Advertisement

Environmental Science and Pollution Research

, Volume 23, Issue 14, pp 14210–14220 | Cite as

Ecological risk assessment of heavy metal (HM) pollution in the ambient air using a new bio-indicator

  • Mohammad Miri
  • Ahmad Allahabadi
  • Hamid Reza Ghaffari
  • Zeynab Abaszadeh Fathabadi
  • Zahra Raisi
  • Mehrab Rezai
  • Mohsen Yazdani Aval
Research Article

Abstract

The aim of this descriptive-analytical study was to measure the concentration of heavy metals (HMs) in the leaf and bark of Ulmus carpinifolia as new biological indicators, and the ecological risk assessment of these metals in the ambient air. To achieve these goals, 48 sampling locations were selected in the city and concentration of four HMs—zinc (Zn), copper (Cu), lead (Pb), and cadmium (Cd)—was measured in the mentioned indicator using atomic absorption spectroscopy method. After this, ecological risk assessment, source appointment, and spatial distribution were conducted. In this regard, the enrichment factor (EF), potential ecological risk factor (E r), potential ecological risk index (RI), correlation coefficient (r), and other indices were calculated. The results showed that the concentration of HMs in the leaf and bark in ascending order is as Cd<Cu<Pb<Zn and Cd<Pb<Cu<Zn, respectively. The EF results indicated that the main origin of all measured HMs except Zn is anthropogenic sources. Also, the principal component analysis (PCA) and spatial distribution proved that the concentration of HMs is mainly originated from the traffic and other human activities. On the other hand, the results RI presented that the majority of locations sampled in the study area was exposed to serious ecological risk in terms of surveyed HMs. The leaf and bark of U. carpinifolia can be applied as bio-indicators of the presence of heavy metals in the ambient air and ecological risk imposed by them.

Keywords

Heavy metals (HMs) Bio-indicator Ulmus carpinifolia Ecological risk assessment Ambient air 

Introduction

High concentrations of heavy metals (HMs) in the environment can increase the risk of the potential adverse effects on human health (Ehrampoush et al. 2015). The soil is well known as a potentially significant source of human exposure to HMs (Zhai et al. 2014) when is contaminated through natural origin (Liu et al. 2013), pedogenic and lithogenic (Zheng et al. 2013), and artificial resources such as atmospheric emissions, wastewater discharges, and agricultural activities (Hernández-Quiroz et al. 2012; Streets et al. 2011; Zhang et al. 2014). In polluted cities, transportation and industrial emissions are the main sources of HMs in environmental media such as plants. There are many reports that show the ambient air in urban and industrial areas has been polluted with HMs worldwide (Ahmadi et al. 2015; Mokhtari et al. 2015; Rodriguez et al. 2012; Sawidis et al. 2011; Serbula et al. 2012; Shen et al. 2012). HMs in the leaf and bark may be originated from the soil and atmosphere, being the last one considered as the main source (Hernández-Quiroz et al. 2012; Sæbø et al. 2012; Sawidis et al. 2012; Zhai et al. 2014).

There are two methods (direct and indirect) to investigate the ambient air pollution with HMs. The direct method is determining the concentration of HMs in air samples (Liu et al. 2013) and indirect method include measurement of heavy metal concentration in rainwater (Rodriguez et al. 2012), soil (Kularatne and De Freitas 2013) and bio-indicators. The bio-indicator samples are more applicable than other methods because of their simpler collection, lesser costs, lower contamination risk, and higher HM concentration (Huston et al. 2012; Sawidis et al. 2011; Zhao et al. 2012).

Many different bio-indicators are applied in the atmospheric pollution monitoring such as lichens, mosses, woody plants, vascular plants, etc. Both the leaf and bark of plants are used in the air pollution study as an indicator of the presence of pollutants (Abdel-Ghaffar et al. 2015; Nath et al. 2014; Sawidis et al. 2011; Sawidis et al. 2012).

The leaf and bark can be used as an indicator for various atmospheric characteristics and pollutants such as pH, electrical conductivity, nitrogen, sulfur, and HMs. They are suitable indicators in industrial and urban areas where other bio-indicators are very scarce (Sawidis et al. 2012; Serbula et al. 2013). Several factors including heavy metal concentration, soil characteristics, throughfall, climatic factors, pollution of other plants, and chemical and physiological properties of the leaf and bark can influence on HM accumulation in the leaf and bark surface. Previous study showed that the smooth barks accumulate metals less than rough barks (Sawidis et al. 2012). Also, the surface of the leaf and bark are affected not only by HM accumulation but also by exchange functions (Barnes et al. 1976; Sawidis et al. 2011; Serbula et al. 2012).

The potential ecological risk index (RI) can clearly show the effects of various pollutants on the living environment under particular conditions. It is extensively applied for assessment and analysis of the potential ecological effects of HMs (Streets et al. 2011).

This paper will focus on the following topics because of major human health issues, ecological problems, and likely risks originating from the HMs in the urban atmosphere:
  • Measurement of HM concentration in the leaf and bark of Ulmus carpinifolia species as bio-indicator

  • Determination of the probable sources of HMs in leaf and bark

  • The potential ecological risk assessment of HMs in the leaf and bark

It would be a good idea that the effects of HMs on the leaf and bark to be considered as an indicator of air pollution in the urban environment. These data also provide essential information for the monitoring, control, and management of HM pollution in the study area and it would be useful for the private and government organization to establish the environmental laws and regulations in order to protect the environment against the anthropogenic pollutants.

Material and methods

Study area, sampling locations, and bio-indicator selection

Yazd, capital of Yazd province, is located in the center of Iran. Yazd city has a total area 2491 km2. It is located in the longitudinal 54° 36′ E, latitude 31° 88′ N, and altitude about 1190 to 1300 m above the sea level. There are various tree species in this city. However, one of the most abundant species is the elm tree that can be seen throughout the city. Therefore, the U. carpinifolia, a species of elm tree, was selected as bio-indicator to investigate HM pollution of the urban atmosphere. Sampling locations were selected on the basis of land use (industrial, commercial, residential, etc.) and traffic level (low, medium, and heavy). So, 48 locations were selected for sampling throughout the city. Figure 1 shows the study area and sampling locations. Four HMs, zinc (Zn), copper (Cu), cadmium (Cd), and lead (Pb) were chosen for the analysis since they are typical pollutants in the urban atmosphere.
Fig. 1

Study area and sampling locations

Sampling and analysis

The sampling was conducted in early summer after 10-day rainless periods. The steel knife was used as a sampling instrument. There were no any defections (insect infestation and other additional waste) in samples. One of the main objectives of the study was the estimation of the leaf and bark’s ability to adsorb HMs in the ambient air. The samples were collected from the different side of trees (about 10–15 years old) outer surfaces same age with at a height of 1.5–2 m above the ground. The stages of the study (sampling, preparation, and examination) are shown in Fig. 2. All results for leaf and bark samples were calculated on a dry weight basis. For the analysis of HMs in the leaf and bark samples by graphite furnace atomic absorption spectrophotometer (GF-AAS) (Varian spectrAA.20, Australia), nitric acid was added to blanks and standards the same as those of the samples.
Fig. 2

The stages of study (sampling, preparation, and analyzing stages)

Statistical analysis for sources identification

The principal component analysis (PCA) and Spearman’s rank correlation coefficients were used to determine the relation between HM concentrations in the leaf and bark and estimate their probable sources. Data analysis was performed by means of the Statistical Package for the Social Sciences (SPSS) version 16.0 for Windows. The Spearman’s rank correlation coefficient calculates the mutual interrelationship strength between HM concentrations. Another effective analytical tool is PCA which can minimize a set of original variables and take out a small number of hidden factors for analyzing relationships between observed variables and samples (Nath et al. 2014; Qingjie et al. 2008). The ordinary kriging method in Arc GIS software version 10.1 was also used to develop independent raster layers for each HM pollutant.

Ecological risk assessment

The enrichment factor (EF) was used (Škrbić and Đurišić-Mladenović 2010; Soltani et al. 2015) to assess the degree of HM pollution and their possible sources (natural or anthropogenic). The mathematical EF equation is defined as follows:
$$ \mathrm{E}\mathrm{F}={\left(\frac{C_n}{C_{\mathrm{ref}}}\right)}_{\mathrm{Sample}}/{\left(\frac{B_n}{B_{\mathrm{ref}}}\right)}_{\mathrm{Background}} $$
(1)
where C n and B n are the concentration of the main element in the sampling and background location, respectively. C ref and B ref are the concentration of the reference element in the sampling and background location, respectively. In this study, Fe was used as the reference element (Wei et al. 2010). The non-polluted place with a 10-km distance away from the city was chosen as the background site. EF can also specify the main source of pollution. In general, an EF value less than 10 and greater than 10 indicates that the source of contamination is a result of human activity and soil material, respectively (Liu et al. 2003; Soltani et al. 2015; Yuen et al. 2012). In addition, EF can also apply for classifying metal pollution. Five pollution classes according to EF value are shown in Table 1 (Yuen et al. 2012).
Table 1

Contamination categories based on enrichment factor

Enrichment factor (EF)

Contamination categories

<2

Deficiency to minimal enrichment

2–5

Moderate enrichment

5–20

Significant enrichment

20–40

Very high enrichment

>40

Extremely high enrichment

In this paper the potential ecological risk index (RI) which was introduced by previous study (Serbula et al. 2013) were used. It is on the basis of elemental abundance and release capacity. The RI shows the differentiation in bioavailability, relative contribution ratio, and geographical space. It is a comprehensive index to reflect the effects of HMs on the ecological environments (Birch and Olmos 2008). In our study, only the soluble and reducible fractions were used in order to assess the bioavailability and toxicity of HMs in the leaf and bark. On the other hand the oxidizable and residual fractions were ignored in the potential ecological risk assessment because they are the non-bioavailable parts of HMs in the leaf and bark (Guo et al. 2010; Hakanson 1980). RI is calculated using three equations as follows:
$$ {C}_f^i={C}^i/{C}_n^i $$
(2)
$$ {E}_f^i={T}_r^i\times {C}_f^i $$
(3)
$$ \mathrm{R}\mathrm{I}={\displaystyle \sum_i^n}{E}_f^i $$
(4)
where C i is the average concentration (mg/kg) of the soluble and reducible fractions of the element i in the leaf and bark, and C n i is the standard reference level (mg/kg) of the element i, C f i is the pollution factor of element i, and E f i is the potential ecological risk factor for element i. T r i is the toxic-response factor of element i which shows the level of toxicity and biological sensitivity of contaminant. T r i for Zn, Cu, Cd, and Pb are 1, 5, 30, and 5, respectively (Zhai et al. 2014).

Result and discussion

HM concentration in the leaf and bark

The descriptive analysis of HM (Zn, Cu, Cd, and Pb) concentration in the leaf and bark are shown in Table 2. As seen from the table, Zn has the highest concentration in both the leaf and bark (mean concentrations are 24.76 and 41.15 mg/kg in the leaf and bark, respectively). Other HM concentration are in descending order Cd<Cu<Pb<Zn for the leaf and Cd<Pb<Cu<Zn for the bark. Other studies also reported that Zn is in higher proportion than other HMs in the leaf and bark of the tree (Hakanson 1980; Huang et al. 2011). This can be due to this fact that Zn is an essential element in the all organisms and plays an important role in the biosynthesis of enzymes, auxins, and some proteins. It should be noted that the high concentration of Zn in trees may cause the loss of production and its low levels may cause leaf deformation (Norouzi et al. 2015).
Table 2

Descriptive statistics of heavy metal (HM) concentrations in the U. carpinifolia leaf and bark

 

Leaf (mg/kg)

Bark (mg/kg)

Zn

Cu

Pb

Cd

Zn

Cu

Pb

Cd

Min

8.6

0.06

0.25

0.36

15

0.05

0.16

0.05

Max

47.15

9.5

12.5

7.5

68.33

17.5

16.67

3.67

Mean

24.76

2.98

3.38

0.81

41.15

3.31

2.54

1.15

Std. deviation

8.345

2.139

2.089

0.993

12.09

3.374

2.624

0.662

Zn, Cu, and Cd concentrations in the bark are higher than in the leaf, but there are higher concentrations of Pb in the leaf. In the previous study, Pb concentration in the leaf was higher than in the bark and Cu concentration was lower than in barks (Sawidis et al. 2011). Pb is taken up in very small amounts by plant roots (Çelik et al. 2005). It has been reported that its uptake into a plane tree leaf from the soil is unlikely since Pb is an element with low plant mobility (Norouzi et al. 2015). Markert (1994) reported that Pb toxic range for plants is between 3 and 20 mg/kg (Günthardt-Goerg and Vollenweider 2007), which in this study the mean Pb concentration is in the toxic range for the leaf (3.38 mg/kg) and lower than toxic range for the bark (2.54 mg/kg). The main reason for this fact may be due to Pb removal from the petrol and other fuels in recent years. The normal range of Cu concentrations for plants is 5–30 mg/kg (Çelik et al. 2005). In this study the mean concentration of Cu in both the leaf (2.98 mg/kg) and bark (3.31 mg/kg) was in the normal range. The range of Cd concentration in our study (0.36–75 mg/kg in the leaf and 0.05–3.67 mg/kg in the bark) was higher than reported concentration in Robinia species by previous studies (Baycu et al. 2006; Kabata-Pendias 2010).

Correlation and principle component analysis

Correlations analysis provides information about HM sources and fate (Baycu et al. 2006; Sawidis et al. 2001). The spearman correlation coefficient was applied to examine dual relationships between HM concentrations in the leaf and bark. The results of this statistic analysis are shown in Table 3. There are no strong dual correlations between all of HM concentration in leaf, but the correlation between Cu and Zn are statistically significant (r = 0.482; p < 0.01). For the bark, statistically significant relationship exists between Pb and Cd (r = 0.681; p < 0.01). The magnitude of the correlation coefficient between the two HMs shows that if these two metals are originated from the same source or from different sources. If the correlation coefficient value be close to ±1 the two elements most likely are originated from the same pollution source (Lu et al. 2010). In this study, the emission source for Zn and Cu in the leaf and Cd and Pb in the bark may be similar because there are strong positive correlations between Zn and Cu in the leaf and Cd and Pb in the bark.
Table 3

Spearman’s rank correlations for heavy metal (HM) concentrations in the leaf and bark

  

Zn

Cu

Pb

Cd

Leaf

Zn

1.000

   

Cu

0.482a

1.000

  

Pb

−0.006

0.156

1.000

 

Cd

0.134

0.321b

0.109

1.000

Bark

Zn

1.000

   

Cu

0.309b

1.000

  

Pb

0.055

0.19

1.000

 

Cd

0.045

0.211

0.681a

1.000

aCorrelation is significant at the 0.01 level (two-tailed)

bCorrelation is significant at the 0.05 level (two-tailed)

Another method for source apportionment is PCA which was proposed by US Environmental Protection Agency (USEPA) (Huang et al. 2009). PCA with varimax rotation method was used to identify the probable sources of HM pollution in leaf and bark (Paatero et al. 2005; Ragosta et al. 2008). Two principal components (PCs) were employed, and it sufficient to explain more than 66 and 76 % of the total variance for leaf and bark samples respectively. The PCA results of HM concentration in the leaf and bark including loadings factor along with a varimax rotation and eigenvalues are shown in Figs. 3 and 4.
Fig. 3

The PCA results of heavy metals (HMs) for bark, CP1 component 1, CP2 component 2, Com commonality

Fig. 4

The PCA results of heavy metals (HMs) for bark, CP1 component 1, CP2 component 2, Com commonality

For the leaf samples, factor 1 is dominated by Cu, Cd, and Zn (40.17 % of the total variance) and for the bark samples, by Cu and Cd (45.61 % of the total variance). This factor reflects that human sources such as industries and road traffic are possible responsible for the emission. The same results have been reported in several previous studies (Fang et al. 2003; Li et al. 2001; Ragosta et al. 2008). Transport emissions contain not only vehicles exhaust emissions but also brake wear, tire, and resuspended dust (Kong et al. 2011). Brake dust is already recognized as a significant carrier of Cu in aerosol composition (Adachi and Tainosho 2004; Huang et al. 2009). Cu is generally used in brake to control heat transfer (Adachi and Tainosho 2004). Generally, the relationship of trace metals in PC1 is maybe related to traffic sources (Huang et al. 2009; Sternbeck et al. 2002). 26.62 % of total variance of the factor 2 in leaf samples is related to Pb and Zn and 30.63 % in barks sample is related to Cd. These elements in atmospheric depositions are also reported to be emitted from road traffic by Bem et al. (2003) (Adachi and Tainosho 2004) and Sternbeck et al. (2002) (Duong and Lee 2011). Generally, the main emission source of atmospheric Pb is leaded gasoline incineration (Bem et al. 2003). Zn may derive from mechanical abrasion of vehicles (Sternbeck et al. 2002) and also from motor vehicles tires and lubricating oils (Manno et al. 2006). The concentration of Cd in the urban atmosphere may also potentially be related to traffic and coal combustion sources (Jiries et al. 2001). The main source of ecological Cd contamination is the ferrous-steel industry, mineral oils, and vehicle wheels. Also waste mud usage may introduce Cd into the air and soil that increases Cd level in the plants (Soltani et al. 2015).

Enrichment factor

The HM enrichment factors were calculated for both leaf and bark sample relative to the background value in the clean area out of the city. In this regard, Fe was taken as the reference element. The mean of the EF for Zn, Cu, Pb, and Cd in leaf samples are 3.03, 54.59, 17.33, and 10 and are 6.51, 49.1, 39.47, 22.56 in bark samples, respectively (Fig. 5). The enrichment situation is classified based on EF value (Tables 1). HMs with mean EF higher than 10 (Cu, Pb, and Cd in both leaf and bark samples) mostly originate from human activities. Although the concentration of Cd was low, its EF value is greater than 10 and indicate that Cd pollution is mainly as result of anthropogenic sources (Yongming et al. 2006). Zn in leaf with mean EF value of 3.03 and in bark with mean EF value of 6.51 is considered to originate primarily from natural sources such as wind-blown soil minerals. The mean EF values for leaf and bark samples presented decreasing trend as Zn<Cd<Pb<Cu.
Fig. 5

Box plot of EF for HMs in the leaf and bark of Ulmus carpinifolia

Table 4

Potential ecological risk factor (E r i ) of single heavy metals (HMs) and potential ecological risk index (RI) of sampling locations

Sampling location

E r i —leaf

RI—leaf

E r i —bark

RI—bark

Zn

Cu

Pb

Cd

Zn

Cu

Pb

Cd

1

3.9

75

100

390

568.9

2.49

31.25

0.8

52.38

86.92

2

5.35

625

781.25

450

1861.6

6.66

104.16

83.33

1746

1940.19

3

5

575

284.37

390

1254.37

5.86

90.62

20.12

585.71

702.32

4

5.39

925

200

540

1670.39

6.69

618.75

24

571.42

1220.87

5

2.35

175

159.37

450

786.72

2.89

63.75

12.8

490.47

569.91

6

1.72

66.66

31.25

300

399.63

4.03

262.5

5.5

571.42

843.46

7

8.29

625

296.87

510

1440.16

7.08

475

14

571.42

1067.51

8

4.69

883.33

50

420

1358.02

6.87

556.25

2.8

21.9

587.82

9

4.68

391.66

131.25

360

887.59

5.22

3.12

40

714.28

762.63

10

4.92

593.33

111.87

354

1064.12

3.07

225

17.5

428.57

674.14

11

5.66

708.33

80

276

1069.99

4.84

215.62

5.15

433.33

658.95

12

5.94

897.5

240.62

384

1528.06

6.43

781.25

16

571.42

1375.1

13

3.44

358.33

228.12

420

1009.89

2.71

11.02

8.82

1120.44

1143.01

14

5.42

391.66

33.12

420

850.21

6.64

106.25

2.95

619.04

734.89

15

4.04

75

96.87

240

415.91

5.70

15.62

6

309.52

336.85

16

9.43

816.66

125

270

1221.09

7.19

256.25

7.5

466.66

737.61

17

5.97

400

146.87

390

942.84

6.18

93.75

10.15

309.52

419.61

18

5.08

250

209.37

390

854.45

4.89

49.37

11.75

376.19

442.21

19

6.02

9.16

184.37

360

559.56

6.49

1093.75

13

619.04

1732.29

20

3.43

16.66

162.5

300

482.59

8.64

243.05

30.55

881.83

1164.09

21

3.79

73.33

58.12

216

351.24

4.32

25.62

6.3

276.19

312.44

22

7.09

481.66

24.37

396

909.13

6.86

97.5

5.1

490.47

599.93

23

4.24

226.66

75

378

683.91

5.39

78.12

7.5

595.23

686.25

24

4.54

375

171.87

420

971.41

5.86

137.5

8.4

761.9

913.66

25

3.53

250

137.5

330

721.03

5.75

81.25

9.9

404.76

501.67

26

6.2

641.66

162.5

450

1260.36

7.15

186.25

13

519.04

725.44

27

4.58

83.33

137.5

360

585.41

5.16

87.5

21.5

571.42

685.59

28

5.59

1583.3

468.75

450

2507.67

4.98

506.25

26.8

1261.9

1799.93

29

3.59

625

56.25

420

1104.84

3.24

203.12

4.25

309.52

520.13

30

8.24

441.66

53.75

288

791.65

7.11

156.25

4

266.66

434.03

31

5.88

1341.66

68.75

4500

5916.29

5.13

181.25

18.5

523.8

728.69

32

3.9

125

143.75

480

752.65

3.62

40.62

10.5

357.14

411.88

33

3.2

308.33

103.12

420

834.65

3.21

97.5

6.15

452.38

559.24

34

4.1

475

128.12

390

997.22

4.5

156.25

10.05

600

770.8

35

4.41

1088.3

306.25

420

1818.99

5.78

289.37

16.3

1261.9

1573.36

36

4.16

1308.3

109.37

360

1781.86

5.19

378.12

7.3

376.19

766.8

37

8.8

325

143.75

450

927.55

4.46

306.25

19.5

571.42

901.64

38

2.78

225

43.75

390

661.53

2.51

76.25

4.9

276.19

359.85

39

5.4

541.66

68.75

450

1065.81

4.74

218.75

6

457.14

686.63

40

5.86

708.33

75

450

1239.19

1.89

253.125

8

571.42

834.45

41

8.07

641.66

34.37

540

1224.11

7.18

160

2.25

171.42

340.86

42

2.89

666.66

306.25

420

1395.8

3.58

200

18.45

1000

1222.04

43

3.52

400

128.12

420

951.64

4.58

131.25

9.9

571.42

717.16

44

4.11

625

246.87

450

1325.98

4.54

200

14.75

785.71

1005

45

4

400

90.62

480

974.62

5.72

93.75

6.15

490.47

596.09

46

3.5

58.33

68.75

540

670.58

4.53

15.62

5.1

452.38

477.63

47

5.02

408.33

71.87

360

845.22

5.81

126.87

5.14

266.66

404.49

48

6.06

608.33

15.62

510

1140.01

6.43

153.12

3.25

233.33

396.13

Mean

5.20

497.81

148.99

486.5

1138.26

5.2

206.95

12.74

548.68

773.59

Max

9.43

1583.33

781.25

4500

5916.29

8.64

1093.75

83.33

1746.03

1940.19

Ecological risk assessment

The results of potential ecological risk coefficient (E r i ) and potential ecological risk index (RI) of Zn, Cu, Pb, and Cd in 48 sampling locations are observed in Table 4 and the categorization based on (Hakanson 1980) is observed in Table 5. According to the mean value of single factor pollution, the RI of Cu and Cd in leaf and bark samples of all locations was in serious pollution level. Pb in leaf samples was in considerable level. Cu in bark samples was in high pollution level. The mean value of Zn concentration was at a low level of potential ecological risk coefficient. The RI shows the sensitivity of various biological populations to toxic material and represents the potential ecological risk caused by HMs. The highest RI value of leaf and bark saw in heavy traffic area (sample no 31 for leaf and sample no 2 for bark). RI values for most sampling locations (85 % of leaf samples and 60 % of bark samples) are bigger than 600 and signify that most areas are at a serious level of ecological risk from the surveyed HMs. In addition, potential ecological RI for 15 % of leaf samples and 37.5 % of bark samples are in considerable level.
Table 5

Categorization of potential ecological risk factor (E r i ) and potential ecological risk index (RI)

E r i

Level of E r i

RI

Level of RI

<40

Low

<150

Low

40–80

Moderate

150–300

Moderate

80–160

Considerable

300–600

Considerable

160–320

High

>600

Serious

≥320

Serious

  

Spatial distribution

Spatial analysis of HMs in the leaf and bark is shown in Figs. 6 and 7, respectively. The results show that maximum concentrations of Pb and Cd are in the area with heavy traffic (center and south area of the city). The maximum level of Cu concentration is close to the ring road of the city since brake wear, tire, and vehicle exhaust emissions are the main source of Cd, Pb, and Cu in the urban area (49, 54). Generally the concentrations of HMs in south area of the city are higher than in the north area. Wind direction may be the main reason for this fact because aerosols and HMs can move from the north to the south and accumulate in the south area.
Fig. 6

Spatial mapping of heavy metals (HMs) in the Ulmus carpinifolia leaf

Fig. 7

Spatial mapping of heavy metals (HMs) in the Ulmus carpinifolia bark

Conclusion

Tree species applied in this study are widely distributed in urban areas worldwide. The elm trees and especially its bark can be considered to be an effective HM accumulator, and so it is an excellent bio-indicator for HM contamination in the urban area.

According to EF value, the main sources of all HMs except Zn in area studied are traffic, metal working industries, and other anthropogenic sources. The RI order of HMs in the leaf is as Zn<Pb<Cd<Cu and is Zn<Pb<Cu<Cd in the bark. The maximum RI was seen in heavy traffic area. The RI in more than 85 % sampling location according to leaf samples and more than 60 %, according to bark samples were in serious pollution level and Cd had the highest effect on RI. Spatial distributions of HMs show that the maximum concentrations are in traffic area and wind direction has an important role in the urban pollutions transfer.

In general, the use of trees as bio-indicators and RI calculation based on HM concentration in the trees has a high applicability worldwide since HMs in tree leaves and barks mostly originate from the soil and air pollution and it can help calculate the risk factor more truly than data which are gained from soil or particle analysis. Also, ecological risk factors obtained from tree study are helpful to develop and complete laws and regulations about HMs in the urban area.

Notes

Acknowledgments

The authors acknowledge the School of Public Health laboratories, Yazd University of Medical Science, and all those who helped us in this research.

References

  1. Abdel-Ghaffar F, Abdel-Gaber R, Bashtar A-R, Morsy K, Mehlhorn H, Al Quraishy S, Saleh R (2015) Hysterothylacium aduncum (Nematoda, Anisakidae) with a new host record from the common sole Solea solea (Soleidae) and its role as a biological indicator of pollution. Parasitol Res 114:513–522CrossRefGoogle Scholar
  2. Adachi K, Tainosho Y (2004) Characterization of heavy metal particles embedded in tire dust. Environ Int 30:1009–1017CrossRefGoogle Scholar
  3. Ahmadi E, Gholami M, Farzadkia M, Nabizadeh R, Azari A (2015) Study of moving bed biofilm reactor in diethyl phthalate and diallyl phthalate removal from synthetic wastewater. Bioresour Technol 183:129–135. doi: 10.1016/j.biortech.2015.01.122 CrossRefGoogle Scholar
  4. Barnes D, Hamadah M, Ottaway J (1976) The lead, copper and zinc content of tree rings and bark a measurement of local metallic pollution. Sci Total Environ 5:63–67CrossRefGoogle Scholar
  5. Baycu G, Tolunay D, Özden H, Günebakan S (2006) Ecophysiological and seasonal variations in Cd, Pb, Zn, and Ni concentrations in the leaves of urban deciduous trees in Istanbul. Environ Pollut 143:545–554CrossRefGoogle Scholar
  6. Bem H, Gallorini M, Rizzio E, Krzemińska M (2003) Comparative studies on the concentrations of some elements in the urban air particulate matter in Lodz City of Poland and in Milan, Italy. Environ Int 29:423–428CrossRefGoogle Scholar
  7. Birch GF, Olmos MA (2008) Sediment-bound heavy metals as indicators of human influence and biological risk in coastal water bodies ICES. J Mar Sci 65:1407–1413Google Scholar
  8. Çelik A, Kartal AA, Akdoğan A, Kaska Y (2005) Determining the heavy metal pollution in Denizli (Turkey) by using Robinio pseudo-acacia L. Environ Int 31:105–112CrossRefGoogle Scholar
  9. Duong TT, Lee B-K (2011) Determining contamination level of heavy metals in road dust from busy traffic areas with different characteristics. J Environ Manag 92:554–562CrossRefGoogle Scholar
  10. Ehrampoush MH, Miria M, Salmani MH, Mahvi AH (2015) Cadmium removal from aqueous solution by green synthesis iron oxide nanoparticles with tangerine peel extract. J Environ Health Sci Eng 13:1CrossRefGoogle Scholar
  11. Fang G-C, Chang C-N, Chu C-C, Wu Y-S, Fu PP-C, Yang I-L, Chen M-H (2003) Characterization of particulate, metallic elements of TSP, PM 2.5 and PM 2.5-10 aerosols at a farm sampling site in Taiwan, Taichung. Sci Total Environ 308:157–166CrossRefGoogle Scholar
  12. Günthardt-Goerg MS, Vollenweider P (2007) Linking stress with macroscopic and microscopic leaf response in trees: new diagnostic perspectives. Environ Pollut 147:467–488CrossRefGoogle Scholar
  13. Guo W, Liu X, Liu Z, Li G (2010) Pollution and potential ecological risk evaluation of heavy metals in the sediments around Dongjiang Harbor Tianjin. Proc Environ Sci 2:729–736CrossRefGoogle Scholar
  14. Hakanson L (1980) An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res 14:975–1001CrossRefGoogle Scholar
  15. Hernández-Quiroz M, Herre A, Cram S, de León CP, Siebe C (2012) Pedogenic, lithogenic- or anthropogenic origin of Cr, Ni, and V in soils near a petrochemical facility in Southeast Mexico. Catena 93:49–57CrossRefGoogle Scholar
  16. Huang S et al (2009) Multivariate analysis of trace element concentrations in atmospheric deposition in the Yangtze River Delta, East China. Atmos Environ 43:5781–5790CrossRefGoogle Scholar
  17. Huang H et al (2011) Quantitative evaluation of heavy metals’ pollution hazards in liquefaction residues of sewage sludge. Bioresour Technol 102:10346–10351CrossRefGoogle Scholar
  18. Huston R, Chan Y, Chapman H, Gardner T, Shaw G (2012) Source apportionment of heavy metals and ionic contaminants in rainwater tanks in a subtropical urban area in Australia. Water Res 46:1121–1132CrossRefGoogle Scholar
  19. Jiries A, Hussein H, Halaseh Z (2001) The quality of water and sediments of street runoff in Amman, Jordan. Hydrol Proc 15:815–824CrossRefGoogle Scholar
  20. Kong S et al (2011) Potential threat of heavy metals in re-suspended dusts on building surfaces in oilfield city. Atmos Environ 45:4192–4204CrossRefGoogle Scholar
  21. Kularatne K, De Freitas C (2013) Epiphytic lichens as biomonitors of airborne heavy metal pollution. Environ Exp Bot 88:24–32CrossRefGoogle Scholar
  22. Li X, Poon C-s, Liu PS (2001) Heavy metal contamination of urban soils and street dusts in Hong Kong. Appl Geochem 16:1361–1368CrossRefGoogle Scholar
  23. Liu Q-T, Diamond ML, Gingrich SE, Ondov JM, Maciejczyk P, Stern GA (2003) Accumulation of metals, trace elements and semi-volatile organic compounds on exterior window surfaces in Baltimore. Environ Pollut 122:51–61CrossRefGoogle Scholar
  24. Liu X et al (2013) Human health risk assessment of heavy metals in soil–vegetable system: a multi-medium analysis. Sci Total Environ 463:530–540CrossRefGoogle Scholar
  25. Lu X, Wang L, Li LY, Lei K, Huang L, Kang D (2010) Multivariate statistical analysis of heavy metals in street dust of Baoji NW China. J Hazard Mater 173:744–749CrossRefGoogle Scholar
  26. Manno E, Varrica D, Dongarra G (2006) Metal distribution in road dust samples collected in an urban area close to a petrochemical plant at Gela Sicily. Atmos Environ 40:5929–5941CrossRefGoogle Scholar
  27. Markert B (1994) Element concentration cadasters in ecosystems. Progress Report on the Element Concentration Cadaster Project ECCE of INTECOL/IUBS 25th General Assembly of IUBS, ParisGoogle Scholar
  28. Mokhtari M, Miri M, Mohammadi A, Khorsandi H, Hajizadeh Y, Abdolahnejad A (2015) Assessment of air quality index and health impact of PM10, PM2.5 and SO2 in Yazd Iran. J Mazandaran Univ Med Sci 25:14–23Google Scholar
  29. Nath B, Birch G, Chaudhuri P (2014) Assessment of sediment quality in Avicennia marina-dominated embayments of Sydney Estuary: the potential use of pneumatophores (aerial roots) as a bio-indicator of trace metal contamination. Sci Total Environ 472:1010–1022CrossRefGoogle Scholar
  30. Norouzi S, Khademi H, Cano AF, Acosta JA (2015) Using plane tree leaves for biomonitoring of dust borne heavy metals: a case study from Isfahan. Centr Iran Ecol Indic 57:64–73CrossRefGoogle Scholar
  31. Paatero P, Hopke PK, Begum BA, Biswas SK (2005) A graphical diagnostic method for assessing the rotation in factor analytical models of atmospheric pollution. Atmos Environ 39:193–201CrossRefGoogle Scholar
  32. Qingjie G, Jun D, Yunchuan X, Qingfei W, Liqiang Y (2008) Calculating pollution indices by heavy metals in ecological geochemistry assessment and a case study in parks of Beijing. J China Univ Geosci 19:230–241CrossRefGoogle Scholar
  33. Ragosta M, Caggiano R, Macchiato M, Sabia S, Trippetta S (2008) Trace elements in daily collected aerosol: level characterization and source identification in a four-year study. Atmos Res 89:206–217CrossRefGoogle Scholar
  34. Rodriguez J, Wannaz E, Salazar M, Pignata M, Fangmeier A, Franzaring J (2012) Accumulation of polycyclic aromatic hydrocarbons and heavy metals in the tree foliage of Eucalyptus rostrata, Pinus radiata and Populus hybridus in the vicinity of a large aluminium smelter in Argentina. Atmos Environ 55:35–42CrossRefGoogle Scholar
  35. Sæbø A, Popek R, Nawrot B, Hanslin H, Gawronska H, Gawronski S (2012) Plant species differences in particulate matter accumulation on leaf surfaces. Sci Total Environ 427:347–354CrossRefGoogle Scholar
  36. Sawidis T, Chettri M, Papaioannou A, Zachariadis G, Stratis J (2001) A study of metal distribution from lignite fuels using trees as biological monitors. Ecotoxicol Environ Saf 48:27–35CrossRefGoogle Scholar
  37. Sawidis T, Breuste J, Mitrovic M, Pavlovic P, Tsigaridas K (2011) Trees as bioindicator of heavy metal pollution in three European cities. Environ Pollut 159:3560–3570CrossRefGoogle Scholar
  38. Sawidis T, Krystallidis P, Veros D, Chettri M (2012) A study of air pollution with heavy metals in Athens city and Attica basin using evergreen trees as biological indicators. Biol Trace Elem Res 148:396–408CrossRefGoogle Scholar
  39. Serbula SM, Miljkovic DD, Kovacevic RM, Ilic AA (2012) Assessment of airborne heavy metal pollution using plant parts and topsoil. Ecotoxicol Environ Saf 76:209–214CrossRefGoogle Scholar
  40. Serbula SM, Kalinovic TS, Ilic AA, Kalinovic JV, Steharnik MM (2013) Assessment of airborne heavy metal pollution using Pinus spp. and Tilia spp. Aerosol Air Qual Res 13:563–573Google Scholar
  41. Shen Z, Liao Q, Hong Q, Gong Y (2012) An overview of research on agricultural non-point source pollution modelling in China. Sep Purif Technol 84:104–111CrossRefGoogle Scholar
  42. Škrbić B, Đurišić-Mladenović N (2010) Chemometric interpretation of heavy metal patterns in soils worldwide. Chemosphere 80:1360–1369CrossRefGoogle Scholar
  43. Soltani N, Keshavarzi B, Moore F, Tavakol T, Lahijanzadeh AR, Jaafarzadeh N, Kermani M (2015) Ecological and human health hazards of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in road dust of Isfahan metropolis, Iran. Sci Total Environ 505:712–723CrossRefGoogle Scholar
  44. Sternbeck J, Sjödin Å, Andréasson K (2002) Metal emissions from road traffic and the influence of resuspension—results from two tunnel studies. Atmos Environ 36:4735–4744CrossRefGoogle Scholar
  45. Streets DG, Devane MK, Lu Z, Bond TC, Sunderland EM, Jacob DJ (2011) All-time releases of mercury to the atmosphere from human activities. Environ Sci Technol 45:10485–10491CrossRefGoogle Scholar
  46. Wei B, Jiang F, Li X, Mu S (2010) Heavy metal induced ecological risk in the city of Urumqi, NW China. Environ Monit Assess 160:33–45CrossRefGoogle Scholar
  47. Yongming H, Peixuan D, Junji C, Posmentier ES (2006) Multivariate analysis of heavy metal contamination in urban dusts of Xi’an, Central China. Sci Total Environ 355:176–186CrossRefGoogle Scholar
  48. Yuen J, Olin PH, Lim H, Benner SG, Sutherland R, Ziegler A (2012) Accumulation of potentially toxic elements in road deposited sediments in residential and light industrial neighborhoods of Singapore. J Environ Manag 101:151–163CrossRefGoogle Scholar
  49. Zhai Y, Chen H, Xu B, Xiang B, Chen Z, Li C, Zeng G (2014) Influence of sewage sludge-based activated carbon and temperature on the liquefaction of sewage sludge: yield and composition of bio-oil, immobilization and risk assessment of heavy metals. Bioresour Technol 159:72–79CrossRefGoogle Scholar
  50. Zhang Q, Ye J, Chen J, Xu H, Wang C, Zhao M (2014) Risk assessment of polychlorinated biphenyls and heavy metals in soils of an abandoned e-waste site in China. Environ Pollut 185:258–265CrossRefGoogle Scholar
  51. Zhao H, Xia B, Fan C, Zhao P, Shen S (2012) Human health risk from soil heavy metal contamination under different land uses near Dabaoshan Mine, Southern China. Sci Total Environ 417:45–54CrossRefGoogle Scholar
  52. Zheng J et al (2013) Heavy metals in food, house dust, and water from an e-waste recycling area in South China and the potential risk to human health. Ecotoxicol Environ Saf 96:205–212CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Mohammad Miri
    • 1
  • Ahmad Allahabadi
    • 2
  • Hamid Reza Ghaffari
    • 3
    • 4
  • Zeynab Abaszadeh Fathabadi
    • 1
  • Zahra Raisi
    • 1
  • Mehrab Rezai
    • 1
  • Mohsen Yazdani Aval
    • 5
  1. 1.Environmental Science and Technology Research Center, Department of Environmental Health, School of Public HealthShahid Sadoughi University of Medical SciencesYazdIran
  2. 2.Department of Environmental Health, School of Public HealthSabzevar University of Medical SciencesSabzevarIran
  3. 3.Social Determinants in Health Promotion Research CenterHormozgan University of Medical SciencesBandar AbbasIran
  4. 4.Department of Environmental Health Engineering, School of Public HealthTehran University of Medical SciencesTehranIran
  5. 5.Department of Medical ScienceTarbiat Modarres UniversityTehranIran

Personalised recommendations