Environ. Sci. Technol. 2009, 43, 1336–1341
Distribution of PAHs in pine (Pinus thunbergii) needles and soils correlates with their gas-particle partitioning Z H E N W A N G , †,‡ J I N G W E N C H E N , * ,† PING YANG,† FULIN TIAN,† XIANLIANG QIAO,† HAITAO BIAN,† AND LINKE GE† Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Department of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China, and National Marine Environmental Monitoring Center, Dalian 116023, China
Received July 24, 2008. Revised manuscript received December 22, 2008. Accepted December 30, 2008.
Pine (Pinus thunbergii) needles and surface soils were simultaneously sampled at 35 sites across Liaoning province, China, to investigate the distribution of polycyclic aromatic hydrocarbons (PAHs) in the two media. We hypothesized that the distribution of PAHs in soils and pine needles was related to the subcooled liquid vapor pressure (p°L) and the gasparticle partition coefficient (KP), since soils accumulate PAHs mainly through dry/wet deposition of particles and pine needles sequester PAHs mainly from the gas phase, and the same physicochemical properties (e.g., p°L) determine the characteristics of PAHs deposition to soils and to needles. To verify the hypothesis, a soil-pine needle quotient (QSP) was defined, which is a dimensionless ratio of PAH concentrations in soils and pine needles. A significant relationship between logQSP and logp°L was observed (r ) 0.94), and the variation of the regression parameters of logQSP∼logp°L and logKP∼logp°L relationships was similar. An adjusted soil-pine needle quotient (Q′SP) was defined by deducting the contributions of particle PAHs to pine needles and the vapor PAHs to soils. LogQ′SP correlated with logp°L and logarithm of the particle to gas ratio (logCP/CA) more evidently than logQSP. In addition, QSP (and Q′SP) could be used to characterize the removal factors of PAHs during atmospheric transport. All the observations proved that QSP (and Q′SP) carry the information of gas-particle partitioning and correlate with p°L.
Introduction The partitioning of semivolatile organic compounds (SOCs) between the gas and particle phases in the atmosphere is a decisive process concerning their potential to undergo longrange atmospheric transport, dry/wet deposition and chemical transformation (1, 2). The gas-particle partition coefficient (3) is defined as KP ) (CP ⁄ TSP) ⁄ CA
(1)
* Corresponding author phone: +86-411-8470 6269; fax: +86-4118470 6269; e-mail:
[email protected]. † Dalian University of Technology. ‡ National Marine Environmental Monitoring Center. 1336
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where CP (ng/m3) and CA (ng/m3) are the pollutant concentrations in the particle and gas phases at equilibrium state, respectively, and TSP (µg/m3) is the total suspended particulate matter concentration, so the unit of KP is m3/µg. It has been well documented that KP correlates with the subcooled liquid vapor pressure (p°L) or octanol-air partition coefficient (KOA) of SOCs (3-5). Generally, SOCs with logKOA < 8.5 exist mainly in vapor phase; SOCs with logKOA > 11 are principally associated with particles; and SOCs with logKOA in the range of 8.5-11 can partition between vapor and particle phases (6). The following linear free energy relationship (LFER) has been frequently used to evaluate KP for groups of structurally related compounds such as polycyclic aromatic hydrocarbons (PAHs) (3-5, 7): log KP ) m log poL + c
(2)
where m and c are regression parameters to be determined. According to Pankow (8), the slope m of eq 2 should theoretically approximate -1 at equilibrium. However, others suggested that m ) -1 was not necessarily an indicator for the equilibrium partitioning. Mandalakis and Stephanou (9) summarized the values of m from the literature and found that shallow slopes (-0.33 to -0.53) were pertinent to rural and coastal sites with lower pollutant levels and more negative values (-0.46 to -0.97) to urban areas with higher pollutant levels. Pankow and Bidleman (3) indicated that a change in the measured m value would cause a change in the measured c, and the measured m and c values exhibited some degree of interrelation. Soil is one of the major reservoirs for SOCs and receives SOCs mainly through dry and wet deposition of particles and litterfall, with dry and wet deposition of particles being the most important (6, 10-12). Several processes lead to loss or accumulation of SOCs in soils. SOCs with lower p°L or higher KOA show less volatilization from soils (10, 11). Generally, SOCs with relatively low p°L, high KOA, high octanol-water partition coefficient (KOW), and high soil organic carbon normalized sorption coefficient (KOC) are favored for accumulation in the upper-most soil layer over time (10). Excellent log/log-linear relationships between the soil/air partition coefficients and p°L (or KOA) of SOCs have been reported (13). Due to lipid-rich cuticle and stomata structures of the surfaces, plant leaves play an important role in removing SOCs from the atmosphere and have been employed as indicators of atmospheric pollution (14, 15). Hydrophobic, persistent organic contaminants reach leaves primarily from the atmosphere, with root uptake and translocation being of very limited importance (16). Many studies indicated that the measured plant/air partitioning of SOCs could be characterized with p°L or KOA (17, 18). Vapor phase SOCs can be absorbed by leaves, and particles can also be intercepted by leaves (19). Yang et al. (19) evaluated the effects of particle deposition on PAH profiles in pine needles by washing off the particles from pine needle surfaces and found that the contributions of deposited particle PAHs to the total concentrations correlated positively with logKOA. To date, pine needles have been widely used as a typical natural passive sampler to implicate the atmospheric pollution of SOCs (20, 21). Conventionally, KP is measured by using active highvolume samplers, which collect pollutants in both gas and particle phases. However, the high-volume samplers are quite expensive, labor intensive, and limited in applicability to locations where power is available. Noting that both the soil/ air and plant/air partitioning of SOCs can be characterized 10.1021/es802067e CCC: $40.75
2009 American Chemical Society
Published on Web 02/04/2009
FIGURE 1. Locations of sampling sites and total PAH concentrations in soils and pine needles at 35 sites in Liaoning province, China. by p°L or KOA (13, 15, 18), that soils accumulate SOCs mainly through dry/wet deposition of particles, and that pine needles sequester SOCs mainly from the gas phase (12, 17, 18), we hypothesized that the distribution of SOCs in soils and pine needles, which is defined by a dimensionless soil-pine needle quotient (QSP), could correlate with SOC physicochemical properties (e.g., p°L) and KP. If this is true, it would be very useful to estimate KP values just by measuring chemicals in these two media instead of measuring the chemicals in gas and particle phases. It thus became the purpose of this study to check the hypothesis by employing PAHs as target chemicals. PAHs are ubiquitous pollutants in the environment that are generally formed by incomplete combustion of fossil fuels or organic matter. PAHs have rather different patterns of distribution because their physicochemical properties span a great range. For example, logp°L (Pa) ranges from 0.71 for acenaphthene to -6.98 for dibenzo(a,h)anthracene, which allow PAHs to be presented in both gas and particle phases. We concurrently measured PAH concentrations in 35 soil and pine needle samples collected from urban to rural/ remote sites across Liaoning province, China. The relations of logQSP∼logp°L and logQSP∼logKP are investigated, and the key factors influencing the relationships are discussed.
Materials and Methods Sampling Area. Liaoning province is located in northeastern China (38°43′-43°26′N, 118°53′-125°46′E), it covers an area
of 145 900 km2 and has 42.17 million inhabitants. Liaoning is a typical traditional industrial area in China. It was estimated that annual coal and coke consumption in the region in 2004 were 139.63 and 15.14 million tons, respectively (22). In addition, biomass burning is a traditional practice for heating and cooking in the rural areas. It is thus expected that Liaoning suffers from severe PAH pollution. Sampling and Preparation. Soil and pine needle samples were collected simultaneously from 35 sites across Liaoning province in April 2005, among which 19 were from urban sites and 16 rural and remote sites (Figure 1). The urban samples were from woodland, grassland, or vegetable garden in urban and suburban areas. The rural and remote samples were from pure pine forests away from cities, roads, or other human activities. About 500 g of soil samples were collected at a depth of 0-5 cm (including the organic top layer and mineral surface horizon A, after removal of the litter layer Oi) with a stainless steel scoop. Needles of Pinus thunbergii were selected for the sampling because the pine is widely distributed in the region. Needles from 4 or 5 pine trees were collected at a consistent height of 3-5 m above the ground using pruning shears, and wrapped in foil precleaned by acetone and stored at -20 °C until further analysis. Only the one-year-old needles that were greenblack and grew around the bud scale scars on the inner branches were analyzed (Figure S1, Supporting Information (SI)). Extraction, Cleanup, and Analysis. Detailed procedures for sample extraction, cleanup, and analytical quality control for soils and pine needles are described elsewhere (19, 23). Briefly, 5 g soil samples mixed with anhydrous sodium sulfate and activated copper powder were extracted twice with 30 mL of acetone/dichloromethane (DCM) (1:1, v:v) in an ultrasonic bath for 30 min. Fresh needle samples (5 g) were extracted ultrasonically in 40 mL of hexane/DCM (1:1) and then shaken for 60 min in sequence. The extracts were concentrated with a rotary evaporator, and purified on an activated silica gel (presoaked in hexane) column with hexane/DCM (1:1) mixture. The eluate was further concentrated and solvent-exchanged to acetonitrile. PAHs were analyzed by an Agilent 1100 HPLC equipped with a variable wavelength fluorescence detector and a Supelcosil LC-PAH (250 × 4.6 mm i.d., 5 µm particle size, Supelco) column. The injection volume was 20 µL and the column temperature was 30.0 °C. The flux was 2.0 mL/min, the gradient elution program was 0-2 min, 65% water and 35% acetonitrile; 2-16 min, gradient to 100% acetonitrile. We quantified 14 PAHs: acenaphthene (Acp), fluorene (Fl),
TABLE 1. Values of Logp°L, logQSP, Median, Geometric Mean (GM), and Minimum and Maximum (Min-Max) Concentrations (ng/g dw) of PAHs in Pine Needles and Soils Collected from Liaoning Province pine needle PAHs
rings
Acp Fl Phe An Flu Pyr BaA Chr BbF BkF BaP DbA BghiP InP ΣPAHs
3 3 3 3 4 4 4 4 5 5 5 5 6 6
a
soil
a L
logp°
logQSP
median
GM(min-max)
median
GM(min-max)
-0.31 -0.67 -1.43 -1.57 -2.53 -2.75 -3.86 -3.80 -4.96 -5.21 -5.36 -6.98 -6.44 -6.33
-0.36 -0.24 -0.15 -0.13 0.17 0.34 0.46 -0.07 0.66 0.81 0.84 0.01 1.10 0.91
10 56 216 12 63 38 13 41 14 4 6 7 3 5 483
10 (3-94) 42 (4-200) 189 (19-764) 12 (3-57) 60 (8-274) 39 (5-151) 14 (3-46) 38 (6-203) 13 (4-30) 3 (1-11) 6 (2-10) 6 (3-7) 3 (1-6) 5 (2-9) 460 (66-1650)
3 20 97 8 85 74 37 29 54 23 43 6 35 36 595
4 (2-36) 24 (12-105) 126 (38-611) 9 (2-60) 93 (11-824) 79 (6-695) 41 (4-389) 27 (5-172) 63 (14-414) 20 (1-145) 38 (4-246) 7 (2-61) 31 (3-276) 34 (4-278) 631 (125-4115)
Logp°L (Pa) were calculated at 283.15 K based on Huang et al. (26).
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FIGURE 2. Average proportions of 14 PAH members in soils and pine needles (the error bars represent the geometric standard deviations, 35 sampling points). phenanthrene (Phe), anthracene (An), fluoranthene (Flu), pyrene (Pyr), benzo(a)anthracene (BaA), chrysene (Chr), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), dibenzo(a,h)anthracene (DbA), benzo(ghi)perylene (BghiP), and indeno(1,2,3-cd)pyrene (InP). Quality Assurance/Quality Control (QA/QC). Steps were taken to allow an assessment of the accuracy and reliability of the data. For every five samples, a method blank (solvent and glassware), a matrix spike, and a sample duplicate were processed together with soil (pine needle) samples by performing the entire pretreatment procedure. The average recoveries in matrix spikes for 14 PAHs ranged from 73% (BkF) to 92% (BaP) for soil samples, and from 72% (BkF) to 94% (BaP) for pine needles. The relative standard deviation of the repeatability was below 20% for both soil and pine needle samples. Limits of detection ranged from 0.30 to 0.60 ng/g for soils and from 0.10 to 0.30 ng/g for pine needles, depending on individual PAH. Results presented in this study were not blank and recovery corrected.
Results and Discussion PAH Levels and Contributions in Soils and Pine Needles. Total PAH concentrations in soils and pine needles at the 35 sites are depicted in Figure 1. The one-sample K-S test indicated that the total PAH concentrations in soils (ΣSPAHs) and pine needles (ΣNPAHs) over different sampling points were log-normally distributed (R ) 0.05) (SI Figure S2). The log-normal distribution of micropollutants in various environmental media has been repeatedly demonstrated in literature (24). Therefore, geometric means and geometric standard deviations were calculated to denote the values of the PAH concentrations in soils and pine needles. For the 35 sampling sites, ΣSPAHs varied from 125 to 4115 ng/g dw (dry weight) with a median value of 595 ng/g dw, and ΣNPAHs from 66 to 1650 ng/g dw with a median value of 483 ng/g dw (Table 1). Both ΣSPAHs and ΣNPAHs showed strong urbansuburban-rural gradients, with concentrations up to 33 times higher in the urban sites than those in the rural sites for soils, and 25 times for pine needles. The enrichment of PAHs in both soils and pine needles at urban sites demonstrates the continuing emission of PAHs in these areas, as both soils and pine needles are good indicators of the surrounding pollution of PAHs (17, 20, 23, 25). Figure 2 shows the geometric mean proportions of PAH members in soils and pine needles. Phe was the most abundant compound in soils and pine needles (43% and 21%, respectively) followed by Flu (14% and 16%, respectively). Clear differences were observed between soils and pine needles: for 3-ring PAHs, the proportions were higher in pine needles than in soils; whereas for 4-, 5-, and 6-ring PAHs, the values were lower in pine needles. The Wilcoxon signed-rank test indicated that there were significant differences between soils and pine needles for 3-, 5-, and 6-ring 1338
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FIGURE 3. Concentrations of PAHs in pine needles for the 35 sampling sits and the corresponding slopes mr of logQSP ∼ logp°L regression equations. PAHs, whereas the differences for the 4-ring PAHs and ΣPAHs were insignificant (R ) 0.05). The observations are within our expectation since PAHs with higher logp°L (such as 3-ring PAHs) mainly exist in the gas phase and can be easily sequestered by pine needles, PAHs with lower logp°L (5- and 6-ring PAHs) mainly associate with particles and can easily deposit into soils, and PAHs with median logp°L (4-ring PAHs) partition between gas and particle phases (10, 27) and contribute to soils and pine needles to a similar extent. Soil-Pine Needle Quotient (QSP) and Its Relation with Logp°L. Similar to eq 2, we employed eq 3 to evaluate the logQSP ∼ logp°L relationship: log QSP ) mr log poL + br
(3)
In eq 3, p°L were calculated based on the method by Huang et al. (26) at the average ambient temperature (10 °C) of the sampling period. Site-specific regression parameters mr, br, and r are given in SI Table S1. Mean values of mr and br together with their standard deviations for all the 35 sampling sites, lead to eq 4: log QSP ) (-0.22 ( 0.02)log poL + (–0.44 ( 0.09) n ) 14, r ) 0.94, p < 0.001 (4) More negative mr values occurred at sites associated with higher ΣNPAHs or ΣSPAHs (Figure 3). For all the sampling sites, mr correlates with ΣNPAHs (r ) -0.72, p < 0.001), and ΣSPAHs (r ) -0.50, p < 0.01) significantly (SI Figure S3-A). A weaker but significant correlation (r ) 0.48, p < 0.01) was also observed between mr and br (SI Figure S3-B), implying they exhibited some degree of interrelation. These results are consistent with the observation for gas-particle partitioning in the atmosphere reported in the literature, i.e., more negative slopes for logKP ) mlogp°L + c were observed in sites with higher pollutant levels, shallow slopes were found for sites with lower pollutant levels, and m and c were found to have some degree of intercorrelation (3, 8, 9). Both logQSP and logKP correlate with logp°L significantly, and the variation of the regression parameters of logQSP∼logp°L and logKP∼logp°L relationships is similar, implying that logQSP and logKP may carry similar information. A similar correlation between logp°L and the soil-pine needle quotients of SOCs (including PAHs) was reported by Weiss (10), who discussed the vegetation/soil distribution of SOCs in relation to their physicochemical properties and gave the following equation: log(cO ⁄ cN1) ) -0.161log poL – 0.498 n ) 36, r 2 ) 0.73 (5) where cO and cN1 were SOC concentrations in the whole humus horizon and Norway spruce needles of remote Austrian forest sites, respectively. We further calculated the site-specific
FIGURE 4. Correlations of log(CP/CA) versus logQSP or logQ′SP for the 12 PAHs. Values of log(CP/CA) were adopted from Sitaras et al. (31) and Terzi and Samara (32). regressions of logQSP∼logp°L for the 24 remote Austrian forest sites based on the PAH concentrations in Norway spruce needles and humus horizon reported by Weiss (28). The regression parameters mr, br, and r are listed in SI Table S2. A weak but significant correlation (r ) 0.48, p < 0.05) between mr and br was also observed (SI Figure S3-D), and the slopes of mr∼br regressions for this study (3.06) and Austrian forest sites (2.94) do not differ significantly (SI Figures S3-B and -D). However, the correlation between mr and PAH concentrations in Norway spruce needles was not significant (p > 0.56) (SI Figure S3-C), which was due to the very limited differences in PAH concentrations in the remote forest sites (28). Adjusted Quotient Q′SP and Its Relation with Log(CP/ CA). Recent studies demonstrated that pine needles not only absorb vapor PAHs but also intercept particles (19, 29). Besides dry/wet deposition of particles and litterfall, soils can also receive PAHs via dry gaseous deposition (13). To deduct the contribution of dry gaseous deposition to the total PAH concentrations in soils, and the contribution of particles to the total PAHs in pine needles, we defined an adjusted quotient Q′SP as follows: Q ′SP ) C ′S ⁄ C ′N ) (CS × f ) ⁄ C ′N
(6)
where CS stands for PAH concentrations in soil samples, C ′S stands for PAH concentrations in soil samples that have deducted the contributions of dry gaseous deposition, C′N is PAH concentration in pine needles after washing off the particles (19), and f is the estimated percent particulate to total airborne PAHs in the atmosphere (30), which was taken as a first rough approximation for those part of soil PAH concentration that is caused by deposition of particles. Q′SP values were calculated for the soil and pine needle samples collected from Dalian (38°14′-40°10′N), a coastal region of southern Liaoning (Figure 1). Figure 4 presents the linear relationships of logQSP (and logQ′SP) versus log(CP/CA). Log(CP/CA) was used in the regression instead of logKP since CP/CA was a dimensionless quantity similar to QSP. Due to lack of the measured log(CP/CA) values in Dalian, the log(CP/CA) data of PAHs in Greece (35-40°N) reported by Sitaras et al. (31) and Terzi and Samara (32) were adopted because of similar latitude and meteorological conditions between Greece and Dalian. Compared with the slope of the logQSP∼log(CP/CA) regression, the slope of the logQ′SP∼log(CP/CA) regression (1.02) approaches 1 more closely (Figure 4). Moreover, compared with the slope of eq 4 for the logQSP∼logp°L regression (-0.22), the slope of the following logQ′SP∼logp°L regression equation further approaches -1. log Q ′SP ) (-0.57 ( 0.06)log poL - (2.07 ( 0.26) n ) 14, r ) 0.93, p < 0.001 (7)
FIGURE 5. Correlations of removal factor Zi versus the fraction φsoil of soil PAHs over the total concentration in soils and pine needles for Liaoning (A) and Dalian (B) samples. φsoil values were calculated using the data for the rural areas. As the same physicochemical properties (e.g., p°L) determine the gas-particle partitioning of PAHs in the atmosphere and the characteristics of the deposition to pine needles and soils (10), it is within expectation that logQSP (logQ′SP) correlates with log(CP/CA) or logKP. The above results also suggest that Q′SP is more evident than QSP in characterizing the partitioning of PAHs between gas and particle phases. The observed correlations as well as the used adjustments are a valid approximation. Many factors are likely to influence the accumulation and distribution of PAHs in soils and pine needles, like meteorological conditions (temperature, wind velocity), different uptake and enrichment behavior, degradation and loss processes, etc (10). These factors may cause deviations for regression parameters of logQSP (or logQ′SP) versus log(CP/CA). Further research is needed to clarify the effects of these factors on the regression parameters. Relation of QSP with Removal Factor Z. The removal factor (Zi) of a specific PAH (i) can be described with the variation of PAH member ratios between source points and background sites (33). Previous studies indicated that Zi of PAHs during atmospheric transport depends on their physicochemical properties, and particularly KP and the particle size distribution are of importance (34). Tsapakis and Stephanou (33) calculated Zi values of PAHs during atmospheric transport from the source points (Heraklion) to the background sites (Finokalia): Zi ) (Ci ⁄ CPhe)Heraklion ⁄ (Ci ⁄ CPhe)Finokalia
(8)
where Ci and CPhe were the total (gaseous and particulate) concentrations of PAH members i and Phe. They found an exponential relationship between Zi and the fraction (φ) of particulate PAHs over the total concentrations at the background site (33). φ can be estimated from KP. The above finding gave a hint to characterize φ and Zi of PAHs during atmospheric transport using the information of their distribution in soils and pine needles given that QSP (and Q′SP) correlates with KP. To verify the assumption, Zi values were calculated from eq 9 using the means of PAH concentrations in soils (CS) and pine needles (CN) for both urban and rural areas. VOL. 43, NO. 5, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Zi )
[(CS + CN)i ⁄ (CS + CN)Phe]urban [(CS + CN)i ⁄ (CS + CN)Phe]rural
(9)
The fraction of soil PAHs (φsoil) over the total concentration of PAHs in soils and pine needles were calculated from eq 10: φsoil )
CS QSP ) CS + CN 1 + QSP
(10)
Exponential relationships between Zi and φsoil for both Liaoning and Dalian samples were also observed (Figure 5). A similar relationship between them was observed for the adjusted data of Dalian samples (SI Figure S4). Zi values for 3-ring PAHs were around 1, and significantly higher values were observed for 5- and 6-ring PAHs indicating the removal factors of 3-ring PAHs are comparable with Phe and PAHs exclusively associated with particles are rapidly removed during atmospheric transport. These are consistent with the trends observed by Tsapakis and Stephanou (33) for atmospheric PAHs in the Eastern Mediterranean. The results indicate that QSP and Q′SP are suitable for characterizing the removal of PAHs during atmospheric transport, as they bear the information of their gas-particle partitioning. This study gives clues of estimating the gas-particle partitioning of SOCs by concurrently collecting soil and pine needle samples that cover a wider spatial range and can be easily collected, and of estimating the soil and pine needle distribution (QSP or Q′SP) by the SOC physicochemical properties (e.g., p°L).
Acknowledgments We thank Dr. W. Peijnenburg and anonymous reviewers for their helpful comments on the manuscript. The study was supported by the National Basic Research Program of China (No. 2004CB418504) and the National Natural Science Foundation of China (No. 20890113).
Supporting Information Available Photograph of the sampled pine needles, detailed information about mr, br and r of the linear regression, and the correlations between Zi and φsoil. This material is available free of charge via the Internet at http://pubs.acs.org.
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