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Application of multivariate data techniques in photochemical study of polycyclic aromatic hydrocarbons (PAHs) and transformed PAH products in road dust
Ecotoxicology and Environmental Safety 196 (2020) 110478
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Application of multivariate data techniques in photochemical study of polycyclic aromatic hydrocarbons (PAHs) and transformed PAH products in road dust
T
Gustav Gbeddy, Ashantha Goonetilleke, Godwin A. Ayoko, Prasanna Egodawatta∗ Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, 4001, Queensland, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Photolysis Ultraviolet photon Transformed PAH product Multivariate data analysis Urban pollution
Road dust is a key repository for PAHs and transformed PAH products (TPPs) generated from natural and anthropogenic sources in the urban environment. Eventhough PAHs and TPPs are prone to post-emission photochemical processes, very limited studies exist on the subject for road dust. This knowledge gap is of particular concern since some of the resultant TPPs are notably more carcinogenic than their precursor PAHs. This study evaluated the role of 254 nm ultraviolet (UV) photons on the photochemistry of PAHs and TPPs in road dust. The findings show that UV irradiation had varying effects on the fate of analytes, particularly naphthalene (NAP), phenanthrene (PHE), 7, 12-dimethylbenz(a)anthracene (DMBA), 1-hydroxypyrene (HPY), 1-nitropyrene (1NPY), pyrene (PYR) and 5-nitroacenaphthene (5NAC). Photochemical relationship was identified between PYR, 1NPY and HPY, and DMBA and benzo(a)anthracene. Unlike carbonyl-PAHs, parent PAHs, nitro-PAHs and hydroxyPAHs can originate from photolysis. Photon irradiation durations of 3, 6 and 7.5 h had the most intense influence on the photolytic process with 7.5 h as optimum. The photochemical rate at optimum irradiation duration shows an increasing trend of NAP < PHE < 1NPY < DMBA < 5NAC < HPY with respective estimates of 0.08, 0.11, 0.21, 0.22, 0.43, and 0.59 mg kg−1 hr−1. Physicochemical properties of analytes such as index of refraction and vapour pressure (in logarithmic form) had an inverse effect on photolysis. The knowledge generated is significant for the in-depth understanding of the fate of PAHs and TPPs on urban road surfaces and contributes to the greater protection of human health and the environment.
1. Introduction Road dust is a major repository for polycyclic aromatic hydrocarbons (PAHs) and transformed PAH products (TPPs) that are released into the environment from natural and anthropogenic sources (Gan et al., 2009; Gupta and Gupta, 2015). TPPs can originate directly and indirectly from combustion and post-emission transformation and degradation processes, respectively (Albinet et al., 2006, 2007). There is growing research interest in the distribution and fate of PAHs and TPPs in the environment due to the detrimental ecological and human health impacts of these pollutants. Some of the PAHs and TPPs are well-known carcinogenic, mutagenic and tumorigenic compounds (Abdel-Shafy and Mansour, 2016; IARC, 1987; IARC, 1983). It is reported that the toxic effects of PAHs and TPPs are influenced by their post-emission transformation and degradation processes (Gan et al., 2009; Jia et al., 2014). Some TPP species such as oxygenated PAHs (OPAHs) and nitrated PAHs
(NPAHs) are potentially more toxic than their corresponding parent PAHs. NPAHs such as nitropyrene and dinitropyrene pose direct mutagenic effects on living organisms without undergoing preliminary enzymatic activation (Albinet et al., 2006, 2007). However, there is lack of studies on the post-emission origins and fate of TPPs (Achten and Andersson, 2015; Albinet et al., 2007) particularly in the road dust matrix even though there is a high potential of human exposure to these hazardous TPPs via inhalation. Photolysis is one of the key processes by which PAHs and TPPs can transform and degrade on urban road surfaces (Gupta and Gupta, 2015; Zhang et al., 2010). Photolysis refers to the breakdown of chemical bonds due to the absorption of solar radiation (Vione et al., 2006). By virtue of their conjugated π-orbital electron systems, PAHs are classified as photoactive pollutants and capable of absorbing photons (Jia et al., 2015; Klessinger and Michl, 1995). PAHs can absorb solar electromagnetic radiation within 280–400 nm and 400–760 nm
∗
Corresponding author. E-mail addresses: [email protected] (G. Gbeddy), [email protected] (A. Goonetilleke), [email protected] (G.A. Ayoko), [email protected] (P. Egodawatta). https://doi.org/10.1016/j.ecoenv.2020.110478 Received 10 February 2020; Received in revised form 11 March 2020; Accepted 12 March 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.
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wavelengths of UV and visible spectra, respectively (Arfsten et al., 1996; Bandowe, 2010; Fu et al., 2012; Mallakin et al., 2000; Yu et al., 2006). The activated PAHs can then undergo photo-physical and photochemical processes especially in the presence of co-existing molecules such as oxygen, ozone, hydrogen, and NOx to yield various transformed PAH products (TPPs). The transformation and degradation processes often produce oxygenated PAHs, nitro-PAHs, aldehyde, ketone, carboxylic acid and phenols (Gbeddy et al., 2019). To the best of our knowledge, there has not been any in-depth evaluation of the photo-transformation and degradation of PAHs and TPPs on road surfaces. Existing studies are primarily related to soil and atmospheric particles with varied compositional characteristics compared to road accumulated particles. As a result, there is limited knowledge on the fate of TPPs in the urban environment and their potential toxic contributions. In this context, this study assessed the role of ultraviolet (UV) photons on the transformation and degradation of PAHs and TPPs laden in urban road dust. The specific objectives of the study included: (i) the application of multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) and principal component analysis (PCA) to characterize the behaviour patterns of parent PAHs and TPPs; (ii) to identify the predominant photo-chemical reactions occurring during photolysis; and (iii) to assess the influence of analyte physicochemical properties on the photolytic process. The outcomes of this study will enhance the understanding of the fate of PAHs and TPPs under the influence of solar irradiation. Secondly, the outcomes will also yield new knowledge for improved protection against exposure to these hazardous pollutants.
Fig. 1. UV photolysis set-up.
Five study sites, encompassing one commercial (BST) and two residential (BVH and BMT) road sites within Benowa suburb, and two industrial (NSS and NHC) roads situated within Nerang suburb were selected for sampling. The location of these sites is shown in Fig. S1. Road dust samples were collected from half the width of each road site after one antecedent dry day (ADD) using a Delonghi Aqualand Model vacuum cleaner with high retrieval efficiency of 92%. Further details of the study area, study sites, sample collection and processing can be found in Gbeddy et al. (2018). The site contaminated with the highest concentration of PAH and TPP analytes was identified via Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) for the photolysis experiment in order to assess the worst-case scenario (Ayoko et al., 2007).
2. Materials and methods 2.1. Chemical reagents Road dust sample of 0.45–425 μm particle size range collected on the 1st antecedent dry day was used to conduct the photolysis experiments. The chemical standards for 26 parent PAHs, 14 potential TPPs (4 CPAHs, 6 NPAHs, 4 HO-PAHs), internal standards and deuterated surrogates were purchased from Novachem Superior Standards, Australia and Sigma-Aldrich Group, Australia, as outlined in Table S1. The TPPs were selected based on their ubiquity in the urban environment as confirmed from previous studies on road dust and soil samples (see Gbeddy et al. (2019)). HPLC grade organic solvents including acetone, cyclohexane, hexane, dichloromethane (DCM), toluene, and methanol were acquired from Sigma-Aldrich Group, Australia. Silica gel 60 (70–230 mesh), alumina, diatomaceous earth and Na2SO4 (pro-analysis quality, water free) were purchased from Novachem Superior Standards, Australia. Na2SO4, silica gel and alumina were heated to 300 °C for 12 h whilst diatomaceous earth was baked to 460 °C for 12 h and cooled to room temperature in a desiccator prior to use. Deactivated silica gel (3%) was prepared from the activated silica gel.
2.3. Photo-transformation and degradation experiment The photolysis experimental set-up as shown in Fig. 1 consisted of a five sided brown paper box, fitted with one UVG–11 Compact UV lamp from John Morris Scientific, and a thermometer. The electrical properties of the UV lamp were, 254 nm wavelength, 4 Watt power, 0.12 Amp current, 230 Voltage and 50/60 Hz frequency. The distance between the irradiated sample and the lamp was 0.095 m, thus resulting in 35.27 W m−2 light intensity. Crucibles filled with 0.5 g of homogenized road dust samples were irradiated in turns for durations of 1.5, 3, 4.5, 6, 7.5 and 9 h. The selected durations were based on the assumption of nine (9) hours solar irradiation daily. The corresponding analyte concentrations were labelled as Ct1.5, Ct3, Ct4.5, Ct6, Ct7.5 and Ct9, respectively. The initial analyte concentration was labelled Ct0. The mean temperature during the experiment was 23 °C. A control experiment consisting of 0.5 g homogenized road dust sample in a similar crucible was kept in the dark. The resultant controlled experiment analyte concentration was labelled as Cct. Duplicate sub-samples from each experimental run were immediately subjected to the analytical methods discussed below in order to extract and quantify the analytes.
2.2. Study area and sampling Gold Coast region of Queensland, Australia, was selected as the study area. Gold Coast is the sixth largest city and one of the rapidly urbanizing regions in Australia (Ma, 2015) and therefore, offers a diverse land uses as study sites. The Gold Coast City has a humid subtropical climate with approximately 300 days of sunshine per year (http://Australia.comAustralia.com, 2017) and mean daily global solar exposure of 15.2 M J m−2 during the sampling period. Gold Coast experiences an average annual noon clear-sky UV index within the range of 8–10 representing “very high” exposures due to the City's latitudinal geographic location at 27.94oS (BOM, 2017). The UV condition offers a favourable environment for the transformation and degradation of micro-organic pollutants such as PAHs and TPPs on urban road surfaces. UV plays a critical role in transforming and degrading these pollutants as identified in a review by Gbeddy et al., 2019. 2
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using the Euclidean distance (Miller and Miller, 2010). FA streamlines the dimensionality of statistical data by decreasing the multicollinearity and number of variables with minimum loss of information, thus serving as a data reduction and variable selection method. The original variables are transformed into new sets of variables called factors (Gbeddy et al., 2020). PROMETHEE in essence computes the degree of preference of one object to another for each criterion based on various modelling scenarios including the choice of suitable preference function and whether low (minimized) or high (maximized) criteria values are preferred (Ayoko et al., 2003). The use of PCA, CA and FA multivariate data techniques was essential to achieve the study aim and objectives. These techniques were primarily used as data reduction and pattern recognition tools. The simultaneous deployment of these techniques ensured that the strengths and weaknesses of individual method is complemented thereby resulting in a more accurate interpretation of the generated research data. For instance, although PCA may depict groups of similar objects, CA is designed purposely to disclose similar groups of objects. Moreover, PCA biplot output does not only reveal the relationship among objects but also between objects and variables, and among variables (Miller and Miller, 2010).
2.4. Analytical method 2.4.1. Simultaneous pressurized fluid extraction of sample and clean-up The pressurized fluid extraction (PFE) method used in this study was adapted from a previous study conducted by Gbeddy et al. (2020) and Lundstedt et al. (2006) to simultaneously extract and separate parent PAHs and TPPs from contaminated soil and road dust. According to the modified method, a glass filter paper was placed at the outlet of the 5 mL extraction cell and a mixture of 10 g deactivated (3%) and 5 g activated alumina was packed into the cell for extract clean-up followed by another filter paper. A homogenous mixture of 0.15 g sample and 0.075 g activated diatomaceous earth was transferred into the cell. The packed cell was also spiked with 50 μL of 2 ng μL−1 recovery standards (RS) solution consisting of eight deuterated PAHs and TPPs and then allowed to equilibrate for 24 h in a desiccator prior to extraction. The loaded accelerated solvent extractor (Dionex 350 ASE) was pressurized to 17 MPa and heated to 200 °C within 9 min. The pressure and heat were held for 5 min during each of the three (3) extraction cycles, with a flush volume of 100% followed by rinsing with more solvent (60% of cell volume). The cell was finally purged with N2 for 90 s. Each cell was sequentially extracted with 100% hexane followed by hexane/DCM (1:1 v/v). The collected extracts were then evaporated to about 5 mL using the rotary evaporator. The extract was further evaporated to dryness using a gentle stream of nitrogen gas and the solvent phase changed by adding 0.9 mL of DCM, filtered and then transferred to a 2 mL glass vial. 100 μL of Fluoranthene-D10 and Chrysene-D12 internal standard solution (1 ng μL−1) was then added to obtain a final volume of 1 mL.
3. Results and discussion 3.1. Selection of sample for photolysis experiment The initial concentrations of PAHs and TPPs obtained for the five samples (NSS1, NHC1, BST1, BVH1 and BMT1) were subjected to PROMETHEE analysis in order to rank the samples. The data matrix consisted of 5 actions (samples) and 34 criteria (analytes). All the criteria (analyte concentrations) were assigned equal weights and maximized. V-shape preference function was used during the analysis since the differences obtained during the pairwise comparison of the samples (actions) for a criterion were not negligible. The PROMETHEE-II results are shown in Table S2, which indicates that BVH1 is the most contaminated sample. The BVH road site is located in an urban residential area with high population density, traffic and close proximity to major roads (Gbeddy et al., 2018). In this regard, BVH1 was regarded as the worst case scenario sample for the photolysis experiments.
2.4.2. GC/MS analysis of extracts The calibration standards, final extract, laboratory and solvent blanks were analysed using Shimadzu Triple Quadrupole (TQ) 8040 GC/MS System containing Rxi-5Sil MS column (30 m × 0.25 mm ID x 0.25 μm thickness) with constant column flow of 1.2 mL min−1. Extracts were analysed using splitless injection and selected reaction monitoring (SRM) mode. The mass spectrometer was operated solely in the electron ionization (EI) mode for all analytes, thereby saving time during analysis (22.83 min). The SRM and EI energies used for analytes can be found in Gbeddy et al. (2020). The GC oven temperature program used included an initial temperature of 50 °C held for 1 min, increased to 260 °C at a rate of 20.0 °C per min, elevated to 280 °C at a rate of 5.0 °C per min and finally increased to 340 °C at a rate of 18.0 °C per min and held for 4 min. The data acquisition, processing and report generation were done using GC/MS real time analysis and LabSolutions GC/MS. The regression coefficient for the calibration curves of the analytes ranged from 0.995 to 1.0 for the Shimadzu TQ instrument. Phenanthrene-d10, anthracene-d10, pyrene-d10, acenaphthene-d10, naphthalene-d8, 9-nitroanthracene-d9, 1-nitropyrene-d9, 1-hydroxypyrene-d9 and benzophenone-d10 surrogates had average percentage recoveries of 98, 111, 114, 58, 53, 43, 71, 76, and 96, respectively.
3.2. Univariate evaluation of photolysis results The mean concentration of PAHs and TPPs measured during the photolysis experiment are shown in Table S3, whilst the trend in analytes concentration is depicted in Fig. 2. The trends clearly demonstrate that PAHs and TPPs laden in road dust are susceptible to transformation and degradation when exposed to UV radiation as hypothesized by Gbeddy et al. (2019). From Fig. 2, the concentration of most analytes remained unchanged during the control experiment (indicated by Cct) and the first 1.5 h of irradiation with the exception of 7, 12-dimethylbenz[a]anthracene (DMBA), 1-nitronaphthalene (NNAP), 1-hydroxypyrene (HPY) and 1, 8-dihydroxyanthraquinone (DHAQ), where minor differences were observed. Furthermore, t-test also confirmed that the differences between initial analyte concentration (Cto), Cct and Ct1.5 were not significant as the probability values obtained (0.141, 0.245 and 0.306) were greater than 0.05 level of significance. Noticeable variations in analyte concentrations were observed between 3 and 7.5 h of irradiation. The highest concentration among all analytes, HPY was produced at 3 h irradiation followed by sharp decrease at 4.5 h. The concentration then increased two-fold between 6 and 7.5 h irradiation followed by a sharp decrease at 9 h. Therefore, it can be inferred that HPY exhibits the greatest level of variation due to UV irradiation as a result of photo-transformation and degradation processes. The NNAP produced initially was completely transformed or degraded at 3 h irradiation. Coincidently, the largest quantum of the lowest molecular weight parent PAH, NAP was produced at 3 h, thus,
2.5. Data analysis The data generated from the photolysis experiment were analysed using multivariate and multicriteria decision-making analytical techniques such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA) and Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) using StatistiXL Version 1.8 and Visual PROMETHEE Academic Edition Version 1.4.0.0, respectively. PCA essentially reduces the dimensionality of data by transforming the original variables into principal components (PC). The PCs are linear combinations of the original variables whereby the coefficients of the various terms ensure that the new variables are less correlated. CA was used to reveal groups of pollutants with similar behaviour during the photolysis, thereby complementing PCA. CA searches for pollutants that are close together in the variable space 3
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Fig. 2. Trends in analytes concentration during photolysis experiment.
From Fig. 3, the duration of UV irradiation had two major effects on the photolysis of PAHs and TPPs where two groups of analytes were formed. Variables St3, St6, and St7.5 are correlated with most of the analytes indicating that 3, 6 and 7.5 h of irradiation had significant effects on the photo-transformation and degradation processes. The cluster of analytes around St3, St6, and St7.5 potentially indicates the group of most reactive pollutants when subjected to UV photons. From Fig. 3a, 7.5 h of 254 nm UV irradiation can be considered as the optimum duration for the alteration of the analytes by virtue of its high correlation with the reactive group of analytes. The photochemical rate of transformation and degradation is shown in Table S5 indicating a decreasing trend of HPY > 5NAC > DMBA > 1NPY > PHE > NAP with corresponding values of 0.59, 0.43, 0.22, 0.21, 0.11 and 0.08 mg kg−1 hr−1, respectively. In this regard, 1-hydroxypyrene can be inferred as the most susceptible analyte to UV driven photolysis in this study. The generation of NPAHs from the photochemical process particularly 1NPY is of critical concern since these pollutants are inherently more toxic compared to their precursor PAHs. Furthermore, Sto, Sct, and St1.5 are highly correlated with the other class of less reactive analytes when exposed to UV radiation. Moreover, the difference between initial and control experiment analytes concentration is not significant (P-value = 0.141 at 0.05 level of significance). This indicates that the dark condition had little effect on the transformation and degradation of PAHs and TPPs laden in road dust. Miller and Miller (2010) noted that PCA can reveal clusters of similar objects. However, CA is the recommended technique for identifying groups of objects. In this context, CA was used to further assess the influence of UV irradiation on PAHs and TPPs using the row standardized photolysis data. Euclidean distance and nearest neighbour cluster method were used during the hierarchical cluster analysis (HCA). The resultant HCA dendrogram is shown in Fig. 4 where two major clusters can be identified, confirming the PCA results. From Fig. 4, it is evident that BeP to DahA form the most reactive class of analytes. CHR and TPL have the highest similarity in behaviour when irradiated with UV, thus agreeing with their isomeric relationship although they have different Dewar's reactivity numbers. PHE and HPY also exhibited similar fate when exposed to UV radiation. The behavioural trend of this reactive class of analytes was further examined over the irradiation period as presented in Fig. 5. Two distinct categories of reactive analytes can be observed in Fig. 5. The first category entails analytes with significant alteration across the irradiation period and this includes HPY, 5NAC, DMBA,
indicating a possible transformation of all the NNAP into its corresponding parent PAH. There was a sharp decrease in the NAP concentration at 4.5 h leading to a complete degradation at 6 h. However, the high concentration of NAP was generated at 7.5 h of irradiation followed by a complete degradation again at 9 h. The potential photochemical transformation between NAP and NNAP is shown in Fig. S2. The maximum concentration of 1NPY was produced after 3 h irradiation followed by a sharp decrease at 4.5 h. The concentration subsequently increased from 6 to 7.5 h followed by a sharp decrease at 9 h. DMBA and PHE followed similar trend of transformation and degradation as 1NPY except for the fact that the maximum concentration was measured at 7.5 h 5NAC was generated between 6 and 7.5 h of irradiation with its maximum concentration at 7.5 h. However, the trends for all other analytes remained relatively unchanged during the entire 9 h period of UV irradiation. In summary, UV photon irradiation had varying effects on the fate of PAHs and TPPs laden in road dust, notably, NAP, PHE, DMBA, HPY, 1NPY, NNAP and 5NAC. Durations of 3–7.5 h of irradiation had the most profound effect on the photo-transformation and degradation of these deleterious pollutants. This can be attributed to the acquisition of threshold quantum of photon energy by these pollutants during these hours in order to undergo the photochemical and photophysical processes as described by Gbeddy et al. (2019). This implies that early and late hours of daily solar irradiation may not lead to significant alterations in the fate of PAHs and TPPs in an urban environment. Based on the findings of the univariate analysis, it can be hypothesized that parent PAHs can produce NPAHs and HO-PAHs from photolysis whilst the CPAHs are less likely to emanate from this process. Similarly, NPAHs and HO-PAHs can also produce parent PAHs via photolysis. In order to further identify the patterns among analytes, the generated data in Table S3 was subjected to multivariate data analysis. 3.3. Multivariate evaluation and identification of TPPs The 27 objects x 8 variables in the data matrix given in Table S3 was subjected to PCA. The data matrix was pre-processed using row standardization (labelled as St) in order to eliminate potential bias due to the variations in initial and subsequent analyte concentrations during the photolysis process. Based on eigenvalues > 1, three principal components (PCs) were found to be significant as shown in Table S4 accounting for 79% variation in the data. The resultant biplots of the analysis are shown in Fig. 3. 4
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Fig. 3. PCA biplots for photolysis experiment data; (a) biplot with PCs 1 and 2; (b) biplot with PCs 1 and 3.
1NPY, PHE, NAP and PYR. The other class has relatively constant alteration, and this comprises BeP, 9FLN, AQN, FRT, CHR, TPL, BaFN, BaP and BghiPE. The findings from the PCA and HCA techniques were combined to help identify potential photochemical transformation and degradation processes. From Figs. 3 and 4, 1NPY, PYR and HPY are highly correlated indicating similar origin, and are found within the same category of most reactive analytes. By virtue of their chemical structure and in line with Dewar's reactivity number concept of localization energy as elaborated by Gbeddy et al. (2019), the following potential phototransformation reactions have been proposed in this study as illustrated in Fig. 6a. Equations (1) and (2) represent the photolytic generation of nitrite and hydroxyl radicals, respectively. From Fig. 3a, BaAN is produced mostly around 9 h of UV irradiation and in line with its chemical structural similarity with DMBA, the reaction in Fig. 6b has been proposed in this study for these analytes.
(2) The reverse reactions for Fig. 6 are also possible resulting in significant changes in the concentrations of these pollutants during the photo-transformation process. The potential mechanism behind the photochemical reaction has been stipulated in the review article by Gbeddy et al. (2019). In summary, PAHs become excited to singlet and triplet states upon absorbing UV photons. The excess energy from the excited PAHs may be transferred to co-existing molecules such as nitrogen oxides (NOx), resulting in the production of free radical species. The photochemical reactions then proceed on complex reaction pathways leading to the formation of TPPs as simplified in Figs. 6 and S2. The results of the photolysis experiment clearly demonstrate that parent PAHs, NPAHs and HO-PAHs are potentially produced from photolysis on urban road surfaces in addition to primary anthropogenic origins such as fuel combustion by vehicles.
(1)
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(PAHs and TPPs) x 13 variables (physicochemical properties). The variables were standardized due to diverse units and variances. The factors were extracted using principal component method from the correlation matrix. The Factor Analysis (FA) results show that 3 factors were significant (Eigenvalue > 1) accounting for 85.5% of the variance in the data as specified in Table S7. Based on correlation coefficients ≥0.8 and ≤ −0.5 (see Table S8), and Varimax rotated factor loadings ≥ 0.8 and ≤ −0.8 (see Table S9), two physicochemical parameters (density (ρ) and water solubility (Sw)) were identified as noisy and redundant variables. Eleven (11) parameters (molecular weight, Mw; number of rings, NOR; number of aromatic rings, NOAR; enthalpy of vaporization, Hv; molar refractivity, Rf; Log octanol/water partition coefficient, logKow; melting point, MP; boiling point, BP; Log vapour pressure, logVp; Log soil adsorption coefficient, logKoc; and index of refraction, IR) were considered as suitable for further multivariate statistical analysis to determine their influence on the photolysis process. The data matrix of 27 objects (PAHs and TPPs) x 19 variables (physicochemical properties and row standardized analyte concentrations) was subjected to PCA for pattern recognition. The data matrix was pre-processed using column standardization to eliminate the effect of different units and variances in the data. The output from the analysis indicated that 5 PCs were significant (Eigenvalue > 1) accounting for 84.9% variance in the data as shown in Table S10. The first two PCs accounted for 56.6% of the total variance in the data, therefore, only their biplot was considered as represented by Fig. 7. The biplot indicates that very high correlation between Mw, NOR, NOAR, MP, BP, logKow, logKoc, Rf and Hv. Therefore, this nine variables can be reduced to a smaller number in any possible prediction assessment for the photolysis of analytes. These highly correlated variables are orthogonal to St3, St6 and St7.5. This indicates that the phototransformation and degradation of the reactive group of analytes is
Fig. 4. Hierarchical clustering dendrogram.
3.4. Influence of analyte physicochemical properties on the photolysis process Physicochemical properties of PAHs and TPPs play a role in their transformation and degradation (Gbeddy et al., 2019). In this regard, thirteen (13) physicochemical properties that are available for the analytes were obtained from ChemSpider.com (2020) and Gbeddy et al. (2020) as indicated in Table S6. The data matrix consists of 27 objects
Fig. 5. Behavioural trend of reactive class of analytes under UV irradiation. 6
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Fig. 6. Photochemical reactions for PAHs and TPPs; (a) photolysis of pyrene (PYR) to 1-nitropyrene (1NPY) and 1-hydroxypyrene (1HPY); (b) photolysis of 7, 12dimethylbenz(a)anthracene (DMBA) to benzo(a)anthracene (BaAN).
independent of these variables. Index of refraction (IR) and logVp have inverse effects on the photolytic fate of reactive PAH and TPP analytes. In this regard, reactive analytes with low IR and logVp are likely to interact with and absorb the irradiated UV photons for subsequent photochemical transformation. 4. Conclusions The outcomes of this research clearly demonstrated that PAHs and TPPs contaminated road dust, particularly parent PAHs, nitro-PAHs (NPAHs) and hydroxy-PAHs (HO-PAHs) are more susceptible to phototransformation and degradation compared to carbonyl-PAHs (CPAHs). UV photon irradiation for 3, 6 and 7.5 h had noticeable impacts on analyte photo-transformation and degradation whilst the control experiment in the dark did not show any significant influence in changes to pollutant concentrations. The results of the photochemical process may exacerbate the health risks posed by PAHs due to the generation of more toxicologically potent NPAHs such as 1-nitropyrene. Most physicochemical properties of analytes except index of refraction and vapour
Fig. 7. PCA biplot for the influence of physicochemical parameters.
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pressure (in logarithmic form) had no influence on the photochemical fate of PAHs and TPPs. The outcomes of this study will contribute to enhancing the in-depth understanding of the fate of PAHs and TPPs on urban road surfaces and for the formulation of robust ecosystem protection measures. We recommend future studies to be conducted on the influence of visible light on the photochemical behaviour and fate of PAHs and TPPs in road dust since these pollutants can also absorb photons in this region of the solar electromagnetic spectrum.
polycyclic aromatic hydrocarbons in animals and plants: a review. Ecotoxicol. Environ. Saf. 33, 1–24. Australia.com, 2017. Gold Coast Weather, Climate & Temperature – Tourism Australia. Ayoko, G.A., et al., 2003. A multicriteria ranking of organotin(IV) compounds with fungicidal properties. Appl. Organomet. Chem. 17, 749–758. Ayoko, G.A., et al., 2007. Exploratory multivariate modeling and prediction of the physico-chemical properties of surface water and groundwater. J. Hydrol. 336, 115–124. Bandowe, B.A., 2010. Oxygen-containing PAH and Their Parent PAHs in Soil- Analysis, Sources and Fate. Johannes Gutenberg-Universität Mainz (PhD Thesis). Bom, 2017. Average Solar Ultraviolet (UV) Index. Bureau of Meteorology, Commonwealth of Australia. ChemSpider.com, 2020. ChemSpider Search and Share Chemistry 2020. Fu, P.P., et al., 2012. Phototoxicity and environmental transformation of polycyclic aromatic hydrocarbons (PAHs)-light-induced reactive oxygen species, lipid peroxidation, and DNA damage. J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev. 30, 1–41. Gan, S., et al., 2009. Remediation of soils contaminated with polycyclic aromatic hydrocarbons (PAHs). J. Hazard Mater. 172, 532–549. Gbeddy, G., et al., 2019. Transformation and degradation of polycyclic aromatic hydrocarbons (PAHs) in urban road surfaces: influential factors, implications and recommendations. Environ. Pollut. 257. https://doi.org/10.1016/j.envpol.2019. 113510. Gbeddy, G., et al., 2020. Dataset for the quantitative structure-activity relationship (QSAR) modeling of the toxicity equivalency factors (TEFs) of PAHs and transformed PAH products. Data Brief 28, 104821. Gbeddy, G., et al., 2018. Variability and uncertainty of particle build-up on urban road surfaces. Sci. Total Environ. 640–641, 1432–1437. Gupta, H., Gupta, B., 2015. Photocatalytic degradation of polycyclic aromatic hydrocarbon benzo[a]pyrene by iron oxides and identification of degradation products. Chemosphere 138, 924–931. IARC, 1987. Overall Evaluations of Carcinogenicity: an Updating of IARC Monographs. (Lyon, . Lyon, France). Iarc, W. s, 1983. IARC Monograph on the Evaluation of the Carcinogenic Risk of Chemicalss to Humans Polynuclear Aromatic Compounds, Part I: Chemical, vol. 32 Environmental and Experimental Data, Lyon, France. Jia, H., et al., 2015. Exchangeable cations-mediated photodegradation of polycyclic aromatic hydrocarbons (PAHs) on smectite surface under visible light. J. Hazard Mater. 287, 16–23. Jia, H., et al., 2014. Transformation of polycyclic aromatic hydrocarbons (PAHs) on Fe (III)-modified clay minerals: role of molecular chemistry and clay surface properties. Appl. Catal. B Environ. 154–155, 238–245. Klessinger, M., Michl, J., 1995. Excited States and Photochemistry of Organic Molecules. VCH Publishers, Inc. Lundstedt, S., et al., 2006. Simultaneous extraction and fractionation of polycyclic aromatic hydrocarbons and their oxygenated derivatives in soil using selective pressurized liquid extraction. Anal. Chem. 78, 2993–3000. Ma, Y., 2015. Human Health Risk of Toxic Chemical Pollutants Generated from Traffic and Land Use Activities. PhD Thesis. Queensland University of Technology. Mallakin, A., et al., 2000. Pathway of anthracene modification under simulated solar radiation. Chemosphere 40, 1435–1441. Miller, J.N., Miller, J.C., 2010. Statistics and Chemometrics for Analytical Chemistry, sixth ed. Pearson Education Limited, England. Vione, D., et al., 2006. Photochemical reactions in the tropospheric aqueous phase and on particulate matter. Chem. Soc. Rev. 35, 441–453. Yu, H., et al., 2006. Photoirradiation of polycyclic aromatic hydrocarbons with UVA light –a pathway leading to the generation of reactive oxygen species, lipid peroxidation, and DNA damage. Int. J. Environ. Res. Publ. Health 3, 348–354. Zhang, L., et al., 2010. Photodegradation of pyrene on soil surfaces under UV light irradiation. J. Hazard Mater. 173, 168–172.
Declaration of competing interest The authors can testify that there is no real or perceived conflict of interest such as personal, financial and connection to person(s) or institution(s) that may have impacted negatively on the outcome of this research. Acknowledgements The authors will like to acknowledge the Queensland University of Technology (QUT), Australia for providing the postgraduate research award to Gustav Gbeddy to undertake this study. The Central Analytical Research Facility (CARF) under the Institute of Future Environments, QUT where the data employed in this paper were acquired is highly appreciated. Access to CARF was facilitated by generous funding from the Science and Engineering Faculty, QUT. The significant role of the Ghana Atomic Energy Commission (GAEC) is highly appreciated for granting study leave to Gustav Gbeddy in order to embark upon this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110478. References Abdel-Shafy, H.I., Mansour, M.S.M., 2016. A review on polycyclic aromatic hydrocarbons: source, environmental impact, effect on human health and remediation. Egyptian Journal of Petroleum 25, 107–123. Achten, C., Andersson, J.T., 2015. Overview of polycyclic aromatic compounds (PAC). Polycycl. Aromat. Comp. 35, 177–186. Albinet, A., et al., 2006. Simultaneous analysis of oxygenated and nitrated polycyclic aromatic hydrocarbons on standard reference material 1649a (urban dust) and on natural ambient air samples by gas chromatography-mass spectrometry with negative ion chemical ionisation. J. Chromatogr. A 1121, 106–113. Albinet, A., et al., 2007. Polycyclic aromatic hydrocarbons (PAHs), nitrated PAHs and oxygenated PAHs in ambient air of the Marseilles area (South of France): concentrations and sources. Sci. Total Environ. 384, 280–292. Arfsten, D.P., et al., 1996. The effects of near ultraviolet radiation on the toxic effects of
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Application of multivariate data techniques in photochemical study of polycyclic aromatic hydrocarbons (PAHs) and transformed PAH products in road dust
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Gustav Gbeddy, Ashantha Goonetilleke, Godwin A. Ayoko, Prasanna Egodawatta∗ Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, 4001, Queensland, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Photolysis Ultraviolet photon Transformed PAH product Multivariate data analysis Urban pollution
Road dust is a key repository for PAHs and transformed PAH products (TPPs) generated from natural and anthropogenic sources in the urban environment. Eventhough PAHs and TPPs are prone to post-emission photochemical processes, very limited studies exist on the subject for road dust. This knowledge gap is of particular concern since some of the resultant TPPs are notably more carcinogenic than their precursor PAHs. This study evaluated the role of 254 nm ultraviolet (UV) photons on the photochemistry of PAHs and TPPs in road dust. The findings show that UV irradiation had varying effects on the fate of analytes, particularly naphthalene (NAP), phenanthrene (PHE), 7, 12-dimethylbenz(a)anthracene (DMBA), 1-hydroxypyrene (HPY), 1-nitropyrene (1NPY), pyrene (PYR) and 5-nitroacenaphthene (5NAC). Photochemical relationship was identified between PYR, 1NPY and HPY, and DMBA and benzo(a)anthracene. Unlike carbonyl-PAHs, parent PAHs, nitro-PAHs and hydroxyPAHs can originate from photolysis. Photon irradiation durations of 3, 6 and 7.5 h had the most intense influence on the photolytic process with 7.5 h as optimum. The photochemical rate at optimum irradiation duration shows an increasing trend of NAP < PHE < 1NPY < DMBA < 5NAC < HPY with respective estimates of 0.08, 0.11, 0.21, 0.22, 0.43, and 0.59 mg kg−1 hr−1. Physicochemical properties of analytes such as index of refraction and vapour pressure (in logarithmic form) had an inverse effect on photolysis. The knowledge generated is significant for the in-depth understanding of the fate of PAHs and TPPs on urban road surfaces and contributes to the greater protection of human health and the environment.
1. Introduction Road dust is a major repository for polycyclic aromatic hydrocarbons (PAHs) and transformed PAH products (TPPs) that are released into the environment from natural and anthropogenic sources (Gan et al., 2009; Gupta and Gupta, 2015). TPPs can originate directly and indirectly from combustion and post-emission transformation and degradation processes, respectively (Albinet et al., 2006, 2007). There is growing research interest in the distribution and fate of PAHs and TPPs in the environment due to the detrimental ecological and human health impacts of these pollutants. Some of the PAHs and TPPs are well-known carcinogenic, mutagenic and tumorigenic compounds (Abdel-Shafy and Mansour, 2016; IARC, 1987; IARC, 1983). It is reported that the toxic effects of PAHs and TPPs are influenced by their post-emission transformation and degradation processes (Gan et al., 2009; Jia et al., 2014). Some TPP species such as oxygenated PAHs (OPAHs) and nitrated PAHs
(NPAHs) are potentially more toxic than their corresponding parent PAHs. NPAHs such as nitropyrene and dinitropyrene pose direct mutagenic effects on living organisms without undergoing preliminary enzymatic activation (Albinet et al., 2006, 2007). However, there is lack of studies on the post-emission origins and fate of TPPs (Achten and Andersson, 2015; Albinet et al., 2007) particularly in the road dust matrix even though there is a high potential of human exposure to these hazardous TPPs via inhalation. Photolysis is one of the key processes by which PAHs and TPPs can transform and degrade on urban road surfaces (Gupta and Gupta, 2015; Zhang et al., 2010). Photolysis refers to the breakdown of chemical bonds due to the absorption of solar radiation (Vione et al., 2006). By virtue of their conjugated π-orbital electron systems, PAHs are classified as photoactive pollutants and capable of absorbing photons (Jia et al., 2015; Klessinger and Michl, 1995). PAHs can absorb solar electromagnetic radiation within 280–400 nm and 400–760 nm
∗
Corresponding author. E-mail addresses: [email protected] (G. Gbeddy), [email protected] (A. Goonetilleke), [email protected] (G.A. Ayoko), [email protected] (P. Egodawatta). https://doi.org/10.1016/j.ecoenv.2020.110478 Received 10 February 2020; Received in revised form 11 March 2020; Accepted 12 March 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.
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wavelengths of UV and visible spectra, respectively (Arfsten et al., 1996; Bandowe, 2010; Fu et al., 2012; Mallakin et al., 2000; Yu et al., 2006). The activated PAHs can then undergo photo-physical and photochemical processes especially in the presence of co-existing molecules such as oxygen, ozone, hydrogen, and NOx to yield various transformed PAH products (TPPs). The transformation and degradation processes often produce oxygenated PAHs, nitro-PAHs, aldehyde, ketone, carboxylic acid and phenols (Gbeddy et al., 2019). To the best of our knowledge, there has not been any in-depth evaluation of the photo-transformation and degradation of PAHs and TPPs on road surfaces. Existing studies are primarily related to soil and atmospheric particles with varied compositional characteristics compared to road accumulated particles. As a result, there is limited knowledge on the fate of TPPs in the urban environment and their potential toxic contributions. In this context, this study assessed the role of ultraviolet (UV) photons on the transformation and degradation of PAHs and TPPs laden in urban road dust. The specific objectives of the study included: (i) the application of multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) and principal component analysis (PCA) to characterize the behaviour patterns of parent PAHs and TPPs; (ii) to identify the predominant photo-chemical reactions occurring during photolysis; and (iii) to assess the influence of analyte physicochemical properties on the photolytic process. The outcomes of this study will enhance the understanding of the fate of PAHs and TPPs under the influence of solar irradiation. Secondly, the outcomes will also yield new knowledge for improved protection against exposure to these hazardous pollutants.
Fig. 1. UV photolysis set-up.
Five study sites, encompassing one commercial (BST) and two residential (BVH and BMT) road sites within Benowa suburb, and two industrial (NSS and NHC) roads situated within Nerang suburb were selected for sampling. The location of these sites is shown in Fig. S1. Road dust samples were collected from half the width of each road site after one antecedent dry day (ADD) using a Delonghi Aqualand Model vacuum cleaner with high retrieval efficiency of 92%. Further details of the study area, study sites, sample collection and processing can be found in Gbeddy et al. (2018). The site contaminated with the highest concentration of PAH and TPP analytes was identified via Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) for the photolysis experiment in order to assess the worst-case scenario (Ayoko et al., 2007).
2. Materials and methods 2.1. Chemical reagents Road dust sample of 0.45–425 μm particle size range collected on the 1st antecedent dry day was used to conduct the photolysis experiments. The chemical standards for 26 parent PAHs, 14 potential TPPs (4 CPAHs, 6 NPAHs, 4 HO-PAHs), internal standards and deuterated surrogates were purchased from Novachem Superior Standards, Australia and Sigma-Aldrich Group, Australia, as outlined in Table S1. The TPPs were selected based on their ubiquity in the urban environment as confirmed from previous studies on road dust and soil samples (see Gbeddy et al. (2019)). HPLC grade organic solvents including acetone, cyclohexane, hexane, dichloromethane (DCM), toluene, and methanol were acquired from Sigma-Aldrich Group, Australia. Silica gel 60 (70–230 mesh), alumina, diatomaceous earth and Na2SO4 (pro-analysis quality, water free) were purchased from Novachem Superior Standards, Australia. Na2SO4, silica gel and alumina were heated to 300 °C for 12 h whilst diatomaceous earth was baked to 460 °C for 12 h and cooled to room temperature in a desiccator prior to use. Deactivated silica gel (3%) was prepared from the activated silica gel.
2.3. Photo-transformation and degradation experiment The photolysis experimental set-up as shown in Fig. 1 consisted of a five sided brown paper box, fitted with one UVG–11 Compact UV lamp from John Morris Scientific, and a thermometer. The electrical properties of the UV lamp were, 254 nm wavelength, 4 Watt power, 0.12 Amp current, 230 Voltage and 50/60 Hz frequency. The distance between the irradiated sample and the lamp was 0.095 m, thus resulting in 35.27 W m−2 light intensity. Crucibles filled with 0.5 g of homogenized road dust samples were irradiated in turns for durations of 1.5, 3, 4.5, 6, 7.5 and 9 h. The selected durations were based on the assumption of nine (9) hours solar irradiation daily. The corresponding analyte concentrations were labelled as Ct1.5, Ct3, Ct4.5, Ct6, Ct7.5 and Ct9, respectively. The initial analyte concentration was labelled Ct0. The mean temperature during the experiment was 23 °C. A control experiment consisting of 0.5 g homogenized road dust sample in a similar crucible was kept in the dark. The resultant controlled experiment analyte concentration was labelled as Cct. Duplicate sub-samples from each experimental run were immediately subjected to the analytical methods discussed below in order to extract and quantify the analytes.
2.2. Study area and sampling Gold Coast region of Queensland, Australia, was selected as the study area. Gold Coast is the sixth largest city and one of the rapidly urbanizing regions in Australia (Ma, 2015) and therefore, offers a diverse land uses as study sites. The Gold Coast City has a humid subtropical climate with approximately 300 days of sunshine per year (http://Australia.comAustralia.com, 2017) and mean daily global solar exposure of 15.2 M J m−2 during the sampling period. Gold Coast experiences an average annual noon clear-sky UV index within the range of 8–10 representing “very high” exposures due to the City's latitudinal geographic location at 27.94oS (BOM, 2017). The UV condition offers a favourable environment for the transformation and degradation of micro-organic pollutants such as PAHs and TPPs on urban road surfaces. UV plays a critical role in transforming and degrading these pollutants as identified in a review by Gbeddy et al., 2019. 2
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using the Euclidean distance (Miller and Miller, 2010). FA streamlines the dimensionality of statistical data by decreasing the multicollinearity and number of variables with minimum loss of information, thus serving as a data reduction and variable selection method. The original variables are transformed into new sets of variables called factors (Gbeddy et al., 2020). PROMETHEE in essence computes the degree of preference of one object to another for each criterion based on various modelling scenarios including the choice of suitable preference function and whether low (minimized) or high (maximized) criteria values are preferred (Ayoko et al., 2003). The use of PCA, CA and FA multivariate data techniques was essential to achieve the study aim and objectives. These techniques were primarily used as data reduction and pattern recognition tools. The simultaneous deployment of these techniques ensured that the strengths and weaknesses of individual method is complemented thereby resulting in a more accurate interpretation of the generated research data. For instance, although PCA may depict groups of similar objects, CA is designed purposely to disclose similar groups of objects. Moreover, PCA biplot output does not only reveal the relationship among objects but also between objects and variables, and among variables (Miller and Miller, 2010).
2.4. Analytical method 2.4.1. Simultaneous pressurized fluid extraction of sample and clean-up The pressurized fluid extraction (PFE) method used in this study was adapted from a previous study conducted by Gbeddy et al. (2020) and Lundstedt et al. (2006) to simultaneously extract and separate parent PAHs and TPPs from contaminated soil and road dust. According to the modified method, a glass filter paper was placed at the outlet of the 5 mL extraction cell and a mixture of 10 g deactivated (3%) and 5 g activated alumina was packed into the cell for extract clean-up followed by another filter paper. A homogenous mixture of 0.15 g sample and 0.075 g activated diatomaceous earth was transferred into the cell. The packed cell was also spiked with 50 μL of 2 ng μL−1 recovery standards (RS) solution consisting of eight deuterated PAHs and TPPs and then allowed to equilibrate for 24 h in a desiccator prior to extraction. The loaded accelerated solvent extractor (Dionex 350 ASE) was pressurized to 17 MPa and heated to 200 °C within 9 min. The pressure and heat were held for 5 min during each of the three (3) extraction cycles, with a flush volume of 100% followed by rinsing with more solvent (60% of cell volume). The cell was finally purged with N2 for 90 s. Each cell was sequentially extracted with 100% hexane followed by hexane/DCM (1:1 v/v). The collected extracts were then evaporated to about 5 mL using the rotary evaporator. The extract was further evaporated to dryness using a gentle stream of nitrogen gas and the solvent phase changed by adding 0.9 mL of DCM, filtered and then transferred to a 2 mL glass vial. 100 μL of Fluoranthene-D10 and Chrysene-D12 internal standard solution (1 ng μL−1) was then added to obtain a final volume of 1 mL.
3. Results and discussion 3.1. Selection of sample for photolysis experiment The initial concentrations of PAHs and TPPs obtained for the five samples (NSS1, NHC1, BST1, BVH1 and BMT1) were subjected to PROMETHEE analysis in order to rank the samples. The data matrix consisted of 5 actions (samples) and 34 criteria (analytes). All the criteria (analyte concentrations) were assigned equal weights and maximized. V-shape preference function was used during the analysis since the differences obtained during the pairwise comparison of the samples (actions) for a criterion were not negligible. The PROMETHEE-II results are shown in Table S2, which indicates that BVH1 is the most contaminated sample. The BVH road site is located in an urban residential area with high population density, traffic and close proximity to major roads (Gbeddy et al., 2018). In this regard, BVH1 was regarded as the worst case scenario sample for the photolysis experiments.
2.4.2. GC/MS analysis of extracts The calibration standards, final extract, laboratory and solvent blanks were analysed using Shimadzu Triple Quadrupole (TQ) 8040 GC/MS System containing Rxi-5Sil MS column (30 m × 0.25 mm ID x 0.25 μm thickness) with constant column flow of 1.2 mL min−1. Extracts were analysed using splitless injection and selected reaction monitoring (SRM) mode. The mass spectrometer was operated solely in the electron ionization (EI) mode for all analytes, thereby saving time during analysis (22.83 min). The SRM and EI energies used for analytes can be found in Gbeddy et al. (2020). The GC oven temperature program used included an initial temperature of 50 °C held for 1 min, increased to 260 °C at a rate of 20.0 °C per min, elevated to 280 °C at a rate of 5.0 °C per min and finally increased to 340 °C at a rate of 18.0 °C per min and held for 4 min. The data acquisition, processing and report generation were done using GC/MS real time analysis and LabSolutions GC/MS. The regression coefficient for the calibration curves of the analytes ranged from 0.995 to 1.0 for the Shimadzu TQ instrument. Phenanthrene-d10, anthracene-d10, pyrene-d10, acenaphthene-d10, naphthalene-d8, 9-nitroanthracene-d9, 1-nitropyrene-d9, 1-hydroxypyrene-d9 and benzophenone-d10 surrogates had average percentage recoveries of 98, 111, 114, 58, 53, 43, 71, 76, and 96, respectively.
3.2. Univariate evaluation of photolysis results The mean concentration of PAHs and TPPs measured during the photolysis experiment are shown in Table S3, whilst the trend in analytes concentration is depicted in Fig. 2. The trends clearly demonstrate that PAHs and TPPs laden in road dust are susceptible to transformation and degradation when exposed to UV radiation as hypothesized by Gbeddy et al. (2019). From Fig. 2, the concentration of most analytes remained unchanged during the control experiment (indicated by Cct) and the first 1.5 h of irradiation with the exception of 7, 12-dimethylbenz[a]anthracene (DMBA), 1-nitronaphthalene (NNAP), 1-hydroxypyrene (HPY) and 1, 8-dihydroxyanthraquinone (DHAQ), where minor differences were observed. Furthermore, t-test also confirmed that the differences between initial analyte concentration (Cto), Cct and Ct1.5 were not significant as the probability values obtained (0.141, 0.245 and 0.306) were greater than 0.05 level of significance. Noticeable variations in analyte concentrations were observed between 3 and 7.5 h of irradiation. The highest concentration among all analytes, HPY was produced at 3 h irradiation followed by sharp decrease at 4.5 h. The concentration then increased two-fold between 6 and 7.5 h irradiation followed by a sharp decrease at 9 h. Therefore, it can be inferred that HPY exhibits the greatest level of variation due to UV irradiation as a result of photo-transformation and degradation processes. The NNAP produced initially was completely transformed or degraded at 3 h irradiation. Coincidently, the largest quantum of the lowest molecular weight parent PAH, NAP was produced at 3 h, thus,
2.5. Data analysis The data generated from the photolysis experiment were analysed using multivariate and multicriteria decision-making analytical techniques such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA) and Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) using StatistiXL Version 1.8 and Visual PROMETHEE Academic Edition Version 1.4.0.0, respectively. PCA essentially reduces the dimensionality of data by transforming the original variables into principal components (PC). The PCs are linear combinations of the original variables whereby the coefficients of the various terms ensure that the new variables are less correlated. CA was used to reveal groups of pollutants with similar behaviour during the photolysis, thereby complementing PCA. CA searches for pollutants that are close together in the variable space 3
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Fig. 2. Trends in analytes concentration during photolysis experiment.
From Fig. 3, the duration of UV irradiation had two major effects on the photolysis of PAHs and TPPs where two groups of analytes were formed. Variables St3, St6, and St7.5 are correlated with most of the analytes indicating that 3, 6 and 7.5 h of irradiation had significant effects on the photo-transformation and degradation processes. The cluster of analytes around St3, St6, and St7.5 potentially indicates the group of most reactive pollutants when subjected to UV photons. From Fig. 3a, 7.5 h of 254 nm UV irradiation can be considered as the optimum duration for the alteration of the analytes by virtue of its high correlation with the reactive group of analytes. The photochemical rate of transformation and degradation is shown in Table S5 indicating a decreasing trend of HPY > 5NAC > DMBA > 1NPY > PHE > NAP with corresponding values of 0.59, 0.43, 0.22, 0.21, 0.11 and 0.08 mg kg−1 hr−1, respectively. In this regard, 1-hydroxypyrene can be inferred as the most susceptible analyte to UV driven photolysis in this study. The generation of NPAHs from the photochemical process particularly 1NPY is of critical concern since these pollutants are inherently more toxic compared to their precursor PAHs. Furthermore, Sto, Sct, and St1.5 are highly correlated with the other class of less reactive analytes when exposed to UV radiation. Moreover, the difference between initial and control experiment analytes concentration is not significant (P-value = 0.141 at 0.05 level of significance). This indicates that the dark condition had little effect on the transformation and degradation of PAHs and TPPs laden in road dust. Miller and Miller (2010) noted that PCA can reveal clusters of similar objects. However, CA is the recommended technique for identifying groups of objects. In this context, CA was used to further assess the influence of UV irradiation on PAHs and TPPs using the row standardized photolysis data. Euclidean distance and nearest neighbour cluster method were used during the hierarchical cluster analysis (HCA). The resultant HCA dendrogram is shown in Fig. 4 where two major clusters can be identified, confirming the PCA results. From Fig. 4, it is evident that BeP to DahA form the most reactive class of analytes. CHR and TPL have the highest similarity in behaviour when irradiated with UV, thus agreeing with their isomeric relationship although they have different Dewar's reactivity numbers. PHE and HPY also exhibited similar fate when exposed to UV radiation. The behavioural trend of this reactive class of analytes was further examined over the irradiation period as presented in Fig. 5. Two distinct categories of reactive analytes can be observed in Fig. 5. The first category entails analytes with significant alteration across the irradiation period and this includes HPY, 5NAC, DMBA,
indicating a possible transformation of all the NNAP into its corresponding parent PAH. There was a sharp decrease in the NAP concentration at 4.5 h leading to a complete degradation at 6 h. However, the high concentration of NAP was generated at 7.5 h of irradiation followed by a complete degradation again at 9 h. The potential photochemical transformation between NAP and NNAP is shown in Fig. S2. The maximum concentration of 1NPY was produced after 3 h irradiation followed by a sharp decrease at 4.5 h. The concentration subsequently increased from 6 to 7.5 h followed by a sharp decrease at 9 h. DMBA and PHE followed similar trend of transformation and degradation as 1NPY except for the fact that the maximum concentration was measured at 7.5 h 5NAC was generated between 6 and 7.5 h of irradiation with its maximum concentration at 7.5 h. However, the trends for all other analytes remained relatively unchanged during the entire 9 h period of UV irradiation. In summary, UV photon irradiation had varying effects on the fate of PAHs and TPPs laden in road dust, notably, NAP, PHE, DMBA, HPY, 1NPY, NNAP and 5NAC. Durations of 3–7.5 h of irradiation had the most profound effect on the photo-transformation and degradation of these deleterious pollutants. This can be attributed to the acquisition of threshold quantum of photon energy by these pollutants during these hours in order to undergo the photochemical and photophysical processes as described by Gbeddy et al. (2019). This implies that early and late hours of daily solar irradiation may not lead to significant alterations in the fate of PAHs and TPPs in an urban environment. Based on the findings of the univariate analysis, it can be hypothesized that parent PAHs can produce NPAHs and HO-PAHs from photolysis whilst the CPAHs are less likely to emanate from this process. Similarly, NPAHs and HO-PAHs can also produce parent PAHs via photolysis. In order to further identify the patterns among analytes, the generated data in Table S3 was subjected to multivariate data analysis. 3.3. Multivariate evaluation and identification of TPPs The 27 objects x 8 variables in the data matrix given in Table S3 was subjected to PCA. The data matrix was pre-processed using row standardization (labelled as St) in order to eliminate potential bias due to the variations in initial and subsequent analyte concentrations during the photolysis process. Based on eigenvalues > 1, three principal components (PCs) were found to be significant as shown in Table S4 accounting for 79% variation in the data. The resultant biplots of the analysis are shown in Fig. 3. 4
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Fig. 3. PCA biplots for photolysis experiment data; (a) biplot with PCs 1 and 2; (b) biplot with PCs 1 and 3.
1NPY, PHE, NAP and PYR. The other class has relatively constant alteration, and this comprises BeP, 9FLN, AQN, FRT, CHR, TPL, BaFN, BaP and BghiPE. The findings from the PCA and HCA techniques were combined to help identify potential photochemical transformation and degradation processes. From Figs. 3 and 4, 1NPY, PYR and HPY are highly correlated indicating similar origin, and are found within the same category of most reactive analytes. By virtue of their chemical structure and in line with Dewar's reactivity number concept of localization energy as elaborated by Gbeddy et al. (2019), the following potential phototransformation reactions have been proposed in this study as illustrated in Fig. 6a. Equations (1) and (2) represent the photolytic generation of nitrite and hydroxyl radicals, respectively. From Fig. 3a, BaAN is produced mostly around 9 h of UV irradiation and in line with its chemical structural similarity with DMBA, the reaction in Fig. 6b has been proposed in this study for these analytes.
(2) The reverse reactions for Fig. 6 are also possible resulting in significant changes in the concentrations of these pollutants during the photo-transformation process. The potential mechanism behind the photochemical reaction has been stipulated in the review article by Gbeddy et al. (2019). In summary, PAHs become excited to singlet and triplet states upon absorbing UV photons. The excess energy from the excited PAHs may be transferred to co-existing molecules such as nitrogen oxides (NOx), resulting in the production of free radical species. The photochemical reactions then proceed on complex reaction pathways leading to the formation of TPPs as simplified in Figs. 6 and S2. The results of the photolysis experiment clearly demonstrate that parent PAHs, NPAHs and HO-PAHs are potentially produced from photolysis on urban road surfaces in addition to primary anthropogenic origins such as fuel combustion by vehicles.
(1)
5
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(PAHs and TPPs) x 13 variables (physicochemical properties). The variables were standardized due to diverse units and variances. The factors were extracted using principal component method from the correlation matrix. The Factor Analysis (FA) results show that 3 factors were significant (Eigenvalue > 1) accounting for 85.5% of the variance in the data as specified in Table S7. Based on correlation coefficients ≥0.8 and ≤ −0.5 (see Table S8), and Varimax rotated factor loadings ≥ 0.8 and ≤ −0.8 (see Table S9), two physicochemical parameters (density (ρ) and water solubility (Sw)) were identified as noisy and redundant variables. Eleven (11) parameters (molecular weight, Mw; number of rings, NOR; number of aromatic rings, NOAR; enthalpy of vaporization, Hv; molar refractivity, Rf; Log octanol/water partition coefficient, logKow; melting point, MP; boiling point, BP; Log vapour pressure, logVp; Log soil adsorption coefficient, logKoc; and index of refraction, IR) were considered as suitable for further multivariate statistical analysis to determine their influence on the photolysis process. The data matrix of 27 objects (PAHs and TPPs) x 19 variables (physicochemical properties and row standardized analyte concentrations) was subjected to PCA for pattern recognition. The data matrix was pre-processed using column standardization to eliminate the effect of different units and variances in the data. The output from the analysis indicated that 5 PCs were significant (Eigenvalue > 1) accounting for 84.9% variance in the data as shown in Table S10. The first two PCs accounted for 56.6% of the total variance in the data, therefore, only their biplot was considered as represented by Fig. 7. The biplot indicates that very high correlation between Mw, NOR, NOAR, MP, BP, logKow, logKoc, Rf and Hv. Therefore, this nine variables can be reduced to a smaller number in any possible prediction assessment for the photolysis of analytes. These highly correlated variables are orthogonal to St3, St6 and St7.5. This indicates that the phototransformation and degradation of the reactive group of analytes is
Fig. 4. Hierarchical clustering dendrogram.
3.4. Influence of analyte physicochemical properties on the photolysis process Physicochemical properties of PAHs and TPPs play a role in their transformation and degradation (Gbeddy et al., 2019). In this regard, thirteen (13) physicochemical properties that are available for the analytes were obtained from ChemSpider.com (2020) and Gbeddy et al. (2020) as indicated in Table S6. The data matrix consists of 27 objects
Fig. 5. Behavioural trend of reactive class of analytes under UV irradiation. 6
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Fig. 6. Photochemical reactions for PAHs and TPPs; (a) photolysis of pyrene (PYR) to 1-nitropyrene (1NPY) and 1-hydroxypyrene (1HPY); (b) photolysis of 7, 12dimethylbenz(a)anthracene (DMBA) to benzo(a)anthracene (BaAN).
independent of these variables. Index of refraction (IR) and logVp have inverse effects on the photolytic fate of reactive PAH and TPP analytes. In this regard, reactive analytes with low IR and logVp are likely to interact with and absorb the irradiated UV photons for subsequent photochemical transformation. 4. Conclusions The outcomes of this research clearly demonstrated that PAHs and TPPs contaminated road dust, particularly parent PAHs, nitro-PAHs (NPAHs) and hydroxy-PAHs (HO-PAHs) are more susceptible to phototransformation and degradation compared to carbonyl-PAHs (CPAHs). UV photon irradiation for 3, 6 and 7.5 h had noticeable impacts on analyte photo-transformation and degradation whilst the control experiment in the dark did not show any significant influence in changes to pollutant concentrations. The results of the photochemical process may exacerbate the health risks posed by PAHs due to the generation of more toxicologically potent NPAHs such as 1-nitropyrene. Most physicochemical properties of analytes except index of refraction and vapour
Fig. 7. PCA biplot for the influence of physicochemical parameters.
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pressure (in logarithmic form) had no influence on the photochemical fate of PAHs and TPPs. The outcomes of this study will contribute to enhancing the in-depth understanding of the fate of PAHs and TPPs on urban road surfaces and for the formulation of robust ecosystem protection measures. We recommend future studies to be conducted on the influence of visible light on the photochemical behaviour and fate of PAHs and TPPs in road dust since these pollutants can also absorb photons in this region of the solar electromagnetic spectrum.
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Declaration of competing interest The authors can testify that there is no real or perceived conflict of interest such as personal, financial and connection to person(s) or institution(s) that may have impacted negatively on the outcome of this research. Acknowledgements The authors will like to acknowledge the Queensland University of Technology (QUT), Australia for providing the postgraduate research award to Gustav Gbeddy to undertake this study. The Central Analytical Research Facility (CARF) under the Institute of Future Environments, QUT where the data employed in this paper were acquired is highly appreciated. Access to CARF was facilitated by generous funding from the Science and Engineering Faculty, QUT. The significant role of the Ghana Atomic Energy Commission (GAEC) is highly appreciated for granting study leave to Gustav Gbeddy in order to embark upon this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110478. References Abdel-Shafy, H.I., Mansour, M.S.M., 2016. A review on polycyclic aromatic hydrocarbons: source, environmental impact, effect on human health and remediation. Egyptian Journal of Petroleum 25, 107–123. Achten, C., Andersson, J.T., 2015. Overview of polycyclic aromatic compounds (PAC). Polycycl. Aromat. Comp. 35, 177–186. Albinet, A., et al., 2006. Simultaneous analysis of oxygenated and nitrated polycyclic aromatic hydrocarbons on standard reference material 1649a (urban dust) and on natural ambient air samples by gas chromatography-mass spectrometry with negative ion chemical ionisation. J. Chromatogr. A 1121, 106–113. Albinet, A., et al., 2007. Polycyclic aromatic hydrocarbons (PAHs), nitrated PAHs and oxygenated PAHs in ambient air of the Marseilles area (South of France): concentrations and sources. Sci. Total Environ. 384, 280–292. Arfsten, D.P., et al., 1996. The effects of near ultraviolet radiation on the toxic effects of
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