Comparison of extraction methods
Four distinct extraction methods were compared relating to ease of application, selectivity and extraction efficiency. The results of PAHs extraction acquired with different methods are indicated in Table 1. Both Soxhlet and MASE techniques outperformed sonication and mechanical stirring through the extraction of greater number of LMW and HMW PAHs based upon specific macroscopic and microscopic reasons. Mass transfer of PAHs from the solid plant matrix to the extraction solvent is regulated by the mechanisms of convection and diffusion. Moreover, PAHs concentration difference between the leaf matrix and solvent is the driving force which controls the rate of extraction (Pinelo et al., 2006).
Soxhlet technique is a semi-batch process involving temperature acceleration (de Castro and Ayuso, 2000). Elevated temperature in Soxhlet extraction expedites the kinetics of mass transfer by increasing the solubility of solute in the solvent and hence improves the extraction efficiency. Besides, high temperature (a) reduces solvent density, viscosity and surface tension which assists in better solvent selectivity, wettability, diffusivity and penetrability for maintaining contact with the solid surface, (b) drastically alters the surface equilibrium by denaturing the analyte-analyte (cohesive) and analyte-matrix (adhesive) interactions owing to the reduction in activation energy of desorption, (c) enhances the solvation power of solvent and (d) allows analyte diffusion into the surface of the matrix along with the mass transfer of targeted solutes into the extracting solvent (Malik and Mandal, 2022). Thus, in Soxhlet method, complete extraction of desired PAHs was possible by virtue of multiple siphoning as the solvent could reach the active sites of the matrix more easily at high temperature and the rapid rate of diffusion promoted mass transfer of PAHs which, in turn, increased the extraction rate. Furthermore, a favourable PAHs concentration gradient (almost constant) was maintained via continuous recycling of fresh solvent through the matrix (with the aid of boiling and condensation) which prevented PAHs saturation in the extractant making the extraction process exhaustive in nature, enhanced the transfer equilibrium and eventually the mass transfer rate in Soxhlet technique (Malik and Mandal, 2022).
In MASE, application of electromagnetic energy (microwaves) to the sample matrix caused damage to the structure of cell membrane, thereby increasing the maximum release of PAHs into the solvent as in the case of Soxhlet. Homogeneous heating by ionic conduction and thermal agitation due to dipole rotation improved the diffusion of solvent into the sample and stimulated the partitioning of PAHs from the matrix to the extractant (Veggi et al., 2013). Induced heat in MASE caused evaporation of residual moisture present in the sample, creating appreciable intracellular pressure which ruptured the cell walls of the leaf matrix and enhanced the leaching of PAHs (Ngamkhae et al., 2022). Increased solubility of PAHs, solvent dispersion and selectivity because of high temperature also raised the extraction efficiency without degradation of analytes. Additionally, the unidirectional flow of heat and mass transfer to the outer side of the cells was responsible for high extraction yield along with reduction in process time and energy loss (Veggi et al., 2013).
Sonication technique transmits the sound energy of ultrasonic waves while propagating through the fluid mix, promoting high solvent inflow into plant matrix, intimate contact between them and rate of mass transfer of analytes. Wave propagation also causes frequent changes in the fluid density (compression and rarefaction) resulting in creation and rupture of vapour bubbles in quick succession (cavitation action) throughout the solvent. Bursting of such bubbles facilitated cell wall breakdown and simultaneous release of PAHs into the extraction solvent. Smith et al. (2006) reported that the ultrasonic-assisted extraction (UAE) is not very much effective for LMW PAHs as compared to Soxhlet and microwave-assisted extraction (MAE) owing to low recovery rates varying between 44–76%. In the present study also, recovery of LMW PAHs (NAP, ACY, ACE, FLU, PHE and ANT) through sonication was not satisfactory (only ACY and ACE could be detected (ref. Table 1)) due to, probably, the lower temperature range (25–35 ⁰C) of the process (Ngamkhae et al., 2022).
Mechanical stirring or agitation at room temperature (in a simple shake flask placed on a magnetic stirrer) is an easy extraction technique with low requirements for glassware. However, the stirring method could not efficiently extract the HMW PAHs and the extraction efficiency was appreciably lower than the other methods because of poor selectivity for PAHs, giving inappropriate results (Berset et al., 1999; Graham et al., 2006). Normally, faster extraction occurred initially when the difference in PAHs concentration was maximum. But, gradually, the extraction rate decelerated with the decrease in concentration difference, thereby causing a sharp decline in the rate of mass transfer. The extended extraction time did not further increase the yield once the saturation point was reached (Kalbe et al., 2008). Considerably higher rate of solvent evaporation was also witnessed in agitation extraction, leading to PAHs losses during evaporation.
The above-cited benefits rendered Soxhlet and MASE more effective over other techniques. Cicero et al. (2000) also obtained comparable recoveries in both the cases for the extraction of polychlorinated biphenyl (PCB) congeners from marine sediments. Highest extraction yield of bioactive compounds was again recorded by Ngamkhae et al. (2022) with MAE in contrast to UAE. A comparative study between MAE and UAE by Ratola et al. (2009) for PAHs extraction from pine needles and bark showed relatively same recovery results for the two methods. Hence, sonication might give better results with high extraction efficiency in some other species under optimized extraction conditions. Many studies have suggested high recoveries of PAHs from leaf matrices of various plant species (Pinus sylvestris, Acer campestre, Pinus koraiensis, Cinnamomum camphora
and Phyllostachys edulis) using Soxhlet extraction (Bi et al., 2018; Chun, 2011; Hubert et al., 2003; Yang et al., 2017). Therefore, in this study, all the other experiments were carried out with the Soxhlet technique.
But, considering the aforesaid advantages of MASE, design and optimization of its parameters is the future aim of the proposed work.
Table 1
GC-MS detection of PAHs in the plant samples: Comparison between extraction methods
| Retention time (min)/Quantification ion (m/z) of PAHs in the analytical standard mix* | Retention time (min)/Quantification ion (m/z) of identified PAHs in the leaf extracts** |
EPA-PAHs | | Mechanical stirring | Sonication | Soxhlet extraction | MASE |
NAP | 6.23, 6.74/128 | 6.64/128.07 | nd | 6.89/128.60 | nd |
ACY | 10.23/152 | 10.26/152.20 | 10.40/152.69 | 10.39/152.02 | 10.35/152.24 |
ACE | 10.60/154 | nd | 10.80/154.51 | 10.76/154.07 | 10.66/154.43 |
FLU | 12.89/166 | 12.77/166.40 | nd | nd | nd |
PHE | 15.37/178 | 15.80/178.03 | nd | 15.17/178.44 | 15.52/178.61 |
ANT | 15.37/178 | 15.80/178.03 | nd | 15.17/178.44 | 15.52/178.61 |
FLA | 19.30/202 | 19.43/202.11 | 19.64/202.52 | 19.99/202.20 | 19.50/202.90 |
PYR | 20.59/202 | nd | 20.45/202.85 | 20.65/202.52 | 20.71/202.43 |
BaA | 24.96/228 | nd | 24.67/228.37 | 24.94/228.17 | 24.64/228.44 |
CHR | 24.96/228 | nd | 24.67/228.37 | 24.94/228.17 | 24.64/228.44 |
BbF | 28.37/252 | 28.89/252.12 | 28.38/252.54 | 28.30/252.65 | 28.51/252.41 |
BkF | 28.37/252 | 28.89/252.12 | 28.38/252.54 | 28.30/252.65 | 28.51/252.41 |
BaP | 29.69/252 | 29.86/252.41 | 29.48/252.38 | nd | 29.74/252.59 |
DB[ah]A | 36.75/278 | nd | 36.82/278.52 | 36.74/278.39 | 36.43/278.65 |
IP | 36.75/276 | nd | 36.82/276.29 | 36.74/276.37 | 36.43/276.57 |
B[ghi]P | 38.89/276 | nd | 38.93/276.45 | 38.89/276.80 | 38.96/276.62 |
Total no. of extracted PAHs | 16 | 9 | 12 | 14 | 14 |
* Reproduced from Mukhopadhyay et al. (2021) for the ease of comparison. |
** Optimization studies done using the plant samples of JDV site only. For other experimental runs, optimized parameters were used. |
‘nd’: Not detected.
Choice of best-suited solvent for PAHs extraction
Separation by solid-liquid extraction is greatly influenced by solvent selection, considering solubility, concentration and nature of analytes, capacity of solvents to penetrate and interact with the sample matrix, net polarities of solvents (correlated to their dipole moments, dielectric constant, viscosity, surface tension and cohesive energy density) and kinetic processes of mass transfer (Baskar et al., 2019). High solute distribution coefficient with minimized solvent losses plays a crucial role in solvent selection and solubility of solute is also mediated by the physicochemical properties and chemical structures of both the solute and solvent (Bonventre, 2014). Table 2 portrays the Soxhlet-based PAHs yields against the studied solvents and the observed trend was: total no. of PAHs extracted with acetone < total no. of PAHs extracted with acetone: n−hexane < total no. of PAHs extracted with DCM < total no. of PAHs extracted with n−hexane < total no. of PAHs extracted with toluene.
Inefficiency of acetone to extract HMW PAHs is evident from Table 2. Similarly, analysis of the extracts obtained with DCM and acetone: n-hexane mixture specified the presence of mainly LMW PAHs and very few HMW PAHs (only BaP, DB[ah]A and IP). But, toluene and n-hexane showed higher selectivity by extracting maximum number of LMW and HMW PAHs from leaf samples. It can be clearly seen that the non-polar solvents were more suitable for the extraction of PAHs. For acetone: n-hexane mixture, although good PAHs recoveries have been reported in case of other solid matrices (Haleyur et al., 2016; Janska et al., 2004), yet, in the current study, lower extraction output was attained when compared with toluene and n-hexane separately. This could have resulted from the complexity of plant cell membrane composed of non-polar lipid tails restricting the transmembrane permeation of polar acetone molecules to a certain degree and hindering, thereby, the diffusion path of n-hexane through the plasma membrane for extraction of PAHs (Orsi et al., 2009; Shinoda, 2016). So, the above phenomenon had affected the extraction efficiency to a great extent. Least extraction capacity of acetone for non-polar PAHs can be ascribed to its high polarity as solvent extraction works on the basic principle of ‘like dissolves like’ (law of similarity and miscibility which states that a solute better dissolves in a solvent having close relative value of polarity) (Kamarudin et al., 2021) and impermeability of biological membrane with lipid bilayer limiting the free entry of highly polar molecules. DCM is a moderately polar solvent and thus, exhibited lower extraction recovery of hydrophobic PAHs. In addition, it is not recommended to be used as an extraction solvent on account of its volatility (boiling point: 39.6 ⁰C) which caused huge solvent loss in Soxhlet method. In contrast, toluene and n-hexane being most non-polar in nature (having low dipole moments), diffusion across the hydrophobic core of lipid bilayer enhanced the extraction performance. According to the results obtained (Table 2), toluene has been proved to be an ideal solvent which solvated almost all the lipophilic PAHs molecules of the matrix completely by means of dispersion interactions. Good relative yields of PAHs from the samples of tree bark, pine needles and moss species with the application of toluene in Soxhlet technique were also documented by Birke et al. (2018) and Oishi et al. (2018). Alternatively, during dynamic sonication-assisted extraction of PAHs from lichen samples, toluene did not show adequate method efficiency implying the significances of the process and matrix properties on extraction yield (Domeno et al., 2006).
Table 2
Variation in PAHs recovery from plant leaves using different solvents in Soxhlet extraction based on GC-MS analysis
| Retention time (min)/Quantification ion (m/z) of identified PAHs extracted from leaf matrix with various solvents |
EPA-PAHs | Acetone | DCM* | n-Hexane | Toluene | Acetone: n-Hexane (1:1) |
NAP | 6.89/128.07 | 6.57/128.06 | 6.93/128.70 | 6.24/128.48 | 6.86/128.24 |
ACY | 10.38/152.09 | 10.36/152.41 | 10.32/152.11 | nd | nd |
ACE | nd | 10.54/154.11 | 10.62/154.49 | 10.63/154.36 | nd |
FLU | nd | 12.82/166.56 | nd | 12.53/166.79 | 12.30/166.05 |
PHE | 15.28/178.03 | 15.80/178.38 | 15.22/178.42 | 15.46/178.20 | 15.70/178.26 |
ANT | 15.28/178.03 | 15.80/178.38 | 15.22/178.42 | 15.46/178.20 | 15.70/178.26 |
FLA | nd | 19.43/202.33 | 19.81/202.72 | 19.85/202.34 | 19.40/202.06 |
PYR | 20.64/202.06 | nd | 20.69/202.57 | 20.68/202.19 | nd |
BaA | 24.87/228.25 | nd | 24.89/228.33 | 24.10/228.53 | nd |
CHR | 24.87/228.25 | nd | 24.89/228.33 | 24.10/228.53 | nd |
BbF | nd | nd | 28.53/252.68 | 28.28/252.89 | nd |
BkF | nd | nd | 28.53/252.68 | 28.28/252.89 | nd |
BaP | nd | 29.89/252.15 | nd | 29.35/252.92 | 29.41/252.12 |
DB[ah]A | nd | 36.65/278.61 | 36.83/278.49 | 36.40/278.66 | 36.49/278.30 |
IP | nd | 36.65/276.39 | 36.83/276.16 | 36.40/276.59 | 36.49/276.62 |
B[ghi]P | nd | nd | 38.90/276.64 | 38.72/276.43 | nd |
Total no. of extracted PAHs | 7 | 10 | 14 | 15 | 8 |
* DCM: Dichloromethane |
Effect of extraction time on PAHs yield
It is necessary to optimize the extraction period in order to protect the analytes, reduce energy consumption and cost of the process. So, duration of extraction demonstrated a strong impact on the effective recovery of PAHs from Murraya leaves. The performance of the extraction experiments at different time intervals employing toluene has been depicted in Table 3. The results revealed that the efficient extraction time (i.e. when equilibrium condition achieved between inside and outside of the solid matrix following convective mass transfer) for obtaining highest number of extracted PAHs was up to 6 h, beyond, which, the extraction efficacy reduced (ref. Table 3). Majority of LMW PAHs were not detected in 5 h of experimental run, probably due to the reason that the extraction equilibrium had not reached (i.e. persistence of a non-equilibrium state between the matrix and the solvent) at that time for all the analytes. Typically, exposure to longer extraction time disrupts plant cell membrane, enhancing the release as well as diffusion of PAHs into the solvent (Christou et al., 2021). However, extraction time exceeding the optimum value can lead to break down or conversion of target analytes owing to heat accumulation. Hence, temperature overshoot with the increase in time might have induced PAHs vapourization or thermal decomposition in the course of 7 h and 8 h of extractions and consequently lowered the recovery. Thus, 6 h of extraction was chosen as the optimum time for further considerations. Prolonged extraction periods were not examined as increase in time may not further affect the extraction process or may degrade or transform the analytes. Comparable extraction period has also been studied by Domeno et al. (2006) and Ray et al. (2021) using lower (Xanthoria parietina) and higher plant species (evergreen trees and shrubs) for PAHs determination via Soxhlet method respectively.
Table 3
GC-MS profiles of foliar PAHs obtained during varying time periods of extraction using Soxhlet technique
| Retention time (min)/Quantification ion (m/z) of identified PAHs in time course of extraction |
EPA-PAHs | 5 h | 6 h | 7 h | 8 h |
NAP | 6.65/128.05 | 6.59/128.21 | 6.62/128.08 | 6.61/128.08 |
ACY | 10.46/152.13 | nd | 10.34/152.38 | nd |
ACE | nd | 10.63/154.36 | nd | nd |
FLU | nd | 12.53/166.79 | nd | nd |
PHE | nd | 15.41/178.23 | nd | nd |
ANT | nd | 15.41/178.23 | nd | nd |
FLA | nd | 19.87/202.39 | nd | nd |
PYR | 20.64/202.52 | nd | 20.90/202.16 | 20.75/202.24 |
BaA | nd | 24.10/228.53 | nd | nd |
CHR | nd | 24.10/228.53 | nd | nd |
BbF | 28.82/252.14 | 28.16/252.77 | 28.22/252.96 | 28.21/252.57 |
BkF | 28.82/252.14 | 28.16/252.77 | 28.22/252.96 | 28.21/252.57 |
BaP | 29.69/252.28 | 29.52/252.81 | 29.69/252.98 | 29.67/252.99 |
DB[ah]A | 36.53/278.30 | 36.40/278.66 | 36.72/278.30 | 36.60/278.53 |
IP | 36.53/276.04 | 36.40/276.59 | 36.72/276.81 | 36.60/276.56 |
B[ghi]P | nd | 38.77/276.46 | nd | nd |
Total no. of extracted PAHs | 8 | 14 | 8 | 7 |
PAHs diffusion in plant leaves: Spatio-temporal variability in pollutant concentrations and their emission sources
Identification of air pollution hotspots by assessing spatial and temporal variations of pollutant concentrations is obligatory for enforcing control actions to reduce elevated risks of negative health impacts. Atmospheric concentration, transport and dispersion of PAHs, weather events, air/plant partitioning and uptake dynamics of PAHs in the plant leaves influence their content in tree foliage (De Nicola et al., 2005). Plant leaves can very well be considered as passive air samplers (or biomonitors) having keen propensity for differentiating even small-scale heterogeneity in the levels of urban air pollution (De Nicola et al., 2011). Table 4 illustrates the site- and season-specific leaf PAHs concentrations in the urban locations. The temporal variabilities observed in the total concentration of foliar PAHs (TPAHs in µg g− 1 d.w.) among the four study points can be described in the order of: TPAHswinter (278.42 ± 3.02−550.79 ± 10.11) > TPAHspostmonsoon (210.52 ± 12.78−401.83 ± 13.61) > TPAHspremonsoon (200.98 ± 2.72−329.17 ± 4.03). Air quality downfall as evidenced in winter months (having highest values of TPAHs) is mostly associated with air stagnation leading to clean, stable and tranquil atmosphere, restricted circulation of air masses (i.e. poor vertical mixing and horizontal dispersion) due to low velocity of the prevailing winds and night-time temperature inversion (Grundstrom et al., 2015). Non-detection of some of the HMW PAHs during winter in some sites (viz. BaP, DB[ah]A and IP at JDV; BaP at RBC and FLA at TGN) might be due to their reaction with the atmospheric oxidants under stable atmospheric stratification, generating secondary pollutants or polar PAHs derivatives (nitrated/oxygenated congeners: NPAHs/OPAHs) (Bandowe and Meusel, 2017). Percentage composition (varying between 59.01−87.44%) of 2-, 3- and 4- ring PAHs, namely NAP, ACY, ACE, FLU, PHE, ANT, FLA, PYR, BaA and CHR, revealed their dominance during premonsoon study of the selected sites. This can be attributed to the conversion of above-stated PAHs into gaseous phases from particulate state in presence of high temperature, enabling their easy permeation into the leaf tissues (internal diffusion by gas-phase transfer), whereas, lower levels of accumulated 5- and 6-ring PAHs may be credited to their photochemical degradation under the same condition (Ambade et al., 2022) which led to an overall decline in TPAHs with respect to other seasons. Contrary to premonsoon, moderate temperature, relatively stable, dry and clear weather in postmonsoon period may be accountable for greater accumulation of PAHs (4.75−23.45% higher TPAHs) in Murraya leaves. The current findings are in line with the conclusions drawn by De Nicola et al. (2005), Prajapati and Tripathi (2008), Ray et al. (2021) and Yang et al. (2017).
The spatial differences in the levels of PAHs were significantly affected by the type and intensity of local human impacts, commercial operations and traffic emissions. Therefore, the source profiles were classified in the current study through the use of distinctive characteristic ratios of PAHs (or molecular DRs: concentration ratios of particular PAHs of same molar masses and inherent properties) for minimizing uncertainties. The estimated values of DRs analysed for discerning the heterogeneous sources of PAHs pollution in the sampling sites on seasonal basis are presented in Table 5. It may be inferred that the seasonal variability of emission sources at the sampling areas of South Kolkata had not been found to be very much prominent. ΣLMW/ΣHMW and ANT/(ANT + PHE) ratios are usually employed to distinguish between the pyrogenic and petrogenic sources of PAHs (Pies et al., 2008; Zhang et al., 2008). As shown in Table 5, ΣLMW/ΣHMW < 1 and ANT/(ANT + PHE) > 0.1 are indicative of coal, wood and biomass combustion as well as automobile exhausts (pyrogenic origin) emitting PAHs into the atmosphere for all the sites. Moreover, FLA, PYR, BaA, CHR, BbF, BkF, BaP, IP and B[ghi]P are categorized as combustion-derived PAHs (Bucheli et al., 2004; Hwang et al., 2003). Foliar concentrations (Table 4) of ACY, FLU, PHE, ANT, FLA, PYR, CHR and BkF in the sites again pointed to the release of PAHs from fly ash of open biomass and coal burning (Kakareka and Kukharchyk, 2003; Larsen and Baker, 2003; Lee et al., 2005), while, in case of TGN site, ΣLMW/ΣHMW > 1 during premonsoon suggested the predominance of petrogenic origin of emission. It is noteworthy that the presence of 4-, 5- and 6- ring PAHs including BaA, CHR, BbF, BkF, BaP, IP and B[ghi]P is a strong indicator of emission from petrol combustion (Dzepina et al., 2007; Ho et al., 2009; Larsen and Baker, 2003; Yunker et al., 2002). The measured DR values for FLA/(FLA + PYR), BaA/(BaA + CHR), BbF/BkF, FLU/(FLU + PYR) and BaP/BghiP had also demonstrated majorly transport-related emissions (from petrol/diesel) and presumed to be the sources for PAHs absorption by the leaves at the study sites. Highest accumulation of TPAHs (µg g− 1 d.w.) at EXM (329.17 ± 4.03−550.79 ± 10.11) than RBC (296.46 ± 15.52−406.14 ± 9.38), JDV (299.03 ± 15.18−388.64 ± 3.94) and TGN (200.98 ± 2.72−278.42 ± 3.02) (ref. Table 4) in all the seasons may be primarily accredited to the huge vehicular emissions at the point of traffic intersection due to movement/idling of vehicles on the congested roadways. However, at RBC, effects of non-exhaust traffic emissions in premonsoon (BaP/BghiP < 0.6) (such as resuspension of dust particulates from road pavements, construction activities, tyres and brakes, burning of solid fuels and street cafes or restaurants) and contribution from fossil fuel combustion in both pre- and post- monsoon (FLA/(FLA + PYR)≈0.4–0.5) were recognized as well (De La Torre-Roche, et al., 2009; Katsoyiannis et al., 2007). FLA/(FLA + PYR) > 0.5 at TGN signified PAHs outflow from biomass burning during premonsoon together with vehicular activities (De La Torre-Roche, et al., 2009). IP/(IP + BghiP) > 0.5 at JDV and TGN reflected mixed origin of PAHs liberated from coal or biomass burning and cooking fume emissions from chimneys of roadside restaurants (Chen et al., 2015). The maximum variation of seasonal concentrations (µg g− 1 d.w.) (ref. Table 4) of BaP (29.47 ± 1.99−30.92 ± 0.69) and DB[ah]A (59.64 ± 2.88−62.81 ± 1.54) at JDV confirmed the sources of oil-based cooking (grilling, smoking, frying, barbecuing, roasting, etc.) in several wayside food stalls or commercial kitchens, corroborating the above inference. Observed concentrations of DB[ah]A at EXM and TGN (Table 4) also represented the influences of cooking processes on the outdoor environment. Mainly at RBC and EXM with IP/(IP + BghiP) lying between 0.2−0.5, petroleum-related PAHs pollution was observed to be widespread. Prevalence of lighter and heavier PAHs (NAP, ACY, FLU, PHE, PYR, BaA, CHR and BghiP) in high and low levels in every site is a pointer towards combustion of petroleum products and exposure to cooking oil smoke (Masih et al., 2012). Occurrence of ACE in the leaf samples due to coal combustion and diesel exhausts was recorded, but in low concentration (6.77 ± 0.21−25.32 ± 2.29 µg g− 1 d.w.) owing to its short half-life and high susceptibility for biodegradation (Chanda and Mehendale, 2005).
Table 4
Locational and seasonal variations in total and individual concentrations (µg g− 1 d.w.) of PAHs measured in Murraya paniculata leaves
| Foliar PAHs concentrations |
| JDV | RBC | EXM | TGN |
EPA-PAHs | Premonsoon | Postmonsoon | Winter | Premonsoon | Postmonsoon | Winter | Premonsoon | Postmonsoon | Winter | Premonsoon | Postmonsoon | Winter |
NAP | 15.24 ± 2.38 | 19.75 ± 0.59 | 27.08 ± 0.49 | 20.42 ± 2.31 | 21.55 ± 1.64 | 24.16 ± 1.70 | 43.61 ± 3.45 | 45.66 ± 1.35 | 39.22 ± 1.27 | 9.97 ± 1.26 | 13.39 ± 0.82 | 14.83 ± 2.52 |
ACY | nd | 9.56 ± 0.67 | 11.28 ± 1.56 | nd | 14.17 ± 1.33 | 12.30 ± 1.79 | nd | 53.29 ± 1.79 | 55.62 ± 2.47 | 5.63 ± 0.54 | 6.35 ± 0.57 | 9.17 ± 1.53 |
ACE | 25.32 ± 2.29 | nd | 7.29 ± 0.28 | 9.72 ± 1.37 | nd | 11.86 ± 0.37 | nd | 6.77 ± 0.21 | 8.11 ± 1.87 | 8.98 ± 0.67 | 9.23 ± 0.88 | 10.04 ± 2.32 |
FLU | 7.74 ± 1.76 | nd | 7.78 ± 0.52 | 9.80 ± 3.47 | 10.49 ± 1.43 | 5.82 ± 0.11 | 13.80 ± 2.73 | 11.32 ± 0.66 | 7.67 ± 1.72 | 10.56 ± 3.17 | nd | 11.74 ± 2.43 |
PHE | 23.40 ± 4.33 | 18.89 ± 0.71 | 18.44 ± 0.30 | 13.63 ± 2.07 | 15.85 ± 1.79 | 17.27 ± 0.66 | 12.91 ± 1.63 | nd | 21.32 ± 1.57 | 28.62 ± 3.77 | 25.86 ± 1.77 | 35.57 ± 3.36 |
ANT | 16.44 ± 2.94 | 43.70 ± 1.62 | 69.11 ± 3.10 | 39.02 ± 1.45 | 41.58 ± 1.46 | 42.83 ± 1.35 | 26.29 ± 2.46 | nd | 34.56 ± 2.99 | 37.84 ± 3.08 | 19.51 ± 0.75 | 26.45 ± 2.86 |
FLA | nd | 39.88 ± 1.37 | 23.10 ± 0.34 | 40.73 ± 2.27 | 46.91 ± 2.33 | 59.52 ± 2.50 | nd | 57.88 ± 2.49 | 70.26 ± 3.78 | 44.52 ± 4.63 | 47.09 ± 2.04 | nd |
PYR | 7.93 ± 2.26 | 32.20 ± 0.89 | 51.25 ± 2.79 | 41.52 ± 3.14 | 55.27 ± 2.69 | 63.09 ± 2.19 | 59.66 ± 2.67 | nd | 75.01 ± 3.89 | 11.92 ± 4.46 | 18.15 ± 1.12 | 21.98 ± 3.43 |
BaA | 48.50 ± 3.76 | nd | 81.20 ± 2.96 | 33.54 ± 1.75 | 37.67 ± 1.87 | 50.22 ± 1.81 | 65.39 ± 3.51 | 78.17 ± 2.61 | 86.95 ± 2.81 | nd | nd | 29.47 ± 2.22 |
CHR | 31.90 ± 1.68 | nd | 48.61 ± 1.65 | 50.83 ± 2.17 | 50.97 ± 2.41 | 52.19 ± 2.07 | 29.59 ± 3.45 | 58.30 ± 0.88 | 64.61 ± 3.13 | nd | nd | 42.17 ± 4.39 |
BbF | nd | 14.57 ± 1.25 | 11.60 ± 1.34 | nd | 24.30 ± 1.32 | 25.70 ± 1.05 | 21.96 ± 1.48 | 22.84 ± 0.51 | 33.42 ± 4.49 | nd | 20.95 ± 1.18 | 36.44 ± 4.15 |
BkF | 10.76 ± 2.63 | 22.37 ± 0.69 | 25.76 ± 0.94 | nd | 23.81 ± 1.82 | 16.46 ± 1.35 | 19.12 ± 1.97 | 15.48 ± 0.68 | 18.36 ± 2.23 | nd | 12.72 ± 0.50 | 13.96 ± 1.26 |
BaP | 29.47 ± 1.99 | 30.92 ± 0.69 | nd | 2.52 ± 0.35 | 13.22 ± 0.83 | nd | 2.82 ± 0.53 | 4.65 ± 0.28 | 9.73 ± 0.75 | 3.64 ± 0.86 | 11.05 ± 1.72 | 11.48 ± 1.63 |
DB[ah]A | 59.64 ± 2.88 | 62.81 ± 1.54 | nd | 16.32 ± 1.44 | 8.26 ± 0.65 | 9.42 ± 0.44 | 31.08 ± 2.21 | 36.71 ± 1.72 | 13.59 ± 1.95 | 23.16 ± 3.82 | 8.54 ± 0.71 | 2.38 ± 0.34 |
IP | 22.69 ± 3.66 | 17.53 ± 0.52 | nd | nd | 1.93 ± 0.10 | 5.18 ± 0.15 | 2.94 ± 0.29 | 3.31 ± 0.52 | 4.37 ± 0.58 | 13.43 ± 2.93 | 17.68 ± 0.73 | 10.10 ± 1.21 |
B[ghi]P | nd | 3.49 ± 0.24 | 6.14 ± 0.82 | 18.41 ± 1.92 | nd | 10.12 ± 1.01 | nd | 7.45 ± 0.47 | 7.99 ± 2.04 | 2.71 ± 1.08 | nd | 2.64 ± 0.34 |
Total PAHs concentration | 299.03 ± 15.18 | 315.67 ± 10.33 | 388.64 ± 3.94 | 296.46 ± 15.52 | 365.98 ± 17.46 | 406.14 ± 9.38 | 329.17 ± 4.03 | 401.83 ± 13.61 | 550.79 ± 10.11 | 200.98 ± 2.72 | 210.52 ± 12.78 | 278.42 ± 3.02 |
Concentration values expressed as (mean ± S.D.) |
Table 5
Molecular DRs with their obtained values (as mean ± S.D.) for identification of foliar PAHs origin in the sampling sites
| DRs for the plant leaves of urban areas |
PAHs ratio | Seasons | JDV | RBC | EXM | TGN |
Value | Range | Source | Value | Range | Source | Value | Range | Source | Value | Range | Source |
ΣLMW/ΣHMW | Premonsoon | 0.42 ± 0.03 | < 1 | Pyrogenic | 0.45 ± 0.01 | < 1 | Pyrogenic | 0.42 ± 0.01 | < 1 | Pyrogenic | 1.02 ± 0.26 | > 1 | Petrogenic |
Postmonsoon | 0.41 ± 0.01 | < 1 | Pyrogenic | 0.40 ± 0.01 | < 1 | Pyrogenic | 0.41 ± 0.00 | < 1 | Pyrogenic | 0.55 ± 0.00 | < 1 | Pyrogenic |
Winter | 0.57 ± 0.02 | < 1 | Pyrogenic | 0.39 ± 0.01 | < 1 | Pyrogenic | 0.43 ± 0.01 | < 1 | Pyrogenic | 0.63 ± 0.01 | < 1 | Pyrogenic |
ANT/(ANT + PHE) | Premonsoon | 0.41 ± 0.01 | > 0.1 | Pyrogenic | 0.74 ± 0.02 | > 0.1 | Pyrogenic | 0.67 ± 0.01 | > 0.1 | Pyrogenic | 0.57 ± 0.01 | > 0.1 | Pyrogenic |
Postmonsoon | 0.70 ± 0.00 | > 0.1 | Pyrogenic | 0.72 ± 0.03 | > 0.1 | Pyrogenic | ࣧ | ࣧ | ࣧ | 0.43 ± 0.01 | > 0.1 | Pyrogenic |
Winter | 0.79 ± 0.01 | > 0.1 | Pyrogenic | 0.71 ± 0.01 | > 0.1 | Pyrogenic | 0.62 ± 0.01 | > 0.1 | Pyrogenic | 0.43 ± 0.05 | > 0.1 | Pyrogenic |
FLA/(FLA + PYR) | Premonsoon | ࣧ | ࣧ | ࣧ | 0.50 ± 0.01 | 0.4–0.5 | Fossil fuel combustion | ࣧ | ࣧ | ࣧ | 0.79 ± 0.06 | > 0.5 | Grass, wood burning and diesel emission |
Postmonsoon | 0.55 ± 0.01 | > 0.5 | Diesel emission | 0.50 ± 0.03 | 0.4–0.5 | Fossil fuel combustion | ࣧ | ࣧ | ࣧ | 0.72 ± 0.00 | > 0.5 |
Winter | 0.31 ± 0.01 | < 0.5 | Petrol emission | 0.49 ± 0.01 | < 0.5 | Petrol emission | 0.48 ± 0.03 | < 0.5 | Petrol emission | ࣧ | ࣧ | ࣧ |
BaA/(BaA + CHR) | Premonsoon | 0.60 ± 0.01 | > 0.35 | Vehicular emission or combustion | 0.40 ± 0.00 | > 0.35 | Vehicular emission or combustion | 0.69 ± 0.04 | > 0.35 | Vehicular emission or combustion | ࣧ | ࣧ | ࣧ |
Postmonsoon | ࣧ | ࣧ | ࣧ | 0.43 ± 0.01 | > 0.35 | 0.57 ± 0.01 | > 0.35 | ࣧ | ࣧ | ࣧ |
Winter | 0.63 ± 0.02 | > 0.35 | Vehicular emission or combustion | 0.49 ± 0.02 | > 0.35 | 0.57 ± 0.02 | > 0.35 | 0.41 ± 0.01 | > 0.35 | Vehicular emission or combustion |
BbF/BkF | Premonsoon | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | 1.15 ± 0.17 | > 0.5 | Diesel emission | ࣧ | ࣧ | ࣧ |
Postmonsoon | 0.65 ± 0.04 | > 0.5 | Diesel emission | 1.02 ± 0.02 | > 0.5 | Diesel emission | 1.48 ± 0.04 | > 0.5 | Diesel emission | 1.65 ± 0.02 | > 0.5 | Diesel emission |
Winter | 0.45 ± 0.04 | na | na | 1.56 ± 0.17 | > 0.5 | Diesel emission | 1.82 ± 0.03 | > 0.5 | Diesel emission | 2.61 ± 0.06 | > 0.5 | Diesel emission |
FLU/(FLU + PYR) | Premonsoon | 0.49 ± 0.01 | < 0.5 | Petrol emission | 0.19 ± 0.04 | < 0.5 | Petrol emission | 0.19 ± 0.02 | < 0.5 | Petrol emission | 0.47 ± 0.18 | < 0.5 | Petrol emission |
Postmonsoon | ࣧ | ࣧ | ࣧ | 0.16 ± 0.01 | < 0.5 | Petrol emission | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ |
Winter | 0.13 ± 0.01 | < 0.5 | Petrol emission | 0.08 ± 0.00 | < 0.5 | Petrol emission | 0.09 ± 0.02 | < 0.5 | Petrol emission | 0.35 ± 0.08 | < 0.5 | Petrol emission |
BaP/BghiP | Premonsoon | ࣧ | ࣧ | ࣧ | 0.14 ± 0.03 | < 0.6 | Non-traffic emission | ࣧ | ࣧ | ࣧ | 1.34 ± 0.54 | > 0.6 | Road traffic emission |
Postmonsoon | 8.86 ± 0.88 | > 0.6 | Road traffic emission | ࣧ | ࣧ | ࣧ | 0.62 ± 0.07 | > 0.6 | Road traffic emission | ࣧ | ࣧ | ࣧ |
Winter | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | 1.22 ± 0.63 | > 0.6 | Road traffic emission | 4.35 ± 0.05 | > 0.6 | Road traffic emission |
IP/(IP + BghiP) | Premonsoon | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | ࣧ | 0.83 ± 0.01 | > 0.5 | Biomass burning, coal combustion and eateries |
Postmonsoon | 0.83 ± 0.01 | > 0.5 | Biomass burning, coal combustion and eateries | ࣧ | ࣧ | ࣧ | 0.31 ± 0.01 | 0.2–0.5 | Combustion of petroleum fuel | ࣧ | ࣧ | ࣧ |
Winter | ࣧ | ࣧ | ࣧ | 0.34 ± 0.01 | 0.2–0.5 | Combustion of petroleum fuel | 0.35 ± 0.11 | 0.2–0.5 | 0.79 ± 0.04 | > 0.5 | Biomass burning, coal combustion and eateries |
Range and sources referred from: Akyuz and Cabuk, 2010; Katsoyiannis et al., 2007; Park et al., 2002; Pies et al., 2008; Ravindra et al., 2008a; Ravindra et al., 2008b; Shukla et al., 2022; Yunker et al., 2002; Zhang et al., 2008. |
Note: ‘ࣧ’ stands for paucity of data due to non-detection of specific PAHs of the binary ratios during concentration analysis. |
‘na’: Not available |