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Environ Monit Assess (2010) 164:101–110

DOI 10.1007/s10661-009-0878-9

Impact of overland traffic on heavy metal levels in highway

dust and soils of Istanbul, TurkeyMert Guney · Turgut T. Onay · Nadim K. Copty

Received: 10 October 2008 / Accepted: 10 March 2009 / Published online: 4 April 2009© Springer Science + Business Media B.V. 2009

Abstract The purpose of this study was to in-

vestigate the impact of overland traffic on the

spatial distribution of heavy metals in urban soils

(Istanbul, Turkey). Road dust, surface, and sub-

surface soil samples were collected from a total

of 41 locations along highways with dense traffic

and secondary roads with lower traffic and ana-

lyzed for lead (Pb), zinc (Zn), and copper (Cu)

concentrations. Statistical evaluation of the heavy

metal concentrations observed along highways

and along the secondary roads showed that thedata were bimodally distributed. The maximum

observed Pb, Zn, and Cu concentrations were

1,573, 522 and 136 mg/kg, respectively, in surface

soils along highways and 99.3, 156, and 38.1 mg/kg

along secondary roads. Correlation analysis of the

metal concentrations in road dust, surface and

20-cm depth soils suggests the presence of a com-

mon pollution source. However, metal concentra-

tions in the deeper soils were substantially lower

than those observed at the surface, indicating low

mobility of heavy metals, especially for Pb andZn. A modified kriging approach that honors the

bimodality of the data was used to estimate the

spatial distribution of the surface concentrations

M. Guney · T. T. Onay · N. K. Copty (B)Institute of Environmental Sciences,Bogazici University, 34342 Bebek, Istanbul, Turkeye-mail: ncopty@boun.edu.tr

of metals, and to identify hotspots. Results indi-

cate that despite the presence of some industrial

zones within the study area, traffic is the main

heavy metal pollution source.

Keywords Heavy metal · Soil pollution ·

Geostatistics · Overland traffic · Urban soils

Introduction

Pollution from overland transportation is an inev-

itable environmental consequence of increasing

commercial and industrial activity in develop-

ing areas, especially in large metropolitan areas

around the world. The rapid growth in overland

transportation activities within many urban cen-

ters is causing the release of numerous pollu-

tants to the environment, including heavy metal

emissions to the atmosphere and its deposition to

nearby roadside soils.

Heavy metals are naturally found in various

amounts in water, air, soils, and sediments. An-

thropogenic sources from various industrial

activities such as mining, foundries, smelters,

combustion, and traffic contribute to the amounts

of heavy metals in various media (Al-Khashman

2004). Although some heavy metals are essential

for vital processes in many living organisms,

including humans (Juvanovic et al. 1995; Lapitajs

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102 Environ Monit Assess (2010) 164:101–110

et al. 1995), these metals are generally toxic when

their concentrations exceed certain thresholds.

The most common heavy metals introduced

to the environment by overland transportation

are lead (Pb), zinc (Zn), and copper (Cu; Kim

et al. 1998; Sezgin et al. 2003; Banerjee 2003; Li

et al. 2004). Use of leaded gasoline is primarilyresponsible for the Pb exposure (Chen et al. 2005),

while tire wear and corrosion of roadside safety

fences contribute to Zn pollution (Blok 2005). Cu

is mainly released from the wear of brake linings,

which is also an important source of Pb and Zn

(SEHPA 2001). All three metals are deposited

in the form of dust and can form aerosols when

resuspended (Han et al. 2007). Moreover, plat-

inum group elements (Pt, Pd, Rh, Ru, and Ir)

originating from catalytic converters have been

detected in limited amounts (in the order of mi-crograms per kilogram) in highway dusts and

plants (Djingova et al. 2003; Hooda et al. 2008).

Field investigations have shown that soil pollu-

tion by heavy metals is generally concentrated in

the first few meters to tens of meters on either side

of the road pavement and then sharply decreases

with distance from the road (Olajire and Ayodele

1997; Blok 2005). Several studies have reported

that heavy metals tend to accumulate within the

top 30 cm of soil despite decades of exposure from

traffic (e.g., Teutsch et al. 2001; Turer et al. 2001).However, soil penetration of metals may increase

when the soil is disturbed due to tillage or traffic

(Panichayapichet et al. 2007).

Geostatistics is a powerful analysis tool that has

been used in numerous studies for the evaluation

of the spatial distribution and behavior of heavy

metals in urban areas. Applications ranged from

environmental risk assessment (Liu et al. 2006) to

the calculation of anthropogenic stock (Saby et al.

2006) and the identification of the spatial patterns

of pollutants in urban areas (Tong and Lam 2000;

Charlesworth et al. 2003; Imperato et al. 2003;

Yongming et al. 2006; Zhang 2006).

Istanbul, with a population exceeding ten mil-

lion, is one of the largest metropolitan areas of 

the world. It is located along the shores of the

“Bosphorus Strait” which connects the Black

Sea to the Marmara Sea and divides the city

into a “European” district and an “Anatolian” or

“Asian” district (Fig. 1). These two districts are

connected to each other by two highway suspen-

sion bridges: the Bosphorus Bridge which receives

an annual average traffic load of about 209,000

vehicles/day and the Fatih Sultan Mehmet Bridge

which carries 173,000 vehicles/day (Turkish Gene-

ral Directorate of Highways 2006).

Although Istanbul is an important transporta-tion center, no study on the extent of soil metal

pollution due to traffic can be found in the liter-

ature. The only related published study investi-

gated metal pollution in street dust at 14 locations

on an 18 km span of a highway located in the

European district of the city (Sezgin et al. 2003).

The study reported elevated concentrations of Pb,

Zn, and Cu in dust, with maximum values of 555.4,

1,852.0, and 1,358.5 mg/kg, respectively.

The purpose of this study is to assess the im-

pact of overland traffic on soil heavy metal (Pb,Zn, and Cu) pollution in the Anatolian district

of Istanbul, Turkey. Specifically, the aims of the

study are (1) to determine the level of pollution of 

the selected metals in dust and soils along major

highways and secondary roads, (2) evaluate the

statistical distribution of metal pollution in the

study area, (3) statistically compare the spatial dis-

tribution of the different heavy metals and iden-

tify potential pollution sources, and (4) identify

hotspots within the study area using geostatistics.

Materials and methods

Sampling procedure

The study area comprising of the Anatolian dis-

trict of the city of Istanbul is shown in Fig. 1.

The district contains numerous residential and

commercial zones and a complex transportation

network including the two suspension bridges that

connect the Anatolian and European districts of 

the city. The Anatolian district also includes a

number of industrial zones which are mostly lo-

cated along the eastern part.

Road dust and soil samples were taken from

a total of 41 locations within the area. Twenty

locations were adjacent to one of the six high-

ways (having four or more lanes and high speed,

dense traffic) in the district. At each of these

20 locations, dust samples were taken from the

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Environ Monit Assess (2010) 164:101–110 103

Fig. 1 Map of the city of Istanbul and the studyarea showing thesampling locations

road pavement, while soil samples were collected

within 1-m distance from the edge of the pave-

ment. Surface soil samples were taken from the

top 0–2 cm of soil, while the subsurface (20-cm

depth) samples were from collected from depths

of 19–21 cm. From the 21 locations next to the

secondary roads (side roads having at most four

lanes and low speed, light traffic), only surface

soil samples (0–2 cm, n = 21) were taken within1-m distance from the end of the pavement. All

samples were taken and processed as duplicates.

Soil samples were taken without disturbing the

soil with a specially manufactured sampling device

consisting of a piston inside a steel cylinder. The

device was driven into the soil with the help of a

hammer, and the sample was removed from the

cylinder by the piston with minimal disturbance

to protect the soil profile. Dust samples were col-

lected by using a brush and a shovel. All samples

were preserved in sealed plastic bags until the time

of analysis.

Metal content

EPA Method 3050B (USEPA 1996) was used for

the analysis of the collected dust and soil sam-

ples. According to the procedure, samples were

dried, crushed, and sieved (sieve size 2 mm) be-

fore the analysis. Acid digestions of samples were

performed with HNO3 and HCl. The recovery

rates of metals for the method are reported in the

protocol as 97% for Pb, 99% for Zn, and 94%

for Cu (USEPA 1996). Digestates were filtered

and heavy metal concentrations in digested sam-

ples were determined by using a flame atomic ab-

sorption spectrophotometer device (Perkin Elmer

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104 Environ Monit Assess (2010) 164:101–110

AAnalyst). All samples were analyzed in dupli-

cates. Detection limit for Pb, Zn, and Cu for

atomic absorption spectrophotometry device was

1.5 μ g/L. The relative standard deviation values

for the duplicate samples were below 15% (13.3%

for Pb, 14.2% for Zn, and 6.7% for Cu, for samples

from the highways; 3.3% for Pb, 2.1% for Zn, and4.6% for Cu, for samples from secondary roads).

Statistical analysis

To evaluate the heavy metal pollution, statistical

analysis of the Pb, Zn, and Cu concentration data

in dust, surface soils, and 20-cm depth soils was

performed. The analysis included the descriptive

statistics for the three datasets and correlations

of co-located metal concentrations to assess the

significance of vehicular traffic on heavy metal

pollution of urban soils and to assess the relative

vertical transport of the different metals.

The spatial variability of the surface soil data

was analyzed using a modified ordinary kriging al-

gorithm. Ordinary kriging is a linear spatial inter-

polator that estimates spatial data at unsampled

locations using a linear weight function of adja-

cent data points (Cressie 1990). The weights are

compared based on the semi-variogram function

which is a measure of the spatial variability of the

data as a function of distance. The geostatistical

program used to compute the semi-variogram and

perform the ordinary kriging is GSLIB (Deutsch

and Journel 1998) which is extensively used in soil

pollution, mining and groundwater problems.

Because the observed heavy metal concentra-

tion data may show multimodality due to differ-

ences in traffic loads, using the entire data set from

the highways and secondary roads jointly in krig-

ing may lead to inaccurate results. To overcome

this, an indicator function was incorporated intothe ordinary kriging. The surface pollution data

were divided into two subsets and separate semi-

variograms were computed for each. The follow-

ing indicator function was defined:

 I ( x, y) = 1 If the unsampled location ( x, y) and

the data point are within the same

subset (i.e., both along the highways

or both near secondary roads)

 I ( x, y) = 0 If the unsampled location ( x, y) and

the data point are not within the

same subset

The modified ordinary kriging equation, thus, be-

comes

C ( x, y) =n

i=1

λi ×C ( xi, yi)×  I ( xi, yi) (1)

Where C ( x, y) is the estimated concentration at

any location, λi is the weight for a given loca-

tion, C ( xi, yi) is the concentration, and I ( xi, yi) is

the value of indicator function (equal to 0 or 1).

The first subset in this study is included in the

50-m wide zone from either side of the highways

(Fig. 1), where extensive pollution due to the

traffic activity is expected to occur. The second

subset is collected from the rest of the area, whichis dominated by a thick web of secondary arterials

and streets with less dense traffic.

Results and discussion

Statistical characteristics of the heavy metal

concentration data

The statistical data are presented for the highways

and secondary roads separately because of thelarge differences observed (Table 1). The results

show that Pb concentrations in dust and surface

soil samples from the highways were significantly

higher than concentrations from the secondary

roads. The Pb concentrations in the highway dust

and surface soil samples were also elevated com-

pared to the 20-cm depth soil samples from high-

way soils. The maximum Pb concentration in the

dust samples collected along the highways was

1,087 mg/kg of dry soil, observed in the arter-

ial carrying traffic to the first suspension bridge

  joining the two districts. The average Pb con-

centration in highway dust was 177 mg/kg. The

highest concentration of Pb in surface soil samples

and in the 20-cm depth samples was 1,573 and

302 mg/kg, respectively, with both samples located

along the same arterial. The corresponding aver-

age Pb concentrations were 191 and 81.2 mg/kg,

respectively. These results suggest that the major

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Environ Monit Assess (2010) 164:101–110 105

Table 1 Descriptive statistics of metal concentrations data (mg/kg dry soil)

Dust samples Surface soil samples 20-cm depth soil samples Surface soil samples

(highways, n = 20) (highways, n = 20) (highways, n = 20) (secondary roads, n = 21)

Avg Max Min SD Avg Max Min SD Avg Max Min SD Avg Max Min SD

Pb 177 1086 12.5 230 191 1573 21.1 355 81.2 302 10.1 82.7 29.7 99.3 8.1 25.1

Zn 245 521 87.0 91.6 255 522 93.4 120 211 450 47.2 109 96.6 155 45.2 29.1

Cu 111 299 14.0 75.5 68.7 136 21.4 35.3 47.0 94.1 12.6 20.7 23.4 38.1 11.0 7.2

pathway for the transport of Pb is through release

into the atmosphere, followed by deposition and

accumulation of metals in dust form and in surface

soils, with limited downwards migration through

the top soil. Although the use of leaded gasoline

in Turkey has substantially decreased in 1990s and

was banned in 2002, Pb concentrations in dust and

soil remain high showing the persistence of the Pb

in soils.A similar pattern was observed for Zn and Cu

concentrations. The highest levels of pollution of 

Zn were observed in dust and surface soil sam-

ples from the highways with very close averages

(245 and 255 mg/kg, respectively) and maximum

values (521 and 522 mg/kg, respectively). Cu pol-

lution was observed only in some locations along

highways, with maximum and average Cu con-

centrations in the surface soil samples of 136 and

68.7 mg/kg, respectively. These values were higher

than the Cu concentrations in the 20-cm depthsamples from highways (maximum of 94.1 mg/kg

and average of 47.0 mg/kg) and in the surface soils

of secondary roads (maximum and average of 38.1

and 23.4 mg/kg, respectively).

Similar studies conducted on urban soils from

other major cities around the world (Table 2) in-

dicate that heavy metal contamination in soils and

highway dusts of urban areas generally exhibits

a wide range in the concentration, varying from

a few milligrams per kilogram to thousands of 

milligrams per kilogram and that the heavy metal

concentrations observed for Istanbul are compa-

rable to that of other cities. While the Pb values

in Istanbul are higher than all other urban areas

(except Naples), the Zn and Cu concentrations are

generally at the lower end. It is important to note

that several factors influence heavy metal concen-

tration including population, traffic density, qual-

ity of the fuel used, proximity of the samplinglocation to the road, and type of the sample.

Bimodal distribution of heavy metal

concentration data

Preliminary inspection of the surface data col-

lected from the highways and secondary roads

suggests that the two sets of data are significantly

different. This is also seen in Fig. 2, which shows

the averages (bars inside the boxes), inner quartile

ranges (top and bottom of the boxes), and max-imum and minimums (the beginning and end of 

the whiskers) for the different metal concentra-

tions. The results of the independent samples t 

test showed that the surface data collected from

the highways and secondary roads are statistically

different (t = 2.023 for Pb, 5.756 for Zn, and 5.635

for Cu; p < 0.05 for Pb, p < 0.01 for Zn and Cu).

The data presented in Table 1 with Fig. 2,

together with the results from the t  test indicate

that the metal pollution observed along highways

Table 2 Summary of results from other selected studies

Location Population Details Pb (mg/kg) Zn (mg/kg) Cu (mg/kg) Reference

Istanbul, Turkey 10,018,735 Highway dusts 105.5–555.4 190.9–1852.0 47.25–1358.5 Sezgin et al. (2003)

Galway, Ireland 65,832 Highway surface soils 25–543 23–656 9–271 Zhang (2006)

Beijing, China 17,430,000 Urban surface soils 25.5–207.5 25.7–196.9 24.1–457.5 Chen et al. (2005)

Hong Kong 6,708,389 Urban surface soils 7.53–496 23–930 1.30–277 Lee et al. (2006)

Ibadan, Nigeria 2,550,593 Highway surface soils 205–730 43.5–213 8.94–80.5 Olajire and Ayodele (1997)

Naples, Italy 3,082,756 Urban surface soils 4–3420 30–2550 6.2–286 Imperato et al. (2003)

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106 Environ Monit Assess (2010) 164:101–110

Fig. 2 Inner quartile ranges, maximums, and minimums of data from surface samples along highways and secondary roads

and secondary roads represent two statistically

different populations, i.e., the data are bimodally

distributed. Consequently, it would not be appro-

priate to consider all the data pooled together for

determination of the semi-variogram and kriging.

To address this issue, an indicator function is in-

corporated into the geostatistical evaluation of the

data as previously described.

Correlations between metal concentrations

The Pearson correlations analysis (n = 20 for

highways, n = 21 for secondary roads) indicate

that Pb, Zn, and Cu dust concentrations are

strongly correlated (r = 0.820 for Pb–Zn, 0.693

for Pb–Cu, and 0.605 for Zn–Cu couple; p <

0.05) which is an indicator that the metals in the

soils may have originated from a common source.

Among the surface data for the different metals,

there is also strong correlation between Pb and

Zn (r = 0.746, p < 0.05) and between Zn and Cu

(r = 0.735, p < 0.05) and a slightly lower corre-

lation for Pb and Cu (r = 0.567, p < 0.05) which

means these pollutants accumulate in a similar

pattern within the soil surface. For the soil sam-

ples from 20-cm depth, only a strong correlation

exists between Pb and Zn (r = 0.810, p < 0.05),while correlations of Pb–Cu and Zn–Cu are weak

(r = 0.210 and 0.302, respectively; p < 0.05).

The Pearson correlations between metal con-

centrations determined from the samples col-

lected from secondary roads are calculated as

0.424 for Pb–Zn, 0.450 for Pb–Cu, and 0.678 for

Zn–Cu couples ( p < 0.05). These results show a

moderate correlation between Pb, Zn, and Cu for

surface soil samples. In comparison, these correla-

tions are slightly weaker than the ones found for

surface soil samples from highways.

Table 3 Correlations of metal concentrations across different media (dust and surface and 20-cm soils) along highways

Soil samples (surface; n = 20) Dust samples (n = 20) Soil samples (20-cm depth; n = 20)

Pb Zn Cu Pb Zn Cu

Pb 0.957 0.716 0.688 0.846 0.649 0.045

Zn 0.663 0.580 0.497 0.869 0.937 0.273

Cu 0.540 0.478 0.366 0.677 0.745 0.698

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Environ Monit Assess (2010) 164:101–110 107

To statistically evaluate the extent of dust depo-

sition and vertical profile of the metal concentra-

tions in soils, Pearson correlations between metal

concentrations observed across different media

(dust, surface soil, and 20-cm depth soil samples)

observed along the highways were computed and

presented in Table 3. Metals in dust samples cor-related moderately to very strongly to the surface

soils, suggesting that there may be a common

source of contamination within the study area.

Concentration of metals in surface soils and 20-cm

depth soils were also strongly correlated, although

the levels of contamination are much lower in

the deeper soils. This indicates that surface con-

tamination is the source of contamination of the

deeper soils but that metal transport is a very slowprocess since the soils around highways has been

exposed to the contamination for a long time.

Fig. 3 Semi-variograms of the heavy metal surface data

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108 Environ Monit Assess (2010) 164:101–110

Geostatistical analysis

The raw semi-variogram was determined using

eight lags with a lag distance of 2.5 km, and a

tolerance of 2 km was selected. An exponential

model was used to fit the raw semi-variogram:

γ  = σ 21− e(

h I )

(2)

Where γ  is semi-variogram function, σ 2 is vari-

ance, h is the separation distance, and I  is

characteristic length parameter. The best fit semi-

variogram model was determined by minimizing

the squared sum of differences between the model

and raw data. The raw semi-variograms along

with the best fit exponential curves are given in

Fig. 3. The semi-variogram parameters are shown

in Table 4.The results of ordinary kriging with the indica-

tor function described in “Materials and methods”

Section are given in Figs. 4, 5, and 6 for Pb, Zn,

and Cu, respectively, based on the surface soils

concentration data. These figures show the dif-

ferences between the metal concentrations near

highways and secondary roads. Due to the large

variation of Pb concentrations in soils near high-

ways roads and secondary roads, a logarithmic

scale was used for better representation. A linear

scale was used in the graphs for Zn and Cu.The overall results clearly exhibit the difference

between concentrations of metals near the high-

ways and the secondary roads. All three maps for

metals of Pb, Zn, and Cu showed similar patterns

of distribution, which is also an indicator of a

common pollution source for these metals.

For the highways, the highest concentrations

were observed near the two arterials (upper left

portion of the study area) carrying traffic to the

two suspension bridges that join the European

Table 4 Best-fit semi-variogram model parameters

Variable Nugget σ 2 I

(ppm2) (ppm2) (km)

Pb (Highways) 0 119,819 2.330

Zn (Highways) 9000 13,681 1.920

Cu (Highways) 750 1,183 1.224

Pb (Secondary roads) 0 602 3.459

Zn (Secondary roads) 0 804 2.656

Cu (Secondary roads) 26.2 49.8 2.440

Fig. 4 Distribution of Pb in surface soils

and Asian portions of the city (see Fig. 1). Also

the arterials and conjunctions close to these roads

showed higher pollution values compared to other

locations within the study area. For the areas

around the secondary roads, elevated concentra-

tions of Pb near the zone around the two arterials

Fig. 5 Distribution of Zn in surface soils

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Environ Monit Assess (2010) 164:101–110 109

Fig. 6 Distribution of Cu in surface soils

to the bridges was apparent. The reason for these

elevated concentrations can be attributed to the

increased traffic activity in the secondary roads

connecting to the highways.

Although the industrial activity within the

Anatolian district of the city is mostly located in

the eastern part of the study area, heavy metal

levels in the soils of these regions were among thelowest. This indicates that these industrial areas

are not a significant source of heavy metal pol-

lution. Therefore, it is concluded that the traffic

activities are the primary source of heavy metal

contamination in the study area.

Conclusions

In this study, concentration data of selected heavy

metals (Pb, Zn, and Cu) in dust, surface, and

subsurface (20-cm depth) soils within the Asian

district of Istanbul, Turkey were statistically ana-

lyzed to assess the impact of overland traffic on

observed heavy metal levels. The data collected

from 41 sampling points located throughout the

study area showed extensive metal pollution in

dust and surface soil samples near the highways

compared to 20-cm depth soils and soils adjacent

to secondary roads. The high levels of correla-

tion between metal concentrations in dust, sur-

face, and 20-cm depth soils indicate the presence

of a common pollution source, namely overland

transportation activities. The relatively low con-

centrations of heavy metals in the 20-cm soils

(as compared to that observed in the dust and

surface soils), coupled with the high correlationcoefficients between the data from the different

media, indicate high attenuation of heavy metals

within the soil environment.

Statistical testing of the data near the highways

and the secondary roads showed that the two

data sets are statistically distinct, which can be

attributed to the fact that overland transportation

within the study area is the main source of metal

pollution in surface and near surface soils and

pollution tends to be limited to short distance on

either side of the roads. Because of the bimodalityof the data collected along highways and along the

secondary roads, indicator-based ordinary kriging

was used to generate maps of the spatial vari-

ability of the heavy metal concentration, which

showed that the highest levels of pollution were

located near the arterials and conjunctions close

to the two suspension bridges that connect the two

districts of the city. Lower heavy metal concen-

trations were observed within the industrial zones

located in the eastern portions of the city. These

results show that overland traffic remains the mainsource of heavy metal pollution within the dust

and urban soils of the city of Istanbul.

Acknowledgements The authors acknowledge the finan-cial support provided by Bogazici University ResearchFund (Project No: 07Y101D).

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