Chemosphere 119 (2015) 794–802
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Seasonal attributes of urban soil PAHs of the Brahmaputra Valley Karishma Hussain a, Raza Rafiqul Hoque a,⇑ a
Department of Environmental Science, Tezpur University, Tezpur, India
h i g h l i g h t s First study on soil PAHs of Brahmaputra Valley. Seasonal variability in attributes of soils PAHs studied. Seasonality in PAHs profiles by rings number. Seasonality in association of PAHs and carbon. PCA–MLR – contributing sources both mobile and stationary.
a r t i c l e
i n f o
Article history: Received 31 January 2014 Received in revised form 9 August 2014 Accepted 11 August 2014 Available online 7 September 2014 Handling Editor: I. Cousins Keywords: Soil PAHs PCA–MLR BC/OC
a b s t r a c t Polycyclic Aromatic Hydrocarbons (PAHs) are ubiquitous organic pollutants, which are both toxic and carcinogenic. In the present study seasonally collected composite soil samples of Guwahati city of the Brahmaputra Valley were analysed for of PAHs by HPLC column. Black carbon (BC) and organic carbon (OC) of soil samples were analysed by thermochemical oxidation method in a TOC analyzer. Mean concentrations P of PAHs (USEPA 16) were found to be 5 570 168 ± 7003, 9052 ± 1292 and 19 294 ± 17 827 ng g1 during monsoon, pre- and post-monsoon seasons, respectively. Two- and 4-ring PAHs dominated and the 2-ring PAHs were particularly abundant during post-monsoon period. The carcinogenic potentials of PAHs were calculated as BaP equivalents, which was found to be maximum (1167.064 ng BaPq g1) at industrial site. Diagnostic ratios of marker species indicated for pyrogenic origins of PAHs. Sources were indentified and contribution of individual sources was quantified by multivariate hyphenated model – Principal Component Analysis–Multiple Linear Regression (PCA–MLR). Mobile sources like the vehicular traffic were found to have contributed 63% to the PAHs load. The correlations of individual PAHs with BC or OC showed seasonal variations. High dependencies of PAHs on BC/OC ratios were found indicating that BC could be interfering with the association of PAHs and OC. However, such relationships showed seasonal bias and high positive dependencies were found during pre-monsoon period only. Strong relationships were found between PAHs and BC/OC during monsoon and post-monsoon seasons. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Polycyclic Aromatic Hydrocarbons (PAHs) are among the most marked semivolatile group of organic pollutants that are toxic and ubiquitous in the environment. The United States Environmental Protection Agency (US EPA) has selected 16 PAHs as the most priority ones to be analyzed in different environmental matrices (USEPA, 1977); among them, Benzo[a]anthracene, Benzo[a]pyrene, Benzo[b]fluoranthene, Benzo[k]fluoranthene, Chrysene, Dibenzo[a]anthracene and Indeno[1,2,3-c,d]pyrene have been regarded as probable human carcinogens (USEPA, 2002). Hence,
⇑ Corresponding author. Tel.: +91 3712 275607. E-mail address: [email protected] (R.R. Hoque). http://dx.doi.org/10.1016/j.chemosphere.2014.08.021 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.
distributions of PAHs in the environment and its potential human risk have grown into the focus of much consideration (Keith and Telliard, 1979 and Clean Water Act, 1993). Combustion products that are emitted to the atmosphere are deposited back onto the soil surface. Soil is, therefore, considered as a medium for accumulation and integration of many pollutants (Wild and Jones, 1995). Primary input of PAHs into soil surface is by air to surface precipitation (Wang et al., 2002 and Tao et al., 2003). Soil is, therefore, considered as one of the major sinks of atmospheric PAHs (Morillo et al., 2008). Further, volatilization, irreversible sorption, leaching, accumulation by plants and biodegradation are the possible pathways for dispersion of PAHs (Reilley et al., 1996). Much of the combustion derived PAHs are present in the top layer of soil (Agarwal, 2009) and human exposure of PAHs through soil has been reported to be greater than that of air and
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water (Menzie et al., 1992). Soil, therefore, can be a good index for PAHs pollution and environmental risk (Liang et al., 2011). Hydrophobic nature and stable chemical structure of PAHs favour adsorption onto soil particles. So, PAHs can sustain in soil matrix for longer time (Means et al., 1980 and Wilcke et al., 2000). Concentration of PAHs is significantly regulated by soil organic carbon (OC) content (Liang et al., 2011). Also, black carbon (BC) is considered to play a major role in distribution of PAHs in soil due to their co-emission and high sorption capacity for PAHs (Agarwal and Bucheli, 2011 and Liu et al., 2011). In this paper, levels of USEPA’s 16 priority PAHs, BC and OC concentrations in soil of Guwahati city have been presented. Spatial variations of soil PAHs together with the composition profile on the basis of number of rings of the compounds have been analysed. Carcinogenic potency of the 16 PAHs have been presented as BaP equivalents. The source characteristics of PAHs have been indentified on the basis of diagnostic ratios of marker species and source contributions are quantified by Principal Component Analysis– Multiple Linear Regression (PCA–MLR). Associations of PAHs with BC and OC have been dealt on the basis of their Pearson’s correlations and linear regressions. There is lack of studies on this aspect and the present study could provide a baseline for future studies on soil PAHs of the region. 2. Methodology 2.1. Study area The study was carried out in Guwahati city, Northeast India situated between the Brahmaputra River and foothills of Shillong plateau; geographically positioned around 26°100 000 N and 92°490 000 E. The region has a warm and humid climate and the monsoon brings in heavy rainfall. As per the 2011 Government of India census, Guwahati has recorded a population of 1.0 million at a decadal growth rate of 8%. Over the last decade Guwahati witnessed a vehicular growth (both light and heavy motor vehicle) of 87% with 58 638 numbers of vehicles plying on road in the first quarter of 2013.
795
individual PAHs in the standard mixture were diverse, which are given in Appendix A. Internal standard mixture (naphthalene-d8, acenaphthene-d10, phenanthrene-d10 and chrysene-d12) was obtained from Supelco, Bellefonte, USA (Catalog No. 48902). All solvents (dichloromethane, cyclohexane, pentane, acetonitrile, etc.) of HPLC and analytical grade were used for sample processing and analysis. High purity milliQ water was used for HPLC analysis. 2.4. Sample extraction and clean up Four gm of soil was extracted in 20 ml of dichloromethane for 30 min by ultrasonic agitation (Bandelin Sonorex) at 20 kHz (UNEP, 1992). Triplicate extracts were then mixed and allowed to settle for few minutes and filtered. Filtrates were concentrated to 1–2 ml in rotary evaporator and exchanged with cyclohexane and kept for clean up. Clean up of the samples was done as per USEPA method 3630C, by a silica gel column made from10 gm of activated silica gel of 100–200 mesh (Merck). The column was capped with Na2SO4 and prewashed with 40 ml pentane at the elution rate of 2 ml min1. The concentrated sample mixture in 4 ml cyclohexane was then loaded in the column and eluted with 25 ml of pentane and discarded. Finally the column was eluted with 25 ml mixture of dichloromethane and pentane in 2:3 ratio (v/v) and the eluate containing desired PAHs was taken and reduced to 0.5–1 ml in rotatory evaporator, which was later adjusted to 1 ml in acetonitrile. 2.5. Analysis 2.5.1. PAHs Analysis was done in HPLC (Waters) equipped with UV detector (W22489) and Waters PAH C18 column (4.6 mm 250 mm, 5 lmparticle size) as per protocol followed by Agarwal, 2009. Quantification of PAHs was done with external and internal calibration standards at detection wavelength of 254 nm. Compound concentrations were calculated using both peak area response and mean Relative Response Factor (RRF) for both internal and external standards.
2.2. Sampling Soil sampling was carried out in 3 consecutive seasons, viz. monsoon (May), post-monsoon (November) and pre-monsoon (February), during 2011–12 to derive seasonal characteristics of the data. Five representative locations were chosen for sampling on the basis of land use of Guwahati city viz. industrial (Noonmati–Narengi area; S1), commercial (Machkhowa-Fancy Bazar; S2), high traffic roadside (Gauhati University; S3), residential (Khanapara–Beltola; S4) and forest site (Basistha; S5) to get a holistic representation of the city. Sampling sites and prevailing wind condition of Guwahati city has been illustrated in Fig. 1 Wind data were procured from Regional Meteorological Centre of India Meteorological Department, Government of India and seasonal windroses of Guwahati city were computed. Top soil (0–2 cm) samples were collected with a stainless steel scoop. Five sub-samples were collected covering the entire site, pooled and homogenized by coning and quartering to make a composite sample. Five composite samples were collected in a season. Samples were air-dried in dark, cleaned from twigs, stones etc, sieved (0.5 respectively are indicative of pyrogenic origin of PAHs (Yunker et al., 2002). Anth/(Anth + Phen) ratios were found to be 0.14 ± 0.1, 0.1 ± 0.1 and 0.14 ± 0.1 during monsoon, post-monsoon and premonsoon respectively. Similarly, Flan/(Flan + Pyr) ratios were 0.71 ± 0.3, 061 ± 0.3 and 0.74 ± 0.24 during monsoon, post-monsoon and pre-monsoon respectively, which inferred pyrogenic input of PAHs in all three seasons. BaA/Chry ratio of 1.11 is considered as specific marker for coal combustion (Dickhut et al., 2000 and Khalili et al., 1995). BaA/Chry ratios were found to be 0.92 ± 1.3 and 1.4 ± 0.9 during post-monsoon and pre-monsoon seasons respectively could explain coal combustion during these two seasons only. Phen/(Phen + Anth) ratio greater than 0.7 is indicative of fossil fuel emissions according to Kavouras et al., 1999. Phen/(Phen + Anth) mean ratio was greater than 0.7 in three seasons indicate source emission from fossil fuels in soil PAHs. The ratio of IP/(IP + BgP) between 0.35 and 0.70 is considered as specific marker for diesel emission (Khalili et al., 1995) and BgP is often considered as a vehicular marker. Mean IP/(IP + BgP) ratio was 0.71 ± 0.2 only during pre-monsoon, while 0.77 ± 0.3 and 0.85 ± 0.13 in monsoon and post-monsoon respectively. This indicated that diesel emission was an important source of soils PAHs during pre-monsoon only. Thus, seasonality of source strengths of PAHs was vividly displayed by the source diagnostic ratios.
vehicular
diesel engine / vehicular emission
Mobile sources 6.966
1.407
54.722 ~63%
Mean diagnostic ratios of PAHs at various sites have been tabulated in Supplementary Table 1. At all the sites the ratios fall within the windows of pyrogenic emission mainly from fossil fuel of gasoline and diesel type. 3.5.2. PCA–MLR To further demonstrate the sources of soil PAHs PCA, was applied to PAH concentrations. PCA is a multivariate statistical tool to transform the original data set into a smaller one that account for most of the variance of the original data (Morillo et al., 2008). By extracting the eigenvalues and eigenvectors from the correlation matrix, principal factors with eigenvalues >1 were chosen. The initial eigenvalues extracted were ‘cleaned up’ by means of Varimax rotation with Keiser Normalization. Five factors explaining 86% of variance were obtained (Table 3). Factor 1 explained 39.8% of variance and had high loadings of Phen, Anth, Flan, BaA, Chry, BbF, BaP, DBA, BgP, and IP. BbF in addition to BgP and Flan is an indication of diesel-engine exhaust (Harrison et al., 1996). However, Anth, Phen, Flan, BaA and Chry, are regarded as specific marker for coal combustion (Khalili et al., 1995 and Simcik et al., 1999) and also Phen, Anth and Flan are typical indicators of wood combustion (Khalili et al., 1995). Thus, factor 1 could be attributed to combination of diesel, coal and wood combustion.
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Table 4 Correlations of PAHs with organic carbon (OC), black carbon (BC) and BC/OC in soil during three seasons. The significant correlation coefficients at p < 0.01 are in bold. LMWPAHs Naph Monsoon OC 0.06 BC 0.15 BC/OC 0.36 Post-monsoon OC 0.98 BC 0.31 BC/OC 0.76 Pre-monsoon OC 0.68 BC 0.23 BC/OC 0.79
HMWPAHs
Acy
Acen
Flu
Phen
Anth
Flan
Pyr
BaA
Chry
BbF
BkF
BaP
DBA
BgP
IP
P PAHs
0.14 0.85 0.71
0.88 0.70 0.65
0.21 0.82 0.66
0.06 0.09 0.16
0.09 0.05 0.17
0.08 0.54 0.48
0.71 0.32 0.53
0.21 0.06 0.20
0.15 0.07 0.18
0.13 0.66 0.43
0.49 0.12 0.11
0.74 0.28 0.50
0.22 0.08 0.16
0.09 0.20 0.06
0.96 0.70 0.71
0.20 0.01 0.24
0.21 0.71 0.38
0.21 0.16 0.19
0.37 0.29 0.09
0.03 0.69 0.27
0.24 0.68 0.39
0.74 0.77 0.85
0.23 0.87 0.40
0.12 0.69 0.28
0.20 0.75 0.27
0.22 0.73 0.38
0.31 0.20 0.15
0.58 0.05 0.55
0.20 0.62 0.32
0.55 0.31 0.29
0.78 0.51 0.88
0.94 0.19 0.71
0.28 0.94 0.74
0.89 0.18 0.63
0.51 0.40 0.28
0.56 0.83 0.66
0.43 0.77 0.88
0.38 0.55 0.51
0.86 0.26 0.64
0.49 0.89 0.91
0.43 0.90 0.90
0.67 0.07 0.70
0.33 0.77 0.33
0.33 0.88 0.79
0.29 0.89 0.78
0.35 0.75 0.71
0.37 0.94 0.86
0.53 0.82 0.94
P P P P Fig. 3. Dependency of PAHs on BC/OC ratios: (a) PAHs on BC/OC during monsoon, (b) PAHs on BC/OC during post-monsoon, (c) PAHs on BC/OC during pre monsoon, (d) LMW PAHs on BC/OC during monsoon, (e) LMW PAHs on BC/OC during post monsoon, (f) LMW PAHs on BC/OC during pre-monsoon, (g) HMWPAHs on BC/OC during monsoon, (h) HMWPAHs on BC/OC during post monsoon and (i) HMWPAHs on BC/OC in pre monsoon.
Factor 2 explained 16% of variance with high loadings of Acy, Phen, Anth, Pyr and DBA. This factor could explain coal combustion (Khalili et al., 1995). Factor 3 had high loading of Acen, Pyr and IP explaining 13% of variance are considered as typical marker for mobile sources (traffic) (Simcik et al. 1999). Factor 4 explained 10% of variance with high loading of Flu, Chry and BkF indicating for vehicular sources. Factor 5 explained 7% of total variance with significant loading of Naph and IP. This factor is marked for diesel powered vehicle. Thus, the sources could be broadly divided into two major categories of pyrogenic origin – factors 1 and 2 representing stationary sources and factors 3, 4 and 5 representing mobile sources. The principal aim of PCA/MLR is used to determine percent contribution of different sources in a given environmental matrix. MLR analysis from extracted factors was conducted in accordance with Larsen and Baker (2003). PCA factor scores 1–5 (corresponding diesel + coal + wood combustion, diesel + coal combustion, traffic, vehicular and diesel engined vehicular emission) were taken as P independent variables and z-scores of sum PAH ( PAHi) as dependent variables. In order of their individual simple correlation from
highest to lowest, each independent variable was added into stepwise regression. The resulting equation 2 was obtained.
Z ¼ 0:38FS1 þ 0:171FS2 þ 0:104FS3 þ 0:021FS4 þ 0:817FS5 ; ðr 2 ¼ 0:852Þ
ð2Þ
The mean contribution of source i (%) was calculated as 100 (Bi/ P P Bi), where Bi/ Bi is the regression coefficient for factor i to the sum of all the regression coefficients and FSi is the factor score for factor i. Mean source contributions were found to be 25.452, 11.454, 6.966, 1.407, and 54.722% for factors 1–5 respectively. Thus, contributions were found to 37 (factors 1 and 2) and 63% (factors 3–5) from stationary and mobile combustion sources respectively. 3.6. Carbon and PAHs Seasonal Pearson’s correlations of individual PAHs with OC, BC P and BC/OC are shown in Table 4. PAHs concentrations showed
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good positive correlation with BC and BC/OC during pre-monsoon and negative correlation during post-monsoon. On the contrary, P during post- monsoon PAHs and OC showed good positive correP lation. Again, during monsoon PAHs and OC showed weak positive correlation. Interestingly, most individual PAHs showed good positive correlations with BC and BC/OC during pre-monsoon, which was not seen during other seasons. To understand the effect of BC on soil PAHs dispersions better, dependencies of PAHs on BC/OC ratios were examined by regressP P P ing PAHs, LMWPAHs and HMWPAHs concentrations over BC/OC ratios. Positive slopes with weak coefficient of determinaP P tion were obtained PAHs (r2 = 0.39, p < 0.03), LMWPAHs P 2 2 (r = 0.39, p < 0.03) and HMWPAHs (r = 0.26, p < 0.10) (Supplementary Fig. 2). However, when the dependencies were tested seaP sonally a distinct seasonal bias was found (Fig. 3). PAHs when regressed on BC/OC a strong positive slope with high coefficient of determination (r2 = 0.89, p < 0.02) was found during pre-monsoon period (Fig. 3c). Similar trends were also observed when P P LMWPAHs and HMWPAHs were regressed over BC/OC; positive slopes with strong coefficient of determinations (r2 = 0.89, p < 0.02 and 0.76, p < 0.05, respectively) (Fig. 3f and i) implying that BC could be an important determinant of PAHs dispersion during the pre-monsoon period. On the other hand negative slopes were obtained for post-monsoon period, which would mean than OC could be the regulator of PAHs dispersion. The relationships of PAHs and BC/OC were not significant for the monsoon period. In soil, this kind of seasonal reversal of PAHs–BC relationship is unusual because soil receives continuous deposition for very long period and, therefore, a characteristic patter is established over a period of time. The observed relationship could be very unique for regions that receive high rainfall during one season of the year. The region represented in the study receives very heavy rainfall during monsoon. Torrential rains during monsoon loosen the topsoil and probably wash away the deposited particles with the storm water. This could remove the signature of soil PAHs of the past. Then after the monsoon, a long spell of dry days - post- and the pre-monsoon periods follow separated by a short spell of winter. After the monsoon, fresh depositions build up. It could be so that during monsoon and post-monsoon period BC contributions are not sufficient enough to control the association of soil PAHs
a b
with OC. During pre-monsoon, however, sufficient deposition of BC on to soil could create a situation where BC starts interfering with OC. Agarwal and Bucheli (2011) reported a study from a dry region of India and inferred that BC could be a better predictor for PAHs distribution in Indian soil than OC. In the high rainfall regions of Indian subcontinent, however, seasonal trend of PAHs–BC relationship is likely, as found in the present study. So, it could be relevant to say that BC could play significant role in soil PAHs dispersion during dry pre-monsoon season. 4. Conclusions P
PAHs in Guwahati soil ranged between 799 and 51 299 ng g1 and the concentrations showed seasonality explicitly. The computed BaPeq values suggest that there is a considerable risk of exposure of soil PAHs. Seasonality was also found in the concentrations of LMWPAHs and HMWPAHs and former were particularly more abundant during the post-monsoon. The PAHs were indentified to be of pyrogenic in origin with dominant contribution from vehicular emission. The relationships of PAHs with soil carbon showed seasonal characteristic and significant strong positive correlations were observed during the pre-monsoon. More elaborate studies of soil PAHs coupled with atmospheric deposition and meteorology could reveal interesting results. Acknowledgements 1. University Grants Commission for Maulana Azad National Fellowship (MANF) to Karishma Hussain (Grant No. F.40-147(M)/ 2009(SA-III/MANF). 2. Tezpur University for the HPLC system and other logistics. 3. Ministry of Earth Sciences (MoES), Government of India for the support received under the project entitled ‘Physico-chemical characterization of aerosol and source apportionment in midBrahmaputra plain in Assam: a modeling approach (No. MoES/ 16/16/10-RDEAS) for analytical support. Appendix A.
Name
Abbreviation
Retention timea
LOD (ppb)a
Standard (ppm)b
TEFs
Naphthalene Acenaphthylene Acenaphthene Fluorene Phenanthrene Anthracene Fluoranthene Pyrene Benzo(a)anthracene Chrysene Benzo(b)fluoranthene Benzo(k)fluoranthene Benzo(a)pyrene Dibenzo(a,h)anthracene Benzo(g,h,i)perylene Indeno(1,2,3-cd)pyrene
Naph Acy Acen Flu Phen Anth Flan Pyr BaA Chry BbF BkF BaP DBA BgP IP
14.35 15.5 16.65 18.42 19.15 19.75 20.65 21.43 22.71 23.75 24.37 24.59 25.43 26.81 27.57 28.02
32.9 8.5 6.2 7.9 5.4 3.6 22.2 1.4 4.2 29.0 4.8 5.1 6.9 7.9 2.8 4.3
10.2 9.2 9.7 8.3 9.9 10.7 9.5 10.0 10.5 9.7 9.6 9.5 9.4 9.7 8.0 8.9
0.001 0.001 0.001 0.001 0.001 0.01 0.001 0.001 0.1 0.01 0.1 0.1 1 1 0.01 0.1
Values are from the present study. Concentrations of calibration standards (Sigma–Aldrich, Germany: Product no. 36979).
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Liu, S., Xia, X., Zhai, Y., Wang, R., Liu, T., Zhang, S., 2011. Black carbon in urban and surrounding rural soils of Beijing, China: Spatial distribution and relationship with polycyclic aromatic hydrocarbons (PAHs). Chemosphere 82, 223–228. Maliszewska-Kordybach, B., Smreczak, B., Klimkowicz, A., Terelak, H., 2008. Monitoring of the total content of polycyclic aromatic hydrocarbons (PAHs) in arable soils in Poland. Chemosphere 73, 1284–1291. Mazzera, D., Hayes, T., Lowenthal, D., Zielinska, B., 1999. Quantification of polycyclic aromatic hydrocarbons in soil at McMurdo station. Antarctica Sci. Total Environ. 229, 65–71. Means, J.C., Wood, S.G., Hassett, J.J., Banwart, W.L., 1980. Sorption of polynuclear aromatic hydrocarbons by sediments and soils. Environ. Sci. Technol. 14, 1524– 1528. Menzie, C.A., Potocki, B.B., Santodonato, J., 1992. Exposure to carcinogenic PAHs in environment. Environ. Sci. Technol. 26, 1278–1284. Morillo, E., Romero, A.S., Madrid, L., Villaverde, J., Maqueda, C., 2008. Characterization and sources of PAHs and potentially toxic metals in urban environments of Sevilla (Southern Spain). Water Air Soil Pollut. 187, 41–51. Nam, J.J., Song, B.H., Eom, K.C., Lee, S.H., Smith, A., 2003. Distribution of polycyclic aromatic hydrocarbons in agricultural soils in South Korea. Chemosphere 50, 1281–1289. Ray, S., Khillare, P.S., Agarwal, T., Shridhar, V., 2008. Assessment of PAHs in soil around the International Airport in Delhi. India J. Hazard Mater. 156, 9–16. Reilley, K.A., Banks, M.K., Schwab, A.P., 1996. Dissipation of polycyclic aromatic hydrocarbons in the rhizosphere. J. Environ. Qual. 25, 212–219. Simcik, M.F., Eisenreich, S.J., Lioy, P.J., 1999. Source apportionment and source/sink relationships of PAHs in the coastal atmosphere of Chicago and Lake Michigan. Atmos. Environ. 33, 5071–5079. Skrbic, B., Cvejanov, J., Durisic-Mladenovic, N., 2005. Polycyclic aromatic hydrocarbons in surface soils of Novi Sad and bank sediment of the Danube River. J. Environ. Sci. Health A Environ. Sci. Eng. 40, 29–42. Tao, S., Cao, H.Y., Liu, W.X., Li, B.G., Cao, J., Xu, F.L., Wang, X.J., Conveny, J.R., Shen, W.R., Qing, B.P., Sun, R., 2003. Fate modeling of phenanthrene with regional variation in Tianjin. China Environ. Sci. Technol. 37, 2453–2459. Tsai, P.J., Shih, T.S., Chen, H.L., Lee, W.J., Lai, C.H., Liou, S.H., 2004. Assessing and predicting the exposures of polycyclic aromatic hydrocarbons (PAHs) and their carcinogenic potencies from vehicle engine exhausts to highway toll station workers. Atmos. Environ. 38, 333–343. UNEP. Reference method for marine pollution studies no.20 (IOC, IAEA), United Nation Environmental Programme, 1992. USEPA. Sampling and analysis procedures for screening of industrial effluents for priority pollutants. U. S. Environmental Protection Agency. Environment Monitoring and Support Laboratory, Cincinnati, 1977. USEPA. Polycyclic Organic Matter, US Environmental Protection Agency, 2002. . Wang, X.L., Tao, S., Dawson, R., Cao, J., Li, B.G., 2002. Modeling the transfer and the fate of benzo(a)pyrene in wastewater irrigated area in Tianjin. J. Environ. Qual. 31, 896–903. Wilcke, W., Amelung, W., Martius, C., Garcia, M.V.B., Zech, W., 2000. Biological sources of polycyclic aromatic hydrocarbons (PAHs) in the Amazonian rain forest. J. Plant Nutr. Soil Sci. 163, 27–30. Wild, S.R., Jones, K.C., 1995. Polynuclear aromatic hydrocarbons in the United Kingdom environment: a preliminary source inventory and budget. Environ. Pollut. 88, 91–108. Yamasaki, H., Kuwata, K., Miyamoto, H., 1982. Effects of ambient temperature on aspects of airborne polycyclic aromatic hydrocarbons. Environ. Sci. Technol. 1982 (16), 189–194. Yang, S.Y.N., Connell, D.W., Hawker, D.W., Kayal, S.I., 1991. Polycyclic aromatic hydrocarbons in air, soil, and vegetation in the vicinity of an urban roadway. Sci. Total Environ. 102, 229–240. Yunker, M.B., Macdonald, R.W., Vingarzan, R., Mitchell, R.H., Goyette, D., Sylvestre, S., 2002. PAHs in the Fraser river basin: a critical appraisal of PAH ratios as indicators of PAH sources and composition. Org. Geochem. 33, 489–515.
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Seasonal attributes of urban soil PAHs of the Brahmaputra Valley Karishma Hussain a, Raza Rafiqul Hoque a,⇑ a
Department of Environmental Science, Tezpur University, Tezpur, India
h i g h l i g h t s First study on soil PAHs of Brahmaputra Valley. Seasonal variability in attributes of soils PAHs studied. Seasonality in PAHs profiles by rings number. Seasonality in association of PAHs and carbon. PCA–MLR – contributing sources both mobile and stationary.
a r t i c l e
i n f o
Article history: Received 31 January 2014 Received in revised form 9 August 2014 Accepted 11 August 2014 Available online 7 September 2014 Handling Editor: I. Cousins Keywords: Soil PAHs PCA–MLR BC/OC
a b s t r a c t Polycyclic Aromatic Hydrocarbons (PAHs) are ubiquitous organic pollutants, which are both toxic and carcinogenic. In the present study seasonally collected composite soil samples of Guwahati city of the Brahmaputra Valley were analysed for of PAHs by HPLC column. Black carbon (BC) and organic carbon (OC) of soil samples were analysed by thermochemical oxidation method in a TOC analyzer. Mean concentrations P of PAHs (USEPA 16) were found to be 5 570 168 ± 7003, 9052 ± 1292 and 19 294 ± 17 827 ng g1 during monsoon, pre- and post-monsoon seasons, respectively. Two- and 4-ring PAHs dominated and the 2-ring PAHs were particularly abundant during post-monsoon period. The carcinogenic potentials of PAHs were calculated as BaP equivalents, which was found to be maximum (1167.064 ng BaPq g1) at industrial site. Diagnostic ratios of marker species indicated for pyrogenic origins of PAHs. Sources were indentified and contribution of individual sources was quantified by multivariate hyphenated model – Principal Component Analysis–Multiple Linear Regression (PCA–MLR). Mobile sources like the vehicular traffic were found to have contributed 63% to the PAHs load. The correlations of individual PAHs with BC or OC showed seasonal variations. High dependencies of PAHs on BC/OC ratios were found indicating that BC could be interfering with the association of PAHs and OC. However, such relationships showed seasonal bias and high positive dependencies were found during pre-monsoon period only. Strong relationships were found between PAHs and BC/OC during monsoon and post-monsoon seasons. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Polycyclic Aromatic Hydrocarbons (PAHs) are among the most marked semivolatile group of organic pollutants that are toxic and ubiquitous in the environment. The United States Environmental Protection Agency (US EPA) has selected 16 PAHs as the most priority ones to be analyzed in different environmental matrices (USEPA, 1977); among them, Benzo[a]anthracene, Benzo[a]pyrene, Benzo[b]fluoranthene, Benzo[k]fluoranthene, Chrysene, Dibenzo[a]anthracene and Indeno[1,2,3-c,d]pyrene have been regarded as probable human carcinogens (USEPA, 2002). Hence,
⇑ Corresponding author. Tel.: +91 3712 275607. E-mail address: [email protected] (R.R. Hoque). http://dx.doi.org/10.1016/j.chemosphere.2014.08.021 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.
distributions of PAHs in the environment and its potential human risk have grown into the focus of much consideration (Keith and Telliard, 1979 and Clean Water Act, 1993). Combustion products that are emitted to the atmosphere are deposited back onto the soil surface. Soil is, therefore, considered as a medium for accumulation and integration of many pollutants (Wild and Jones, 1995). Primary input of PAHs into soil surface is by air to surface precipitation (Wang et al., 2002 and Tao et al., 2003). Soil is, therefore, considered as one of the major sinks of atmospheric PAHs (Morillo et al., 2008). Further, volatilization, irreversible sorption, leaching, accumulation by plants and biodegradation are the possible pathways for dispersion of PAHs (Reilley et al., 1996). Much of the combustion derived PAHs are present in the top layer of soil (Agarwal, 2009) and human exposure of PAHs through soil has been reported to be greater than that of air and
K. Hussain, R.R. Hoque / Chemosphere 119 (2015) 794–802
water (Menzie et al., 1992). Soil, therefore, can be a good index for PAHs pollution and environmental risk (Liang et al., 2011). Hydrophobic nature and stable chemical structure of PAHs favour adsorption onto soil particles. So, PAHs can sustain in soil matrix for longer time (Means et al., 1980 and Wilcke et al., 2000). Concentration of PAHs is significantly regulated by soil organic carbon (OC) content (Liang et al., 2011). Also, black carbon (BC) is considered to play a major role in distribution of PAHs in soil due to their co-emission and high sorption capacity for PAHs (Agarwal and Bucheli, 2011 and Liu et al., 2011). In this paper, levels of USEPA’s 16 priority PAHs, BC and OC concentrations in soil of Guwahati city have been presented. Spatial variations of soil PAHs together with the composition profile on the basis of number of rings of the compounds have been analysed. Carcinogenic potency of the 16 PAHs have been presented as BaP equivalents. The source characteristics of PAHs have been indentified on the basis of diagnostic ratios of marker species and source contributions are quantified by Principal Component Analysis– Multiple Linear Regression (PCA–MLR). Associations of PAHs with BC and OC have been dealt on the basis of their Pearson’s correlations and linear regressions. There is lack of studies on this aspect and the present study could provide a baseline for future studies on soil PAHs of the region. 2. Methodology 2.1. Study area The study was carried out in Guwahati city, Northeast India situated between the Brahmaputra River and foothills of Shillong plateau; geographically positioned around 26°100 000 N and 92°490 000 E. The region has a warm and humid climate and the monsoon brings in heavy rainfall. As per the 2011 Government of India census, Guwahati has recorded a population of 1.0 million at a decadal growth rate of 8%. Over the last decade Guwahati witnessed a vehicular growth (both light and heavy motor vehicle) of 87% with 58 638 numbers of vehicles plying on road in the first quarter of 2013.
795
individual PAHs in the standard mixture were diverse, which are given in Appendix A. Internal standard mixture (naphthalene-d8, acenaphthene-d10, phenanthrene-d10 and chrysene-d12) was obtained from Supelco, Bellefonte, USA (Catalog No. 48902). All solvents (dichloromethane, cyclohexane, pentane, acetonitrile, etc.) of HPLC and analytical grade were used for sample processing and analysis. High purity milliQ water was used for HPLC analysis. 2.4. Sample extraction and clean up Four gm of soil was extracted in 20 ml of dichloromethane for 30 min by ultrasonic agitation (Bandelin Sonorex) at 20 kHz (UNEP, 1992). Triplicate extracts were then mixed and allowed to settle for few minutes and filtered. Filtrates were concentrated to 1–2 ml in rotary evaporator and exchanged with cyclohexane and kept for clean up. Clean up of the samples was done as per USEPA method 3630C, by a silica gel column made from10 gm of activated silica gel of 100–200 mesh (Merck). The column was capped with Na2SO4 and prewashed with 40 ml pentane at the elution rate of 2 ml min1. The concentrated sample mixture in 4 ml cyclohexane was then loaded in the column and eluted with 25 ml of pentane and discarded. Finally the column was eluted with 25 ml mixture of dichloromethane and pentane in 2:3 ratio (v/v) and the eluate containing desired PAHs was taken and reduced to 0.5–1 ml in rotatory evaporator, which was later adjusted to 1 ml in acetonitrile. 2.5. Analysis 2.5.1. PAHs Analysis was done in HPLC (Waters) equipped with UV detector (W22489) and Waters PAH C18 column (4.6 mm 250 mm, 5 lmparticle size) as per protocol followed by Agarwal, 2009. Quantification of PAHs was done with external and internal calibration standards at detection wavelength of 254 nm. Compound concentrations were calculated using both peak area response and mean Relative Response Factor (RRF) for both internal and external standards.
2.2. Sampling Soil sampling was carried out in 3 consecutive seasons, viz. monsoon (May), post-monsoon (November) and pre-monsoon (February), during 2011–12 to derive seasonal characteristics of the data. Five representative locations were chosen for sampling on the basis of land use of Guwahati city viz. industrial (Noonmati–Narengi area; S1), commercial (Machkhowa-Fancy Bazar; S2), high traffic roadside (Gauhati University; S3), residential (Khanapara–Beltola; S4) and forest site (Basistha; S5) to get a holistic representation of the city. Sampling sites and prevailing wind condition of Guwahati city has been illustrated in Fig. 1 Wind data were procured from Regional Meteorological Centre of India Meteorological Department, Government of India and seasonal windroses of Guwahati city were computed. Top soil (0–2 cm) samples were collected with a stainless steel scoop. Five sub-samples were collected covering the entire site, pooled and homogenized by coning and quartering to make a composite sample. Five composite samples were collected in a season. Samples were air-dried in dark, cleaned from twigs, stones etc, sieved (0.5 respectively are indicative of pyrogenic origin of PAHs (Yunker et al., 2002). Anth/(Anth + Phen) ratios were found to be 0.14 ± 0.1, 0.1 ± 0.1 and 0.14 ± 0.1 during monsoon, post-monsoon and premonsoon respectively. Similarly, Flan/(Flan + Pyr) ratios were 0.71 ± 0.3, 061 ± 0.3 and 0.74 ± 0.24 during monsoon, post-monsoon and pre-monsoon respectively, which inferred pyrogenic input of PAHs in all three seasons. BaA/Chry ratio of 1.11 is considered as specific marker for coal combustion (Dickhut et al., 2000 and Khalili et al., 1995). BaA/Chry ratios were found to be 0.92 ± 1.3 and 1.4 ± 0.9 during post-monsoon and pre-monsoon seasons respectively could explain coal combustion during these two seasons only. Phen/(Phen + Anth) ratio greater than 0.7 is indicative of fossil fuel emissions according to Kavouras et al., 1999. Phen/(Phen + Anth) mean ratio was greater than 0.7 in three seasons indicate source emission from fossil fuels in soil PAHs. The ratio of IP/(IP + BgP) between 0.35 and 0.70 is considered as specific marker for diesel emission (Khalili et al., 1995) and BgP is often considered as a vehicular marker. Mean IP/(IP + BgP) ratio was 0.71 ± 0.2 only during pre-monsoon, while 0.77 ± 0.3 and 0.85 ± 0.13 in monsoon and post-monsoon respectively. This indicated that diesel emission was an important source of soils PAHs during pre-monsoon only. Thus, seasonality of source strengths of PAHs was vividly displayed by the source diagnostic ratios.
vehicular
diesel engine / vehicular emission
Mobile sources 6.966
1.407
54.722 ~63%
Mean diagnostic ratios of PAHs at various sites have been tabulated in Supplementary Table 1. At all the sites the ratios fall within the windows of pyrogenic emission mainly from fossil fuel of gasoline and diesel type. 3.5.2. PCA–MLR To further demonstrate the sources of soil PAHs PCA, was applied to PAH concentrations. PCA is a multivariate statistical tool to transform the original data set into a smaller one that account for most of the variance of the original data (Morillo et al., 2008). By extracting the eigenvalues and eigenvectors from the correlation matrix, principal factors with eigenvalues >1 were chosen. The initial eigenvalues extracted were ‘cleaned up’ by means of Varimax rotation with Keiser Normalization. Five factors explaining 86% of variance were obtained (Table 3). Factor 1 explained 39.8% of variance and had high loadings of Phen, Anth, Flan, BaA, Chry, BbF, BaP, DBA, BgP, and IP. BbF in addition to BgP and Flan is an indication of diesel-engine exhaust (Harrison et al., 1996). However, Anth, Phen, Flan, BaA and Chry, are regarded as specific marker for coal combustion (Khalili et al., 1995 and Simcik et al., 1999) and also Phen, Anth and Flan are typical indicators of wood combustion (Khalili et al., 1995). Thus, factor 1 could be attributed to combination of diesel, coal and wood combustion.
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Table 4 Correlations of PAHs with organic carbon (OC), black carbon (BC) and BC/OC in soil during three seasons. The significant correlation coefficients at p < 0.01 are in bold. LMWPAHs Naph Monsoon OC 0.06 BC 0.15 BC/OC 0.36 Post-monsoon OC 0.98 BC 0.31 BC/OC 0.76 Pre-monsoon OC 0.68 BC 0.23 BC/OC 0.79
HMWPAHs
Acy
Acen
Flu
Phen
Anth
Flan
Pyr
BaA
Chry
BbF
BkF
BaP
DBA
BgP
IP
P PAHs
0.14 0.85 0.71
0.88 0.70 0.65
0.21 0.82 0.66
0.06 0.09 0.16
0.09 0.05 0.17
0.08 0.54 0.48
0.71 0.32 0.53
0.21 0.06 0.20
0.15 0.07 0.18
0.13 0.66 0.43
0.49 0.12 0.11
0.74 0.28 0.50
0.22 0.08 0.16
0.09 0.20 0.06
0.96 0.70 0.71
0.20 0.01 0.24
0.21 0.71 0.38
0.21 0.16 0.19
0.37 0.29 0.09
0.03 0.69 0.27
0.24 0.68 0.39
0.74 0.77 0.85
0.23 0.87 0.40
0.12 0.69 0.28
0.20 0.75 0.27
0.22 0.73 0.38
0.31 0.20 0.15
0.58 0.05 0.55
0.20 0.62 0.32
0.55 0.31 0.29
0.78 0.51 0.88
0.94 0.19 0.71
0.28 0.94 0.74
0.89 0.18 0.63
0.51 0.40 0.28
0.56 0.83 0.66
0.43 0.77 0.88
0.38 0.55 0.51
0.86 0.26 0.64
0.49 0.89 0.91
0.43 0.90 0.90
0.67 0.07 0.70
0.33 0.77 0.33
0.33 0.88 0.79
0.29 0.89 0.78
0.35 0.75 0.71
0.37 0.94 0.86
0.53 0.82 0.94
P P P P Fig. 3. Dependency of PAHs on BC/OC ratios: (a) PAHs on BC/OC during monsoon, (b) PAHs on BC/OC during post-monsoon, (c) PAHs on BC/OC during pre monsoon, (d) LMW PAHs on BC/OC during monsoon, (e) LMW PAHs on BC/OC during post monsoon, (f) LMW PAHs on BC/OC during pre-monsoon, (g) HMWPAHs on BC/OC during monsoon, (h) HMWPAHs on BC/OC during post monsoon and (i) HMWPAHs on BC/OC in pre monsoon.
Factor 2 explained 16% of variance with high loadings of Acy, Phen, Anth, Pyr and DBA. This factor could explain coal combustion (Khalili et al., 1995). Factor 3 had high loading of Acen, Pyr and IP explaining 13% of variance are considered as typical marker for mobile sources (traffic) (Simcik et al. 1999). Factor 4 explained 10% of variance with high loading of Flu, Chry and BkF indicating for vehicular sources. Factor 5 explained 7% of total variance with significant loading of Naph and IP. This factor is marked for diesel powered vehicle. Thus, the sources could be broadly divided into two major categories of pyrogenic origin – factors 1 and 2 representing stationary sources and factors 3, 4 and 5 representing mobile sources. The principal aim of PCA/MLR is used to determine percent contribution of different sources in a given environmental matrix. MLR analysis from extracted factors was conducted in accordance with Larsen and Baker (2003). PCA factor scores 1–5 (corresponding diesel + coal + wood combustion, diesel + coal combustion, traffic, vehicular and diesel engined vehicular emission) were taken as P independent variables and z-scores of sum PAH ( PAHi) as dependent variables. In order of their individual simple correlation from
highest to lowest, each independent variable was added into stepwise regression. The resulting equation 2 was obtained.
Z ¼ 0:38FS1 þ 0:171FS2 þ 0:104FS3 þ 0:021FS4 þ 0:817FS5 ; ðr 2 ¼ 0:852Þ
ð2Þ
The mean contribution of source i (%) was calculated as 100 (Bi/ P P Bi), where Bi/ Bi is the regression coefficient for factor i to the sum of all the regression coefficients and FSi is the factor score for factor i. Mean source contributions were found to be 25.452, 11.454, 6.966, 1.407, and 54.722% for factors 1–5 respectively. Thus, contributions were found to 37 (factors 1 and 2) and 63% (factors 3–5) from stationary and mobile combustion sources respectively. 3.6. Carbon and PAHs Seasonal Pearson’s correlations of individual PAHs with OC, BC P and BC/OC are shown in Table 4. PAHs concentrations showed
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good positive correlation with BC and BC/OC during pre-monsoon and negative correlation during post-monsoon. On the contrary, P during post- monsoon PAHs and OC showed good positive correP lation. Again, during monsoon PAHs and OC showed weak positive correlation. Interestingly, most individual PAHs showed good positive correlations with BC and BC/OC during pre-monsoon, which was not seen during other seasons. To understand the effect of BC on soil PAHs dispersions better, dependencies of PAHs on BC/OC ratios were examined by regressP P P ing PAHs, LMWPAHs and HMWPAHs concentrations over BC/OC ratios. Positive slopes with weak coefficient of determinaP P tion were obtained PAHs (r2 = 0.39, p < 0.03), LMWPAHs P 2 2 (r = 0.39, p < 0.03) and HMWPAHs (r = 0.26, p < 0.10) (Supplementary Fig. 2). However, when the dependencies were tested seaP sonally a distinct seasonal bias was found (Fig. 3). PAHs when regressed on BC/OC a strong positive slope with high coefficient of determination (r2 = 0.89, p < 0.02) was found during pre-monsoon period (Fig. 3c). Similar trends were also observed when P P LMWPAHs and HMWPAHs were regressed over BC/OC; positive slopes with strong coefficient of determinations (r2 = 0.89, p < 0.02 and 0.76, p < 0.05, respectively) (Fig. 3f and i) implying that BC could be an important determinant of PAHs dispersion during the pre-monsoon period. On the other hand negative slopes were obtained for post-monsoon period, which would mean than OC could be the regulator of PAHs dispersion. The relationships of PAHs and BC/OC were not significant for the monsoon period. In soil, this kind of seasonal reversal of PAHs–BC relationship is unusual because soil receives continuous deposition for very long period and, therefore, a characteristic patter is established over a period of time. The observed relationship could be very unique for regions that receive high rainfall during one season of the year. The region represented in the study receives very heavy rainfall during monsoon. Torrential rains during monsoon loosen the topsoil and probably wash away the deposited particles with the storm water. This could remove the signature of soil PAHs of the past. Then after the monsoon, a long spell of dry days - post- and the pre-monsoon periods follow separated by a short spell of winter. After the monsoon, fresh depositions build up. It could be so that during monsoon and post-monsoon period BC contributions are not sufficient enough to control the association of soil PAHs
a b
with OC. During pre-monsoon, however, sufficient deposition of BC on to soil could create a situation where BC starts interfering with OC. Agarwal and Bucheli (2011) reported a study from a dry region of India and inferred that BC could be a better predictor for PAHs distribution in Indian soil than OC. In the high rainfall regions of Indian subcontinent, however, seasonal trend of PAHs–BC relationship is likely, as found in the present study. So, it could be relevant to say that BC could play significant role in soil PAHs dispersion during dry pre-monsoon season. 4. Conclusions P
PAHs in Guwahati soil ranged between 799 and 51 299 ng g1 and the concentrations showed seasonality explicitly. The computed BaPeq values suggest that there is a considerable risk of exposure of soil PAHs. Seasonality was also found in the concentrations of LMWPAHs and HMWPAHs and former were particularly more abundant during the post-monsoon. The PAHs were indentified to be of pyrogenic in origin with dominant contribution from vehicular emission. The relationships of PAHs with soil carbon showed seasonal characteristic and significant strong positive correlations were observed during the pre-monsoon. More elaborate studies of soil PAHs coupled with atmospheric deposition and meteorology could reveal interesting results. Acknowledgements 1. University Grants Commission for Maulana Azad National Fellowship (MANF) to Karishma Hussain (Grant No. F.40-147(M)/ 2009(SA-III/MANF). 2. Tezpur University for the HPLC system and other logistics. 3. Ministry of Earth Sciences (MoES), Government of India for the support received under the project entitled ‘Physico-chemical characterization of aerosol and source apportionment in midBrahmaputra plain in Assam: a modeling approach (No. MoES/ 16/16/10-RDEAS) for analytical support. Appendix A.
Name
Abbreviation
Retention timea
LOD (ppb)a
Standard (ppm)b
TEFs
Naphthalene Acenaphthylene Acenaphthene Fluorene Phenanthrene Anthracene Fluoranthene Pyrene Benzo(a)anthracene Chrysene Benzo(b)fluoranthene Benzo(k)fluoranthene Benzo(a)pyrene Dibenzo(a,h)anthracene Benzo(g,h,i)perylene Indeno(1,2,3-cd)pyrene
Naph Acy Acen Flu Phen Anth Flan Pyr BaA Chry BbF BkF BaP DBA BgP IP
14.35 15.5 16.65 18.42 19.15 19.75 20.65 21.43 22.71 23.75 24.37 24.59 25.43 26.81 27.57 28.02
32.9 8.5 6.2 7.9 5.4 3.6 22.2 1.4 4.2 29.0 4.8 5.1 6.9 7.9 2.8 4.3
10.2 9.2 9.7 8.3 9.9 10.7 9.5 10.0 10.5 9.7 9.6 9.5 9.4 9.7 8.0 8.9
0.001 0.001 0.001 0.001 0.001 0.01 0.001 0.001 0.1 0.01 0.1 0.1 1 1 0.01 0.1
Values are from the present study. Concentrations of calibration standards (Sigma–Aldrich, Germany: Product no. 36979).
802
K. Hussain, R.R. Hoque / Chemosphere 119 (2015) 794–802
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