Source diagnostics of polycyclic aromatic hydrocarbons in urban road runoff, dust, rain and canopy throughfall

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Environmental Pollution 153 (2008) 594e601 www.elsevier.com/locate/envpol

Source diagnostics of polycyclic aromatic hydrocarbons in urban road runoff, dust, rain and canopy throughfall Wei Zhang, Shucai Zhang, Chao Wan, Dapan Yue, Youbin Ye, Xuejun Wang* MOE Laboratory of Earth Surface Processes, College of Environmental Sciences, Peking University, Beijing 100871, PR China Received 12 June 2007; received in revised form 3 September 2007; accepted 9 September 2007

Urban road runoff and road dust, canopy throughfall and rain were considered as a system for diagnostics of PAH sources. Abstract Diagnostic ratios and multivariate analysis were utilized to apportion polycyclic aromatic hydrocarbon (PAH) sources for road runoff, road dust, rain and canopy throughfall based on samples collected in an urban area of Beijing, China. Three sampling sites representing vehicle lane, bicycle lane and branch road were selected. For road runoff and road dust, vehicular emission and coal combustion were identified as major sources, and the source contributions varied among the sampling sites. For rain, three principal components were apportioned representing coal/oil combustion (54%), vehicular emission (34%) and coking (12%). For canopy throughfall, vehicular emission (56%), coal combustion (30%) and oil combustion (14%) were identified as major sources. Overall, the PAH’s source for road runoff mainly reflected that for road dust. Despite site-specific sources, the findings at the study area provided a general picture of PAHs sources for the road runoff system in urban area of Beijing. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: PAHs; Road runoff; Source diagnostics; Urban area

1. Introduction Due to rapid urbanization and motorization in the past decades, road storm runoff has been identified as a major cause of urban water quality degradation in both developed and developing countries (Motelay-Massei et al., 2006; Keller et al., 2005; Chen et al., 2004). Both organic and inorganic pollutants may be present in urban road runoff, including polycyclic aromatic hydrocarbons (PAHs), halogenated phenols, metals, and de-icing salts (Brown and Peake, 2006; Hoffman et al., 1984). Among these pollutants, PAHs are of particular concern in such runoff because of their relatively high concentration level, diverse sources, and toxicity to aquatic organisms (Beasley and Kneale, 2002).

* Corresponding author. Tel./fax: þ86 10 62759190. E-mail address: [email protected] (X. Wang). 0269-7491/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2007.09.004

Urban road runoff pollution has been extensively studied since 1970’s (Tsihrintzis and Hamid, 1997). Motor vehicle emission, crankcase oil leakage, vehicle tyre wearing and asphalt road surfaces erosion are the commonly recognized sources of PAHs in urban road environment (Walker et al., 1999; Harrison et al., 1996). Researchers surmised that each PAH source is featured with a specific source fingerprint profile, and the relative abundance and predominance of certain PAH species might be used in combination with auxiliary parameters to distinguish emissions from particular pollution sources (Park and Kim, 2005; Mastral and Callen, 2000). In previous studies, receptor-oriented approach that does not require prior information on source composition was widely used to determine sources (Zhang et al., 2005). However, there are two significant concerns regarding source apportionment of PAHs in road runoff in previous studies. First, the tendency of various PAHs to partition between the dissolved and particle phases may complicate the

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characteristic source signatures. Therefore, source apportionment based on either particle phase or dissolved phase PAHs may be inappropriate (Brown and Peake, 2006; Kumar et al., 2001; Chen et al., 2004). Second, road runoff is the collection of several pollutant inputs including road dust, rainfall, and canopy throughfall. Road runoff should be considered as the secondary receptor, and the PAH sources may be complicated by the features of the different inputs. In this study, receptor-oriented approaches including diagnostic ratio and multivariate analysis were utilized to apportion PAH sources for road runoff and the related inputs. The identified PAH sources for road runoff were compared with the results for road dust, rain and canopy throughfall. In order to avoid problems associated with PAHs partitioning in water, PAHs in the dissolved and particle phases in runoff, rain and canopy throughfall samples were analyzed separately and the total PAHs in both dissolved and particle phases were submitted for source identification. Beijing is a megacity under rapid development, suffering serious traffic and industrial pollution. The pollution feature of a city in fast urbanization process is different from the developed cities. Other big cities in China, such as Shanghai and Guangzhou, are under the similar development process with Beijing, and also face similar urban environmental problem. From this aspect, the methods and findings of this study can generalize to many cities in China and other developing countries. 2. Materials and methods 2.1. Sampling site and procedure A one-year storm runoff sampling campaign was conducted in 2006 in Beijing, China. Beijing is in the warm temperate zone with semiarid weather, and the raining season is mainly in spring and summer (from May to September). In 2006, road runoff occurred in six storm events, and the runoff samples were collected in each event. The study area was selected in a composite residential and commercial catchment of Beijing city. According to the Urban Planning of Beijing City (2004e2020), a trunk road (Chengfu Road, with a representative traffic volume of 30,000 vehicles per day) and a branch road (Haidian Road, with a representative traffic volume of 20,000 vehicles per day) were selected for road runoff sampling. For the trunk road, the bicycle lane and motor vehicle lane were separately sampled because the bicycle lane is separated from vehicle lane by a greenbelt, and bicycle lanes account for a significant part of roadways in Beijing. Because industries have been relocated to the suburb area of Beijing, the selected sampling sites would be possible to extend the findings of the subject area to the entire urban area of Beijing. Detailed information on the sampling sites and storm events are described elsewhere (Zhang et al., in press). Runoff sampling was performed with an automatic sampler (GRASP BC9600) equipped with pre-cleaned 3-L glass bottles. Ultrasonic Doppler sensor was used in the flow meter (GRASP WL-1A) for level measurement. The runoff sampler and flow meter were deployed at the gully of the storm sewer system. The hollow steel sampling head (1.5 cm diameter) was merged in runoff for effective sampling of suspended particles in runoff. According to the intensity of rainfall, sampling was conducted at 5e15 min intervals during the first hour and at 30 min intervals during the second and third hours. Rain and canopy throughfall were sampled simultaneously with runoff sampling. Rainwater was collected in aluminum containers and then transferred to pre-cleaned glass bottles immediately. Canopy throughfall samples were collected 1 m above ground level under a roadside tree (Sophora japonica) at the branch road. The tree was approximately 6 m in height and the leaf area index is 3.1. Road dust was sampled at the three runoff sampling sites using a vacuum cleaner prior to rainfall.

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2.2. Laboratory analysis Samples of runoff, rain and canopy throughfall were filtered through prebaked (450  C for 4 h) glass fiber filters (142-mm diameter and 0.7-mm pore size) in a stainless-steel filter holder within 24 h after sampling. The filters were freeze-dried for at least 72 h. After being freeze-dried, the filters were extracted overnight with 100 mL of dichloromethane in a soxhlet apparatus. The extracts were concentrated, solvent-exchanged to hexane, and passed through an alumina:silica (1:2) column chromatography. The column was eluted with 20 mL of hexane and then 70 ml of dichloromethane:hexane (20:50). The combined collected solvent was rotary evaporated (Mai et al., 2003). The dissolved PAHs were recovered by passing 1-L volume filtrate sample through a C18 extraction cartridge (6 mL, 0.5 g, Supelco). The cartridges were conditioned prior to use by washing five times with 10 mL of dichloromethane, activating three times with 6 mL of methanol, and then washing twice with 10 mL of deionized water. Samples were passed through the cartridges under vacuum. The trapped compounds were extracted from the sorbent cartridge with 10 mL of dichloromethane. Road dust samples were extracted in a soxhlet apparatus for 20 h with 90 mL dichloromethane:acetone (1:1) mixture. The extracts were concentrated with a rotary evaporator, solvent-exchanged to hexane, and then passed through a silica column. The column was eluted with 20 mL of hexane and 35 mL of dichloromethane. The solution was collected and then rotary evaporated. The final extracts of the runoff, rain, canopy throughfall and dust samples were concentrated to 1 mL, known quantities of internal standards 2-fluorobiphenyl and p-terphenyl-d14 were added, and then transferred into vials before instrumental analysis. The concentrations of PAHs in the extracts were determined by an Agilent 6890 gas chromatograph equipped with a 5973N mass selective detector. The identity of each PAH was confirmed using a standard PAH mixture (610/525/550 in methanol from Chem Service, U.S.) containing the 16 PAHs. The standard PAH mixture was analyzed by GC/MS (Agilent 6890 GC, 5973 MSD) at full scan mode. The molecular ions were selected as the target ion for quantification and another two or three characteristic ions were selected for confirmation. The following 16 USEPA priority PAHs were analyzed: naphthalene (NAP), acenaphthene (ANE), acenaphthylene (ACY), fluorene (FLO), phenanthrene (PHE), anthracene (ANT), fluoranthene (FLA), pyrene (PYR), benz(a)anthracene (BaA), chrysene (CHR), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), dibenz(a,h)anthracene (DahA), indeno(l,2,3-cd )pyrene (IcdP) and benzo( g,h,i)perylene (BghiP). All data were subject to strict quality control procedures. Quantification was done using an internal calibration method. The samples were spiked with five surrogate standards (naphthalene-d8, acenaphthene-d10, phenanthrene-d10, chrysene-d12, and perylene-d12). Surrogate recoveries were 48  13% for naphthalene-d8, 56  13% for acenaphthene-d10, 63  22% for phenanthrene-d10, 50  22% for chrysene-d12, and 68  26% for perylene-d12 with filtrate samples, were 43  20% for naphthalene-d8, 64  21% for acenaphthene-d10, 77  22% for phenanthrene-d10, 61  11% for chrysene-d12, and 77  18% for perylene-d12 with suspended particle samples, and were 63  22% for naphthalene-d8, 81  26% for acenaphthene-d10, 89  21% for phenanthrene-d10, 90  25% for chrysene-d12, and 80  28% for perylene-d12 with dust samples. For each batch of samples, two procedural blanks were processed, and all data were blank corrected. The relative percent difference for individual PAHs identified in method duplicate samples was <15%. The method recoveries were 22e106% for suspended particle samples, 41e117% for filtrate samples, and 58e101% for dust samples. The detection limits were 0.7e7.9 ng L1 with suspended particle samples and 0.7e2.8 ng L1 with filtrate samples for 1 L runoff samples, and were 2.6e22.8 ng g1 with road dust for 0.4 g sample.

3. Results and discussion 3.1. Concentration analysis Normality test showed that the measured PAH concentrations followed logarithmic normal distribution. Concentrations

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of 16 PAHs in the road runoff, dust, rain and canopy throughfall samples are presented in Tables S1eS3 (Supplementary material). The temporal variability of PAH concentrations in runoff, rain, canopy throughfall and road dust samples was examined using ANOVA method. Significant difference was observed between samples collected from May to June and those from July to August. Generally, the spring in Beijing is from middle April to middle June, and the summer is from late June to late August. The temporal variation can be illustrated by spring/summer ratios (Table 1). Table 1 shows that PAH concentrations in each medium are generally higher in spring than in summer. In Beijing, rainfall is mainly concentrated in late spring and summer, and there is little rainfall in fall and winter. Generally, spring is sunny and cool, and summer is featured with strong sunshine and high temperature. According to the meteorological data recorded during the sampling period, the spring events were characterized by relatively low temperature (18.8  C), low dew point (17.6  C), and mild wind speed (1.6 m/s). The summer events were characterized by high temperature (23.3  C), high dew point (21.5  C), and mild wind speed (1.6 m/s). Apart from seasonal variation of source emissions, the prevailing atmospheric condition in summer may favor PAHs dispersion and decomposition, and result in relatively low PAH levels.

3.2. Diagnostic ratio analysis The PAH compositional ratios used in this paper include light molecular weight (LMW, 2e3 ring) PAHs/heavy molecular weight (HMW, 4e6 ring) PAHs, FLA/PYR, BaA/CHR, and BbF/BkF, which have similar thermodynamic partitioning and kinetic transfer properties (Dickhut et al., 2000). To avoid misdiagnosis of PAH origins caused by PAH partitioning between dissolved and particle phases, total PAH concentrations

(particle þ dissolved) were used to calculate the diagnostic ratios (Table 2). According to previous studies, LMW/HMW < 1 indicates pyrogenic sources including incomplete combustion of fossil fuels or wood, and LMW/HMW > 1 signals petrogenic sources including spilt oil or petroleum products (Soclo et al., 2000). According to the LMW/HMW ratios in Table 2, the PAHs in runoff possibly have a pyrogenic origin, while for road dust, rain and canopy throughfall, petrogenic sources are likely. The disagreement on PAH sources for runoff and the pollutant inputs can be explained through further analysis of the seasonal variation of the LMW/HMW ratios. LMW/ HMW ratios were calculated separately for spring and summer events (Table 2). In spring, LMW/HMW ratios for rain and canopy throughfall indicated pyrogenic sources; while in summer, LMW/HMW ratios for road dust showed pyrogenic sources. It should be noted that the source origin indicated by LMW/HMW ratios for dust, canopy throughfall and rain changed from spring to summer, and this seasonal changes are consistent with the observations in Section 3.1. Previous studies suggested a process of volatilization of LMW PAHs from contaminated ground surface (Wania et al., 1998; Gustafson and Dickhut, 1997; Dimashki et al., 2001). Under higher temperature in summer, LMW species may be depleted in road dust and enriched in the ambient air. In rain and canopy throughfall samples, PAHs mainly source from vapor and aerosol phases in ambient air, and these phases are scavenged by rain. Therefore, in summer the observed PAH profile for road dust is depleted with LMW PAHs; while for rain and canopy throughfall, the observed profile is enriched with LMW PAHs. PAH isomer pairs with similar molecular weight were further applied for source apportionment. According to previous studies, a ratio of PHE/ANT > 15 indicates petrogenic sources, and PHE/ANT < 10 signals pyrogenic sources

Table 1 Spring/summer ratios of PAH concentrations in road runoff, road dust, rain and canopy throughfall samples PAHs

NAP ACY ACE FLO PHE ANT FLA PYR BaA CHR BbF BkF BaP IcdP DahA BghiP

Runoff

Rain

Branch road

Vehicle lane

Bicycle lane

Rp

Rd

Rp

Rd

Rp

Rd

0.5 2.5 1.2 2.9 4.6 4.4 4.3 4.2 5.5 4.6 16.1 8.2 7.0 28.6 39.1 9.6

2.5 2.4 1.7 1.6 5.2 3.4 3.6 3.5 3.2 3.0 9.0 7.1 19.4 27.0 47.9 21.2

5.5 7.5 8.3 9.6 12.7 10.4 11.0 10.5 13.6 12.0 44.2 29.2 23.3 88.4 104.8 45.4

2.7 2.7 2.2 2.5 4.1 3.4 2.3 1.7 1.3 1.0 3.3 1.8 3.5 4.8 8.7 3.6

3.1 3.9 4.3 5.2 7.6 6.3 8.0 7.1 14.5 13.5 69.2 41.9 30.0 272.3 136.9 48.9

2.5 3.1 3.3 2.6 3.2 3.2 3.2 2.8 2.5 2.0 6.1 5.5 10.5 13.3 29.7 10.0

Rp ¼ spring/summer ratio for particle-bound PAHs. Rd ¼ spring/summer ratio for dissolved PAHs.

Canopy

Dust

Rp

Rd

Rp

Rd

Branch road

Vehicle lane

Bicycle lane

1.0 1.3 1.3 1.9 1.9 5.8 3.0 2.3 5.2 4.5 12.3 17.4 18.9 4.4 5.7 6.9

21.4 2.1 1.9 2.4 2.2 1.9 2.5 2.3 1.1 1.1 1.9 1.3 5.8 6.8 5.1 1.8

3.4 1.4 1.4 2.3 2.4 2.3 3.4 2.7 4.3 3.9 14.6 15.0 15.5 8.7 17.2 6.7

0.6 0.8 1.4 0.0 0.7 1.5 1.9 1.6 0.9 1.0 2.9 1.6 3.4 10.9 2.4 3.1

11.3 3.5 4.0 7.0 5.1 4.2 2.9 2.6 1.7 1.7 2.0 2.3 2.6 9.8 1.0 3.2

2.7 1.8 4.6 2.7 4.0 3.6 2.9 2.4 1.4 1.8 2.0 2.1 2.4 6.8 1.0 2.7

5.6 3.0 5.8 4.3 5.6 4.1 3.2 3.1 2.3 1.6 1.8 1.9 3.6 11.2 0.9 3.5

10.94 1.24 0.59 0.24 1.34 9.59 1.33 0.57 0.19 1.75 Rsp ¼ diagnostic ratio for spring events. Rsu ¼ diagnostic ratio for summer events. Rt ¼ diagnostic ratio for the total events.

Rsu Rsp

12.29 1.14 0.61 0.31 0.77 8.88 1.02 0.58 0.25 1.15

Rt Rsu

8.34 0.97 0.56 0.24 1.46 9.54 1.08 0.61 0.26 0.77

Rsp Rt

10.13 1.46 0.58 0.27 1.21 9.35 1.47 0.55 0.31 0.81

Rsu Rsp

10.65 1.45 0.60 0.25 1.48 14.17 1.22 0.65 0.27 1.24

Rt Rsu

11.72 1.26 0.64 0.23 0.87 15.81 1.19 0.66 0.30 1.49

Rsp Rt Rsu

7.84 1.23 0.6 0.34 0.68

Rsp

9.39 1.12 0.63 0.33 1.23

Rt

7.78 1.51 0.54 0.27 0.69 7.07 1.25 0.53 0.25 0.87

Rsu Rsp

8.41 1.73 0.54 0.29 0.53 8.09 1.48 0.62 0.26 0.91

Rt Rsu

7.58 1.16 0.59 0.26 1.30

Rsp

3.3. Principal component analysis

8.53 1.76 0.65 0.27 0.57 6.85 1.54 0.61 0.33 0.88

Rt Rsu

6.61 1.07 0.61 0.31 0.99

Rsp

7.16 2.15 0.62 0.35 0.74 PHE/ANT BbF/BkF FLA/(FLA þ PYR) BaA/(BaA þ CHR) LMW/HMW

Rain Canopy throughfall Vehicle lane Bicycle lane Dust

Branch road Vehicle lane Bicycle lane Branch road

Road runoff Diagnostic ratio

Table 2 Diagnostic ratios for PAHs (dissolved þ particle) in road runoff, dust, rain and canopy throughfall samples

597

(Takada et al., 1991). According to Table 2, PHE/ANT ratios are generally smaller than 10 for runoff, rain and canopy throughfall, and slightly higher than 10 for road dust. This result suggests a dominance of pyrogenic sources for runoff, rain and canopy throughfall. Another widely used indicator for 3and 4-ring PAHs is FLA/(FLA þ PYR). The ratio lower than 0.4 signals petrogenic sources, and higher than 0.5 indicates combustion processes (Budzinski et al., 1997; Yunker et al., 2002). In Table 2, the FLA/(FLA þ PYR) ratios for each media were all above 0.5, indicating combustion sources for the runoff system. For 4-ring PAH isomer indicator BaA/ (BaA þ CHR), the ratio higher than 0.35 signals combustion sources, lower than 0.2 indicates petrogenic sources, and between 0.2 and 0.35 could be either petrogenic or combustion sources (Yunker and Macdonald, 1995; Soclo et al., 2000). The BaA/(BaA þ CHR) values in Table 2 range between 0.19 and 0.35, indicating the PAH sources could be either petrogenic or combustion origins for each media of the runoff system. Overall, the above PAH compositional ratios basically indicate a dominance of pyrogenic sources for PAHs in runoff, rain and canopy throughfall. Road dust has both petrogenic and pyrogenic sources. Similar observations were reported in previous studies, where crankcase oil dominated with petrogenic profile displays feature of pyrogenic signature as the oil contacts with the exhaust gases in the engine cylinders (Wang et al., 2000). Asphalt, the pavement material of the sampling sites, contains significant amounts of PAHs and also has a mix of petrogenic and pyrogenic character (Brandt and De Groot, 2001).

8.77 1.17 0.62 0.34 1.01

Rt

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Because diagnostic ratios provide only qualitative information about pollutant sources, multiple variant techniques including principal component analysis (PCA) and multiple linear regression analysis (MLRA) were used to quantify PAH sources for the road runoff system. Since NAP is a very volatile member and the abundance of this compound may mask the variability of the remaining species, total concentration (dissolved þ particle) of 15 parent PAHs excluding NAP were considered in PCA and MLRA. Prior to analysis, non-detectable values were replaced with random numbers between zero and the sample-dependent reporting limits. By this way, the influence of spurious correlations between compounds that were not detected in some samples may be avoided (Mai et al., 2003). PCA was conducted with Varimax rotation using SPSS software. Principal components with eigenvalue > 0.5 were retained, and the principal component loading accounting for >3% of the variance was considered. The principal components were explained by factor loadings of the PAH species and used to identify source emission composition. Because road runoff is the collection of PAHs from several inputs, the principal factors for runoff may actually contain overabundant informative components. Therefore, PCA and MLRA were applied to both road runoff and the

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inputs in order to better differentiate PAHs source information for the road runoff system. For road runoff samples, two principal components were identified. The variance loadings and factor loadings are summarized in Table S4 (Supplementary material). The principal components for runoff at the branch road and the vehicle lane have similar profile: factor 1 is mostly associated with HMW PAHs including BkF, BaP, IcdP, and BghiP, and factor 2 is highly weighted in LMW PAHs including ACY, FLO, PHE, ANT, FLA and PYR. According to literature, BghiP is a tracer of vehicular emissions (Harrison et al., 1996); BkF indicates diesel vehicle origins (Venkataraman et al., 1994); IcdP also signals diesel and gas engine emissions (Larsen and Baker, 2003). Therefore, factor 1 at the branch road and the vehicle lane can be attributed to vehicular sources, including diesel and gasoline engine emissions. Because FLO, PHE, ANT, FLA and PYR are predominantly coal combustion tracers (Harrison et al., 1996; Simcik et al., 1999; Masclet et al., 1986), factor 2 is considered as indicative of coal combustion sources. At the bicycle lane, factor 1 is heavily loaded on LMW species, and may originate from coal combustion sources. Factor 2 is predominately weighted in BkF, BaP, IcdP, DahA and BghiP, representing vehicular sources. The Varimax rotated factor matrix for road dust, rain and canopy throughfall is presented in Table S5. For road dust, three principal components are extracted. At the bicycle lane and vehicle lane of the trunk road, factor 1 has high loading on IcdP and BghiP, representing vehicular emission; factor 2 is heavily loaded on FLO, PHE, ANT, FLA and CHR and signals coal combustion; factor 3 is predominant with DahA, which has no specific source meaning (this is further investigated in Section 3.4). At the branch road, factor 1, with high weight on IcdP and BghiP, also represents vehicular emission; factor 2 is mostly associated with BaA, BkF, BbF, and BaP, which are markers for oil combustion (Larsen and Baker, 2003; Harrison et al., 1996); factor 3 is dominated by ACY, ACE and PYR, of which PYR is a signal of coal combustion (Simcik et al., 1999). Three principal components are extracted for rain and canopy throughfall, respectively (Table S5). For rain, factor 1 signals coal/oil combustion, factor 2 indicates vehicular emissions, and factor 3 is primarily loaded with ANT, which is a marker of coke production (Larsen and Baker, 2003).

For canopy throughfall, factor 1 can be attributed to vehicular emission, factor 2 signals coal combustion source, and factor 3 mainly consists of ACE and FLO, indicating oil combustion source. Comparison of the source information indicates that PAH profile of the principal components for road dust and runoff is similar. Although contribution of other inputs is not negligible, the source feature of road dust contributes significantly to road runoff. Because there are no coal combustion, oil combustion and coking industry sources in the local area during the sampling season, the source feature of coal/oil combustion and coking industry for rain and canopy throughfall may indicate either atmospheric transport of PAHs from remote area, or volatilization from contaminated ground. As stated in previous sections, volatile PAH species may subject to volatilization from contaminated ground under high temperature. In order to assess the impact of the temperature-depended volatilization, auxiliary parameters including daily average temperature and wind speed were submitted to PCA for rain and canopy throughfall samples. The results indicate that factor loading of temperature and wind speed is negative on the principal components weighted dominantly with LMW PAHs (Table S6). The inverse relationship between LMW PAHs and temperature and wind speed on principal factors indicates that LMW PAHs in rain and canopy throughfall are unlikely to result from PAH volatilization from contaminated ground. Therefore, LMW PAHs in such media may be attributed to atmospheric transport of oil/coal originated PAHs from remote area. 3.4. Multiple linear regression In order to determine the contribution of PAH sources to the road runoff system, multiple linear regression was performed stepwise using SPSS software (Table S7). The mean contributions of principal factors are summarized in Table 3. The source contributions for road runoff are presented in Fig. 1. At the branch road, some events contained apparent negative source contributions, which is physically impossible. Such negative source contribution was also reported by Larsen and Baker (2003) when PCA/MLRA was applied for estimation of PAHs sources to ambient air in Baltimore. PCA’s ability to generate negative source contributions is a known

Table 3 Contribution of PAH sources for road runoff, dust, rain and canopy throughfall Media

Sampling site

Factor 1

Factor 2

Factor 3

Source

Contribution (%)

Source

Contribution (%)

Source

Contribution (%)

Road runoff

Branch road Vehicle lane Bicycle lane

Vehicular emission Vehicular emission Coal combustion

60 59 50

Coal combustion Coal combustion Vehicular emission

40 41 50

e e e

e e e

Road dust

Branch road Vehicle lane Bicycle lane

Vehicular emission Vehicular emission Vehicular emission

45 57 47

Oil combustion Coal combustion Coal combustion

35 42 50

Coal combustion No specific source No specific source

20 1 3

Rain

Coal/oil combustion

54

Vehicular emission

34

Coking

12

Canopy throughfall

Vehicular emission

56

Coal combustion

30

Oil combustion

14

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15 PAHs (ng/L)

50000 40000 30000 20000 10000 0 -10000

May 26

June 28

July 23

July 31

Aug. 8

Branch road 50000

15 PAHs (ng/L)

concern, and this is attributed to PCA/MLRA’s inability to effectively model extreme data. In Larsen and Baker’s study, PCA/MLRA analysis was rerun without the extreme data, and the truncated PCA showed reasonable result. In this study, because significant seasonal variation of PAH concentrations in road runoff was observed (refer to Section 3.1), PCA/ MLRA was applied separately for spring and summer runoff samples. The season-based source contributions are integrated and displayed in Fig. 2. Compared with Fig. 1, the negative source contribution at the branch road is largely improved in Fig. 2 through the seasonal analysis. It should be noted that in both Figs. 1 and 2, the source contributions become very small in the summer. This is consistent with Section 3.1, and may partly attribute to the prevailing atmospheric condition in summer that favor PAHs dispersion and decomposition. Similarly, MLRA was conducted for road dust, rain and canopy throughfall samples. For road dust, the contribution

599

40000 30000 20000 10000 0

50000

-10000

May 4

May 26

June 28

July 23

July 31

Aug. 8

Vehicle lane

30000

50000

20000

40000

10000 0 -10000 May 4

May 26

June 28

July 23

July 31

Aug. 8

15 PAHs (ng/L)

15 PAHs (ng/L)

40000

30000 20000 10000 0

Vehicle lane

-10000 May 4

50000

15 PAHs (ng/L)

June 28

July 23

July 31

Aug. 8

Bicycle lane

40000 30000

Fig. 2. Seasonal integrated daily source contributions of

P 15 PAHs.

20000 10000 0 -10000 May 4

May 26

June 28

July 23

July 31

Aug. 8

Bicycle lane 50000 40000

15 PAHs (ng/L)

May 26

30000 20000 10000 0 -10000 May 26

June 28

July 23

July 31

Aug. 8

Branch road Vehicular emission

Fig. 1. Time series of daily source contributions of MLRA.

Coal combustion

P

15 PAHs based on PCA/

of vehicular emission predominates at the vehicle lane and the branch road. At the bicycle lane, the contribution of vehicular emission and coal combustion is almost equal. For road dust at the bicycle lane and vehicle lane, the regression coefficients for factor 3 are not significant ( p > 0.05), indicating that factor 3 is not significantly correlated with total PAHs. This is consistent with the PCA result in Section 3.3. For rain, coal/oil combustion has the largest contribution. This indicates that atmospheric transportation is a major PAHs source for rainwater. For canopy throughfall, the contribution of vehicular emission is predominant. Compared with rainwater, this result illustrates a significant influence of motor traffic on the roadside canopy throughfall. The sampling sites were located in a composite residential and commercial area. Because industries have been relocated to the suburb area of Beijing in recent years, the findings of the study area can be generalized to approximately represent the entire urban area of Beijing. Although site-specific sources may exit, the source information observed at the study area can provide a general picture of PAHs sources in road runoff

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and the related inputs in urban area of Beijing. Based on the identified PAHs sources, control of PAHs contamination in road runoff system in Beijing urban area would generally fall into two ways: (1) control of traffic emission (improve gasoline/diesel quality, control traffic volume and reduce traffic jam); and (2) replace coal combustion with cleaner energy such as natural gas. 4. Conclusion Through PAH compositional ratios, a dominance of pyrogenic sources for runoff, rain and canopy throughfall is observed, and road dust is featured with both petrogenic and pyrogenic sources. Principal component analysis showed that vehicular emission and coal combustion were the major PAHs sources for the runoff system in the study area. For both road runoff and road dust, source of vehicular emission dominated at the sites with traffic load (vehicle lane and branch road), and at the bicycle lane, the contribution of vehicular emission and coal combustion were almost equal. For rain, the identified main sources included coal/oil combustion, vehicular emission and coking, and the contributions were 54%, 34% and 12%, respectively. For canopy throughfall, vehicular emission, coal combustion and oil combustion were identified as major sources, and the contributions were 56%, 30% and 14%, respectively. Overall, PAHs in road runoff mainly reflected the source information of road dust. Acknowledgments This study was funded by the National Scientific Foundation of China (Grant 40525003), National Basic Research Program of China (Grant 2003CB15004), and Key MOE Research Project (Grant 306019). We thank Dr. Jundong Hu and Dr. Kaiyan Wang for their help on sampling. We also thank Mrs. Yu Liu, Mrs. Bingjun Meng and Mrs. Yanhua Liu for their assistance in laboratory analysis.

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