Ecological risk assessment of heavy metal (HM) pollution in the ambient air using a new bio-indicator
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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 airIntroduction
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).
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Measurement of HM concentration in the leaf and bark of Ulmus carpinifolia species as bio-indicator
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Determination of the probable sources of HMs in leaf and bark
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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
Sampling and analysis
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
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 |
Result and discussion
HM concentration in the leaf and bark
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
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 |
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
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
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
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
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