JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D03302, doi:10.1029/2011JD016400, 2012
Source and formation of secondary particulate matter in PM2.5 in Asian continental outflow J. L. Feng,1 Z. G. Guo,2 T. R. Zhang,3 X. H. Yao,3 C. K. Chan,4,5 and M. Fang5 Received 14 June 2011; revised 7 October 2011; accepted 3 December 2011; published 3 February 2012.
[1] Fifty-five 48-h PM2.5 samples were collected from March 2003 to January 2004
at Changdao, a resort island in Bohai Sea/Yellow Sea in Northern China. Sulfate, nitrate and ammonium accounted for 54 9% of the PM2.5 mass concentration (annual average 47 mg m3) while organic matter and K+ contributed to 27 7% and 7 7% of the total mass, respectively. The ratios of SO2 4 to NO3 mass concentrations could be divided into two regimes and demarcated at nitrate concentration of 5 mg m3. In the low NO 3 2 + regime, NO 3 , SO4 and EC were well correlated to K , and the estimated contributions of 2 NO 3 , SO4 and EC from biomass burning were 50 27%, 38 24% and 47 27%, respectively. These correlations substantially decreased in the high NO 3 regime reflecting fossil fuel combustion and formation of ammonium nitrate and the estimated contributions 2 of NO 3 , SO4 and EC from biomass burning were 16 12%, 28 18% and 27 16%, respectively. In most samples, the equivalent ratios of total anion to total cation concentrations were greater than unity, suggesting that the aerosols were acidic. When [H+] > 0, a moderately good linear correlation of the estimated aerosol acidity [H+] with the water-soluble organic carbon (WSOC) was observed with R2 = 0.70 and an increase of [H+] by 100 neq m3 would increase 1.2 mg m3 WSOC in PM2.5. When [H+] > 0, an increase of [H+] by 100 neq m3 would increase 1.4 mg m3 of secondary organic carbon (SOC) in PM2.5. Moreover, the correlation analysis result suggested that 60% of the estimated SOC (on average) in PM2.5 were water-soluble. Citation: Feng, J. L., Z. G. Guo, T. R. Zhang, X. H. Yao, C. K. Chan, and M. Fang (2012), Source and formation of secondary particulate matter in PM2.5 in Asian continental outflow, J. Geophys. Res., 117, D03302, doi:10.1029/2011JD016400.
1. Introduction [2] The impact of the Asian continental outflow on the coastal/oceanic biogeochemical cycle of the east China Seas (Bohai Sea, Yellow Sea and East China Sea) and North Pacific Ocean has been recognized for many years, while more recently, the attention has been on the impact on the regional and global climate [Rahn et al., 1977; Duce et al., 1980; Gao et al., 1992, 1996; Ma et al., 2003; Nakamura et al., 2005; Uno et al., 2009]. The effect of sulfate and particulate carbonaceous species on the climate has been recognized despite the existence of significant uncertainties, in particular, in the particulate carbonaceous species data [Intergovernmental Panel on Climate Change, 2007]. For 1 Institute of Environmental Pollution and Health, Shanghai University, Shanghai, China. 2 Center for Atmospheric Chemistry Study, Department of Environmental Science and Engineering, Fudan University, Shanghai, China. 3 Key Lab of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, Qingdao, China. 4 Division of Environment, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. 5 Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Copyright 2012 by the American Geophysical Union. 0148-0227/12/2011JD016400
example, uncertainties in the simulation of secondary organic carbon (SOC) could be as a large as a factor of 20 [Heald et al., 2005], suggesting that most of the important formation mechanisms of SOC were still unknown. Fu et al. [2008] reported that the uncertainties significantly reduced when acid-catalyzed aqueous-phase reactive uptake of dicarbonyls was included in their modeling efforts. However, the importance of acid-catalyzed aqueous-phase formation of SOC in the atmosphere is still under debate [Q. Zhang et al., 2007; Nopmongcol et al., 2007; Li et al., 2008, 2010; Tanner et al., 2009; Pathak et al., 2011; Ding et al., 2011]. More field measurements are needed to examine the acid-catalyzed aqueous-phase formation of SOC in various environments. [3] Biomass burning has been found to be one of the important sources of inorganic and organic aerosols in the atmosphere upwind of the Yellow Sea [Duan et al., 2004; Kaneyasu and Takada, 2004; Kanakidou et al., 2005; Li et al., 2007], and Ma et al. [2003] reported that very concentrated biomass burning plumes can reach the Yellow Sea. Lee et al. [2005] reported that smoke aerosols from Russian forest fires traveled for thousands of kilometers from 60°N to 40°N, crossing the entire Yellow Sea to reach the cities downwind. High concentrations of biogenic and anthropogenic gases and particulate matter have been detected over the Yellow Sea [Ma et al., 2003; Y. Wang et al., 2006; Shi
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Figure 1. Map of sample site of Changdao Island. et al., 2010] and the submicron aerosols have been reported to be acidic due to insufficient NH3 to neutralize the acidic components [W. Wang et al., 2006]. Thus, it is interesting to investigate the role of acid-catalyzed aqueous-phase formation of SOC in the Yellow Sea. [4] Although there are short-term studies on the size distribution, transport and deposition of particulate matter in the coast of the Yellow Sea [Gao et al., 1996; Zhang et al., 2000; Hu et al., 2002; Ma et al., 2003; Nakamura et al., 2005; Uno et al., 2007], long-term monitoring at a relatively clean location has yet to be reported which could shed insight to the problem mentioned. Changdao Island (37.93°N and 120.72°E), located at the demarcation line between Bohai Sea and Yellow Sea (Figure 1), was selected in this study for this purpose. Changdao is a resort with little industry at 7 km north of the Shandong Peninsula. It is in the transport path of the Asian continental outflow to the Pacific Ocean. In this study, a yearlong measurement of PM2.5 was made at Changdao to study the sources and formation pathways of particulate matter.
2. Experimental Methods [5] The sampling site is located on the rooftop of a radar station at the south tip of Changdao Island in the Bohai Sea/ Yellow Sea. Fifty-five PM2.5 samples were collected with pre-baked quartz fiber filters (Whatman, QM-A 90 mm, baked at 450 °C for 5 h before use) during four seasons (20 March to 25 April 2003, Spring; 29 July to 1 September 2003, Summer; 10 October to 11 November 2003, Autumn; and 24 December, 2003 to 15 January 2004, Winter) using a medium-volume sampler (Beijing Geological InstrumentDickel Co., Ltd. Model Number: TSP/PM10/PM2.5-II). The flow rate was 77 L minl and the sampling duration was 48 h. Details on the sampling are given by Feng et al. [2007]. Small samples punched from the filters (5.8 cm2 each) were ultrasonically extracted with 10 ml nano-pure water, which was deionized to a resistivity of 18 MW cm1. After passing through microporous membranes (pore size of 0.45 mm), 2 eight anions (Cl, NO 2 , NO3 , SO4 , oxalate, malonate, succinate, glutarate) and five cations (Na+, NH+4 , K+, Ca2+, Mg2+) were analyzed by Ion Chromatography (Dionex 600)
following the method reported by Yao et al. [2002]. The anions were analyzed using AS11 column (4 mm) with an AG11 guard column and the cations with CS12 column. The eluents used were 0.4–6 mM NaOH (gradient) for the anions and 20 mM methane sulfonic acid (MSA) for the cations. The average concentrations, in mg ml1, of the ions in the field blanks were 0.07, 0.09, 0.05, 0.01, 0.06, 0.06, 0.01, + + 2+ 2 + 0.06 for Cl, NO 3 , SO4 , oxalate, Na , NH4 , K , Ca , respectively (n = 4). NO2 , malonate, succinate, glutarate and Mg2+ were not detected in the field blanks. The blank concentrations were less than 10% of the average sample concentrations even for Cl and Ca2+. The presented concentrations of ions were blank corrected. Part of the water extract was used to measure the concentration of WSOC with a total organic carbon analyzer (Shimadzu TOC-5000A). The carbon analyzer was calibrated with potassium hydrogen phthalate solutions of 0 ppm to 30 ppm in each run (n = 5, R2 = 0.99). The concentration of WSOC in the field blank was 0.5 mg ml1 and was deducted from the reported concentrations. [6] Organic carbon (OC) and elemental carbon (EC) concentrations of the samples were analyzed by thermal/optical carbon analyzer (Sunset Laboratory Inc., Forest Grove, OR) with the NIOSH temperature program [Birch, 1998]. Briefly, the sample was first heated in four increasing temperature steps (250, 500, 650 and 850 °C) under a completely oxygen-free helium environment; then the sample was heated in 2% oxygen/helium atmosphere at four temperature steps ranging from 550 to 940 °C. The evolved carbon-containing gases were oxidized to CO2 over a manganese dioxide catalyst and then reduced to methane which was detected by a flame ionization detector. A laser beam of 680 nm was applied to monitor the transmittance of the filter under heating to determine the speciation of OC/EC. [7] Levoglucosan was measured by GC-MS and the details are given by Feng et al. [2007].
3. Results and Discussion 3.1. Composition and Seasonal Variations [8] The mass concentration of PM2.5 was not measured because of the large weighing artifact associated with quartz fiber filters [Wang et al., 2009; Yao et al., 2009]. Instead, the
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Figure 2. Time series of concentrations of chemical species in PM2.5 at Changdao (mg m3).
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sum of the concentration of all measured species is used to estimate the mass concentration of PM2.5 and the organic matter is estimated from the OC concentration by multiplying by 1.6 [Turpin and Lim, 2001]. A previous yearlong PM2.5 measurement in Beijing, upwind of Changdao, showed that the contribution of mineral dust and trace elements to the total mass was less than 10% [He et al., 2001]. Due to the nature of the current sampling site and the high forest coverage, the contribution of mineral dust and trace elements to the PM2.5 total mass was also expected to be less than 10%. Sulfate, nitrate and ammonium in total accounted for 54 9% of the estimated PM2.5 mass concentration (annual average of 47 mg m3). Organic matter and K+ contributed 27 7% and 7 7% of the total mass, respectively. The relative contribution of sulfate, nitrate and ammonium to the PM2.5 at Changdao was obviously higher than that in Beijing and other cities in China (30–40%), while the organic matter was obviously less [He et al., 2001; Chan and Yao, 2008]. Feng et al. [2007] reported that air mass was predominantly from the northwest in the fall and winter, and was partially from the southeast in the spring and the summer. The percentages of the organics and the sum of sulfate, nitrate and ammonium to the total mass were close to the values in Toronto, where transported particulate matters are the major contributor to PM2.5 [Lee et al., 2003]. The EC (4%) in the PM2.5 was obviously lower than the 5–15% reported for the other cities of China [Fang et al., 2009] because of low anthropogenic emissions nearby this sampling site. K+ accounted for about 7% of the PM2.5 mass concentration which is much higher than the 2–5% reported for the other cities in China [Chan and Yao, 2008]. High K+, characteristic of biomass burning in China, has been reported in the Asian continental outflow [Ma et al., 2003]. [9] Since Changdao is an island in the Bohai Sea, it is reasonable that the sum of [Na+] and [Cl-] accounted for 6% of the PM2.5 mass concentration. Ca2+ and Mg2+ accounted for only 2% of the PM2.5 possibly due to the abundance of trees and vegetation surrounding the sampling site which lowered the concentration of suspended dust. Ca2+ substantially increased in three samples taken in the Asian dust storm episodes (Figure 2c). [10] Large day-to-day variations in the concentrations of sulfate, nitrate, OC, EC, oxalate, K+, Na+ and Ca2+ were found (Figures 2a–2d). Using sulfate as an example, the maximum concentration was 21, 12, 18 and 4 times of the minimum concentration in spring, summer, fall and winter. However, student’s t-test shows that the difference in the mean concentration of sulfate between the seasons was not statistically significant with 95% confidence. Theoretically, the sulfate concentration in PM2.5 is determined by the SO2 concentration, the conversion rate of SO2 to sulfate and meteorological conditions. Although the SO2 concentration in upwind cities of the northern China increased by one or two orders of magnitude in winter due to space-heating [Yao et al., 2002], the sulfate concentration did not increase accordingly because of the lack of fast conversion processes such as cloud-processing [He et al., 2001; Yao et al., 2003]. The oxidation reaction of SO2 via OH free radical was likely slow in winter due to weak solar radiation. Yao et al. [2002] reported that the molar ratio of SO2 to (SO2+SO2 4 ) in Beijing in winter was usually less than 0.1. In general, the dispersion conditions for air pollutants is poor in winter in
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northern China due to low precipitation and low mixing heights, and the synoptic wind favored the transport of air pollutants to Changdao [Mao, 2003]. [11] Nitrate exhibited pronounced seasonal trend. The mean concentration of nitrate in summer was significantly lower than the other seasons (which were about the same) because high temperature did not favor the partitioning of nitrate to the particulate phase as NH4NO3, and the clean air from the Yellow Sea in the east would dilute the air pollutants [Feng et al., 2007]. Volatilization of particulate nitrate during sampling could also lead to lower nitrate concentrations in summer [Pathak and Chan, 2005]. [12] K+ has been used as a marker of biomass burning [Duan et al., 2004; Pio et al., 2008]. Other studies suggested that part of K+ could be from crustal sources [Andreae and Merlet, 2001; Yang et al., 2005; Pio et al., 2008]. In our data set, [Ca2+] was much smaller than [K+], except for three episodes, and K+ from crustal sources accounted for only 1–3% of the total K+. In the three episodes, the contribution of K+ from crustal sources accounted for 6–8% of the total K+ using the method proposed by Pio et al. [2008]. The seasonal variation in the mean concentration of K+ was statistically insignificant although very high concentrations were observed in a few days. High K+ concentrations observed at Changdao could be due to domestic biomass burning for cooking and house-heating upwind, while the spikes were likely due to the burning of crop residues and/or forest fires outside of the island. In Shandong Province alone, the burning of agricultural crop residue was estimated to be 5*104 Gg per year, 25% of which was burned in the field and the rest as domestic fuel [Zhang et al., 2008]. [13] Seasonal concentrations of OC, EC and WSOC at Changdao were reported by Feng et al. [2007], but the statistical difference of OC and WSOC between seasons was not discussed. In this study, more statistical analysis was done and the results were summarized as below: 1) The mean concentration of OC in the winter was significantly higher than that in other three seasons and the mean concentration of OC in the summer was significantly lower than that in the other three seasons; 2) The difference of the mean concentration of WSOC between seasons was not statistically significant and the same was true for the difference of the mean concentration of oxalate. Thus, it can be inferred that the higher mean concentration of OC in winter was not due to more WSOC and/or SOC formed. It is likely associated with low ambient temperature. The WSOC in the four seasons accounted for 54 12% of the total OC and the R2 between them was 0.83. The carbon in the diacids (oxalate, malonate, succinate and glutarate) accounted for 2 1% of the total OC. In summer, the measured carbon in the diacids accounted for 4 1% of the total OC, suggesting that SOC was a more significant fraction of OC. The measured WSOC in summer accounted for 60 13% of the total OC which was also higher than the annual average. Biomass burning can be a major source of OC. Feng et al. [2007] used levoglucosan as a marker to evaluate the contribution of biomass burning at Changdao. However, levoglucosan has been reported to be unstable in the atmosphere [Hennigan et al., 2010] which could lead to the underestimation of biomass burning contribution for a rural site like Changdao. However, Zhang et al. [2010] reported that the monthly variation of levoglucosan coincided well with MODIS fire counts in
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southeastern U.S. where the concentration of K+ was low and had no apparent seasonal trends and exhibited poor correlation with fire counts. Considering that much higher concentrations of K+ were observed in Changdao, K+ was thereby used as a biomass burning marker in this study and was compared to the use of levoglucosan as a biomass burning marker. We did not correlate the contribution of biomass burning to the fire counts because 75% of biomass was used for cooking and space heating in northern China, which would not be detected by MODIS. In addition, we combined measurements of inorganic and organic to study acid-catalyzed aqueous-phase formation of SOC and/or WSOC in PM2.5 in this study. 3.2. Correlation of Chemical Components in PM2.5 for Source Identification 3.2.1. Correlation of the Inorganic Species [14] In the correlation analysis of the data set, six outlying points (referred to as A, B, C, D, E, F hereafter) were detected, which turned out to be episodic events and they will be excluded in the regression calculations. These points, however, provided other insight to the formation of aerosols at Changdao and section 3.3 will be dedicated to detailed discussion. Pearson correlation was used for most of data analysis. Linear correlation was conducted only when the slope was studied. [15] The linear correlation of nitrate and sulfate can be classified into two regimes. Regime 1 was characterized 3 and Regime 2 includes samples with by NO 3 < 5 mg m 3 > 5 mg m (Figure 3a). Linear regression yielded NO 3 3.9 and 1.6 for slopes, and R2 of 0.78 for Regime 1 and Regime 2, respectively. [16] In Regime 1 when the outlying point B was removed, 2 the Pearson correlations of NO 3 , SO4 , OC, EC, WSOC, and + oxalate with K were moderately good with the Pearson correlation coefficient (r) ranging from 0.53 to 0.81 (Table 1). Point B was possibly related to an occasional biomass burning event as will be discussed later. The Regime 1 samples were mainly collected in spring, summer and fall. In 2 Regime 2, correlations of NO 3 , SO4 , OC, EC and WSOC + with K markedly decreased when compared to those in Regime 1 (Table 1). The Regime 2 samples were collected in spring, fall and winter. The high correlation with K+ in Regime 1 suggested that biomass burning had important 2 contribution to NO 3 , SO4 , OC, EC and WSOC, while the low correlations in Regime 2 suggested that the combustion of fossil fuels probably took over biomass burning in the contribution of these species. [17] The moderately good linear correlations between 2 ([K+] + [NH+4 ]) and ([NO 3 ] + [SO4 ]) and between the sum of cation and the sum of anion are shown in Figures 4a and 4b. About 70% of the samples had equivalent ratios 2 + + of ([NO 3 ] + [SO4 ]) to ([K ] + [NH4 ]) > 1, suggesting that the sulfate aerosol were not completely neutralized. Moreover, about 60% of the samples had equivalent anion to cation ratios of > 1 (Figure 4b). This further supported the presence of incompletely neutralized sulfate aerosol. 3.2.2. Formation of Nitrate in PM2.5 [18] The NO 3 in the Regime 1 samples was probably mostly associated with K+ and other metal ions, and the formation of NH4NO3 was not likely for the following reasons: a) low NO 3 concentration; b) the Pearson correlation
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Table 1. Pearson Correlations of Important Particulate Species With K+ at the 0.01 Level (2-Tailed) NO 3 a
Regime 1 Regime 2a
0.81 0.43c
SO24
Cl
0.79 0.64
b
fail fail
OC
EC
WSOC
Oxalate
0.67 0.61
0.81 0.54
0.53 0.37
0.55 fail
a
Remove outlying point B. p > 0.05. Correlation is significant at the 0.05 level (2-tailed).
b c
Nitrate associated with K+ and other metal ions only played a minor role. Like the formation of ammonium nitrate, ammonium chloride could be formed under NH3 rich conditions. This is supported by a moderately good Pearson correlation between nitrate and chloride with r = 0.68 (Figure S1b). In Regime 2, some of the nitrate and chloride were likely from the same source, e.g., biomass/coal burning, and/or the meteorological and chemical conditions favoring the formation of NH4NO3 and NH4Cl [Yao et al., 2002; Yamasoe et al., 2000; da Rocha et al., 2005; Pio et al., 2008]. In some samples of Regime 2, the sulfate was likely incompletely neutralized and did not favor the formation of NH4NO3 and NH4Cl in the atmosphere. Since the duration of our sample collection was 48 h long, incompletely neutralized sulfate aerosol and NH4NO3 and NH4Cl may be formed in different time periods during sample
Figure 3. Correlations (a) between sulfate and nitrate and (b) between nitrate and potassium (in Figure 3b, only data in the Regime 1 is included to avoid clustering). between nitrate and K+ (r = 0.81) was high; and c) most of the samples were collected in summer and there were no winter samples. The high temperature in summer did not favor the formation of NH4NO3 because of gas-particle partitioning. Ma et al. [2003] reported that about 60% of plumes from North China were affected by the open-field and/or domestic burning of biomass. The nighttime reaction of N2O5 with H2O was recently proposed as an important pathway of nitrate formation in ammonia poor environment in China [Pathak et al., 2009]. The reaction would form nitrate and nitrite simultaneously, however, the observed ratio of nitrite to nitrate in ambient aerosol could be much less than unity because of the instability of nitrite. At nighttime, the relative humidity was generally over 80% at this sampling site. The Pearson correlation between nitrate and nitrite in Regime 1 was moderately good with r = 0.72 (Figure S1a), suggesting that the reaction of N2O5 with H2O could occur at nighttime.1 [19] On the other hand, in Regime 2, the formation of NH4NO3 was likely the overwhelming source of nitrate which can be seen from the high NO 3 concentration, the moderately good Pearson correlation between NH+4 and NO 3 with r = 0.62 (not shown), the poor Pearson correlation between nitrate and K+, and the lack of summer samples. 1 Auxiliary materials are available in the HTML. doi:10.1029/ 2011JD016400.
+ 2 + Figure 4. (a) ([NO 3 ] + [SO4 ]) versus ([K ] + [NH4 ]) and (b) anion versus cation in PM2.5 in Changdao (full and empty symbols represent Regime 1 and Regime 2, respectively).
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collection. Yao et al. [2002, 2003] reported that formation of NH4NO3 is not likely to occur under high temperature and low relative humidity in daytime, but the low temperature and high relative humidity at night would favor the formation of NH4NO3 in Beijing (upwind of Changdao). 3.2.3. Correlation of the Inorganic and Organic Components in PM2.5 [20] The aqueous glyoxal system has been found to form dicarboxylic acids through oxidation reactions and to form high molecular weight compounds through acid-catalyzed reactions [Li et al., 2008; Galloway et al., 2009]. On the other hand, several recent studies showed that the role of the acid-catalyzed reactions may not be as significant as anticipated [Peltier et al., 2007; Q. Zhang et al., 2007; Li et al., 2008, 2010]. In this section, we will examine the correlation of aerosol acidity with SOC and WSOC. Using an approach widely adopted in the literature [Turpin and Lim, 2001; Ho et al., 2003; Mo et al., 2004; Lin et al., 2009; Snyder et al., 2009; Cheng et al., 2011], SOC is estimated as below: ½SOC ¼ ½OC ð½OC=½ECÞMin *½EC
ð1Þ
([OC]/[EC])Min is the minimum ratio of [OC]/[EC] in each season, which is 3.2, 2.6, 2.0 and 2.6 for spring, summer, autumn and winter in our data set. The estimated SOC accounted for 44 15% of the total OC. As the samples with the minimum OC/EC ratio at Changdao could also contain SOC, the estimated contribution of SOC using the EC-tracer method would be underestimated. [21] The relative strong acidity (H+, or the difference between the equivalence concentrations of the sum of anion concentration and the sum of the cation concentration) in the aerosol phase could be approximated by the following equation [Saxena et al., 1993; Kerminen et al., 2001; Schwab et al., 2004]: ½Hþ ¼ ½Anion-½Cation
ð2Þ
A negative [H+] suggests the absence of acidic aerosols. Since the strong acidity was not measured, error may exist in the estimated [H+] [Yao et al., 2006]. When [H+] < 0, the linear correlation coefficient between [SOC] and [H+] was less than 0.1 (Figure 5a). When [H+] > 0, the regression equation was [SOC] = 0.014*[H+] + 1.5 with R2 of 0.42. The slope suggested that an increase of [H+] by 100 neq m3 would increase 1.4 mg m3 SOC in PM2.5. However, the intercept of 1.5 suggested that part of SOC was unrelated to 2 strong acidity. When [SOC] was plotted with [SO2 4 ], the R + + was less than 0.2 when [H ] was > 0 (Figure S2). When [H ] was < 0, the correlation between [SOC] and [SO2 4 ] was moderately good with R2 = 0.69 (Figure S2). This high correlation coefficient could suggest that SOC and SO2 4 were possibly formed by similar processes, but the acidcatalyzed formation of SOC was not expected. Take note that the two points with high [SOC] and [SO2 4 ] concentrations greatly influenced the correlation when [H+] was < 0, and when the two points were removed, the R2 dropped to 0.05. [22] When [WSOC] was plotted against [H+] (Figure 5b), no significant correlation was found when [H+] < 0, but for the samples with [H+] > 0, the correlation was good ([WSOC] = 0.012*[H+] + 2.1, R2 = 0.70) when the outlying
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point F was excluded. The slope suggested that an increase of [H+] by 100 neq m3 would increase 1.2 mg m3 WSOC in PM2.5. It is obvious that the correlation between [WSOC] and [H+] was better than that between [SOC] and [H+] when [H+] > 0. Since the [SOC] was estimated by equation (1), the error could be large. To the best of our knowledge, it is still a big challenge to accurately estimate SOC in atmospheric particles. When SOC was plotted with WSOC, the regression equation is [WSOC] = 0.60*[SOC] + 1.7 with R2 = 0.77. The slope suggested that about 60% of the SOC was water-soluble while the intercept indicated that part of WSOC was not from secondary reactions. It is not surprising that the intercept was present because some WSOC such as amino acids, urea etc. are not SOC [Shi et al., 2010]. On the other hand, some of the high molecular weight SOC is less water-soluble, especially under neutral or acidic conditions [Kanakidou et al., 2005]. [WSOC] was positively correlated to [SO2 4 ] and the regression equation was [WSOC] = 0.19* 2 [SO2 4 ] + 1.6 with R = 0.44 after the outlying point F was excluded. The reason for the relatively weak correlation was that some of the sulfate aerosols were completely neutralized and played no role in forming WSOC. [23] When the Pearson correlations between [H+] and the dicarboxylic acids including oxalate, malonate, succinate and glutarate were examined (Table 2), the correlation coefficient (r) ranged from 0.44 to 0.56. However, r substantially increased (0.68 to 0.81) when only samples with [H+] > 0 were analyzed. It has been reported that acidic aerosol could enhance the uptake coefficient of gaseous precursors of diacids [Liggio and Li, 2006]. [24] Moderately good Pearson correlations between the 2 dicarboxylic acids and NO 3 , SO4 , WSOC were found (Table 2). It is interesting that correlations of succinate with NO 3 and WSOC were evidently higher than the other diacids, which is to be explained. The correlation of sulfate with malonate and succinate were evidently weaker (Table 2). In eastern China, cloud-processing has been reported to play an important role in the formation of sulfate [Zhuang et al., 1999; Yao et al., 2002; Yu et al., 2005]. Meanwhile, the mass ratio of malonate to succinate in this study was 1.5, suggesting they were photochemically formed [Kawamura and Ikushima, 1993]. So the weaker correlation of sulfate with malonate and succinate could be due to their different formation pathways. [25] The Pearson correlation coefficients between dicarboxylic acids were larger than 0.79. This suggested that these diacids could share the same precursors, formed by similar aging processes or from the same sources [Kawamura and Ikushima, 1993; Yao et al., 2004]. 3.3. The Outlying Data Points: Episodic Events [26] There were six outlying data in the 55 samples, and they are believed to be episodic events. [27] Outlying point A occurred in summer (Table 3) under high temperature and the equivalent ratio of anion to cation was 1.35; both conditions do not favor the formation of NH4NO3. K+ dominated the cations suggesting strong contribution from biomass burning. Back trajectory analysis (Figure S3) indicated that the air masses moved very slowly during the period and the poor dispersion conditions favored the accumulation of air pollutants. The back trajectory also showed that air parcels reaching Changdao passed over
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Figure 5. Correlations (a) between secondary OC and aerosol acidity and (b) between WSOC and aerosol acidity. polluted industrial zones in Shangdong (Zibo and Weihai) [Gao et al., 2011], thus the mixing of anthropogenic pollutants with emissions from biomass burning could exist, yielding high nitrate probably in the form of KNO3. However, the sulfate in high concentration was probably incompletely neutralized, which would be partially associated with ammonium, K+ and other metal ions. [28] Outlying point B also occurred in summer. The concentration of K+ was higher than sulfate. This scenario usually occurs in biomass burning aerosol that are close to the source [Yamasoe et al., 2000; Gao et al., 2003; Schmidl et al., 2008] and was rarely observed in transported biomass burning emissions [Falkovich et al., 2005; da Rocha et al., 2005]. It is possible that a local forest or bush fire was responsible for this, although no record was available to confirm this.
Table 2. Pearson Correlations of Dicarboxylic Acids With Other Species for All Databases at the 0.01 Level (2-Tailed, Outlying Point F Excluded) H+ H+ > 0 NO 3 SO2 4 WSOC Oxalate Malonate Succinate
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Oxalate
Malonate
Succinate
Glutarate
0.57 0.68 0.64 0.65 0.65
0.44 0.81 0.71 0.57 0.71 0.86
0.53 0.80 0.82 0.62 0.84 0.79 0.86
0.43 0.68 0.66 0.71 0.68 0.85 0.83 0.82
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Table 3. Concentrations of Inorganic and Organic Species in the Outlying Points (Unit: mg m3) Sampling time SO2 4 NO 3 NH+4 Cl Na+ K+ Ca2+ OC EC WSOC Oxalate Malonate Succinate Glutarate
Outlying Point A
Outlying Point B
Outlying Point C
Outlying Point D
Outlying Point E
Outlying Point F
2003-8-1214 35.3 8.0 4.8 0.5 1.1 12.0 0.7 7.1 2.5 3.5 0.6 0.04 0.02 0.02
2003-8-1820 14.9 2.9 2.6 0.1 1.3 15.8 1.0 3.7 1.0 2 0.4 0.05 0.02 0.03
2004-1-1415 14.0 15.7 4.5 3. 7 0.6 4.5 0.5 19.9 3.5 8.7 0.4 0.03 0.02 0.02
2004-1-57 28.5 20.9 3.4 17.0 8.6 3.3 4.4 12.3 4.8 7.1 0.5 0.07 0.04 0.04
2003-11-13 20.8 14.0 3.5 9.9 0.8 2.5 0.6 11.2 2.3 8.3 1.1 0.1 0.09 0.05
2003-4-2325 56.4 29.6 6.1 2.9 1.2 12.6 0.7 13.1 4.0 5.6 0.8 0.02 0.02 0.03
[29] In Outlying point C, the concentration of nitrite was the highest of all samples, and the concentration of nitrate was higher than that of the sulfate. A strong formation of nitrate and nitrite could occur in the sample. The sample was collected winter and the strong photochemical reaction was not expected. Formation of nitrate and nitrite was likely due to aqueous-phase reactions. [30] In Outlying point D, high concentration of Ca2+ indicates the outbreak of the Asian dust storm. Back-trajectories showed that the air masses were from northwest China. The strong winds associated with dust storm would substantially increase the sea-salt concentration in the aerosol. The mass ratio of chloride to sodium was almost same as the sea-salt, indicating the high concentration of chloride was most likely completely from sea-salt aerosol. [31] In Outlying point E, high chloride concentration was also detected, but the concentration of sodium was too low for the inclusion of sea-salt as a contributor. Coal/biomass burning should be the main source of the chloride [Yao et al., 2002; Li et al., 2007]. Extremely high concentration of particulate chloride and nitrate was reported in the Kanto Plain of Japan due to the formation of NH4Cl and NH4NO3 [Kaneyasu et al., 1999] when air was stagnant. Changdao is in proximity of the great North China Plain which has strong anthropogenic emission sources [Gao et al., 2011], it is very likely that high concentrations of chloride could be measured at Changdao when dispersion condition is poor. [32] In Outlying point F, the highest concentrations of sulfate (56 mg m3) and nitrate (30 mg m3) were observed. From back-trajectory analysis, the air pollutants were from the Shandong Peninsula. Furthermore, high K+ concentration suggested that the episode was the combined contribution from biomass burning and other anthropogenic emissions. In this sample, the equivalent ratio of anion to cation was 2.3, suggesting that the aerosol were very acidic. Such acidic aerosol was also reported in Guangzhou, China [Huang et al., 2011]. 3.4. Estimating the Contribution of Biomass Burning to PM2.5 Using K+ and Levoglucosan 2 [33] SO2 4 from biomass burning ([SO4 ]BB) is estimated using the equation below: 2 SO4 BB ð%Þ ¼ ½Kþ *Ratiosource = SO2 4 *100
ð3Þ
where [K+] and [SO2 4 ] are the measured concentrations of 2 + K+ and SO2 4 . Ratiosource is the ratio of [SO4 ]/[K ] in biomass burning source generally not available. Alternatively, + the minimum mass ratio of [SO2 4 ]/[K ] (0.9) based on the current database is used and the value is almost the same as tropical forest biomass burning aerosols in the Amazon Basin reported by Yamasoe et al. [2000]. In samples with the minimum ratios, part of the SO2 4 still could be from nonbiomass burning sources. Thus, the estimation should be considered as the upper limit. In the three episodes with high concentrations of Ca2+, the contribution of K+ from crustal sources was subtracted using the method proposed by Pio et al. [2008]. [34] Using a similar approach based on K+, the estimated contribution of biomass burning to the major species in PM2.5 is listed in Table 4. In Regime 1, the average contri2 bution of biomass burning to NO 3 , SO4 , OC, EC, WSOC varied from 40 to 50%, depending on the species. The average contribution varied from 16 to 27% in Regime 2, indicating a substantial decrease of the contribution from biomass burning. However, the average contributions of biomass burning to oxalate was 18% in Regime 1 and 19% Regime 2 suggesting biomass burning was not the major source of oxalate. [35] Levoglucosan is widely used as a tracer of biomass burning and most of the reported levoglucosan/PM2.5 ratios of the biomass burning emitted fine particles ranged from 0.02 to 0.1 [Sheesley et al., 2003; Dhammapala et al., 2007; Mazzoleni et al., 2007; Y. X. Zhang et al., 2007], and the ratio varied with the fuel type and burning conditions [Mazzoleni et al., 2007]. Assuming that the average levoglucosan/PM2.5 Table 4. Contribution of Biomass Burning to Particulate Matter in PM2.5 in Changdao Nitrate
Sulfate
Oxalate
OC
EC
WSOC
Average Median Standard deviation
50% 49% 27%
Regime 1 38% 18% 30% 16% 24% 16%
40% 33% 27%
45% 45% 27%
41% 33% 29%
Average Median Standard deviation
16% 12% 12%
Regime 2 28% 19% 24% 13% 18% 20%
19% 14% 11%
27% 23% 16%
23% 18% 15%
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ratio for the biomass burning emission in northern China is 0.04, the biomass burning contribution thus estimated would be less than 5% in most samples. In addition, even if a low ratio of 0.02 was used, the estimated contribution of biomass burning in Changdao would still be less than 10%. In comparison, the contribution of K+ alone to the PM2.5 mass reached 7%. Hennigan et al. [2010] found that almost 90% of the levoglucosan could be lost after 3–4 h of exposure to hydroxyl radicals. The low estimation in this study could be attributed to the possible degradation of levoglucosan [Saarikoski et al., 2008; Hennigan et al., 2010; Mochida et al., 2010]. Thus, in the particular case of Changdao, using K+ as the biomass burning marker would be a reasonable choice because of the high K+ concentrations.
4. Conclusion 2 [36] Analysis of eight anions (Cl, NO 2 , NO3 , SO4 , oxalate, malonate, succinate, glutarate) and five cations (NH+4 , Na+, K+, Ca2+, Mg2+) in 55 seasonal PM2.5 samples in Changdao revealed that the aerosol were from mixed sources of anthropogenic pollution and biomass burning. Six outlying data points were detected and they were attributed to episodic events. These outlying data provided additional insight to the formation of particles in Changdao. [37] Because of the wide presence of domestic biomass burning in the proximity of Changdao, K+ concentration was high and was a useful marker for source identification. There was no seasonal trend for biomass burning. The estimated contribution of biomass burning to the major particulate components varied from 16 to 50%, depending on the chemical species. [38] The extent of mixing of the anthropogenic and biomass burning particles was highly variable and two regimes can be identified. In Regime 1, nitrate concentration was 5 mg m3, and the correlation between K+ and other ions evidently decreased. In addition, most of nitrate should be in the form of NH4NO3 since the concentrations of K+ and other metal ions were too low to account for the measured nitrate. [39] The fine particles at Changdao were generally acidic. Correlations of aerosol strong acidity with WSOC suggest that an increase of [H+] by 100 neq m3 would increase 1.2 mg m3 WSOC in PM2.5 and about 60% of the SOC appeared to be water-soluble.
[40] Acknowledgments. The study was financially supported by Natural Science Foundation of China (grants 20877052 and 40776062) and Natural Science Foundation of Shandong Province (grant J2003E02) for which the authors are grateful.
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