4 A

Environ Geochem Health (2007) 29:197–207 DOI 10.1007/s10653-006-9065-x

ORIGINAL PAPER

The investigation of metal concentrations in street dust samples in Aqaba city, Jordan Omar Ali Al-Khashman

Received: 5 February 2006 / Accepted: 12 October 2006 / Published online: 8 February 2007
Springer Science+Business Media B.V. 2007

Abstract The concentrations of metals (Fe, Zn, Cu, Cr, Pb, Cd, Ni, Mn and Co) in 140 street dust samples were collected from Aqaba city, Jordan. These samples were determined using flame atomic absorption spectrophotometry after digestion with aqua regia. The highest levels of metal concentrations were found in the samples from heavy traffic. While the lowest levels of metal ions were noted in the street dust samples from hospital and health centers and school gardens. The results of this study were compared with several cities around the world. The levels of the metal concentrations found were generally below the mean world-wide values of street dust samples. Metal values in urban street dust samples were several times higher than the control levels. The statistical analyses were applied to the data matrix to determine the analytical results and to identify the possible source of pollution in the studied area. Correlations between the metal concentrations of the street dust samples were obtained. Factor analysis showed that the area was mainly influenced by three sources, namely lithogenic, traffic, and industrial.

O. A. Al-Khashman (&) Prince Faisal Center for Dead Sea, Environmental and Energy Research, Mutah University, Al-Karak 61710, Jordan e-mail: [email protected]

Keywords Street dust
Metals
Statistical analysis
Pollution
Traffic
Jordan

Introduction Human activity increases the levels of metals contamination in the environment. Metals pollution accumulates in the street dust, soil, and surface water samples and influences the ecosystem in the world (Al-Radady, Davis, & French, 1994; Tu¨zen, 2003). The determination of metal in environmental samples including dusts, plants, soils and surface water is very necessary for monitoring environmental pollution (Tu¨zen, 2003; Zhou, Wong, Koh, & Wee, 1997). Metals are released into the biosphere by natural processes and by anthropogenic activities. They are predominantly transferred as molecules or particulate matter via the atmosphere, mostly on large scales. The amount of anthropogenically derived metals has increased continuously since the beginning of the industrial revolution, and the awareness and concern about associated environmental and health risks has risen sharply over the last few decades (Gunter & Komarnicki, 2005). In urban areas, metals may come from many different sources, including vehicle emission, industrial discharges and weathered materials (Al-Khashman, 2004; Al-Khashman & Shawabakh, 2006; Gibson &

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Farmer, 1986; Harrison, Laxen, & Wilson, 1981; Li, Poon, & Pui, 2001; Thornton, 1991). Vehicle emission is one of the sources of emission of metals, and street dust metal pollution in urban areas arises from a multitude of sources via water transported material from surrounding soils and slopes, dry and wet atmospheric deposition, biological inputs, road surface wear, road paint degradation, vehicle wear and lubricating oil, and particulate emissions (Banerjee, 2003; Sutherland & Tolosa, 2000). Particulate matter can be attributed to different sources according to its particle size; on the other hand, fine particles are derived from the combustion process and are mainly attributed to anthropogenic sources (Gunter & Komarnicki, 2005). Street dust is environmental material for monitoring the levels of metal ions (Divrikli, Soylak, Elic, & Dogan, 2003; Ramlan & Badri, 1989). Two main factors known to influence the levels

Fig. 1 Location map of the study area

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of trace metals in dust have been reported to be traffic and industrial activities. The monitoring of the metals contents of dust samples is an efficient way of obtaining information on the current environmental state of large areas (Divrikli et al., 2003). The main purpose of the present work was to determine the concentrations of metals in street dust samples collected from various stations in Aqaba city and to identify their natural or anthropogenic sources. The study area The study area is located at the north shore of the Gulf of Aqaba, in the southwestern part of Jordan. It is about 51 m above the sea level, and bounded by latitude 2933¢ N and longitude 350¢ E (Fig. 1). Aqaba is a coastal city with a population of 120,000 at time of this study

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(Department of Statistics, 2004). The main industrial activities in Aqaba city are (Jordan Phosphate C., 1985), cement, petroleum industry, Jordan Fertilizer industries plant, 1982), and some shells. The Red Sea port of Aqaba is an important commercial shipping center. Aqaba has a modern airport and port facilities sitting alongside the remains of the ancient city of Eyla. The mean traffic density in the streets with heavy, medium, light and low traffic were >500, 300, 150 and <100 vehicles/h respectively. The climate of Jordan is predominantly Mediterranean; it is marked by sharp seasonal variations in both temperature and precipitation. Aqaba city climate is very hot in the summer and warm in the winter and is characterized by an extremely small amount of precipitation, that is, around (17.0 mm/year). Minimum temperature in the study area is 9.7C in January and 26.3C in July (Department of Meteorology, 2004). The investigated area is located in the northwest

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part of the Arabian plate, whereas most of Jordan is located within the stable part of the plate. Upper cretaceous carbonaceous facies dominated the central part of the country, whereas ancient basement (pre-Cambrian) and Cambrian Nubian sandstone dominated in the southern part of the country. Basalt desert in the northeast and the rift valley form Jordan’s western borders (Fig. 2). Furthermore, the sandy facies within the carbonate rock increases southward through the country (Banat, Howari, & Al-Hamad, 2005; Bender, 1974). Two geological formations occur predominantly in and around the study area. Jordan’s oldest rocks (pre-Cambrian age, 570 million years old) are the main component of the mountains behind Aqaba. They mainly consist of granite, which is crisscrossed with sheets of intruded igneous rock, known as dykes. At the time of dyke formation, the granitic rocks were deep below the surface. In this area, sandstones are present and composed primarily of fine and

Fig. 2 Geology of the study area and surrounding areas (according to Banat et al., 2005)

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medium-sized quartz grain, small amounts of kaolinite and illite (Bender, 1974). The sandstone consists of yellow-brown and red-brown medium to coarse grained arkosic and subarkosic sandstone. The Gulf of Aqaba represents the southern portion of the Dead Sea fault system (Jordan rift), which connects the spreading of the Red Sea seafloor with continental rift propagation (Al-Zoubi, Abu-Hamatteh, & Abdelalkaderer, 2006).

Materials and methods Sample collection A total of 140 street dust samples were collected from various locations in Aqaba during the period of June 2004 to August 2004 (Table 1). The sampling was chosen at the end of the dry summer months following at least four rainless months. Rain has no effect on the trace metals, which accumulated in these months. A sampling point within each unit was selected at random, and approximately 250 g of the dust particles that had accumulated on impervious surfaces (gutter, road pavement) within a 5-m radius circle around the selected sampling point were collected (Baptista and De Miguel, 2005). The dust sampling was carried out from pavement edges using a plastic dustpan and brushes. Dust samples were not collected adjacent to site specific pollution sources e.g. industrial sites and gasoline stations.

Briefly, the samples were collected from both sides of the road on a dry day and a composite sample out of the two collected was obtained after coning and quartering (Table 1). The samples were transferred to an air-tight polyethylene bag for transport to the laboratory. The samples were left to dry at room temperature for 5 days and were sieved through a 2-mm plastic sieve to remove extraneous matter such as small pieces of brick, paving stone, and other debris. Care was taken to reduce the disturbance of the fine particles, which are readily lost by resuspension (Baptista and De Miguel, 2005; Li et al., 2001; Madrid, Barrientos, & Madrid, 2002; Manta, Angelone, Bellanca, Neri, & Sprovieri, 2002). Control samples (n = 13), were collected from the remote areas outside of Aqaba that were not affected by traffic and any source of contamination (background sites). The samples were collected from this area for comparison purposes. Preparation for the analysis Two grams of the sieved sample were weighed and transferred to a Pyrex tube. Then 10 ml of aqua regia (1HNO3 and 3 HCL) was added. The sample was transferred to a heating block for 8 h to complete digestion, then centrifuged and transferred to a volumetric flask and made up to 25 ml with 1 M HNO3 (Divrikli et al., 2003). Metals in the final solutions were determined using a Shimadzu atomic absorption spectrophotometer (model AA-6200). The pH was measured

Table 1 Description of sampling locations Location/sampling point

Number of vehicles/h

Description

School gardens (A), S1–S15 (n = 15) Health and hospital centers (B), HS1–HS15 (n = 15) Residential sites (C), R1–R18 (n = 18) Industrial sites (D), IN1–IN16 (n = 16)

125 150

Residential area, low traffic Low traffic, beside the parks

275 300

Parks (E), P1–P18 (n = 18)

–

Heavy traffic (F), HT1–HT15 (n = 15) Moderate traffic (G), MT1–MT15 (n = 15) Light traffic (H), LT1–LT15 (n = 15) Background sites (I), BG1–BG13 (n = 13)

>500 350 <250 0

Residential areas, medium traffic Major industries: phosphate, cement, and petrochemicals Urban parks, highway type road, grass, and some old trees Heavy traffic areas Medium traffic areas Low traffic areas Rural area

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in 1 (sample, w):2.5 (distilled water, v) mixtures (Banerjee, 2003; Okalebo, Gathua, & Woomer, 1993). The percentage of the organic matter of selective samples was determined by the chromic acid digestion method (Jackson, 1973). Standard stock solutions for all the elements were procured from Merck as well as prepared in the laboratory following the procedures as described in APHA (1989). To minimize the sources of error, blanks were run simultaneously for analysis of all the elements (Banerjee, 2003). Reagent blanks and duplicate samples were used in the analytical programs to assess contamination, precision and bias. The precision and bias in the analysis were generally <7%. The glassware used was Pyrex, which was washed several times with soap, distilled water and diluted nitric acid to remove any impurities. All procedures of sampling and handling were carried out without contact with the metals, to avoid potential contamination of the samples. Statistical analysis Statistical analyses were performed with SPSS for Windows 10.0.5. Data were log-transformed prior to principle component analysis (PCA) to reduce the influence of high data (Moller, Muller, Abdullah, Abdelgawad, & Utermann, 2005). Principle Component Analysis was conducted using factor extraction with an eigenvalue larger than 1 after varimax rotation. Pearson’s correlation coefficient was used to measure the degree of correlation between logarithms of the metal data (Garcia & Millan, 1998).

Results and discussion Physico-chemical parameters Street dust is also potentially a source of metals for people living in urban communities. The values of metals in street dust samples collected from Aqaba city were determined using a Shimadzu atomic absorption spectrophotometer after digestion with aqua regia (one part concentrated HNO3 and three parts concentrated HCL). The values of different metals in the street dust

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vary greatly according to the strength and direction of wind, composition of dust and pH values. The pH values show relatively equal distribution in the area, with their higher values in the street dust samples collected in the residential area. The pH values were relatively similar across the area, with higher values in street dust samples collected closer to a workplace with construction materials. The relatively high average pH values measured in this area is due to the neutralization of acidity in the dust by carbonate. The higher pH > 7, removal metals from aqueous phase to solid phase. The organic matter in dust samples ranged from 1.23 to 1.96%. The highest value of organic matter was found in the northeast of the investigated area; however, the distribution pattern of organic matter reflects the variable dust of vegetation and plant cover in the investigated area. Concentration of metals The concentrations of metals in street dust samples are given in Table 2. The street dust samples in the investigated area were classified into nine groups, i.e., schools and gardens, health and hospital centers, residential, industrial, parks, streets with heavy traffic, streets with moderate traffic, streets with light traffic and control samples out of Aqaba city. The pH value varied from 7.6 in moderate traffic stations (MT10) to 8.20 in light traffic stations (LT4) beside the construction material site (Table 2), which suggests neutral to sub-alkaline conditions for all the dust samples. The mean values of the metals are shows in Fig 2. The highest levels of metal ions were found in the samples from street dust of the heavy traffic area, except for manganese and cobalt, the highest level of which were found in the samples from the industrial areas. As shown in Table 2, the mean metal concentration values are arranged in the following order: CFe > CZn > CPb > CNi > CMn > CCu > CCr > CCo > CCd where C stands for concentration. The lowest mean values of metals were observed at school gardens compared with the other sites in the investigated area. Lead pollution in the environmental samples including soil, dust, sediment and natural water comes from combustion of gasoline that contains tetraethyl lead as an anti-knock agent (Tu¨zen, 2003). The

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Table 2 Mean and standard deviation for the descriptive parameters of street dust samples (lg g–1), n = 140 samples Site pH A B C D E F G H I

8.1 7.9 8.1 7.8 7.9 7.7 7.6 8.2 7.9

± ± ± ± ± ± ± ± ±

0.1 0.2 0.3 0.1 0.1 0.2 0.1 0.6 0.2

OM%

Fe

1.4 1.8 1.7 1.9 1.7 1.6 1.3 1.2 1.7

4,540 4,999 5,125 5,343 5,477 6,865 5,678 5,126 3,340

± ± ± ± ± ± ± ± ±

0.1 0.3 0.2 0.2 0.1 0.2 0.1 0.1 0.3

± ± ± ± ± ± ± ± ±

9.2 8.7 6.4 9.1 6.1 0.1 5.7 5.5 7.5

Zn

Cu

Cr

Pb

Cd

103 ± 2.1 106 ± 4.7 114 ± 4.1 118 ± 3.2 127 ± 3.5 160 ± 3.2 146 ± 4.1 126 ± 2.5 48 ± 1.5

21 ± 1.2 24 ± 8.2 26 ± 4.5 29 ± 1.9 24 ± 4.3 56 ± 2.1 50 ± 4.9 32 ± 4.5 1.4 ± 0.1

11 ± 0.1 16 ± 0.4 20 ± 2.4 22 ± 6.5 36 ± 7.5 41 ± 7.1 32 ± 4.5 21 ± 2.7 2.1 ± 0.1

93 ± 7.1 109 ± 5.2 138 ± 6.2 145 ± 5.1 164 ± 6.2 212 ± 6.8 194 ± 1.5 103 ± 4.5 19 ± 0.5

2.5 2.2 2.0 2.1 2.6 2.9 2.1 1.9 1.2

Ni ± ± ± ± ± ± ± ± ±

0.4 53 ± 3.1 0.3 60 ± 4.3 0.1 51 ± 5.5 0.2 65 ± 2.6 0.1 70 ± 4.5 0.1 115 ± 4.5 0.2 66 ± 4.1 0.1 55 ± 1.5 0.2 10.2 ± 1.1

Mn

Co

51 ± 2.1 15 ± 2.1 54 ± 3.2 10 ± 0.1 59 ± 1.5 11 ± 0.8 90 ± 1.6 18 ± 0.8 76 ± 1.9 19 ± 0.1 107 ± 3.5 21.4 ± 0.6 65 ± 2.6 16 ± 0.2 48 ± 1.4 18 ± 0.7 32 ± 1.8 5.3 ± 0.1

A: School gardens B: Health and hospital centers C: Residential areas D: Industrial areas E: Parks F: Heavy traffic G: Moderate traffic H: Light traffic I: Background sites

highest lead values have been detected in dust collected from streets carrying heavy traffic (Divrikli et al., 2003; Fergusson, 1991). As shown in Table 2, the minimum mean lead concentration found in street dust samples from school gardens was 93 lg g–1 (Fig. 3). The concentrations of lead in the dust samples was highest in dust from streets with heavy traffic (mean = 372.9 lg g–1). The high value of lead in the dust samples from heavy traffic sites comes from traffic, because all the samples were in the street dust with high traffic levels (>500 v/h). However, the mean concentration of lead for street dust samples was lower than for dust samples reported by other literature, such as 697.2 lg g–1 for Bahrain, 230.5 lg g–1 for Xian, China, 514 lg g–1 for Aviles, Spain, 1,030 lg g–1 for London, UK, 265 lg g–1 for Manchester, UK, and 236 lg g–1 for Amman, Jordan. On the other hand, the mean concentration of lead in street dust from Aqaba city was higher than reported by other literature, such as 48 lg g–1 for Bursa, Turkey, 69.2 lg g–1 for Yozgat, Turkey and 47.1 lg g–1 for Coventry, UK. The mean contents of copper in the dust samples was within the range of 21–56 lg g–1. While the lowest mean value of copper was found in the samples from

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school gardens, the highest mean copper value was found in the samples from heavy traffic areas (Fig. 3). The source of copper in the street dust was indicated by research as being due to corrosion of metallic parts of cars derived from engine wear, thrust bearing, brushing, and bearing metals (Al-Khashman, 2004; Al-Khashman & Shawabkeh, 2006; Divrikli et al., 2003; Jaradat & Momani, 1999; Thornton, 1991). The levels of copper in the samples from Aqaba city were generally lower than the world-wide values (100– 300 lg g–1; Fergusson, 1991). The present study found that copper levels were low compared with several cities in the world (Table 3). The level of chromium in the dust samples was found to be within the range of 11 lg g–1 in school gardens to 41 lg g–1 in heavy traffic sites (Fig. 2). The chromium in dust was associated with the chrome plating of some motor vehicle parts (Al-Shayep & Seaward, 2001). Chromium highest concentrations were observed in the samples from streets with heavy traffic (51 lg g–1). On the other hand, the lowest value for chromium was observed in school gardens (Fig. 3). The level of chromium in the investigated area was generally lower than those determined in other cities in developed countries, except Luanda in Angola (Table 3).

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203 0.12 Mn

0.08

0.06 0.05 0.04 0.03 0.02 0.01 0

Cu

0.06 0.04 0.02 0

A

B

C

D

E F Sites

G

H

I

0.05

A

Cr

0.04 0.03

ppm

ppm

ppm

ppm

0.1

0.02 0.01 0 A

B

C

D E Sites

F

G

H

I

B

C

D E Sites

F

G

H

Fe

7000 6000 5000 4000 3000 2000 1000 0 A

B

C

D

E Sites

F

G

H

I

Pb

0.01

0

B

C

D E Sites

F

G

H

A

I

0.003

Cd

ppm

0.002 0.0015 0.001 0.0005 0 A

B

C

D E Sites

B

C

D

E

F

G

H

I

Sites

0.0025 ppm

0.015

0.005

A

F

G

H

0.15

ppm

Co

0.02 ppm

ppm

0.025

0.25 0.2 0.15 0.1 0.05 0

I

I

0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0

Zn

A

B

C

D

E F Sites

G

H

I

Ni

0.1 0.05 0 A

B

C

D

E F Sites

G

H

I

Fig. 3 Mean concentrations of the metals in the street dust samples. A school gardens, B health and hospital centers, C residential areas, D industrial areas, E parks, F heavy traffic, G moderate traffic, H light traffic, I background sites

Higher concentrations of zinc and cadmium in heavy traffic zones indicates that fragmentation of car tires is a likely source of these metals (Elik,

2003). The highest mean cadmium content was found in the streets with heavy traffic (2.9 lg g–1). The cadmium is released as a combustion product

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Table 3 Mean concentration of metals (lg g–1) in street dust in several cities City

Fe

Cu

Cd

Pb

Zn

Cr

Co

Mn

Ni

Digestion

Ammana Avilesb Bahrainc Birminghamd Coventrye Kayserif Londong Manchesterh LuandaI Tokatj Xiank Yozgatl

7,132 42,200 – – – – 26,000 8,767 11,572 – – –

177 183 – 466.9 226.4 66.7 155 113 42 38 94.9 37.7

1.7 22.3 72 1.6 .9 10.1 3.5 – 1.1 5.4 – 2.97

236 514 697.2 48 47.1 165.5 1,030 265 315 266 230.5 69.2

358 4,829 151.8 534 385 – 680 653 317 98 421.4 –

– 41.6 144.4 – – 72.8 – – 26 41 167.3 32.7

– – – – – 26.1 – – 2.9 22 – 24.3

– – – – – 274 – – 258 415 687 852

88 – – – – 57 – – 10 128 – 77

HCL+HNO3 HCL+HNO3 HCL+HNO3 HCLO4+HNO3+H2SO4 HCLO4+HNO3+H2SO4 HNO3 HCL+HNO3 HNO3 HCL+HNO3 HCL+HNO3 HCLO4+HNO3+H2SO4 HNO3

a Al-Khashman (2006); b Ordonez, Loredo, De Miguel, & Charlesworth (2003); c Akhter and Madany (1993); d Charlesworth et al., (2003); e Charlesworth et al., (2003); f Divrikli et al., (2003); g Schwar, Moorcroft, Laxen, Thompson, & Armorgie (1988); h Robertson, Taylor, & Hoon (2003); I Baptista and De Miguel (2005); j Tu¨zen (2003); k Yongming et al., (2006); l Divrikli et al., (2003)

in the accumulators of motor vehicles or in carburetors (Charlesworth, Everett, McCarthy, Ordonez, & de Miguel, 2003; Divrikli et al., 2003). The value of cadmium in street dust samples from Amman, Jordan was reported to be within the range of 1.01–2.9 lg g–1 (Al-Khashman, 2006). Many researchers have reported the mean concentration of cadmium in several cities (Table 3). The range of the median value of cadmium in street dust samples world-wide has been examined as being 0.5–4.0 lg g–1 (Fergusson & Kim, 1991). In general, the high values of Cr, Cd, Ni, and Cu were found in Aqaba city, which might be due to the phosphate handling activities of the port of Aqaba, which emits phosphate aerosol through the atmosphere. The zinc concentration was found in the range of 103–160 lg g–1. The zinc levels found in the present study were lower than those determined in other cities in developing countries (Table 3). Iron was the most abundant metal content in street dust. The possible source of iron particles were brake lining material (brake dust; Adachi & Tainosho, 2004; Garg et al., 2000; Hildemann, Markowski, & Cass, 1991). The highest mean iron level was found in the streets with heavy traffic (6,865 lg g–1). However, the mean concentration of iron in street dust samples was lower than determined in several cities in developing countries (Table 3). Manganese content in the dust samples was found to be within the range of 48–107 lg g–1. The main

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source of manganese in dust samples was from the geological material in Aqaba city (lithogenic source), from traffic, and from tire wear (Divrikli et al., 2003). The mean manganese concentration in street dust samples was 300–800 lg g–1 (Fergusson, 1991). The concentration of cobalt in dust samples was within the range of 17.8– 22.3 lg g–1. The major source of manganese is considered to be natural, sandstone and basement rock being the main source of manganese in dust samples. The highest mean level of cobalt was found in heavy traffic sites (21.4 lg g–1). The level of cobalt in dust samples was found to be higher than mean values around the world, and the level of cobalt in the present study does not vary much from the investigated area in other localities. Also, the source of cobalt in the dust samples from heavy traffic comes from the corrosion of metallic parts of vehicles. Nickel values in dust samples were found to be within the range of 51–114.6 lg g–1.The highest mean value of nickel was found in the street dust samples and the lowest mean value of nickel was found in the control site. The range of mean nickel concentration in world-wide street dust samples was 50–100 lg g–1 (Fergusson & Kim, 1991). Nickel pollution on a local scale is caused by emissions from vehicle engines that use nickel gasolene and by the abrasion and corrosion of nickel from vehicle parts (Al-Shayeb & Seaward, 2001).

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Statistical analyses and data treatment Correlation coefficient analysis Pearson’s correlation coefficient can be used to measure the degree of correlation between logarithms of the metal data (Garcia & Millan, 1998). The correlations of metals in street dust samples of Aqaba city are depicted in Table 4. Highly significant correlation data (>0.65) were found between Cu and Cr, Cu and Pb, Cr and Ni, Pb and Co, and Cd and Mn (Table 4). These high correlations show that the origin of the metals in the investigated area is highly related to the heavy traffic, industrial activities, structure of the soils, and street dust emissions. Lead values showed very weak positive correlations with Mn, Zn, Ni, and Cd varying from 0.194 to 0.402. A meaningful correlation has not been found between Mn and Pb. The source of Pb in the street dust samples may be from heavy traffic activities in the studied area. The Mn–Co (0.113) and Mn–Cr (0.224) pairs were weakly correlated at 95% confidence level. Principle component analysis Statistical analyses were performed with SPSS for Windows 10.0.5. Data were log-transformed prior to PCA to reduce the influence of high data (Moller et al., 2005). PCA was used to help

identify the source of pollutants more accurately using factor extraction with an eigenvalue larger than 1 after varimax rotation. The results of statistical analysis are tabulated in Table 5. Table 5 displays the factor loadings with a varimax rotation, as well as the eigenvalues. The results show that two eigenvalues are greater than 1 and the total variance for the two factors is about 74%. The third eigenvalue, which is 1.11, explains about 10% of the total variance, which is a significant contribution toward the explanation of cumulative variance. Factor 1 was dominated by Cu, Cr, Cd, Ni, Co, and Zn, and accounts for 41.12% of the total variance, in this case. Zn and Cd loadings (0.625 and 0.676 respectively) are not as high as the loadings of the other elements in the group; such a domination may be caused by the behavior of the metals within the group. This factor represents pollution caused by industrial activities and emissions from traffic (Banerjee, 2003; Chen, Wong, Zhou, & Wong, 1997). The second factor is highly dominated by pH, OM and Fe and Mn and accounts for 23.24% of the total variance. This factor was composed of the characteristics of street dust pH and organic matter and metals (Fe and Mn), and represents the contribution of metals from lithogenic and anthropogenic sources. This factor’s high loadings of the elements Fe and Mn suggest the influence of natural sources and some anthropogenic input. Relatively, the high loadings of Fe and Mn in the

Table 4 Correlation matrix between metals in urban samples; cells show the Pearson correlation coefficient and the corresponding P value, n = 140 samples

Cr Pb Cd Ni Mn Co Fe Zn

Cu

Cr

Pb

Cd

Ni

Mn

Co

Fe

0.851 0.000 0.722 0.000 0.51 0.000 0.552 0.003 0.410 0.000 0.144 0.002 0.521 0.000 0.281 0.000

0.762 0.000 0.313 0.000 0.692 0.000 0.224 0.002 0.232 0.000 0.434 0.000 0.223 0.000

0.201 0.000 0.402 0.001 0.194 0.000 0.764 0.000 0.681 0.000 0.344 0.000

0.384 0.001 0.692 0.000 0.522 0.000 0.404 0.000 0.481 0.000

0.564 0.000 0.070 0.001 0.184 0.000 0.390 0.000

0.113 0.000 0.007 0.002 0.310 0.002

0.401 0.000

0.321 0.001

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Table 5 Factor loadings for varimax rotated PCA of metal data in street dust samples (loadings in bold type are statistically significant) Parameters pH OM Cu Cr Pb Cd Ni Mn Co Fe Zn Eigenvalues % Variance % Cumulative

Factor 1 –1.370 –0.803 0.823 0.805 –9.652 0.676 0.881 –0.126 0.745 0.290 0.625 4.523 41.122 41.122

Factor 2 0.908 0.775 0.222 –0.164 –1.619 0.414 0.129 0.908 0.338 0.672 0.332 2.556 23.238 64.359

Factor 3 –2.264 –0.335 –0.232 0.323 0.973 –0.152 –0.250 0.245 –0.162 0.199 1.109 10.082 74.441

Communities 0.825 0.765 0.780 0.781 0.876 0.633 0.855 0.901 0.696 0.537 0.540

dusts are interpreted here to be the result of natural enrichment by weathering and pedogenesis (Al-Khashman, 2006). The third factor was correlated very strongly with Pb, which has a high loading value (0.973), and explains 10.08% of the total variance. The source of this factor may be explained by contributions mainly from traffic, especially cars and minibuses, which consume leaded gasolene. High levels of Pb in street dust samples have been recognized for a long time to be linked mainly to traffic activities due to this utilization of leaded gasolene (Day, Hart, & Robinson, 1975; Yongming, Peixuan, Junji, & Posmentier, 2006).

Conclusion This study performed on street dust samples from Aqaba city, Jordan revealed a clear accumulation of Pb, Zn, and Ni. This study determined that the increases in anthropogenic metals such as Pb, Zn, Cu, and Ni in street dust in Aqaba city can most likely be attributed to rapid development, increased traffic emissions to the atmosphere, industrial activities, and lack of sophisticated management of wastes and effluents from plants. The highest metals values were found in the heavy traffic sites, while the lowest concentrations of the metals were found

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in school gardens and hospital and health centers. The mean concentrations of the studied metals were as follows: CFe > CZn > CPb > CNi > CMn > CCu > CCr > CCo > CCd where C stands for concentration. Results showed that dust samples contained significant levels of the metals studied with the control values. The statistical analysis, metal value analysis, and correlation analysis were applied as an effective tool to identify the source of metals in street dust samples. The examined elements were classified into three main groups according to their sources: natural, traffic, and industrial. PCA analysis coupled with correlation analysis was used to gain additional insight into the origin of these metals in street dust samples from Aqaba city. The trend of increasing industrial and traffic activities in the city indicates the need for pollution control in the city environment.

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