**Abstract**

In the chapter will be presented: scientific substantiation on the models used worldwide to evaluate the contamination of soil, respectively vegetables and fruits; development of theoretical models to evaluate the impact of soil contamination by heavy metals on vegetables and fruits; testing of theoretical models in real conditions based on data obtained from laboratory; development of mathematical models to evaluate the impact on soil contamination on vegetables and fruits and thus on consumers health. The research presented in the chapter aim to develop some original models on the correlation between the level of soil contamination, respectively the remanence of heavy metal in vegetables and fruits harvested for consumption in fresh state. The statistical mathematical models elaborated by the interpolation of the experimental data are models with practical applications in both scientific research and agricultural management.

**Keywords:** contamination of soil, vegetables and fruits, heavy metal, mathematical models, accumulation of heavy metals

### **1. Introduction**

The rapid development of industry and urbanization in developing countries has led to the chaotic increase in levels of toxic heavy metals in the environment. In addition, heavy metal contamination of agricultural soils and crop plants in these countries, because of the use of industrial waste water it can have negative effects on human health. Other sources of heavy metals from agriculture include manure, fertilizers and pesticides, and contamination from the air due to excessive use of cars [1].

The most important pollution in agriculture is the accumulation of heavy metals which are very toxic to soil, water, plants and humans. Although heavy metals have an key role in nature for soil conservation, their concentration above certain limits can have toxic effects [2]. Therefore, in order to understand the phenomenon of accumulation in soil and plants, it is necessary to know the following definitions [3]:

• Bioamplification is the accumulation of toxic substances in the tissues of tolerant organisms as the trophic level increases. This increase can occur if: the substance cannot be decomposed by environmental processes; the concentration of the

substance gradually increases as it moves into a food chain; the impossibility of internal degradation or elimination of the substance.


Bioconcentration and bioaccumulation occur within an organism, bioamplification occurs at trophic levels (food chain).

Excess accumulation of heavy metals in crops from contaminated agricultural soils it results in soil pollution and low quality of food. Soil is the key factor, it is the basis of the food chain that determines food safety. Vegetables and fruits are plants that are commonly used in food due to their content in nutrients (iron, calcium, proteins), vitamins, minerals, fiber, beneficial to health. Consumption of contaminated fruit and vegetables entail risks for health therefore many researchers have studied food safety in this regard [1, 4–8].

The key functions of metals in plants are involvement in redox reactions and an integral part of enzymes. The essential metals for plants Fe, Cu, Mo, Zn play a major role in the formation of enzymes, the transport of electrons, the sustaining metabolism. In the soil, metals are known as essential trace elements, others non-essential (Hg, Ag, Pb, Ni, etc) and as ultra-trace elements [2].

Heavy metals are present in the environment and they are pollutants in both aquatic and terrestrial ecosystems. The most hazardous heavy metals (Cr, Ni, Cu, Zn, Cd, Pb, Hg) in the environment have three characteristics: their persistence in the environment, toxicity to the soil, water, plants, and organisms, their bioaccumulation in the structure of the soil, the composition of water, the tissues and organs of plants, the body of organisms [9].

The negative effect of metals on the activity of microbes in the soil indirectly affects plant growth. Plants that grow under the stress of heavy metals consume more energy for their survival, what affects other physiological processes, such as: absorption of nutrients, photosynthesis, respiration, metabolism and reproduction, water balance. Due to metallic stress, a lot of reactive oxygen accumulates in the plant [10–12]. Plant reactions to heavy metal toxicity may include: necrosis, chlorosis, senescence and wilting, slowing growth, metabolic disorders, loss of yield, nutrient deficiency, reduced ability to fix atmospheric nitrogen, the small number of seeds and, finally, death [2].

The accumulation of soil metals in plants depends on a number of factors, such as: the structure of the plant, the life cycle of the plant, the vigor of the plant, the pH of the soil, the depth of the root system, temperature, partial pressure of oxygen, carbohydrate level, respiration rate, nutrient exchange and microbial activity [2, 13].

Generally, plants can be integrated into three categories, given their reaction against metals: exclusions, accumulators and indicators based on the mechanism of action for to survive under stress, as suggested [2, 14]. Exclusions react to entering of metals into the vegetative aerial parts making this impossible by stopping the metal in the roots. Accumulators are plants that accumulate metals in the vegetative aerial

*Evaluate the Impact of Soil Contamination on Vegetables and Fruits DOI: http://dx.doi.org/10.5772/intechopen.110445*

**Figure 1.**

*The shape of curves for mathematical modeling of heavy metal concentrations in the soil-plant system [15].*

parts greater than the metals in the soil. Indicators are species that continuously accumulate metals in vegetative aerial tissues tolerating metal concentrations and indicating the amount of metals in the soil. The behavior of plants in relation to the increase in the concentration of heavy metals in the soil is shown in **Figure 1** [15].

Hyperaccumulation of heavy metals is the process by which plants accumulate excess metals 0.1–1% of their dry weight, and concentrate them into roots, stems, or leaves [16]. A hyperaccumulating plant can accumulate and/or tolerate high levels amounts of metals. As an ecological adaptation, some plant species have the ability to grow in heavycontaminated metal soils and accumulate them [2, 17]. As hyperaccumulating species, about 400 species of plants from 22 families are known. The Brassicaceae family contains a large number of these plants, which includes 87 species from 11 genera [15].

According to the paper [18] the soil factors that influence the absorption of metal by plants are: the concentration of metal in the soil, the processes and properties of the soil and the vegetable factors. These are: the refeeding of ions in the rhizosphere, the kinetic parameters that adjusts the absorption of metals from plants, the tolerance of metal by the plant.

For the assessment of phytoremediation of soils by means of plants, there are many specialized papers [12, 19–21], which uses the bioconcentration factor (BCF) and the translocation factor (TF) as parameters. Through BCF, the concentration of metals in plant tissues is determined in relation to their growth medium, while TF determines way metals are translocated in the aerial vegetative parts of plants [12, 19–21]. As plant species used to remove metals from the environment, they can be listed: peas [1, 22], lettuce [23], wood species (poplar, willow, ash) [24], oleander [25], flax and hemp [26], jute, rooster crest, field thyme [27], rapeseed and Indian mustard [28], sunflower and corn [29], cucumber [22, 30], cherry tomato [15], sweet pepper [31], cabbage and broccoli [32], spinach [33, 34].

The way plants tolerate, absorb, transport, capture, sequester and bioaccumulate metals differs depending on several factors, such as: plant species, phenology, type of metal, soil type and quality, climate, type of source that contaminates, chemical and physical behavior of the plant, environmental factors [12].

The following will be presented different parameters for the assessment of soils and plants with heavy metals, calculated with different mathematical formulas. This parameters have been used in many papers by researchers who have studied this major environmental issue currently existing globally at the moment.

• **The contamination factor (CF)** is the ratio of the metal tracked from the soil to the background value of the heavy metal, expressed in mg/kg<sup>1</sup> dry matter, as it is presented in the papers [35, 36] and is determined with the formula (1):

CF ¼ Concentration of heavy metal in the sample*=*Background value of the heavy meta (1)

The contamination factor values are classified as such: CF < 1 (low contamination), 1 < CF < 3 (moderate contamination), 3 < CF < 6 (high contamination) and CF > 6 (very high contamination) [35, 36].

• **Geoaccumulation index (Igeo)** can be used when desired effective environmental planning of pollution. It is used successfully to assess soil contaminated with heavy metals from natural or anthropogenic sources. It can be determined with the formula (2):

$$\mathbf{I}\_{\text{geo}} = \log\_2 \left[ \frac{\mathbf{C}\_{\text{n}}}{\mathbf{1}.5 \bullet \mathbf{B}\_{\text{n}}} \right] \tag{2}$$


In the papers [35, 36] geoaccumulation index has been classified into the following six categories: unpolluted environment (Igeo ≤ 0), unpolluted environment to moderately polluted environment (0 < Igeo ≤ 1), moderately polluted environment (1 < Igeo ≤ 2), moderately to heavily polluted environment (2 < Igeo ≤ 3), highly polluted environment (3 < Igeo ≤ 4), strongly to extremely polluted environment (4 < I geo ≤ 5).

• **Enrichment factor (EF)** is also important to be able to assess the level of heavy metal pollution from anthropogenic sources, and it is calculated as the ratio of the concentration of the studied metal (the chosen reference metal must be in combination with very fine surface solids and occur naturally and evenly in the environment) and geochemical background, expressed also in mg kg�1 dry matter, calculated with the formula (3):

$$\text{EF} = \text{sample metal/Background metal} \tag{3}$$

The values of enrichment factor were classified into: EF < 1 (soil without enrichment in Ref. metal), 1 < EF < 3 (soil less enriched in Ref. metal), 3 < EF < 5 (soil moderately enriched in Ref. metal), 5 < EF < 25 (soil severely enriched in Ref. metal), 25 < EF < 50 (soil very severely enriched in Ref. metal) si EF > 50 (soil extremely severely enriched in Ref. metal) [35, 37].

• **Degree of contamination (DC)** is presented in literature [35, 36] as a simple evaluation method for controlling anthropogenic pollution, as an indication of dangerous or not. A classification has been proposed for degree of contamination in three categories: DC < 6 (low degree of contamination), 6 < DC < 12 (moderate degree of contamination) and 12 < DC < 24 (considerable degree of contamination). It can be calculated as the sum of the contamination factor of each metal concerned, with the following formula (4):

$$\text{DC} = \sum \text{nCF} \tag{4}$$

*Evaluate the Impact of Soil Contamination on Vegetables and Fruits DOI: http://dx.doi.org/10.5772/intechopen.110445*

• **Potential Ecological Risk** is a mathematical model that evaluates as the degree of soil pollution based on toxicity from heavy metals as well as assessment of environmental threats because of metals in response to environmental factors. The model is important because it shows which metal is more dangerous to the environment [38]. In the eqs. (5), Eri is calculated for the determination of environmental risk and PERI (Eq. (6)) is calculated for determination as the sum of all the risk values presented by heavy metals in the soil.

$$\mathbf{E}\_{\text{ri}} = \mathbf{T}\_{\text{ri}} \bullet \mathbf{CF} \tag{5}$$

where Tri represents the value of the toxic or lethal response; and CF is the contamination factor.

$$\text{PERI} = \sum\_{\text{f}=1}^{n} \text{E}\_{\text{ri}} \tag{6}$$

The two formulas helps establish the degree of threat to soils because to heavy metals by the indication of limits and to measuring the environmental sensitivity for the metals concerned. Therefore, [36] classified the two parameters as follows: Eri < 40 (low ecological risk), 40 < Eri < 80 (moderate ecological risk), 80 < Eri < 160 (considerable ecological risk), 160 < Eri < 320 (high ecological risk) and Eri > 320 (very serious ecological risk). In the same way, PERI was classified as follows: PERI < 95 (low ecological risk), 95 < PERI <190 (moderate ecological risk), 190 < PERI < 380 (considerable ecological risk) and PERI >380 (very high ecological risk).

Soil pollution with each metal was determined in the paper [1, 39] using the **pollution load index, (PLI)**, calculated as the ratio of the concentration of heavy metal in polluted soils to the concentration of heavy metal in unpolluted soils.

In the paper [40] the level of chemical pollution of the soil was determined using the **anthropogenic coefficient (Kc),** (Eq. (7)) of the concentration of a metal in a sample, calculated as the ratio of the content to the metal in a studied land (C) to the base level of the metal (CF).

$$\mathbf{K\_{c}} = \mathbf{C/CF} \tag{7}$$

It is known that soil pollution due to anthropogenic activities may have different sources, therefore in the paper [40] the **total pollution index (Zc)**, (Eq. 8)) was calculated as a result of a group of heavy metals in a studied area.

$$\mathbf{Z\_{c}} = \sum\_{i=1}^{n} \mathbf{K\_{c}} - (\mathbf{n} - \mathbf{1}) \tag{8}$$

where, Kc it is the anthropic coefficient of the concentration of a metal in a sample; n is the number of samples analyzed.

In mathematical modeling of the phenomenon of accumulation of metals in the soil-plant system, depending as a lot of factors such as was previously mentioned, it is necessary that the behavior of the plant be regularly monitored in order to study the prediction of dynamics. For this purpose, in the papers [40, 41] was determined the **biological absorption coefficient**, noted **(Kbp <sup>i</sup>** Þ, (Eq. (9)).

$$\mathbf{K}\_{\mathrm{i}}^{\mathrm{bp}} = \mathbf{C}\_{\mathrm{i}}^{\mathrm{r}} / \mathbf{C}\_{\mathrm{i}}^{\mathrm{p}} \tag{9}$$

Cr <sup>i</sup> is the content of the i-th heavy metal in a plant, mg/kg; C<sup>p</sup> <sup>i</sup> is the content of the i-th heavy metal in soil, mg/kg.

In the paper [42] was determined the **normalized difference vegetation index (NDVI)**, (Eq. 10)), from mining areas where the soil is polluted with heavy metals. This index (NDVI) depend on the following factors: the topographic position index (TPI), wind speed (WP), precipitation (P), atmospheric dustfall (D), and surface temperature (W).

NDVI it is useful to be able to determine the cover of vegetation from an area, and it is calculated as the ratio of the value of the difference and the total value both near infrared bands, as well as visible infrared bands, and is calculated with the formula (10) [42]:

$$\text{NDVI} = \text{NIR} - \text{red} / \text{NIR} + \text{red} \tag{10}$$

under NDVI este normalized difference vegetation index, NIR arată the value of the near-infrared light, iar roșu arată the value of the visible infrared light.

The study [42] concluded that atmospheric dust was the main factor that increased the heavy metal content in the soil, being strongly influenced by wind speed and topography of the soil.

• **Mitotic index (MI),** helps to determine the number of cells in the leaves, as a growth parameter of the plant, and is determined as a ratio between the number of cells that divide and the total number of cells in the leaves, expressed as a percentage (%), and calculated with the formula (11) [31]:

$$\mathbf{MI} = \begin{pmatrix} \text{number of dividing cells/total number of cells} \end{pmatrix} \mathbf{x100} \tag{11}$$

In a statistical analysis [30] which describes the modeling of heavy metals in the soil-plant system, rice was used in the study and was used as algorithms: multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and cubist. They have helped predict the bioaccumulation coefficient of metals in rice and to identify the potential for transfer of metals into the tissues of rice plants. The flow diagram of the study [30] carried out in China is shown in **Figure 2**.

**Figure 2.** *Flowchart of the modeling of heavy metals in soil–rice system [30].*

In order to establish the relationship between soil and the environment, in science and engineering, many forms of models have been used, such as they are: the Philip model, the Richard equation, the Hortonn model [43].

Modeling the distribution of heavy metals and their bioaccumulation in plants has been a major concern for many researchers because the concentration and distribution of heavy metals because the concentration and distribution of heavy metals concern both soil and crop quality. Therefore, the use of a precise model is essential for estimating the real values of heavy metals in the soil and their distribution for effective management in agriculture.

Three models (linear, logarithmic, polynomial, and quadratic models) have often been developed to test their adequacy to experimental data correlated to heavy metals [43, 44]. Validation of these models by direct analysis and comparison of the experimental data with the precise ones, indicates the percentage deviation (DV), determined with the relation (Eq. (12)):

$$\mathbf{D}\_{\mathbf{v}} = \left[\frac{\mathbf{D}\_{\mathbf{p}} + \mathbf{D}\_{\mathbf{e}}}{\mathbf{D}\_{\mathbf{e}}}\right] \mathbf{x} \mathbf{100} \tag{12}$$

where, Dv is deviation, Dp is predicted values and De is experimented values [43].

Regression models has been widely used in the prediction of soil properties, especially the degree of pollution, because of their ease and wide use. These models show a global model of the problem studied and through a single regression equation can represent the process [43, 45].

Statistical models for predicting metal concentrations in plants have as main independent variables metal concentrations in the soil. Based on the available analytical data, the concentrations are total or only the bioavailable portion.
