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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: [email protected]
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).
References
Al-Khashman, O. A. (2004). Heavy metal distribution indust, street dust and soils from the work place in KarakIndustrial Estate, Jordan. Atmospheric Environment, 38, 6803–6811. doi:10.1016/j.atmosenv.2004.09.011.
Banerjee, A. D. K. (2003). Heavy metal levels and solidphase speciation in street dusts of Delhi, India.Environmental Pollution, 123, 95–105. doi:10.1016/S0269-7491(02)00337-8.
Blok, J. (2005). Environmental exposure of road borders tozinc. The Science of the Total Environment, 348, 173–190. doi:10.1016/j.scitotenv.2004.12.073.
8/3/2019 Impact of Overland Traffic on Heavy Metal Levels in Highway
http://slidepdf.com/reader/full/impact-of-overland-traffic-on-heavy-metal-levels-in-highway 10/10
110 Environ Monit Assess (2010) 164:101–110
Charlesworth, S., Everett, M., McCarthy, R., Ordonez,A., & De Miguel, E. (2003). A comparative study of heavy metal concentration and distribution in depo-sited street dusts in a large and a small urbanarea: Birmingham and Coventry, West Midlands, UK.Environment International, 29, 563–573. doi:10.1016/S0160-4120(03)00015-1.
Chen, T., Zheng, Y., Lei, M., Huang, Z., Wu, H., Chen,H., et al. (2005). Assessment of heavy metal pollu-tion in surface soils of urban parks in Beijing,China. Chemosphere, 60, 542–551. doi:10.1016/j.chemosphere.2004.12.072.
Cressie, N. (1990). Statistics for spatial data (1st edn.). NewYork: Wiley.
Deutsch, C. V., & Journel, A. G. (1998). GSLIB Geosta-tistical software library and user’s guide (2nd edn.).Oxford: Oxford University Press.
Djingova, R., Kovacheva, P., Wagner, G., & Mark-ert, B. (2003). Distribution of platinum group el-ements and other traffic related elements amongdifferent plants along some highways in Germany.
The Science of the Total Environment, 308, 235–246.doi:10.1016/S0048-9697(02)00677-0.
Han, L., Zhuang, G., Cheng, S., Wang, Y., & Li,J. (2007). Characteristics of re-suspended road dustand its impact on the atmospheric environment inBeijing. Atmospheric Environment, 41(35), 7485–7499.doi:10.1016/j.atmosenv.2007.05.044.
Hooda, P. S., Miller, A., & Edwards, A. C. (2008). Theplant availability of auto-cast platinum group ele-ments. Environmental Geochemistry and Health, 30,135–139. doi:10.1007/s10653-008-9134-4.
Imperato, M., Adamo, P., Naimo, D., Arienzo, M.,Stanzione, D., & Violante, P. (2003). Spatial dis-tribution of heavy metals in urban soils of Naplescity (Italy). Environmental Pollution, 124, 247–256.doi:10.1016/S0269-7491(02)00478-5.
Juvanovic, S., Carrot, F., Deschamps, N., & Vukotic, P.(1995). A study of the air pollution in the surround-ings of an aluminum smelter using Epiphytic andLithophytic Lichens. Journal of Trace MicroprobeTechniques, 13, 463–471.
Kim, K. W., Myung, J. H., Ahn, J. S., & Chon, H. T. (1998).Heavy metal contamination in dusts and stream sedi-ments in the Taejon area, Korea. Journal of Geochem-ical Exploration, 64, 409–419. doi:10.1016/S0375-6742(98)00045-4.
Lapitajs, G., Greg, U., Dunemann, L., Begerow, J., Moens,
L., & Verrept, P. (1995). ICP-MS in the determinationof trace and ultra trace elements in the human body. International Laboratory, 5, 21–27.
Lee, S. L., Li, X. D., Shi, W. Z., Cheung, S. C. N., &Thornton, I. (2006). Metal contamination in urban,suburban, and country park soils of Hong Kong: Astudy based on GIS and multivariate statistics. TheScience of the Total Environment, 356, 45–61. doi:10.1016/j.scitotenv.2005.03.024.
Li, X., Lee, S., Wong, S., Shi, W., & Thornton, I. (2004).The study of metal contamination in urban soils of Hong Kong using a GIS-based approach. Environ-
mental Pollution, 129, 113–124. doi:10.1016/j.envpol.2003.09.030.
Liu, X., Wu, J., & Xu, J. (2006). Characterizing therisk assessment of heavy metals and sampling un-certainty analysis in paddy field by geostatisticsand GIS. Environmental Pollution, 141, 257–264.doi:10.1016/j.envpol.2005.08.048.
Olajire, A., & Ayodele, E. T. (1997). Contamination of roadside soil and grass with heavy metals. Environ-ment International, 23(1), 91–101. doi:10.1016/S0160-4120(96)00080-3.
Panichayapichet, P., Nitisoravut, S., & Simachaya, W.(2007). Spatial distribution and transport of heavymetals in soil, ponded-surface water and grass in a Pb-contaminated watershed as related to land-use prac-tices. Environmental Monitoring and Assessment, 135,181–193. doi:10.1007/s10661-007-9642-1.
Saby, N., Arrouays, D., Boulonne, L., Jolivet, C., & Pochot,A. (2006). Geostatistical assessment of Pb in soilaround Paris, France. The Science of the Total Envi-ronment, 367 , 212–221. doi:10.1016/j.scitotenv.2005.
11.028.Sezgin, N., Ozcan, H. K., Demir, G., Nemlioglu, S., &
Bayat, C. (2003). Determination of heavy metalconcentrations in street dusts in Istanbul E-5highway. Environment International, 29, 979–985.doi:10.1016/S0160-4120(03)00075-8.
Stockholm Environment and Health Protection Adminis-tration (SEHPA) (2001). Metal emissions from stock-holm traffic. Stockholm: SEHPA.
Teutsch, N., Erel, Y., Halicz, L., & Banin, A. (2001).Distribution of natural and anthropogenic lead inMediterranean soils. Geochimica et Cosmochimica Acta, 65(17), 2853–2864. doi:10.1016/S0016-7037(01)00607-X.
Tong, S. T. Y., & Lam, K. C. (2000). Home sweet home? Acase study of household dust contamination in HongKong. The Science of the Total Environment, 256, 115–123. doi:10.1016/S0048-9697(00)00471-X.
Turer, D., Maynard, J. B., & Sansalone, J. J. (2001).Heavy metal contamination in soils of urbanhighways: Comparison between runoff and soil con-centrations at Cincinnati, Ohio. Water, Air, andSoil Pollution, 132, 293–314. doi:10.1023/A:1013290130089.
Turkish General Directorate of Highways (2006). Annual report on traffic and transportation. Istanbul: TurkishGeneral Directorate of Highways.
United States Environmental Protection Agency (1996).Method 3050B: Acid digestion of sediments, sludgesand soils (Revision 2). Washington, DC: USEPA.
Yongming, H., Peixuan, D., Junji, C., & Posmentier, E.S. (2006). Multivariate analysis of heavy metal con-tamination in urban dusts of Xi’an, Central China.The Science of the Total Environment, 355, 176–186.doi:10.1016/j.scitotenv.2005.02.026.
Zhang, C. (2006). Using multivariate analyses and GIS toidentify pollutants and their spatial patterns in urbansoils in Galway, Ireland. Environmental Pollution, 142,501–511. doi:10.1016/j.envpol.2005.10.028.