ArticlePDF Available


The total concentrations of Cd, As, Pb, Cr, Ni, Co, Zn, Cu, Ag, Hg, and Mo were determined in the atmospheric dust of the city of Yerevan by atomic absorption spectrometry (AAnalyst PE 800). Heavy metal pollution levels were evaluated by calculating geo-accumulation (Igeo) and summary pollution (Zc) indices. Potential human health risk was assessed using the United States Environmental Protection agency’s human health risk assessment model. The results show that mean contents of all elements tested except Ni and Cr were substantially higher than local geochemical background values. According to the Igeo, Yerevan territory is strongly-to-extremely polluted by As, Ag, Hg, Mo, and Cd. The Zc assessment indicated that very high pollution was detected in 36 % of samples, high in 32 %, average in 12 %, and low in 20 %. The health risk assessment revealed a non-carcinogenic risk (HI >1) for children at 13 samplings sites and for adults at one sampling site. For children the risk was due to elevated levels of Mo, Cd, Co, and As, while for adults, only Mo. Carcinogenic risk (>1:1,000,000) of As and Cr via ingestion pathway was observed in 25 and 14 samples, respectively. This study, therefore, is the base for further detailed investigations to organize problematic site remediation and risk reduction measures.
Assessment of pollution levels and human health risk of heavy
metals in dust deposited on Yerevan’s tree leaves (Armenia)
N. Maghakyan
G. Tepanosyan
O. Belyaeva
L. Sahakyan
A. Saghatelyan
Received: 6 April 2016 / Revised: 23 July 2016 / Accepted: 23 August 2016
ÓScience Press, Institute of Geochemistry, CAS and Springer-Verlag Berlin Heidelberg 2016
Abstract The total concentrations of Cd, As, Pb, Cr, Ni,
Co, Zn, Cu, Ag, Hg, and Mo were determined in the
atmospheric dust of the city of Yerevan by atomic
absorption spectrometry (AAnalyst PE 800). Heavy metal
pollution levels were evaluated by calculating geo-accu-
mulation (I
) and summary pollution (Z
) indices.
Potential human health risk was assessed using the United
States Environmental Protection agency’s human health
risk assessment model. The results show that mean con-
tents of all elements tested except Ni and Cr were sub-
stantially higher than local geochemical background
values. According to the I
, Yerevan territory is strongly-
to-extremely polluted by As, Ag, Hg, Mo, and Cd. The Z
assessment indicated that very high pollution was detected
in 36 % of samples, high in 32 %, average in 12 %, and
low in 20 %. The health risk assessment revealed a non-
carcinogenic risk (HI[1) for children at 13 samplings sites
and for adults at one sampling site. For children the risk
was due to elevated levels of Mo, Cd, Co, and As, while for
adults, only Mo. Carcinogenic risk ([1:1,000,000) of As
and Cr via ingestion pathway was observed in 25 and 14
samples, respectively. This study, therefore, is the base for
further detailed investigations to organize problematic site
remediation and risk reduction measures.
Keywords Urban dust Heavy metals Pollution levels
Health risk assessment
1 Introduction
Dust, one of the basic atmospheric pollutants, is an
aggregation of naturally occurring and anthropogenic solid
particles. Dust can have a negative impact on human health
(Al Jallad et al. 2013; Zhou et al. 2014; Lu et al. 2015). The
character and degree of basic impacts of dust depend on
particle size, composition, and duration of exposure.
Atmospheric dust is known as a carrier of toxic substances,
especially heavy metals (Duzgoren-Aydin et al. 2006;
Chaudhari et al. 2012). Heavy metals in dust may penetrate
the human organism through inhalation, ingestion, and skin
absorption and induce negative effects such as hematoge-
nesis disorders and problems in the central nervous, cardio-
vascular, and urogenital systems (Li et al. 2013). More-
over, individual heavy metals are known to trigger specific
diseases such as Alzheimer’s and Parkinson’s (Oves et al.
Pollution by dust and heavy metals is of particular
concern in urban areas because of high population density
and numerous sources of pollution—motor transport,
industrial plants, domestic refuse, corrosion of roadway
surfaces, etc. (Charlesworth and Lees 1999; Sharma et al.
2008; Wei et al. 2010; Cai et al. 2013). Extensive recent
research has sought to estimate pollution levels, identify
sources, and assess potential health risks—both to children
and adults—associated with heavy metals in dust. An
essential element of such studies has been the investigation
of street dust and its heavy metal contents (Lu et al.
2009,2015; Kong et al. 2011; Du et al. 2013; Zhang et al.
2013; Li et al. 2014; Wang et al. 2014). Another way to
study urban atmospheric dust is to use biomonitors such as
higher plants. Although higher plants are not ideal
biomonitors like mosses and lichens, in industrialized and
urban areas that are missing these vegetation types, higher
&N. Maghakyan
Department of Environmental Geochemistry, Center for
Ecological-Noosphere Studies of the National Academy of
Sciences, Abovian-68, 0025 Yerevan, Republic of Armenia
Acta Geochim
DOI 10.1007/s11631-016-0122-6
plants can be used as well. Airborne particles including
dust and heavy metals deposit on the surface of tree leaves
via wet and dry atmospheric precipitation (Mingorance and
Oliva 2006; Pavlı
´k et al. 2011). According to the Toma-
ˇevic et al. (2005) study, tree leaf deposits directly reflect
the level of atmospheric pollution by heavy metals.
Yerevan is an old city and, being an industrial center with
dense population and heavy traffic, it has been exposed to
high levels of atmospheric pollution for years (Saghatelyan
and Arevshatyan 2003; Saghatelyan 2004; Sahakyan 2006;
Saghatelyan et al. 2014). During the Soviet period, the
spatial planning of the city was quite ordinary, and there
were more heavily polluted industrial pockets of the city.
After the collapse of the Soviet Union, during social and
economic transformations in the 1990s and now, during
recovery of industry, the spatial distribution of industrial
units throughout Yerevan reflects a more mosaic character,
i.e. irregularly spread across the city. This complicated the
process of identification of pollution sources for exact con-
taminants. Although geochemical investigations have been
conducted for many decades, it should be stressed that
assessment of dust and heavy metal-induced health risks to
Yerevan’s residents has never been done before. The goal of
this research was to assess levels of heavy metals pollution
in urban dust using tree leaves as dust accumulators and
assessing health risks to different groups of the population
(children and adults).
2 Materials and methods
2.1 Study site
Yerevan (latitude 40°1004000N, longitude 44°3004500E),
Armenia’s capital, covers an area of 223 km
with a pop-
ulation of just over one million people. The relief is rather
diverse and is represented by plains, plateaus, foothills, and
the River Hrazdan canyon. The city is situated at a height
of 850–1420 m a.s.l. The climate is typically dry conti-
nental; the amount of annual precipitation is 250–400 mm.
Mean air temperature varies from 22 to 26 °C in summer
and -4to-6°C in winter. Persistent snow cover occurs in
January and February, but not regularly. Northeastern
winds dominate the city year-round, but the air cycle is
substantially complicated because of the topography (in-
termountain trough) and specificity of site development.
Thermal inversions occur during winter. Dry steppe and
semi-desert natural landscapes are common.
The main industrial branch is processing. The city con-
tains metallurgical plants, manufacturing enterprises (pro-
duction of concrete, wood- and metalware, food, medicines,
paper, etc.), and stone- and woodworking workshops. In the
area of Yerevan and its outskirts there are active sandpits
and quarries of basalt, tuff, and clay. Presently, the city area
is under intense construction; in most cases, construction
sites are not properly isolated.
2.2 Sampling and analysis
Sampling density in local-scale projects does not follow
any exact rules, as it mainly depends on the objectives of
the project and available funds (Demetriades et al. 2015).
In the case of multifunctional cities having mosaic distri-
bution of pollution sources such as Yerevan, this issue
becomes more complicated; collection and analysis costs
can restrict sampling scale and sample size.
In this study, sampling sites were based on the geo-
morphological features and the peculiarities of the spatial
distribution of urban green areas which cover 5.6 %
(12.5 km
) of the territory of Yerevan. The number of
samples was determined taking into account available
funds and the long-term air monitoring points of the Center
for Ecological-Noosphere Studies (CENS) NAS RA in
order to ensure continuity of research.
In the summer 2011, 25 tree-leaf samples were collected
from the city. Samples were collected from the most wide-
spread tree species having relatively good dust absorption
properties (Kretinin and Selyanina 2006): white elm (Ulmus
laevis), Chinese elm (Ulmus parvifolia), Persian walnut
(Juglans regia), oriental plane tree (Platanus orientalis),
common lilac (Syringa vulgaris), white poplar (Populous
alba), and white mulberry tree (Morus alba) (Fig. 1).
Leaves were sampled at a height of 1.5–2 m above the
ground from at least three trees of the same species per sam-
pling site then placed in paper bags and transported to the
Central Analytical Laboratory CENS accredited by ISO-IEC
After the sampled leaves had been dried at room tem-
perature, they were washed with de-ionized water (MilliQ).
The generated liquid underwent filtration using a weighed
ash free filter (retention limit 2–3 lm).
Dry residue was dissolved in nitric acid (1:1), then the
acid was evaporated and MilliQ water was added to the
residual solution until 20 ml was achieved.
In the filtrated matter the contents of eleven elements—
Cd, As, Pb, Cr, Ni, Co, Zn, Cu, Ag, Hg, and Mo—were
determined by AAnalyst 800 AAS PE, USA.
Concentrations below detection levels of the employed
analytical method were given a value half of the detection
limit as proposed by Reimann et al. (2008).
2.3 Assessment of pollution with heavy metals
in dust
Based on the concentrations of heavy metals in Yerevan’s
atmospheric dust, single-element and multi-element
Acta Geochim
pollution level assessments were done. Particularly, the
degree of each heavy metal contaminant in the dust was
characterized by geoaccumulation index (I
) (Muller
1969; Lu et al. 2009; Johnson et al. 2011):
Igeo ¼log2
where C
is the concentration of element i in dust, while B
is the local geochemical background concentration from
Tepanosyan et al. (2016) of the ith element in Yerevan’s
soil. The following classification is given for the I
unpolluted (I
B0), unpolluted to moderately polluted
(0 \I
B1), moderately polluted (1 \I
B2), mod-
erately to strongly polluted (2 \I
B3), strongly pol-
luted (3 \I
B4), strongly to extremely polluted
(4 \I
B5), and extremely polluted (5 \I
) (Muller
1969; Lu et al. 2009; Johnson et al. 2011).
For an integral description of heavy metal pollution, the
summary pollution level was assessed and a contamination
index (Z
) was calculated (Perelman and Kasimov 2000;
Johnson and Demetriades 2011) according to formulas (2)
and (3).
Kcn1ðÞ ð3Þ
where K
is a concentration coefficient, C
is the content of
ith metal in dust, C
is local background content of the ith
element in soil from Tepanosyan et al. (2016), and nis the
number of elements in the same sample with K
[1. The
summary pollution level was classified as low (Z
moderately hazardous (16 \Z
\32), high/hazardous
(32 \Z
\128), or very high/extremely hazardous
[128) (Perelman and Kasimov 2000).
Finally, to obtain qualitative and quantitative charac-
teristicics of heavy metals in soil, decreasing geochemical
series were created.
2.4 Risk assessment
Taking into account toxic and carcinogenic effects of
heavy metals, calculations of both non-carcinogenic and
carcinogenic risks were done. Two pathways of exposure
of humans to dust heavy metals—direct ingestion of dust
particles and dermal absorption of dust heavy metals (Lu
et al. 2009; RAIS 2014)—were considered for non-car-
cinogenic risk assessment. Risk from inhalation was not
assessed as undifferentiated dust was investigated.
Health risks to children and adults posed by heavy
metals in dust were calculated in a manner consistent with
a health risk model developed by the US Environmental
Protection Agency (US EPA 1989,2002; RAIS 2013).
Fig. 1 Position of Yerevan in Armenia’s area, sampling points and sampled tree species
Acta Geochim
2.5 Non-carcinogenic risk
Non-carcinogenic risk was calculated with respect to all the
detected heavy metals: Hg, Pb, Mo, Cd, Zn, Cu, Ni, Ag,
Co, Cr, and As.
Per metal chronic daily intake (CDI) per pathway of
exposure was calculated by formulas (4) and (5) (US EPA
US 1989,2002; RAIS 2013).
kg day
¼CIngR EF ED 106
kg day
Non-carcinogenic hazard quotient per element via each
pathway was calculated by formulas (6) and (7):
HQing ¼CDIing
HQdermal ¼CDIdermal
Description and values of factors used in formulas (47)
are given in Tables 1and 6.
RfD values (Table 1), which underpinned the assess-
ment of non-carcinogenic risk, were taken from the risk
assessment information system (RAIS 2013). There exists
no Oral RfD value for Pb, so this research used the value
from the WHO guidelines for drinking water quality (WHO
RAIS lacks dermal RfD values, so instead, with respect
to all the elements, oral chronic RfD
values multiplied
by respective gastrointestinal absorption factors (US EPA
2002,2004; RAIS 2013) were used.
The sum of all HQ values represents a Hazard Index
. In addition, a single-element HI including
ingestion and dermal absorption pathways was calculated
to reveal priority of elements, while multi-element HI of all
studied elements via two pathways was evaluated to
describe total human health risk. HI\1 indicates the
absence of harmful effect on the health, whereas HI[1
denotes a possibility of adverse health effects.
2.6 Carcinogenic risk
Carcinogenic risk is defined as occurrence probability for
any type of cancer during the whole lifetime in case of
exposure to a carcinogenic element. The allowable risk
limits are defined as 10
(Lu et al. 2009). Particu-
larly, in the case of a single element, allowable carcinogenic
risk limit is 10
, while for multi-element carcinogenic risk
the allowable limit is\10
(TCEQ Regulatory Guidance).
According to the International Agency for Research on
Cancer, Cr, Cd, As, Ni, and Co are considered to have a
carcinogenic effect (Cao et al. 2014). Taking into consid-
eration the existence of slope factors, the carcinogenic risks
of Cr and As through ingestion were assessed.
The lifetime (LT =70) average daily dose was calcu-
lated for the ingestion pathway by formula (8) (US EPA
1989,2002; RAIS 2013):
LADDing ¼CEF 106
EDchild IngRchild
þEDadult EDchild
Carcinogenic risk from ingestion for each element was
assessed by formula (9) (RAIS 2013), while multi-element
Table 1 Description and values of factors used in the risk assessment equations
Factors (measurement unit) Description Value Source
Adults Children
IngR (mg/day) Ingestion rate 100 200 (US EPA 2002)
EF (day/year) Exposure frequency 90* 90* –*
ED (year) Exposure duration 30 6 (US EPA 2002)
BW (kg) Average body weight 70 15 (US EPA 1989)
SA (cm
) Skin area 5700 2800 (US EPA 2002)
AF (mg/cm
) Skin adherence factor 0.07 0.2 (US EPA 2002)
AT Average time In the case of non-carcinogenic exposure AT =365 9ED (US EPA 2002)
Dermal absorption (ABS =0.03 for As, ABS =0,001 for the rest of elements. (RAIS 2013)
(mg/kg-day), Chronic reference dose Chemical specific (RAIS 2013)
Cancer slop factor Chemical specific (RAIS 2013)
*Values used for this particular research
Acta Geochim
carcinogenic risk (RI) was calculated by summing single
element carcinogenic risk values.
Oral Risk ¼LAADing SF ð9Þ
Description and values of the coeffients are given in
Tables 1and 7, while carcinogenic risk level classification
is given in Table 2(Rapant et al. 2010).
3 Results and discussion
3.1 Contents of heavy metals in atmospheric dust
Ni, Co, Ag, Cr, Pb, Mo, Cd, Zn, and Cu were detected in all
samples, whereas As in two samples and Hg in six samples
had concentrations below the detection limit. A value of
half of the detection limit was given in these cases as
proposed by Reimann et al. (2008).
Descriptive statistical parameters and local geochemical
background values of heavy metals are given in Table 3.
As demonstrated in Table 3, asymmetry and excess
values of all the elements differ from 0 which proves
deviation from normal distribution.
The standard error (k) of the mean of studied heavy
metals at the level of 5 % (p \0.05) reflects the repre-
sentativeness of the 25 samples’ studied. Mean standard
errors (Table 3) of heavy metals in dust deposits of Yere-
van territory’s tree leaves show that studied heavy metals
categorized into the following three groups: (1) Ni, Cr, Zn,
and Co (k\20 %); (2) Ag, Hg, Cu, and Pb
(20 % \k\50 %); and (3) As, Mo, and Cd (k[50 %).
The highest standard mean errors were observed for As,
Mo, and Cd: 24.38 (155.2 %), 1914.43 (188.7 %) and
60.51 (124.3 %), respectively. After removing some
extreme values of these elements which were thought to be
the results of discharges from point pollution sources, the
standard mean error values became As 39.2 %, Mo 40.2 %,
and Cd 44.1 %. According to Revich et al. (1982), allow-
able standard mean error to be considered background sites
(or natural) is kB30 %. For this study k\50 % can be
considered sufficient, but further detailed investigations
should be carried out to reveal sources and spatial distri-
bution peculiarities of As, Mo, and Cd.
Meanwhile, Ni, Co, and Zn may be regarded as having
an approximately normal distribution according to the rule
of ±3 (Beus et al. 1976). Also, typical of Ni, Co, and Zn
are relatively low values of the coefficient of variation
(CV): 26, 47 and 40, respectively. Relatively low CV was
determined for Cr, Ag, Hg, Pb, and Cu. However, their
distribution significantly deviated from normal distribution,
which might be due to the presence of outliers and extreme
values of manmade contents. CV values of As, Mo, and Cd
were 396, 481, and 317, respectively. This is attributed to
extremely high manmade contents of heavy metals detec-
ted in some dust samples.
Mean contents of almost all elements except Cr and Ni
exceeded background contents (Tables 3,4). As seen from
the arranged geochemical series (Table 4), mean contents
of Mo, Cd, Hg, and Ag are manifold excessive versus the
background: by 579.8, 155.9, 33.5, and 26.8 times,
respectively. At their highest contents Mo, Cd, As, and Hg
showed the highest excesses versus the background: by
13,974.86, 2510.9, 453.5 and 139.4 times, respectively.
It should be noted that collation of statistical charac-
teristics of Ni, Co, and Zn with results obtained during
comparison of mean of these elements versus local back-
ground values suggests that Ni may have a natural origin,
while Co and Zn come from both natural and anthro-
pogenic sources. The known source for heavy metals,
especially Mo, in the Yerevan area is Mo concentrate
smelting and processing in plants located in the south of
Yerevan, where all studied elements with the exception of
Ni exceeded local background values. In addition, for Cd,
vehicle tire wear is supposed to be one of the main sources
of pollution. For Hg, sources include combustion of diesel,
jet fuel, medical waste disposal facilities, dental offices,
and some consumer products. Arsenic may have come
from metal smelting, glass, textiles, and paper production.
At the lowest contents Hg, Ag, Pb, Zn, and Cu are also
excessive versus the background (Tables 3,4).
3.2 Heavy metal pollution assessment
3.2.1 Geoaccumulation index
The calculated results of I
of heavy metals in dust are
presented in Table 5. The mean values of I
decrease in
the order of Cd[Mo[Ag[Hg[Pb[Cu[Zn[Co[As[
Ni[Cr. The I
ranges from -1.56 to 0.07 with a mean
value of -0.70 for Ni (unpolluted); -7.96 to 2.1 with a
mean value of 0.68 for Co (moderately polluted); -4.88 to
8.24 with a mean value of 0.56 for As (unpolluted to
moderately polluted); 2.06 to 6.35 with a mean value of
Table 2 Carcinogenic risk level classification (Rapant et al. 2010)
Risk level Calculated cases of cancer
Cancer risk
Very low
II 10
III 10
IV 10
Very high
Acta Geochim
3.59 for Ag (strongly polluted); 0.23 to 6.54 with a mean
value of 3.46 for Hg (strongly polluted); -2.21 to 0.49 with
a mean value of -1,09 for Cr (moderately polluted); 0.75
to 3.91 with a mean value of 1.96 for Pb (moderately
polluted); -0.77 to 13.18 with a mean value of 3.65 for Mo
(strongly polluted); -2.4 to 10.7 with a mean value of 4.82
for Cd (strongly to extremely polluted); 0.13 to 2.12 with a
mean value of 1.15 for Zn (moderately polluted); and 0.005
to 3.66 with a mean value of 1.83 for Cu (moderately
Table 3 Mean contents of heavy metals, descriptive statistical parameters, and background values
Value Ni Co As Ag Hg Cr Pb Mo Cd Zn Cu
29.09 62.26 15.71 6.68 0.57 54.23 32.43 1014.68 48.66 281.93 294.65
Std. error (p \0.05), k2.91 11.50 24.38 2.93 0.23 10.05 8.10 1914.43 60.51 44.36 90.47
28.65 70.66 2.05 3.40 0.43 48.03 30.15 24.89 12.84 288.73 261.05
SD 7.44 29.34 62.19 7.49 0.59 25.64 20.67 4883.76 154.36 113.17 230.80
Skewness 0.27 -1.14 4.93 2.30 1.43 2.45 2.30 5.00 4.87 0.56 0.83
Std. error of skewness 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46
Kurtosis 0.19 0.43 24.52 4.96 2.18 7.63 7.08 25.00 24.07 -0.53 -0.60
Std. error of kurtosis 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
15.48 0.09 0.04 1.56 0.03 23.11 12.10 1.54 0.09 129.01 60.24
47.77 100.90 312.94 30.63 2.37 149.81 108.47 24456.00 783.43 511.45 757.70
CV, % 26 47 396 112 102 47 64 481 317 40 78
30.4 15.65 0.69 0.25 0.017 71.1 4.8 1.75 0.312 78.38 40.03
values are described in mg/kg: n =25
Tepanosyan et al. (2016)
Table 4 Geochemical series arranged according to highest, mean, and lowest contents
Contents Geochemical series
Highest Mo
Mean Mo
Lowest Ag
Table 5 Values of I
and single-element pollution levels
Ni Co As Ag Hg Cr Pb Mo Cd Zn Cu
Max 0.07 2.10 8.24 6.35 6.54 0.49 3.91 13.19 10.71 2.12 3.66
Min -1.56 -7.96 -4.89 2.06 0.23 -2.21 0.75 -0.77 -2.41 0.13 0.005
Mean -0.70 0.68 0.56 3.59 3.46 -1.09 1.96 3.65 4.82 1.15 1.83
SD 0.38 2.46 2.91 1.18 2.09 0.55 0.77 2.44 2.15 0.59 1.22
Samples (%)
B0 96.0 16.0 24.0 0.0 0.0 96.0 0.0 4.0 4.0 0.0 0.0
B1 4.0 0.0 28.0 0.0 24.0 4.0 12.0 4.0 0.0 44.0 32.0
B2 0.0 76.0 28.0 0.0 0.0 0.0 36.0 8.0 0.0 48.0 16.0
B3 0.0 8.0 8.0 32.0 12.0 0.0 44.0 20.0 0.0 8.0 32.0
B4 0.0 0.0 4.0 36.0 12.0 0.0 8.0 36.0 16.0 0.0 20.0
B5 0.0 0.0 4.0 16.0 28.0 0.0 0.0 12.0 40.0 0.0 0.0
0.0 0.0 4.0 16.0 24.0 0.0 0.0 16.0 40.0 0.0 0.0
Acta Geochim
The concentrations of Ni, Co, As, Cr, Mo, and Cd reg-
ister at the unpolluted level (I
B0) in 96 %, 16 %,
24 %, 96 %, 4 %, and 4 % of all samples, respectively.
The unpolluted to moderately polluted (0 \I
B1) level
was determined in 4, 28 %, 24 %, 4 %, 12 %, 4 %, 44 %,
and 32 % of all samples for Ni, As, Hg, Cr, Pb, Mo, Zn,
and Cu, respectively. The moderately polluted (1 \I
B2) level included 76 %, 28 %, 36 %, 8 %, 48 %, and
16 % of all samples for Co, As, Pb, Mo, Zn, and Cu,
respectively. The moderately-to-strongly polluted
(2 \I
B3) level was observed in 8 %, 8 %, 32 %,
12 %, 44 %, 20 %, 8 %, and 32 % of all samples for Co,
As, Ag, Hg, Pb, Mo, Zn, and Cu respectively. The strongly
polluted (3 \I
B4) level was detected in 4 %, 36 %,
12 %, 8 %, 36 %, 16 %, and 20 % of all samples for As,
Ag, Hg, Pb, Mo, Cd, and Cu, respectively. The concen-
trations of As, Ag, Hg, Mo, and Cd belong to the strongly-
to-extremely polluted (4 \I
B5) level in 4%, 16%,
28%, 12%, and 40 % of all samples, and to the extremely
polluted (I
[5) level in 4 %, 16 %, 24 %, 16 %, and
40 % of all samples, respectively. Only for Ni, Co, Cr, and
Zn were concentrations belonging to the strongly polluted
to extremely polluted levels not observed.
3.2.2 Summary pollution level
The summary pollution levels (Z
) are given in Fig. 2. The
contamination index value varied from 21 to 14,172,
averaging 805.5 (extremely hazardous level). According to
the contamination index, 36 % of samples exhibited a very
high level of pollution, i.e. extremely hazardous; 32 % a
high level of pollution, i.e. hazardous degree; 12 % a mean
level, i.e. moderately hazardous degree; and 20 % a low
level of pollution. Very high and high levels of pollution
were detected in the southern (industrial) and central
(densily populated and exposed to heavy traffic load) dis-
tricts of Yerevan.
As seen from Fig. 2, a relatively large share in the
contamination index of almost all the samples belongs to
Mo, Cd, Hg, and Ag. In addition, Cd approximating 93 %
and 83 % of the contamination index falls on sampling
points N8 and N15 located in the north and west of the city.
Fig. 2 Levels of the
contamination index and shares
of individual elements in it (%)
Acta Geochim
Table 6 Non-carcinogenic health risk of heavy metals in dust
Element Group HQ
HI =PHQi RFDing (mg/kg day
min max mean min max mean min max mean
Hg Child 6.16E-04 4.87E-02 1.17E-02 1.73E-06 1.36E-04 3.26E-05 6.18E-04 4.88E-02 1.17E-02 1.60E-04
Adult 6.60E-05 5.22E-03 1.25E-03 2.64E-07 2.08E-05 4.98E-06 6.63E-05 5.24E-03 1.25E-03
Pb Child 1.14E-02 1.02E-01 3.06E-02 2.12E-04 1.90E-03 5.71E-04 1.16E-02 1.04E-01 3.12E-02 3.50E -03
Adult 1.22E-03 1.09E-02 3.28E-03 3.24E-04 2.90E-03 8.72E-04 1.54E-03 1.38E-02 4.15E-03
Mo Child 1.01E-03 1.61E?01 6.67E-01 2.84E-06 4.50E-02 1.87E-03 1.02E-03 1.61E?01 6.69E-01 5.00E-03
Adult 1.08E-04 1.72E?00 7.15E-02 4.33E-07 6.87E-03 2.85E-04 1.09E-04 1.73E?00 7.18E-02
Cd Child 2.89E-04 2.58E?00 1.60E-01 3.24E-05 2.88E-01 1.79E-02 3.22E-04 2.86E?00 1.78E-01 1.00E-03
Adult 3.10E-05 2.76E-01 1.71E-02 4.95E-06 4.40E-02 2.74E-03 3.59E-05 3.20E-01 1.99E-02
Zn Child 1.41E-03 5.60E-03 3.09E-03 3.96E-06 1.57E-05 8.65E-06 1.42E-03 5.62E-03 3.10E-03 3.00E-01
Adult 1.51E-04 6.01E-04 3.31E-04 6.04E-07 2.40E-06 1.32E-06 1.52E-04 6.03E-04 3.32E-04
Cu Child 4.95E-03 6.23E-02 2.42E-02 1.39E-05 1.74E-04 6.78E-05 4.97E-03 6.25E-02 -4.00E-02
Adult 5.30E-04 6.67E-03 2.59E-03 2.12E-06 2.66E-05 1.04E-05 5.33E-04 6.70E-03 2.61E-03
Ni Child 2.54E-03 7.85E-03 4.78E-03 1.78E-04 5.50E-04 3.35E-04 2.72E-03 8.40E-03 5.12E-03 2.00E -02
Adult 2.73E-04 8.41E-04 5.12E-04 2.72E-05 8.39E-05 5.11E-05 3.00E-04 9.25E-04 5.63E-04
Ag Child 1.03E-03 2.01E-02 4.39E-03 7.18E-05 1.41E-03 3.07E-04 1.10E-03 2.16E-02 4.70E-03 5.00E-03
Adult 1.10E-04 2.16E-03 4.70E-04 1.10E-05 2.15E-04 4.69E-05 1.21E-04 2.37E-03 5.17E-04
Co Child 1.03E-03 1.11E?00 6.82E-01 2.88E-06 3.10E-03 1.91E-03 1.03E-03 1.11E?00 6.84E-01 3.00E-04
Adult 1.10E-04 1.18E-01 7.31E-02 4.40E-07 4.73E-04 2.92E-04 1.11E-04 1.19E-01 7.34E-02
Cr Child 2.53E-02 1.64E-01 5.94E-02 2.84E-03 1.84E-02 6.66E-03 2.82E-02 1.83E-01 6.61E-02 3.00E -03
Adult 2.71E-03 1.76E-02 6.37E-03 4.33E-04 2.81E-03 1.02E-03 3.15E-03 2.04E-02 7.38E-03
As Child 3.84E-04 3.43E ?00 1.72E-01 3.22E-05 2.88E-01 1.45E-02 4.16E-04 3.72E?00 1.87E-01 3.00E -04
Adult 4.11E-05 3.67E-01 1.84E-02 4.92E-06 4.40E-02 2.21E-03 4.60E-05 4.11E-01 2.07E-02
In italics [1 values are given
Acta Geochim
Rather a large share of Mo (98 % of the contamination
index) falls on a sampling point in the south of the city near
the Mo concentrate smelting and processing plants.
3.3 Non-carcinogenic risk assessment
Results of assessment of non-carcinogenic risk of heavy
metals in leaf dust are given in Table 6. Both to children
and adults, the major route of exposure from multi-ele-
mental risk is ingestion followed by skin absorption. For
children, major (HQ[1) risk is determined for Mo, Cd, Co,
and As; in adults, for Mo only. In the case of children, the
single-elemental HI for Mo varies from 0.001 to 16.1; for
Cd, 0.0003 to 2.86; for Co, 0.001 to 1.11; and for As,
0.0004 to 3.72, with means of 0.67, 0.18, 0.68, and 0.19,
respectively. Moreover, for children, single-elemental HI
was greater than 1 in one sample in the cases of Mo, Cd,
and As, and in three samples in the case of Co. Single-
elemental HI for adults for Mo varies from 0.0001 to 1.73,
with a mean of 0.072, and HI greater than 1 was detected in
one sample.
Mean single-elemental HI values of studied metals are
represented by the following decreasing series for both
children and adults:
Co [Mo [As [Cd [Cr [Pb [Cu [Hg [
Ni [Ag [Zn
Multi-elemental HI varies from 0.27 to 17.51 for chil-
dren and 0.03 to 1.86 for adults with means of 1.89 and 0.2,
respectively. Moreover, a probable heavy metal–induced
non-carcinogenic risk to children and adults is posed by
thirteen and one dust samples, respectively (Fig. 3).
3.4 Carcinogenic risk assessment
To assess carcinogenic risk of Cr and As, appropriate SF
values were taken from RAIS (2013). However, RAIS
Fig. 3 Multi-elemental risk to
children and adults according to
sampling point
Table 7 Summary of carcinogenic risk via ingestion and inhalation
exposure of dust, based on min., max., and mean concentrations
Element Value Oral risk SF(mg/kg-day)
Cr min 4.42E-06 5.00E-01
max 2.86E-05
mean 1.04E-05
As min 2.01E-08 1.50E?00
max 1.80E-04
mean 9.01E-06
min 4.44E-06
max 1.89E-04
mean 1.94E-05
In italics[10
values are given for single-element carcinogenic risk,
for multi-element carcinogenic risk values
Acta Geochim
contains no values for ingestion pathway for the rest of the
detected elements.
The results of the carcinogenic risk calculation are given
in Table 7.
It is evident (Table 7) that minimum carcinogenic risk
values of Cr via ingestion pathway belong to the low risk
level, while mean and maximum carcinogenic risk values
of Cr belong to the medium risk level. In the case of As,
carcinogenic risk was detected only for mean (low risk
level) and max (high risk level) values. From all 25 sam-
ples, a low level ([10
and \10
) of Cr and As car-
cinogenic risk was detected in seventeen and twelve
samples, respectively; and a medium level ([10
) in eight and one samples, respectively. A high
level ([10
) of carcinogenic risk was observed
only in the case of As in one sampling site. Multi-element
carcinogenic risk ([10
) via ingestion pathway has been
observed only by the maximum RI
4 Conclusions
The concentrations, pollution levels, and health risks of
heavy metals (Cd, As, Pb, Cr, Ni, Co, Zn, Cu, Ag, Hg, and
Mo) in tree leaf dust deposits from Yerevan territory were
studied. The obtained results show that mean contents of
all studied elements, except Cr and Ni, exceed background
values. A geochemical series of mean contents of heavy
metals is represented as Mo
. Statistical
descriptions of the contents indicate that high contents of
As, Mo, and Cd detected in Yerevan are due to man-made
sources, while Ni has a natural origin, and Co and Zn
originate from both natural and anthropogenic sources.
This fact was also complemented by low levels of I
these elements. Summary pollution levels based on the
contamination index show that 36 % of all samples
exhibited a very high level of pollution; 32 %, high; 12 %,
mean; and 20 %, low.
The results of risk analysis suggest that both in children
and adults the major pathway of risk is ingestion of dust
particles. Children, as compared with adults, are at a higher
risk, and such a level of air pollution with heavy metals can
trigger serious health problems. A probable cause of non-
carcinogenic risk to children was found to be Mo, Cd, Co,
and As; to adults, Mo only. From very low to high levels of
carcinogenic risk were observed for single-element and
medium levels for multi-element ingestion of Yerevan
It should be stressed that there are some limitations in
the used risk assessment model: (1) all employed coeffi-
cients are set for US citizens, (2) lack of respective coef-
ficients for individual pathways of some elements, (3) dust
particle size and rate of penetration are not taken into
account, (4) seasonal variations are not taken into account,
and (5) calculations are based on total concentrations of
heavy metals. Despite this, application of the described risk
model helped us get a better vision of probable health risks
to Yerevan’s residents.
Acknowledgments This research was implemented in the frames of
a theme ‘‘Studying geochemical stream of elements in atmospheric air
of Yerevan’’ (No 13-1E220, 2011) under agreement-based (thematic)
financial support of the State Committee of Science to the Ministry of
Education and Sciences RA.
Al Jallad F, Al Katheeri E, Al Omar M (2013) Levels of particulate
matter in Western UAE desert and factors affecting their
distribution. In: Longhurst JWSC, Brebbia A (eds) Air Pollution
XXI. WIT Press, Southampton, pp 111–122
Beus AA, Grabovskaya LI, Tikhonov NV (1976) Environmental
Geochemistry. Nedra, Moscow
Cai QY, Mo CH, Li HQ et al (2013) Heavy metal contamination of
urban soils and dusts in Guangzhou, South China. Environ Monit
Assess 185:1095–1106. doi:10.1007/s10661-012-2617-x
Cao S, Duan X, Zhao X et al (2014) Health risks from the exposure of
children to As, Se, Pb and other heavy metals near the largest
coking plant in China. Sci Total Environ 472:1001–1009. doi:10.
Charlesworth SM, Lees J a (1999) The distribution of heavy metals in
deposited urban dusts and sediments, Coventry, England.
Environ Geochem Health 21(2):97–115
Chaudhari PR, Gupta R, Gajghate DG, Wate SR (2012) Heavy metal
pollution of ambient air in Nagpur City. Environ Monit Assess
184:2487–2496. doi:10.1007/s10661-011-2133-4
Demetriades A, Birke M, Albanese S et al (2015) Continental,
regional and local scale geochemical mapping. J Geochem
Explor 154:1–5. doi:10.1016/j.gexplo.2015.02.011
Du Y, Gao B, Zhou H et al (2013) Health risk assessment of heavy
metals in road dusts in urban parks of Beijing, China. Procedia
Environ Sci 18:299–309. doi:10.1016/j.proenv.2013.04.039
Duzgoren-Aydin NS, Wong CSC, Aydin A et al (2006) Heavy metal
contamination and distribution in the urban environment of
Guangzhou, SE China. Environ Geochem Health 28:375–391.
Johnson CC, Demetriades A (2011) Urban geochemical mapping: a
review of case studies in this volume. In: Johnson CC,
Demetriades A, Locutura J, Ottesen RT (eds) Mapping the
chemical environment of urban areas. Wiley, New York,
pp 7–27
Johnson CC, Demetriades A, Locutura J, Ottesen RT (eds) (2011)
Mapping the chemical environment of urban areas. Wiley,
Kong S, Lu B, Ji Y et al (2011) Levels, risk assessment and sources of
PM10 fraction heavy metals in four types dust from a coal-based
city. Microchem J 98:280–290. doi:10.1016/j.microc.2011.02.
Kretinin VM, Selyanina ZM (2006) Dust retention by tree and shrub
leaves and its accumulation in light chestnut soils under forest
shelterbelts. Eurasian Soil Sci 39:334–338. doi:10.1134/
Li H, Qian X, Hu W et al (2013) Chemical speciation and human
health risk of trace metals in urban street dusts from a
Acta Geochim
metropolitan city, Nanjing, SE China. Sci Total Environ
456–457:212–221. doi:10.1016/j.scitotenv.2013.03.094
Li X, Zhang S, Yang M (2014) Accumulation and risk assessment of
heavy metals in dust in main living areas of Guiyang City,
Southwest China. Chinese J Geochemistry 33:272–276. doi:10.
Lu X, Li LY, Wang L et al (2009) Contamination assessment of
mercury and arsenic in roadway dust from Baoji, China. Atmos
Environ 43:2489–2496. doi:10.1016/j.atmosenv.2009.01.048
Lu F, Xu D, Cheng Y et al (2015) Systematic review and meta-
analysis of the adverse health effects of ambient PM2.5 and
PM10 pollution in the Chinese population. Environ Res
136:196–204. doi:10.1016/j.envres.2014.06.029
Mingorance MD, Oliva SR (2006) Heavy metals content in N.
oleander leaves as urban pollution assessment. Environ Monit
Assess 119:57–68. doi:10.1007/s10661-005-9004-9
Muller G (1969) Index of geoaccumulation in sediments of the Rhine
River. Geo J 2:108–118
Oves M, Khan, Zaidi A, Ahmad E (2012) Soil contamination,
nutritive value, and human health risk assessment of heavy
metals: an overview. In: Zaidi A, Wani PA, Khan (eds) Toxicity
of heavy metals to legumes and bioremediation. Springer,
Vienna, pp 1–27
´k M, Pavlı
´D, Zemanova
´V et al (2011) Trace elements
present in airborne particulate matter -stressors of plant
metabolism. Ecotoxicol Environ Saf 79:101–107. doi:10.1016/
Perelman AI, Kasimov NS (2000) Landscape geochemistry. Astrea,
RAIS (2013) RAIS. The Risk Assessment Information System. http://
RAIS (2014) RAIS. The Risk Assessment Information System. http://
Rapant S, Fajc
´K, Khun M, Cvec
´V (2010) Application of
health risk assessment method for geological environment at
national and regional scales. Environ Earth Sci 64:513–521.
Reimann C, Filzmoser P, Garret RG, Dutter R (2008) Statistical data
analysis explaned. Wiley, Chichester
Revich BA, Smirnova RS, Sorokina EP (1982) Methodological
guidance for geochemical assessment of polluted sites by
chemical elements. IMGRE, Los Angeles
Saghatelyan A (2004) The peculiarities of heavy metal distribution on
Armenia’s territory. CENS NAS RA, Yerevan
Saghatelyan AK, Arevshatyan SH, Sahakyan LV (2003) Ecological-
geochemical assessment of heavy metal pollution of the territory
of Yerevan. Electron J Nat Sci 1(1):36–41
Saghatelyan A, Sahakyan L, Belyaeva O, Maghakyan N (2014)
Studying atmospheric dust and heavy metals on urban sites
through synchronous use of different methods. J Atmos Pollut
2:12–16. doi:10.12691/jap-2-1-3
Sahakyan LV (2006) The assessment of heavy metal stream in the air
basin of Yerevan. Chinese J Geochem 25:95–96. doi:10.1007/
Sharma RK, Agrawal M, Marshall FM (2008) Atmospheric deposi-
tion of heavy metals (Cu, Zn, Cd and Pb) in Varanasi City, India.
Environ Monit Assess 142:269–278. doi:10.1007/s10661-007-
Tepanosyan G, Sahakyan L, Belyaeva O, Saghatelyan A (2016)
Origin identi fi cation and potential ecological risk assessment of
potentially toxic inorganic elements in the topsoil of the city of
Yerevan, Armenia. J Geochem Explor 167:1–11. doi:10.1016/j.
´M, Vukmirovic
´Z, Rajs
´S et al (2005) Characterization
of trace metal particles deposited on some deciduous tree leaves
in an urban area. Chemosphere 61:753–760. doi:10.1016/j.
US EPA (1989) Risk assessment guidance for superfund. Volume I
human health evaluation manual (Part A). United States
Environmental Protection Agency, Washington
US EPA (2002) Supplemental guidance for developing soil screening
levels for superfund sites. United States Environmental Protec-
tion Agency, Washington
US EPA (2004) Risk assessment guidance for superfund volume I:
human health evaluation manual. National Center for Environ-
mental Assessment, Washington
Wang L, Lu X, Ren C et al (2014) Contamination assessment and
health risk of heavy metals in dust from Changqing industrial
park of Baoji, NW China. Environ Earth Sci 71:2095–2104.
Wei B, Jiang F, Li X, Mu S (2010) Contamination levels assessment
of potential toxic metals in road dust deposited in different types
of urban environment. Environ Earth Sci 61:1187–1196. doi:10.
WHO (1998) Guidelines for drinking-water quality, 2nd edn. WHO,
Hong Kong
TCEQ Regulatory Guidance Risk Levels, Hazard Indices, and
Cumulative Adjustment
Zhang J, Deng H, Wang D et al (2013) Toxic heavy metal
contamination and risk assessment of street dust in small towns
of Shanghai suburban area, China. Environ Sci Pollut Res
20:323–332. doi:10.1007/s11356-012-0908-y
Zhou M, Liu Y, Wang L et al (2014) Particulate air pollution and
mortality in a cohort of Chinese men. Environ Pollut 186:1–6.
Acta Geochim
... The sampling site where Mo and Pb outliers and extreme values have been detected are spatially located near the plant of "Pure Iron" producing Ferromolybdenum and metal Mo. It needs to be mentioned that at this site extremely high contents of these elements have been observed in soil and leaves dust deposition too (Maghakyan et al., 2016;Saghatelyan et al., 2014). ...
... Conversely, in our case, Ni and Cu leaf content does not differ significantly during the studied seasons, indicating the possibility of anthropogenic emissions (likely, traffic related (Sgrigna et al., 2016), which induce the absorption of these elements by plants, compensating their internal use. For Pb and Mo, which have been previously reported to be the main urban pollutants of Yerevan (Maghakyan et al., 2016;Saghatelyan et al., 2014;Tepanosyan et al., 2017Tepanosyan et al., , 2016, accumulation in leaf tissues during the vegetation period is expected and, indeed, observed, since their measured content increases from May to September. A different behavior is observed for the Zn contents in leaves, since it is not statistically different in the two seasons in the case of F. excelsior L., while it decreases with the vegetation period for P. orientalis L. Just like Ni and Cu, Zn too is an element which is used by plants, and thus its contents should decrease during the vegetation period. ...
The recognition of the features and capabilities of potentially toxic elements (PTE) uptake from urban tree leaves is crucial for mitigating pollution and optimizing the allocation of green infrastructures of an urban environment. Therefore, Pb, Ni, Mo, Cu, Zn contents and spatiotemporal variation were investigated in the leaves of the most widespread urban trees in Yerevan (Armenia) (Fraxinus excelsior L. and Platanus orientalis L.) by means of a chemical approach based on atomic-absorption spectroscopy, after having washed them. The obtained results showed similarities in leaves Ni, Cu, Pb and Mo uptake. Meanwhile, only biologically non-essential elements (Mo and Pb) tend to accumulate in leaves during the vegetation season. This allows for the identification of localized pollution sources. Spatiotemporal variation of Zn contents suggested that P. orientalis L. is the less efficient tree species in Zn uptake. The study of the relationship of Pb, Ni, Mo, Cu, and land use by means of clr-biplots showed the absence of any potential links. Moreover, it was revealed that the element contents of leaves in green areas are similar to those observed in industrial and residential sites. The latter highlighted the need for the expansion of green areas with the use of scientifically justified species as a means of nature-based solution for pollution mitigation and better urban environmental management.
... Polluted sites specifically with various metal pollutants were monitored frequently by the lower plants such as mosses and lichens due to their more accumulation ability (Jiang et al., 2018 and Yatawara and Dayananda 2019). Nowadays, higher plants (trees) are widely accepted in urban areas with more levels of pollution where mosses and lichens are rarely distributed (Arslan et al., 2009;Khattak and Jabeen, 2012;Deepalakshmi et al., 2014 andMaghakyan et al., 2016). The absorption, adsorption and accumulation process on the leaves' surface are greater compared than other parts of the plants such as roots, barks and stem. ...
... (Norouzi et al., 2015 andNaderizadeh et al., 2016). Mn in the air originated from the soil, Fe emitted into the air both from natural and anthropogenic sourcesUgulu et al., 2012 andMaghakyan et al., 2016). Cr originated from motor vehicles. ...
Full-text available
Biomonitoring of heavy metals is one of the economic methods to identify and improve the quality of air. The aim of this work was to identify the concentration of nine heavy metals viz. Fe, Pb, Cu, Zn, Al, Cd, As, Cr and Mn in the ambient air deposited on the leaves of five tree species such as Saraca asoca, Terminalia catappa, Syzygium cumini, Ficus religiosa and Pongamia glabra collected from six sites such as Pallavarmedu (Site I), CSI hospital (Site II), Moongilmandapam (Site III), Collectrate (Site IV), Near Cancer Institute (Site V) and VellaGate (Site VI) of the Kanchipuram town of TamilNadu State, in the months of February - March 2019. Even with some differences in the concentration of nine heavy metals on the species, few were identified with significant correlation, suggesting that these pollutants were emitted from similar sources. The deposition of iron (235.53mg/kg) and aluminium (157.91mg/kg) were higher on the leaves of S.asoca compared with other species. The metals such as Cu, Cd, As, Pb and Cr were nil and not detected on the leaves, but Pb concentration was high (185.79 mg/kg) only on P. glabra at Site 2 and Cr (2.37 mg/kg) was found on the leaves of S. asoca at Site 1. The heavy metal dust deposited on the leaf surface was probably due to vehicular emission and other anthropogenic activities. The analysis showed that all the selected tree species acted as a biomonitor and should be grown that may help to improve the air quality of the area.
... Accumulation capacity of lower plants such as lichens and mosses were higher, but these organisms are rarely available in urban areas which are subjected to high level of pollution [13][14][15][16][17][18][19]. Nowadays, lower plants are replaced by higher plants for bio monitoring purpose [20,21]. ...
Full-text available
Plant species can be utilized for biomonitoring the quality of the environment and reform the extent of pollution in both urbanized and industrial regions. In this current study, the quantity of nine heavy metal components viz. Al, As, Cd, Cr, Cu, Fe, Mn, Zn, and Pb absorbed on the leaves of Saraca asoca and Syzygium cumini were examined by using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) during “February-March, 2019”. The samples were gathered from six different sites namely, Vellagate (Site1), CSI hospital (Site2), Near Cancer Institute (Site3), Moongilmandapam (Site4), Collectrate (Site5), Pallavarmedu (Control Site 6), of the Kanchipuram town, Tamil Nadu State. Metals Fe, Al, Zn and Mn were identified on the leaves of S.asoca and S.Cumini in all sites with varying concentrations out of which Fe (234.49mg/kg) and Al (364.18mg/kg) were higher level. Pb was identified only on the leaves of S.asoca in the sites 2 (2.21mg/kg) and site 4 (2.81mg/kg) which are subjected to heavy traffic and Cu was found only in site 3 and site 4 with minimum levels. The metals such as As, Cd, Cr was not identified on both species in all selected sites. Absorption of heavy metals on the leaves was probably due to emissions from vehicle, nonemission sources and mainly from other man-made activities. This work showed that both the selected species S.asoca and S.Cumini were suitable bio indicators, bio-monitors and used as greenbelt around the industrial areas for the mitigation of pollutants in the environment.
... Currently, global focus has been directed towards heavy metals due to their damaging effects on human health (Bortey-Sam et al., 2015;Gündüz, 2015;Kashyap et al., 2018). Heavy-metal exposure and pollution is a severe problem because they are abundant in the environment, non-biodegradable, persistent, and highly toxic even at trace levels (Auyeung et al., 2002;Arjouni et al., 2015;Emenike et al., 2017;Maghakyan et al., 2017;Jiang et al., 2018), and they exert deleterious effects likely by disturbing the cell metabolism, ionic transportation, protein folding, and DNA modification (Sirot et al., 2009;Jaishankar et al., 2014). Pb is one of the most poisonous metals; chronic exposure to Pb affects the normal functioning of the nervous system, cardiovascular system, reproductive system, and kidneys, leading to reduced consciousness, increased blood pressure, loss of appetite, hyperactivity, anemia, and fatigue (Auyeung et al., 2002;Bellinger, 2008;Buettner et al., 2009;Barakat, 2011;Bolan et al., 2016;Bassil et al., 2018). ...
Full-text available
In order to serve population health better, the first integrated tiered decision tree for cumulative risk assessment of co-exposure of Pb-, Cd-, and As-associated health risks in food homologous traditional Chinese medicine (TCM) was designed, after measuring their concentrations by inductively coupled plasma-mass spectroscopy (ICP-MS). Basically, our three-step decision tree involving hazard quotient (HQ), hazard index (HI), and target-organ toxicity dose (TTD) modification of the HI method was developed to evaluate the potential risks of 949 batches of 15 types of food homologous TCM. To acquire a real-life exposure scenario, the cumulative risk assessment model was established by optimizing key parameters, such as ingestion rates, frequency, and duration of exposure to food homologous TCM based on questionnaire data. As a result, the mean concentrations of Pb, Cd, and As in 949 batches of food homologous TCM were 0.896, 0.133, and 0.192 mg/kg, respectively. The HQ values of As for Angelica sinensis (Oliv.) Diels and Houttuynia cordata Thunb. were 1.04 and 1.01, respectively, for females. Other HQs of Pb, Cd, or As in food homologous TCM were lower than 1 for both males and females. However, after rapid screening of the co-exposure health risks of heavy metals by the HI method, cumulative risk assessment results acquired by TTD modification of the HI method implied that the potential health risks associated with the co-exposure of Pb, Cd, and As in Lonicera japonica Thunb. and Houttuynia cordata Thunb. ingested as both TCM and food were of concern in the clinic. Additionally, the cumulative risks of Pb, Cd, and As in Mentha canadensis L., Chrysanthemum indicum L., and Zaocys dhumnades (Cantor) only used as food exceeded the human tolerance dose. Collectively, our innovation on the tiered strategy of decision tree based on a real-life exposure scenario provides a novel approach engaging in the cumulative risk assessment of heavy metals in food homologous TCM. All in all, such effort attempts to scientifically guide the rational use of TCM in the treatment of the complex diseases and the improvement of population health.
... It is well known that heavy metals can lead to tissue damage when their intake exceeds certain limits , harming organism growth and even posing a threat to life. For Ga, however, median lethal doses (LD ₅₀ ), and comprehensive understanding of morphological transformation, in vivo migration, and physiological effects on various organisms have yet to be established (Maghakyan et al., 2017). Available LC 50 data for Ga for different aquatic organisms (Table 3) indicate that high Ga concentrations in the aquatic environment may pose a threat to life therein. ...
Full-text available
Gallium exhibits weak metallic properties owing to its proximity to non-metals in the periodic table, yet is volatile in extra-terrestrial bodies and fairly reactive in nature. It has been used extensively to elucidate the Solar System evolution, planet interior differentiation, and terrestrial processes. However, Ga speciation and transformation in various planetary compartments and the dynamics of its trans-reservoir pathways remain to be fully resolved. Although recent studies and the development of modern analytical techniques for Ga isotopes have markedly improved our understanding of Ga geochemistry, a systematical summary of state-of-the-art knowledge appears to be long overdue. Here we provide an overview of the geochemical properties of Ga in different reservoirs, including meteorites and Earth's interior and exterior compartments, and a timely review of Ga isotopic geochemistry. We also provide a first tentative estimate of total masses of Ga in different compartments and the trans-reservoir Ga flux, based on the published data. This compilation reveals clearly the lack of geochemistry data of Ga in Earth's interior, the imbalance of oceanic Ga budget and the potential implications of Ga isotopes, stimulating future systematic studies of Ga and its isotopes in geosciences and related fields.
... Finally, in the smaller current EaEU states, namely Armenia, Belarus, and Kyrgyzstan, environmental issues remain (see Akopova, Nursapa, and Kuderin 2018 for an excellent summary) but are nowhere near the scale seen in Russia and Kazakhstan 30 years after the demise of the Soviet Union. Armenia, as a country dependent upon mining (mainly copper and molybdenum), suffers some of the same issues as its larger Union-mates, including heavy metals in the air around capital Yerevan (Maghakyan et al. 2017) and in the soil around major mining sites (Gevorgyan et al. 2017), although there have been novel attempts to rectify these long-standing issues (Ghazaryan et al. 2019). In Belarus as well, the legacy of Soviet heavy industry, the radioactive contamination from Chornobyl, and the lack of economic restructuring have resulted in pollution in water reservoirs directly related to proximity to industrial sites (Vlasov and Gigevich 2006), although the country's lack of natural resources means it has avoided the worst of the pollution seen by other EaEU members. ...
While there is a sizable body of evidence linking greater economic freedom to better environmental outcomes, there is an ambiguous relationship of trade to the environment. What occurs when trade expands among countries that already have shown that the environment is not a priority? Such an example comes from real life with the Eurasian Economic Union (EaEU), a collection of autocracies that have pursued integration but without any extensive, market-based liberalization. This paper examines the role of the EaEU in increasing trade among its member states and shows that the EaEU did indeed lead to more environmentally unfriendly production.
... [6] From the perspective of pollution, they are ubiquitous pollutants in the environment, and can accumulate with similar substances in the environment and food, thereby causing harm to human health. [7][8][9] Natural medicines refer to botanicals, animal medicines, and mineral medicines that have been proven to have a certain pharmacological activity by the modern medical system. [10][11][12] Botanical medicines usually refer to raw materials and preparations that use plant primary metabolites (such as proteins and polysaccharides) or secondary metabolites (such as alkaloids, phenols, and terpenes) as their active ingredients. ...
Full-text available
Ramulus Mori alkaloids, also known as SangZhi alkaloids (SZ-A), is a natural medicine used for the treatment of type 2 diabetes mellitus in China. SZ-A is extracted from Morus alba L., which grows in the natural environment and may be contaminated by heavy metals and harmful elements. These contaminants can enter SZ-A products during the extraction of M. alba, thereby posing a threat to patient health. Therefore, it is necessary to formulate scientific and reasonable limits to ensure patient safety. For this purpose, in this study, we used the extraction process of SZ-A as the object of investigation and determined the content of five harmful elements: Cd, Pb, As, Hg, and Cu in the herb raw material, SZ-A product, and its intermediates obtained in different extraction steps. Next, the transfer rate of harmful elements in the extraction process was used as an indicator to evaluate the ability of different operations to remove harmful elements. Subsequently, the health risks of heavy metals and harmful elements in SZ-A were assessed. Our results demonstrated that M. alba has little risk of contamination by Hg. The cation and anion resin refining processes are the best effective method to remove Cd, Pb, and Cu from the products. However, As is not easily eliminated during the water extraction. There is as much as 87% of As transferred from the herb raw material to the water-extracted intermediate, while Cd, Pb, and Cu are rarely transferred (6% to 17%) under the same conditions. Overall, the results indicate that the regulatory standard limits for Cd, Pb, As, Hg, and Cu contained in natural medicine Ramulus Mori alkaloids are set to 1, 5, 2, 0.2, and 20 μg/g, respectively, which is the most scientific and it can guarantee the safety of patients. Graphical abstract
... The probability of ingestion by adults was higher than that by children, with a large difference (Fig. 6). This is mainly because the ingestion rate of adults is greater than that of children (Maghakyan et al., 2017). The probability of inhalation was higher for children than adults, mainly because children's respiratory system is fragile, which can easily cause health risks. ...
This study aims to analyze the pollution characteristics and sources of heavy metal elements for the first time in the Zhundong mining area in Xinjiang using the linear regression model. Additionaly, the health risks with their probability and infleuencing factors on different groups of people's were also evaluated using Monte Carlo (MC) simulation approach. The results shows that 89.28% of Hg was from coal combustion, 40.28% of Pb was from transportation, and 19.54% of As was from atmospheric dust. The main source of Cu and Cr was coal dust, Hg has the greatest impact on potential ecological risks. which accounted for 60.2% and 81.46% of the Cu and Cr content in soil, respectively. The all samples taken from Pb have been Extremely polluted (100%). 93.3% samples taken from As have been Extremely polluted. The overall potential ecological risk was moderate. Adults experienced higher non-carcinogenic risks of heavy metals from their diets than children. Interestingly, body weight was the main factor affecting the adult's health risks. This research provides more comprehensive information for better soil management, soil remediation, and soil pollution control in the Xinjiang mining areas.
... This is due to the continuous technical improvement in the automotive field, usage of unleaded fuel and stringent rules and regulations made on the heavy metals emissions in the air. 23 The level of Pb in the ambient air was reduced to 89 percentage by the stringent rules and regulatory efforts taken and followed by Environmental Protection Agency. 24 metals.The sources of the heavy metal pollution are not only from the vehicular emission, but also from the greater application of pesticides and the combustion process. ...
In the present study, the concentrations of lead (Pb), cadmium (Cd), arsenic (As), mercury (Hg), and copper (Cu) in 2245 batches of Chinese herbal medicines (CHMs) were measured using inductively coupled plasma-mass spectroscopy (ICP-MS). We developed a risk assessment strategy that assessed the heavy metal-associated health risk of CHMs based on our large dataset. Using a combination of the mean and 95th percentile (P95) values of the chronic daily intake (CDI), hazard quotient (HQ), hazard index (HI), and lifetime cancer risk (CR), the health risks of the average exposure population and the high exposure population were estimated, respectively. To obtain a precise and realistic risk assessment, the exposure frequency and exposure duration were determined using questionnaire data from 20,917 randomly selected volunteers. Additionally, given the specific ingestion characteristics of CHMs, the safety factor and the transfer rates of heavy metals were highlighted as well. The concentrations of Pb, Cd, As, Hg, and Cu in 2245 batches of CHMs were 1.566, 0.299, 0.391, 0.074, and 8.386 mg/kg, respectively. The mean HI values indicated that consumption of most CHMs would not pose an unacceptable health risk to the average exposure population, except for argy wormwood leaf (1.326), morinda root (2.095), plantain herb (1.540), chrysanthemum flower (1.146), and Indian madder root (2.826). In addition, CR assessment for Pb and As revealed that, for the average exposure population, the risk of developing cancers was lower than the acceptable levels (1 × 10⁻⁴) in the clinic. However, the P95 of the HI and CR values indicated that more attention should be paid to the systemic effects of CHMs in terms of both non-carcinogenic and carcinogenic health risks for the high exposure population. Furthermore, in order to serve population health better, national and international guidelines have now been established. The risk assessment strategy developed in this study is the first of its kind, and contributed to the risk assessment, guidelines, and safety standards for heavy metals in CHMs.
Full-text available
Outdoor dust as a pollutant is also a transit environment for different pollutants emphasizing heavy metals. Commonly, it is urban population, who is exposed to the maximal adverse impact of dust and associated pollutants. In most cases, urban atmosphere researches are implemented on a few permanent monitoring stations. Data obtained from these stations cannot be sufficient enough to provide a real picture of atmospheric pollution. The most detailed information is obtained from synchronous instrumental sampling (aspiration) and studies of indicator environments (snow cover, leaves). This research pursued assessment of levels of dust and heavy metal pollution of near-surface air through different methods on the example of city of Yerevan (Armenia). The city area comprises a complex mosaic of natural and man-made sources of dust and heavy metals. So, for many years Yerevan has been exposed to high dust and associated heavy metals pollution levels. The research was implemented in 2011 through 2012 and included spatially coherent snow and tree leaf sampling, and instrumental sampling of dust and allowed assessing dust and heavy metal load and contents on the entire territory of Yerevan, identifying pollution sources, contouring ecologically unfavorable sites and finally identifying risk groups among the population.
Conference Paper
Full-text available
Levels of particulate matter and meteorological variables (atmospheric temperature, relative humidity and wind speed) for 2009 to 2011were analyzed and evaluated. Data used in this paper were obtained from an ambient air quality station located in the western desert of Abu Dhabi Emirate-United Arab Emirates. The variation patterns of PM10 concentrations were explored, and their relationships with meteorological parameters were identified. The study area is characterized by relatively low wind speed, high temperatures and humidity and elevated levels of suspended particle concentrations. Hourly levels of PM10 were found to range between 4 to 3474μg/m3 with 27% of the daily average values exceeding the national standard limit of 150μg/m3. The diurnal variation pattern of PM10 showed two concentration peaks, the first of which occurred in the afternoon whereas the second peak occurred at 16:00. The highest level of PM10 was observed on Tuesdays, while the lowest level was on Fridays. The highest main value of PM10 was observed on July where a level of 204μg/m3 was reported and lowest level of 47μg/m3 was reported in January. Pearson’s analysis revealed a positive correlation between PM10 and temperature, low humidity (≤13%) and wind speed conditions. On the other hand, a strong inverse relationship was observed between PM10 concentrations and relative humidity higher than 13%. Keywords: particulate matter, meteorological parameters, Abu Dhabi, statistical analysis.
The total concentrations of Ti, Fe, Ba, Mn, Co, V, Pb, Zn, Cu, Ni, Cr, Mo, Hg and As were determined in 1356 topsoil samples collected from the area of the city of Yerevan in order to: (1) determine the spatial distribution peculiarity and the origin of potentially toxic inorganic elements in Yerevan soils; and (2) assess the potential ecological risk of potentially toxic inorganic elements. The spatial distribution features of these elements were illustrated by environmental geochemical mapping. Pollution indexes (PIs) of As, Ti, Mn, Fe, Ba, and Co were between the range of 0.9–1.1, while PI of Cu, Zn, Ni, Cr, V, Hg, Mo (1.5–6.8) and especially Pb (22.9) was higher. Multivariate geostatistical analyses suggested that the concentrations of Pb, Cu, Zn, Hg, Cr, Ni and Mo observed in the topsoil bore the influence of anthropogenic and industrial activities. Moreover, according to the main findings of Principal component analysis (PCA) Pb and Zn have two distinct sources of origin: (1) vehicle emission and social activities (PC2); and (2) industrial activities (PC3). The potential ecological risk was quantitatively estimated for each sampling site and a risk map for the assessment was created. Among the investigated elements, Pb and Hg showed a higher potential ecological risk, than the others
Introduction: As the largest developing country, China has some of the worst air quality in the world. Heavy smog in January 2013 led to unprecedented public concern about the health impact of exposure to particulate matter. Conducting health impact assessments of particulate matter has thus become an urgent task for public health practitioners. Combined estimates of the health effects of exposure to particulate matter from quantitative reviews could provide vital information for epidemiology-based health impact assessments, but estimates for the Chinese population are limited. Methods: On December 31, 2013, we systematically searched the PubMed, Web of Science, and China National Knowledge Infrastructure databases using as keywords names of 127 major cities in Mainland China, Hong Kong, and Taiwan. From among the 1464 articles identified, 59 studies were manually screened. Random-effects or fixed-effects models were used to combine their risk estimates, the funnel plots with Egger test were performed to evaluate the publication bias and Meta regression were run to explore the association between exposure to particulate matter with aerodynamic diameters less than 10 and 2.5 µm (PM10 and PM2.5) and the resulting health effects by the Comprehensive Meta Analysis. Results: In terms of short-term effects, the combined excess risks of total non-accidental mortality, mortality due to cardiovascular disease, and mortality due to respiratory disease were 0.36% (95% confidence interval [95%CI]: 0.26%, 0.46%), 0.36% (95%CI: 0.24%, 0.49%), and 0.42% (95%CI: 0.28%, 0.55%), for each 10 μg/m(3) increase in PM10. A 10 μg/m(3) increase in PM2.5 was associated with a 0.40% (95%CI: 0.22%, 0.59%) increase in total non-accidental mortality, a 0.63% (95%CI: 0.35%, 0.91%) increase in mortality due to cardiovascular disease, and a 0.75% (95%CI: 01.39%, 1.11%) increase in mortality due to respiratory disease. For constituent-specific mortality, increases of 0.40-3.11% were associated with an increase of 10 ng/m(3) for nickel in PM. The summary estimate ranges of hospital utilization were 0.08% ~ 0.72% and -0.58% ~ 1.32% for a 10 μg/m(3) increase in PM10 and PM2.5. In terms of long-term effects, a 10 μg/m(3) increase of PM10 corresponded to 23-67% increase in the risk of mortality. Conclusion: Short exposures to PM10 and PM2.5 are associated with increases in mortality, but evidence of constituent-associated health effects, long-term effects and morbidity in China is still inadequate.
IntroductionMethodologies and strategies for urban samplingChemical analysisQuality controlInterpreting and presenting the resultsLegislationCommunicationFuture trendsReferences
This comprehensive text focuses on the increasingly important issues of urban geochemical mapping with key coverage of the distribution and behaviour of chemicals and compounds in the urban environment. Clearly structured throughout, the first part of the book covers general aspects of urban chemical mapping with an overview of current practice and reviews of different aspects of the component methodologies. The second part includes case histories from different urban areas around Europe authored by those national or academic institutions tasked with investigating the chemical environments of their major urban centers.
Guiyang is a famous tourist city located in southwestern China. In this study, dust from eleven residential areas, seven city squares, and nine schools was collected to measure the heavy metal levels and evaluate its risk. At each sampling site, 4–5 sub-samples were taken as a bulk sample. All samples were air-dried, ground, passed through a 0.105 mm nylon sieve, digested with HNO3-HClO4 to determine the concentrations of Cd, Cu, Ni, Pb and Zn by ICP-MS, and digested with 1:1 aqua regia to determine As by AFS. The results show that the concentrations of As, Cd, Cu, Ni, Pb and Zn in dust of Guiyang City follow normal distribution with means of 16.1, 1.54, 138, 47.7, 129 and 479 mg/kg, respectively. Levels of As, Cd, Cu, Ni, Pb and Zn exceed the background level of soil in Guizhou Province by 33%, 96%, 100%, 78%, 96%, and 100%, respectively. Cd, Cu, Pb and Zn are heavily accumulated in dust of living areas with accumulation factors of 4.10, 5.12, 4.12 and 5.51, respectively. City square possesses the highest geometric means of As, Cd, Cu, Pb, and Zn. The risks of heavy metal exposure to teenagers are not obvious and in an order of As>Pb>Cu>Ni>Zn (Cd).