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Identifying Speed Hump, a Traffic Calming Device, as a Hotspot for Environmental Contamination in Traffic-Affected Urban Roads

  • Indian Institute of Technology (Indian School of Mines) Dhanbad

Abstract and Figures

Despite several studies on traffic calming devices, information on particulate matter contribution by vehicle abrasion and wear nearby vertical deflections of speed humps is scant. Many studies have been performed in the recent past on heavy metal contamination at roads mainly at intersections. On the other hand, the traffic calming devices were studied for their effectiveness in reducing the vehicle speed and thereby increasing road safety, but their environmental effects are neglected. In the present study, the relation between the concentrations of Cu and Zn (marker heavy metals for traffic sources) at speed humps were nearly thrice to that in intersections, while for another marker heavy metal Pb, it was found nearly twice in comparison. Pollution load index >3 was observed upto 7.5–8.8 m distances of speed humps, and these were identified as hotspot zones for traffic-generated pollution. Furthermore, this heavy-metal-laden speed hump soil can pose a threat to living beings by virtue of resuspension produced by vehicular movements. Therefore, it is necessary to manage this emerging environmental issue, and we propose a traffic calming device with wheel cut-out provision for different vehicle classes as an alternate.
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Identifying Speed Hump, a Trac Calming Device, as a Hotspot for
Environmental Contamination in Trac-Aected Urban Roads
Ravi Sahu and Suresh Pandian Elumalai*
Department of Environmental Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004,
Jharkhand, India
ABSTRACT: Despite several studies on trac calming devices, information on
particulate matter contribution by vehicle abrasion and wear nearby vertical
deections of speed humps is scant. Many studies have been performed in the
recent past on heavy metal contamination at roads mainly at intersections. On the
other hand, the trac calming devices were studied for their eectiveness in reducing
the vehicle speed and thereby increasing road safety, but their environmental eects
are neglected. In the present study, the relation between the concentrations of Cu and
Zn (marker heavy metals for trac sources) at speed humps were nearly thrice to that
in intersections, while for another marker heavy metal Pb, it was found nearly twice in
comparison. Pollution load index >3 was observed upto 7.58.8 m distances of speed
humps, and these were identied as hotspot zones for trac-generated pollution.
Furthermore, this heavy-metal-laden speed hump soil can pose a threat to living
beings by virtue of resuspension produced by vehicular movements. Therefore, it is
necessary to manage this emerging environmental issue, and we propose a trac
calming device with wheel cut-out provision for dierent vehicle classes as an alternate.
Road characteristic is a well-known factor as a substantial
contributor to aect tracow, travelers safety, intrusive noise,
air quality of nearby road microenvironments, and associated
environmental issues.
Over the last decade, unprecedented
growth of personal vehicle usage in developing countries with
limited development in road facilities has resulted in increasing
time share in traveling and growing trac accidents.
at road networks where mixed trac prevails, these personal
vehicles travel with a much higher speed than slowly moving
public vehicles. As a consequence of these factors, the
augmented road accidents have further increased and are the
growing menace on the roads the world over. This has resulted
in increased frequency of trac calming devices.
Most of the
urban air, road dust, and soil quality studies are carried out near
intersections and street canyons,
but information on heavy
metal near these trac calming devices is scant.
Nowadays, the most common trac safety measures are
related to vertical changes of the roads; such as speed humps,
speed bumps, and speed tables; and horizontal changes on the
alignment; such as roundabouts and chicanes.
Most of the
trac calming devices were studied for their eectiveness in
reducing the vehicle speed and thereby increasing road
The drastic change in trac speed near trac
calming devices was highly correlated to gaseous pollutant
In developing countries, owing to lack of trac
rules to monitor fast moving vehicles, navigating to unpaved
shoulders at the speed hump is also a unique driving behavior.
Therefore, at rst look, roads in these countries appear to have
greater soil from the unpaved shoulders. In recent times, the
variation in driving cycles of vehicle types in cities have been
observed from their legislative ones.
Furthermore, it is clear
that the safer speed limits and the type and amount of tracin
developing countries are quite dierent from those in
developed countries. Thus, the impact of trac calming devices
on nearby environment in such developing countries may dier
signicantly and therefore is required to be studied precisely in
Concentrations of vehicular emitted heavy metals in roadside
soils result in long-term environmental damage.
It was
reported that Cu, Zn, and Pb could indicate trac pollution
and could continue to accumulate in urban environment due to
their nonbiodegradability and long residence time; thus, they
are also known as chemical time bombs.
Many studies
have been performed on contamination of road soil due to
vehicle-emitted marker heavy metals, Cu (brake wear), Zn (tire
wear), and Pb (exhaust and nonexhaust emission), around the
However, detailed investigation on trac-
generated heavy metals around speed humps in road soils is not
yet reported.
The present study deals with the characterization and
distribution of speed humps and intersection soils along the
road to nd the information on interheavy metal relationship
and impact of distance on speed hump road soil contamination.
The assessment of the contamination level of heavy metals at
these speed humps was compared to that at other sites using
the contamination factor (CF), pollution load index (PLI),
modied degree of contamination (mCd), geoaccumulation
Received: May 26, 2017
Accepted: August 21, 2017
Published: September 5, 2017
© 2017 American Chemical Society 5434 DOI: 10.1021/acsomega.7b00683
ACS Omega 2017, 2, 54345444
This is an open access article published under an ACS AuthorChoice License, which permits
copying and redistribution of the article or any adaptations for non-commercial purposes.
index (Igeo), and ecological risk index (RI). Much has been
written on the impact of vehicular emission on the road dust
and nearby soil, but its impact on speed hump microenviron-
ment is limited. Therefore, the present study provides useful
information on trac contaminants and identies an emerging
urban hotspot zone for trac-generated pollution.
Furthermore, resolution of heavy metal issues in immediate
eect as well as in long term at speed hump microenvironments
is discussed. The present study draws the attention of
environmentalists and strategy makers of developing countries
toward speed humps deteriorating eect on ecology and
proposes an alternative to encourage the management of this
emerging environmental issue with the help of scientic
Heavy Metal Concentrations in Road Soil. The
concentration proles of Cu, Zn, and Pb in trac sites were
observed in the undertaken study at distances of 1, 2, 3, 5, 10,
50, 100, and 200 m away from the speed humps. Heavy metal
concentrations at speed humps and intersections are presented
in Figure 1. A total number of 255 heavy metal values (17 sites
×5 samples ×3 metals) along the road nearby speed humps
and at ve intersections are measured.
Among the Heavy Metals. Among the heavy metals, the
mean concentrations of Cu, Zn, and Pb were obtained in the
range of 51.9 ±8.8 to 186.5 ±18.7 mg kg1, 220.5 ±55.3 to
548.6 ±49.6 mg kg1, and 22.9 ±11.6 to 61.1 ±9.6 mg kg1,
respectively. High concentration of heavy metals at speed
humps may be a result of excessive brake wear, tire wear, and
tailpipe emission. Heavy metals at speed humps were found in
the order of Zn > Cu > Pb. The concentrations of heavy metals
are very high with respect to their background values (their
corresponding concentrations in preindustrial soil), which are
45, 95, and 20 mg kg1, respectively.
At speed humps, metal
concentrations were found to be >4-fold for Cu and Zn and
3-fold for Pb with respect to their background values. The
observed mean concentrations of Cu, Zn, and Pb at
intersections were 56.0 ±7.61, 158.4 ±7.56, and 32.4 ±
5.35 mg kg1, respectively. Heavy metal mean concentrations at
intersections were 1.21.6 times greater than background
values. Their concentrations were found slightly greater than 2-
fold with respect to the values at speed humps. Results
Figure 1. Spatial distribution of mean concentrations of heavy metals in road soil on both sides (1 and 2) of speed humps at a 1200 m distance and
at intersections (I).
Figure 2. Variations in the contamination factor (CF) of heavy metals in road soil on both the sides (1 and 2) of speed humps at 1200 m distance
and at trac intersections (I).
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indicated that these speed humps were even more contami-
nated than the intersections in terms of these heavy metals.
Eect of Speed Humps on Contamination with
Respect to Distance. Among the heavy metals nearby
speed humps, the highest mean concentrations obtained at 1
m for Cu, Zn, and Pb on side 1 were 186.5 ±18.7 mg kg1at 1
m, 541.6 ±27.4 mg kg1at 2 m, and 57.5 ±12.6 mg kg1.
Lowest values obtained for Cu, Zn, and Pb were 51.9 ±8.8 mg
kg1(at 200 m), 158.4 ±7.56 mg kg1(at intersections), and
22.9 ±11.6 mg kg1(at 200 m), respectively. On moving along
the road and away from speed humps on side 2, highest mean
concentrations obtained for Cu, Zn, and Pb were 183.0 ±17.4
mg kg1at 1 m, 548.6 ±49.6 mg kg1at 1 m, and 61.1 ±9.6
mg kg1at 2 m, respectively. The sharp decrease in
concentrations of heavy metals away from speed humps proves
them as hotspots. Especially, at speed humps, deceleration is
signicantly high due to brakes exerted by drivers to minimize
mechanical damage to vehicles. The high concentration nearby
speed humps may be a result of excessive use of brakes by
drivers while maneuvering their vehicles over them. Con-
sequently, tailpipe emission also increases to accelerate just
after the vehicle passes the speed humps. This could have
resulted in the elevated heavy metal emission in nearby speed
Assessment of Road Contamination Using Indices. CF
values of the road soil along with the dierent grades of CF are
presented in Figure 2. Values of CF for Cu revealed that upto 2
m it was strongly contaminated, whereas it was moderately to
strongly contaminated upto 5 m. For Zn, upto 3 m signicance
of estimated CF values showed that upto 3 m strong to very
strong contamination occurred. For Zn, upto 50 m distance,
moderate to strong contamination was observed for Zn. The
estimated signicance of CF values for Pb showed that upto 3
m of distance was moderately to strongly contaminated. It has
provided the basic information on the contamination of road
soil near speed humps. Continuous emission of heavy metals
from vehicles may result in a long-term environmental damage.
For the assessment of environmental pollution or contami-
nation at speed humps, other indices were used based on CF
values. Heavy metals Cu and Zn at speed humps indicated that
their values were nearly thrice to that in intersection road soil,
whereas for Pb, it was nearly twice in comparison.
PLI values of heavy metals in road soil on both the sides (1
and 2) of speed humps at 1200 m distance and at trac
Figure 3. Pollution load index (PLI) of heavy metals in road soil on both the sides (1 and 2) of speed humps at 1200 m distance and at trac
intersections (I).
Figure 4. Variations in the modied degree of contamination (mCd) in road soil on both the sides (1 and 2) of speed humps at 1200 m distance
and at trac intersections (I).
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intersections are presented in Figure 3. PLI values (>1)
indicated progressive deterioration of the analyzed sites nearby
speed humps. Distances upto 5 m showed PLI >3, whereas for
distance >10 m, PLI values were <3. In the case of intersections
also, it was observed to be 1.44, which showed that they are
contaminated by heavy metals emitted from vehicles.
On the basis of cumulative eects due to heavy metals, mCd
was estimated for road soil using their CF values for the
respective distances. Estimated mCd values for each location
are presented in Figure 4. Signicance of mCd showed that
upto 3 m distance, high degree of contamination occurred,
whereas upto 50 m, moderate degree of contamination was
estimated nearby speed humps. mCd values for speed humps
were more than twice the values observed for intersections.
mCd values indicate that at intersections moderate degree of
contamination exists.
On the other hand, Igeo index values can be a quantitative
measure of the degree of pollution in road soil. Igeo values of
the road soil are presented in Figure 5. At speed humps, the
observed Igeo for Cu, Zn, and Pb showed that contamination is
not of huge concern. According to the Igeo index, only upto 5
m for Cu, 50 m for Zn, and 2 m for Pb were the distances that
had contamination of moderate level. Rest distances were
recognized as either uncontaminated to slightly contaminated
(upto distances of 50 m (Cu), 200 m (Zn), and 10 m (Pb)) or
practically uncontaminated thereafter. Negative Igeo values
were observed for Cu and Pb at 100200 and 50200 m,
respectively, which showed that the road soil is uncontaminated
with these metals. For Zn, road soil is moderately contaminated
upto 50 m and after that it is uncontaminated. For
intersections, Igeo for all three metals in road soil depicted
that they are uncontaminated.
The Igeo and CF values showed that the contamination
levels were in order of Zn > Cu > Pb. They indicated the
anthropogenic inuence on those sites and hence their
contamination level. Furthermore, speed humps had Igeo >1
for all three metals. This indicates that the road soils are
moderately contaminated by the metals derived from
anthropogenic sources. Results indicate that the road environ-
ment nearby speed humps is more than 2 times more polluted
than that of intersections, which are already known sites for
environmental pollution.
Variations in the risk index (RI) of heavy metals in the
collected road soil are presented in Figure 6. However, the
estimated RI values (with maximum RI = 41) indicated no
ecological risk at the studied speed humps because there was
variation observed in dierent indices while identifying a
distance upto which environmental pollution occurred near
speed humps. Therefore, to exactly identify the length of
hotspot for contamination due to trac sources nearby speed
humps, a zone was determined using PLI values.
Identication of Hotspot Zones for Contamination
Due to Trac Sources Nearby Speed Humps. Data
analysis at speed humps showed that distribution of heavy
metals on both the sides (sides 1 and 2) followed a nonlinear
pattern and is presented in Figure 7. The observed data in the
contamination of road soils encourages us to nd the actual
prone area or zone that is under the high inuence of
contamination due to trac sources. Hence, the mathematical
evaluation of this hotspot zone for pollutants using PLI values
may provide necessary information to environmentalists and
policy makers to act upon this emerging issue in urban speed
Figure 5. Variations in geoaccumulation index (Igeo) of heavy metals in road soil on both the sides (1 and 2) of speed humps at 1200 m distance
and at trac intersections (I).
Figure 6. Variations in the risk index (RI) of heavy metals in road soil
on both the sides (1 and 2) of speed humps at 1200 m distance and
at trac intersections (I).
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hump microenvironments. The prole for distribution of heavy
metals at the dierent distances on both sides from the speed
humps upto 200 m is presented in Figure 7a. Results showed
that the observed heavy metals were distributed in a logarithmic
pattern along the roads at the speed humps. Mean values were
traced and obtained logarithmic equations of their distribution
are presented in eq 1 (with R2= 0.971) and eq 2 (with R2=
0.976), respectively.
=− + =
xR0.56 ln( ) 4.226 ( 0.971)
=− + =
xR0.53 ln( ) 4.131 ( 0.976)
where, y1and y2are the values of PLI (unitless and
dimensionless) for road soil at sides 1 and 2 of speed humps,
respectively, and xis the distance in meters.
Proles of PLI of heavy metals on both the sides of speed
humps for distances 10 m is presented in Figure 7b. Average
values of PLI due to three heavy metals Cu, Zn, and Pb in the
zone of upto 10 m on both the sides were also found to be in a
nonlinear pattern and in a decreasing exponential form. Mean
values were traced, and equations of their distribution are
presented in eq 3 (with R2= 0.957) and eq 4 (with R2= 0.943),
respectively. For this zone, the obtained equations were of
exponential type and are expressed as follows
R4.366 e ( 0.957)
0.05 2 (3)
R4.266 e ( 0.943)
0.04 2 (4)
where y3and y4are the values of PLI (unitless and
dimensionless) for road soil at sides 1 and 2 of speed humps,
respectively, and xis the distance in meters.
Using these distribution patterns, an area or a zone was
estimated around the speed humps that represented the most
aected area, as far as the environment is concerned. Distances
at which PLI 3 for heavy metals were considered highly
contaminated, and the zones in between these distances were
identied here as new hotspots for pollution on roads. These
hotspots for road soil contamination due to heavy metals at
speed humps in urban roadways of developing countries may
pose harmful health eects to the commuters as well as residing
inhabitants. The hotspots for metal contamination on road
were estimated using eqs 3 and 4. Results revealed that upto
7.50 and 8.80 m distances on either sides of the speed humps
needed strategies to decrease road soil contamination on urgent
Statistical Analysis of Heavy Metal Data. Analysis of
data for its quality was performed using dierent statistical
tools. Signicant Spearmansρcorrelation among Cu, Zn, and
Pb with varying distances from the speed humps are presented
in Table 1. Data analysis revealed that a signicant negative
correlation was present among heavy metals Cu, Zn, and Pb
and distances from speed humps on either sides. Therefore,
while moving away from the speed humps, heavy metal
Figure 7. Proles of PLI of heavy metals on both the sides of speed humps along the road and respective trendlines for distances upto 200 m (a) and
distances in between 1 and 10 m (b).
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concentrations were found to decrease. Observed signicant
Spearmansρcorrelation coecients (r)(p0.01) were
0.917, 0.938, and 0.802 between increasing distances and
Cu, Zn, and Pb, respectively. On the other hand, a strong
positive correlation between the couple heavy metals (CuZn,
CuPb, and ZnPb) with rvalues 0.881, 0.809, and 0.781 were
observed. Results signied that each paired heavy metal had
common contamination sources, that is, trac in this study. On
the other hand, variation in their rvalues could be due to the
fact that their generation was from dierent sources within the
trac. However, physicochemical properties and metal
associations were not analyzed in the present study, to help
in ascertaining these results. The analysis revealed that heavy
metals had aected the speed hump microenvironments.
One-way ANOVA was performed to test the overall
inuence of the distance from speed humps on heavy metal
concentration. On detection of signicant dierences (p
0.05) between the mean concentrations at varying distances
from speed humps, Tukeys honestly signicant dierence
(HSD) test was performed. Tukeys HSD was performed to
identify the variation pattern between the distances for their
heavy metal concentrations. Tukeys HSD analysis showed
homogeneity in Cu concentration at 13 and 35 m, whereas
signicant dierence in the mean concentration was observed
at a 510 m distance. Tukeys HSD analysis showed
homogeneity in Pb concentration in road soil collected at 1
5 and 310 m, whereas signicant dierence in the mean
concentration was observed at a 1050 m distance. The
analysis showed a homogeneity in Zn concentration at
distances 13, 35, and 510 m, whereas signicant variations
in mean concentrations were observed at 1050 and 50200
m. Data analysis for heavy metal concentrations at dierent
distances at speed humps showed that the contamination of
heavy metals indicated less variation upto 10 m distance.
Proposed New Trac Calming Device for Developing
Countries. The new design focused on avoiding mechanical
disturbances to the vehicles in low-trac regions without
aecting its target speed reduction. Without simplicity and low
costs there will never be any large scale use. The driving
behavior to navigate over the unpaved surface at speed humps
may be avoided by implementation of vertical structures in the
estimated hotspot zone and providing wheel cut-outs (Figure
8) (longitudinal gap provided to allow vehicles to avoid
traveling over the vertical hump) of width slightly greater than
that of wheels of dierent vehicle types. It is expected here that
these wheel cut-outs may turn the heterogeneous type trac
(characteristics of city trac mainly in developing countries)
into homogenous type and also allow unimpeded passage by
emergency vehicles. Provision to warn drivers of the presence
of speed hump by posting suitable advance warning sign should
be placed 40 m before the speed hump. Speed limit imposition
combined with trac law enforcement is one of the best ways
to make vehicles slow down. Studies in many countries have
indicated that the introduction of speed limits often has only a
short-term eect in reducing speeds unless police regularly
enforce the limits.
Posted speed limits alone will not
guarantee compliance. It is only when backed up by strict
police enforcement that speed limits reduce speed. Further-
more, it should be painted with luminous and alternate color
bands. Regular cleaning of this trac calming device may
increase its performance level as well. After some new trac
calming devices design implementation like this, one can
gradually build up a general design for developing countries.
Limitations of the Proposed New Trac Calming
Device. The proposed calming device has its limitations, and it
may aect the trac in very crowded roads. The proposed
trac calming device considers only the emission reduction as
the area of concern and not the other parameters concerned
with ow of trac. This proposed trac calming device may
perform well in low-tracked arterial roads as well as in urban
roads of developing countries like India, prevailing mixed trac
and where the average speed itself has been reported to be
lesser than that of other developed countries. For example,
Adak et al. developed emission factors of three major vehicle
types: two wheelers, three wheelers, and four wheelers for
Dhanbad, India, using real-world driving cycle.
They have
reported the average speeds of these three types of vehicles to
be 27.8, 13.4, and 17.4 km h1, respectively, which is much
lower than the designed speed for the roads. In another study,
real-world vehicle emission was observed to vary with the
corresponding driving cycle of the country used for regulatory
They have reported that the road driving occurred at
lower average speeds with higher frequency and magnitudes of
accelerations. Moreover, in India, for speed breakers, the
preferred advisory crossing speed of 25 km h1was also
specied in the guidelines of IRC 99 (1996).
the eect of the proposed trac calming device on tracow
in dierent cities of developing countries may vary with their
respective trac characteristics. Therefore, a performance study
of the proposed trac calming device can be conducted to
examine its eect on tracow.
Existing Mitigation Techniques. The proposed design for
new trac calming device could turn into a useful alterative for
the identied hotspot by its implementation along with the
existing measures.
Improving the Environment of Speed Humps by
Washing. The vehicle-emitted heavy metal buildup can
accumulate (do not degrade with time) on road surfaces and
roadside soil, and hence it contributes in air, soil, and water
A provision to wash othe paved road
Table 1. SpearmansρCorrelation Coecients Among the
Heavy Metals and Distances from the Speed Humps
distance Cu Zn Pb
distance 1
Cu 0.917
Zn 0.938
Pb 0.802
Correlation is signicant at the 0.01 level (2-tailed).
Figure 8. Proposed trac calming device with the wheel cut-out
provision for dierent vehicle class.
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contaminated surface at an area nearby speed humps may
provide the immediate and cost-eective solution to remove
the heavy metals from road surfaces. It was assumed in this
study that regular cleaning of road dust buildup at this speed
hump hotspot with water may bring about a signicant
improvement to environment by removing entrained particles
at the hotspot zone. The study attempted for washing of leaves
nearby pollution sources showed signicant decrease in heavy
metals impact on plants.
However, as the hotspot zone is
identied, control of the resuspension process could diminish
the further contamination to produce better and promising
In previous published studies, suitable analytical parameters
were identied for road surface cleaning, runowaters, etc.
Researchers have recognized that wash-ois inuenced by
water intensity, duration, and runovolume.
buildup contaminants wash-ofrom the road surface was
usually analyzed using exponential equations.
these parameters to wash oat speed hump hotspots may play
a better role in this process. In general, water quality models
like storm water management model (SWMM) use a constant
value of k. The value of kis site-specic and may vary with the
road soil type, rainfall intensity, catchment area, and catchment
A constant value was reported to perform notably
well in the estimation process, and use of a constant value of k
had reduced the wash-oequations complexity.
Similarly, for
speed humps, the best possible values of kmay produce reliable
results using the theory of least squares to replicate the
observed wash-opatterns by providing water jets of calculated
ow rate, amount, and duration.
Additional Parameters To Improve Road Environ-
ment at Speed Humps. Road gradient was reported as
another important parameter in road surface cleaning or runo
simulation studies.
The water jets could be drained passively
from water outlet nozzles, placed at a relatively higher road
grade, to the other end along with the heavy metals removed
from that zone for that particular period. The contaminated
water can be collected in storage tanks for its treatment by the
ltration process with a provision to recycle it back to the upper
end using pumps. Filter strips, swales, inltration trenches, lter
drains, and soakways for road surface runotreatment have
been studied for car parking runo.
Furthermore, pollutants
at speed humps could be removed through mechanisms
adopted by previous researchers in laboratories, such as
ltration, adsorption, sedimentation, and biological uptake
factors aecting the phenomenon was also important in terms
of pollutant mass transport in road dust cleaning.
In addition
to this, dierent paving materials having a higher advection
property at roads with environmental pollution may also be
used to reduce its pollutant load.
The ndings of this study encourage the environmentalists,
planners, and strategy makers in the developing countries to
implement immediate alternatives to not only remove but also
avoid continuously increasing trac-emitted toxic heavy metals
and their long-term persistence nearby trac calming speed
humps. Therefore, the proposed new trac calming device,
especially nearby sensitive locations in cities, such as schools,
hospitals, residential areas, and minor roads, prone to higher
accidental cases, may act as an ecient and a low-cost alternate
to this existing environmental concern without aecting its
safety aspects. Furthermore, eective monitoring with a detailed
study on the performance of the proposed trac calming
device is necessary.
Sample Collection. Road soil was collected following the
method described by Fujiwara et al.
Samples were collected at
speed humps along the road (sides 1 and 2) and intersections
in urban roads (Figure 9). To study the environmental eects
caused by speed humps, road soils were collected from the
sampling sites at distances of 1, 2, 3, 5, 10, 50, 100, and 200 m
from the speed humps.
Figure 9. Study sites for collection of road soil at speed humps and trac intersections in Dhanbad, India (Map source:
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The concentration of heavy metals Cu, Zn, and Pb was
determined in the collected samples. These three selected
heavy metals are well-known markers of trac-associated
emission. Cu, Zn, and Pb in soil were reported to be generated
from brake wear, tire wear, and exhaust, respectively.
Chemicals and Reagents. Digestion of road soil samples
for heavy metal analysis was done using the method described
by Ogundele et al.
Road soil samples were air-dried, then
crushed with a clean dry mortar and pestle, and then sieved
through a 2 mm sieve to make it ne. Sieved samples, weighed
3 g, were then digested with a mixture of 10 mL of
concentrated hydrochloric acid and 3.3 mL of concentrated
nitric acid. For better mixing, they were left overnight.
Distilled water was added to the digested sample, and then the
sample was ltered with a Whatman No. 42 lter, pore size 2.5
μm, and topped upto 20 mL volumetric ask with distilled
The solutions were transferred into sampling bottles
for analysis.
The inltrates were examined for metal concentration level
from their acid extracts using atomic absorption spectropho-
tometer (AAS) (GBC Avanta PM, Australia) at wavelengths, λ,
Cu = 324.8 nm, Zn = 213.9 nm, and Pb = 217.0 nm, using air
acetylene ame. Standard Reference Materials (AccuTrace,
AccuStandard Inc.; Matrix 25% HNO3; CRM uncertainty ±
5%; veried against NIST SRM#3128 for Pb; 3168 for Zn; and
3114 for Cu) were used for the preparation and calibration of
each analytical batch.
Evaluation Method. To interpret and assess the
contamination status for heavy metals in collected samples,
several indices, such as CF, PLI, mCd, Igeo, and RI, were
estimated using eqs 510. The level of heavy metal
contaminations with respect to its corresponding values in
native soil before industrialization took place can be expressed
by the CF. Hence, CF is the ratio of the concentration of heavy
metal in the sample to that in its corresponding background.
It is an eective tool for monitoring the pollution over a period
of time and dened as
where Cnis the concentration of heavy metal nin the sample
and Bnis the concentration of heavy metal nin the
background. The contamination levels were classied by
based on their intensities on a scale ranging from
1 to 6, as presented in Table 2. The highest number indicates
that the metal concentration is 100 times greater than what it
could be expected in the earth crust.
Using the CF values estimated for heavy metals, other four
indices (PLI, mCd, Igeo, and RI) were estimated. The PLI was
proposed by Tomlinson et al.
It provides some understanding
about the measure of a component in the particular
environment. PLI for each site was evaluated as indicated by
= × × ··· ×
where Nis the total number of heavy metals analyzed (three in
the present study) and CF is the contamination factor.
Zero PLI value indicates perfection, a value of one indicates the
presence of only baseline levels of heavy metals, and values
above one would indicate progressive deterioration of the
analyzed site.
Another contamination identifying index mCd was also used
to examine the contamination in road soil by heavy metals and
was calculated based on the equation provided by Abrahim and
mCd 1C
where CF is the contamination factor, Nis number of heavy
metals in the study, and mCd is the modied and generalized
form of the degree of contamination (Cd) proposed by
It is calculated by summing all individual
contamination factors and dened as
where CF is the contamination factor, Nis the total number of
heavy metals analyzed (three in the present study), and Cd is
the degree of contamination. Abraham and Parker
reported that this generalized formula allows the incorporation
of several heavy metals without the restraint of an upper limit.
The mCd may be classied into dierent classes, which are
presented in Table 3.
Pollution levels of heavy metals around speed humps could
be characterized by the Igeo. This method has been used by
Müller for several heavy metal studies throughout the world.
It is computed using the following equation
=CBIgeo log ( /1.5 )
where Cnis the measured concentration of individual heavy
metal in the sample and Bnis the background value of
individual heavy metal. The control samples were taken to
represent the background, and 1.5 is the unvarying factor. Table
4represents seven classes of Igeo as proposed by Müller.
provided the ecological risk index (RI), which
integrates the factors of ecological risk potentials (Er) for each
heavy metal and associates their ecological and environmental
eects with their toxicology.
Its calculation is done as
Table 2. Interpretation of Heavy Metal Contamination
Using Contamination Factor (CF)
CF contamination level
0 none
1 none to medium
2 moderate
3 moderate to strong
4 strong
5 strong to very strong
6 very strong
Table 3. Interpretation of Heavy Metal Contamination
Using Modied Degree of Contamination (mCd)
ranges signicance
mCd < 1.5 nil to very low degree of contamination
1.5 mCd <2 low degree of contamination
2mCd <4 moderate degree of contamination
4mCd <8 high degree of contamination
8mCd <16 very high degree of contamination
16 mCd <32 extremely high degree of contamination
mCd 32 ultrahigh degree of contamination
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ACS Omega 2017, 2, 54345444
RI Er ; Er Tr ;
ii i
where Cf
icorresponds to the pollution factor for individual
heavy metals, C01
icorresponds to heavy metals concentration
in the sample, Cn
iis the background concentration, Eriis the
ecological risk potential of the heavy metals, Triis the toxic
response coecient developed by Hakanson
(toxic response
coecients for Cu = 5, Zn = 1, and Pb = 5), and RI is the
ecological risk index. The interpretation categories for RI are
presented in Table 5.
Statistical Analysis. To determine statistical parameters
and for further analysis of data for its quality, one-way analysis
of variance (ANOVA) and Spearman ρcorrelation analysis
were conducted using Statistical Package for Social Science
(SPSS) of IBM Statistics version 21.0.
Corresponding Author
*E-mail: Tel: (+91)
Suresh Pandian Elumalai: 0000-0003-4104-1776
The authors declare no competing nancial interest.
The authors would like to thank the Department of
Environmental Science and Engineering, Indian Institute of
Technology (Indian School of Mines), Dhanbad, India, for
providing the research facilities and appreciate the useful
contribution of Dr. Prasenjit Adak (Ph.D.) for developing the
computer-generated image (Figure
), Dr. Manisha (Ph.D.) and
Mr. Anil Kumar (Ph.D. student) for providing assistance during
the experiments, and Dr. Ashwini Kumar (Ph.D.) for assisting
in operating AAS during sample analysis. We express our
sincere thanks to anonymous reviewers for their valuable
suggestions to improve the quality of this manuscript.
(1) Silvano, A. P.; Bang, K. L. Impact of speed limits and road
characteristics on free-flow speed in urban areas. J. Transp. Eng. 2015,
142, No. 04015039, DOI: 10.1061/(ASCE)TE.1943-5436.0000800.
(2) Rothman, L.; Macpherson, A.; Buliung, R.; Macarthur, C.; To, T.;
Larsen, K.; Howard, A. Installation of speed humps and pedestrian-
motor vehicle collisions in Toronto, Canada: a quasi-experimental
study. BMC Public Health 2015,15, No. 774.
(3) Hallmar, S.; Knapp, K. Temporary Speed Hump Impact
Evaluation. Final Report for Project CTRE 00-37; IOWA Department
of Transportation, 2002.
(4) Colucci, J. M.; Charles, R. B. Carcinogenic air pollutants in
relation to automotive traffic in New York. Environ. Sci. Technol. 1971,
5, 145150.
(5) Goel, A.; Kumar, P. Vertical and horizontal variability in airborne
nanoparticles and their exposure around signalised traffic intersections.
Environ. Pollut. 2016,214,5469.
(6) Suman, S.; Sinha, A.; Tarafdar, A. Polycyclic aromatic
hydrocarbons (PAHs) concentration levels, pattern, source identi-
fication and soil toxicity assessment in urban traffic soil of Dhanbad,
India. Sci. Total Environ. 2016,545546, 353360.
(7) Thaker, P.; Gokhale, S. The impact of traffic-flow patterns on air
quality in urban street canyons. Environ. Pollut. 2016,208, 161169.
(8) Ewing, R. Trac Calming State of Practice. Prepared by the Institute
of Transportation Engineers (ITE); Federal Highway Administration
(FHWA), U.S. Department of Transportation: Washington, D.C.,
(9) Ponnaluri, R. V.; Groce, P. W. Operational effectiveness of speed
humps in traffic calming. Institute of Transportation Engineers. ITE J.
2005,75, 26.
(10) Grana, A.; Giuffrè
, T.; Guerrieri, M. Exploring effects of area-
wide traffic calming measures on urban road sustainable safety. J.
Sustainable Dev. 2010,3,3849.
(11) Catharinus, F. J.; van Langevelde, F. Effects of scale and
efficiency of rural traffic calming on safety, accessibility and wildlife.
Transp. Res. Part D: Transp. Environ. 2011,16, 486491.
(12) Chen, L.; Chen, C.; Ewing, R.; McKnight, C. E.; Srinivasan, R.;
Roe, M. Safety countermeasures and crash reduction in New York
City-Experience and lessons learned. Accid. Anal. Prev. 2013,50, 312
(13) Höglund, P. G.; Niittymä
ki, J. In Estimating Vehicle Emissions
and Air Pollution related to Driving Patterns and Trac Calming. Urban
Transport Systems Conference Proceedings, Lund, 1999.
(14) Vá
rhelyi, A. The effects of small roundabouts on emissions and
fuel consumption: a case study. Transp. Res. Part D: Transp. Environ.
(15) Daham, B.; Andrew, G. E.; Li, H.; Patridge, M.; Bell, M. C.;
Tate, J. E. Quantifying the Eects of Trac Calming on Using on-Road
Measurement; Society of Automotive Engineers, 2005; pp 155164.
(16) Ahn, K.; Rakha, H. A field evaluation case study of the
environmental and energy impacts of traffic calming. Transp. Res. Part
D: Transp. Environ. 2009,14, 411424.
(17) Ghafghazi, G.; Hatzopoulou, M. Simulating the environmental
effects of isolated and area-wide traffic calming schemes using traffic
simulation and microscopic emission modeling. Transportation 2014,
41, 633649.
(18) Chadda, H. S.; Cross, S. E. Speed (road) bumps: Issues and
opinions. J. Transp. Eng. 1985,111, 410418.
(19) Adak, P.; Sahu, R.; Elumalai, S. P. Development of emission
factors for motorcycles and shared auto-rickshaws using real-world
driving cycle for a typical Indian city. Sci. Total Environ. 2016,544,
Table 4. Seven Descriptive Classes of Geoaccumulation
Index (Igeo)
classes description
Igeo < 0 practically uncontaminated
01 uncontaminated to slightly contaminated
12 moderately contaminated
23 moderately to highly contaminated
34 highly contaminated
45 highly to very highly contaminated
Igeo > 5 very highly/strongly contaminated
Table 5. Interpretation Categories for the Pollution Factor,
Potential Ecological Risk, and Ecological Risk Index
value category
potential ecological risk
Eri< 40 low
40 Eri80 moderate
80 Eri160 considerable
160 Eri320 high
320 Erivery high
risk index
RI 150 low
150 RI 300 moderate
300 RI 600 considerable
600 RI high
ACS Omega Article
DOI: 10.1021/acsomega.7b00683
ACS Omega 2017, 2, 54345444
(20) De Silva, S.; Ball, A. S.; Huynh, T.; Reichman, S. M. Metal
accumulation in roadside soil in Melbourne, Australia: effect of road
age, traffic density and vehicular speed. Environ. Pollut. 2016,208,
(21) Wang, C.; Ye, Z.; Wang, W.; Jin, M. Traffic-Related Heavy
Metal Contamination in Urban Areas and Correlation with Traffic
Activity in China. Transp. Res. Rec. 2016,2571,8089.
(22) Wang, G.; Zeng, C.; Zhang, F.; Zhang, Y.; Scott, C. A.; Yan, X.
Traffic-related trace elements in soils along six highway segments on
the Tibetan Plateau: Influence factors and spatial variation. Sci. Total
Environ. 2017,581582, 811821.
(23) Alloway, B. J. Soil Processes and the Behaviour of Metals. In
Heavy Metals in Soils; St Edmundsburg Press: Glasgow, Great Britain,
1990; pp 728.
(24) Wik, A.; Dave, G. Occurrence and effects of tire wear particles in
the environmenta critical review and an initial risk assessment.
Environ. Pollut. 2009,157,111.
(25) Goodman, G. T.; Roberts, T. M. Plants and Soils as Indicators
of Metals in the Air. Nature 1971,231, 287292.
(26) Harrison, R. M.; Laxen, D. P.; Wilson, S. J. Chemical
associations of lead, cadmium, copper, and zinc in street dusts and
roadside soils. Environ. Sci. Technol. 1981,15, 13781383.
(27) Knasmüller, S.; Gottmann, E.; Steinkellner, H.; Fomin, A.; Pickl,
C.; Paschke, A.; Kundi, M.; et al. Detection of genotoxic effects of
heavy metal contaminated soils with plant bioassays. Mutat. Res., Genet.
Toxicol. Environ. Mutagen. 1998,420,3748.
(28) Councell, T. B.; Duckenfield, K. U.; Landa, E. R.; Callender, E.
Tire-wear particles as a source of zinc to the environment. Environ. Sci.
Technol. 2004,38, 42064214.
(29) Birmili, W.; Allen, A. G.; Bary, F.; Harrison, R. M. Trace metal
concentrations and water solubility in size-fractionated atmospheric
particles and influence of road traffic. Environ. Sci. Technol. 2006,40,
(30) Dolan, L. M. J.; Van Bohemen, H.; Whelan, P.; Akbar, K. F.;
Omalley, V. et al. Towards the Sustainable Development of Modern
Road Ecosystems. In The Ecology of Transportation: Managing Mobility
for the Environment; Springer: Netherlands, 2006; Vol. 10, pp 275
(31) Schauer, J. J.; Lough, G. C.; Shafer, M. M.; Christensen, W. C.;
Arndt, M. F.; DeMinter, J. T.; Park, J. S. Characterization of Emissions
of Metals Emitted from Motor Vehicles, HEI Research Report; Health
Eects Institute: Boston, MA, 2006; p 133.
(32) Hjortenkrans, D. S.; Bergbäck, B. G.; Häggerud, A. V. Metal
emissions from brake linings and tires: case studies of Stockholm,
Sweden 1995/1998 and 2005. Environ. Sci. Technol. 2007,41, 5224
(33) Harrison, R. M.; Jones, A. M.; Gietl, J.; Yin, J.; Green, D. C.
Estimation of the contributions of brake dust, tire wear, and
resuspension to nonexhaust traffic particles derived from atmospheric
measurements. Environ. Sci. Technol. 2012,46, 65236529.
(34) Adamiec, E.; Jarosz-Krzemińska, E.; Wieszała, R. Heavy metals
from non-exhaust vehicle emissions in urban and motorway road
dusts. Environ. Monit. Assess. 2016,188, No. 369.
(35) Shen, H.; Peters, T. M.; Casuccio, G. S.; Lersch, T. L.; West, R.
R.; Kumar, A.; Kumar, N.; Ault, A. P. Elevated Concentrations of Lead
in Particulate Matter on the Neighborhood-Scale in Delhi, India As
Determined by Single Particle Analysis. Environ. Sci. Technol. 2016,50,
(36) Yang, Y.; Vance, M.; Tou, F.; Tiwari, A.; Liu, M.; Hochella, M.
F. Nanoparticles in road dust from impervious urban surfaces:
distribution, identification, and environmental implications. Environ.
Sci.: Nano 2016,3, 534544.
(37) Turekian, K. K.; Wedepohl, K. H. Distribution of the elements
in some major units of the Earths crust. Geol. Soc. Am. Bull. 1961,72,
(38) Jain, M.; Singh, A. P.; Bali, S.; Kaul, S. Speed-Breaker Early
Warning System. In NSDR, 2012.
(39) Pathak, S. K.; Sood, V.; Singh, Y.; Channiwala, S. A. Real world
vehicle emissions: Their correlation with driving parameters. Transp.
Res. Part D: Transp. Environ. 2016,44, 157176.
(40) IRC 99 Tentative Guidelines on the Provision of Speed Breakers for
Control of Vehicular Speeds on Minor Roads; IRC (Indian Road
Congress), 1996.
(41) Herngren, L. Build-up and Wash-oProcess Kinetics of PAHs
and Heavy Metals on Paved Surfaces Using Simulated Rainfall. Ph.D.
Thesis, Queensland University of Technology, 2005.
(42) Duzgoren-Aydin, N. S.; Wong, C. S. C.; Song, Z. G.; Aydin, A.;
Li, X. D.; You, M. Fate of heavy metal contaminants in road dusts and
gully sediments in Guangzhou, SE China: A chemical and
mineralogical assessment. Hum. Ecol. Risk Assess. 2006,12, 374389.
(43) Wei, B. G.; Yang, L. S. A review of heavy metal contaminations
in urban soils, urban road dusts and agricultural soils from China.
Microchem. J. 2010,94,99107.
(44) Zhao, H.; Li, X.; Wang, X. Heavy metal contents of road-
deposited sediment along the urbanrural gradient around Beijing and
its potential contribution to runoff pollution. Environ. Sci. Technol.
2011,45, 71207127.
(45) Liu, A.; Liu, L.; Li, D.; Guan, Y. Characterizing heavy metal
build-up on urban road surfaces: Implication for stormwater reuse. Sci.
Total Environ. 2015,515516,2029.
(46) Swaileh, K. M.; Hussein, R. M.; Abu-Elhaj, S. Assessment of
heavy metal contamination in roadside surface soil and vegetation
from the West Bank. Arch. Environ. Contam. Toxicol. 2004,47,2330.
(47) Sartor, J. D.; Boyd, G. B.; Agardy, F. J. Water Pollution Aspects of
Street Surface Contaminants (No. EPA-R2-72/081); US Environmental
Protection Agency: Washington, DC, 1974.
(48) Wicke, D.; Cochrane, T. A.; OSullivan, A. D. Atmospheric
deposition and storm induced runoff of heavy metals from different
impermeable urban surfaces. J. Environ. Monit. 2012,14, 209216.
(49) Alley, W. M. Estimation of impervious-area Washoff Parameters.
Water Resour. Res. 1981,17, 11611166.
(50) Millar, R. Analytical Determination of Pollutant Was8.h-Off
Parameters. J. Environ. Eng. 1999,125, 989992.
(51) Revitt, D. M.; Lundy, L.; Coulon, F.; Fairley, M. The sources,
impact and management of car park runoff pollution: a review. J.
Environ. Manage. 2014,146, 552567.
(52) Huber, W. C.; Dickinson, R. E. Stormwater Management Model
(SWMM), version 4, Users manual (No. EPA/600/3-88/001a); U.S.
Environmental Protection Agency: Athens, 1988.
(53) Davis, A. P.; Hunt, W. F.; Traver, R. G.; Clar, M. Bioretention
technology: Overview of current practice and future needs. J. Environ.
Eng. 2009,135, 109117.
(54) Murphy, L. U.; Cochrane, T. A.; Osullivan, A. The influence of
different pavement surfaces on atmospheric copper, lead, zinc, and
suspended solids attenuation and wash-off. Water, Air, Soil Pollut.
2015,226, No. 232.
(55) Fujiwara, F. G.; Gomez, D. R.; Dawidowski, L.; Perelman, P.;
Faggi, A. Metals associated with airborne particulate matter in road
dust and tree bark collected in a megacity (Buenos Aires, Argentina).
Ecol. Indic. 2011,11, 240247.
(56) Ogundele, D. T.; Adio, A. A.; Oludele, O. E. Heavy Metal
Concentrations in Plants and Soil along Heavy Traffic Roads in North
Central Nigeria. J. Environ. Anal. Toxicol. 2015,5,1.
(57) Rashid, M. H.; Fardous, Z.; Chowdhury, M. A. Z.; Alam, M. K.;
Bari, M. L.; Moniruzzaman, M.; Gan, S. H. Determination of heavy
metals in the soils of tea plantations and in fresh and processed tea
leaves: an evaluation of six digestion methods. Chem. Cent. J. 2016,10,
No. 7.
(58) Mwegoha, W. J. S.; Kihampa, C. Heavy metal contamination in
agricultural soils and water in Dar es Salaam city, Tanzania. Afr. J.
Environ. Sci. Technol. 2010,4, 763769.
(59) Hakanson, L. An ecological risk index for aquatic pollution
control: a sedimentological approach. Water Res. 1980,14, 9751001.
(60) Cicchella, D.; Giaccio, L.; Lima, A.; Albanese, S.; Cosenza, A.;
Civitillo, D.; Vivo, B. D. Assessment of the topsoil heavy metals
ACS Omega Article
DOI: 10.1021/acsomega.7b00683
ACS Omega 2017, 2, 54345444
pollution in the Sarno River basin, south Italy. Environ. Earth Sci. 2014,
71, 51295143.
(61) Tomlinson, D. L.; Wilson, J. G.; Harris, C. R.; Jeffrey, D. W.
Problems in the assessment of heavy-metal levels in estuaries and the
formation of a pollution index. Helgoländer Meeresuntersuchungen 1980,
33, 566.
(62) Müller, G. Index of geoaccumulation in sediments of the Rhine
River. Geol. J. 1969,2, 108118.
(63) Angulo, E. The Tomlinson pollution load index applied to heavy
metal Mussel-Watchdata: a useful index to assess coastal pollution.
Sci. Total Environ. 1996,187,1956.
(64) Krishna, A. K.; Govil, P. K. Soil contamination due to heavy
metals from an industrial area of Surat, Gujarat, Western India.
Environ. Monit. Assess. 2007,124, 263275.
(65) Sharma, B. K. Environmental Chemistry,6thed.;GOEL
Publishing House, 2001.
(66) Abrahim, G. M. S.; Parker, R. J. Assessment of heavy metal
enrichment factors and the degree of contamination in marine
sediments from Tamaki Estuary, Auckland, New Zealand. Environ.
Monit. Assess. 2008,136, 227238.
(67) Müller, G. The heavy metal pollution of the sediments of
Neckars and its tributary: a stocktaking. Chem.-Ztg. 1981,105, 157
(68) Ogunkunle, C. O.; Fatoba, P. O. Pollution loads and the
ecological risk assessment of soil heavy metals around a mega cement
factory in southwest Nigeria. Pol. J. Environ. Stud. 2013,22, 487493.
ACS Omega Article
DOI: 10.1021/acsomega.7b00683
ACS Omega 2017, 2, 54345444
... This indicates that on higher ADT roads congestion and road surface roughness can cause increased concentrations of metals due to tire and break pad wear related particles. In another study by Sahu and Elumalai (2017), hot spots of Cu, Zn, and Pb were observed 1-2 m away from speed bumps used to control the speed of traffic in urban areas. In the same study the authors also observed that Cu, Zn, and Pb concentrations were two to three times higher at these speed humps in comparison to at intersections. ...
... In the same study the authors also observed that Cu, Zn, and Pb concentrations were two to three times higher at these speed humps in comparison to at intersections. These results indicate that speed humps might create higher concentration hot spots of heavy metals compared to intersections (Sahu and Elumalai 2017). Further, the results from the literature also indicate that increased traffic volume may be blowing some of the material off of the road when there is no congestion. ...
... Other studies have observed similar results for material collected from the shoulder of the road (Van Dolah et al. 2005). The results found in the current study could again be due to hotspots similar to those found in Sahu and Elumalai (2017) caused by traffic control devices. ...
Street sweeping is a routine roadway maintenance activity that functions as a nonstructural stormwater best management practice. Further, the road-deposited sediment collected during sweeping operations has the potential for beneficial reuse in a number of different applications but first must be characterized in terms of its toxicity to ensure that it is safe to do so. This study provides a chemical characterization of this material regarding heavy metals, polycyclic aromatic hydrocarbons, and oil and grease, and attempts to predict the concentration of these contaminants using average daily traffic, land cover, and particle size. Seventy-nine locations were selected from six average daily traffic (ADT) categories ranging from 1–400 to greater than 10,000 vehicles per day and four land cover categories including developed, open space; developed, low intensity; developed, medium intensity, and developed, high intensity. Average concentrations of As, Pb, Se, Ba, Cr, Ag, Cd, Cu, and Zn were 0.39, 7.3, 0.32, 14, 6, 0.046, 0.83, 0.89, and 30 mg=kg, respectively. The average total polycyclic aromatic hydrocarbons (PAH) concentration of all 79 sites was 17,000 μg=kg. Oil and grease concentrations in the material ranged from 34 to 3,400 mg=kg. The results showed that both average daily traffic and land cover cannot be used to predict the contaminant load of this material. However, a strong correlation was observed between particle size and the concentrations of all heavy metals and PAHs.
... (1) Chicanes in residential areas can change the perception of drivers toward the road structure. Thus, drivers move at a suitable speed, thereby improving driving comfort [3]. (2) Chicanes can moderately reduce noise and air pollution, beautify pavement landscape, and respond to road engineering strategies for green environmental protection [4]. ...
... (2) Chicanes can moderately reduce noise and air pollution, beautify pavement landscape, and respond to road engineering strategies for green environmental protection [4]. (3) Chicanes are suitable for one-way, two-way, and nonmixed roads with sufficient parking space. (4) Chicanes are generally designed with a road arch or a speed buffer zone. ...
... Traffic calming is described by the Institute of Transportation Engineers (ITE) as a set of physical steps that minimize the negative effects of motor vehicle usage, change driver behavior, and improve conditions for non-motorized street users (The Institute of Transportation Engineers, 2021;Federal Highway Administration, 2017;Sahu & Elumalai, 2017). Commonly, the speed hump is the most effective traffic calming measure that has been implemented all around the world. ...
For several reasons, professions today cope with complicated activities that place a high demand on employees’ skills. Unlike earlier research that primarily focused on regular and less challenging activities, the current study looked at leaders and managers dealing with highly complicated and non-repetitive work daily. This paper seeks to develop an initial review on public servants, especially leaders, from a public service perspective to outline the future-ready conceptual skill to improve activities, thus enhancing their service performance. The conceptual paper focuses on the development processes leading to complex problem-solving as one of the future-ready conceptual skills relate to previous research and existing theory. In order to make good governance practice successful, complex problem-solving may contribute to certain behaviours. According to the findings of the literature study, there is a relationship between complex problem-solving and the effectiveness of good governance practices. Perhaps more importantly, this paper examines how the skill is distinctive and most practically cultivated in a public organisation.
... What is not clear however, is the amount of airborne particulate matter that falls out into the freshwater system. Sahu and Elumalai (2017) identified a threefold increase in concentrations of Zn and Cu particulates on roadsides adjacent to traffic calming devices. It would seem sensible to assume that ford crossings act similarly to traffic calming devices as vehicles slow down on approach. ...
Full-text available
Numerous studies have identified the issue of road surface runoff as a source of contamination into waterways but the impact of vehicular wash-off is less well understood. A ford crossing provides a pathway for vehicle-derived contaminants emanating from both road surface runoff and vehicular wash-off into a river system. Twyford Lane Ford (Ford 1) and Birchgrove Lane Ford (Ford 2), located ca. 600 m apart on a tributary of the River Ouse in Sussex (UK), were the focus of this study. A combination of biomonitoring (assessment of benthic macroinvertebrates) and chemical assessments of water and sediments has been undertaken to determine any detrimental impacts, such as a lack in biodiversity, resulting from the ford crossings. Sediment concentrations of chromium (Cr³⁺), lead (Pb) and zinc (Zn) were generally elevated at Ford 1, attenuating at sampling points between the fords to then peak at Ford 2. However, soil organic matter (SOM) and sediment particle size were seen to have an influence on elemental concentrations, in general with an increase in elemental concentrations associated with a higher percentage of fine-grained sediments (≤63 μm). Elevated concentrations of Zn and magnesium (Mg) were identified within water samples taken during a precipitation event following a prolonged dry period. The biomonitoring results found reduced BMWP (Biological Monitoring Working Party) scores at positions close to the ford crossings, and where the stream was in proximity to the roadside. Sensitive Ephemeroptera were largely absent at sampling points closest to the fords, which is likely to be associated with elevated Zn. The results suggest that careful consideration should be applied when selecting crossing points over sensitive waters.
... The risks caused by traffic exhaust in the central area were higher than other areas, whereas those in the western and eastern areas were lower than those of other areas in the study area. The densities of vehicles in the central area were higher than those in other areas (Fig. S3), and the high-traffic conditions enhanced the release of heavy metals [86]. The influence of traffic exhaust declined as the distance from traffic increased, which resulted in weaker impacts on the surrounding areas [87]. ...
To explore the spatial variation of source-specific ecological risks and identify critical sources of heavy metals in road dust, 36 road dust samples collected in Beijing in March 2017 were analyzed for heavy metals. A new method that takes into consideration the heavy-metal toxic response and is flexible to changes in the number of calculated heavy metals, called the Nemerow integrated risk index (NIRI), was developed for ecological risk assessment. The NIRI indicated that heavy metals posed considerable to high risks at the majority of sites, and 22 % of the sites suffered extreme risk in spring (NIRI > 320). Four main sources were identified based on positive matrix factorization (PMF): traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Owing to the lower toxic response factors of representative heavy metals of fuel combustion than those of other sources, although fuel combustion had the highest contribution (34.21 %) to heavy metals in spring, it only contributed 5.57 % to ecological risks. Critical sources and critical source areas were determined by considering the contributions to both heavy metals and ecological risks. The use of pesticide and fertilizer and traffic-related exhaust were identified as critical sources of heavy metals in spring. Source-specific ecological risks and critical sources of heavy metals changed with the changing seasons, which suggests that different strategies should be adopted in different seasons.
... By grouping the samples based on traffic volume and speed (Guangzhou Transport Planning Research Institute, 2012), it was found that the concentrations of Cu and Zn in road dust slightly increased with the increasing traffic volume (Fig. S2). Some studies have reported that a high occurrence of braking on a road, particularly during heavily congested traffic periods, produces more Cu/ Zn/Pb contamination in road dust (Sahu and Elumalai, 2017;Harrison et al., 2012). The effect of traffic speed on trace metal variation was not obvious (Fig. S3). ...
Trace metal contamination prevails in various compartments of the urban environment. Understanding the roles of various anthropogenic sources in urban trace metal contamination is critical for pollution control and city development. In this study, the source contribution from various contamination sources to trace metal contamination (e.g., Cu, Pb, Zn, Co, Cr and Ni) in different environmental compartments in a typical megacity, Guangzhou, southern China, was investigated using the receptor model (Absolute Principal Component Scores-Multiple Linear Regression, APCS-MLR) coupled with the Kriging technique. Lead isotopic data and APCS-MLR analysis identified industrial and traffic emissions as the major sources of trace metals in surface soil, road dust, and foliar dust in Guangzhou. Lead isotopic compositions of road dust and foliar dust exhibited similar ranges, implying their similar sources and potential metal exchange between them. Re-suspended soil contributed to 0-38% and 25-58% of the trace metals in the road dust and foliar dust, respectively, indicating the transport of the different terrestrial dust. Spatial distribution patterns implied that Cu in the road dust was a good indicator of traffic contamination, particularly with traffic volume and vehicle speed. Lead and Zn in foliar dust indicated mainly industrial contamination, which decreased from the emission source (e.g., a power plant and steel factory) to the surrounding environment. The spatial influence of industry and traffic on the contamination status of road dust/foliar dust was successfully separated from that of other anthropogenic sources. This study demonstrated that anthropogenic inputs of trace metals in various environmental compartments (e.g., urban soil, road dust, and foliar dust) can be evaluated using a combined APCS-MLR receptor model and geostatistical analysis at a megacity scale. The coupled use of APCS-MLR analysis, geostatistics, and Pb isotopes successfully deciphered the spatial influence of the contamination sources in the urban environment matrix, providing some important information for further land remediation and health risk assessment.
... Because the emitted particle contains hazardous elements and the majority of them get deposited near to the road periphery (Wawer et al. 2015;Zhang and Peng 2014;Kaul and Sharma 2009;Cadle and Williams 1979), it causes associated effects on human health (Gupta and Elumalai 2017a;WHO 2013;Guttikunda and Goel 2013;Zuurbier et al. 2010;Gauderman et al. 2007), local climate (Gupta and Elumalai 2017b;Singh et al. 2016), vegetation (Hariram et al. 2018), and soil (Sahu and Elumalai 2017;Aryal et al. 2017;Sahu et al. 2016). ...
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Most assessments of road dust have focused largely on the resuspension of materials from the paved road while the contribution from unpaved shoulder to particulate matter is poorly understood. We evaluated the role of unpaved road shoulders in the contribution of particulate matter emitted by analyzing elements in the road dust. We collected road dust samples and employed US-EPA empirical equations. The results of TSP emission reveal that unpaved shoulder adjacent to paved roads (43.1–29.9%) is a potential emitter than that at roundabouts (27%). In paved road environment, the contribution of TSP emission was 54.9–25.6% from unpaved shoulders based on driving share of vehicles. TSP emission results suggest that waste material is frequently exchanged from paved to unpaved shoulder, which leads to seasonal variations in paved road. The observed particle size of paved surface waste material shows that about 36% particles were less than 2.5 μm and 52% were greater than 10 μm, suggesting that dust is resuspendable and presents a health risk due to being respirable. Elemental analysis confirmed the presence of the toxic elements Cr, Ni, Cu, Zn, Pb, Sn, Sb, and Ba in waste material. Moreover, receptor models indicate that the waste material comprised of elements from tire wear (31%), mineral dust (27%), brake wear (17%), vehicle exhaust (14%), and coal (7%). The elemental contribution of coal is a location-specific source identified from principal component analysis and hierarchical cluster analysis, which originated spillage during transportation. The study illustrates the contributions of PM emission from the different road networks and the mechanism of exchange of waste materials. Graphical Abstract Microscopic observation of resuspension and transportation of road dust due to vehicular movement leads to advection mechanism at the roundabout and the paved road having unpaved shoulders. Open image in new window
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In this study, a detailed investigation was conducted to collect the concentrations of heavy metals including cadmium, copper, nickel, lead and zinc from five geographic areas to reflect the different land use and traffic conditions in Zhaoyuan, China. Analytical results of heavy metal concentrations showed that the mean and median values of these elements in soils were clearly higher than the corresponding background values, indicating pollution from the urban area. To generate a continuous surface from the collected discrete samples, Kriging interpolation was introduced to determine the spatial distribution patterns of heavy metal contamination. As a major source of urban heavy metal contamination, the traffic area was carefully analyzed. Interelemental relationships showed that most heavy metals in the traffic area had similar anthropogenic origins. In addition, the research team explored the relationships between heavy metals and traffic characteristics. The results indicated that heavy metal concentrations in roadside soils not only decreased with rising altitude and increasing distance from the trunk and branch roads on both sides but also could be affected by traffic volume. Finally, three evaluation methods including the geoaccumulation index, the pollution index, and the improved pollution index were introduced to assess heavy metal contamination levels fully in the traffic area. The pollution index evaluation method produced results indicating more severe levels of contamination than the other two evaluation methods.
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Nanoparticles (NPs) resulting from urban road dust resuspension are an understudied class of pollutants in urban environments with strong potential for health hazards. The objective of this study was to investigate the heavy metal and nanoparticle content of PM2.5 generated in the laboratory using novel aerosolization of 66 road dust samples collected throughout the mega-city of Shanghai (China). The samples were characterized using an array of techniques including inductively-coupled plasma mass spectrometry, aerosol size distribution measurements, and scanning and transmission electron microscopy coupled with elemental characterization and electron diffraction. Principal metal concentrations were plotted geospatially. Results show that metals were generally enriched in aerosolized samples relative to the bulk dust. Elevated concentrations of metals were found mostly in downtown areas with intense traffic. Fe-, Pb-, Zn-, and Ba-containing NPs were identified using electron microscopy, spectroscopy, and diffraction, and we tentatively identify most of them as either engineered, incidental, or naturally occurring NPs. For example, dangerous Pb sulfide and sulfate NPs likely have an incidental origin and are also sometimes associated with Sn; we believe that these materials originated from an e-waste plant. Size distributions of most aerosolized samples presented a peak in the ultrafine range (<100 nm). We estimate that 3.2 ± 0.7 μg mg-1 of Shanghai road dust may become resuspended in the form of PM2.5. Aerosolization, as done in this study, seems to be a very useful approach to study NPs in dust.
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The main sources of non-exhaust vehicular emissions that contribute to road dust are tire, brake and clutch wear, road surface wear, and other vehicle and road component degradation. This study is an attempt to identify and investigate heavy metals in urban and motorway road dusts as well as in dust from brake linings and tires. Road dust was collected from sections of the A-4 motorway in Poland, which is part of European route E40, and from urban roads in Katowice, Poland. Dust from a relatively unpolluted mountain road was collected and examined as a control sample. Selected metals Cd, Cr, Cu, Ni, Pb, Zn, Fe, Se, Sr, Ba, Ti, and Pd were analyzed using inductively coupled plasma-mass spectrometry, inductively coupled plasma (ICP)-optical emission spectroscopy, and atomic absorption spectroscopy on a range of size-fractionated road dust and brake lining dust (<20, 20–56, 56–90, 90–250, and >250 μm). The compositions of brake lining and tire dust were also investigated using scanning electron microscopy-energy-dispersive spectroscopy. To estimate the degree of potential environmental risk of non-exhaust emissions, comparison with the geochemical background and the calculations of geo-accumulation indices were performed. The finest fractions of urban and motorway dusts were significantly contaminated with all of the investigated metals, especially with Ti, Cu, and Cr, which are well-recognized key tracers of non-exhaust brake wear. Urban dust was, however, more contaminated than motorway dust. It was therefore concluded that brake lining and tire wear strongly contributed to the contamination of road dust.
The accumulation of traffic-related trace elements in soil as the result of anthropogenic activities raises serious concerns about environmental pollution and public health. Traffic is the main source of trace elements in roadside soil on the Tibetan Plateau, an area otherwise devoid of industrial emissions. Indeed, the rapid development of tourism and transportation in this region means it is becoming increasingly important to identify the accumulation levels, influence distance, spatial distribution, and other relevant factors influencing trace elements. In this study, 229 soil samples along six segments of the major transportation routes on the Tibetan Plateau (highways G214, S308, and G109), were collected for analysis of eight trace elements (Cr, Co, Ni, As, Cu, Zn, Cd, and Pb). The results of statistical analyses showed that of the eight trace elements in soils, Cu, Zn, Cd, and Pb were primarily derived from traffic. The relationship between the trace element accumulation levels and the distance from the roadside followed an exponential decline, with the exception of Segment 3, the only unpaved gravel road studied. In addition, the distance of influence from the roadside varied by trace element and segment, ranging from 16m to 144m. Background values for each segment were different because of soil heterogeneity, while a number of other potential influencing factors (including traffic volume, road surface material, roadside distance, land cover, terrain, and altitude) all had significant effects on trace-element concentrations. Overall, however, concentrations along most of the road segments investigated were at, or below, levels defined as low on the Nemero Synthesis index.
High mass concentrations of atmospheric lead particles are frequently observed in the Delhi, India metropolitan area, although the sources of lead particles are poorly understood. In this study, particles sampled across Delhi (August - December 2008) were analyzed by computer-controlled scanning electron microscopy with energy dispersive x-ray spectroscopy (CCSEM-EDX) to improve our understanding of the spatial and physicochemical variability of lead-rich particles (> 90% lead). The mean mass concentration of lead-rich particles smaller than 10 µm (PM10) was 0.7 μg/m3 (1.5 μg/m3 std. dev.) with high variability (range: 0 - 6.2 μg/m3). Four samples (16% of 25 samples) with PM10 lead particle concentrations >1.4 µg/m3 were defined as lead events and studied further. The temporal characteristics, heterogeneous spatial distribution, and wind patterns of events, excluded regional monsoon conditions or common anthropogenic sources from being the major causes of the lead events. Individual particle composition, size, and morphology analysis indicate informal recycling of lead-acid batteries as the likely source of the lead events. This source is not included in emission inventories, and the observed isolated hotspots with high lead concentrations could represent an elevated exposure risk in certain neighborhoods of Delhi.
Rural residential streets typically experience low traffic volumes and high operational speeds. The case study described in this feature collected and evaluated pre- and post-installation characteristics of speed humps deployment along rural residential streets. This work was successful mainly due to extensive public participation.
Vehicular population in developing countries is expected to proliferate in the coming decade, centred on Tier II and Tier III cities rather than large metropolis. WLTP is being introduced as a global instrument for emission regulation to reduce gap between standard test procedures and actual road conditions. This work aims at quantifying and discernment of the gap between WLTC and real-world conditions in an urban city in a developing country on the basis of driving cycle parameters and simulated emissions for gasoline fuelled light passenger cars. Real world driving patterns were recorded on different routes and varying traffic conditions using car-chasing technique integrated with GPS monitoring and speed sensors. Real-world driving patterns and ambient conditions were used to simulate emissions using International Vehicle Emissions model for average rate (g/km) and Comprehensive Modal Emissions Model for instantaneous emission (g/s) analysis. Cycle parameters were mathematically calculated to compare WLTC and road trips. The analyses revealed a large gap between WLTC and road conditions. CO emissions were predicted to be 155% higher than WLTC and HC and NOx emissions were estimated to be 63% and 64% higher respectively. These gaps were correlated to different driving cycle parameters. It was observed that road driving occurs at lower average speeds with higher frequency and magnitudes of accelerations. The positive kinetic energy required by road cycles, was 100% higher than WLTC and the Relative Positive Acceleration (RPA) demanded by road cycles, was found to be 60% higher in real-world driving patterns and thereby contribute to higher emissions.
We measured size–resolved PNCs in the 5–560 nm range at two different types (4– and 3–way) of TIs in Guildford (Surrey, UK) at fixed sites (~1.5 m above the road level), sequentially at 4 different heights (1, 1.5, 2.5 and 4.7 m), and along the road at five different distances (10, 20, 30, 45 and 60 m). The aims were to: (i) assess the differences in PNCs measured at studied TIs, (ii) identify the best fit probability distribution curves for the PNCs, (iii) determine vertical and horizontal decay profiles of PNCs, (iv) estimate particle number emission factors (PNEFs) under congested and free–flow traffic conditions, and (v) quantify the pedestrian exposure in terms of respiratory deposition dose (RDD) rates at the TIs. Daily averaged particle number distributions at TIs reflected the effect of fresh emissions with peaks at 5.6, 10 and 56nm. Despite the relatively high traffic volume at 3–way TI, average PNCs at 4–way TI were about twice as high as at 3–way TI, indicating less favourable dispersion conditions. Generalised extreme value distribution fitted well to PNC data at both TIs. Vertical PNC profiles followed an exponential decay, which was much sharper at 4–way TI than at 3–way TI, suggesting ~40% less exposure for people at first floor (4.7 m) to those at ground floor around 4-way TI. Vertical profiles indicated much sharper (~132–times larger) decay than in horizontal direction, due to close vicinity of road vehicles during the along-road measurements. Over an order of magnitude higher PNEFs were found during congested, compared with free–flow, conditions due to frequent changes in traffic speed. Average RDD rate at 4–way TI during congested conditions were up to 14–times higher than those at 3–way TI (0.40×1011 h˗1). Findings of this study are a step forward to understand exposure at and around the TIs.