Journal Archive

Bilaspur is the second largest city in the Chhattisgarh state of India. The city is administered by Municipal Corporation with a metropolitan population of 616,000 (Source: Bilaspur, India Metro Area Population, 2021). The geographical location of Bilaspur is 22°05′N 82°09′ E/22°09′N 82°15′E with an average elevation of 262 m (860 ft) (Gajbhiye et al., 2016c). The climate of the city is pleasant in winter and generally hot and humid in summer. The temperature varies approximately from 9°C to 45°C. The monsoon generally arrives in the second week of June and continues until September. Average annual rainfall in the city is 1,259 mm (Source: TBC, IGKV, Bilaspur). Random sewer projects and unplanned roadways are responsible for regular traffic jams in the city. Big industrial areas (e.g. Sirgitti, Tifra and Silpahari) are located in and around Bilaspur. Moreover, large thermal power plants of the National Thermal Power Corporation (NTPC) and mining areas of South East Central Coalfields Limited (SECL) are located around the city.

In this study, 6 different roadside sites were selected for the collection of foliar dust. To this end, emphasis was given to represent diverse number and nature of traffic activities. (1) Turkadih Arpa Bridge near Koni: a it is a bypass road which connects three national highways (NH 130 with NH 130A and NH 45). Most of the heavy vehicles passes through it with a reduced speed, (2) near Koni residential roadside area: it was affected by both heavy and light vehicular traffic which connect residential area to main road, (3) roadside in front of Guru Ghasidas Central University, Bilaspur (GGU roadside) gate: affected by moderate vehicular activities; (4) Near the Bilasa Park Gate: on the road side affected by moderate vehicular activities, (5) Agricultural college gate: near to site 4, and (6) Seepat Chowk: a major traffic square in the city affected by heavy vehicle congestion and impacted by residential/commercial activities (Fig. 1).

Fig. 1. 
Geographic locations of study sites in the city of Bilaspur (C.G.), India (Source=Google earth application-.kml/.kmz).

At each study site, plant species, especially trees/shrubs, were selected for the collection of dust depending on their availability, ease of sampling and height (Table S1). Consequently, a total of 35 dust samples were collected from 6 different study sites: 4 from site 1 (Annona squamosa, Calotropis procera, Citrus limon and Primula pulverea), 10 from site 2 (Annona squamosa, Pongamia pinnata, Bambusa bambos, Butea monosperma, Capparis zeylanica, Ficus religiosa, Hemidesmus indicus, Alangium lamarckii, Senna siamea and Alstonia scholaris), 11 from site 3 (Ailanthus altissima, Alstonia scholaris, Antigonon leptopus Alba, Carissa carandas, Ficus benghalensis, Mitragyna parvifolia, Paulownia tomentosa, Ricinus communis, Saraca asoca, Alangium lamarckii and Senna siamea), 5 from site 4 (Artocarpus heterophyllus, Gmelina arborea, Psidium guajava, Senna siamea and Syzygium cumini), 3 from site 5 (Alstonia scholaris, Butea monosperma and Mitragyna parvifolia) and 2 from site 6 (Butea monosperma and Mangifera indica). The foliar dust samples from each site were collected within 1 day to minimize temporal effects. This time represents the dry weather conditions near the end of the winter season. For foliar dust, leaf samples were collected at a height of around 1.5 m above ground level at all sites to match the ambient height and to minimize road dust re-suspension during sample collection. Three leaves (corresponding to three replicates) from each plant were collected at once toward the road side face in ziploc plastic bags and brought to the laboratory.

The leaves of individual plant were immersed in 100 mL of deionized water (using a 200-mL beaker) and mixed firmly for five minutes. The water with the suspended dust was kept on a hot plate at 100°C for evaporation and recovery of the dust and 0.5 g of dust sample was separated for digestion. Sample preparation for the analysis of elemental species was performed using a microwave extraction system (Milestone, START D, USA) with nitric acid solution (US EPA Method 3051A) (Fig. 2). The dust samples were soaked in digestion reagent (HNO₃, 10 mL) in a Teflon (perfluoroalkoxy, PFA) vessel and digested under extreme pressure and temperature conditions conditions using the following procedures: (i) Temperature and pressure of the microwave digestion system were increased up to 180°C and 100 bar, respectively, for 10 min, with 1,000 W power; (ii) The temperature and pressure were steadily maintained for 15 min under above conditions with the same power; (iii) Teflon vessels were cooled down to reduce the temperature and pressure. Digestion vessels were cleaned by operating 2 blank runs using only 10 mL HNO₃ followed by washing with ultrapure water and finally drying with air. Extracted solutions were then transferred into a volumetric flask (25 mL volume) after filtration with Whatman PVDF syringe filter (0.45 μm) and the total volume was brought up to 25 mL with ultrapure water. As mentioned in method 3051A, it may not reflect the total metal content for certain metals (Al, Ba, Cr, Fe, Mg, and V: they require addition of HCl to be comparable with method 3050). Therefore, total mineralization methods with more aggressive acids (such as microwave-aquaregia+ HF) can also be used, however, with proper care, as it can also underestimate the concentration of metals such as Pb, Al, Ca, Fe, Mg, and Ba (Chen and Ma, 2001).

Fig. 2. 
A schematic diagram showing major steps of methodology in this study.

The sample solutions prepared using the above procedures were analyzed for various metals species using ICP-OES (Perkin Elmer, Model OPTIMA 7300DV, USA). The quality control of the analytical procedure was performed by analyzing standard reference material. The recovery analysis was conducted to evaluate the validity of the analytical procedure of various elemental species using certified reference material, CRM 1646a (Estuarine Sediment: National Institute of Standards and Technology, USA). The procedure and reagent used were the same as for the digestion and preparation of dust samples. The detection limit values achieved for target elements (expressed in μg mL^-1) were: Al (1.2), Fe (3.9), Ca (0.6), Na (1.6), K (3.9), Mg (1.5), Mn (0.3), Zn (1.8), V (2.3), Cr (0.6), Pb (1.2), Cu (0.3), Ni (1.6), Co (0.7), S (7.8), and Cd (0.3). The repeatability was well below the 5% level for all the target elements, when expressed in terms of relative standard error (%) (e.g., 0.1% (Ca) to 4.4% (Ba)). The average recovery percentages obtained for available target species were determined and expressed in mean ±RSD (relative standard deviation %) are as follows: Al (44.6±9.4%), Fe (88.8±3.8%), Ca (99.6±2.5%), Na (86.8±1.9%), K (71.8±12.3%), Mg (91.5±2.6%), Mn (77.9±9.7%), Zn (81.8±3.3%), V (71.2±8.7%), Cr (86.1±2.6%), Pb (104.0±7.9%), Cu (113.5±2.4%), Ni (90.3±4.3%), Co (91.8±2.8%), S (96.5±9.9%), and Cd (98.5±2.0%).

The Normality of data were checked by a Shapiro-Wilk test and visual inspection of normal Q-Q plot through SPSS v16.0 (Razali and Wah, 2011). The summary statistics of this normality test is shown in the form of Table S2 and Fig. S1. As such, data were not normally distributed in most cases. As 19 metals of diverse nature were quantified in present study, it was important to identify their major sources. Principal component analysis (PCA) is frequently applied in environmental pollution research to assess relative information regarding pollution sources (Li et al., 2017; Tang et al., 2017). Moreover, PCA results in a considerable reduction in the number of variables and the detection of structure in the relationships of different variables. As such, PCA was applied using all the data of different metals measured at six sites as variables through XLSTAT 2018. The PCA was run based on the correlation matrix (Pearson (n)) type which standardized and scaled up the data. As there was not any missing data, do not accept missing data option was selected. For extraction of components, Eigen value (above 1), scree plot, and the total variance explained (above 70%) were considered. The bi-plots obtained were correlation biplots.

We quantified 19 elements (18 metals/transition metals/alkali metals/alkaline earth metals and 1 non-metal [S]) in foliar dust samples. Their concentrations ranged from 1.53±0.37 mg/kg for Co to 177,728±32,793 mg/kg for Fe. Subsequently, the metals were divided into 2 groups depending on their inclusion in hazardous air pollutant (HAP) listing: (i) metals/elements other than HAP list and (ii) metals in the US EPA HAP list.

The mean concentrations of metals determined in this study were compared with concentrations reported in numerous previous studies from Calcutta, India (Chatterjee and Banerjee, 1999), Hangzhou, China (Lu et al., 2008), Huizhou, China (Qiu et al., 2009), Vienna, Austria (Simon et al., 2011), Miskolc, Hungary (Simon et al., 2016) and different locations in Bilaspur, India (Gajbhiye et al., 2016a, c). The mean Al concentration (12,352 mg/kg) was more than 7 times higher than the Al concentration (1,683 mg/kg) in foliar dust of P. acerifolia leaf in Miskolc, Hungary. The mean Fe concentration (57,181 mg/kg) was more than 26 times higher than previously reported from other locations of Bilaspur, India (2,062 mg/kg) and Vienna, Austria (2,136 mg/kg) and 2 times higher than reported in Calcutta (21,800 mg/kg). The location of sites and traffic density is directly reflected on metal concentration in foliar dust (Zhang et al., 2017). As such, in present study, significantly high vehicular activities compared to previous study might have contributed in significant deposition and enrichment of Fe in foliar dust. If we compare the concentration of metals across different sites in the present study, it reflects the influence of environmental factors such as traffic activity. For instance, as Site 6 was main traffic square of city with the highest traffic load, it showed the highest concentration of metals (Al, Fe, Ba, V, Cu, Mn, Cr, Pb, Ni, Co, and Cd) in comparison to other sites (Table 1). The mean Ca concentration in the present study (38,520 mg/kg) was higher than in foliar dust in Vienna, Austria (29,427 mg/kg). However, mean K, Mg, and S concentrations were several times lower than in Vienna, Austria. Conversely, metals in the second group (Na, Ba, Sr, Zn, V, and Cu) showed comparably lower concentrations than previous studies. For example, the mean Na concentration (184 mg/kg) was more than 50 times lower than in Vienna, Austria. Similarly, mean Sr (23.2 mg/kg), Zn (26.5 mg/kg) and V (23.4 mg/kg) concentrations were approximately 4, 37, and 2 times lower than in Calcutta, India, respectively.

The mean concentration of metals (Mn, Cr, Pb, Ni, Co, and Cd) in the HAP category also showed highly variable patterns. The mean Mn level (138 mg/kg) in the present study was significantly lower than in previous studies. For example, Mn level was more than 2 times lower than in Bilaspur, India (392 mg/kg) and Miskolc, Hungary (364 mg/kg). In contrast, the mean Cr concentration (24.9 mg/kg) was nearly 2 times higher than in Bilaspur, India (15.2 mg/kg) and Miskolc, Hungary (12 mg/kg). Pb (17.4 mg/kg) was slightly lower than in Bilaspur (21.3 mg/kg) and Vienna, Austria (18 mg/kg). The mean Ni concentration (4.95 mg/kg) was 7 times lower than in Calcutta (38 mg/kg). If we compare the concentration of metal sin HAP category across different sites, it reflects the influence of variable source activities (mainly traffic activity). For instance, Site 6 showed the highest concentration of HAP metals (Pb, Ni, Cr, Mn, Co, and Cd) similar to other metals among all the sites (Table 1). Moreover, the micrometeorological conditions can also affect the concentrations of these metals up to a certain degree.

If the Eigen values above 1 is considered, there were four principal components extracted (Table S4). However, as shown in the scree plot, the cumulative variance explained by two principle components were 71.8% (Fig. 3), only two principle components (PC1 and PC2) were considered. As shown in Table 2, PC1 was dominated by Cr, Co, V, Cu, Cd, Pb, Mn, Fe, Al, Ti, and Ba explaining 48.8% of total variance. The presence of metals such as Cr, Co, Cu, Cd, Mn, and Pb and with high factor loadings/correlations of 0.962, 0.945, 0.896, 0.893, 0.880, and 0.856, respectively in PC1 indicated their likely origin from corrosion in diesel/gasoline vehicles (braking, wearing of rust) use of fuel, lubricant, corrosion of metallic and engine parts, breakdown of vehicle tires and body parts, and vehicular exhaust (Gajbhiye et al., 2019; Wu and Lu, 2018; Bourliva et al., 2017; Li et al., 2017). In the present study, all samples (foliar dust) were collected from the surface of leaves located near roadsides. Therefore, depending on the location of the sites and activities at the source, the primary source was apparently vehicular activities. The results of present study can be compared with previous studies conducted at sites affected by similar source characteristics. For instance, in present study, concentration of Cr (24.7 mg/kg) was considerably two times higher than previous studies such as Miskolc, Hungary (12 mg/kg) and Bilaspur, India (15.2 μg g^-1) (Gajbhiye, 2016a; Simon et al., 2016). The mean Co concentration of present study was (2.88 mg/kg) nearly 3 orders of magnitude lower than those in Calcutta, India (9.73 mg/kg) and Guangzhou, China (8.09 mg/kg). However, it was relatively higher than those of Coimbatore, India (2.03 mg/kg) (Subpiramaniyam et al., 2021; Liang et al., 2019; Chatterjee and Banerjee, 1999). The mean concentration of Cu (13.1 mg/kg) in present study was much lower than those of Calcutta, India (269 mg/kg); Guangzhou, China (225 mg/kg); Outer-Ring Highway of Shanghai, China (191.8 mg/kg) (Liang et al., 2019; Yin et al., 2014; Chatterjee and Banerjee, 1999). The mean concentration of Cd (9.91 mg/kg) in foliar dust was around 4 time higher than those of Hangzhou, China (2.62 mg/kg) and Panzhihua, China (2.70 mg/kg) (Yang et al., 2016; Lu et al., 2008). In case of Pb, it was (17.4 mg/kg) 5 and 17 order of magnitude lower than those in Miskolc, Hungary (98 mg/kg) and Panzhihua, China (306.95 mg/kg), respectively (Simon et al., 2016; Yang et al., 2016).

Fig. 3. 
The results of PCA in form of different plots considering all data of concentrations of 19 metals.

The PC2 showed 23.05 % of total variance with Zn, K, Sr, Ti, and S, as main contributors with a factor loading/correlation of 0.875, 0.800, 0.719, 0.689, and 0.680, respectively. There are a number of studies that reported a significant increase in elements such as Zn, Sr, Ti, K, and S in urban PM due to firework events (Dickerson et al., 2017; Baranyai et al., 2015). The study area covered in this study is also affected by significant firework events during several festivals throughout the year. Moreover, there is a usual practice of huge road processions in religious, political, and marriage events during which high amount of firework products are combusted for celebrations. As such, the high loadings of these metals in PC2 can be attributed to firework events at least in partial sense. However, when the mean concentration of metals of present study was compared with the metals emitted by fireworks events in previous studies, it was found that mean concentration of Sr (23.2 mg/kg) and Ti (69.7 mg/kg) was 20 and 600 order of magnitude higher than those of Coimbatore, India (Sr: 1.24 mg/kg; Ti: 0.10 mg/kg) (Subpiramaniyam et al., 2021). In contrast, mean concentration of S (1,425 mg/kg) and K (1,697 mg/kg) was 6 and 12 order of magnitude lower than those of 9,417 mg/kg and 22,159 mg/kg south east part of Debrecen, Hungary, respectively (Baranyai et al., 2015). Alternatively, mean concentration of Zn (26.5 mg/kg) was 12 orders of magnitude lower than those of Debrecen, Hungary (346 mg/kg) and Vienna, Austria (311 mg/kg) (Baranyai et al., 2015; Simon et al., 2011). Hence, it indicated that the factors other than vehicular activities such as firework events along with industrial (e.g., presence of power plants for sulfur emission)/commercial and/or regional/transboundary pollution could also potentially affect the distributions of metals at roadside locations in urban environment (Yadav et al., 2018; Ismail et al., 2017).

The foliar dust samples were collected from different plants growing near a given site. Thus, when the concentration of a given metal was compared across different plants at a given site, considerable variation was observed. Most toxic metals (e.g., metals listed as HAPs, such as Mn, Cr, Co, Ni, and Pb) showed the average variation in their concentration was 2-fold or less across different plants from different sites, except Cd where it was more than 4 times at site 6. The occurrence of significantly high level of Cd at site 6 is consistent with comparable concentrations found in foliar dust samples (23.5 to 34.3 mg/kg) near the same site in other study (Tiwari and Pandey, 2016). Site 6 (Seepat chowk) is a major traffic square of the city and adjacent to Nehru chowk where NH130A and NH 49 originate from NH 130 and connects Bilaspur City to other states of India. Hence, this site has significantly high traffic density and heavy vehicular load which might have contributed in the significantly high deposition and enrichment of Cd in foliar dust samples. It has been reported that traffic and industrial emissions were two main sources of Cd including with other HAP listed metals in suspended dust around road side environment (Hou et al., 2019; Wang et al., 2010). Emission of Cd results from the gasoline use, aging of automobile tires, car body wear, and brake lining wear (Weckwerth, 2001). Moreover, Cd can also possibly be emitted from paints used on the external surface of nearby public facilities (Li et al., 2018). Moreover, the comparison of other metals (Al, Fe, Ca, Mg, and V) showed a more than 10-fold difference between their concentrations at site 3. Morphological variation in plant leaves has been reported to influence the retention and accumulation of dust on their surface (Gajbhiye et al., 2019; Chen et al., 2017). The deposition of foliar dust tends to remain temporary on leaf surfaces due to climatic conditions (rainfall, humidity, wind speed and direction, season, and temperature) (Schleicher et al., 2011; Nowak et al., 2006). Hence, the climatic conditions can also exert direct impacts on metal concentration through foliar dust.