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Environ Geochem Health DOI 10.1007/s10653-017-0011-x

ORIGINAL PAPER

Concentrations, input prediction and probabilistic biological risk assessment of polycyclic aromatic hydrocarbons (PAHs) along Gujarat coastline Haren B. Gosai . Bhumi K. Sachaniya . Dushyant R. Dudhagara . Rahul K. Rajpara . Bharti P. Dave

Received: 10 March 2017 / Accepted: 1 August 2017 Ó Springer Science+Business Media B.V. 2017

Abstract A comprehensive investigation was conducted in order to assess the levels of PAHs, their input prediction and potential risks to bacterial abundance and human health along Gujarat coastline. A total of 40 sediment samples were collected at quarterly intervals within a year from two contaminated sites—Alang-Sosiya Shipbreaking Yard (ASSBRY) and Navlakhi Port (NAV), situated at Gulf of Khambhat and Gulf of Kutch, respectively. The concentration of RPAHs ranged from 408.00 to 54240.45 ng g-1 dw, indicating heavy pollution of PAHs at both the contaminated sites. Furthermore, isomeric ratios and principal component analysis have revealed that inputs of PAHs at both contaminated sites were mixed-pyrogenic and petrogenic. Pearson co-relation test and regression analysis have disclosed Nap, Acel and Phe as major predictors for bacterial abundance at both contaminated sites. Significantly, cancer risk assessment of the PAHs has been exercised based on incremental lifetime cancer risks. Overall, index of cancer risk of PAHs for ASSBRY and

Electronic supplementary material The online version of this article (doi:10.1007/s10653-017-0011-x) contains supplementary material, which is available to authorized users. H. B. Gosai  B. K. Sachaniya  D. R. Dudhagara  R. K. Rajpara  B. P. Dave (&) Department of Life Sciences, Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat 364001, India e-mail: [email protected]

NAV ranged from 4.11 9 10-6–2.11 9 10-5 and 9.08 9 10-6–4.50 9 10-3 indicating higher cancer risk at NAV compared to ASSBRY. The present findings provide baseline information that may help in developing advanced bioremediation and bioleaching strategies to minimize biological risk. Keywords Polycyclic aromatic hydrocarbons (PAHs)  Biological risk assessment  Input prediction  Bacterial abundance

Introduction The versatility of contaminants in the sediments of marine ecosystems has extensively amplified since last few decades (Lewis and Devereux 2009). Natural and anthropogenic activities such erosion, urbanization, industrialization and agricultural activities collectively call for an increased threat to exploitation of sediment quality. Loading of contaminants including pathogens, chemical entities, heavy metals in the coastal sediments poses threat to the ecosystem biodiversity and also to the [50% of the world population residing within 100 miles distance from the coastlines (Wakeham 1996; Bouloubassi et al. 2001). Among the several pollutants causing concomitant threat to the marine and in close proximity to terrestrial habitats, polycyclic aromatic hydrocarbons

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(PAHs) are ubiquitously persistent along the marine ecosystem and other shallow estuaries which are open to a variety of anthropogenic activities (Lipiatou et al. 1997; Baumard et al. 1998; Martı´nez-Llado´ et al. 2007). Prime occurrence of PAHs is reported due to either incomplete combustion of organic matter or fossil fuels, or abundance of petroleum components in the marine environment (Parastar et al. 2011). As PAHs are hydrophobic in nature, they tend to accumulate in marine sediments by getting adsorbed to suspended particulates and hence persist as long-term contaminant repository in estuaries which may lead to contamination of food chains, which could cause a potential risk to human health (Kipopoulou et al. 1999; Jiang et al. 2011; Yang et al. 2014). The instantaneous area of the Gujarat coastline is being exceedingly surrounded by petroleum refineries, chemical industries, ship loading and ship-breaking activities and heavy transportation. Commercial vessels such as oil tankers and container ships use the coastline to transport manufactured goods and raw material for energy sources (coal, oil, etc.). In context to the same, there are many ports, viz. Alang-Sosiya Shipbreaking and Recycling Yard (ASSBRY), Navlakhi Port (NAV), Kandla, Pipavav and Mundra, situated along the Gujarat coastline. Specifically, these ports have drawn attention from several researchers due to extensive accumulation of PAHs. Several studies have tried to determine the PAHs concentration and pollution status of ASSBRY (Dudhagara et al. 2016). However, so far there is dearth of research on the PAHs contamination level at NAV and the studies describing their impact on human health and microbial load of these two ports of Gujarat are also scarce. The current study was implemented with an aim to observe chemometric footprints in the surface sediments of ASSBRY and NAV regions of the Gulf of Khambhat and Gulf of Kutch, respectively, along the Gujarat coastline at varied seasonal time points within a period of 1 year. The main objectives of the present study were assessment of PAHs to get a better understanding of their probable human cancer risk assessment and relationships with the bacterial abundance inhabiting the sediment. The study also includes the prediction of PAHs inputs using isomeric ratios followed by multivariate analysis based on PAHs concentrations and their structural attributes. To the best of our knowledge, this is one of the first studies

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taking into account the impact of PAHs accumulation on bacterial abundance and probabilistic cancer risk assessment at ASSBRY and NAV sites.

Materials and methods Study sites ASSBRY and NAV sites were considered for the current study as oil- and coal-contaminated areas along Gulf of Khambhat and Gulf of Kutch, Gujarat, respectively. The coastal sediment samples were collected at quarterly intervals during May 2015 to April 2016 to investigate the change in the contamination levels of PAHs along with inputs of PAHs. Each site was divided into five sampling stations, and a total of 20 samples from each site were collected at quarterly intervals during May 2015, August 2015, December 2015 and April 2016 (Fig. 1). The distance between successive sampling stations at each site considered for current study was 500 m so as to obtain an overall representation of the contamination levels at the sites (Kujawinski et al. 2011). Each of the five samples from the corresponding sampling stations comprised of pooled three samples collected within the 250 m of diameter of the respective station (Fig. 1). The sediment samples collected from ASSBRY were designated as 1A–20A (1A–5A: May 2015; 6A–10A: August 2015; 11A–15A: December 2015; and 16A– 20A: April 2015). Similarly, the samples collected from NAV were designated as 1N–20N (1N–5N; May 2015; 6N–10N: August 2015; 11N–15N: December 2015; and 16N–20N: April 2016). The samples were collected in amber glass bottles to prevent photooxidation of low molecular weight (LMW) PAHs. The samples were sieved through 2-mm sieve to remove gravel and silt prior to freeze-drying and stored at -20 °C until further experimentation. Extraction of PAHs Solvent extraction method was used for PAHs extraction from sediment samples as described by Dudhagara et al. (2016). PAHs extraction was carried out using 50 mL of DCM–acetone (2:1) mixture with sonication in an ultrasonicator (Toshcon-SW2, India) five times with 1 min of rest. Solvent phase was collected through filtration using Whatman filter paper

Environ Geochem Health

Fig. 1 Study areas and sampling locations

(No. 4), and the same process was repeated thrice. Collected solvent was air-dried overnight, and the extract was further re-suspended in 10 mL of DCM– acetone mixture for cleanup procedure. Cleanup procedure was performed as a crucial step for removal

of contaminants like decanes, humic acid materials and elemental sulfur which might interfere during the GC–MS analysis. The extracted samples were passed through silica gel column to get rid of impurities. Samples were further air-dried up to 1 mL to

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concentrate PAHs prior to GC–MS analysis (Shimadzu QP2010þ, Japan) (Barakat et al. 2001; Rahmanpoor et al. 2014). Analysis of PAHs and quality control PAHs assessment of the concentrated extract was performed by GC–MS using Rtx-5MS capillary column (0.25 mm internal diameter, 0.25 mm film thickness and 30 m long). Initially, standard mixture of US Environmental Protection Agency (USEPA) listed 16 priority pollutant PAHs (Sigma-Aldrich, Bellefonte, USA) was injected for quantification and calibration purposes (Dudhagara et al. 2016). Standard mixture contained PAHs: naphthalene (Nap), acenaphthylene (Acel), acenaphthene (Ace), fluorene (Flu), phenanthrene (Phe), anthracene (Ant), fluoranthene (Flt), pyrene (Pyr), chrysene (Chr), benz(a)anthracene (BaA), benzo(a)pyrene (BaP), Benzo(k)fluoranthene (BkF), indeno (1,2,3-cd)pyrene (IP), dibenz(a,h)anthracene (DahA), perylene (Per) and benzo (ghi)perylene (BghiPer). MS program was run at a temperature of 60 °C, hold of 3 min, followed by a sequential 10 °C increase till 160 °C, hold of 5 min, again consecutive 5 °C increase up to 280 °C and followed by the final hold of 10 min. As a carrier gas, pure helium (99.999%) was used at flow rate of 1.5 mL min-1. 1 lL of solvent– dissolved sample was injected into the injector with temperature of 280 °C with splitless injection mode using autoinjector (AOC-20i, Shimadzu) (Mohajeri et al. 2010; Xu and Lu 2010). The RPAHs concentration of total PAHs (TPAHs) was calculated on the basis of individually resolved peaks and then summed to examine TPAHs at contaminated sites. PAHs determination was subjected to quality control using method blank (solvent), three spiked blanks (16 PAHs spiked into solvent) and three matrix spiked (soil spiked with 16 PAHs). PAHs were quantified using the internal calibration method based on five-point calibration curves for individual compounds. The average recoveries of 16 PAHs in spiked blanks and matrix spiked samples were 115.43 ± 10.21 and 68.53 ± 11.26, respectively. The procedural blank did not contain target analytes. The method detection limit (MDL) of PAHs was estimated from a signal-to-noise ratio of 3, as MDL: 2 ng g-1 dw. To examine the primary productivity of the sediment samples, total microbial load (CFU/100 mL) was also determined (Jose and Pasicolan 2013).

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Multivariate analysis The concentrations of PAHs were analyzed using Mean ± SD. Due to the bunch of data obtained for 40 samples generated by the GC–MS analysis, the use of multivariate statistics is emphatically a certain and appropriate approach. Thus, in order to predict the inputs of PAHs, various isomeric ratios such as fluoranthene/fluoranthene ? pyrene(Flt/Flt ? Pyr), anthrancene/anthracene ? phenanthrene (Ant/Ant ? Phe), flouranthrene/pyrene (Flt/Pyr), anthracene/ phenanthrene (Ant/Phe), high molecular weight/low molecular weight (HMW/LMW) were used (Yunker et al. 2002; Chen and Chen 2011; Tobiszewski and Namies´nik 2012). Their characteristic values were used for the input prediction as depicted in Table 1. Principal component analysis (PCA), a multivariable tool, was used to reduce the set of variables and to extract the latent factors to analyze the relationship among variables, with an aim to define toxicity loading and inputs prediction of PAHs at contaminated sites (Palanisami et al. 2012; Saeedi et al. 2012). PCA was performed with varimax rotation, and [1.0 Eigen value was taken for consideration of principal components to derive significant outline and link between detected individual PAHs at contaminated sites using Minitab 17.0. To perform PCA, PAHs concentrations below the detection level were replaced by random values ranging from zero to MDL (Yunker and Macdonald 2003; Oros and Ross 2004; Dudhagara et al. 2016). Hierarchical cluster analysis (HCA) was performed using Minitab 17.0 to emphasize natural grouping of PAHs at both the contaminated sites (Calle´n et al. 2013). Effect of PAHs on bacterial abundance Total bacterial abundance was determined by classical microbiological method (CFU/mL). The effect of PAHs on total bacterial abundance was carried out by Pearson’s correlation followed by multiple linear regression analysis (MLRA) using Minitab version 17.0 (Jose and Pasicolan 2013). Pearson correlation analysis was used to test the degree of linear relationship among the individual PAHs and total microbial load. In the correlation analysis, the statistical level of significance used was at 0.005, 0.01 and 0.05. After determining the significant parameters, multiple regression analysis using stepwise method was used

Environ Geochem Health Table 1 Characteristic isomeric ratio values for inputs prediction of PAHs

Ratio

Petrogenic

Pyrogenic

References

Ant/Phe

\0.1

C0.1

Dudhagara et al. (2016)

Flt/Flt ? Pyr

\0.4

C0.4

Brandli et al. (2007)

Flt/Pyr

\0.1

C0.1

Rajpara et al. (2017)

Ant/Ant ? Phe

\0.1

C0.1

Brandli et al. (2007)

HMW/LMW

\1.0

C1.0

Edokpayi et al. (2016)

to test if linear dependence existed among the PAHs and total bacterial abundance. Risk assessment Toxicity equivalent (TEQ) method was used to assess the ecotoxicological risk at both the contaminated sites. The total BaP equivalent concentration (BaPeq) was calculated by the sum of BaPeq for each PAH using TEQ factors (Yang et al. 2014). People living at both the contaminated sites form a complete and mutually interdependent community. Therefore, the incremental lifetime cancer risk (ILCR) was employed to evaluate the potential risk of PAHs in soils of ASSBRY and NAV in this study. The ILCRs for adults in terms of direct ingestion, dermal contact and inhalation were calculated using the following equations: ILCRSIngestion pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  CS  CSFIngestion  3 ðBW=70Þ  IRsoil  EF  ED ¼ BW  AT  cf

ð1Þ ILCRSDermal pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  CS  CSFDermal  3 ðBW=70Þ  SA  AF  ABS  EF  ED ¼ BW  AT  cf

ð2Þ ILCRSInhalation pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  CS  CSFInhalation  3 ðBW=70Þ  IRair  EF  ED ¼ BW  AT  PEF  cf

ð3Þ -1

where CS is the PAHs concentration of soils (lg kg ), which was obtained by converting concentrations of PAHs using the TEQ factor of BaP (Liao and Chiang 2006; Yang et al. 2014). The carcinogenic slope factor (mg kg-1 day-1)-1 (CSF) was based on the cancercausing ability of BaP: CSFIngestion, CSFDermal and CSFInhalation of BaP were 7.3, 25 and 3.85

(mg kg-1 day-1)-1, respectively (Wang et al. 2007). BW is body weight (kg): 70 kg; AT is average life span (year): 70 years; EF is exposure frequency (days year-1): 350 days year-1; ED is the exposure duration (year): 30 years; IRsoil is the soil intake rate (kg day21): 0.0001 kg day-1; IRair is the inhalation rate (m3 day-1): 20 m3 day-1; SA is the dermal surface exposure (cm2 day-1): 5000 cm2 day-1; cf is the conversion factor: 106; AF is the dermal adherence factor (kg cm-2): 0.00001 kg cm-2; ABS is the dermal adsorption fraction (unitless): 0.1; and PEF is the soil dust produce factor (m3 kg-1): 1.32 9 109 m3 kg-1 (Peng et al. 2011). The total risks were the sum of risks of ILCRs in terms of direct ingestion, dermal contact and inhalation.

Results and discussion PAHs distribution patterns and concentrations The distribution pattern of individual PAHs at the sampling stations at various time intervals is shown in Tables S-1, S-2 and S-3. Surface sediments at ASSBRY and NAV were found to contain a wide range of individual PAHs concentrations, ranging from very low (\3 ng g-1 dw) to very high ([5000 ng g-1 dw) illustrating significant differences in the distribution patterns among sampling stations and time intervals. Among the detected PAHs at both the contaminated sites, Ace—a LMW PAH and DbChr—A HMW PAH were found to be in negligible concentrations. Maximum concentration of RPAHs (30,396.25 ng g-1dw) was observed during December 2015 at ASSBRY followed by May 2015, April 2016 and August 2015 as revealed from the sum as well as mean values of PAHs at the sampling stations at each time interval (Table 2) while in case of NAV, maximum concentration of RPAHs (54,240.45 ng g-1dw)

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Environ Geochem Health Table 2 Concentrations and distribution patterns of PAHs at the contaminated sites Sampling sites

ASSBRY

Concentration of PAHs (ng/g) dw

May 2015

R PAHs

NAV August 2015

December 2015

April 2016

15819.1

May 2015

August 2015

December 2015

April 2016

54240.45

19878.05

734.83

30,396.25

8078.95

1309.65

39290.90

R LMW PAHs

4348.10

326.80

15853.65

7187.45

4329.70

898.15

15120.90

8156.60

R HMW PAHs

15529.95

408.00

14542.60

8631.60

3749.20

411.50

24170.00

46,074.80

Mean R PAHs

3975.61

146.966

6079.25

3163.821

1615.79

261.93

7858.18

10848.09

Mean R LMW

869.62

65.36

3170.73

1437.499

865.94

179.63

3024.18

1633.12

Mean R HMW

3105.99

81.608

2908.52

1726.322

749.84

82.30

4834.00

9214.96

was observed during April 2016 followed by December 2015, May 2016 and August 2015 (Table 2). In our previous study, during 2013–2014 the concentration of RPAHs at ASSBRY reported was in the range of 5020 to 981,180 ng g-1 dw which supports the RPAHs contamination level of the present study (Dudhagara et al. 2016). The differences in concentration of PAHs across time intervals within a period of a year may be due to variation in the industrial and anthropogenic activities at the respective sites indicating seasonal ship-breaking, scrapping activities and coal transportation at ASSBRY and NAV, respectively. Elevated concentrations of RPAHs at both the sites during dry seasons (December 2015, May 2015 and April 2016) summarize the higher pollution level of PAHs compared to monsoon (August 2015). The abundance of HMW PAHs was observed to be dominant compared to LMW in most of the samples at ASSBRY and NAV (Table 2). However, this result is paradoxical with previous study undertaken during 2013–2014 (Dudhagara et al. 2016). The possible explanation may be that LMW PAHs are more soluble and degradable and drained into the aquatic environment, while HMW PAHs are more recalcitrant because of their hydrophobic nature, resistance to decomposition, and their ability to adsorb to sinked particles and reach to the sediment bed (Kafilzadeh 2015). Pre-dominance of HMW PAHs indicates the pyrogenic origin of PAHs both the contaminated sites (Yunker et al. 2002). PAHs were further classified according to the number of rings present in the individual PAHs, i.e., 2–3-, 4- and 5–6-ring PAHs. (Zuo et al. 2007; Barakat et al. 2011; Dudhagara et al. 2016). Overall, both the sites were highly polluted with 2–3-ring PAHs,

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followed by 5–6-ring and 4-ring PAHs (Fig. 2). Elevation in the 2–3-ring PAHs concentration points out the petrogenic inputs of contamination at both the sites (Yunker et al. 2002). The broad image of prominent PAHs concentration levels at ASSBRY and NAV imitates a higher input of PAHs to these areas, apparently due to (1) wide-ranged ship-breaking activities, oil spillage, heavy transportation and other industrial activities at ASSBRY and (2) coal transport activities, heavy transportation, fossil fuel, and other local industrial and human-related activities at NAV throughout the year. The results of this study suggest that several areas require continuous monitoring by policy makers and stake holders for possible remediation and restoration of these contaminated sites. Prediction of PAHs input using isomeric ratios Besides the PAHs input prediction based on the number of rings and molecular weight, isomeric ratios of detected PAHs also categorize the possible inputs of environmental PAHs contamination (Budzinski et al. 1997; Soclo et al. 2000). To predict input of PAHs at the contaminated sites, ratios of detected isomeric PAHs (Ant/Phe Vs Flt/Flt ? Pyr, Flt/Pyr Vs Ant/ Ant ? Phe, and HMW/LMW Vs Flt/Flt ? Pyr) were computed and analyzed as described by Yunker et al. (2002). Results revealed that based on the Ant/Phe and Ant/ Ant ? Phe ratios, all the ASSBRY samples except 14A were contaminated by pyrogenic inputs. The ratios of Flt/Flt ? Pyr indicated three samples (13A, 15A and 18A) being petrogenic, and Flt/Pyr ratio revealed three sediments samples (10A, 16A and 17A) in the petrogenically originating list of sediments

Environ Geochem Health Fig. 2 Frequency distribution of different ring PAHs at A ASSBRY and B NAV

A

B

(Fig. 3). HMW/LMW showed 6A, 7A and 11A also to be petrogenic apart from 10A, 15A and 18A samples which were also earlier classified under petrogenic (Fig. 3). Overall, the ratios depicted that majority of contaminated sediments in ASSBRY during the first half of the year are originating from pyrogenic inputs, and in the latter half, i.e., December 2015 and April 2016, petrogenic contamination was observed episodically. The isomeric ratios of Ant/Phe and Ant/Ant ? Phe revealed that all the NAV samples were contaminated

by pyrogenic inputs. The ratios of Flt/Flt ? Pyr and Flt/Pyr during the latter half of the study, i.e., December 2015 and April 2016, showed petrogenic contamination. HMW/LMW ratio-based sample distribution showed the samples from May 2015 and August 2015 to be contaminated via petrogenic inputs except the sampling stations 1N, 3N and 7N (Fig. 4). Overall, at NAV the contamination of sediments was confirmed to be mixed inputs of PAHs, majorly affected with PAHs contamination via pyrogenic inputs followed by partial contribution from the

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A

B

C

Fig. 3 Isomeric ratios of a Ant/Phe versus Flt/Flt ? Pyr, b Flt/ Pyr versus Ant/Ant ? Phe, c HMW/LMW versus Flt/Flt ? Pyr of ASSBRY. (1A–5A: May 2015; 6A–10A: August 2015; 11A– 15A: December 2015; and 16A–20A: April 2015) A = ASSBRY

petrogenic activities. Further, confirmation of PAHs origin was carried out using principal component analysis (PCA). PCA model The application of PCA in conjunction with PAHs analysis helps to predict the PAHs inputs and their association which therefore helps us to characterize prominent risk of contamination of PAHs so that

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A

B

C

Fig. 4 Isomeric ratios of a Ant/Phe versus Flt/Flt ? Pyr, b Flt/ Pyr versus Ant/Ant ? Phe, c HMW/LMW versus Flt/Flt ? Pyr of NAV. (1 N–5 N: May 2015; 6 N–10 N: August 2015; 11 N– 15 N: December 2015; and 16 N–20 N: April 2015) N = NAV

informed decisions can be made for site remediation by government agencies and policy makers. PCA analysis of ASSBRY showed three clear clusters in the PC1 (32.8%) and PC2 (31.7%) plot comprising total 64.5% of total variability (Fig. 5a). The first cluster indicated significant positive loading of B(ghi)Per, Acel, Phe and Flu, indicating petrogenic origin of contamination of PAHs (Zhang et al. 2009; Pietzsch

Environ Geochem Health

A

B

Natural groupings of PAHs using HCA HCA revealed the distribution of sinked PAHs at both the sites based on sum of individual PAHs. The PAHs having similar chemical and structural characteristics are presented into the same cluster (Wang et al. 2010). The hierarchical clustering revealed a single cluster comprising of two subgroups of similar PAHs. A remarkably different observation in this case was that Nap stood out as a single out-group from the rest of the PAHs, a probable reason being its very high concentration in majority of samples compared to the other PAHs observed at ASSBRY (Fig. 6a). One of the subgroups of the cluster included Ant, Ace, IP, Db(a,k)A and DbChr which were present in lower concentrations in the sediments. The other subgroup comprised rest of PAHs with Phe as an out-group, wherein as observed earlier Phe also showed significant correlation suggesting its vital impact on the bacterial abundance at the site.

A

Fig. 5 PCA model of a ASSBRY and b NAV

et al. 2010), supporting the excessive ship-breaking and oil spillage activity throughout the year while second and third cluster showed mixed inputs of contamination of PAHs at ASSBRY. For NAV sediments, the first two components showed a variance of 35.2% (PC1) and 34.9% (PC2), comprising total 70.1% of the total variance (Fig. 5b). As observed in the previous results, the overall PAHs concentrations at NAV were comparatively less, which was also confirmed by the PCA outcomes wherein the bottom left cluster comprised of LMW and 4-ring PAHs as compared to lower loading of 5–6-ring PAHs concentrations. In addition to that, a clear cluster of 5–6-ring PAHs was formed at the upper right side of the plot revealing their elevated loading of this group, suggesting pyrogenic inputs of contamination (Pietzsch et al. 2010). In general, input prediction model projected and centered on the diagnostic ratios and the PCA suggested mix inputs of PAHs revealing inconsistencies regarding the inputs contamination.

B

Fig. 6 Natural groupings of detected PAHs at a ASSBRY, b NAV. 1. Nap, 2. Acel, 3. Ace, 4. Flu, 5. Phe, 6. Ant, 7. Flt, 8. Pyr, 9. Chr, 10. B(a)A, 11. B(k)F, 12. B(a)P, 13. Db(a,k)A, 14. IP, 15. B(ghi)Per, 16. Per, 17. Db Chr

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For NAV, two definite clusters having homogenous groups of PAHs have been obtained. The first cluster contained two subgroups and comprised of PAHs with considerably lower concentrations in the sediments. The first subgroup included Nap, Acel, Phe, B(a)A, Db(a,k)A, Flt and Ant, at a similarity level of [80%, which were all moderately concentrated in the NAV samples (Fig. 6b). The second subgroup of the first cluster comprised of the less concentrated PAHs (Ace, DbChr, Flu, IP, Chr, B(k)F, B(ghi), Per) at similarity level [90%. This cluster was associated with the second cluster at similarity level of 38.65%. The second cluster comprised of three PAHs, viz. Pyr, B(a)P and Per, which were prevalent in the highest concentrations in most of the samples. Effect of PAHs on bacterial abundance Microbiological analyses were performed to compute the influence of individual PAHs concentration on microbial population in sediments at each study site using Pearson correlation analyses followed by regression. The results revealed significant negative correlation between the bacterial abundance and Nap

Table 3 Correlation of total microbial load with individual PAHs abundance at the contaminated sites Parameters

ASSBRY

Nap

-0.998***

0.094

Acel

-0.999***

-0.635

Ace

-0.395

-0.725*

Flu

0.256

-0.587

Phe

-0.985**

-0.787*

Ant

-0.710*

-0.758*

Flt

-0.475

-0.514

Pyr

-0.955

0.410

Chr

-0.593

-0.711*

B(a)A

-0.504

-0.000

B(k)F

-0.657

0.024

B(a)P

-0.088

0.032

Db(a,k)A

-0.157

-0.243

IP

-0.985**

B(ghi)Per Per

0.305 -0.068

Db Chr

0.219

* p \ 0.05; ** p \ 0.01; *** p \ 0.005

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NAV

0.120 -0.475 -0.725* -0.036

(p \ 0.005), Acel (p \ 0.005), Phe (p \ 0.01), IP (p \ 0.01) and Ant (p \ 0.05) (Table 3). Further, stepwise regression analysis revealed that Nap and Acel were significant predictors for bacterial population at ASSBRY at an R-value of 0.999. Moreover, almost similar trend was observed at NAV, i.e., a significant negative correlation (p \ 0.05) of PAHs such as Ace, Phe, Ant, Chr and Per with bacterial abundance. Further, regression analysis revealed Ace and Phe to be the significant influencing factors for this site at an R-value of 0.997 (Table 3). The results also corroborate with earlier findings that a higher prevalence of bacteria, and its diversity is observed in less hydrocarbon polluted samples compared to hydrocarbon accumulated sites (Amund and Igiri 1990; Akpor et al. 2007; Esedafe et al. 2015). A very surprising result was observed that BaP despite being highly carcinogenic, does not have any negative influence on total bacterial abundance. This rare observation has been supported by the study of Mermillod-Blondin et al. (2013). The occurrence of BaP can have both negative and positive effects on microorganisms (through organic matter enrichment). These effects generally take place in the presence of high concentration of BaP also reported by Juhasz and Naidu (2000). Mermillod-Blondin et al. (2013) have also suggested that microbial population could be more affected by contaminant-induced changes in microbe–invertebrate interactions rather than by direct effect of contaminants on microorganisms. Risk assessment of PAHs on human health Toxicity equivalent quotient (TEQ) was used to assess the ecotoxicological risk imposed at the contaminated sites. BaPeq was calculated by the sum of BaPeq for each PAH using toxicity equivalent factors (Yang et al. 2014). Based on the BaPeq value, incremental lifetime cancer risk (ILCR) is used to evaluate the human health risk. Generally, an ILCR between 10-6 and 10-4 indicates a potential risk. Table 4 shows the ILCRs levels calculated in the ASSBRY and NAV soils, indicating a high human health risk from the exposure PAHs. The total ILCRs values of ASSBRY soils ranged from 4.11 9 10-6 to 2.11 9 10-5 exhibiting vast contribution of ILCRingestion indicating high potential carcinogenic risk via inhalation throughout the year. ILCRs value of NAV soil ranged

Environ Geochem Health Table 4 Descriptive statistics of data on incremental lifetime cancer risks (ILCRs) in soils from ASSBRY and NAV

Sampling period

ILCRS

ASSBRY

May 2015

ILCRSIngestion

2.91 9 10-6

2.09 9 10-6

4.99 9 10

-8

3.58 9 10-4

2.30 9 10

-9

1.67 9 10-10

2.96 9 10

-6

3.61 9 10-4

1.20 9 10

-7

0.50 9 10-7

2.1 9 10-5

9.03 9 10-6

ILCRSDermal ILCRSInhalation Total August 2015

ILCRSIngestion ILCRSDermal

-12

0.42 9 10-11

-5

ILCRSIngestion

2.11 9 10 2.14 9 10-6

9.08 9 10-6 2.48 9 10-6

ILCRSDermal

3.67 9 10-8

4.25 9 10-4

ILCRSInhalation

1.70 9 10-10

0.19 9 10-9

ILCRSInhalation Total December 2015

Total April 2016

ILCRSIngestion ILCRSDermal ILCRSInhalation

Total

from 9.08 9 10-6 to 4.50 9 10-3 indicating higher cancer risk as compared to ASSBRY. ILCRDermal ranged from 9.03 9 10-6 to 4.48 9 10-3 indicating the high risk of cancer via dermal contact. All the ILCRInhalation values ranged between 10-9 and 10-12 at both the sites, indicative of negligible carcinogenic risk of PAHs. The additive assumption of this approach is quite uncertain and may lead to inaccurate assessments. Despite this limitation, this approach has the merit of taking into account the actual PAHs profiles encountered in environmental settings reported in this study.

Conclusion The present work is probably the first report on the combined PAHs assessment and their effect on microbial population and human health risk assessment at Gulf of Khambhat (ASSBRY) and Gulf of Kutch (NAV). The establishment of industries and urbanization at larger scale, during last decade, shows visible signs of impact at a first glance at both the contaminated sites. Maximum concentration of RPAHs at ASSBRY (30,396.25 ng g-1 gw) and NAV (54240.45 ng g-1 gw) disclosed high contamination of PAHs in comparison with global

NAV

1.0 9 10

2.17 9 10

-6

4.27 9 10-4

4.11 9 10

-6

2.61 9 10-5

7.04 9 10

-9

4.48 9 10-3

0.32 9 10

-9

2.09 9 10-9

4.11 9 10-6

4.50 9 10-3

contamination. Isomeric ratios and PCA model revealed mixed origin of PAHs at both the contaminated sites. Thus, it can be postulated that both the sites are exposed to various LMW and HMW PAHs by the joint inputs of petrogenic and pyrogenic approaches. Higher concentrations of some PAHs are influencing bacterial abundance at both the sites and need further study. The ILCRs of PAHs showed that PAHs concentrations are likely to be harmful to human health almost throughout the year. However, NAV still needs more attention of policy makers and stake holders for restoration of the site. Despite the inherent limitations of this research, the adopted approach in the present study highlights baseline lessons for future references which may include the entire Gujarat coast for the purpose of promoting our knowledge of the sediment quality of coastal ecosystems and applying the findings for developing remediation strategies for conservation of the coastal resource quality. Acknowledgement Authors are thankful to Earth Science and Technology Cell (ESTC), Ministry of Science (MoES), Government of India (GoI), New Delhi, for financial assistance to carry out research. Thanks are also due to Dr N.C. Desai, Professor and Head, Department of Chemistry, Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat, for his help in the chemistry of PAHs.

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References Akpor, O. B., Lgbinosa, E. O., & Lgbinosa, O. O. (2007). Studies on the effect of petroleum hydrocarbon on the microbial and physico-chemicals characteristics of soil. African Journal of Biotechnology, 6, 1939–1943. Amund, O. O., & Igiri, O. C. (1990). Biodegradation of petroleum hydrocarbons under tropical estuarine conditions. World Journal of Microbiology & Biotechnology, 6, 255–262. Barakat, A. O., Mostafa, A., Wade, T. L., Sweet, S. T., & El Sayed, N. B. (2011). Distribution and characteristics of PAHs in sediments from the Mediterranean coastal environment of Egypt. Marine Pollution Bulletin, 62, 1969–1978. Barakat, A. O., Qian, Y., Kim, M., & Kennicutt, M. C. (2001). Chemical characterization of naturally weathered oil residue in arid terrestrial environmental in Al-Alamein, Egypt. Environmental Science and Technology, 27, 291–310. Baumard, P., Budzinski, H., & Garrigues, P. (1998). Polycyclic aromatic hydrocarbons in sediments and mussels of the western Mediterranean sea. Environmental Toxicology and Chemistry, 17, 765–776. Bouloubassi, I., Fillaux, J., & Saliot, A. (2001). Hydrocarbons in surface sediments from there Changjiang (Yangtze River) Estuary, East China Sea. Marine Pollution Bulletin, 42, 1335–1346. Brandli, R. C., Bucheli, T. D., Kupper, T., Mayer, J., Stadelmann, F. X., & Tarradellas, J. (2007). Fate of PCBs, PAHs and their source characteristic ratios during composting and digestion of source-separated organic waste in fullscale plants. Environmental Pollution, 148, 520–528. Budzinski, H., Jones, I., Bellocq, J., Pie´rrad, C., & Garrigues, P. (1997). Evaluation of sediment contamination by polycyclic aromatic hydrocarbons in the Gironde estuary. Marine Chemistry, 58, 85–97. Calle´n, M. S., Lo´pez, J. M., & Mastral, A. M. (2013). Influence of organic and inorganic markers in the source apportionment of airborne PM10 in Zaragoza (Spain) by two receptor models. Environmental Science and Pollution Research, 20, 3240–3251. Chen, C. W., & Chen, C. F. (2011). Distribution, origin and potential toxicological significance of polycyclic aromatic hydrocarbons (PAHs) in sediments of Kaohsiung Harbor, Taiwan. Marine Pollution Bulletin, 63, 417–423. Dudhagara, D. R., Rajpara, R. K., Bhatt, J. K., Gosai, H. B., Sachaniya, B. K., & Dave, B. P. (2016). Distribution, sources and ecological risk assessment of PAHs in historically contaminated surface sediments at Bhavnagar coast, Gujarat, India. Environmental Pollution, 213, 338–346. Edokpayi, J. N., Odiyo, J. O., Popoola, O. E. & Msagati, T. A. (2016). Determination and distribution of polycyclic aromatic hydrocarbons in rivers, sediments and wastewater effluents in Vhembe District, South Africa. International Journal of Environmental Research and Public Health, 13(4), 387. Esedafe, W. K., Fagade, O. E., Umaru, F. F., & Akinwotu, O. (2015). Bacterial degradation of the polycyclic aromatic hydrocarbon (PAH)-fraction of refinery effluent.

123

International Journal of Environmental Bioremediation & Biodegradation, 3, 23–27. Jiang, Y. F., Wang, X. T., Wu, M. H., Sheng, G. Y., & Fu, J. M. (2011). Contamination, source identification, and risk assessment of polycyclic aromatic hydrocarbons in agricultural soil of Shanghai, China. Environmental Monitoring and Assessment, 183, 139–150. Jose, A. S., & Pasicolan, S. A. (2013). Carrying capacity of net primary productivity of Laguna Lake, Philippines. Global Advanced Research Journal of Agricultural Science, 2, 289–298. Juhasz, A. L., & Naidu, R. (2000). Bioremediation of high molecular weight polycyclic aromatichydrocarbons: A review of the microbial degradation of benzo(a)pyrene. International Biodeterioration & Biodegradation, 45, 57–88. Kafilzadeh, F. (2015). Distribution and sources of polycyclic aromatic hydrocarbons in water and sediments of the Soltan Abad River, Iran. The Egyptian Journal of Aquatic Research, 41, 227–231. Kipopoulou, A. M., Manoli, E., & Samara, C. (1999). Bioconcentration of polycyclic aromatic hydrocarbons in vegetables grown in an industrial area. Environmental Pollution, 106, 369–380. Kujawinski, E. B., Soule, M. C. K., Valentine, D. L., Boysen, A. K., Longnecker, K., & Redmond, M. C. (2011). Fate of dispersants associated with the deepwater horizon oil spill. Environmental Science and Technology, 45, 1298–1306. Lewis, M. A., & Devereux, R. (2009). Non-nutrient anthropogenic chemicals in seagrass ecosystems: Fate and effects. Environmental Toxicology and Chemistry, 28, 644–661. Liao, C. M., & Chiang, K. C. (2006). Probabilistic risk assessment for personal exposure to carcinogenic polycyclic aromatic hydrocarbons in Taiwanese temples. Chemosphere, 63, 1610–1619. Lipiatou, E., Tolosa, I., Simo, R., Bouloubassi, I., Dachs, J., Marti, S., et al. (1997). Mass budget and dynamics of PAH in the western Mediterranean Sea. Deep-Sea Research, 44, 881–905. Martı´nez-Llado´, X., Gibert, O., Martı´, V., Dı´ez, S., Romo, J., & Bayona, J. M. (2007). Distribution of polycyclic aromatic hydrocarbons (PAHs) and tributyltin (TBT) in Barcelona harbor sediments and their impact on benthic communities. Environmental Pollution, 149, 104–113. Mermillod-Blondin, F., Foulquier, A., Gilbert, F., Navel, S., Montuelle, B., Bellvert, F., et al. (2013). Benzo(a)pyrene inhibits the role of the bioturbator Tubifex tubifex in river sediment biogeochemistry. Science of the Total Environment, 450–451, 230–241. Mohajeri, L., Aziz, H. A., Isa, M. H., Zahed, M. A., & Mohajeri, S. (2010). Ex-situ Bioremediation of Crude Oil in Soil, a Comparative Kinetic Analysis. Bulletin of Environmental Contamination and Toxicology, 85, 54–58. Oros, D. R., & Ross, J. R. M. (2004). Polycyclic aromatic hydrocarbons in San Francisco Estuary sediments. Marine Chemistry, 86, 169–184. Palanisami, T., Mallavarapu, M., & Naidu, R. (2012). Multivariate analysis of mixed contaminants (PAHs and heavy

Environ Geochem Health metals) at manufactured gas plant site soils. Environmental Monitoring and Assessment, 184, 3875–3885. Parastar, H., Radovic, J. R., Heravi, M. J., Diez, S., Bayona, J. M., & Tauler, R. (2011). Resolution and quantification of complex mixtures of polycyclic aromatic hydrocarbons in heavy fuel oil samples by means of GC 9 GC-ToFMS combined to multivariate curve resolution. Analytical Chemistry, 83, 9289–9297. Peng, C., Chen, W. P., Liao, X. L., Wang, M. E., & Ouyang, Z. Y. (2011). Polycyclic aromatic hydrocarbons in urban soils of Beijing: Status, sources, distribution and potential risk. Environmental Pollution, 159, 802–808. Pietzsch, R., Patchineelam, S. R., & Torres, J. P. M. (2010). Polycyclic aromatic hydrocarbons in recent sediments from a subtropical estuary in Brazil. Marine Chemistry, 118, 56–66. Rahmanpoor, S., Ghafourian, H., Hashtroudi, S. M., & Bastami, K. D. (2014). Distribution and sources of polycyclic aromatic hydrocarbons in surface sediments of the Hormuz strait, Persian Gulf. Marine Pollution Bulletin, 78, 224–229. Rajpara, R. K., Dudhagara, D. R., Bhatt, J. K., Gosai, H. B. & Dave, B. P. (2017). Polycyclic aromatic hydrocarbons (PAHs) at the Gulf of Kutch, Gujarat, India: Occurrence, source apportionment, and toxicity of PAHs as an emerging issue. Marine Pollution Bulletin, 119(2), 231–238. Saeedi, M., Loretta, Y. L., & Salmanzadeh, M. (2012). Heavy metals and polycyclic aromatic hydrocarbons: Pollution and ecological risk assessment in street dust of Tehran. Journal of Hazardous Materials, 227, 9–17. Soclo, H. H., Garrigues, P., & Ewald, M. (2000). Origin of polycyclic aromatic hydrocarbons (PAHs) in coastal marine sediments: Case studies in Cotonou (Benin) and Aquitaine (France) areas. Marine Pollution Bulletin, 40, 387–396. Tobiszewski, M., & Namies´nik, J. (2012). PAH diagnostic ratios for the identification of pollution emission sources. Environmental Pollution, 162, 110–119.

Wakeham, S. G. (1996). Aliphatic and polycyclic aromatic hydrocarbons in Black Sea sediments. Marine Chemistry, 53, 187–205. Wang, Z., Chen, J. W., Qiao, X. L., Yang, P., & Tian, F. L. (2007). Distribution and sources of polycyclic aromatic hydrocarbons from urban to rural soils: A case study in Dalian, China. Chemosphere, 68, 965–971. Wang, H. S., Liang, P., Kang, Y., Shao, D. D., Zheng, G. J., Wu, S. C., et al. (2010). Enrichment of polycyclic aromatic hydrocarbons (PAHs) in mariculture sediments of Hong Kong. Environmental Pollution, 158, 3298–3308. Xu, Y., & Lu, M. (2010). Bioremediation of crude oil-contaminated soil: Comparison of different biostimulation and bioaugmentation treatments. Journal of Hazardous Materials, 183, 395–401. Yang, Y., Woodward, L. A., Li, Q. X., & Wang, J. (2014). Concentrations, source and risk assessment of polycyclic aromatic hydrocarbons in soils from midway atoll, North Pacific Ocean. PLoS One, 9, 1–7. Yunker, M. B., & Macdonald, R. W. (2003). Alkane and PAH depositional history, sources and fluxes in sediments from the Fraser River Basin and Strait of Georgia, Canada. Organic Geochemistry, 34, 1429–1454. Yunker, M. B., Macdonald, R. W., Vingarzan, R., Mitchell, H., Goyette, D., & Sylvestre, S. (2002). PAHs in the Fraser River basin: A critical appraisal of PAHs ratios as indicators of PAH source and composition. Organic Geochemistry, 33, 489–515. Zhang, W., Feng, H., Chang, J., Qu, J., Xie, H., & Yu, L. (2009). Heavy metal contamination in surface sediments of Yangtze River intertidal zone: An assessment from different indexes. Environmental Pollution, 157, 1533–1543. Zuo, Q., Duan, H. Y., Yang, Y., Wang, J. X., & Tao, S. (2007). Source apportionment of polycyclic aromatic hydrocarbons in surface soil in Tianjin, China. Environmental Pollution, 147, 303–310.

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