A study of acidity on PM2.5 in Hong Kong using online ionic chemical composition measurements

Atmospheric Environment 45 (2011) 7081e7088

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A study of acidity on PM2.5 in Hong Kong using online ionic chemical composition measurements Jian Xue a, Alexis K.H. Lau a, Jian Zhen Yu a, b, * a b

Division of Environment, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China Department of Chemistry, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 July 2011 Received in revised form 18 September 2011 Accepted 20 September 2011

Particle in-situ pH (pHIS), defined as pH of the aqueous phase on aerosols, is an important factor in influencing aerosol-phase chemistry and uptake of gaseous species by particles. In this study, a continuous system, Particle-into-Liquid System (PILS) coupled with two ion chromatographs, was used to obtain PM2.5 ionic chemical composition at a time resolution of 30 min at a suburban site in Hong Kong under three different synoptic conditions. The chemical composition data and meteorological parameters (e.g., temperature, relative humidity (RH)) are input into Aerosol Inorganic Model (AIM-III) for estimation of in-situ pH through calculation of Hþ amount and aerosol liquid water content (LWC). The particle pHIS ranged from 1.87 to 3.12, with an average at 0.03, indicating the PM2.5 particles in Hong Kong are highly acidic. Unlike particle strong acidity, which was dominated by sulfate concentration, the amount of aerosol liquid water content could significantly influence in-situ particle acidity. Principal factor analysis has identified the equivalent concentration ratio between cations and anions (i.e., Rþ/) and RH to be the two most important factors influencing the particle pHIS. pHIS under different synoptic conditions in this study could be well approximated by a single linear regression equation (slope: 0.95, R2: 0.93), i.e., pHIS ¼ 4.94 Rþ/ þ 3.11 RH  5.70. Such an empirical equation provides a convenient mean in estimating particle in-situ acidity for assessing the role of acid-catalyzed aerosol reactions. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Particle acidity Ionic chemical composition Acid-catalyzed reactions PILS-IC

1. Introduction PM2.5 (particulate matter with aerodynamic diameter smaller  þ than 2.5 mm) contains SO2 4 , NO3 , NH4 , organic and element carbon as the major constituents. In many locations, there is no sufficient ammonia to fully neutralize the acidic components, making aerosol acidic. Deposition of acidic particles causes damage of building materials and adversely affects the well-being of forest and aquatic ecosystems. Particle acidity is also reported to be a contributing factor to lung and laryngeal cancers in humans (Sathiakumar et al., 1997; Hsu et al., 2008). In addition to impacts on human health and the ecosystems, particle acidity is an important factor in influencing aerosol-phase chemistry and uptake of gaseous species by particles. For example, aerosol acidity is critical to the conversion of insoluble iron to soluble Fe, the deposition of which provides a major source of bioavailable iron for organisms in the ocean and thereby affects the

* Corresponding author. Department of Chemistry, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. E-mail address: [email protected] (J.Z. Yu). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.09.040

ocean’s primary productivity (e.g., Luo et al., 2005). Acid-catalyzed heterogeneous reactions have been reported to contribute to secondary organic aerosol (SOA) formation in chamber studies (e.g., Jang et al., 2002). Increase in SOA mass and certain SOA products (e.g., organosulfates) were observed with enhanced aerosol acidity in chamber studies of SOA formation from a number of common volatile organic compounds such as isoprene (Surratt et al., 2007a, 2007b), limonene (Iinuma et al., 2007), and b-caryophyllene (Chan et al., 2011). However, the link between particle acidity and SOA formation in ambient environment is less clear. Chu (2004) observed simultaneous increase of OC and 24-h average particle acidity during regional high PM2.5 episodes in Eastern U.S. Nopmongcol et al. (2007) considered acid-mediated organic aerosol formation in their model simulation of a wood smoke episode in Houston and concluded that acid-catalyzed reactions of carbonyls might contribute to SOA formation. In a few studies using semicontinuous or high time resolution continuous measurements, no or unclear enhancement of organics was observed in the presence of acidic aerosol (Zhang et al., 2005, 2007; Takahama et al., 2006; Tanner et al., 2009; Minerath and Elrod, 2009). Zhang et al. (2007) suggested that the lack of observation of SOA enhancement by particle acidity was possibly due to ambient particles not as acidic

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as those encountered in the chamber studies. The discrepancies between chamber and field observations demonstrate the need for the determination of particle acidity in ambient environments. There are two indicators of particle acidity, strong acidity, denoted as [Hþ]s hereafter, and in-situ acidity. Strong acidity (nmoles m3) is free hydrogen ion (Hþ) and hydrogen ion available from either undissociated sulfuric acid or undissociated bisulfate ion (USEPA, 1999). It is measured by titration or by measuring pH of the aerosol extract in an aqueous solution of perchloric acid at a pH of 4.00. It can also be determined from concentrations of non-Hþ cations and strong acid anions on the basis of charge balance (Ito et al., 1998). Strong acidity gives an indication of the total Hþ amount that can be made available when the particles are removed from the atmosphere (Takahama et al., 2006). It is a good parameter to indicate heath effects of acidic aerosol and impact of acid deposition on ecosystem; however, it is inadequate in studying how aerosol acidity affects aerosol-phase chemical reactions or how aerosol surface affects mass transfer between gas and aerosol phases. These processes are more directly linked to the actual pH in the aqueous phase on particles (i.e., in-situ pH, termed pHIS hereafter), which also strongly depends on the liquid water content (LWC) on the particles. Direct measurements of pHIS have not been realized yet. An indirect mean is to calculate from aerosol LWC and Hþ amount (AHþ) for a given aerosol population. Both LWC and AHþ can be inferred from aerosol ionic chemical composition data. A number of thermodynamic models have been developed since the mid-1980s, including EQIL (Bassett and Seinfeld, 1983), MARS (Saxena et al., 1986), SEQUILIB (Pilinis and Seinfeld, 1987), ISORROPIA (Nenes et al., 1998), and Aerosol Inorganic Model (AIM) (Clegg et al., 1998a, b). Of the various thermodynamic models, AIM was considered to be the most accurate in describing the thermodynamics of gas-particle system (Seinfeld and Pandis, 2006; Yao et al., 2006). For this reason, AIM has been selected in this work. Past studies on particle composition and acidity estimation mostly rely on filter-based measurements. The approach using filter-based measurements for acidity estimation has two shortcomings due to the typical need for sampling for 12 h or longer to collect enough material for offline analysis. First, significant changes in aerosol chemical composition and LWC, therefore pHIS, in time periods of less than 1 h could be common. The average acidity values over a coarse time resolution such as 12 h or longer would be inadequate for investigating effects of particle acidity on heterogeneous chemical processes. Second, the accurate determination of nitrate is crucial for particle acidity estimation. It is well known that filter-based nitrate determination is subjected to various sampling artifacts due to gas-particle interactions and/or evaporation of semi-volatile (Schaap et al., 2004). Nitrate underestimation caused by evaporation loss due to temperature variation during sampling may result in significant underestimation of particle acidity. In this work, a continuous system, PILS (particle-into-liquid system) coupled to two ion chromatographs (IC), was used to obtain PM2.5 ionic chemical composition at a time resolution of 30 min in Hong Kong under three different synoptic conditions. The objective of this study is to use these data, in conjunction with meteorological parameters, to examine [Hþ]s and pHIS, in PM2.5 and their diurnal variations. Results from this work provide basic data for assessing impact of acid deposition and on SOA formation in this region.

in Supplementary materials). The PRD region is among the more economically developed regions in China and has the reputation of being called China’s world factory. Under influence of regional transport, pollution in the PRD has a large impact on PM2.5 and other pollutant levels in HK. Aerosol sampling was conducted on the campus of the Hong Kong University of Science and Technology (HKUST). The site is a few kilometers away from commercial centers and dense residential areas in the city. The sampling systems were placed on rooftop of an academic building, 20 m above the ground and 65 m above the sea level. Meteorological data were obtained from instruments located on top of a separate building 100 m away. Three sampling periods are discussed in this work: (I) 21e25 October 2008; (II) 6e13 November 2008; (III) 29 Junee3 July 2009. Back trajectory analysis (Draxler and Rolph, 2003) was conducted at 500 m height for every sampling period to examine the aerosol mass origins. The back trajectories are shown in Fig. S1b. During Period I, air parcels initially originated from the Pacific Ocean to the East, then shifted northward passing the northeast part of the PRD. In Period II, cold air masses originated from the inner Mainland dominated HK, causing rapid drop in temperature and relative humidity (RH). In Period III, air masses affecting HK arrived from the South China Sea.

2. Experimental section

2.3. Validation of PILS measurements

2.1. Sampling periods and location

The PILS measurements were validated against filter-based offline measurements. Sampling inlets were >2 m above ground and 5 m apart to avoid airflow interference. Two filter-based setups were used in the validation experiments. They consisted of

Hong Kong (HK) is located at the edge of the Pearl River Delta (PRD) in southeast China, extending to the South China Sea (Fig. S1a

2.2. Measurements of ionic chemical composition by PILS A PILS-IC system (Weber et al., 2001; Orsini et al., 2003) was used to obtain PM2.5 ionic chemical composition. Ambient air was drawn into PILS at a flow rate of 16.0 L min1. Fitted upstream of the PILS were a cyclone (URG, Chapel Hill, USA) to remove particles large than 2.5 mm in aerodynamic diameter and two denuders (URG, 3 channels and 242 mm long). The first denuder was coated with 2% Na2CO3/2% (w/v) glycerol in a mixed solvent of water and methanol (50%:50%, v/v) to strip gaseous SO2, HNO3, and HCl. The second denuder was coated with 3% (w/v) citric acid in methanol containing 2% (w/v) glycerol to remove NH3. The IC detection system consists of an anion IC (Metrom model 761 with suppressor, coupled with a Supp 1e250 mm column) and a cation IC (Metrom model 861 without suppressor, coupled with a C4-150 mm column). The PILS was operated to provide analysis of one sample per 30 min, with 8 min air sampling for each sample. A 1-ml sampling loop was used. Detection limits (DLs) are calculated to be the average concentration plus three times of standard deviations of blank for each ion. The DL values are 0.02 mg m3 for Cl, 3 3 for SO2 for Kþ, 0.08 mg m3 for NO 3 , 0.04 mg m 4 , <0.01 mg m þ  3 þ 3 0.09 mg m for Na , and 0.12 mg m for NH4 . Three ions, NO2 , Ca2þ and Mg2þ, were not quantified in this study since their concentrations were under their respective DLs during most of the sampling time. The blank was achieved by placing one Teflon filter upstream of the inlet to eliminate the particles (Orsini et al., 2003). Measurement uncertainties in the air concentrations comprise of two parts, uncertainty in flow rate and uncertainty of the IC determination. The sampling flow rate was checked daily and found to have a variation of 3%. The IC analysis uncertainty was determined to be 5% by measuring a standard once every two days. NHþ 4 was found to have the highest overall measurement uncertainty of 8%, while the other species had an uncertainty less than 7%.

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a PM2.5 cyclone, two denuders identical to those used with the PILS system, and one or three filters. In the first set-up, only one quartz filter was used downstream the two denuders. A total of 21 samples of variable sampling times (8e24 h) were collected. With this set-up, the determination of þ þ þ is considered reliable; however, the SO2 4 , NH4 , Na , and K  collection of aerosol-phase NO 3 and Cl is known to be subjected to negative sampling artifacts due to evaporation loss (Schaap et al., 2004; Pathak et al., 2004b; Chow et al., 2005; Keck and Wittmaack, 2006). Contamination of Naþ from syringe filters used in the treatment of this batch of samples prevented the quantification of Naþ in the filters collected with this set-up. The second denuder/filter set-up employed three filters connected in tandem, one quartz filter, one nylon filter (P5PJ047, PALL Corporation, USA), and one Teflon filter (R2PJ047, PALL Corporation, USA) coated with citric acid. Gaseous HCl and HNO3 volatized from the quartz filter were collected on the nylon filter while evaporated NH3 was collected on the acid-treated Teflon filter. Seven sets of 24h samples and one set of 11.5-h samples were collected with the second set-up. The nylon filters were cleaned with distilled water before use. The Teflon filters were coated with 2% citric (w/v) and 2% glycerol (w/v) in methanol. These filters were dried in an N2 stream before use. The IC analysis of the filter samples follows the analytical procedures described in Method MLD064 (California Air Resource Board). The final water extracts (25 ml) of the quartz filters were diluted by 8 or 16 times before they were injected into IC. The purpose is to decrease the concentration of target species to the range of that calibrated by PILS. The measurement uncertainty was assessed by measuring one sample 7 times. The variations of all species were below 5%. 3. Results and discussion 3.1. Comparison of PILS and filter-based measurements Table 1 lists the comparison results of the PILS versus the filterbased measurements in this study. Multiple 30-min PILS data were averaged over the filter collection time intervals for the comparisons. Slope and intercept values in Table 1 were calculated with trust-region LevenbergeMarquardt ordinary least orthogonal distance method (Zwolak et al., 2007), which takes consideration of measurement errors in independent variables. The two sets of data are highly correlated, with r2 ¼ 0.73 for Kþ and r2 > 0.92 for other  þ  þ ions (i.e., SO2 4 , NO3 , Cl , NH4 , and Na ). The slope values of the linear regression lines of PILS data versus filter data deviate less þ þ than 10% from unity for SO2 4 , Na , and K and less than 20% from

SO2c 4 NHþ 4 d NO 3  Cl Naþ Kþe

1.06 1.14 1.19 0.84 1.07 1.01

     

0.04 0.06 0.10 0.06 0.13 0.11

R2

Nb

Conc Range (mg m3)

0.31 0.28 0.18 0.04 0.08 0.02

0.97 0.92 0.96 0.97 0.92 0.73

29 29 8 8 8 29

1.7e24.1 0.4e7.7 0.3e1.9 0.08e1.1 0.1e0.6 0.1e0.8

0.40 0.22 0.08 0.03 0.04 0.04

3.2. Particle strong acidity, [Hþ]s In this study, particle strong acidity, [Hþ]s is estimated using the following equation:

h i i h i h i h i h i h i h  þ H þ ¼ 2 SO2 þ NO  NHþ  Kþ 4 3 þ Cl 4  Na s

(1)

 þ  þ þ where [Hþ]s, [SO2 4 ], [NO3 ], [Cl ], [NH4 ], [Na ], and [K ] are the air 3 þ concentrations in nmol m . In some studies, K was not included in the calculation of [Hþ]s due to its typically low concentrations. If Kþ is neglected, [Hþ]s could be overestimated by w7 nmol m3 in our samples. We also found it is necessary to include Naþ and Cl. Otherwise, the calculated [Hþ]s could be overestimated by >100% in samples characterized with clearly Cl deficit. This formula (Eq. (1)) ignores the organic anionic species (e.g., formate, acetate, oxalate) that are counter-balanced by Naþ, Kþ, and NHþ 4 ; nevertheless, this approximation has been shown to be in agreement with the standard method and are widely used (Kerminen et al., 2001; Pathak et al., 2004a; Schwab et al., 2004; Ziemba et al., 2007). The average [Hþ]s values of PM2.5 in the three sampling periods were 89 nmol m3 in the fall period, 48 nmol m3 in the cold frontinfluenced period, and 27 nmol m3 in the summer period (Table 2). These values were of the same magnitude as those estimates (27, 49 and 97 nmol m3) derived from filter-based measurements made at three locations in Hong Kong during year 2000e2001 reported by Pathak et al. (2003). [Hþ]s observed in this study is also comparable with those (28e101 nmol m3) observed in other suburban areas round the world (Keeler et al., 1991; Liu et al., 1996; Zhang et al., 2007), but higher than those in the urban areas (ranging from being alkaline to 44 nmol m3) (Koutrakis et al., 1988; Lee et al., 1993, 1999; Chang et al., 2007). This suburbaneurban difference may be due to higher level of NH3 in

Sampling Period

Intercepta      

þ  unity for NO 3 , Cl , and NH4 . The intercepts are 0.31  þ  0.40, 0.28  0.22, 0.18  0.08 mg m3 for SO2 4 , NH4 and NO3 , 3  þ þ respectively and <0.1 mg m for Cl , Na and K . The comparison data shows that the PILS system can provide reliable continuous measurements of major soluble ions in the ambient aerosols. We also evaluated the impact of the analytical uncertainty in PILS on particle in-situ pH estimates (Section 3.3). It was found that the 2 moderate overestimation of NHþ 4 and SO4 by PILS in comparison with the filter-based measurements would lead to an underestimation of hourly in-situ pH of PM2.5 by 0.03 unit, a rather small value compared with the range of in-situ pH encountered in our samples.

Table 2 Summary of meteorological data, ionic chemical compositions, and particle acidity in three sampling periods of different synoptic conditions.

Table 1 Comparison of PILS and filter-based measurements. Slopea

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a Calculated using trust-region LevenbergeMarquardt ordinary least orthogonal distance method. b Number of filter samples used for comparison. c Orsini et al. (2003) reported a slope value of 1.03  0.02 and Hogrefe et al. (2004) reported slope values of 0.71, 0.85, 1.11, and 1.15 for comparison of sulfate measurements by PILS versus filter-based integrated samplers. d Hogrefe et al. (2004) reported a slope value of 1.05 for comparison of nitrate measurements by PILS versus filter-based samplers (with denuder installed upstream). e Measurements

Period I 21e25 Oct. 2008

No. PILS samples 127 T (K) 298.15  1.79 RH (%) 77  6 þ 3 89.48  42.96 [H ]s (nmol m ) 20.24  13.56 Free Hþ (nmol m3) 1.96  0.96 LWC (mmol m3) pHIS 0.59  0.43 Ionic strength (M) 15.06  4.63 3 2 18.37  5.03 SO4 (mg m ) 3 3.86  4.14 NO 3 (mg m )  3 0.33  0.22 Cl (mg m ) 3 5.89  2.26 NHþ 4 (mg m ) 0.87  0.24 Naþ (mg m3) 0.33  0.17 Kþ (mg m3) 2 [NHþ 1.71  0.46 4 ]/[SO4 ] (mol m3/mol m3)

Period II 6e13 Nov. 2008 180 292.46 48 48.28 3.28 0.11 0.45 34.48 8.40 1.21 0.11 2.60 0.17 0.55 1.62

             

Period III 29 Jun.e3 Jul. 2009

213 2.21 301.63  1.12 9 75  6 18.65 27.38  13.18 3.69 9.07  1.12 0.10 0.39  0.12 0.59 0.08  0.81 10.51 11.66  2.62 3.43 3.22  0.93 0.39 0.46  0.20 0.08 0.39  0.12 1.14 0.81  0.37 0.14 0.29  0.10 0.20 Below detection 0.20 1.32  0.33

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urban areas, resulting from various human activities (Atkins and Lee, 1993; Liu et al., 1996). þ Fig. 1a shows the scatter plot of SO2 4 versus [H ]s of all the samples. [Hþ]s showed a strong correlation (r2 ¼ 0.62) with SO2 4 . That is expected because SO2 4 contributes 84  7% of the total anion equivalent concentration. The slope value of the linear regression between molar concentration of [Hþ]s and SO2 4 is 0.40, 2 indicating that HSO 4 and SO4 were the dominant forms of S (VI) on PM2.5. NHþ 4 was the major neutralizing component of PM2.5 and 2 highly correlated with SO2 4 (r ¼ 0.95, the slope: 1.76, mole based). þ Time series of [H ]s in the three sampling periods are shown in Figs. S2eS4. Significant variation of [Hþ]s was observed among the three sampling periods. Period I was associated with the highest [Hþ]s, with an average value of 89.5  43.0 nmol m3 (average  standard deviation), about 2 times and 3 times of those in period II (48.3  18.6 nmol m3) and period III (27.4  13.2 nmol m3), respectively. The highest [Hþ]s (205 nmol m3) peaked at 0:00 on October 25. Increase in inorganic soluble species, most notably for SO2 4 , was also observed at this time (Fig. S2). This increase in major ionic aerosol components coincided with shift in the air mass origin from the Ocean to the northeastern part of the PRD (Fig. S1b), bringing in more polluted air in the region. Our PILS data in Period I confirmed the important regional influence observed in previous filter-based measurements (Louie et al., 2005a, 2005b). þ During Period II, the concentration of SO2 4 and NH4 , as well as þ [H ]s, were much lower than those in Period I, except for Kþ (Table 2 and Fig. S3). The reason is most likely that stronger wind

[H + ]s (nmol m -3)

a

300 y = 0.40 x + 12.97

period I period II period III

2

R = 0.62

200

100

0 0

100

200

300

400

200 300 sulfate (nmol m -3)

400

sulfate (nmol m -3) Proton activity in aerosol aqueous phase (M)

b

100 period I period II period III

80 60 40 20 0 0

100

Fig. 1. (a) Scatter plot of strong acidity versus sulfate demonstrates positive correlation between the two. (b) Scatter plot of proton activity, aHþ, in aerosol solution versus sulfate shows a lack of correlation between the two, as aHþ is also strongly influenced by LWC.

(3.3  2.6 m s1) during this time effectively dispersed pollutants. In comparison, Period I experienced much weaker wind (1.2  0.9 m s1). The stronger wind was caused by a cold front, as evidenced by a rapid drop in temperature and RH. The average [Hþ]s in particles collected in Period III was 27.34  13.2 nmol m3, about one-third and half of the values observed in Periods I and II, respectively. In addition, a lower [NHþ 4] to [SO2 4 ] ratio (1.32  0.33) was observed during Period III than those in Period I (1.70  0.52) and Period II (1.61  0.19). The lower [Hþ]s during Period III could be explained if one considers that air parcels arriving HK during this time were clean air from the South China sea, low in the anthropogenic acidic constituents such as sulfate and nitrate and their precursors. In summary, the levels of [Hþ]s depends on the concentrations of the acidic constituents and the relative abundance of the acidic and the basic constituents, which in turn are influenced by emission sources, atmospheric chemistry, and meteorological conditions. 3.3. Particle in-situ acidity and in-situ pH Particle in-situ pH (pHIS) is calculated from Eq. (2),

pHIS ¼ logaHþ ¼ log½gHþ  nHþ  1000  r=m

(2)

where aHþ is activity of Hþ in mol L1 in the aqueous phase on the particle, gHþ is the activity coefficient of Hþ, and nHþ is free Hþ in the unit of mol m3 of air. r and m are density in g cm3 and air concentration in g m3 of the aerosol aqueous phase, respectively. gHþ, nHþ, and LWC are calculated using AIM. m is taken to be the sum 2 of concentrations of all ionic solutes plus LWC. NHþ 4 and SO4 are the major ionic species. The average molar ratio of the two was found to be 1.52  0.39, therefore the aqueous particle phase could be approximated to be that of (NH4)3H(SO4)2 aqueous solution. r was estimated with solution density empirical equations provided by Tang and Munkelwitz (1994). The estimated r for individual particle solutions varied from 1.10 to 1.44 g cm3, with an average of 1.26 g cm3. 3.3.1. AIM model Detailed description of AIM is given by Clegg et al. (1998a,b). There are two versions of AIM, AIM-II and AIM-III. AIM-II considers  þ þ a SO2 4 eNO3 eNH4 eH system and allows variable temperature, while AIM-III also considers Naþ and Cl in addition to  þ þ SO2 4 eNO3 eNH4 eH , but only allows modeling at a fixed temperature of 298.15 K. Sensitivity tests were conducted to evaluate the performance of the two AIM versions on estimation of pHIS. The results indicated that assuming a fixed temperature at 298.15 K affects little the calculated pHIS while the omission of sea salt ions would lead to over/underestimation of pHIS by about 0.3. Conse quently, in this study AIM-III, which runs with a SO2 4 eNO3 e þ þ þ  NH4 eH eNa eCl system at a fixed temperature of 298.15 K, was used so that the contribution of sea salt ions could be accounted for. In the calculation using AIM-III, a deliquescent mode is adopted. That is, particles are assumed to exist as aqueous solution at ambient RH above the deliquescence RH (DRH) of the solid phase (e.g., w40% for NH4HSO4 and w80% for (NH4)2SO4 at 25  C) and as solids below DRH. When the RH is below DRH, particles would crystallize and no particle liquid water content (LWC) would exist. Consequently, pHIS becomes undefined. For this reason, 23 out of 180 samples collected in Period II were excluded when we calculated particle in-situ parameters in this study. Supersaturated solutions are not considered in this study. 3.3.2. Particle in-situ pH in different sampling periods Particle pHIS and other relevant parameters in the three sampling periods are given in Table 2. The particle pHIS ranged

J. Xue et al. / Atmospheric Environment 45 (2011) 7081e7088

from 1.87 to 3.12. Fig. 2 shows the occurrence frequency distribution of pHIS in 0.4 pH unit intervals in the range from 2.0 to 3.2. The most frequent pHIS values appear in the range of 0.4e0.8 in Period I, 0e0.4 in Period II, and 0.4 to 0 in Period III. The pHIS estimates indicated that the PM2.5 particles in HK are highly acidic. These values are within the range of those reported in the previous studies that investigated pHIS using filter-based measurements. Yao et al. (2006) evaluated pHIS of PM2.5 collected at three locations in 2000e2001 in HK using different thermodynamic methods, including AIM-II with gas aerosol partitioning calculation disabled, ISORROPIA and SCAPE2. They estimated an average pHIS of 0.4 and a range of 2.2 to 1.1. Pathak et al. (2004a) derived PM2.5 ionic chemical composition from the measurements of PM10 at six sites in HK in 2001and reported a higher average pHIS value of 0.25 and a range of 0.62 to 2.35. Fridlind and Jacobson (2000) estimated the pHIS in fine mode SO2 4 aerosols in a remote marine environment to be mostly between 0 and 2 but could be as low as 0.8. Meng and Seinfeld (1994) estimated pHIS in California, USA to be between 1 and 3. Period I was characterized with the highest levels of [Hþ]s and major ion constituents, but it was associated with the lowest particle in-situ acidity (or the highest pHIS). The contradiction in the two indicators of particle acidity could be explained by the dominant dilution effect resulting from the high LWC on PM2.5 in this period. The average LWC during Period I was w18 times that in Period II and 5 times that in Period III. The higher water content dilutes molarity of acidic solution on aerosols. This example also illustrates that high concentrations of particle Hþ do not necessarily translate to high particle in-situ acidity. It would be inappropriate if one uses strong acidity to represent in-situ particle acidity in ambient samples when investigating acid-catalyzed reactions on aerosol surfaces. Period II had the highest particle in-situ acidity, with an average pHIS of 0.45  0.59, significantly lower than the values in Period I (0.59  0.43) and Period III (0.08  0.81). The highest pHIS was less than 0.4 throughout the whole Period II, indicating long duration of strong particle acidity. In Period II, a cold front intruded HK, evidenced by a sudden drop of temperature and RH (Fig. S3). The low RH condition (average RH ¼ 48%) was primarily responsible for the high particle in-situ acidity. In Period III, the calculated pHIS fell between those encountered in Periods I and II. This was a result of the combined effects of the LWC and relative abundance of acidic and basic constituents. In this 2 period, aerosols were characterized by the lowest [NHþ 4 ] to [SO4 ] ratio, indicating the lack of NH3 in neutralizing acidic components. Although the RH conditions were similar to those in Period I (w75%), the major ionic species were much lower, leading to significantly lower LWC in Period III. 3.3.3. Factors affecting particle in-situ pH At a fundamental level, pHIS is determined by LWC and the amount of Hþ in the aerosol solution. The important role of LWC in determining pHIS is demonstrated by the lack of correlation

30 20

120

Period II

Period III

50 40 30 20

10

10

0

0

no LWC in 23 cases

No. of cases

40

between aHþ and [SO2 4 ] (Fig. 2b), in contrast to the strong correlation between [Hþ]s and [SO2 4 ] (Fig. 2a). LWC on a particle is strongly RH-dependent and affected by chemical composition of the water-soluble fraction of the particle mass. The amount of Hþ in the aerosol solution is strongly dependent on the relative abundance of acidic and basic constituents in the aerosol solution. Pathak et al. (2004a) simplified aerosols to be made of SO2 4 and þ NHþ 4 and demonstrated that pHIS is highly dependent on [NH4 ]/ 2 þ 2 [SO4 ] and RH. The lower the [NH4 ]/[SO4 ] is, the more acidic the aerosol solution is. This could be easily understood in the simplified þ system that only contains SO2 4 and NH4 . However, in real atmois also an important acidic component, spheric particles, NO 3 especially in urban areas (Kaneyasu et al., 1999; Pathak et al., 2011). If we take as an example PM2.5 samples collected in the early morning (from 2:00 to 6:30) of 23 October, [NO 3 ] dramatically increased during this time and exceeded [SO2 4 ]. Since more NH3 þ dissolved in the aerosol aqueous phase to neutralize NO 3 , [NH4 ]/ [SO2 4 ] during this time reached 3.38  0.53, much higher than the average value of 1.60  0.31 during the rest time in Period I. During þ 2 the high NO 3 hours, apparently the [NH4 ]/[SO4 ] ratio is not adequate to describe pHIS. We subsequently evaluate the effect of NO 3 on pHIS by considþ  ering SO2 4 eNH4 eNO3 aerosol solutions. We examined the variation of pHIS as a function of the degree of SO2 4 being replaced by þ þ NO 3 while keeping [NH4 ] and [H ]s fixed and RH at 80%. The reference aerosol composition adopts the average measurements of ion species in all of our samples. The pHIS corresponding to the reference composition is 0.30. Our calculation indicates that the in-situ acidity increases as the aerosol ion composition shifts to higher nitrate scenarios while maintaining the same [NHþ 4 ] and [Hþ]s. When half of SO2 4 in the reference aerosol composition is replaced by NO 3 , the pHIS dropped by w0.7 pH unit to 0.41. The underlying reason is that the part of strong acidity attributed by 2 þ bisulfate ion (HSO 4 ) would become free H as SO4 is replaced with  NO3 . The result indicates particles that under conditions of the þ  same [Hþ]s, a SO2 4 eNH4 eNO3 particle would be more acidic than 2 þ a SO4 eNH4 particle. Hence, NO 3 must be considered in formulating relationships linking pHIS with aerosol ionic constituents. We next use Principal Component Analysis (PCA) to identify suitable indicators to describe pHIS. PCA is a commonly used statistic method that reduces data dimensionality by performing a covariance analysis between factors. In this study, PCA is performed on nine variables and 497 samples. The nine variables include RH, pHIS, Rþ/ (equivalent ratio of total cations to anions), 2 2 RN/S (i.e., [NHþ 4 ]/[SO4 ] by mole), and mole percentages of SO4 , þ  þ ¼, P , P , P þ , Cl , NH , and Na (denoted as P , and P NO 3 4 SO4 NO3 Cl NH4 Naþ , respectively). Mole percentages rather than their mole concentration were used because correlation analysis shows that the mole percentages are more related with particle pHIS. The PCA analysis yielded four principal components, explaining more than 99.5% of variance of each variable except for PNaþ (89%) and PCl (92%) (Table S1). pHIS has high loadings in factor 2 and factor 4. In factor 2, pHIS, Rþ/ and RN/S show high positive loading values (>0.75) while

60

Period I

50

No. of cases

No. of cases

60

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80

40

0

-2 -1.6-1.2-0.8-0.4 0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2

-2 -1.6-1.2-0.8-0.4 0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2

in Situ pH

in Situ pH

-2 -1.6-1.2-0.8-0.4 0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2

Fig. 2. Occurrence frequency distribution of pHIS in three sampling periods.

in Situ pH

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PSO¼4 shows a high negative loading of 0.80. A negative loading value for SO2 4 is expected because pHIS is negatively correlated with PSO¼4 Of these variables, Rþ/ has the highest loading (0.841), indicating that Rþ/ may be considered as a more robust parameter linking aerosol ionic composition to particle pHIS. Rþ/ and RN/S co-vary as the NHþ 4 is the major cation and sulfate is the major anion in a majority of samples. In factor 4, both RH and pHIS have high loadings, signaling that RH is another variable that controls ambient particle pHIS. Subsequently, pHIS is regressed as a function of Rþ/ and RH for samples collected in all the three sampling periods. pHIS is highly correlated with Rþ/ and RH, with R2 ¼ 0.93. The regression equation is.

pHIS ¼ 4:94Rþ= þ 3:11 RH  5:70

(3)

Alternatively, pHIS can be expressed as a function of RN/S and RH:

pHIS ¼ 1:34RN=S þ 3:25 RH  54:24

(4)

Fig. 3 plots the calculated pHIS by Eqs. (3) and (4) versus the AIM-derived pHIS, demonstrating the good agreement between the two and that pHIS can be well approximated by both equations. In comparison, performance of Eq. (3) is significantly better, with slope and R2 near unity (Fig. 3). It is noted that for three samples, marked in a circle in Fig. 3a, their approximated pHIS values by Eq. (3) are w1.3 units lower than the AIM-derived pHIS values. These exceptional samples were collected during 8:00 am to 10:00 am on 30 June 2009 and characterized by exceedingly lower [Hþ]s (<2 nmol m3) and free Hþ (close to 0 nmol m3) values. In these samples, only a very small

Calculated pH IS using linear regression (Eq. 3)

a

Linear regression variables: R +/- and RH 5 4 y = 0.95 x + 0.00 3 R2 = 0.93 2 1 0 -1 -2 -3 -3 -2 -1 0 1 2 3 4 5 AIM-derived pH IS

Calculated pH IS using line regression (Eq. 4)

b

Linear regression variables: R N/Sand RH 5 4 3 2 1 0 y = 0.75 x - 0.01 -1 R2 = 0.75 -2 -3 -3 -2 -1 0 1 2 3 4 5 AIM-derived pH IS

Fig. 3. Comparison of calculated pHIS using the two regression lines with the AMI-derived pHIS.

fraction (<1%) of sulfate existed as bisulfate. The deviation was due to the large uncertainty in the calculated [Hþ]s. This result indicates Eq. (3) would underestimate pHIS when applied to neutralized particles. There are eight cases (marked in the larger circle in Fig. 3b), in which the Eq. (4)-derived pHIS values are significantly overestimated in reference to the AIM-derived pHIS values. These samples were collected on the early morning of October 23, a period characterized by high concentrations of PM2.5 NO 3 (Fig. S2). As our earlier analysis has indicated, their deviations from the Eq. (4) regression line could be attributed to the overlook of NO 3 in Eq. (4). The extra exceptional cases using Eq. (4) indicates that Rþ/ is a more robust indicator than RN/S for predicting particle pHIS. The predicting capability of the regression equation (Eq. (3)) was evaluated by randomly selecting 90% of the total of 498 samples to obtain a regression line and subsequently applying the regression equation to the remaining 10% samples. The average bias, calculated to be the difference between AIM-derived pHIS and pHIS predicted by the regression equation, was less than 0.05 pH units in all three testing cases. The results indicate that the linear regression equation using Rþ/ and RH as variables can well estimate fine particle pHIS. The strong dependence of pHIS on RH implies that the in-situ particle acidity would tend to be higher during the day and lower during the night following the typical diurnal pattern of RH being low in the daytime and high in the nighttime. Such a predicted diurnal variation in pHIS was generally observed (e.g., Figs. S3 and S4), although there were some exceptions to this diurnal pattern when the influence of Rþ/ dominated that of RH (e.g., on some days in Period I, see Fig. S2). The higher particle acidity (i.e., lower pHIS) in the middle of the day may suggest that particle acid-catalyzed reactions that generate SOA may be more prominent in the middle of a day. We note that in our calculation of pHIS using AIM, organic aerosol (OA) components have not been considered. Neither did previous studies on aerosol acidity (e.g., Pathak et al., 2004a, 2004b; Zhang et al., 2007; Yao et al., 2006). The reasons are given below. Certain OA components, such as organic acids, may contribute free [Hþ] and affect partitioning/dissociation of inorganic species on acidic particles. In a recent paper, Huang et al. (2010) reported that oxalic acid, the most abundant single OA compound identified so far (Huang and Yu, 2007), contributed less than 3% to the total free acidity in rainwater collected in a city adjacent to Hong Kong during five years’ observation. Such rainwater data implies the contribution of organic acids to aerosol free [Hþ] is most likely minor. Some recently identified organosulfates in ambient aerosols are mono-esters (i.e., R-OSO3H) (Surratt et al., 2007b), leaving one proton associated with the sulfate group. The acidity contribution from this type of organosulfates was not considered in our calculation. Measurements of organosulfates in ambient samples were reported in recent literature, typically at the level of a few 10 ng m3 or lower for individual organosulfate compounds (e.g., Chan et al., 2010; Olson et al., 2011). An organosulfate monoester at 10 ng m3 would release w0.1 nmol m3 Hþ if we assume a lower-end molecular weight of 155 (i.e., the molecular weight for glycolic acid sulfate, a C2 organosulfate). This level was more than two orders of magnitude lower in comparison with the [Hþ]s concentrations estimated from inorganic anion and cation balance (Table 2). As such, the contribution to free Hþ due to dissociation of organosulfates is likely small. Hygroscopic organic aerosol components could also affect pHIS through enhancement of water uptake. Ansari and Pandis (2000) found that water absorption by SOA was significantly less than the common atmospheric inorganic salts such as (NH4)2SO4 and NaCl. In summary, OA could influence pHIS through multiple ways, but there is not yet evidence to suggest a dominant influence from OA on pHIS.

J. Xue et al. / Atmospheric Environment 45 (2011) 7081e7088

4. Summary In this study, a PILS-IC System was used to obtain PM2.5 ionic chemical composition at a time resolution of 30 min at a suburban site in Hong Kong during three different types of synoptic conditions, corresponding to a high pollution period, a cold frontimpacted period, and a low-pollution period. Comparison of the PILS-IC measurements with those integrated filter samples showed that the PILS system provides reliable continuous measurements of major soluble inorganic ions in the ambient aerosols of Hong Kong. The high-temporally resolved chemical composition data and meteorological parameters (e.g., temperature, relative humidity) were used for studying particle strong acidity and particle in-situ pH on PM2.5 in Hong Kong. Particle strong acidity was dominated by sulfate concentration in the PM2.5 samples. It was on average 89 nmol m3 in the fall period of high pollution, 48 nmol m3 in the cold front-impacted period in November, and 27 nmol m3 in the summer low-pollution period. The in-situ pH on PM2.5 sampled in Hong Kong was estimated using Aerosol Inorganic Model. It ranged from 1.87 to 3.12, with an average and standard variation of 0.03  0.77, indicating the PM2.5 particles in HK are highly acidic. The major factors that control in-situ pH were identified using principal factor analysis to be the equivalent concentration ratio between cations and anions (i.e., Rþ/) and relative humidity. In-situ particle pH, despite under different synoptic conditions, could be well approximated by a single linear regression equation (slope: 0.95, R2: 0.93), i.e.,

pHIS ¼ 4:94Rþ= þ 3:11 RH  5:70: The equation is not valid under ambient RH lower than deliquescence RH of NH4HSO4 (w37% at 30  C and 48% at 0  C), as pHIS is undefined under these conditions. pHIS estimates by this equation would also have large uncertainties for particles that are close to be fully neutralized (with [Hþ]s<2 nmol m3). Under conditions of relative stable aerosol ionic composition, the diurnal variation in RH would drive the particle in-situ acidity to be higher during the day and lower during the night. Such diurnal variation in pHIS would in turn have implications for acid-catalyzed aerosol-phase processes, such as acid-catalyzed secondary organic aerosol formation. In summary, such an empirical equation provides a convenient mean in estimating particle in-situ acidity for assessing the role of acid-catalyzed aerosol reactions. Acknowledgments This work was supported by the Research Grants Council of Hong Kong (615406) and Hong Kong University Grant Council Special Equipment Grant (SEG HKUST07). Appendix. Supplementary material Supplementary material related to this article can be found online at doi:10.1016/j.atmosenv.2011.09.040. References Ansari, A.S., Pandis, S.N., 2000. Water absorption by secondary organic aerosol and its effect an inorganic aerosol behavior. Environmental Science & Technology 34, 71e77. Atkins, D.H.F., Lee, D.S., 1993. Indoor concentrations of ammonia and the potential contribution of humans to atmospheric budgets. Atmospheric Environment 27A, 1e7. Bassett, M.E., Seinfeld, J.H., 1983. Atmospheric equilibrium model of sulfate and nitrate aerosols. Atmospheric Environment 17, 2237e2252. Chan, M.N., Surratt, J.D., Claeys, M., Edgerton, E.S., Tanner, R.L., Shaw, S.L., Zheng, M., Knipping, E.M., Eddingsaas, N.C., Wennberg, P.O., Seinfeld, J.H., 2010. Characterization and quantification of isoprene-derived epoxydiols in ambient aerosol

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