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

Abstract

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.

Full text

Identifying Speed Hump, a Traffic Calming Device, as a Hotspot for
Environmental Contamination in Traffic-Affected 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 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.
■INTRODUCTION
Road characteristic is a well-known factor as a substantial
contributor to affect trafficflow, traveler’s safety, intrusive noise,
air quality of nearby road microenvironments, and associated
environmental issues.
1
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 traffic accidents.
2
Especially,
at road networks where mixed traffic 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 traffic calming devices.
3
Most of the
urban air, road dust, and soil quality studies are carried out near
intersections and street canyons,
4−7
but information on heavy
metal near these traffic calming devices is scant.
Nowadays, the most common traffic 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.
3
Most of the
traffic calming devices were studied for their effectiveness in
reducing the vehicle speed and thereby increasing road
safety.
3,8−12
The drastic change in traffic speed near traffic
calming devices was highly correlated to gaseous pollutant
emission.
13−17
In developing countries, owing to lack of traffic
rules to monitor fast moving vehicles, navigating to unpaved
shoulders at the speed hump is also a unique driving behavior.
18
Therefore, at first 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.
19
Furthermore, it is clear
that the safer speed limits and the type and amount of trafficin
developing countries are quite different from those in
developed countries. Thus, the impact of traffic calming devices
on nearby environment in such developing countries may differ
significantly and therefore is required to be studied precisely in
detail.
Concentrations of vehicular emitted heavy metals in roadside
soils result in long-term environmental damage.
20−22
It was
reported that Cu, Zn, and Pb could indicate traffic 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”.
23,24
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
world.
21,22,25−36
However, detailed investigation on traffic-
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 find 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),
modified degree of contamination (mCd), geoaccumulation
Received: May 26, 2017
Accepted: August 21, 2017
Published: September 5, 2017
Article
http://pubs.acs.org/journal/acsodf
© 2017 American Chemical Society 5434 DOI: 10.1021/acsomega.7b00683
ACS Omega 2017, 2, 5434−5444
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 traffic contaminants and identifies an emerging
urban hotspot zone for traffic-generated pollution.
Furthermore, resolution of heavy metal issues in immediate
effect 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 hump’s deteriorating effect on ecology and
proposes an alternative to encourage the management of this
emerging environmental issue with the help of scientific
approach.
■RESULTS AND DISCUSSION
Heavy Metal Concentrations in Road Soil. The
concentration profiles of Cu, Zn, and Pb in traffic 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 five 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 kg−1, 220.5 ±55.3 to
548.6 ±49.6 mg kg−1, and 22.9 ±11.6 to 61.1 ±9.6 mg kg−1,
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 kg−1, respectively.
37
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 kg−1, respectively. Heavy metal mean concentrations at
intersections were 1.2−1.6 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 1−200 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 1−200 m distance
and at traffic 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.
Effect 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 kg−1at 1
m, 541.6 ±27.4 mg kg−1at 2 m, and 57.5 ±12.6 mg kg−1.
Lowest values obtained for Cu, Zn, and Pb were 51.9 ±8.8 mg
kg−1(at 200 m), 158.4 ±7.56 mg kg−1(at intersections), and
22.9 ±11.6 mg kg−1(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 kg−1at 1 m, 548.6 ±49.6 mg kg−1at 1 m, and 61.1 ±9.6
mg kg−1at 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
significantly 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
humps.
Assessment of Road Contamination Using Indices. CF
values of the road soil along with the different 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 significance
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 significance 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 1−200 m distance and at traffic
Figure 3. Pollution load index (PLI) of heavy metals in road soil on both the sides (1 and 2) of speed humps at 1−200 m distance and at traffic
intersections (I).
Figure 4. Variations in the modified degree of contamination (mCd) in road soil on both the sides (1 and 2) of speed humps at 1−200 m distance
and at traffic 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 effects 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. Significance 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 100−200 and 50−200 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 influence 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 different 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 traffic sources nearby speed
humps, a zone was determined using PLI values.
Identification of Hotspot Zones for Contamination
Due to Traffic 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 find the actual
prone area or zone that is under the high influence of
contamination due to traffic 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 1−200 m distance
and at traffic 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 1−200 m distance and
at traffic intersections (I).
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hump microenvironments. The profile for distribution of heavy
metals at the different 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.
=− + =
y
xR0.56 ln( ) 4.226 ( 0.971)
1
2(1)
=− + =
y
xR0.53 ln( ) 4.131 ( 0.976)
2
2
(2)
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.
Profiles 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
==
−
y
R4.366 e ( 0.957)
x
3
0.05 2 (3)
==
−
y
R4.266 e ( 0.943)
x
4
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
affected 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
identified 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 effects 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
basis.
Statistical Analysis of Heavy Metal Data. Analysis of
data for its quality was performed using different statistical
tools. Significant Spearman’sρcorrelation among Cu, Zn, and
Pb with varying distances from the speed humps are presented
in Table 1. Data analysis revealed that a significant 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. Profiles 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 significant
Spearman’sρcorrelation coefficients (r)(p≤0.01) 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 (Cu−Zn,
Cu−Pb, and Zn−Pb) with rvalues 0.881, 0.809, and 0.781 were
observed. Results signified that each paired heavy metal had
common contamination sources, that is, traffic in this study. On
the other hand, variation in their rvalues could be due to the
fact that their generation was from different sources within the
traffic. 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 affected the speed hump microenvironments.
One-way ANOVA was performed to test the overall
influence of the distance from speed humps on heavy metal
concentration. On detection of significant differences (p≤
0.05) between the mean concentrations at varying distances
from speed humps, Tukey’s honestly significant difference
(HSD) test was performed. Tukey’s HSD was performed to
identify the variation pattern between the distances for their
heavy metal concentrations. Tukey’s HSD analysis showed
homogeneity in Cu concentration at 1−3 and 3−5 m, whereas
significant difference in the mean concentration was observed
at a 5−10 m distance. Tukey’s HSD analysis showed
homogeneity in Pb concentration in road soil collected at 1−
5 and 3−10 m, whereas significant difference in the mean
concentration was observed at a 10−50 m distance. The
analysis showed a homogeneity in Zn concentration at
distances 1−3, 3−5, and 5−10 m, whereas significant variations
in mean concentrations were observed at 10−50 and 50−200
m. Data analysis for heavy metal concentrations at different
distances at speed humps showed that the contamination of
heavy metals indicated less variation upto 10 m distance.
Proposed New Traffic Calming Device for Developing
Countries. The new design focused on avoiding mechanical
disturbances to the vehicles in low-traffic regions without
affecting 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 different vehicle types. It is expected here that
these wheel cut-outs may turn the heterogeneous type traffic
(characteristics of city traffic 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 traffic 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 effect in reducing speeds unless police regularly
enforce the limits.
38
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 traffic calming device may
increase its performance level as well. After some new traffic
calming device’s design implementation like this, one can
gradually build up a general design for developing countries.
Limitations of the Proposed New Traffic Calming
Device. The proposed calming device has its limitations, and it
may affect the traffic in very crowded roads. The proposed
traffic calming device considers only the emission reduction as
the area of concern and not the other parameters concerned
with flow of traffic. This proposed traffic calming device may
perform well in low-trafficked arterial roads as well as in urban
roads of developing countries like India, prevailing mixed traffic
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.
19
They have
reported the average speeds of these three types of vehicles to
be 27.8, 13.4, and 17.4 km h−1, 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
purpose.
39
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 h−1was also
specified in the guidelines of IRC 99 (1996).
40
Furthermore,
the effect of the proposed traffic calming device on trafficflow
in different cities of developing countries may vary with their
respective traffic characteristics. Therefore, a performance study
of the proposed traffic calming device can be conducted to
examine its effect on trafficflow.
Existing Mitigation Techniques. The proposed design for
new traffic calming device could turn into a useful alterative for
the identified 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
pollution.
41−45
A provision to wash offthe paved road
Table 1. Spearman’sρCorrelation Coefficients Among the
Heavy Metals and Distances from the Speed Humps
distance Cu Zn Pb
distance 1
Cu −0.917
a
1
Zn −0.938
a
0.881
a
1
Pb −0.802
a
0.809
a
0.781
a
1
a
Correlation is significant at the 0.01 level (2-tailed).
Figure 8. Proposed traffic calming device with the wheel cut-out
provision for different vehicle class.
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contaminated surface at an area nearby speed humps may
provide the immediate and cost-effective 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 significant
improvement to environment by removing entrained particles
at the hotspot zone. The study attempted for washing of leaves
nearby pollution sources showed significant decrease in heavy
metal’s impact on plants.
46
However, as the hotspot zone is
identified, control of the resuspension process could diminish
the further contamination to produce better and promising
results.
In previous published studies, suitable analytical parameters
were identified for road surface cleaning, runoffwaters, etc.
47,48
Researchers have recognized that wash-offis influenced by
water intensity, duration, and runoffvolume.
47,48
Furthermore,
buildup contaminant’s wash-offfrom the road surface was
usually analyzed using exponential equations.
46
Considering
these parameters to wash offat 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-specific and may vary with the
road soil type, rainfall intensity, catchment area, and catchment
slope.
49−51
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-offequation’s complexity.
52
Similarly, for
speed humps, the best possible values of kmay produce reliable
results using the theory of least squares to replicate the
observed wash-offpatterns by providing water jets of calculated
flow 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 runoff
simulation studies.
44
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
filtration process with a provision to recycle it back to the upper
end using pumps. Filter strips, swales, infiltration trenches, filter
drains, and soakways for road surface runofftreatment have
been studied for car parking runoff.
51
Furthermore, pollutants
at speed humps could be removed through mechanisms
adopted by previous researchers in laboratories, such as
filtration, adsorption, sedimentation, and biological uptake
factors affecting the phenomenon was also important in terms
of pollutant mass transport in road dust cleaning.
53
In addition
to this, different paving materials having a higher advection
property at roads with environmental pollution may also be
used to reduce its pollutant load.
54
The findings 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 traffic-emitted toxic heavy metals
and their long-term persistence nearby traffic calming speed
humps. Therefore, the proposed new traffic 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 efficient and a low-cost alternate
to this existing environmental concern without affecting its
safety aspects. Furthermore, effective monitoring with a detailed
study on the performance of the proposed traffic calming
device is necessary.
■MATERIALS AND METHODS
Sample Collection. Road soil was collected following the
method described by Fujiwara et al.
55
Samples were collected at
speed humps along the road (sides 1 and 2) and intersections
in urban roads (Figure 9). To study the environmental effects
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 traffic intersections in Dhanbad, India (Map source: Wikimapia.org).
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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 traffic-associated
emission. Cu, Zn, and Pb in soil were reported to be generated
from brake wear, tire wear, and exhaust, respectively.
21,32−34
Chemicals and Reagents. Digestion of road soil samples
for heavy metal analysis was done using the method described
by Ogundele et al.
56
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 fine. 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.
57,58
Distilled water was added to the digested sample, and then the
sample was filtered with a Whatman No. 42 filter, pore size 2.5
μm, and topped upto 20 mL volumetric flask with distilled
water.
56
The solutions were transferred into sampling bottles
for analysis.
The infiltrates 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 flame. Standard Reference Materials (AccuTrace,
AccuStandard Inc.; Matrix 2−5% HNO3; CRM uncertainty ±
5%; verified 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 5−10. 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.
37
It is an effective tool for monitoring the pollution over a period
of time and defined as
=CBCF /
n
n
(5)
where Cnis the concentration of heavy metal “n”in the sample
and Bnis the concentration of heavy metal “n”in the
background. The contamination levels were classified by
Hakanson
59
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.
59,60
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.
61
It provides some understanding
about the measure of a component in the particular
environment. PLI for each site was evaluated as indicated by
= × × ··· ×
P
LI (CF CF CF )
N12
N(6)
where Nis the total number of heavy metals analyzed (three in
the present study) and CF is the contamination factor.
62−64
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.
60,64,65
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
Parker
66
∑
=
=
N
mCd 1C
F
i
N
i
1(7)
where CF is the contamination factor, Nis number of heavy
metals in the study, and mCd is the modified and generalized
form of the degree of contamination (Cd) proposed by
Hakanson.
59
It is calculated by summing all individual
contamination factors and defined as
∑
=
=
Cd CF
i
N
i
1(8)
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
66
have
reported that this generalized formula allows the incorporation
of several heavy metals without the restraint of an upper limit.
The mCd may be classified into different 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
Muller for several heavy metal studies throughout the world.
62
It is computed using the following equation
=CBIgeo log ( /1.5 )
nn
2(9)
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 Muller.
67
Hakanson
59
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
effects with their toxicology.
68
Its calculation is done as
Table 2. Interpretation of Heavy Metal Contamination
Using Contamination Factor (CF)
59
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 Modified Degree of Contamination (mCd)
66
ranges significance
mCd < 1.5 nil to very low degree of contamination
1.5 ≤mCd <2 low degree of contamination
2≤mCd <4 moderate degree of contamination
4≤mCd <8 high degree of contamination
8≤mCd <16 very high degree of contamination
16 ≤mCd <32 extremely high degree of contamination
mCd ≥32 ultrahigh degree of contamination
ACS Omega Article
DOI: 10.1021/acsomega.7b00683
ACS Omega 2017, 2, 5434−5444
5441

∑
==×=
−
CC C
C
RI Er ; Er Tr ;
ii i
f
i
f
i
i
n
i
01
(10)
where Cf
icorresponds to the pollution factor for individual
heavy metals, C0‑1
icorresponds to heavy metal’s concentration
in the sample, Cn
iis the background concentration, Eriis the
ecological risk potential of the heavy metals, Triis the toxic
response coefficient developed by Hakanson
59
(toxic response
coefficients 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.
■AUTHOR INFORMATION
Corresponding Author
*E-mail: suresh.pe.ese@ismdhanbad.ac.in. Tel: (+91)
9471191703.
ORCID
Suresh Pandian Elumalai: 0000-0003-4104-1776
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
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
8
), 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.
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