Accepted Manuscript

Title: Sources and Ecological Risk of Heavy Metals in Soils of Different Land Uses in

Bangladesh

Author: Md. Saiful ISLAM, Md. Kawser AHMED, Md. Habibullah-AL-MAMUN, Shah Md.

Asraful ISLAM

PII: S1002-0160(17)60394-1

DOI: 10.1016/S1002-0160(17)60394-1

Reference: NA

To appear in:

Received date: NA

Revised date: NA

Accepted date: NA

Please cite this article as: Md. Saiful ISLAM, Md. Kawser AHMED, Md. Habibullah-AL-MAMUN, Shah Md. Asraful

ISLAM, Sources and Ecological Risk of Heavy Metals in Soils of Different Land Uses in Bangladesh, Pedosphere (2017),

10.1016/S1002-0160(17)60394-1.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are

providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the

resulting proof before it is published in its final form. Please note that during the production process errors may be

discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

PEDOSPHERE

Pedosphere ISSN 1002-0160/CN 32-1315/P doi:10.1016/S1002-0160(17)60394-1

Sources and Ecological Risk of Heavy Metals in Soils of Different Land Uses in

Bangladesh

Md. Saiful ISLAM

1,2,*, Md. Kawser AHMED

3, Md. Habibullah-AL-MAMUN

2,4, Shah Md. Asraful ISLAM

5

1Departmentof Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali-8602,

Bangladesh 2Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogaya-ku,

Yokohama, Kanagawa 240-8501, Japan 3Department of Oceanography, University of Dhaka, Dhaka-1000, Bangladesh

4Department of Fisheries, University of Dhaka, Dhaka-1000, Bangladesh

5Departmentof Plant Pathology, Patuakhali Science and Technology University, Dumki, Patuakhali-8602,

Bangladesh

*Corresponding author. E-mail: msaifulpstu@yahoo.com, islam-md.saiful-nj@ynu.jp

ABSTRACT

Soil pollution, influenced by both natural and anthropogenic factors, significantly reduces environmental

quality. Six heavy metals i.e., chromium, nickel, copper, arsenic, cadmium and lead in eight different land-use

soils from the Patuakhali district in Bangladesh were assessed. Metals were measured using an inductively

coupled plasma mass spectrometer (ICP-MS). The concentrations of Cr, Ni, Cu, As, Cd and Pb in soils were

1.3–87, 4.9–152, 4.1–181, 0.26–80, 0.17–24 and 5.2–276 mg kg-1

, respectively. The enrichment factor (EF),

pollution load index (PLI) and contamination factor ( i

fC ), were used to assess the ecological risk posed by

heavy metals in soils. The range of PLI was 0.78–2.66, indicating baseline levels to progressive deterioration of

soil due to metal contamination. However, i

fC values of Cd ranging from 1.8 to 12 which showed that the

studied soils were strongly impacted by Cd. Considering the severity of the potential ecological risk (i

rE ) for a

single metal, the descending order of contaminants was Cd > As > Pb > Cu > Ni > Cr. In relation to the

potential ecological risk (PER), soils from all land uses showed moderate to very high ecological risk.

Key Words: Ecological risk, Heavy metals, Land use, Patuakhali, Bangladesh.

INTRODUCTION

The natural concentration of heavy metals and metalloids like Cr, Ni, Cd, Cu, Pb and As in soils tends to

remain low and therefore ensure an optimum ecological equilibrium. However, the concentrations of heavy

metals and metalloids in soils frequently increase due to human activities. Indeed, the concentrations of heavy

metals and metalloids in agricultural soils are well-known and have adversely affected many parts of the world

(Kheir, 2010; Sun et al., 2010), including Bangladesh (Islam et al., 2014). Soil is a dynamic natural resource for

the survival of human life and regarded as the key receiver of persistent toxic heavy metals and metalloids due

to expanding (Luo et al., 2007; Karim et al., 2014). In recent decades, there has been significant concern

regarding soil contamination by various toxic metals due to expanding industrialization and urbanization (Chen

et al., 2010; Sun et al., 2010). However, in urban areas heavy metals and metalloids may originate from various

sources such as industrial activities, power generation, mining, smelting, waste spills or fossil fuel combustion

and waste disposal (Wei and Yang, 2010; Li and Feng, 2012; Rodriguez Martin et al., 2014; Karim et al., 2014).

The excessive input of heavy metals and metalloids into urban soils may lead to the deterioration of soil biology

and function, changes in soil physicochemical properties which may create other environmental problems (Papa

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

et al., 2010). Therefore, the accumulation of heavy metals and metalloids in soils are of increasing concern due

to their potential risks and harmful effects on soil ecosystems (Cui et al., 2004; Li et al., 2009; Yu et al., 2012;

Yuan et al., 2014). As a result, studies on the assessment of ecological risk from heavy metals and metalloids

contamination in soil have gained more attention.

There is concern about the ecological risks and sources of heavy metals and metalloids in soils due to the rate

of urbanization in developing countries including Bangladesh. Different methods have been widely used to

assess the contamination of heavy metals and metalloids in soil such as enrichment factor (EF), contamination

factor (CF), geoaccumulation index (Igeo) etc. (Rashed, 2010; Liu et al., 2014). To evaluate the combined risk of

numerous heavy metals and metalloids in soil, the pollution load index (PLI) and potential ecological risk index

(PER) have also been developed (Huang et al., 2013). Thus, Sayadi and Sayyed (2011) noted that natural and

human-related metal enrichment in soils should be differentiated prior to the assessment of metal contamination

processes by enrichment factor (EF). The EF of an area indicates the relative enrichment in any contaminant

when compared to pre-industrial soil from a similar environment (Dias et al., 2014; However et al., 2013). On

the other hand, study of the distribution and source identification of heavy metals and metalloids in soils utilized

for different land uses is very important in identifying pollution status (Carr et al., 2008; Acosta et al., 2011;

Yuan et al., 2014).

The area of Patuakhali district is about 3,204.58 square kilometers; the total population is about 1,444,340

and population density is 451 people per square kilometer (Islam et al., 2015a). The study area has gained

attention due to its environmental pollution and is facing serious threats caused by the district's rapid

expansion, congestion and activities from industries (Islam et al., 2015a). In the study area, increasing air,

water and soil pollution emanating from traffic congestion and industrial waste are serious problems that

adversely affect public health. Several studies have reported metal concentration in urban agricultural soils in

Bangladesh (e.g. Kashem, and Singh, 1999; Ahmad and Gani, 2010; Rahman et al., 2012; Islam et al., 2014)

consider the land-use angle for measuring the metals concentrations. Nevertheless, these studies did not

consider land use aspect for the metal concentration in urban soils (Cerqueira et al., 2012; Saikia et al., 2014;

Martinello et al., 2014). Therefore, in this study, the levels of heavy metals and metalloids in soils of

different land uses were investigated. Trace element concentrations in soils from different land uses has

scarcely been investigated and is of critical importance in assessing heavy metals and metalloids’

contamination in soils and their impact on the environment (Chen et al., 2005; Luo et al., 2007; Islam et al.,

2015b,c). The hypothesis of the present study states that metal concentration values depend on soils utilized

for different land uses. Therefore, the objectives of this study are to address the problem of Cr, Ni, Cu, As,

Cd and Pb pollution in different urban soils, to identify the sources of heavy metals and metalloids and to

assess the ecological risk of heavy metals and metalloids in soils utilized for different land uses.

MATERIALS AND METHODS

Study area and sampling

This study focused on different urban soils of Patuakhali District, located in the southern part of Bangladesh

as shown in Figure 1. The study was conducted within eight different land use urban soils i.e. brick field (BF),

ferry ghat (FG), launch terminal (LT), power station (PS), agriculture field (AF), residential area (RA), market

(M) and play ground (PG). The study area receives domestic raw sewage, household waste, and industrial waste

from surrounding habitation. The basic information of the study area is presented in Table SI. Fifty three

composite soil samples were collected during August and September, 2013. To represent the preindustrial

sample, soil was taken by means of a percussion hammer corer (50–80 cm in length) for metal analysis

(Schottler and Engstrom, 2006). Lead-210 dating by alpha spectrometry method was used to determine the soil

age and accumulation rates (Islam et al., 2015d). The samples were subjected to chemical treatment in order to

obtain sources of alpha activity of 210

Pb (Sikorski and Goslar, 2003). The measurement of alpha radiation was

performed using of an alpha spectrometer (Canberra- 7401, PIPS®). The specific activity of

210Pb was

determined using the method described by (Sikorski and Bluszcz, 2008) which makes allowance for short lived

isotope decay, efficiency of the chemical procedure and the geometric efficiency of the spectrometric

measurement. Samples were air-dried at room temperature for fifteen days then ground and homogenized. The

dried soil samples were crumbled with a porcelain mortar, pestle, sieved and stored to clean Ziploc bags under

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

freezing conditions until chemical analysis.

Measurement of soil physicochemical properties

The pH of the soil was measured in 1:2.5 soils to water ratio. The suspension was allowed to stand overnight

and the pH was measured using a pH meter. For electrical conductivity (EC) determination, 5.0 g of soil was

taken in 50 mL polypropylene tubes and 30 mL of Milli-Q water was added to the tube. The lid was closed and

it was shaken for 5 minutes. EC was then measured using an EC meter (Horiba D-52). Organic carbon of soil

was measured using an elemental analyzer (model type: Vario EL III, Elenemtar, Germany). The catalytic

combustion was carried out at a permanent temperature of up to 1200 °C. The carbon concentration from the

detector signal and the sample weight on the basis of stored calibration curves were measured. Particle size

distribution was determined using the hydrometer method (Zhao et al., 2014). The soil particles were classified

for their size using the United States Department of Agriculture (USDA) classification system (gravel [>2 mm],

sand [2–0.05 mm], silt [0.05–0.002 mm] and clay [< 0.002 mm]).

Sample analysis

Analytical grade chemical reagents were used for sample extraction. Milli-Q (Elix UV5 and MilliQ,

Millipore, USA) water was used for the preparation of solution. For metal analysis, about 0.5 g powdered soil

sample was treated with 4.5 mL 35% HCl (Kanto Chemical Co, Tokyo, Japan) and 1.5 mL 69% HNO3 (Kanto

Chemical Co, Tokyo, Japan) in a Teflon vessel and was digested using the Microwave Digestion System

(Berghof speedwave®, Eningen, Germany). The digested soil samples were then transferred into a Teflon beaker

and the total volume was made up to 50 mL with Milli-Q water. The extracted solution was then filtered using a

syringe filter (DISMIC® - 25HP PTFE, pore size = 0.45 µm) (Arenas-Lago et al., 2014; Cerqueira et al., 2011;

Silva et al., 2011) Toyo Roshi Kaisha, Ltd., Tokyo, Japan and stored in 50 mL polypropylene tubes (Nalgene,

New York). Afterwards, the Teflon vessels were cleaned by Milli-Q water and air dried. Finally, blank digestion

with 5 mL HNO3 following the said digestion procedures was carried out to clean up the digestion vessels

(Berghof’s product user manual, 2008).

Instrumental analysis and quality control

For the measurement of heavy metals and metalloids, samples were analyzed using an inductively coupled

plasma mass spectrometer (ICP-MS, Agilent 7700 series). For the preparation of calibration curve

multi-element Standard XSTC-13 (Spex Certi Prep®, Metuchen, USA) solutions were used. Multielement

solution (Agilent Technologies, USA) 1.0 µg/L was used as tuning solution covering a wide range of masses of

metals. All test batches were evaluated using an internal quality approach and validated to ensure Internal

Quality Controls. Instrument operating conditions and parameters for metal analysis are presented in Table SII.

Data calculation

Enrichment factors (EFs)

Enrichment factor (EF) is an appropriate methodology which helps to differentiate a metal source of

anthropogenic origin from those occurring naturally in the environment (Zhang et al., 2009). Enrichment factor

is calculated by normalizing the given metals’ concentration in soils to the percentage of Al (Luoma and

Rainbow, 2008; Islam et al., 2015b). The EF is calculated according to the following equation:

backgroundAlMsampleAlM CCCCEF )//()/( (1)

Where, (CM/CAl) sample is the ratio of concentration of heavy metals (CM) to that of aluminum (CAl) in soil sample,

and (CM/CAl) background is the same reference ratio in the pre-industrial sample. Generally, an EF value of about 1

suggests that a given metal may originate entirely from crustal source or natural processes of weathering

(Rashed, 2010). Samples having enrichment factor > 1.5 is considered indicative of human influence and

arbitrarily, an EF of 1.5-3, 3-5, 5-10 and > 10 is considered as evidence of minor, moderate, severe, and very

severe contamination respectively (Islam et al., 2015c,d).

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

Contamination factor ( i

fC )

The contamination factor ( i

fC ) is the ratio obtained by dividing the concentration of each metal in soil by

the baseline or background value (heavy metals and metalloids in the pre-industrial soil sample of the study

area):

background

heavymetali

fC

CC

(2)

The contamination levels may be classified based on their intensities on a scale ranging from 1 to 6: low degree

( i

fC < 1), moderate degree (1 ≤ i

fC < 3), considerable degree (3 ≤ i

fC < 6), and very high degree ( i

fC ≥ 6)

(Luo et al. 2007; Rashed, 2010; Islam et al., 2015a) (Table 4). Thus, i

fC values can monitor the enrichment of

one given metal in soils over a period of time.

Pollution load index (PLI)

To assess the soil quality, an integrated approach of pollution load index of five heavy metals and one

metalloid is calculated. The PLI is defined as the nth

root of the multiplications of the contamination factor ( i

fC )

of metals (Bhuiyan et al., 2010; Islam et al., 2015a,b). n

ni

f

i

ffii

f CCCCPLI /1321 )......( (3)

PLI gives an overall toxicity assessment status of the soil sample and also the contribution of the studied heavy

metals and metalloids.

Potential ecological risk (PER)

Potential ecological risk load index (PER) is used to assess the degree of metal contamination in soils. The

equations for PER calculation were proposed by Guo et al. (2010) and are as follows.

i

n

ii

fC

CC ,

n

i

i

fd CC1

(4)

i

f

i

r

i

r CTE ,

m

i

i

rEPER1

(5)

Where, i

fC is the contamination factor of a single metal, iC is the content of metal in samples and i

nC is the

background value of the heavy metal. The background value (pre-industrial samples of the study area) of Cr, Ni,

Cu, As, Cd and Pb in soils were 29, 32, 27, 6.5, 0.82 and 23 mg/kg, respectively. The sum of contamination

factor ( i

fC ) for all metals represents the integrated pollution degree ( dC ). i

rE is the potential ecological risk

index and i

rT is the biological toxic factor of an individual metal. The toxic-response factors for Cr, Ni, Cu, As,

Cd and Pb were 2, 6, 5, 10, 30 and 5, respectively (Xu et al., 2008; Guo et al., 2010; Islam et al., 2015a,b). PER

is the comprehensive potential ecological risk index, the sum ofi

rE .

Statistical analysis

The data was statistically analyzed using the statistical package, SPSS 22.0 (SPSS, USA). The means and

standard deviations of heavy metals in soils were calculated. Multivariate method of principal component

analysis (PCA) was used to interpret the sources of heavy metals in soil. The Multivariate Post-hoc Tukey test

was performed to test significant differences of metal concentrations in soils of different land uses. Varimax

normalized rotation using Ward’s Method was applied to minimize the number of variables with a high loading

on each component.

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

RESULTS AND DISCUSSION

Physicochemical properties and metal concentration in soil

The physicochemical properties of soil are presented in Table I. The studied soils were slightly acidic to

neutral where the range of pH was 5.4 to 7.2. Soil acidity favors the availability of cations in soil (Adeniyi et al.,

2008). The effects of soil pH on nutrient intake are mainly indirect, caused by increasing the solubility of toxic

metals in low-pH soils (pH < 5.0); the hydrogen ion exists as a hydrated proton and become a toxicant (Liu et

al., 2007). The range of organic carbon was 4.3 to 8.1 (g kg-1

) and the highest value was observed in soil

collected from the FG site (8.1 g kg-1

). According to the United States Department of Agriculture soil

classification system, the textural analysis revealed that the soil belonged to the sand, sandy loam, loamy sand

and sandy clayey loam class (Table I). The concentrations of heavy metals and metalloids (Cr, Ni, Cu, As, Cd

and Pb) in soils are presented in Table II. The levels of heavy metals and metalloids varied among the sampling

sites and followed the descending order of: LT > PS > BF > FG > M > AF > RA > PG. It was observed that

some heavy metals showed higher standard deviation for some land uses. Such high deviation can be due to the

lack of uniformity of the distribution of heavy metals across the site (Arenas-Lago et al., 2013, 2014; Cerqueira

et al., 2011, 2012; Silva et al., 2011; Quispe et al., 2012). Other possible causes might have been due to the

intensification of land use activities, such as digging, excavation, and construction, as well as other natural

processes, such as weathering and erosion, which could alter stabilization of the soil environment (Amuno,

2013).

The highest concentration of Cr was obtained at the PS site (52±25 mg kg-1

) followed by the LT site (47±23

mg kg-1

) (Table II) which could have resulted from the disposal and application of untreated waste from the

station and district urban area (Srinivasa et al., 2010). A statistically significant difference (P < 0.05) was

observed for Cr concentration in some of the land use areas within the study (Table II). The highest mean

concentration of Ni was observed in soil at the BF site (83±69 mg kg-1

) followed by the LT site (76±49 mg kg-1

).

The high level of Ni in soils of BF, LT, FG and PS sites may have resulted from localized additions or accidental

spillages from the brick field, launch terminal, ferry ghat and power station sites containing Ni (Krishna and

Govil, 2007). From Table II, it was also observed that individual concentrations of some metals (Ni, Cu, As and

Cd) in some samples exceeded Bangladesh background values, as well as Dutch and Canadian soil quality

guidelines values but were lower than the National Environment Protection Measures (NEPM) guidelines for

soil contamination by metals and metalloids (NEPC, 2013). The extent of contamination of soil is evaluated by

comparing total concentration against the NEPM Health Investigation Levels (HILs) criteria for contaminated

soil (NEPC, 2013). A site may be assessed based on investigation levels, or a site specific assessment can be

undertaken. The extent of contamination of soil is evaluated by comparing total concentration against the

criteria for contaminated soil referred to as NEPMs (NEPC, 2013).

The highest level of Cu was observed in soil at the LT site (73±31 mg kg-1

) followed by the PS site (67±77

mg kg-1

) (Table II). An elevated level of Cu at this site may be due to emissions from different waste burning

activities (Kashem and Singh, 1999; Srinivasa et al., 2010). The highest mean concentration of As was found in

soil collected from the BF site (38±12 mg kg-1

) followed by the LT site (15±12 mg kg-1

), where most samples

exceeded the Dutch target value and Canadian guidelines value (Table II). In the study area, very large amount

of As contaminated ground water (Polizzotto et al., 2013; Neumann et al., 2010) is being used for irrigation

along with various As-enriched fertilizers and pesticides in the agricultural fields (Bhuiyan et al., 2011; Alam et

al., 2003). Moreover, emission and waste from brick fields and incineration activities may contribute to the high

concentration of As in the study area (Olawoyin et al., 2012).

Sources of heavy metals in soil

Principal component analysis (PCA) was performed to identify the sources of heavy metals and metalloids

in soils of different land uses (Bai et al., 2011; Anju and Banerjee, 2012). In the present study, three principal

components were obtained which accounted for 74.7% of all the total variation (Fig. 2). Metals like Ni and Pb

were included in the first principal component (PC1) explaining the greatest variance (40.4%); the second

component (PC2) was composed of As and Cd explaining 19.5% of the variance. The third component (PC3)

included Cr and Cu explaining 14.8% of the total variance (Table III and Fig. 2). The anthropogenic and

geogenic elements have been considered to be Ni and Pb in the surface soils in many previous studies (Li et al.

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

2006; Liu et al., 2011). Moreover, their distribution patterns were generally invariant in soils of different land

uses in the study area. Although Cr and Cu were included in PC3, the distribution of these two metals in the

studied soils confirmed that they were derived from the parent material of soil. From Fig. 2, it was implied that

As and Cd in PC2 were considered to mainly originate from the regional lithogenic factors of natural parent

soils as reported by previous reports (Zhou et al., 2008; Franco-Urı´a et al., 2009). However, the elements in

PC2 may also derive from anthropogenic factors such as the agrochemicals (including fertilizers and pesticides)

as mentioned in previous reports (Nziguheba and Smolders, 2008; Karim et al., 2008). PCA results were

consistent with the distribution of heavy metals in different land use soils and further confirmed the sources of

heavy metals and metalloids in the studied area.

Toxic unit analysis

Potential toxicity of heavy metals and metalloids in soils can be estimated as the sum of toxic units (∑TUs),

the ratio of the determined concentration of metal in soil to probable effect levels (PELs) (Zheng et al., 2008;

Islam et al., 2015a). The toxic unit (TU) and sum of toxic units (∑TUs) for heavy metals and metalloids in

different land use soils are presented in Fig. 3. Toxic units for the studied metals were in the descending: Cd >

Ni > As > Pb > Cr > Cu. The sum of toxic units for BF (7.8), LT (7.0), PS (6.7) and FG (5.0) land use soils were

greater than 4 (Fig. 3), indicating a moderate to serious toxicity of heavy metals and metalloids (Bai et al.,

2011).

Ecological risk assessment

The enrichment factor (EF), pollution load index (PLI), contamination factor ( i

fC ) and degree of

contaminations ( dC ) were applied to assess metal contamination in soils of different land uses. The values of

EF for different land use soils are presented in Table IV. Among the sites the EFs decreased in the order: BF >

PS > LT > PS > FG > PG > AF > M > RA. As a whole, the EFs values for the studied metals were in the

descending: Cd > Pb > As > Cu > Ni > Cr. From Table IV, it was noted that the EF of Cd was higher than 1.5 for

most of the sites indicating strong human influence from various urban activities in the Patuakhali district

(Rashed, 2010; Islam et al., 2015a,b). Generally, studies have shown that low enrichment values indicate a large

contribution from crustal source in soil, while high EFs indicate a significant contribution from anthropogenic

sources (Yadao and Rajamani, 2006; Rashed, 2010).

Pollution load index (PLI) value equal to zero indicates perfection; value of one indicates the presence of

only baseline levels of contaminants and values above one indicate progressive deterioration of soil due to metal

contamination (Rashed, 2010; Islam et al., 2015a). Extent of pollution increases with the increase of numerical

PLI value. Using the above indicators, soils studied were considerably polluted, since the PLI of most lands was

higher than one (Fig. 4). Among the land uses, the highest PLI values were observed at site LT (2.66) followed

by BF (2.29) and PS (2.22) sites. An elevated level of pollution load index (PLI) for LT, BF and PS sites

suggested that the activities from launch terminal, industries, emissions from BF and PS may cause some risks

(Bhuiyan et al., 2010; Liu et al., 2014; Islam et al., 2015c,d).

Hakanson (1980) defines four categories of i

fC , four categories of dC , five categories ofi

rE , and four

categories of PER, as shown in Table V. The contamination factor ( i

fC ) for individual metals and the degree of

contamination ( dC ) are presented in Table VI. Among the land uses, BF, FG, LT and PS sites showed very high

contamination ( i

fC > 6) with Cd concentration in the soil (Table VI), indicating Cd may pose potential risks to

the surrounding ecosystems (Rashed, 2010). Overall, the i

fC for studied metals were in the descending order:

Cd > As > Pb > Cu > Ni > Cr. The assessment of soil contamination was based on the degree of contamination

( dC ). Considering dC , BF, LT and PS sites showed a very high degree of metal contamination ( dC > 20) (Table

VI).

Combining the potential ecological risk index of individual metals (i

rE ) and the potential ecological risk

index (PER) (Table VII) with their grades (Table V), soils from BF, FG, LT, AF, PG and PS were classified at

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

considerable to very high ecological risk for Cd and low to moderate potential ecological risk for the remaining

heavy metals. i

rE in the soil was in the descending order: Cd > As > Pb > Cu > Ni > Cr. Among the land uses,

considerable variations were observed for the potential ecological risk index (i

rE ) of individual metals which

indicated ecological risks of heavy metals varied with different land uses (Table VII). PER represents the

sensitivity of various biological communities to different toxic substances and illustrates the potential ecological

risk caused by heavy metals. In this study PER values of the environments of different land uses were found in

the descending: PS > BF > LT > FG > AF > PG > RA > M. According to other authors (Mass et al., 2010; Luo

et al., 2012), Cd contributes significantly to the PER of the environment and may originate from anthropogenic

activities (application of phosphate fertilizers and industrial activities) (Rodríguez Martín et al., 2013). Overall,

the range of PER for all kinds of land use is 86–449, indicating moderate to very high potential ecological risk.

The maximum value of PER (449 in soil at PS site) denotes very high potential ecological risk for land

receiving waste from coal burning power station.

CONCLUSIONS

Soils from launch terminal (LT), power station (PS) and brick field (BF) sites were contaminated by heavy

metals and metalloids especially Ni, Cu, As, Cd and Pb. The study also confirmed that metal concentrations in

the urban soils of Bangladesh varied with different land-uses. Some metal groups indicate that they have the

same origin, whereas the distribution of Cr, Cu, As and Cd in the studied soils confirmed that these metals are

derived from the parent material. Metals in soils of different land uses showed a moderate to high degree of

contamination. Considering the individual metals, only Cd showed a very high ecological risk for brick field

(BF) and power station (PS) sites, whereas the potential ecological risk indexes of other metals showed

moderate to very high potential ecological risks. Therefore, a long-term risk assessment needs to be carried out

on the leach ability and migration potential of these heavy metals and metalloids at the contaminated sites.

Moreover, different remediation measures (phyto-remediation or bio-remediation) should be taken promptly to

remove or reduce existing metal contamination in the study areas.

ACKNOWLEDGMENTS

The authors thank the authority of Patuakhali Science and Technology University (PSTU), Bangladesh and

Yokohama National University (YNU), Japan, for providing laboratory facilities. The samples collected in

Bangladesh were brought into Japan based on the permission issued by the Yokohama Plant Protection Station

(Import permit No. 25Y324 and 25Y1009). The authors are also delighted to express their gratitude and

sincerest thanks to Professor Dr. Md. Shams-Ud-Din (Vice Chancellor, PSTU), for his valuable suggestions,

cooperation and provision of funding to carry out this research. Furthermore, we are thankful for the kind help

from the members of the Soil Science Department, Patuakhali Science and Technology University (PSTU),

Bangladesh, during field sampling.

REFERENCES

Acosta, J. A., Faz, A., Martínez-Martínez, S., Zornoza, R., Carmona, D. M. and Kabas, S. 2011. Multivariate

statistical and GIS-based approach to evaluate heavy metals behavior in mine sites for future reclamation. J.

Geochem. Explor. 109: 8–17.

Adeniyi, A. A., Yusuf, K. O. and Okedeyi, O. O. 2008. Assessment of the exposure of two fish species to metals

pollution in the Ogun river catchments, Kettu, Lagos, Nigeria. Environ. Monit. Assess. 137: 451–458.

Ahmad, J. U. and Goni, M. A. 2010. Heavy metal contamination in water, soil, and vegetables of the industrial

areas in Dhaka, Bangladesh. Environ. Monit. Assess. 166: 347–357.

Alam, M. G. M., Snow, E.T. and Tanaka, A. 2003. Arsenic and heavy metal contamination of vegetables grown

in Samta village, Bangladesh. Sci. Total Environ. 308: 83–96.

Amuno, S. A. 2013. Potential ecological risk of heavy metal distribution in cemetery soils. Water Air Soil Pollut.

224: 1435–1446.

Anju, M. and Banerjee, D. K. 2012. Multivariate statistical analysis of heavy metals in soils of a Pb–Zn mining

area, India. Environ Monit Assess. 184: 4191–4206.

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

Arenas-Lago, D., Vega, F. A., Silva, L. F. O. and Andrade, M. L. 2014. Copper distribution in surface and

subsurface soil horizons. Environ. Sci. Pollut. Res. 21: 10997–11008.

Arenas-Lago, D., Vega, F. A., Silva, L. F. O. and Andrade, M. L. 2013. Soil interaction and fractionation of

added cadmium in some Galician soils. Microchem. J. 110: 681–690.

Bai, J., Xiao, R., Cui, B., Zhang, K., Wang, Q., Liu, X., Gao, H. and Huang, L. 2011. Assessment of heavy

metal pollution in wetland soils from the young and old reclaimed regions in the Pearl River Estuary, South

China. Environ. Pollut. 159: 817–824.

Bhuiyan, M. A. H., Parvez, L., Islam, M., Dampare, S. B. and Suzuki, S. 2010. Heavy metal pollution of coal

mine-affected agricultural soils in the northern part of Bangladesh. J. Hazard. Mater. 173: 384–392.

Bhuiyan, M. A. H., Suruvi, N. I., Dampare, S. B., Islam, M. A., Quraishi, S. B., Ganyaglo, S. and Suzuki, S.

2011. Investigation of the possible sources of heavy metal contamination in lagoon and canal water in the

tannery industrial area in Dhaka, Bangladesh. Environ. Monit. Assess. 175: 633–649.

Carr, R., Zhang, C. S., Moles, N. and Harder, M. 2008. Identification and mapping of heavy metal pollution in

soils of a sports ground in Galway City, Ireland, using a portable XRF analyser and GIS. Environ. Geochem.

Health. 30: 45–52.

CCME (Canadian Council of Ministers of the Environment). 2003. Canadian environmental quality guidelines.

Winnipeg, Canada.

Cerqueira, B., Vega, F. A., Serra, C., Silva, L. F. O. and Andrade, M. L. 2011. Time of flight secondary ion mass

spectrometry and high-resolution transmission electron microscopy/energy dispersive spectroscopy: A

preliminary study of the distribution of Cu2+

and Cu2+/

Pb2+

on a Bt horizon surfaces. J. Hazard. Mater. 195:

422–431.

Cerqueira, B., Vega, F. A., Silva, L. F. O. and Andrade, L. 2012. Effects of vegetation on chemical and

mineralogical characteristics of soils developed on a decantation bank from a copper mine. Sci. Total

Environ. 421–422: 220–229.

Chen, T. B., Zheng, Y. M., Chen, H., Wu, H. T., Zhou, J. L., Luo, J. F. and Zheng, G. D. 2005. Arsenic

accumulation in soils for different land use types in Beijing (in Chinese). Geogr. Res. 24: 229–235.

Chen, X., Xia, X. H., Zhao, Y. and Zhang, P. 2010. Heavy metal concentrations in roadside soils and correlation

with urban traffic in Beijing, China. J. Hazard. Mater. 181: 640–646.

Cui, Y., Zhu, Y., Zhai, R., Chen, D., Huang, Y. and Qiu, Y. 2004. Transfer of metals from soil to vegetables in

an area near a smelter in Nanning, China. Environ. Int. 30: 785–791.

Dias, C. L., Oliveira, M. L. S., Hower, J. C., Taffarel, S. R., Kautzmann, R. M. and Silva, L. F. O. 2014.

Nanominerals and ultrafine particles from coal fires from Santa Catarina, South Brazil. Int. J. Coal Geol.

122: 50-60.

Franco-Uria, A., Lopez-Mateo, C., Roca, E. and Fernandez-Marcos, M. L. 2009. Source identification of heavy

metals in pasture land by multivariate analysis in NW Spain. J. Hazard. Mater. 165: 1008–1015.

Guo, W., Liu, X., Liu, Z. and Li, G. 2010. Pollution and potential ecological risk evaluation of heavy metals in

the sediments around Dongjiang Harbor, Tianjin. Proc. Environ. Sci. 2: 729–736.

Håkanson, L. 1980. An ecological risk index for aquatic pollution control: a sedimentological approach. Water

Res. 14: 975–1001.

Hower, J. C., O’Keefe, J. M. K., Henke, K. R., Wagner, N. J., Copley, G., Blake, D. R., Garrison, T., Oliveira,

M. L. S., Kautzmann, R. M. and Silva, L. F. O. 2013. Gaseous emissions and sublimates from the Truman

Shepherd coal fire, Floyd County, Kentucky: A re-investigation following attempted mitigation of the fire.

Int. J. Coal Geol. 116: 63–74.

Huang, L., Pu, X., Pan, J. F. and Wang, B. 2013. Heavy metal pollution status in surface sediments of Swan

Lake lagoon and Rongcheng Bay in the northern Yellow Sea. Chemosphere. 93: 1957–1964.

Islam, M. S., Ahmed, M. K. and Al-Mamun, M. H. 2015b. Metal speciation in soil and health risk due to

vegetables consumption in Bangladesh. Environ. Monit. Assess. 187: 288–303.

Islam, M. S., Ahmed, M. K., Al-Mamun, M. H. and Hoque, M. F. 2015a. Preliminary assessment of heavy metal

contamination in surface sediments from a river in Bangladesh. Environ. Earth Sci. 73: 1837–1848.

Islam, M. S., Ahmed, M. K., Al-mamun, M. H. and Masunaga, S. 2014. Trace metals in soil and vegetables and

associated health risk assessment. Environ. Monit. Assess. 186: 8727–8739.

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

Islam, M. S., Ahmed, M. K., Al-Mamun, M. H. and Masunaga, S. 2015c. Potential ecological risk of hazardous

elements in different land-use urban soils of Bangladesh. Sci. Total Environ. 512–513: 94–102.

Islam, M. S., Ahmed, M. K., Raknuzzaman, M., Al-Mamun, M. H. and Islam, M. K. 2015d. Heavy metal

pollution in surface water and sediment: A preliminary assessment of an urban river in a developing country.

Ecol. Ind. 48: 282–291.

Karim, R. A., Hossain, S. M., Miah, M. M. H., Nehar, K. and Mubin, M. S. H. 2008. Arsenic and heavy metal

concentrations in surface soils and vegetables of Feni district in Bangladesh. Environ. Monit. Assess. 145:

417–425.

Karim, Z., Qureshi, B. A., Mumtaz, M. and Qureshi, S. 2014. Heavy metal content in urban soils as an indicator

of anthropogenic and natural influences on landscape of Karachi—A multivariate spatio-temporal analysis.

Ecol. Ind. 42: 20–31.

Kashem, M. A. and Singh, B. R. 1999. Heavy metal contamination of soil and vegetation in the vicinity of

industries in Bangladesh. Water Air Soil Pollut. 115: 347–361.

Kheir, R. B. 2010. Spatial soil zinc content distribution from terrain parameters: a GIS-based decision-tree

model in Lebanon. Environ. Pollut. 158: 520–528.

Krishna, A. K., Govil, P. K. 2007. Soil contamination due to heavy metals from an industrial area of Surat,

Gujarat, Western India. Environ. Monit. Assess. 124: 263–275.

Li, J. H., Lu, Y., Yin, W., Gan, H. H., Zhang, C., Deng, X. L. and Lian, J. 2009. Distribution of heavy metals in

agricultural soils near a petrochemical complex in Guangzhou, China. Environ. Monit. Assess. 153: 365–

375.

Li, J., Xie, Z. M., Xu, J. M. and Sun, Y. F. 2006. Risk assessment for safety of soils and vegetables around a

lead/zinc mine. Environ. Geochem. Health. 28: 37–44.

Li, X. and Feng, L. 2012. Multivariate and geostatistical analyzes of metals in urban soil of Weinan industrial

areas, Northwest of China. Atmos. Environ. 47: 58–65.

Liu, G. D., Dunlop, J., Phung, T. and Li, Y. C. 2007. Physiological responses of wheat phosphorus-efficient and

-inefficient genotypes in field and effects of mixing other nutrients on mobilization of insoluble phosphates

in hydroponics. Commun. Soil Sci. Plant Anal. 38: 2239–2256.

Liu, G., Yu, Y., Hou, J., Xue, W., Liu, X., Liu, Y., Wang, W., Alsaedi, A., Hayat, T. and Liu, Z. 2014. An

ecological risk assessment of heavy metal pollution of the agricultural ecosystem near a lead-acid battery

factory. Ecol. Ind. 47: 210–218.

Liu, P., Zhao, H., Wang, L., Liu, Z., Wei, J., Wang, Y., Jiang, L., Dong, L. and Zhang, Y. 2011. Analysis of

heavy metal sources for vegetable soils from Shandong Province, China. Agricul. Sci. China. 10: 109–119.

Luo, W., Lu, Y., Gisey, J. P., Wang, T., Shi, Y., Wang, G. and Xing, Y. 2007. Effects of land use on

concentrations of metals in surface soils and ecological risk around Guanting Reservoir, China. Environ.

Geochem. Health. 29: 459–471.

Luo, X. S., Yu, S., Zhu, Y. G. and Li, X. D. 2012. Trace metal contamination in urban soils of China. Sci. Total

Environ. 421–422: 17–30.

Luoma, S. N. and Rainbow, P. S. 2008. Metal contamination in aquatic environments: Science and lateral

management (573 pp.). Cambridge: Cambridge University Press.

Martinello, K., Oliveira, M. L. S., Molossi, F. A., Ramos, C. G., Teixeira, E. C., Kautzmann, R. M. and Silva, L.

F. O. 2014. Direct identification of hazardous elements in ultra-fine and nanominerals from coal fly ash

produced during diesel co-firing. Sci. Total Environ. 470-471: 444–452.

Mass, S., Scheifler, R., Benslama, M., Crini, N., Lucot, E., Brahmia, Z., Benyacoub, S., Muhammad, S., Shah,

M. T. and Khan, S. 2011. Heavy metal concentrations in soil and wild plants growing around Pb–Zn sulfide

terrain in the Kohistan region, northern Pakistan. Microchem. J. 99: 67–75.

NEPC. 2013. National Environment Protection Measures. Assessment of Site Contamination – Guideline on

Investigation Levels for Soil and Groundwater 2013. National Environmental Protection Council: Canberra,

Australia.

Neumann, R. B., Ashfaque, K., Badruzzaman, A. B. M., Ali, M. A., Shoemaker, J. and Harvey, C. 2010.

Anthropogenic influences on groundwater arsenic concentrations in Bangladesh. Nature Geosci. 3: 46–52.

Nziguheba, G. and Smolders, E. 2008. Inputs of trace elements in agricultural soils via phosphate fertilizers in

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

European countries. Sci. Total Environ. 390: 53–57.

Olawoyin, R., Oyewole, S. A. and Grayson, R. L. 2012. Potential risk effect from elevated levels of soil heavy

metals on human health in the Niger delta. Ecotoxicol. Environ. Saf. 85: 120–130.

Papa, S., Bartoli, G., Pellegrino, A. and Fioretto, A. 2010. Microbial activities and trace element contents in an

urban soil. Environ. Monit. Assess. 165: 193–203.

Polizzotto, M. L., Lineberger, E. M., Matteson, A. R., Neumann, R. B., Badruzzaman, A. B. M. and Ali, M.

2013. Arsenic transport in irrigation water across rice-field soils in Bangladesh. Environ. Pollut. 179: 210–

217.

Quispe, D., Pérez-López, R., Silva, L. F. O. and Nieto, J. M. 2012. Changes in mobility of hazardous elements

during coal combustion in Santa Catarina power plant (Brazil). Fuel. 94: 495–503.

Rahman, S. H., Khanam, D., Adyel T. M., Islam, M. S., Ahsan, M. A. and Akbor, M. A. 2012. Assessment of

heavy metal contamination of agricultural soil around Dhaka Export Processing Zone (DEPZ), Bangladesh,

implication of seasonal variation and indices. Appl. Sci. 2: 584–601.

Rashed, M. N. 2010. Monitoring of contaminated toxic and heavy metals, from mine tailings through age

accumulation, in soil and some wild plants at Southeast Egypt. J. Hazard. Mater. 178: 739–746.

Rodríguez Martín, J. A., Gutiérrez, C., Escuer, M., García-González, M. T., Campos-Herrera, R. and Águila, N.

2014. Effect of mine tailing on the spatial variability of soil nematodes from lead pollution in La Union

(Spain). Sci. Total Environ. 473–474: 518–529.

Rodríguez Martín, J. A., Ramos-Miras, J. J., Boluda, R. and Gil, C. 2013. Spatial relations of heavy metals in

arable and greenhouse soils of a Mediterranean environment region (Spain). Geoderma. 200–201: 180–188.

Saikia, B. K., Ward, C. R., Oliveira, M. L. S., Hower, J. C., Braga, M. and Silva, L. F. 2014. Geochemistry and

nano-mineralogy of two medium-sulfur Northeast Indian coals. Int. J. Coal Geol. 121: 26–34.

Sayadi, M. H. and Sayyed, M. R. G. 2011. Comparative assessment of baseline concentration of the heavy

metals in the soils of Tehran (Iran) with the comprisable reference data. Environ. Earth Sci. 63: 1179–1188.

Schottler, S. P. and Engstrom, D. R. 2006. A chronological assessment of Lake Okeechobee (Florida) sediments

using multiple dating markers. J. Paleolimnol. 36: 19–36.

Sikorski, J. and Bluszcz, A. 2008. Application of α and γ spectrometry in the 210

pb method to model

sedimentation in artificial retention reservoir. Geochron. 31: 65–75.

Sikorski, J. and Goslar, T. 2003. Inventory of sediments of the dammed lake in Kozłowa Góra and first

measurements of 210

Pb activities in the lake deposits. Geochron. 22: 55–62.

Silva, L. F. O., Oliveira, M. L. S., Neace, E. R., O’Keefe, J. M. K., Henke, K. R. and Hower, J. C. 2011.

Nanominerals and ultrafine particles in sublimates from the Ruth Mullins coal fire, Perry County, Eastern

Kentucky, USA. Int. J. Coal Geol. 85: 237–245.

Srinivasa, G. S., Reddy, M. R. and Govil, P. K. 2010. Assessment of heavy metal contamination in soils at

Jajmau (Kanpur) and Unnao industrial areas of the Ganga Plain, Uttar Pradesh, India. J. Hazard. Mater. 174:

113–121.

Sun, Y. B., Zhou, Q. X., Xie, X. K. and Liu, R. 2010. Spatial, sources and risk assessment of heavy metal

contamination of urban soils in typical regions of Shenyang, China. J. Hazard. Mater. 174: 455–462.

VROM (Volkshuisvesting, RuimtelijkeOrdeningenMilieubeheer). 2000. Circular on target values and

intervention values for soil remediation. Spatial Planning and Environment, Netherlands: Ministry of

Housing, Spatial Planning and Environment.

Wei, B. G. and Yang, L. S. 2010. A review of heavy metal contaminations in urban soils, urban road dusts and

agricultural soils from China. Microchem. J. 94, 99–107.

Xu, Z. Q., Ni, S. J. and Tuo, X. G. 2008. Calculation of heavy metals toxicity coefficient in the evaluation of

potential ecological risk index. Environ. Sci. Technol. 31: 112–115.

Yadao, S. and Rajamani, V. 2006. Air quality and trace metal chemistry of different size fractions of aerosols in

N–NW India—implications for source diversity. Atmos. Environ. 40: 698–712.

Yu, J., Huang, Z., Chen, T., Qin, D., Zeng, X. and Huang, Y. 2012. Evaluation of ecological risk and source of

heavy metals in vegetable-growing soils in Fujian province, China. Environ. Earth Sci. 65: 29–37.

Yuan, G. L., Sun, T. H., Han, P., Li, J. and Lang, X. X. 2014. Source identification and ecological risk

assessment of heavy metals in topsoil using environmental geochemical mapping: Typical urban renewal

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

area in Beijing, China. J. Geochem. Explor. 136: 40–67.

Zhang,W., Feng, H., Chang, J., Qu, J., Xie, H. and Yu, L. 2009. Heavy metal contamination in surface

sediments of Yangtze River intertidal zone: An assessment from different indexes. Environ. Pollut. 157:

1533–1543.

Zhao, L., Xu, Y., Hou, H., Shangguan, Y. and Li, F. 2014. Source identification and health risk assessment

of metals in urban soils around the Tanggu chemical industrial district, Tianjin, China. Sci. Total Environ.

468–469: 654–662.

Zheng, N., Wang, Q., Liang, Z. and Zheng, D. 2008. Characterization of heavy metal concentrations in the

sediments of three freshwater rivers in Huludao City, Northeast China. Environ. Pollut. 154: 135–142.

Zhou, J., Ma, D. S., Pan, J. Y., Nie, W. M. and Wu, K. 2008. Application of multivariate statistical approach to

identify heavy metal sources in sediment and waters: a case study in Yangzhong, China. Environ. Geol. 54:

373–380.

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

TABLE I

Physicochemical properties (mean ± SD) of soils, at different land uses of Patuakhali District, Bangladesh.

Land

types

pH (1:2.5 H2O)

EC (µScm-1

)

Organic carbon

(gkg-1

)

Sand Silt Clay Soil texturea

(gkg

-1 in < 2 mm)

BF 6.5±1.3 70±16 7.0±1.8 730 150 120 Sandy loam

FG 5.4±1.1 34±7.9 8.1±1.2 888 77 35 Sand

LT 5.7±0.72 67±13 6.8±2.6 582 188 230 Sandy clayey loam

PS 7.2±0.65 26±4.9 5.8±2.1 670 240 90 Sandy loam

AF 6.9±1.4 51±19 7.5±3.5 700 190 110 Sandy loam

RA 5.8±0.84 35±5.5 4.3±1.1 805 150 45 Loamy sand

M 7.1±1.6 39±11 6.6±2.1 565 145 290 Sandy clayey loam

PG 6.1±0.75 22±6.8 6.4±1.2 580 165 255 Sandy clayey loam aAccording to the United States Department of Agriculture soil classification system.

TABLE II

Metal concentration (mgkg-1

) in soils of different land uses and guidelines value.

Vertically letters a and b show statistically significant differences at (p < 0.05, Multivariate Post-hoc Tukey test) among the land uses

of each metal.

Sites Al Cr Ni Cu As Cd Pb

Brick field (n=6) Mean±SD 13±5.7 43±14a 83±69a 51±37a 38±12b 10±7.7a 42±27a

Range 5.2-21 19-58 21-271 15-90 5.1-80 3.9-24 9.0-73

Ferry ghat (n=4) Mean±SD 12±4.8 39±23ab 56±20a 54±32a 8.0±3.2a 6.3±5.2a 38±27a

Range 6.0-17 17-70 29-75 13-89 3.5-11 1.3-11 16-76

Launch terminal (n=6) Mean±SD 15±11 47±23a 76±49a 73±31a 15±12a 5.8±5.1a 137±78b

Range 4.1-31 18-87 21-152 26-122 5.5-36 0.81-13 51-276

Power station (n=4) Mean±SD 14±1.8 52±25a 51±54a 67±77a 10±12a 11±8.8a 54±22a

Range 12-16 16-74 22-131 15-181 2.8-28 2.0-23 26-78

Agricultural field (n=9) Mean±SD 15±8.2 19±16ab 27±27a 34±21a 9.3±6.0a 3.1±2.2a 24±16a

Range 6.0-28 6.3-58 8.3-95 6.2-65 4.0-22 1.0-7.2 10=63

Residential area (n=10) Mean±SD 15±7.1 10±11b 31±24a 28±22a 3.3±2.7a 2.4±1.3a 33±23a

Range 7.6-32 2.4-39 6.7-89 6.2-61 1.0-10 0.87-4.2 10=70

Market (n=4) Mean±SD 16±6.7 14±8.1ab 21±5.8a 77±44a 6.3±4.8a 1.5±0.93a 17±20a

Range 7.1-21 7.5-26 16-29 15-111 2.3-13 0.54-2.7 5.2-47

Play ground (n=10) Mean±SD 14±5.5 22±19ab 28±23a 21±18a 3.0±2.9a 4.8±5.2a 27±22a

Range 9.4-28 1.3-62 4.9-76 4.1-57 0.26-8.0 0.17-17 9.4-81

Background value of the present study 29 32 27 6.5 0.82 23

Background value of Bangladesh soilc NA 22 27 3 0.01-0.2 20.0

Dutch soil quality standard (Target Value)d 100 35 36 29 1 85

Dutch soil quality standard (Intervention Value)d 380 210 190 55 12 530

Canadian Environmental Quality Guidelinese 64 50 63 12 1.4 70

National Environment Protection Council, Canberra,Australiaf

100 400 7000 100 20 300

cKashem and Shingh, 1999

dVROM, 2000

eCCME, 2003

fNEPC, 2013

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

14

TABLE III

Total variance explained and component matrices for the heavy metals in surface soils collected from Patuakhali

district, Bangladesh.

Initial Eigen values Extraction Sums of Squared Loadings Rotation Sums of Squared

Loadings

Compone

nt

Tota

l

% of

Variance

Cumulative

%

Tota

l % of Variance

Cumulative

% Total % of Variance Cumulative %

1 2.4 40.4 40.4 2.4 40.4 40.4 1.7 28.0 28.0

2 1.2 19.5 59.9 1.2 19.5 59.9 1.5 24.8 52.8

3 0.89 14.8 74.7 0.89 14.8 74.7 1.3 21.9 74.7

4 0.68 11.3 86.0

5 0.44 7.4 93.4

6 0.40 6.6 100

Elements Component matrix Rotated Component Matrix

PC1 PC2 PC3 PC1 PC2 PC3

Component

Matrix

Cr 0.73 0.372 0.11 0.45 0.28 0.63

8

Ni 0.77 -0.211 -0.38 0.82 0.33 -0.03

Cu 0.33 0.80 0.357 -0.02 -0.04 0.93

As 0.54 -0.45 0.45 0.08 0.826 0.01

3

Cd 0.63 -0.36 0.359 0.21 0.78 0.09

Pb 0.71 0.14 -0.53 0.88 0.02 0.19

Extraction Method: Principal Component Analysis

TABLE IV

Enrichment factor of heavy metals in soil from different land uses in Patuakhali, Bangladesh Land use type Cr Ni Cu As Cd Pb

Brick field (BF) 0.83±0.27 0.89±0.65 0.99±0.59 3.3±2.8 6.7±5.3 1.0±0.65

Ferry ghat (FG) 0.67±0.39 0.88±0.31 1.0±0.61 0.62±0.25 3.9±3.2 0.84±0.60

Launch terminal (LT) 0.82±0.40 1.2±0.77 1.4±0.59 1.2±0.96 3.6±3.1 3.0±1.7

Power station (PS) 0.91±0.44 0.80±0.85 1.3±1.4 0.78±0.92 6.7±5.4 1.2±0.48

Agricultural field (AF) 0.33±0.28 0.43±0.43 0.64±0.39 0.73±0.46 1.9±1.4 0.53±0.35

Residential area (RA) 0.17±0.19 0.50±0.39 0.53±0.41 0.25±0.21 1.5±0.81 0.73±0.50

Market (M) 0.25±0.14 0.34±0.09 1.4±0.82 0.49±0.37 0.90±0.57 0.37±0.45

Play ground (PG) 0.38±0.32 0.44±0.36 0.40±0.33 0.23±0.23 3.0±3.2 0.60±0.47

TABLE V

Indices and grades of potential ecological risk of trace metals pollution (Luo et al., 2007). Contamin

ation

factor

(i

fC )

Contamination

degree of

individual metal

Degree of

contamination

( dC )

Contamination

degree of

the environment

i

rE Grade of ecological risk of

individual

metal

Potential ecological risk

(PER)

i

fC < 1 Low

dC < 5 Low contamination i

rE < 40 Low risk PER < 65 Low risk

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

15

1 ≤ i

fC <

3

Moderate 5 ≤ dC < 10

Moderate

contamination 40 ≤

i

rE<80

Moderate risk 65 ≤ PER <

130

Moderate

risk

3 ≤ i

fC <

6

Considerable 10 ≤ dC < 20

Considerable

contamination 80 ≤

i

rE <

160

Considerable risk 130 ≤ PER<

260

Considerabl

e risk

i

fC ≥ 6 High dC

≥ 20

High contamination 160 ≤

i

rE <

320

High risk PER ≥ 260 Very high

risk

i

rE

≥ 320 Very high risk

TABLE VI

Contamination factor, degree of contamination and contamination level of heavy metals and metalloids in soils

collected from Patuakhali district, Bangladesh. Land uses Contamination factor (C

if) Degree of Contamination

Cr Ni Cu As Cd Pb contamination level

Brick field (BF) 1.5 1.6 1.8 5.8 12 1.8 24.4 Very high

Ferry ghat (FG) 1.3 1.7 2.0 1.2 7.6 1.7 15.6 Considerable

Launch terminal (LT) 1.6 2.4 2.7 2.3 7.0 6.0 22.0 Very high

Power station (PS) 1.8 1.6 2.5 1.5 13 2.3 23.0 Very high

Agricultural field (AF) 1.4 0.5 2.8 5.1 4.3 2.2 16.3 Considerable

Residential area (RA) 0.34 0.98 1.1 0.50 2.9 1.4 7.2 Moderate

Market (M) 0.50 0.67 2.8 0.97 1.8 0.73 7.5 Moderate

Play ground (PG) 0.76 0.86 0.79 0.45 5.9 1.2 10 Considerable

Note: Bold indicates very high contamination (Cif > 6.0)

TABLE VII

Potential ecological risk factor, risk index and pollution degree of heavy metals and metalloids in soils collected

from Patuakhali district, Bangladesh. Land uses Potential ecological risk factor (E

ir) Potential Risk

PER

Pollution

degree Cr Ni Cu As Cd Pb

Brick field (n=6) 3.0 10 9 58 359 9.2 447 Very high risk

Ferry ghat (n=4) 2.7 10 10 12 229 8.3 272 Very high risk

Launch terminal (n=6) 3.2 14 14 23 211 30 295 Very high risk

Power station (n=4) 3.6 10 12 15 396 12 449 Very high risk

Agricultural field (n=9) 2.8 2.9 14 51 130 11 211 Considerable risk

Residential area (n=10) 0.68 5.9 5.3 5.0 87 7.2 111 Moderate risk

Market (n=4) 1.0 4.0 14 10 54 3.7 86 Moderate risk

Play ground (n=10) 1.5 5.2 3.9 4.5 177 6.0 198 Considerable risk

Note: Bold indicates very high contamination (Eir >320)

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

16

Fig. 1 Map of the study area of Patuakhali district in Bangladesh.

Fig. 2 Principal component analysis (PCA) of heavy metals in soils collected from different land uses in

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

17

Patuakhali, Bangladesh.

Fig. 3 Estimated toxic units (∑TUs) in soils of different land use types in Bangladesh.

Fig. 4 Pollution load index (PLI) of heavy metals in soils of different land use types in Bangladesh (Error bar

represents ±SE).

Accep

ted M

anus

cript

ACCEPTED MANUSCRIPT

18

TABLE SI

Descriptions of different land uses and their respective number of sites under present investigation. Different types of

land use

Numb

er Description about the sampling sites

of

sites

Residential area

(RA)

10 The area consists of houses.

Play ground (PG) 10 Playground for children, adults and other residents. Agriculture field

(AF)

9 Traditional farming growing different types of vegetables and cereal crops with chemical

fertilizers and pesticides

Brick field (BF) 6 Burning of coal and wood for making bricks.

Launch terminal

(LT)

6 Transport of goods from various industries of Dhaka City and dispose waste from the terminal.

Power station (PS) 4 Burning coal for producing electricity, dispose of some waste from the station.

Market (M)

4 Shopping activity, breaking down of electronic components, dispose waste from the shop

keepers.

Ferry ghat (FG)

4 Boating activity, transportation of different raw materials from various industries, dispose waste

from boat.

TABLE SII

The ICP-MS operating conditions and measurement parameters.

Operating conditions

Spray chamber Scott double-pass

Nebulizer pump (rps) 0.1

RF power (W) 1550

RF Matching (V) 0.2

Sample depth (mm) 8

Torch-H (mm) 0.2

Torch-V (mm) 0.2

Plasma gas flow rate (L min-1

) 15

Carrier gas (Ar) flow rate (L min-1

) 0.9 (optimized daily)

Measurement parameters

Scanning mode Peak hop

Resolution (amu) 0.7

Readings/replicate 1

No. of replicates 3

Isotopes 52

Cr, 60

Ni, 63

Cu, 75

As, 111

Cd, 208

Pb

Accep

ted M

anus

cript