation coefficients between different sites for these CAs were shown in Table 3. It was indicated in Fig. 4 that the concentrations of these acids were evenly distributed among the urban sites (BNU, CS, YH), except that oxalic acid showed higher concentrations at BNU than at YH and CS in the winter season. This variation indicated that traffic activities might be the major source of oxalic acid in the urban area. The correlations of each acid among these urban sites were

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Table 3 The correlation coefficients between different sites for formic, acetic and oxalic acids in PM2.5 Summer and winter −

HCOO CH3COO C2O2− 4

Autumn

BNU-CS

BNU-YH

CS-YH

BNU-MY

0.75 0.16 0.53

0.82 0.40 0.23

0.80 0.16 0.70

0.19 − 0.60 0.22

Bold: correlation is significant at p b 0.01 level.

prominent for formic and oxalic acids, but weak for acetic acid, indicating that formic and oxalic acids had regional sources in the urban scale, while acetic acid was more regulated by local factors. The concentration of formic acid was comparable at BNU (traffic urban) and MY (rural), while acetic and oxalic acids had higher concentrations at MY and BNU, respectively. Since the samples used here were collected in autumn, the harvest season in Beijing, biomass burning of straws was prominent in the field of rural areas. The higher acetic level at MY might be related to the biomass burning processes. The higher oxalic level at BNU indicated that secondary formation and traffic emissions might be the important sources. No strong correlations were observed between BNU and MY for these acids, indicating a large difference between urban and rural sites. 3.2.4. Diurnal variation The diurnal variations of CAs were shown in Fig. 5. Generally, the concentrations of formic and oxalic acids were higher during daytime. This fact may be attributed to the CAs formed by photochemical reactions and/or emitted directly by fossil fuels combustion and cooking activities which occur with more frequency at daytime. Additionally, RH was higher at nighttime (average: 70%) than at daytime (average: 55%) in winter, thus removal mechanisms could lead to a decrease in CAs levels during night. The diurnal variation of formic acid was more obvious than that of oxalic acid, indicating that formic acid was mainly from the photochemical oxidation, while oxalic acid might have many sources besides the photochemical oxidation. Undoubtedly, there were some exceptions of this variation. For example, formic acid on December 24 and acetic/oxalic acids on December 25 at CS had higher concentrations during night, which was likely due to the special industrial processes at this industrial site.

Fig. 4. The variations of formic, acetic and oxalic acids among urban (BNU, CS, YH) and rural (MY) sites in PM2.5.

3.2.5. Size distribution The relationship of the concentrations of formic, acetic and oxalic acids in PM2.5 with those in PM10 was

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Fig. 5. Diurnal variations of formic, acetic, and oxalic acids in PM2.5 aerosols collected at BNU (1–8 pairs), CS (9–14 pairs) and YH (15–21 pairs) during winter 2002.

shown in Fig. 6. It can be seen clearly that formic and acetic acids were irregularly scattered between PM2.5 and PM10, and had mass in both fine and coarse modes. Their mass median diameter seemed larger in summer than in winter. The concentrations of oxalic acid in PM2.5 showed strong correlations with those in PM10. The linear regression equations were PM2.5 = 0.767 * PM10 (R 2 = 0.81) in summer and PM 2.5 = 0.869 * PM 10 (R2 = 0.91) in winter, respectively. The slope indicated that oxalic mainly presented in the fine mode, especially in winter. The different size distributions between formic/acetic acids and oxalic acid could be related to their different physical characteristics. Formic and acetic acids are more volatile than oxalic acid. Formic and acetic acids in the fine mode could easily volatilize to the gas phase, part of which could be absorbed on the coarse particles. The higher temperature in summer (average: 27 °C) than that in winter (average: − 4 °C) might be the main reason for the seasonal difference.

burning, Cl− for waste or coal burning, NO2− for traffic emission, SO42− and MSA for secondary formation of different mechanism. In addition, wind speed and cloud

3.3. Possible sources of carboxylic acids in aerosols 3.3.1. Source identification by correlation analysis Formic, acetic and oxalic acids together with other ions/elements and meteorological factors were subjected to correlation analysis by SPSS 11.0 in order to understand their possible sources and formation mechanism. The results were shown in Table 4. The selected source indicators include Al for crust, K+ for biomass

Fig. 6. The distribution of formic, acetic and oxalic acids in PM2.5 and PM10 collected during the summer and winter seasons at urban sites. (Note: acetic acid in winter was omitted for its low concentrations.)

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Table 4 The correlation coefficients between formic, acetic, oxalic and several source indicators, meteorological parameters for all the samples collected in Beijing −

HCOO CH3COO− C2O2− 4

A1

K+

C1−

NO2

MSA

SO2− 4

Wind speed

Cloud cover

−0.06 0.13 0.08

0.54 − 0.05 0.08

0.60 − 0.08 0.07

0.07 0.60 0.42

0.06 − 0.04 0.11

0.55 − 0.01 0.08

−0.41 0.18 0.01

0.12 0.01 − 0.10

Bold: correlation is significant at p b 0.01 level; italic: correlation is significant at p b 0.05 level.

cover have been used as additional parameters to illustrate the atmospheric behaviors of CAs. HCOO− correlated with Cl− , SO42− and K+ , indicating that it might be from the direction emissions of coal/biomass/waste burning or from the secondary formations of previous gases. CH3COO − showed strong correlation with NO2− and moderate correlation with Al, indicating that primary emission or secondary formation from traffic activities might be its possible sources. C2O42− only showed strong correlation with NO2− , so motor exhaust hydrocarbon emissions might be its major source (Nolte et al., 1999). Wind speed showed negative correlation with HCOO − and positive correlation with CH3COO− , indicating that HCOO− was mainly generated from local sources, while CH3COO− might be principally related to long range transport. Sulfate and MSA have been used as references to investigate the major formation routes of DCAs (Huang et al., 2005). In Beijing, sulfate is largely formed by incloud progress (Yao et al., 2002a), while MSA is formed by gas-phase reactions (Yuan et al., 2004). Previous studies also showed that in-cloud and heterogeneous formations can yield a good correlation between sulfate and oxalate (Yao et al., 2002b, 2003), while gas-phase oxidation followed by gas-particle condensation can yield a high correlation between MSA and oxalate (Kerminen et al., 1997). As shown in Table 4, HCOO− showed strong correlation with SO42− (r = 0.55), but poor correlation with MSA. On the contrary, C2O42− showed moderate correlation with MSA (r = 0.11), but weak correlation (r = 0.08) with SO42−, suggesting that in-cloud and heterogeneous formations play a more important role in the formation of formic acid, while gas-phase formation is important for the formation of oxalic acid in Beijing. This can be verified by the moderate correlation between HCOO − and cloud cover, and the poor correlation between C2O42− and cloud cover. The result is contrary to the conclusions of Yao et al. (2003), Huang et al. (2005) and Yu et al. (2005), but similar with the results of Uchiyama (1996) and Kawamura et al. (1996a, b). CH3COO− did not show any correlations with both

MSA and SO42−, verifying that it was mainly from primary emissions. 3.3.2. Relative proportion of primary sources and secondary sources Primary emissions and photochemical transformations were suggested as the major sources of CAs (Yu, 2000). Previous discussions in Section 3.3.1 indicated that acetic acid was mainly from primary emissions, while formic acid was largely from secondary transformations. Thus, the ratio of acetic to formic acid (A/F) might be an indicator of the relative importance of direct emissions (high ratio) and in situ formation by photochemical processes (low ratio). In order to verify this assumption, A/F values were collected from major primary emission sources including vehicular emission, biomass burning, and soil and vegetation emissions, and from secondary sources reported by previous studies. These A/F values were listed in Table 5. It might be reasonable to conclude that, even though the A/F value is not uniform in primary emissions and is too complicated to figure out in secondary emissions, it shows significant difference between primary (N1) and secondary (b1) sources. Therefore, A/F has been proved to be a suitable indicator to study the relative contributions of primary and secondary sources to CAs. The annual average A/F was 0.71 in PM2.5 in Beijing, indicating that the secondary formation was the

Table 5 The ratio of acetic to formic acid (A/F) from different sources Sources

A/F

References

Primary sources Biomass burning

N1 2–10 3–4 1.75–2.38 2–3 2.1–2.3 1.6 a 2.0a b1

Talbot et al. (1988) Hartmann et al. (1991) Talbot et al. (1988) Grosjean (1989) Grosjean (1992) Talbot et al. (1990) Servant et al. (1991) Talbot et al. (1988)

Vehicular emissions

Vegetation (tropical forests) emissions Secondary sources a

Calculated value.

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dominant source of CAs. The A/F in spring, summer, autumn and winter were 18.00, 4.88, 0.39 and 0.03, respectively, in PM2.5 at BNU. The extremely high value in spring might be somewhat related to the dust storms in this season. The result also indicated a slight increase of secondary formation with depressed temperature from summer, autumn, to winter, suggesting that temperature might be the controlling factor in determining the relative contribution of the primary and secondary sources to these acids. There are various types of atmospheric reactions forming formic, acetic and oxalic acids at urban sites, such as the oxidation of unsaturated fatty acids originating from cooking, the oxidation of aromatic hydrocarbons (Kawamura and Ikushima, 1993), and the ozone oxidation of olefins emitted from vehicular exhausts (Scheff and Wadden, 1993). The larger extent of precursors' accumulation in the lower mixing layer during winter might be one reason for the high secondary transformation. The lower primary emissions from soil and vegetation in the cold season might be another reason for the high proportion of secondary sources in winter. A/F was lower at BNU (0.39) than at MY (1.51) in autumn, suggesting a large contribution of the secondary sources in urban area and of the primary sources in rural area. There are lots of burning activities in the rural area of Beijing in autumn; thus, the burning of straw and/or other plants might contribute to the CAs significantly. 3.3.3. The contribution of biomass burning As discussed above, the biomass burning might be one of the major sources of CAs, especially in the rural area in autumn. Duan et al. (2004) suggested that biomass burning in Beijing was a significant repetitive pollution factor. We do need to address the question here: to what extent the biomass burning contributes to the degradation of air quality in Beijing? It was reported that the contribution of the biomass burning to organic carbon concentrations in Beijing was high (10–43% in urban area and 50–70% in rural area) (Duan et al., 2004); thus, it is also desirable to estimate the contribution of biomass burning to the CAs in Beijing. As K+ has been used as an unambiguous tracer of biomass burning (Yamasoe et al., 2000; Ikegami et al., 2001), the ratio of CA to K+ (CA/K+) could be an indicator to distinguish the biomass burning from other sources of CA. Here, a simple method was developed to study the contribution of biomass burning to CA quantitatively. The principles and assumptions for the estimation are: (1) K+ only originates from the biomass burning; (2) CA could be regarded as the sum of two fractions, one from the biomass burning and associated with K+, the other from other

sources, i.e. CA = (CA)biomass + (CA)non-biomass and (CA)biomass = (CA/K+ )biomass × K+ ; (3) CA/K + from the direct biomass burning, (CA/K+ )biomass, is a constant. According to the three assumptions mentioned above, the equation of CA ¼ ðCA=Kþ Þbiomass  Kþ þ ðCAÞnonbiomass can be used to distinguish the contribution of the biomass burning to CA in aerosols. The approach is the use of regression analysis to evaluate the quantitative relationship between CA and K+ , i.e. CA = a × K+ + b, where the slope a = (CA/K+ )biomass and the intercept b = (CA) non-biomass, and then calculating the biomass contribution based on (i) (CA − b) / CA × 100% or (ii) (a × K+ ) / CA × 100%. If CA correlates with K+ and the slope of the linear regression equation of CA = a × K+ + b is within the range of (CA / K + )biomass calculated from direct biomass burning plumes, the three assumptions can be met and the two methods discussed above can be used for quantitatively estimating the contribution of the biomass burning to CA. CAs and K+ were submitted to regression analysis for PM2.5 and PM10 samples collected at different sites in different seasons in Beijing. (CA / K+)biomass values of 0.008–0.06, 0.09–0.7 and 0.02–0.1 for formic, acetic and oxalic acids calculated from the average composition of chemical species in biomass burning plumes of Cerrado and the tropical forest (Yamasoe et al., 2000) were used to check the slopes of the regression lines. Only meaningful results were presented in Table 6. The results showed that the contributions of the biomass burning to CA calculated from (i) (CA − b) / CA × 100% and (ii) (a × K+) / CA × 100% were similar. Overall, the biomass burning contributed about 30–60% to formic and oxalic acids. The seasonal variation of the contribution followed the order of autumn N summer N winter, which was consistent with the land farming activities in Beijing. Biomass burning was most active in autumn when lots of straws and other plants were burned in the field. In summer, the contribution of the biomass burning was also high, as the biomass burning was still active in spite of the low atmospheric CAs and K+ concentrations in this rainy season. In contrast, the CAs level was high in winter, but the contribution from biomass burning was low, suggesting that CAs in particles predominantly originated from other sources than biomass burning in winter. The contribution of biomass burning was higher in PM10 than in PM2.5, suggesting that CAs in coarse particles were more from this primary source. It must be noted that the biomass burning was not likely

Y. Wang et al. / Atmospheric Research 84 (2007) 169–181

179

Table 6 The meaningful linear regression equations between CA and K+, average concentrations of K+ and CA, and the calculated contribution of biomass burning to CA Species

HCOO−

C2O2− 4

Sampling

BNU-Sum-PM10 BNU-Win-PM10 CS-Win-PM10 YH-Sum-PM10 BNU-Aut-PM2.5 BNU-Win-PM2.5 BNU-Sum-PM2.5 CS-Sum-PM2.5 YH-Sum-PM2.5 BNU-Sum-PM10 CS-Sum-PM10 YH-Sum-PM10 BNU-Aut-PM2.5 BNU-Sum-PM2.5 CS-Sum-PM2.5 YH-Sum-PM2.5

CA = aK+ + b

r

a

b

0.013 0.033 0.037 0.030 0.051 0.031 0.020 0.021 0.015 0.132 0.087 0.095 0.139 0.093 0.080 0.080

0.014 0.146 0.077 0.059 0.030 0.142 0.020 0.061 0.045 0.181 0.113 0.179 0.126 0.171 0.073 0.181

0.90 0.59 0.80 0.76 0.66 0.66 0.70 0.59 0.82 0.91 0.77 0.85 0.86 0.71 0.76 0.83

N

21 18 16 17 20 18 21 21 21 21 21 21 20 21 21 21

Conc. (μg m− 3)

Biomass contribution (%)

+

K

CA

(CA − b)/ CA × 100

K+ × a/ CA × 100

1.80 2.05 3.30 3.22 0.97 2.11 1.25 1.53 1.21 1.80 1.84 1.68 0.97 1.25 1.53 1.21

0.04 0.21 0.20 0.15 0.08 0.21 0.05 0.09 0.06 0.42 0.27 0.34 0.26 0.29 0.20 0.28

62.4 31.8 61.1 61.8 62.4 3.3 55.6 34.2 29.0 56.8 58.6 47.3 51.7 40.6 62.7 34.9

63.0 31.6 61.7 62.6 62.2 31.7 55.4 34.6 28.7 56.8 58.6 47.0 51.8 40.4 62.4 34.9

r: correlation coefficient between CA and K+; N: number of samples.

the major direct source of acetic acid, which showed poor correlations with K+ . 4. Conclusions This study has investigated the water-soluble carboxylic acid species (formic, acetic and oxalic acids) in PM2.5 and PM10 aerosols in Beijing. Some principal conclusions were presented as follows: (1) The annual average mass concentrations of PM2.5 and PM10 were 147.5 and 207.5 μg m − 3 , respectively, in Beijing. Particulate pollution, especially the fine part, is still serious although governments have taken many controlling policies in recent years. PM2.5 concentration had a seasonal variation of spring N winter N autumn N summer and evenly distributed among urban and rural sites. These variations were regulated by the emission strength and the meteorological conditions. (2) The average concentration of total carboxylic acids (including formic, acetic and oxalic acids) were 541 and 615 ng m− 3 in PM2.5 and PM10, contributing 0.4% and 0.3% to the mass of the aerosols, respectively. Oxalic acid was the most abundant species in aerosols, while acetic acid had the lowest concentration with the largest variation. (3) Formic and acetic acids displayed different seasonal variations (formic: springb summerb autumnb winter; acetic: spring N summer N autumn N winter).

Oxalic acid had a seasonal cycle of spring, winterN summer, autumn at the traffic site, while its seasonal variation was not obvious at industrial and residential sites. The seasonal variations of formic and acetic acids were more obvious than that of oxalic acid. CAs changed concurrently and distributed evenly among the urban sites, while they had little connections between urban and rural areas. The concentrations of CAs were higher at daytime than at nighttime. Formic and acetic acids had mass in both fine and coarse modes, while oxalic acid was mostly present in the fine mode. (4) Formic acid was largely from secondary in-cloud and heterogeneous transformations of previous gases emitted from coal/biomass/waste burning. Acetic acid was mainly from primary vehicular/ vegetation/soil emissions. Oxalic acid was from both the primary traffic/coal burning/meat cooking emissions and the secondary gas-phase formations. Secondary formation was the dominant source of CAs in Beijing suggested by the aceticto-formic ratio (A/F). Based on the correlations of CAs with K+ as well as their corresponding ratios, biomass burning was estimated to contribute about 30–60% to formic and oxalic acids. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant Nos. 30230310,

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20477004 and 40575062), Beijing Natural Science Foundation (Grant No. 8041003), and also in part supported by SKLLQG, the Institute of Earth Environment, CAS and LAPC, the Institute of Atmospheric Physics, CAS, and the Swedish International Development Cooperation Agency (SIDA) through the Asian Regional Research Program on Environmental Technology (ARRPET) at the Asian Institute of Technology. References Andreae, M.O., Talbot, R.W., Andreae, T.W., Harris, R.C., 1988. Formic and acetic acids over the central Amazon region, Brazil, dry season. Journal of Geophysical Research 93, 1616–1624. Blando, J.D., Turpin, B.J., 2000. Secondary organic aerosol formation in cloud and fog droplets: a literature evaluation of plausibility. Atmospheric Environment 34, 1623–1632. Chebbi, A., Carlier, P., 1996. Carboxylic acids in the troposphere, occurrence, sources, and sinks: a review. Atmospheric Environment 30 (24), 4233–4249. Duan, F., Liu, X., Yu, T., Cachierd, H., 2004. Identification and estimate of biomass burning contribution to the urban aerosol organic carbon concentrations in Beijing. Atmospheric Environment 38 (9), 1275–1282. Fornaroa, A., Gutz, I.G.R., 2003. Wet deposition and related atmospheric chemistry in the São Paulo metropolis, Brazil: Part 2. Contribution of formic and acetic acids. Atmospheric Environment 37, 117–128. Galloway, J.N., Gaudry, A., 1984. The composition of precipitation on Amsterdam Island, Indian Ocean. Atmospheric Environment 18, 2649–2656. Gregory, G.L., Harris, R.C., Talbot, R.W., Rasmussen, R.A., Garstang, M., Andreae, M.O., Hilton, R.R., Browell, E.V., Beck, S.M., Sebacher, D.I., Khalil, M.A.K., Ferek, R.J., Harris, S.V., 1986. Air chemistry over the tropical forests of Guyana. Journal of Geophysical Research 91, 8603–8612. Grosjean, D., 1989. Organic acids in southern California air: ambient concentrations, mobile source emissions, in-situ formation and removal processes. Environmental Science and Technology 23, 1504–1506. Grosjean, D., 1992. Formic and acetic acids: emissions, atmospheric formation and dry deposition at two southern California locations. Atmospheric Environment 26A, 3279–3286. Hara, K., Osada, K., Matsunaga, K., Sakai, T., Iwasaka, Y., Furuya, K., 2002. Concentration trends and mixing states of particulate oxalate in Arctic boundary layer in winter/spring. Journal of Geophysical Research [Atmospheres] 107 (D19), AAC 12/1–AAC 12/14. Hartmann, W.R., Santana, M., Hermoso, M., Andreae, M.O., Sanhueza, E., 1991. Diurnal cycles of formic and acetic acids in the northern part of Guyana shield, Venezuela. Journal of Atmospheric Chemistry 13, 63–72. Huang, X.F., Hu, M., He, L.Y., Tang, X.Y., 2005. Chemical characterization of water-soluble organic acids in PM2.5 in Beijing, China. Atmospheric Environment 39 (16), 2819–2827. Ikegami, M., Okada, K., Zaizen, Y., Makino, Y., Jensen, J.B., Gras, J.L., Harjanto, H., 2001. Very high weight ratios of S/K in individual haze particles over Kalimantan during the 1997 Indonesian forest fires. Atmospheric Environment 35, 4237–4243. Jacobson, M.C., Hanson, H.C., Noone, K.J., Charlson, R.J., 2000. Organic atmospheric aerosols: review and state of the science. Reviews of Geophysics 38, 267–294.

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