Research Article

 

Ecological evaluation of heavy metal pollution of different soil-climatic regions of Armenia by biogeochemical coefficients

 

Astghik Sukiasyan*, Armen Kirakosyan

 

Faculty of Chemical technology and Environmental engineering, National Polytechnic University of Armenia, Yerevan, Armenia
*Correspondence: Astghik Sukiasyan, sukiasyan.astghik@gmail.com, Faculty of Chemical technology and Environmental Engineering, National Polytechnic University of Armenia, 0009,Yerevan, 105, Teryan Str., Armenia, Tel.:+37494568740, https://orcid.org/0000-0001-5549-3146

 

ABSTRACT: 

 

We assessed environmental risk factors, based on the biogeochemical coefficients of several heavy metals in the coastal areas of different rivers in Armenia. Environmental risk factors of some heavy metals in coastal areas of the Rivers Debet, Shnokh, and Araks (Armenia) were evaluated in various soil-climatic regions. A combined method for appraising biogeochemical risk factors in soil-plant systems of coastal areas of rivers was used. Values of several biogeochemical coefficients served as input Zea Maize L. being chosen as the model plant. The total indicator of contamination with heavy metals in the hazard group was calculated, according to the Russian GOST. We found the most polluted soil in Hushakert and the least contaminated in Tekhut. The use of European classification of heavy metals, by hazard class, to determine the maximum permissible additives was considered. Hazard classes, which allow categorizing heavy metals, primarily depend on the synergism of biota response to the degree of contamination. Using annual plants allows a comprehensive assessment of the migration characteristics of heavy metals in the examined soil-plant systems. As a result, one can expand the use of Zea Maize L. indicator plant, so that it becomes a natural filter of coastal areas for monitoring heavy metals migration.

 

Keywords: Heavy metal, Soil, Climate, Ecology, Armenia

 

Full Article PDF ⬇️

 

Cite as: Sukiasyan and Kirakosyan. Ecological evaluation of heavy metal pollution of different soil-climatic regions of Armenia by biogeochemical coefficients. DRC Sustainable Future 2020, 1(2). 94-102. DOI: 10.37281/1.2.2 

 

  1. Introduction

 

Industry development goes along with inevitable large-scale environmental pollution. Most of the pollutants enter the environment as waste from a specific source of pollution. Heavy metals (HMs) represent a major part of these pollutants, although in most cases these toxic elements are present only in trace concentrations (Wong et al., 2002, Seaward, 2004). As HMs are natural components of the earth crust, it is important to determine a baseline of their content in the environment. Deposition of HMs ions in the soil “governs” in a certain way the physicochemical properties of the soil, changing some of its basic characteristics, such as mobilization, sorption, and adsorption of the soil cover (Mónok et al.,  2017) Anthropogenic concentration changes of HMs contribute to their uncontrolled distribution in the environment. Eventually, HMs reach elevated concentrations and undergo chemical modifications, so that they may exert toxicity, detrimental to the habitat of living organisms (Mihaly-Cozmuta et al., 2005). One important soil property is its buffering capacity, which accounts for the resistance of soils to anthropogenic effects. Soil contamination with increased HM concentrations contributes to the gradual degrading of the environment. The process of soil recovery depends on the dynamics of HM migration, and the mechanisms of HM sorption and accumulation in plants. Susceptibility of plant species to one single chemical element is very specific and largely depends on the intensity of metabolic processes in which the element is involved. Nonetheless, in the geochemical assessment of the state of the environment, one should consider the distribution of chemical elements in plants. This approach allows us to categorize HMs according to the degree of threat they pose to the environment.

 

As early as in 1973, the UN Global Monitoring Program listed only Pb, Cd, and Hg as HMs (Dobrovol’skiy, 1983). Later, within the UN Environment Program, the list was expanded to comprise in addition to HMs (Cu, Sn, V, Cr, Mo, Co, and Ni) metalloids (Sb, As, and Sе), as well (State of the environment. UNEP 1980). At the same time, the Russian sanitary and hygienic GOST 17.4.102-83 was adopted, according to which As, Cd, Hg, Se, Pb, and Zn belong to highly hazardous HMs, Ni, Mo, Cu, and Sb are moderately hazardous, while Ba, V, W, Mn, and Sr belong to the low-hazardous group (GOST, 2008). Comprehensive assessment based on an improved evaluation model includes weight factor of HM pollution in soil was used in China (Hong-Gui et al., 2012; Ren and Tian, 2012)

 

Altered biochemical, physiological, and metabolic processes have been identified in plants that grow in regions with significant metal pollution (Förstner et al., 1981; Gao et al., 2016; Sukiasyan et al., 2018). In the meanwhile, availability of metals like Cu, Mn, Co, Zn, and Cr in trace amounts is needed by plants for their regular metabolic activities (Srivastava et al., 2017).

 

Remediation of soil requires appropriate attention to protect soil quality, the ecosystem, and human health. Physical and chemical HMs remediation technologies are relatively expensive, often destructive to the local ecosystem, and produce hazardous wastes in large amounts (Yao et al., 2012). By contrast, developing biological technologies, such as phytoremediation, shows great potential, being environment friendly and less expensive than other conventional methods (Sukiasyan et al., 2018).

 

There are crop plants, such as Brassica carinata, Hellianthusannus L., Glycine max. L. Merr, Sorghum bicolor, Medicago sativa L., and Zea Maize L. that can be used as bioindicators of the presence of some heavy metals. (Pari et al., 2010; Zürrer and Bachofen, 1985; Demirta et al., 2010; Theuretzbacher et al., 2012;Slepetys et al., 2012). Each crop plant has individual sensitivity to specific HMs and shows differences in HM accumulation. Response of plants to HM pollution can vary, when cultivated in distinct ecological zones with different degrees of pollution (Xiong et al., 2017).

 

Various countries worldwide set particular standards for evaluating the HM content of soils. For example, according to ecological and toxicological data obtained by European ecologists, heavy metals/metalloids form the following series of hazard degree in soils (Crommentuijn et al., 1997):

 

 

 

Se > Tl > Sb > Cd > V > Hg > Ni > Cu > Cr > As > Ba

 

 

 

This sequence substantially differs from the succession of heavy elements listed in the general toxicological Russian GOST (State Norms and Standards of Russian Federation), which considers As, Cd, Hg, Se, Pb, and Zn to be severely hazardous elements, whereas Co, Ni, Mo, Sb, and Cr are rated as moderately hazardous (Vodyanitskii, 2016).

 

Development goals are based on selecting appropriate indicators as the targets from existing sets or introducing new ones (Hák et al., 2016). A flexible approach to the classification scale of HMs in Europe is based on the principle of human-mediated use of soil resources. This approach primarily considers the agro-industrial complex, the sowing of agricultural products (Crommentuijn et al., 2017; Van de Plassche and De Bruijn, 1992). The analytical relevance of trace metals and HMs speciation analysis must be evaluated in environmental and biophysical-chemical systems, to secure reliable and efficient assessment and monitoring of trace metals (Benson et al., 2013). The total pollution indicator is, indeed, a quantitative measure for HMs in the form of a sum of the excess of scattering coefficients of HMs, with their specific geochemical level. However, to assess the intensity of migration of HMs in the soil-plant system, various criteria were developed, among which the pollution indices correspond to the measure of total pollution for an individual risk group of HMs (Kowalska et al., 2018). The contamination factor (or bioaccumulation factor), ecological risk factor, enrichment factor, and index of geo-accumulation can illustrate the level of pollution (Qingjie et al., 2008; Hu et al., 2017; Salman et al., 2019).

 

Review of recent literature revealed three strategies that take advantage of specific biochemical and common parameters to estimate soil quality via heavy metals contamination and evaluate soil remediation potential: (1) the use of simple indexes; (2) estimation of individual properties; or (3) the use of complex indexes derived from combinations of various parameters or inferred from statistical procedures (Gil-Sotres et al., 2005; Gao et al., 2016; Kowalska et al., 2018).

 

The aim of this work was to study the hazard degree of soil heavy metal pollutions and the ecological potential of soil remediation using Zea Maize L. We determined biogeochemical coefficients for several heavy metals in different ecological zones of Armenia.

  

  1. Methods 

To conduct our research, we selected as a model plant the widely cultivated variety of semi-dentate Zea Mays L., which is widespread in the Lori region of Armenia, along the Debet River (Odzun –41°03′06″ N, 44°36′55″ E) and its tributary of the Shnokh River (Shnokh – 41°08′52″ N, 44 °50′16″ E, Tekhut – 41°07′05″ N, 44°50′45″ E) It can also be found in the Armavir region, along the Araks River (Hushakert – 40°04′52″ N, 43°55′35″ E) (Figure 1).

 

  • Preparation of soil samples. Soil samples were collected under dry weather condition, from the depth of 120 mm, which corresponds to the growth of the root system of the investigated plants. Combined samples were taken from each site by the envelope method, which consisted of at least 5-point samples. Sampling was done with metal-free instruments. Next, the samples were placed in dark glass containers and transported to the laboratory, at the temperature of +4 °C. Instrumental analysis was performed within 24 h. After removing the remnants of the root system, insects, and other solid constituents, the soil was ground in a mortar and pestle, and sieved through a sieve with diameter holes ≤ 1 mm.
  • Preparation of plant samples (Zea Maize L.) Air- drying of ripe maize grains was conducted in a fume hood, at room temperature. For ash formation, the plant material was placed in a muffle furnace, using pre-calcined porcelain cups, at the temperature of +400 °C, for up to 1 h. Then, the dry residue samples (ash) were transferred in a desiccator for being stored until further instrumental measurements.
  • Concentration measurement of chemical elements. Prepared samples (Zea Maize L. grain and soil ash) were placed in XRF Sample Cups (USA), i.e., plastic tubes with the diameter of 32 mm. Prior to introducing the samples, a special polypropylene film was inserted to the bottom of the cups. Proper seals were placed at the upper part of the samples and then closed with a lid, which allowed for compressing the sample to the desired state. Analysis involved directing X-rays onto the sample for up to 210 s, using a Thermo Scientific™ Niton™ XRF Portable Analyzer.

 

 

 

Figure 1. Modified map with marked experimental regions of Armenia (Montanarella et al., 2013).

 

 

 

  • Calculation of biogeochemical coefficients. To characterize the HM pollution of soil and the processes of absorption and accumulation of HMs in plants, the following parameters were calculated:

 

  1. Potential biochemical mobility of HMs from soil to plant (PBM):

 

PBM = Cp/Cs                              (1)

 

where Cp – the HMs content in the ash of Zea Maize, sampled from its growth region, mg/kg; Cs – HMs content in the relevant soil of growth, mg/kg (Perel’man and Kasimov, 1999)

 

  1. Concentration coefficient (Cc):

 

Cc = Cs/Bf                                   (2)

 

where Bf – background content of HMs in the soil of growth, mg/kg (Kasimov and Vlasov, 2005)

 

  1. Total pollution indicator (Zc):

 

               (3)

 

when Zc < 16, the value corresponds to the level of danger acceptable to humans, 16 < Zc <32 – moderate danger level, 32 < Zc < 128 – dangerous level, Zc > 128 – extremely dangerous level, n – number of HMs

 

  1. Pollution index (Ipol):

 

Ipol = (Cc1 × Cc2 ×…× Ccn)1/n         (4)

 

the value of pollution intervals is defined as the ratio PBM/Ipol. So, PBM/Ipol < 0.10 corresponds to insignificant pollution; 0.11 < PBM/Ipol. < 0.20– low pollution range; 0.21 < PBM/Ipol. < 4.0 – moderate pollution range; 4.1 < PBM/Ipol.< 8.0– the range of severe pollution; PBM/Ipol > 8.1– excessive pollution range; n is the number of HMs;

 

  1. Quantitative expression of the environmental risk factor (ERF):

 

ERF = PBM / Ct                          (5)

 

 where Ct is the toxicity coefficient for a given polluting chemical element (Crommentuijn et al., 1997), and the categories used to describe ERF are as follow: ERF < 40 (low environmental risk), 40 < ERF < 80 (moderate environmental risk), 80 < ERF < 160 (significant environmental risk), 160  < ERF < 320 (high environmental risk), ERF > 320 (very high environmental risk) (Hakanson, 1980).

 

  1. f) Index of geo-accumulation for classification of the studied soil samples, according to the degree of contamination HMs:

 

Igeo = log2 (Cs / 1,5 Х Bn)               (6)

 

where: Cs – HMs content in the relevant soil of growth, in mg kg-1; а Bn – geochemical background value (median) for each type of soil according to Unanyan (2010), in mg kg-1. The degree of soil contamination was assessed according to the Müller scale, where I is a practically unpolluted background of Igeo ≤ 0; II – uncontaminated to moderate 0 < Igeo < 1; III – moderately polluted 1 ≤ Igeo ≤ 2; IV – slightly contaminated to strong 2 ≤ Igeo< 3; V – highly contaminated 3 ≤ Igeo< 4; VI – highly to extreme lypolluted 4 ≤ Igeo< 5; VII – very heavily polluted Igeo ≥ 5 (Förstner and Müller, 1981). Igeo was found to be the most reliable parameter for assessing sediment pollution (Nowrouzi and Pourkhabaz, 2015).

 

  • Statistical processing. All experiments had 10 biological and up to five technical replicates. Results were processed using MatLab software, considering Student’s t-test. The observed differences were statistically significant at a significance level of P < 0.05. (Kirakosyan and Sukiasyan, 2005; Dobrovol’skiy, 1983).

 

  1. Results and discussion

 

In most cases, anthropogenic pollution itself is inherently multi-elemental; therefore, statistical parameters of HMs content of soils and their distribution can serve as a regional characteristic of soil contamination. Dominance of mountain relief in Armenia enforces that most coastal river areas should be adopted for agricultural use. This makes it is necessary to estimate the level of toxic HMs in the soil, based on the ecotoxic principle, namely, by comparing the effects of different chemical elements on soil biota and plants.  Spatial distribution of soil HMs, in different land uses of industrial areas, had to be considered, as well (Mahmoudabadi et al., 2015). Based on the values of concentration coefficient (Cc) and total pollution indicator  (Zc) a sum of the excess of Cc values over background indicators was calculated (Kasimov and Vlasov, 2015), followed by assigning HMs to hazard classes, according to the Russian GOST (GOST, 2008).  Results are listed in Table 1.

 

 

 

Table 1. Values of coefficients of concentrations (Cc) and total indicator of pollution (Zc) in different soil-climatic regions of the Republic of Armenia

 

Classification of heavy metals

 by GOST, 2008

Soil-climatic regions

 

Tekhut

Shnokh

Odzun

Hushakert

Slightly toxic

Ba

0.499

1.214

0.421

0.183

Sr

0.490

0.550

0.577

0.689

W

9.652

19.254

14.095

41.056

Mn

0.594

0.872

0.551

1.299

V

0.907

0.964

1.0140

1.930

Zc

8.143

18.855

12.658

41.158

Moderately toxic

Sb

18.349

25.831

9.382

31.177

Mo

1.232

2.639

1.759

2.136

Cu

1.241

16.138

2.664

1.641

Ni

1.1954

1.269

0.877

1.106

Co

7.080

14.583

12.745

14.244

Cr

0.780

0.762

1.069

4.017

Zc

24.877

56.221

23.496

49.320

Highly toxic

Cd

9.567

25.054

9.879

26.290

As

1.701

8.389

1.958

3.144

Pb

0.680

4.341

1.037

0.799

Zn

0.724

3.398

0.866

1.315

Hg

69.260

75.761

100.894

81.972

Zc

77.932

112.944

110.634

109.520

 

Low-risk class HMs are within the permissible hazard level in Tekhut and Odzun regions and show a slight increase of 6% relative to the Shnokh region. In the village of Hushakert, the level Zc for low-hazardous HMs is within the limits considered as dangerous level. When analyzing the class of moderately toxic HMs, a similar pattern is observed, with uniformly exceeding meaning of Zc from dangerous level in Tekhut and Odzun to moderate, and in Shnokh and Hushakert up to dangerous levels. When considering the class of highly toxic HMs, soils from all four regions have Zc in the dangerous concentration level.

It is appropriate to use for the quantitative assessment of the toxic effects of HMs on soil biota and plants, the value of maximum allowable supplement (MAS) of chemical elements, grouping them into classes by the risk of exposure (Crommentuijn et al., 1997). In this case, the MAS calculated based on a variety of eco-toxic studies that were carried out only for some of the elements (Vodyanskiy, 2012). 

Adhering to the above approach, the potential biochemical mobility of HMs from soil to plant (PBM) was determined, and from this the pollution index (Ipol) was calculated. According to the values of the highly toxic HMs class, moderate contamination was observed in the Shnokh settlement, while in Tekhut and Odzun there is a significant contamination, and according to the Ipol. value, the contamination in Hushakert is very high. Ipol values are low for the moderately hazardous group of HMs in all the studied territories. In the class of low-hazardous HMs, Tekhut is considered significantly contaminated, but judged by the Ipol value, only moderate contamination is observed. Further, to assess the state of the environment, the ratio of PBM/Ipol was considered (Table 2).

 

Table 2. Values of biochemical mobility of heavy metals by Crommentuijn et al., 1997 from soil to plant (PBM) and ranges of contamination (PBM/ Ipol) in different soil-climatic regions of the Republic of Armenia

 

Classification of heavy metals

Soil-climatic regions

Tekhut

Shnokh

Odzun

Hushakert

PBM

PBM / Ipol.

PBM

PBM / Ipol.

PBM

PBM / Ipol.

PBM

PBM / Ipol.

Highly toxic

Se

1.4

4

2.4

0.9

1.3

0.3

2.7

0.3

Sb

0.5

0.3

0.7

0.2

1.1

0.3

0.7

0.3

Cd

78.4

20.1

13.3

4.7

39.4

10.3

164.1

10.3

Moderately toxic

V

0.2

0.3

0.3

0.7

0.2

0.23

0.1

0.1

Hg

1.7

3.3

2.9

6.1

4.5

6.6

3.0

4.6

Ni

0.3

0.5

0.5

1.0

1.2

1.7

2.0

3.1

Cu

4.9

9.2

0.6

1.2

2.9

4.3

3.8

5.8

Cr

1.2

2.3

2.6

5.5

0.8

1.2

0.2

0.4

As

0.3

0.6

0.1

0.2

0.3

0.4

0.4

0.7

Ba

0.1

0.1

0.1

0.1

0.1

0.2

0.3

0.4

Slightly toxic

Mo

18.8

6.8

17.3

6.6

15.7

3.9

7.1

2.7

Pb

0.9

0.3

0.1

0.1

0.4

0.2

1.1

0.5

Zn

23.9

8.7

10.3

3.9

43.33

10.6

21.7

8.3

Co

0.2

0.1

0.2

0.1

0.2

0.1

0.1

0.1

 

 

 

It was observed that among the group of highly dangerous HMs, Se and Sb possess a large PBM value, which ranges from 13 (Shnokh) to 164 (Hushakert). According to the Se and Sb content, the soil-plant system is in a state of moderate contamination or pollution. Evaluating Cd, one can state that in all investigated regions the soil-plant system is excessively polluted. In the group of moderately toxic HMs, according to the indications of PBM, Zea Maize L. extensively absorbs and accumulates Hg and Cr in from soils from Shnokh. Similar data was found in soils from Tekhut, along with strong absorption of Cu by the plant. In Odzun and Hushakert, there is a comparable migration trend from soil to plant, for Hg, Cu, and Ni.

 

To obtain a reliable picture of the degree of contamination, the PBM/Ipol ratios were compared. According to this parameter, in Tekhut – Hg, and Cr, in Shnokh– Cu and Ni, while in Odzun and Hushakert only Ni caused moderate pollution. At the same time, excessive Cu contamination was noticed in the Tekhut area. Particularly in Odzun and Hushakert, Hg and Cr pollution was found, and a severe Hg and Cu pollution was recorded in the village of Shnokh. Out of the elements of the second group, for Ba, As, and V, PBM coefficient values do not exceed the unity, hence, the numerical equivalent ratio of PBM/Ipol ranges corresponds to slightly and moderately polluted areas.

 

For low-toxicity HMs, there is little mobility of Co and Ba, and the range of ​PBM/Ipol ratio is limited to medium and moderate values. Focusing on the PBM values, Zea Maize L. is prone to absorb and accumulate HMs, its highest Mo value being detected in Tekhut (18, 8), while the maximum value for Zn was determined in Odzun (43, 33). The lowest Mo value was found in Hushakert (7, 1) and lowest Zn in Shnokh (3, 9). Zn contamination is excessive in all areas studied, except for Shnokh. This finding is exacerbated by concentration changes in natural resources, which are primarily anthropogenic, such as industrial and transportation developments, extensive mining of minerals, and the active chemicalization of agriculture (Kroik et al., 2012).

 

Assessment of the range of contamination of the soil-plant system is insufficient for getting a complete picture of the environmental pollution in coastal areas. Therefore, it is necessary to quantify the environmental risk factor (ERF) for each element, according to the toxic classes they belong to. According to results obtained for the class of highly dangerous HMs, Se and Sb have a low level of environmental risk (ERF < 40) for all investigated areas (Table 3).

 

In Shnokh and Odzun low ecological risk of Cd is noted (ERF < 80), while in Hushakert it reaches a significant level (ERF < 160). In the class of moderately hazardous HMs, ERF for all elements was low in the studied areas.

 

There was a significant difference between poorly toxic HMs. For Co, the soil-plant system had a low ERF value, while for Pb it exhibited moderate (Tekhut, Shnokh) or significant values (Odzun, Hushakert). One should stress the very high value of ERF for Zn and Mo in each contaminated area. Nevertheless, the total pollution index had greater values because it included HMs, which were active during the migration in the water-soil-plant system (Sukiasyan et al., 2018a). These can affect the growth of agricultural plants and their productivity, since the coastal soil acts as a natural filter, when using anthropogenic polluted river water. The latter is an active source of dangerous microelements, spread in the regions considered. Given that the basic carrier of HMs in plants is water, the physiological response of plants differ from one cultivation region to another, being governed by various degrees of stress caused by drought (Sukiasyan and Pirumyan, 2018).

 

 

 

Table 3. Quantitative assessment of the environmental risk factoring different soil-climatic regions of the Republic of Armenia

 

Classification of heavy metals

Soil-climatic regions

Tekhut

Shnokh

Odzun

Hushakert

Highly toxic

Se

0.153

0.269

0.139

0.298

Sb

0.286

0.359

0.599

0.382

Cd

59.6

10.1

29.9

124.7

Moderately toxic

V

0.186

0.345

0.193

0.093

Hg

3.295

5.432

8.541

5.756

Ni

0.729

1.278

2.989

5.225

Cu

17.12

2.031

10.19

13.24

Cr

4.697

9.863

3.025

0.892

As

1.399

0.481

1.323

1.923

Ba

0.710

0.630

1.004

2.384

Slightly toxic

Mo

4757

4388

3977

1792

Pb

50.95

4.74

22.08

61.44

Zn

383.0

165.0

693.3

347.5

Co

5.302

4.615

3.838

3.329

 

 

 

The geo-accumulation index was calculated, which served as the basis for classification of soil samples, when considering the degree of contamination by HMs. According to our results, listed in Table 4, soil samples from the Hushakert settlement contained Mn, Cu, and Mo, which corresponded to slightly polluted condition of the soil, while concentrations of Co and Zn revealed a heavily polluted status. Calculation of the Igeo value for soil samples originating from Odzun showed that the environment was moderately polluted with Cu and Mo, while Mn content was relatively low. As in Odzun, these soil samples were found heavily polluted with Co and Zn.

 

Soil samples from Tekhut were slightly polluted with Mo, Mn, and Cu, but heavily polluted with Co and Zn. Analysis of soil samples from Shnokh indicated a moderate Mn contamination and heavy Mo contamination.

 

 

 

Table 4. The index values of geo-accumulation (Igeo) and pollution by category contaminated soil classes for some heavy metals in different soil-climatic regions of the Republic of Armenia

 

Heavy metal

Hushakert

Category

Odzun

Category

Tekhut

Category

Shnokh

Category

Mn

2.4±0.2

IV

0.9±0.1

I

1.3±0.1

III

1.9±0.1

III

Cu

2.9±0.1

IV

2.9±0.1

IV

2.1±0.3

IV

5.8±0.3

VII

Co

5.80±0.02

VII

5.5±0.1

VII

4.6±0.1

VI

5.6±0.1

VII

Zn

7.1±0.5

VII

5.9±0.3

VII

6.4±0.4

VII

8.6±0.4

VII

Mo

3.6±0.3

V

3.5±0.1

V

3.2±0.5

V

4.3±0.6

VI

 

 

 

The cumulative activity of HMs in plants is regulated by the extent of water absorption of the soil and the decrease in the absorbing capacity of the root system. Because of disturbed water exchange, plant leaves lose turgor, which affects adversely their physiological processes (Ilin et al., 2001). Revealed differences in the cumulative effect of HMs on the geo-accumulation index (Igeo) and the excellent response to drought in the intensity of plant transpiration (T) indicates the unequal effectiveness of adaptation mechanisms involved in plant defense. Reactions depend on both soil and climatic growth conditions.

 

Migration of HMs in the soil-plant system promotes metal accumulation in the plant organism, reducing the intensity of transpiration (Table 5). Summarizing our results, regardless of the chosen approach for classifying the danger of HMs, there is a certain susceptibility of maize to absorption and accumulation of Cd, Mo, Zn, and Hg.

 

Considering the concentration coefficients, HMs categorized as low or moderately hazardous are poorly accumulated in Zea Maize L. When using maize as a model plant, it is possible to monitor the degree of contamination in the soil-plant system, thereby identifying migration patterns in the soil cover by Cd, Mo, Zn, Hg, Cr, and Cu. Actually, the definition of hazard classes for categorizing HMs is primarily based on the response of the biota to the degree of contamination in situ.

 

 

 

Table 5. Correlation coefficient between the values of the geo-accumulation index (Igeo) and plant transpiration intensity (T)

 

 

 

Soil-climatic regions

Tekhut

Shnokh

Odzun

Hushakert

Pair correlation coefficient

-0.995

-0.994

-0.960

-0.950

 

 

 

Use of an annual plant allows to fully assess migration characteristics of HMs in the soil-plant system. This enables to extend the use of maize as an indicator plant and as a natural filter of coastal areas in the migration of heavy metals (Sukiasyan, 2018b).

 

  1. Conclusions

Pollution of soils by HMs is a foremost environmental problem. It is suggested that when determining hazardous concentration levels of HMs, one should consider changes in soil structure, caused by agricultural work. It is proposed to set the maximum permissible cumulative HMs along with the established standard values of the maximum permissible concentrations. Owing to its vegetative period from 110 to 140 days, Zea Maize can be used as a model plant in the study of migration of HMs in soil-plant systems. Therefore, Zea Maize was chosen for evaluating and categorizing the danger posed by various HMs. This was enabled by a certain propensity of Zea Maize to absorb and accumulate elements like Cd, Mo, Zn, and Hg. By this, one can identify and differentiate low and moderately dangerous areas. Maize as a model plant allows to monitor degrees of contamination in soil-plant systems, thereby ascertaining migration patterns in the soil cover for HMs, including Cd, Mo, Zn, Hg, Cr, and Cu. Also, we performed a quantitative assessment of contamination in the investigated areas based on the environmental risk factor for each HMs. This examination revealed high HMs levels. Nevertheless, the total pollution index exhibits greater values as it includes HMs during their migration. Subsequent accumulation of HMs in the plant can cause metabolic changes that primarily affect the growth of agricultural plants and their productivity. Consequently, we propose to complement our study of the described areas, addressing migration changes of HMs in the soil, caused by various anthropogenic factors. As the planned continuation of our work at your journal, you can submit the following continuation   “We propose to complement our study of the described areas, addressing migration changes of HMs in the soil, caused by various anthropogenic factors. The exploitation of the Teghut copper-molybdenum enterprise caused an environmental load on the concentration of some heavy metals (autumn 2016), the migration of which continued after its work was stopped (autumn 2018). The concentration changes of Mo, Zn, and Cu in water samples of the river and coastal soils near the tailings of the enterprise we will investigate.

 

 

 

References

 

  1. Benson, N.U., Anake, W.U., Olanrewaju, I.O. (2013). Analytical Relevance of Trace Metal Speciation in Environmental and Biophysicochemical Systems.American J. Anal. Chem. 4, 633-641.
  2. Mónok, D., Füleky, G. (2017). Investigation of soil cadmium pollution using a ryegrass (Lolium perenne L.) biotest. Agrokémia és Talajtan, 66 (2), 333-347.
  3. Crommentuijn, T., Polder M. D., Van de Plassche, E. J. (1997). Maximum permissible concentrations and negligible concentrations for metals, considering background concentrations. RIVM Report 601501001. Bilthoven. Netherlands. p. 260.
  4. Demirta, C., Yazgan, S., Candogan, B.N, Sincik, M., Buyukcangaz, H., Goksoy, A.T. (2010). Quality and yield response of soybean (Glycine max Merrill) to osmotic stress in sub–humid environment. African J. Biotech. 9, 6873-6881.
  5. Dobrovol’skiy, V.V. Geography of microelements. Global scattering. M.: Mysl., 1983, p. 271.
  6. Förstner, U., Müller, G. (1981). Concentrations of heavy metals and polycyclic aromatic hydrocarbons in river sediments: geochemical background, man’s influence, and environmental impact. GeoJournal. 5, 417–432.
  7. Gao, Q., Li, Yi, Cheng, Q., Yu, M., Hu, Bo., Wang, Zh., Yu, Zh. (2016). Analysis and assessment of the nutrients, biochemical indexes and heavy metals in the Three Gorges Reservoir, China, from 2008 to 2013. Water Research. 92, 262-274.
  8. Gil-Sotres, F., Trasar-Cepeda, C., Leirós, M.C., Seoane, S. (2005). Different approaches to evaluating soil quality using biochemical properties. Soil Biology and Biochemistry. 37, 877-887.
  9. GOST 17.4.1.02–1983. Nature protection. Soils. Classification of chemicals for pollution control. Moscow: Standartinform Publ., 2008, 8p.
  10. Hakanson, L. (1980). An ecological risk index for aquatic pollution control: a sedimentological approach. Water Res. 14, 975–1001.
  11. Hák,, Janoušková, S., Moldan, B. (2016). Sustainable Development Goals: A need for relevant indicators. Ecological Indicators. 60,565-573.
  12. Hong-Dui, D., Teng-feng, Gu., Ming-Hui, Li., Xu, D. (2012). Comprehensive Assessment Model on Heavy Metal Pollution in Soil. International Journal of Electrochemical Science. 7(6), 5286-5296.
  13. Hu, B., Jia, X., Hu, J., Xu, D., Xia, F., Li, Y. (2017). Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China. International Journal of Environmental Research and Public Health14, 1042.
  14. Ilin, V.B., Syso, A.I. (2001). Trace Elements and Heavy Metals in Soils and Plants of the Novosibirsk Region // Novosibirsk: Publishing House of the Siberian Branch of the Russian Academy of Sciences. pp. 1-229.
  15. Kasimov, N.S., Vlasov, D.V. (2015). Clarks of chemical elements as reference standards in ecogeochemistry. Bulletin of the Moscow University. Series 5. Geography. 2, 7-17.
  16. Kirakosyan, A. A., Sukiasyan, A. R. (2005). Using MATLAB as an express method for evaluating experimental results. Proceeding of International Conference “Information technology”, Yerevan, 23-25 June 2005 Yerevan. 34-37.
  17. Korosov, A.V., Gorbach, V.V. (2017). Computer processing of biological data. Petr GU. pp. 1-97.
  18. Kowalska, J.B., Mazurek, R., Gąsiorek, M., Zaleski, T. (2018). Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination-A review. Environ Geochem Health. 40, 2395-2420.
  19. Kroik, A. A., Gotvyanskaya, V. A., Didenkul, M. G.(2012). Regularities of accumulation and distribution of heavy metals in the system soil of a plant. Journal of Geology, Geography and Ecology. 32, 90-93.

20. Mahmoudabadi, E., Sarmadian, F.,Nazary Moghaddam, R. (2015). Spatial distribution of soil heavy metals in different land uses of an industrial area of Tehran (Iran). Int. J. Environ. Sci. Technol. 12, 3283.

  1. Mihaly-Cozmuta, A., Mihaly-Cozmuta, L., Viman, V., Vatca, G., Varga, C. (2005). Spectrometric methods used to determine heavy metals and total cyanides in accidental polluted soils. American J. Appl. Sciences2, 358-362.
  2. Montanarella, L., Panagos, P.,Yigini, Y. (2013). Resources of Mediterranean and Caucasus Countries /Ed.: Yigini Y., Panagos P., Montanarella L. / Luxembourg: Publications Office of the EU, 243 p.
  3. Nowrouzi, M., Pourkhabbaz,(2014). Application of geoaccumulation index and enrichment factor for assessing metal contamination in the sediments of Hara Biosphere Reserve, Iran,Chemical Speciation & Bioavailability.26, 99-105.
  4. Pari, L., Assirelli, A., Suardi, A. (2010). Evaluation of Brassica napus and Brassivacarinata losses during harvesting: three years of experience. 18th European Biomass Conference and Exhibition, Lyon, 3–7 May 2010, France, pp. 1790-1793.
  5. Perelman, A. I., Kasimov, N. S. (1999). Geochemistry of the landscape. Moscow: Astreya, p. 768.
  6. Ren, C., Tian, X. (2012). Comprehensive Evaluation Model for Soil Heavy Metal Pollution. In: Cao B.Y., Xie, X.J. (Eds) Fuzzy Engineering and Operations Research. Advances in Intelligent and Soft Computing, vol 147. Springer, Berlin, Heidelberg.
  7. 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. of China University of Geosciences. 19, 230-241.
  8. Salman, S.A., Zeid, S.A.M., Seleem, EM.M., Abdel-Hafiz M.A. (2019). Soil characterization and heavy metal pollution assessment in Orabi farms, El Obour, Egypt. Bull. Natl. Res. Cent. 43, 42.
  9. Seaward, M.R.D. (2004). The use of lichens for environmental impact assessment. Symbiosis.37, 293-305.
  10. Slepetys, J., Kadziuliene, Z., Sarunaite, L., Tilvikiene, V., Kryzeviciene, A. (2012). Biomass potential of plants grown for bioenergy production. Renewable Energy and Energy Efficiency, Growing and Processing Technologies of Energy Crops. 66–72.

31. Srivastava, V., Sarkar, A., Singh, S., Singh, P., de Araujo Ademir. S. F., Singh Rajeev. P. (2017). Agroecological responses of heavy metal pollution with special emphasis on soil health and plant performances. Front. Environ. Science, 5.

  1. State of the environment. United Nations Environment Program M.: VINITI. 1980, 162 p.
  2. Sukiasyan, A.R., Pirumyan, G.P. (2018). Influence of heavy metals in water and soil on plant ecological stress in various climatic zones of the Republic of Armenia. Water and ecology: problems and solutions. 2, 87-94.
  3. Sukiasyan, A.R., Tadevosyan, A.V., Pirumyan, G. P. (2018a). Reaction to the drought of various lines of armenian maize depending on soil and climatic conditions. Vestnik VSU, Serie: Geography. Geoecology. 2, 96-102.
  4. Sukiasyan, А.R. (2018b). Influence of heavy metals content in water of small rivers used for irrigation of maize of Armenian population. Theoretical and Applied Ecology. 4, 40–45.
  5. Theuretzbacher, F., Kravanja, P., Becker, M., Bauer, A., Enguidanos, R., Amon, B., Friedl, A., Potthast, A., Amon, T. (2012). Whole plant utilization of different Sorghum bicolor Moench varieties for combined bioethanol and biogas production. F. Rovira-Más (Ed.), International Conference of Agricultural Engineering, CIGR AgEng 2012. International Conference of Agricultural Engineering CIGR-AgEng 2012, pp. 1-6.
  6. Unanyan, S.А. (2010). Agromonitoring of the ecosystem of technogenic zones of the Republic of Armenia and the development of measures to restore soil fertility. Ref. dis. Dr. Sel.-household. sciences. Yerevan. p. 40.
  7. Van de Plassche, E.J., De Bruijn, J.H.M. (1992). Towards integrated environmental quality objectives for surface water, sediments, and soil for nine metals. RIVM Report 679101005. Netherlands. Bilthoven. pp. 1-130.
  8. Vodyanitskii, Yu.N. (2016). Standards for the contents of heavy metals in soils of some states. Annals of Agrarian Science. 14, 257-263.
  9. Vodyanskiy, Yu. N. (2012). Norms оf the content оf heavy metals and metalloids in soils. Soil Science. 3, 368-375.
  10. Wong, S. C., Li, X. D., Zhang, G., Qi, S. H., Min, Y. S. (2002). Heavy metals in agricultural soils of the Pearl River Delta, South China. Environmental Pollution. 119, 33-44.
  11. Xiong, Q., Zhao, W., Zhao, J., Zhao, W., Jiang, L. (2017). Concentration levels, pollution characteristics and potential ecological risk of dust heavy metals in the metropolitan area of Beijing, China. International journal of environmental research and public health. 14, 1159.
  12. Yao, Zh., Li, J., Xie, H., Yu, C. (2012). Review on Remediation Technologies of Soil Contaminated by Heavy Metals. Procedia Environmental Sciences. 16, 722-729.
  13. Zürrer, H., Bachofen, R. (1985). Yields of three cultivars of sunflowers in Switzerland. Biomass. 7, 297-302.

 

 

 

 

Copyright © 2019 Dama Research Center limited. All rights reserved.

Beverley Commercial Centre, 87-105 Chatham road South, Tsim Sha Tsui Kowloon, Hong Kong