Received 3 October 2015; accepted 11 January 2016; published 14 January 2016
Air pollution has become a growing problem in megacities and large urban areas of the World   . The estimated number of light and heavy vehicles in India registered is >120 million. A large fraction (>40%) of the air pollution is contributed by road transport, emitting a wide range of gaseous air pollutants and suspended particulate matter of different sizes and compositions   . Transport-related air pollution causes a number of health problems i.e. increased risk of death, particularly from cardiopulmonary causes, and it increases the risk of non-allergic respiratory symptoms and disease  -  . Urban street dusts are sinks of the various pollutants (i.e. ions, metals, organics, etc.) depositing by sources i.e. motor vehicles, industry, weathered materials, etc.  - . Therefore, in the proposed work, contamination assessment of ions and metals i.e. F−, Cl−, , , , Na+, K+, Mg2+, Ca2+, As, Cr, Mn, Fe, Ni, Cu, Zn, Pb and Hg in the highway road dusts of India is described.
2. Materials and Methods
2.1. Study Area
The road dust samples were collected from 42 locations of the country, near high way (Figure 1). The most of sampling locations were chosen from the Chhattisgarh state of the country due to running of several industries and coal based thermal power plants. Other samples were taken from 5 cities and towns of India.
Figure 1. Representation of sampling locations in India.
2.2. Sample Collection
Total 42 surface road dust samples (0 - 10 cm) over area of 6 × 6 cm2 were collected from various locations of the country in year, 2008. Four samples from different points of each location were collected, and a composite sample was prepared by mixing them in equal mass ratio. In years, 2009-2014, one composite sample from location: Siltara-I was collected in each year. The samples were collected by using plastic spoon in month of May during years from 2008 to 2014. They were kept in a glass bottle (250 mL) and dried at 60˚C in an oven for overnight (12 hr). The samples were crushed into fine particles by mortar and sieved out the particles of mesh size <0.1 mm.
2.3. Sample Preparation and Analysis
The color of the dust was differntiatted by using the standard color chart. A 10 g dust sample was extracted with 50 mL deionized hot water (»50˚C) for 6 hrs in the shaker. The extract was filtered by using filter paper (pore size, 2 µm). The pH values were measured by using Hanna pH meter.The fluoride content was monitored with Metrohm ion meter-781 equipped with fluoride ion selective electrode and calomel electrode by using buffer in an equal ratio. The content of ions i.e. Cl−, , , , Na+, K+, Mg2+ and Ca2+ was analyzed by using Dionex DX1100 ion chromatography, equipped with anion separation column (AS9-HC, 250 × 4 mm), cation separation column (CS12A, 250 × 4 mm) and conductivity detector. The eluents, 9 mM Na2CO3 (1.4 mL/min) and 20 mM methyl sulfonic acid (0.8 mL/min) were used for leaching of the anions and cations, respectively. The content of ions was determined by using the standard calibration curves.
The dust samples were digested with HNO3:H2O2 in closed vessel microwave digestion system (MARS 5). The Varian Liberty AX Sequential ICP-AES and Varian AA280FS atomic absorption spectrophotometer equipped VGA-77 (plasma flow: 15 L/min, auxiliary flow: 1.5 L/min) were used for analysis of the metals in the dust. The VARIAN “SpectrAA 55B equipped with hydride/cold vapor regenerator accessories was employed for analysis of elements i.e. As and Hg. The urban dust reference material, QUA NAS from EU was used for the quality control.
The principal component analysis (PCA) method was used for analyzing relationships among the observed variables  . The statistical window software STATISTICA 7.1 was employed for the statistical analysis.
3. Results and Discussion
3.1. Dust Characteristics
The physical characteristics of the dusts are shown in Table 1. The color of dusts was varied from yellow to black. The urban dusts were blackish to black in color. The pH value of dust (n = 42) was ranged from 6.4 - 9.5 with mean value of 7.4 ± 0.2 at 95% probability. The dust of coal burning site of country like Korba was found to be slightly acidic due to the relatively higher content of ions i.e. Cl− and.
3.2. Ions in Road Dust
The contents of water soluble ions in the road dusts (n = 42) is summarized in Table 2. The concentration of ions i.e. Cl−, , , , Na+, K+, Mg2+ and Ca2+ was ranged from 276 - 12,718, 48 - 1423, 243 - 10,580, 11 - 539, 290 - 46,484, 110 - 7716, 84 - 1771 and 595 - 15,955 mg/kg with mean value of 3734 ± 895, 592 ± 895, 2859 ± 662, 143 ± 29, 4826 ± 2049, 1565 ± 411, 837 ± 121 and 8545 ± 1288 mg/kg, respectively. Ions i.e. Ca2+, Na+, Cl− and were found to be major contributing species in the road dusts. Their highest concentration was observed in the dust of Delhi followed by Jabalpur. The concentration of water soluble F− in the road dust samples (n = 42) was ranged from 75 - 895 mg/kg with mean value of 224 ± 43 mg/kg (at 95% probability) (Table 2). The highest concentration of Ca2+ in all locations (except Delhi and Raigarh) was marked. In Delhi, >50% fraction of the road dust was contributed by Na+. In all urban and industrial regions of the country, ≥25% fraction of the dust was contributed by anions i.e. Cl− and due to huge fuel burning. The high- est concentration of F− and was seen in the Raipur and Korba region due to running of Aluminium plant and thermal power plants (Figure 2). However, the highest concentration of was marked in the remote area, Ambagarh Chouki. The [Σanion]/[Σcation] ratio (n = 42) was ranged from 0.05 - 0.77 with mean value of 0.27 ± 0.05, showing alkaline nature of the dusts. The temporal variation of ions from year 2008-2014 at site:
Table 1. Characteristics of road dust of India.
B = Brown, Bl = Black, DB = Dark brown, LB = Light brown, YB = Yellow brown, G = Grey.
Table 2. Concentration of ions in road dust, mg/kg.
Siltara-I is presented in Figure 3. A temporal increase in the concentration of all ions was observed due to increase in the frequency of the vehicles at rate of ≥2% - 8% in the Raipur region of the country. In duration of six years (from 2008 to 2014), the sum of total concentration of 9 ions was increased >2-folds. The concentration of ions in the dust was observed in following increasing order: < F− < Mg2+ < NO3− < K+ < < Na+ < Cl− < Ca2+.
Figure 2. Spatial variation of ions, R = Raipur, Ko = Korba, AC = Ambagarh Chouki.
Figure 3. Temporal variation of ions at Siltara-I location.
3.3. Metals in Road Dust
The concentration of the metals i.e. As, Cr, Mn, Fe, Ni, Cu, Zn, Pb and Hg in the road dusts of Raipur city is shown in Table 3. The content (n = 9) of metals i.e. As, Cr, Mn, Fe, Ni, Cu, Zn, Pb and Hg in the road dusts was ranged from 24 - 42, 164 - 526, 1711 - 5218, 63,850 - 110,853, 47 - 62, 81 - 720, 166 - 450, 92 - 295 and 0.05 - 0.12 mg/kg with mean value of 31 ± 4, 246 ± 82, 3002 ± 851, 82,581 ± 11,214, 54 ± 4, 206 ± 145, 241 ± 64, 171 ± 42 and 0.08 ± 0.02 mg/kg, respectively (at 95% probability). Among them, Fe exhibited extremely high concentration in all locations of the Chhattisgarh state. The ferro-alloy metals i.e. Fe, Cr, Ni and Hg showed the higher concentration in the industrial locations. However, metals i.e. Cu, Zn and Pb exhibited the higher concentration in the highway sites i.e. Tatibandh due to vehicular and tire emissions. The concentration of the metals in the dust was seen in the following increasing order: Hg < As < Ni < Cu < Pb < Cr » Zn < Fe. A temporal enhancement in the content of metals in the road dust of Siltara-I is shown in Figure 4. The metal content was found to increase at rate of >2% from year 2008-2014 due to an enormous increase in number of vehicles and iron industries. The content of metals in the road dust of studied area was found to be higher than other regions of the World due to increased mineral roasting and coal burning activities  - .
3.4. Salinity of Dust
The salinity is a sum of content of water soluble ions (i.e. Cl−, , , , Na+, K+, Mg2+ and Ca2+), and ranged (n = 42) from 0.28% - 9.10% with mean value of 2.23% ± 0.44%, respectively. Among them, the highest value (9.10%) was observed at Delhi, may be due to highest vehicle frequency (>6,000,000). The mean salinity value was found to be at least 10-times higher than the recommended value of 0.25%.
3.5. ESP and SAR Values
The presence of excessive amounts of exchangeable sodium causes deflocculating of soil. A soil is considered “sodic” when the exchangeable sodium percentage (ESP) is 6% or greater. Sodium adsorption ratio (SAR) is a ratio of the sodium (detrimental element) to the combination of calcium and magnesium in relation to known effects on soil dispensability. The ESP and SAR value was ranged from 14.2% - 67.1% and 2.2 - 95.3 with mean value of 26.6% ± 3.4% and 13.1 ± 4.1, respectively. Among them, the highest value was recorded at Delhi, due to the largest anthropogenic activities in the capital city of the country. The higher ESP and SAR values were observed in the locations i.e. industrial area, bus stand, etc. due to increased human activities. The ESP and SAR value of the road dusts of the country were found to be much higher than recommended value of 15% and 6, respectively.
3.6. Contamination Factor
The contamination factor (Cf) is a concentration ratio of element from the road dust to the background level
Table 3. Concentration of metals in road dust, mg/kg.
R1, R2, R3, R4, R5, R6, R7 & R8 = Tatibandh, Vivekan and Aashram, Fafadih chowk, Urla, Bhanpuri, Sankara, Siltara-I & Siltara-II.
Figure 4. Temporal variation of metals in road dust at Siltara-I.
present in the earth crust  . The Cf value for F−, Ni, Hg, Fe, Cr, Zn, Mn, Cu, Pb, Cl− and was found to be 1.1, 1.7, 2.1 2.2, 2.6, 3.9, 7.3, 10, 10 and 15, respectively. Among them, Cu, Pb, Cl− and were highly contaminated at all locations. However, other metals i.e. Hg, Fe, Cr, Zn and Mn were poorly contaminated in the road dust.
3.7. Cluster Analysis
The dendrogram of the sample sites is presented in Figure 5. Cluster analysis was performed on the dataset by Ward’s method using Euclidean distance as similarity measure. The variables were interrelated to each other according their maximum similarities. First, the interrelation takes place between two variables which had the most similarity and the next repetition other similar pair clusters were related together  . Three groups of sample sites were observed: Group A (n = 1) with the sample site No. 40 which is Nizamuddin railway station located in Delhi. The sample site No.40 stands as an outlier. Group B (n = 10) which contained the sample sites nos. 7, 8, 13, 14, 17 located in Raipur, 20 - 22 in Bhilai, 31 in Korba and 38 in Jabalpur. Group C (n = 31) is formed by the rest of sample sites located in Raipur, Bhilai, Korba and the others locations, except Jabalpur. The discriminating parameters between the three groups were pH, F− and inorganic nitrogen (and) which are highlighted in Figure 6.
3.8. Correlation Matrices
The correlation matrices of the ions and metals in the dusts are presented in Table 4, Table 5. The F− content of the road dust had either no correlation or poor negative correlation with other ions, may be due to its reactive nature. Other ions i.e. Cl−, , , , Na+, K+, Mg2+ and Ca2+ among themselves were well correlated, and expected their emission from multiple sources i.e. vehicular combustion, road material weathering, etc. Among heavy metals, four metals i.e. As, Cu, Zn and Pb were fairly correlated coming from similar sources. Iron was found to be well correlated only with Cr.
Figure 5. Discriminating parameters for groups.
Figure 6. Dendrogram for differential of road dust samples.
Table 4. Correlation matrix of ions in road dust of Raipur city.
Table 5. Correlation matrix of metals in road dust of Raipur city.
3.9. Factor Analysis
Factor analysis was executed on 18 variables for the 8 sample sites. Four factors were extracted for 94.76% of the total variance. Factor-1 accounted for 50.22% of the total variance. The variables Cl−, , , , Mg2+ and Ca2+ had each one a strong positive loading value. Na+ and Cu showed some moderate loading values on Factor-1. It characterized the presence of salts and organic matter for which Cu had a great affinity. Factor-2 accounted for 22.50% of the total variance. Chromium and Ni had a strong positive loading value, and Fe presented a moderate loading value. Mercury had a negative loading value on Factor-2, by denoting different sources between Cr or Ni and Hg.
Factor-3 represented 14.23% of the total variance. Manganese and Pb showed absolute strong loading values on Factor-3. Manganese was in opposite relation with metals i.e. Pb, Zn, Cr, Cu, Ni and Fe. This can be explained by the different sources of Mn in relation to the trace metals cited above.
Factor-4 accounted for 7.81% of the total variance. Metals i.e. Pb, Zn and K had each one a strong positive loading value on Factor-4. Arsenic presented a negative loading value, this denoted also different sources between As and Pb or Zn. The results of factor analysis highlighted the complexity of the different sources of metals.
A higher concentration of Zn and Pb in the road dust was found, and their prominent sources expected were ZnO and Pb used in tire thread and in the motor vehicle wheel balance weights, respectively  . The possible source of the metals i.e. As, Cr, Mn, Fe, Ni and Hg are road materials, automobile rust, motorcar exhaust, steel plants and coal burning  .
The main dominating species in the road dust is the Fe, contributing »75% fraction of the content of 18 elements (i.e. F−, Cl−, , , , Na+, K+, Mg2+, Ca2+, As, Cr, Mn, Fe, Ni, Cu, Zn, Pb and Hg). However, the fraction of Na and Ca includes 4% and 8%, respectively. The road dust is a sodic in nature at hazardous levels. The motor vehicle exhaust emissions are expected to be main sources for contaminating the road dust with Cl−, , Cu, Zn and Pb nearby highways. The higher concentration of F− was marked in two locations: Raipur and Korba of the country due to huge coal burning and running of an Aluminium Plant.
We are thankful to the Alexander von Humboldt Foundation, Bonn for granting fellowship to one of them: KSP.
 Goyal, S.K., Ghatge, S.V., Nema, P. and Tamhane, S.M. (2006) Understanding Urban Vehicular Pollution Problem Vis-a-Vis Ambient Air Quality—Case Study of a Megacity (Delhi, India). Environmental Monitoring Assessment, 119, 557-569.
 Nagpure, A.S., Sharma, K. and Gurjar, B.R. (2013) Traffic Induced Emission Estimates and Trends (2000-2005) in Megacity Delhi. Urban Climate, 4, 61-73.
 Amigou, A., Sermage-Faure, C., Orsi, L., Leverger, G., Baruchel, A., Bertrand, Y., Nelken, B., Robert, A., Michel, G., Margueritte, G., Perel, Y., Mechinaud, F., Bordigoni, P., Hémon, D. and Clavel, J. (2011) Road Traffic and Childhood Leukemia: The ESCALE Study (SFCE). Environmental Health Perspectives, 119, 566-572.
 Tétreault, L.F., Perron, S. and Smargiassi, A. (2013) Cardiovascular Health, Traffic-Related Air Pollution and Noise: are Associations Mutually Confounded? A Systematic Review. International Journal of Public Health, 58, 649-666.
 Rogge, W.F., Mederios, P.M. and Simoneit, B.R.T. (2012) Organic Compounds in Dust from Rural and Urban Paved and Unpaved Roads taken during the San Joaquin Valley Fugitive Dust Characterization Study. Environmental Engineering Science, 29.
 Zhang, Q., Shen, Z., Cao, J., Ho, K.F., Zhang, R., Bie, Z., Chang, H. and Liu, S. (2014) Chemical Profiles of Urban Fugitive Dust over Xi’an in the South Margin of the Loess Plateau, China. Atmospheric Pollution Research, 5, 421-430.
 Akbar, K.F., Hale, W.H.G., Headley, A.D. and Athar, M. (2006) Heavy Metal Contamination of Roadside Soils of Northern England. Soil and Water Research, 1, 158-163.
 Amato, F., Pandolfi, M., Viana, M., Querol, X., Alastuey, A. and Moreno, T. (2009) Spatial and Chemical Patterns of PM10 in Road Dust Deposited in Urban Environment. Atmospheric Environment, 43, 1650-1659.
 Christoforidis, A. and Stamatis, N. (2009) Heavy Metal Contamination in Street Dust and Roadside Soil along the Major National Road in Kavala’s Region Greece. Geoderma, 151, 257-263.
 Faiz, Y., Tufail, M., Javed, M.T., Chaudhry, M.M. and Siddique, N. (2009) Road Dust Pollution of Cd, Cu, Ni, Pb and Zn along Islamabad Expressway, Pakistan. Microchemical Journal, 92, 186-192.
 Gunawardana, C., Goonetilleke, A., Egodawatta, P., Dawes, L. and Kokot, S. (2012) Source Characterisation of Road Dust Based on Chemical and Mineralogical Composition. Chemosphere, 87, 163-170.
 Han, L., Zhuang, G., Cheng, S., Wang, Y. and Li, J. (2007) Characteristics of Re-Suspended Road Dust and Its Impact on the Atmospheric Environment in Beijing. Atmospheric Environment, 41, 7485-7499.
 Joshi, U.M., Vijayaraghavan, K. and Balasubramanian, R. (2009) Elemental Composition of Urban Street Dusts and Their Dissolution Characteristics in various Aqueous Media. Chemosphere, 77, 526-533.
 Kuruneri-Chitepo, C. and Shackleton, C.M. (2011) The Distribution, Abundance and Composition of Street Trees in Selected Towns of the Eastern Cape, South Africa. Urban Forestry and Urban Greening, 10, 247-254.
 Liu, E., Yan, T., Birch, G. and Zhu, Y. (2014) Pollution and Health Risk of Potentially Toxic Metals in Urban Road Dust in Nanjing, a Mega-City of China. Science of the Total Environment, 476-477, 522-531.
 McKenzie, E.R., Wong, C.M., Green, P.G., Kayhanian, M. and Young, T.M. (2008) Size Dependent Elemental Composition of Road-Associated Particles. Science of the Total Environment, 398, 145-153.
 Nazzal, Y., Rosen, M.A. and Al-Rawabdeh, A.M. (2012) Assessment of Metal Pollution in Urban Road Dusts from Selected Highways of the Greater Toronto area in Canada. Environmental Monitoring Assessment, 185, 1847-1858.
 Tokalioglu, S. and Kartal, S. (2006) Multivariate Analysis of the Data and Speciation of Heavy Metals in Street Dust Samples from the Organized Industrial District in Kayseri (Turkey). Atmospheric Environment, 40, 2797-2805.
 Zhao, P., Feng, Y., Zhu, T. and Wu, J. (2007) Characterizations of Resuspended Dust in Six Cities of North China. Atmospheric Environment, 40, 5807-5814.
 Atiemo, M.P., Ofosu, G.F., Kuranchie-Mensah, H., Tutu, A.O., Linda Palm, N.D.M. and Blankson, S.A. (2011) Contamination Assessment of Heavy Metals in Road Dust from Selected Roads in Accra, Ghana. Research Journal of Environmental and Earth Sciences, 3, 473-480.
 Duong, T.T.T. and Lee, B.K. (2011) Determining Contamination Level of Heavy Metals in Road Dust from Busy Traffic Areas with Different Characteristics. Journal of Environmental Management, 92, 554-562.
 Lee, P.K., Youm, S.J. and Jo, H.Y. (2013) Heavy Metal Concentrations and Contamination Levels from Asian Dust and Identification of Sources: A Case-Study. Chemosphere, 91, 1018-1025.
 Du, Y., Gao, B., Zhou, H., Ju, X., Hao, H. and Yin, S. (2013) Health Risk Assessment of Heavy Metals in Road Dusts in Urban Parks of Beijing China. Procedia Environmental Sciences, 18, 299-309.
 Loska, K. and Wiechula, D. (2003) Application of Principle Component Analysis for the Estimation of Source of Heavy Metal Contamination in Surface Sediments from the Rybnik Reservoir. Chemosphere, 51, 723-733.