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Article

Assessment of Heavy Metals Accumulation in Soil and Native Plants in an Industrial Environment, Saudi Arabia

by
Saud S. Aloud
1,
Khaled D. Alotaibi
1,
Khalid F. Almutairi
2,* and
Fahad N. Albarakah
1
1
Soil Science Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11362, Saudi Arabia
2
Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11362, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(10), 5993; https://doi.org/10.3390/su14105993
Submission received: 12 April 2022 / Revised: 2 May 2022 / Accepted: 9 May 2022 / Published: 15 May 2022

Abstract

:
Industrial activities are associated with various heavy metals (HMs) being emitted into the environment, which may pose a threat to humans and animals. The rapid increase in an industrial activity in major cities in Saudi Arabia (SA) has raised concerns regarding the accumulation of HMs in the environment. The aim of this study is to assess the accumulation of HMs in soil and native plants in an industrial environment. We collected 36 surface soil samples and 12 plant species from 12 sites in an industrial city in central SA. The results showed that the HMs content in the soil followed a descending order of (Fe > Ni > Zn > Pb > Cu> Cr > Cd). The enrichment factor (EF) of HMs in the soil ranged from 0.20 to 7336. Up to 100%, 16.6%, and 6.2% of soil samples were extremely highly enriched with Cd, Ni, and Pb, respectively. Plant species Cyperus laevigatus accumulate Cd, Pb, and Ni. Citrullus colocynthis accumulate Cd and Pb in significantly (p < 0.001) higher amounts than other studied species. The Pollution Load Index (PLI) values for the 12 sites ranged from 0.52–1.33 with S5 and S2 PLI >1.0 indicating progressive deterioration of these sites. The Bioaccumulation Factor (BF) ranged from 0.04–2.76 and revealed that some plant species may be candidates for phytoextraction potential. The most promising plant species for phytoextraction and remediation were annuals or perennials such as Malva parviflora, Sisymbrium irio and Citrullus colocynthis, especially for Cr and Cu. This study suggests that these native plant species may be useful for phytoremediation in the area.

1. Introduction

Soil pollution with heavy metals (HMs) is one of the most pressing environmental problems worldwide. HMs refer to elements with large atomic weight and relatively high densities [1,2]. After being discharged, HMs can stay in the environment for a long time due to their non-degradability, and as a result, they can adversely affect soils, plants, and humans [3,4]. HM pollution may originate from the soil parent material (lithogenic source) and/or from various anthropogenic sources [5]. For example, soils at industrial sites can have distinct groups of HM contaminants, which depend on the respective industries and their raw materials and products. However, HM pollution is mainly related to human activities, including mining and processing of metal ores, fertilizers, and pesticides, sewage sludge, transportation, and others industrial processes [6].
HM pollution mainly results from anthropogenic activities, such as industrial activities, mining, use of agrochemicals, and automobiles. However, industrial activities are considered the major source of HMs that accumulate in our environment and reach toxic levels, thereby causing health risks and adversely affecting ecosystems [7,8,9,10]. Mining and industrial zones are pointed to as sources of global deterioration of environmental quality due to the release of HMs. HM pollution is considered to be one of the most serious and dangerous hazards affecting humans and animals [11,12]. Moreover, HMs are non-biodegradable elements that exist in the soil for a period of time longer than any other contaminants of the biosphere [13]. Human activities could be responsible for the environment’s contamination by high concentrations of toxic metals, including Zn, Cu, and Cd. Various manufacturing activities in industrial areas have led to a marked increase in the total amount of HMs in nearby soils. HMs such as As, Cd, Cr, Ni, Pb, etc., have been classified as carcinogenic to humans and wildlife [13,14,15]. The result of poor solid and liquid waste disposal from industrial activities in soils alongside Buddha Nullah, Ludhiana, (Punjab) India caused increases in the concentration of HMs, such as Cd, Co, Cu, Pb, and Zn, which can directly influence human health and ecological systems [16]. The soil’s microbial diversity and function are also affected by HMs contamination [17,18]. In samples collected from urban and suburban areas of Belgrade, HM contamination of soils and water caused by industrial and traffic actives in the urban environment were arranged in the order Zn > Ni > Pb > Cr > Cu > As > Hg > Cd [19]. In most cases, the maximum concentrations of metals were found in the surface (0.0–30 cm) in the order (Fe > Mn > Zn > Ni > Pb > Cu > Co). A pot experiment conducted with EDTA showed that some plant species, including Lamium purpureum, accumulated a relatively higher amount of As, while the Amaranthus reflexus accumulated higher Cd compared to the other plant species studied. It was also concluded that chelates can improve phytoremediation of HMs from contaminated soils through increasing solubility and uptake by high biomass plants and accelerating the remediation process [20,21]. After evaluation of the efficiency of different diluted chelate solutions to mobilize HMs—namely, Cd, Pb, Cr, and As—upward by capillary force in soil under greenhouse conditions, mobilization of specifically Cd and Pb upward to the surface layer was greater than 35% of the added concentration [22]. For a plant to be a hyperaccumulator of As, Pb, Cu, Ni, and Co, it should be greater than 1000 mg·kg−1 dry shoot mass, while for Cd it should be greater than 500 mg·kg−1 [23,24]. Different plants, such as S. lycopersicum and B. juncea, have been found to tolerate and accumulate HMs, which can used to eliminate the HMs soil contamination. However, plants that grow in aridic soil are subjected not only to HMs, but also to alkaline and saline conditions due to high pH and EC. These chemical and environmental stresses do not allow the survival of HMs accumulator plants. Thus, the best option is to use native plants, which are able to thrive and grow in their native environment, for phytoremediation of HMs contaminated sites [23,25]. However, information regarding the use of plant species indigenous to this region of extreme environmental conditions is lacking. Therefore, the objectives of this study were (1) to assess HM pollution in soil in the vicinity of the industrial zone in Riyadh city and (2) to assess the content of HMs in the native plants growing in the study area and their potential use for remediating HMs contaminated sites in the region.

2. Materials and Methods

2.1. Study Area

The study area is an industrial city located south of Riyadh Capital in central SA (Figure 1). The Second Industrial City, which is located 12 km south of Riyadh City, capital of Saudi Arabia, was established in 1976. It has been developed in four stages to a total area of more than 18 million square meters. It houses more than 1115 different industrial units including smelting, metal chemical industries, electroplating, energy, fuel production, and cement factories. The area of study is in a desert region, where the climate is dominated by high temperatures and limited rainfall with an annual precipitation range of 90–100 mm and a high evaporation rate.

2.2. Soil Sampling and Analyses

Surface soil samples from depths of 0 to 20 cm were randomly collected from 12 sites of the study area using a soil auger. The global positioning system (GPS) device was used to point to sample locations (Figure 1). Each site was divided into three sections, and three soil samples were collected from each section and combined to provide a composite sample. Soil samples were then air-dried at room temperature (20–25 °C) for one week and sieved through a 2-mm sieve. The soil was analyzed for basic chemical and physical properties. The hydrometer method was applied to determine the particle size distribution [26]. A calcimeter method was used to estimate the soil calcium carbonate (CaCO3) content [27]. The content of the soil organic matter (SOM) was measured following the method of Nelson and Sommers [28]. The soil pH and electrical conductivity (EC) were measured in distilled water extracts in the soil and distilled water ratio of 1:1 after equilibration for 24 h [29]. The chemical and physical characteristics of the soil are provided in (Table 1. A 0.5 g of oven dry soil was accurately weighed and digestated using a Teflon beaker following the addition of a mixture of acids (HF + HCl) at 130–140 °C for complete digestion. The digested extract was diluted with double distilled water and filtered into 100 mL volumetric flasks using a Whatman no. 42 filter and analyzed for HMs using ICP-AES.

2.3. Plant Sampling and Analyses

According to their importance for adaptation to the environment, 12 native plant species were identified and sampled from the 12 sites of the study area (Figure 1). Three individuals of each species were randomly selected from each sampled site. It was found that the identified 12 plant species belong to 11 families (Table 2). All plants were washed and cleaned with distilled water, oven dried at 70 °C, and ground into powder with an electric grinder. Plant samples of 2.0 g were taken in a Pyrex beaker and digested with a mixture of acids (HNO3 + HClO4 and aqua regia). The mixture of acids (HNO3 + HClO4) extract was analyzed for HMs using ICP-AES [30].

2.4. Data Precision and Accuracy

All reagents were of analytical grade. Stock standard solutions of 1000 µg/mL for all seven elements were prepared either from salts or pure metals supplied by J. T. Baker (Deventer, The Netherlands), Merck (Darmstadt, Germany), and Aldrich Chemical Company (Milwaukee, WI, USA). Working multi-elemental standard solutions were prepared by appropriate dilution with deionized water from stock solutions and by the addition of 8 mL of concentrated nitric acid (s.p. Merck) per 100 mL of standard solution. The calibration curve was prepared for each of the metals investigated by least square fitting.

2.5. Quantification of Soil Contamination of the Study Area

2.5.1. Enrichment Factor (EF)

A common approach to estimate how much the sediment is impacted (naturally and anthropogenically) by HMs is to calculate the Enrichment Factor (EF) for metal concentrations above un-contaminated background levels [31]. The EF normalizes the measured HMs content with respect to a sample’s reference such as Fe, Al, or Zn [32]. The Fe is used to determine the relative degree of metal contamination, and the comparisons in this study were made using Fe background concentrations in the earth’s crust as a reference element with the assumption that iron content in the crust is not affected by anthropogenic activity [33]. The EF of an HM in soil can be calculated with the following formula:
EF = C metal C normalizer   [ soil ]   C metal C normalizer [ control ]
where Cmetal and Cnormalizer are the concentrations of HMs and normalizer in the sediment and the unpolluted control [34]. The EF can be used to differentiate between the metals originating from anthropogenic activities and those from natural processes, and to assess the degree of anthropogenic influence. Five contamination categories are recognized, and they are (1) EF < 2 is deficiency to minimal enrichment; (2) EF 2–5 is moderate enrichment; (3) EF 5–20 is significant enrichment; (4) EF 20–40 is very high enrichment; and (5) EF > 40 is extremely high enrichment [35].

2.5.2. Contamination Factor (CF)

The contamination factor (CF) for the sediment samples was calculated by the equation below, where CF is the ratio obtained by dividing the concentration of each metal in the soil (Cmetal) by the baseline or background (Cbackground) value (average earth crust abundance or concentration in unpolluted soil):
CF = C metal C background
Contamination levels may be classified based on their intensities on a scale ranging from 1 to 6 (0 = none, 1 = none to medium, 2 = moderate, 3 = moderate to strong, 4 = strongly polluted, 5 = strong to very strong, 6 = very strong) [36].
The highest number indicates that the metal concentration is 100 times greater than what would be expected in the crust.

2.5.3. Pollution Load Index (PLI)

For the entire sampling site, the PLI has been determined as the nth root of the product of the n CF in this study to measure the PLI in the studied sites. The PLI for a single site is the nth root of n number of the contamination factor (CF) values multiplied together [37]. The CF is the quotient obtained as follows:
PLI = (CF1 × CF2 × CF3 × ··· × CFn)1/n
This provides a simple, comparative means for assessing the level of HM pollution. For the all-sampling site, the PLI has been calculated as the nth root of the product of the n CF [38].

2.5.4. Bioaccumulation Factor for Plants (BF)

The Plants Bioaccumulation Factor (BF) is a useful tool to evaluate whether a specific plant is an HM hyperaccumulator. The bioaccumulation factor is defined as the ability of a plant to accumulate HM concentrations. The ability of a plant to accumulate HMs from soils can be estimated using the BF, which is the ratio of HM concentrations in plants and soil [39,40,41]. The BF is computed according to the following formula:
BF = C 1 C 2
where C1 and C2 are the average concentrations of metal in plants and soil, respectively.

2.6. Statistical Analyses

Statistical analyses were made for all of the data using (SPSS; IBM Corp., New York, NY, USA) and Microsoft Excel. The figures were drawn using Microsoft Excel. Data mean and standard deviation were calculated using Microsoft Excel. Comparison of the mean of HMs in plants across all sites was made with Statistical Package for Social Studies (SPSS; IBM Corp., New York, NY, USA) using one-way ANOVA.

3. Results and Discussion

3.1. Soil

3.1.1. Physicochemical Parameters

The soil’s basic physical and chemical properties are shown in Table 2. In the topsoil from the different sampling sites in the area investigated, 58% had sandy clay loam and 42% sandy loam. The average soil texture class for all sites was sandy clay loam texture with very low organic matter content ranging from 0.43% to 0.98% with an average of 0.65%. The soil pH values ranged between 7.4 and 8.7 with an average of 8, indicating a high alkalinty for all sites. For electrical conductivity (EC), the value ranged between 6.65 and 12.80 dsm−1 and with an average of 9.19 dsm−1, and for CaCO3%, the value ranged between 28.5% and 42.2% and with an average of 35%. Results revealed that all sites are saline and alkaline soils. The soil’s chemical proprieties indicate that HMs in such environments would precipitate in the topsoil surface with limited movement and bioavailability for plants due to high calcium carbonate content in addition to alkaline pH.

3.1.2. Heavy Metal Concentration in Soils

The concentrations of HMs (Cd, Cr, Cu, Fe, Pb, Zn, and Ni) in the soil samples collected from the 12 sites (S) of the study area are summarized in Table 3. The values of the contents of the eight HMs in the soils of the study area followed a descending order of Fe > Ni > Zn > Pb > Cu> Cr > Cd. The average value for Cd in all sites was 8.58 mg·kg−1 with ranges between 1.1–22 mg·kg−1 and for Cr the value was 12.38 mg·kg−1 with ranges between 1.25 to 34.2 mg·kg−1. The Cu value was 15.11 mg·kg−1 with ranges between 8.2 to 22.4 mg·kg−1 and for Fe the value was 3522.7 mg·kg−1 with ranges between 655 to 6479 mg·kg−1. The Pb value was 15.81 mg·kg−1 with ranges between 3.35 to 25.5 mg·kg−1 and for Zn the value was 22.73 mg·kg−1 with range between 11.55 to 42.6 mg·kg−1, while for Ni the average value was 45.13 mg·kg−1 with a range from 21.7 to 72.8 mg·kg−1. The soil’s physical and chemical properties have a strong influence on the environmental fate and the mobility of HMs in soils [42]. However, the soils of the study area have 35% CaCO3 and pH value of 8.2, which indicate that HMs in such environments would precipitate in the topsoil surface with limited movement and bioavailability for plants. Cement and stone factories may produce pollutants that are widely dispersed and exceed the standard limit. In addition, the harmful chemical gases and dusts from cement factories, power plants, and vehicles in the this study area cause a pollution risk [43]. The comparison of the mean values showed that contents of Cd in S2 and S4 and Ni in S4 and Pb in S2 and S12 in the study area were found to be significantly (p < 0.001) higher than those in other sites. The high level of Cd and Pb on S2, S4, and S12 indicate a higher accumulation load from adjacent factories as well as due to the contamination by other sources such as oil refineries, petrochemicals, battery, and cement factories as well as heavy vehicle traffic. The correlation between HM content and soil’s physico-chemical properties is given in (Table 4). Significant negative correlations between the percent of sand and Cu concentration (p > 0.05) were observed. Silt content was positively correlated with Pb (p > 0.05), while clay was negatively correlated with Pb (p > 0.01). Dragović also found silt to be positively correlated (p > 0.01) with concentrations of Pb in soils in the Zlatibor mountains [44]. Soil pH, organic matter, EC, and calcium carbonate in soil showed no significant correlation with HM content in the soil in the study area.

3.2. Indices of Pollution

In this study, the enrichment factor (EF), contamination factor (CF), and pollution load index (PLI) have been applied to assess HM contamination in the soil of the study area. In addition, the bioaccumulation factor (BF) for each plant was applied and investigated in this study.

3.2.1. Enrichment Factor (EF)

The most common indices of pollution are deaccumulation and enrichment factors (EF). The EF was used in this study to differentiate between the metals originating from anthropogenic activities and those from natural processes in order to assess the degree of anthropogenic influence. The results of the calculated EF of HMs in soil samples are shown in Table 5 and Table 6. The EF of HMs ranged from 0.20 to 7336. The low values of EF indicate that the enrichment of soils by HMs is due to a natural process while high values indicate enrichment by anthropogenic activities. Based on contamination categories, 100%, 16.6%, and 6.2% of the samples fell under extremely high enrichment for Cd, Ni, and Pb, respectively. Of the soil samples, 33.3% showed very high enrichment for Pb, and 8.3% of soil samples showed very high enrichment for Cu, Zn, and Ni. Across all sites, 33.4% showed moderate Cr enrichment, while 66.6% fell under deficiency to minimal enrichment for Cr. The EF in the soil of the study area was generally very high, which indicates a significant contribution from anthropogenic sources, especially for Cd and Ni, such as from industrials spills [35,45].

3.2.2. Contamination Factor (CF) and Pollution Load Index (PLI)

The CF values were calculated from the concentration of HMs in the soil samples of the study area sites and the normal soil crust for metals introduced by Lindsay (1979) as a background concentration values. The CF can be used to evaluate the levels of contamination of HMs in the soils by comparing it with the background concentrations of metals in the earth’s crust [33].
The range of value of CF for Cd is 18.33–366.70, Cr: 0.01–0.34, Cu: 0.27–0.75, Fe: 0.02–0.17, Pb: 0.34–2.55, Zn: 0.23–0.85 and Ni: 0.54–1.82 (Table 7). The highest contamination factors are found in site S2 for Cd (CF, 366.7) and Pb (CF, 2.55). The average values of these metals are higher than one (Stevanović et al. 2018), indicating the contamination of soil by these metals. According to Muller (1969), all sites were very polluted in regard to Cd with (CF > 6). The very high values of Cd and Pb could be attributed to the influence of industrial activities taking place at the study area such as disposal of substances containing metals from vehicles batteries, the automotive parts industry, smelting, and so on [36].
The PLI calculated from CF values is given in Table 7. The PLI has been widely used to estimate the relative degree of the pollution level of heavy metals in soils [46,47,48]. It provides a simple comparative means of the level of HMs in the study area, which represents the number of times certain metal content in the soil exceeds the natural background concentration average. The PLI values in this study ranged from 0.52 to 1.33 with a mean value of 0.82 for soil samples collected from the 12 sites. The highest PLI value (1.33) was found in S5 followed by S2 (1.19), with medium contamination of soil by the investigated HMs. The rest of the sites are considered to have low levels of pollution by HMs. On the other hand, it is evident from the results that contamination factors for Cd and Pb were higher, giving rise to a higher PLI value in the study area. The PLI as presented provides a tool for comparison to evaluate site quality: a value of zero indicates perfection, a value equal to one indicates only baseline levels of pollutants are present, and values above one would represent progressive deterioration of the site as found in S5 and S2 [37]. This indicates contamination from the study area activities which include oil refineries, petrochemicals, batteries, and cement factories as well as heavy vehicle traffic.

3.2.3. Heavy Metal Concentration in Plants

The contents of HMs in native plant samples are summarized in Table 8 and Table 9 and Figure 2 and Figure 3. The concentrations of Cd ranged from 0.2 to 2.4 mg·kg−1, Cr (1.2 to 7.8 mg·kg−1), Cu (4.0 to 6.3 mg·kg−1), Fe (125.9 to 230.0 mg·kg−1), Ni (7.5 to 28.0 mg·kg−1), Pb (0.7 to 22.1 mg·kg−1), and Zn (6.6 to 12.75 mg·kg−1). Plant species that showed the highest concentration of metals were Citrullus Colocynthis (Cu and Ni), Cassia Italica (Cr), Phragmite australis (Ni), Cyperus Laevigatus (Ni), Argemone mexicana, Rhazya stricta (Ni), Conocarpus Lancifolius (Cd), and Sisymbrium irio (Fe). Results showed that the values for Fe and Cu concentrations compared to other HMs were highest in all plant species. Contents of Cd in all plant species ranged from 0.2 to 2.4 mg·kg−1 and the highest value was in Cyperus laevigatus and lowest in Alsola imbricata. Contents of Cd in plant species Calotropis Procera and Citrullus colocynthis were 1.2 and 1.9 mg·kg−1, respectively. These results were similar to those reported by Alyemeni where the average concentrations of Cd were 1.4 and 2.0 mg·kg−1 in Calotropis procera and Citrullus Colocynthis, respectively [15]. These values were higher than the data reported by Cardwell, who found 0.11 to 0.85 mg·kg−1 for 13 plant species from contaminated urban streams from southeast Queensland, Australia [49]. However, the Cd values found were not in the phytotoxic range of 5–700 mg·kg−1 reported by Chaney [50]. Cd contents in normal plants grown in uncontaminated soils usually range from 0.05 to 0.2 mg·kg−1. When the Cd concentration is higher than 2 mg·kg−1, it could cause toxicity to most plant species, adversely affecting the photosynthesis process and inhibiting plant growth and development, thus resulting in plant death. The concentration of Cr ranged from 1.2 to 7.8 mg·kg−1 and was the highest in Cyperus laevigatus and the lowest in Phragmite australis. Cr can cause toxicity in plants when concentrations exceed 2 mg·kg−1 [2,51,52]. Cr in all plants except Phragmite australis and Sisymbrium irio were above the safe limit and could be harmful to the local public as reported by Shah et al. and Lim et al. [53,54]. The concentrations of Pb ranged from 0.7 to 22.1 mg·kg−1 with a maximum in Rhazya stricta and a minimum in Cassia italica. The concentration of Zn ranged from 6.6 to 22.1 mg·kg−1 and was the highest in Cyperus laevigatus and the lowest in Salsola imbricata. Most plants accumulate less than 20 mg·kg−1 of Zn, which is considered to be deficient [53,54,55]. As a result, most plant species in this study were found to be deficient in Zn contents. Considering the maximum acceptable limits of HMs in plants, this study found that native plants have accumulated higher concentrations of the Fe, Cu, Ni, and Pb. Across all sites of the statistical analyses, one-way ANOVA (Figure 3) revealed that accumulation of Cd, Pb, and Ni by Cyperus laevigatus and Cd and Pb accumulation by Citrullus colocynthis were significantly (p < 0.001) higher than other studied species. In addition, Cassia italica showed significant (p < 0.001) uptake for Cr and Cd and Rhazya stricta showed significant (p < 0.001) uptake for Ni, Cr, and Zn. Accumulation of Cu and Fe by Malva parviflora was significantly (p < 0.001) higher compared to other studied species.

3.2.4. Bioaccumulation Factor (BF)

The BF in Table 10 can be used to estimate the ability of plants to accumulate metals from soils, which is defined as the ratio of metal concentration in plants to that in the soil [37]. The process of phytoextraction generally requires the translocation of HMs to the easily harvestable plant parts, i.e., shoots [56]. The BF values in the current study ranged from 0.04–2.76. Among the 12 native plant species, Malva parviflora growing at S11 had the highest BF value of 2.76 and 1.16 for Cu and Cr, respectively. Several plant species, including Malva parviflora, Sisymbrium irio and Citrullus colocynthis, showed highest BF values in S2, S11, and S12 as well as the highest Cr and Cu uptake, which could indicate the ability of these plant species to accumulate HMs compared to other identified species. In general, the BF for the native plants indicate that some species could be candidates for phytoextraction potential, such as Conocarpus lancifolius for Pb and Malva parviflora for Cu and Cr. Usually, BF values less than one are unsuitable for phytoextraction, and as a result, most of the studied plants were limited as phytoremediators for HMs in contaminated soil [57].

4. Practical Implications

The rapid growth of industrial activities in the major industrial cities of SA has caused an accumulation of pollutants, including HMs. The environments of these cities as well as nearby areas have been affected. There is a lack of active research in SA on the bioremediation of anthropogenically affected areas. It is an urgent need to find solutions for remediating such sites. There is also a lack of active research on finding an indigenous plant species that would be able to accumulate HMs and be applied in the phytoremediation of contaminated sites. Many native plants were found to tolerate and accumulate HMs, and we found that the most promising plant species for phytoextraction and remediation were annuals or perennials such as Malva parviflora, Sisymbrium irio, and Citrullus colocynthis, especially for Cr and Cu. This study suggests that these native plant species may be applied for phytoremediation in the area. This study provides guidance for determining the status of HMs in soils and native plants species in an industrial zone in the industrial city of Riyadh. Further research should be done in other industrial cities in the region with emphasis on identifying plant species that could be used for phytoremediation with the aim of preserving biodiversity.

5. Conclusions

This study was conducted to determine HM contents in soil and in the second industrial city zone in Riyadh city, SA as well as to assess the native plants growing in the area and their potential use for remediating sites contaminated by HMs in the region. Th Indices of metal pollution (EF and CF) used in study indicate that the investigated area was highly polluted by Cd and Pb as well as slightly polluted by Ni and Zn. This could be due to the contamination generated from manufacturing activities in the study area, which include smelting, metal, chemicals, electroplating, energy, fuel production, and cement factories and industries, in addition to heavy traffic flow, where the assessed sites are located in a dense industrial zone. The release of HMs by various industrial activities is a significant reason for soil pollution by HMs. As the study area has experienced quick development and industrialization over the past decades, the risk of pollution by HMs has also become gradually prominent. In order to decrease hazards of HMs to health of local inhabitants, phytoremediation can be adapted to clean up HM contamination sites. Using hyperaccumulator native plants will be essential for preventing pollution of the environment. This requires appropriate approaches to be used to protect the inhabitants and ecosystem that are in the vicinity of these contaminated sites in addition to implementation of an efficient remediation method to reduce contamination levels and minimize health and environmental risks. According to the results of the current study, the most promising species for phytoextraction and remediation were annuals or perennials such as Malva parviflora, Sisymbrium irio, and Citrullus colocynthis, especially for Cr and Cu. These native plant species can be used for phytoremediation of HM contaminated sites.

Author Contributions

Conceptualization, S.S.A. and F.N.A.; methodology, K.F.A. and K.D.A.; formal analysis, K.D.A. and S.S.A.; data curation, K.F.A. and F.N.A.; writing—original draft preparation, S.S.A.; writing—review and editing, K.F.A., K.D.A. and F.N.A.; supervision, S.S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This Project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (14-BIO785-02).

Data Availability Statement

The data used in this study is available from the corresponding author upon reasonable request.

Acknowledgments

The authors extend their deep appreciation to the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alloway, J.B. Heavy Metals in Soils; Springer: Dordrecht, The Netherlands, 2013; ISBN 978-94-007-4469-1. [Google Scholar]
  2. Kabata-Pendias, A. Trace Elements in Soils and Plants, 4th ed.; CRC Press: Boca Raton, FL, USA, 2010; ISBN 9780429192036. [Google Scholar]
  3. Wu, G.; Kang, H.; Zhang, X.; Shao, H.; Chu, L.; Ruan, C. A Critical Review on the Bio-Removal of Hazardous Heavy Metals from Contaminated Soils: Issues, Progress, Eco-Environmental Concerns and Opportunities. J. Hazard. Mater. 2010, 174, 1–8. [Google Scholar] [CrossRef] [PubMed]
  4. Rahman, Z.; Singh, V.P. The Relative Impact of Toxic Heavy Metals (THMs) (Arsenic (As), Cadmium (Cd), Chromium (Cr)(VI), Mercury (Hg), and Lead (Pb)) on the Total Environment: An Overview. Environ. Monit. Assess. 2019, 191, 419. [Google Scholar] [CrossRef] [PubMed]
  5. Seleiman, M.; Santanen, A.; Mäkelä, P. Recycling Sludge on Cropland as Fertilizer—Advantages and Risks. Resour. Conserv. Recycl. 2019, 155, 104647. [Google Scholar] [CrossRef]
  6. He, Z.L.; Yang, X.E.; Stoffella, P.J. Trace Elements in Agroecosystems and Impacts on the Environment. J. Trace Elem. Med. Biol. 2005, 19, 125–140. [Google Scholar] [CrossRef]
  7. Tang, J.; Chai, L.; Li, H.; Yang, Z.; Yang, W. A 10-Year Statistical Analysis of Heavy Metals in River and Sediment in Hengyang Segment, Xiangjiang River Basin, China. Sustainability 2018, 10, 1057. [Google Scholar] [CrossRef] [Green Version]
  8. Aloud, S.S. Impact of Municipal and Industrial Waste on the Distribution and Accumulation of Some Heavy Metals in Sandy Soils of Al-Qassim Region at Central of Saudi Arabia. J. Environ. Sci. Technol. 2008, 1, 135–142. [Google Scholar] [CrossRef] [Green Version]
  9. Kumpiene, J.; Lagerkvist, A.; Maurice, C. Stabilization of As, Cr, Cu, Pb and Zn in Soil Using Amendments—A Review. Waste Manag. 2008, 28, 215–225. [Google Scholar] [CrossRef]
  10. Wang, Y.; Bai, Y.; Wang, J. Distribution of Urban Soil Heavy Metal and Pollution Evaluation in Different Functional Zones of Yinchuan City. Huan Jing Ke Xue 2016, 37, 710–716. [Google Scholar]
  11. Saxena, G.; Purchase, D.; Mulla, S.I.; Saratale, G.D.; Bharagava, R.N. Phytoremediation of Heavy Metal-Contaminated Sites: Eco-Environmental Concerns, Field Studies, Sustainability Issues, and Future Prospects. Rev. Environ. Contam. Toxicol. 2020, 249, 71–131. [Google Scholar] [CrossRef] [Green Version]
  12. Badawy, S.H.; Helal, M.I.D.; Chaudri, A.M.; Lawlor, K.; McGrath, S.P. Soil Solid-Phase Controls Lead Activity in Soil Solution. J. Environ. Qual. 2002, 31, 162–167. [Google Scholar] [CrossRef]
  13. Beyersmann, D.; Hartwig, A. Carcinogenic Metal Compounds: Recent Insight into Molecular and Cellular Mechanisms. Arch. Toxicol. 2008, 82, 493–512. [Google Scholar] [CrossRef] [PubMed]
  14. Sanyaolu, V.; Adeniran, A. Determination of Heavy Metal Fallout on the Surrounding Flora and Aquifer: Case Study of A Scrap Metal Smelting Factory in Odogunyan Area, Ikorodu, Lagos-State, Nigeria. Int. Res. J. Environ. Sci. 2014, 3, 93–100. [Google Scholar]
  15. Alyemeni, M.; Sher, H.; El-Sheikh, M.; Eid, E. Bioaccumulation of Nutrient and Heavy Metals by Calotropis Procera and Citrullus Colocynthis and Their Potential Use as Contamination Indicators. Sci. Res. Essays 2011, 6, 966–976. [Google Scholar]
  16. Kaur, J.; Bhat, S.A.; Singh, N.; Bhatti, S.S.; Kaur, V.; Katnoria, J.K. Assessment of the Heavy Metal Contamination of Roadside Soils Alongside Buddha Nullah, Ludhiana, (Punjab) India. Int. J. Environ. Res. Public Health 2022, 19, 1596. [Google Scholar] [CrossRef]
  17. Deng, J.; Li, B.; Zhang, S.; Li, Z.; Zu, Y.; He, Y.; Chen, J.; Li, T. Plant Species Diversity of Plant Communities and Heavy Metal Accumulation in Buffer Zone of Momianhe Stream Along a Long-Term Mine Wastes Area, China. Bull. Environ. Contam. Toxicol. 2021, 107, 1136–1142. [Google Scholar] [CrossRef]
  18. Lenart-Boroń, A.; Wolny-Koładka, K. Heavy Metal Concentration and the Occurrence of Selected Microorganisms in Soils of a Steelworks Area in Poland. Plant Soil Environ. 2015, 61, 273–278. [Google Scholar] [CrossRef] [Green Version]
  19. Crnković, D.; Ristić, M.; Antonović, D. Distribution of Heavy Metals and Arsenic in Soils of Belgrade (Serbia and Montenegro). Soil Sediment Contam. Int. J. 2006, 15, 581–589. [Google Scholar] [CrossRef]
  20. Aloud, S.S. Extraction of Heavy Metals by Phosphate and Oxalate from a Contaminated Soil. In Proceedings of the 1st IASTED International Conference on Unconventional Oil, Calgary, AB, Canada, 4–6 July 2011. [Google Scholar] [CrossRef]
  21. Aloud, S.S. Mutual Effect of EDTA and Some Plant Species on Phytoremediation of Arsenic and Cadmium Contaminated Soil. Egypt. J. Appl. Sci. 2007, 8A, 264–275. [Google Scholar]
  22. Aloud, S.S. Enhancement of Heavy Metals Movement by Capillary Forces by Addition of Chelate Solution in Polluted Soil. In Proceedings of the 6th International Conference on Environmental Science and Technology (ICEST), Houston, TX, USA, 25–29 June 2012; pp. 339–340. [Google Scholar]
  23. Baker, A.; Brooks, R. Terrestrial Higher Plants Which Hyperaccumulate Metallic Elements, A Review of Their Distribution, Ecology and Phytochemistry. Biorecovery 1989, 1, 81–126. [Google Scholar]
  24. Küpper, H.; Leitenmaier, B. Cadmium-Accumulating Plants. Met. Ions Life Sci. 2013, 11, 373–393. [Google Scholar] [CrossRef]
  25. Rodríguez-Bocanegra, J.; Roca, N.; Febrero, A.; Bort, J. Assessment of Heavy Metal Tolerance in Two Plant Species Growing in Experimental Disturbed Polluted Urban Soil. J. Soils Sediments 2018, 18, 2305–2317. [Google Scholar] [CrossRef]
  26. Holliday, V.T. Methods of Soil Analysis, Part 1, Physical and Mineralogical Methods (2nd Edition), A. Klute, Ed., 1986, American Society of Agronomy, Agronomy Monographs 9(1), Madison, Wisconsin, 1188 pp., \$60.00. Geoarchaeol. Int. J. 1990, 5, 87–89. [Google Scholar] [CrossRef]
  27. Loeppert, R.H.; Suarez, D.L. Carbonate and Gypsum. In Methods of Soil Analysis; John Wiley & Sons: Hoboken, NJ, USA, 1996; pp. 437–474. ISBN 9780891188667. [Google Scholar]
  28. Nelson, D.W.; Sommers, L.E. Total Carbon, Organic Carbon, and Organic Matter. In Methods of Soil Analysis; John Wiley & Sons: Hoboken, NJ, USA, 1983; pp. 539–579. ISBN 9780891189770. [Google Scholar]
  29. Thomas, G.W. Soil PH and Soil Acidity. In Methods of Soil Analysis; John Wiley & Sons: Hoboken, NJ, USA, 1996; pp. 475–490. ISBN 9780891188667. [Google Scholar]
  30. Estefan, G. Methods of Soil, Plant, and Water Analysis: A Manual for the West Asia and North Africa Region, 3rd ed.; International Center for Agricultural Research in the Dry Areas (ICARDA): Beirut, Lebanon, 2013. [Google Scholar]
  31. Ho, H.H.; Rudy, S.; Damme, A. Distribution and Contamination Status of Heavy Metals in Estuarine Sediments near CauOng Harbor, Ha Long Bay, Vietnam. Geology Belgica. Geol. Belgica 2010, 13, 37–47. [Google Scholar]
  32. Lizárraga-Mendiola, L.; Durán-Domínguez-de-Bazúa, M.D.C.; González-Sandoval, M.-R. Environmental Assessment of an Active Tailings Pile in the State of Mexico (Central Mexico). Res. J. Environ. Sci. 2008, 2, 197–208. [Google Scholar] [CrossRef] [Green Version]
  33. Lindsay, W.L. Chemical Equilibria in Soils; John Wiley & Sons: Hoboken, NJ, USA, 1979. [Google Scholar]
  34. Demková, L.; Jezný, T.; Bobulska, L. Assessment of Soil Heavy Metal Pollution in a Former Mining Area—Before and after the End of Mining Activities. Soil Water Res. 2017, 12, 2017. [Google Scholar] [CrossRef] [Green Version]
  35. Sutherlad, R. Bed Sediment-Associated Trace Metals in an Urban Stream, Oahu, Hawaii. Environ. Geol. 2000, 39, 611–627. [Google Scholar] [CrossRef]
  36. Muller, G. Index of Geoaccumulation in Sediments of the Rhine River. GeoJournal 1969, 2, 108–118. [Google Scholar]
  37. Tomlinson, D.L.; Wilson, J.G.; Harris, C.R.; Jeffrey, D.W. Problems in the Assessment of Heavy-Metal Levels in Estuaries and the Formation of a Pollution Index. Helgoländer Meeresunters. 1980, 33, 566–575. [Google Scholar] [CrossRef] [Green Version]
  38. Usero, J.G.; Amarillo, J.F. Evaluación de La Calidad de Las Aguas y Sedimentos Del Litoral de Andalucía; Consejería de Medio Ambiente: Sevilla, Spain, 2000; 164p. [Google Scholar]
  39. Ma, L.Q.; Komar, K.M.; Tu, C.; Zhang, W.; Cai, Y.; Kennelley, E.D. A Fern That Hyperaccumulates Arsenic. Nature 2001, 409, 579. [Google Scholar] [CrossRef]
  40. Soda, S.; Hamada, T.; Yamaoka, Y.; Ike, M.; Nakazato, H.; Saeki, Y.; Kasamatsu, T.; Sakurai, Y. Constructed Wetlands for Advanced Treatment of Wastewater with a Complex Matrix from a Metal-Processing Plant: Bioconcentration and Translocation Factors of Various Metals in Acorus Gramineus and Cyperus Alternifolius. Ecol. Eng. 2012, 39, 63–70. [Google Scholar] [CrossRef]
  41. Rai, U.; Upadhyay, A.; Singh, N.; Dwivedi, S.; Tripathi, R. Seasonal Applicability of Horizontal Sub-Surface Flow Constructed Wetland for Trace Elements and Nutrient Removal from Urban Wastes to Conserve Ganga River Water Quality at Haridwar, India. Ecol. Eng. 2015, 81, 115–122. [Google Scholar] [CrossRef]
  42. Wilson, S.C.; Tighe, M.; Paterson, E.; Ashley, P.M. Food Crop Accumulation and Bioavailability Assessment for Antimony (Sb) Compared with Arsenic (As) in Contaminated Soils. Environ. Sci. Pollut. Res. Int. 2014, 21, 11671–11681. [Google Scholar] [CrossRef] [PubMed]
  43. Salman, A.; Al-Tayib, M.; Hag-Elsafi, S.; Al-Duwarij, N. Assessment of Pollution Sources in the Southeastern of the Riyadh and Its Impact on the Population/Saudi Arabia. Arab. J. Geosci. 2016, 9, 328. [Google Scholar] [CrossRef]
  44. Dragović, S.; Mihailović, N.; Gajić, B. Heavy Metals in Soils: Distribution, Relationship with Soil Characteristics and Radionuclides and Multivariate Assessment of Contamination Sources. Chemosphere 2008, 72, 491–495. [Google Scholar] [CrossRef] [PubMed]
  45. Yadav, S.; Rajamani, V. Air Quality and Trace Metal Chemistry of Different Size Fractions of Aerosols in N-NW India—Implications for Source Diversity. Atmos. Environ. 2006, 40, 698–712. [Google Scholar] [CrossRef]
  46. Chen, H.; Teng, Y.; Lu, S.; Wang, Y.; Wang, J. Contamination Features and Health Risk of Soil Heavy Metals in China. Sci. Total Environ. 2015, 512–513, 143–153. [Google Scholar] [CrossRef] [PubMed]
  47. Stevanović, V.; Gulan, L.; Milenković, B.; Valjarević, A.; Zeremski, T.; Penjišević, I. Environmental Risk Assessment of Radioactivity and Heavy Metals in Soil of Toplica Region, South Serbia. Environ. Geochem. Health 2018, 40, 2101–2118. [Google Scholar] [CrossRef] [PubMed]
  48. Zheng, Y.; Shen, D.; Wu, S.; Han, Y.; Li, S.; Tang, F.; Lin, N.; Mo, R.; Liu, Y. Uptake Effects of Toxic Heavy Metals from Growth Soils into Jujube and Persimmon of China. Environ. Sci. Pollut. Res. 2018, 25, 31593–31602. [Google Scholar] [CrossRef]
  49. Cardwell, A.J.; Hawker, D.W.; Greenway, M. Metal Accumulation in Aquatic Macrophytes from Southeast Queensland, Australia. Chemosphere 2002, 48, 653–663. [Google Scholar] [CrossRef]
  50. Chaney, R.L. Toxic Element Accumulation in Soils and Crops: Protecting Soil Fertility and Agricultural Food-Chains. In Inorganic Contaminants in the Vadose Zone; Bar-Yosef, B., Barrow, N.J., Goldshmid, J., Eds.; Springer: Berlin/Heidelberg, Germany, 1989. [Google Scholar]
  51. Bonanno, G.; Lo Giudice, R. Heavy Metal Bioaccumulation by the Organs of Phragmites Australis (Common Reed) and Their Potential Use as Contamination Indicators. Ecol. Indic. 2010, 10, 639–645. [Google Scholar] [CrossRef]
  52. Hassan, Z.; Aarts, M. Opportunities and Feasibilities for Biotechnological Improvement of Zn, Cd Or Ni Tolerance and Accumulation in Plants. Environ. Exp. Bot. 2011, 72, 53–63. [Google Scholar] [CrossRef]
  53. Muhammad, S.; Shah, M.T.; Khan, S.; Saddique, U.; Gul, N.; Khan, M.U.; Malik, R.N.; Farooq, M.; Naz, A. Wild Plant Assessment for Heavy Metal Phytoremediation Potential along the Mafic and Ultramafic Terrain in Northern Pakistan. Biomed. Res. Int. 2013, 2013, 194765. [Google Scholar] [CrossRef] [PubMed]
  54. Lim, H.-S.; Lee, J.-S.; Chon, H.-T.; Sager, M. Heavy Metal Contamination and Health Risk Assessment in the Vicinity of the Abandoned Songcheon Au-Ag Mine in Korea. J. Geochem. Explor. 2008, 96, 223–230. [Google Scholar] [CrossRef]
  55. National Research Council. Nutrient Requirements of Beef Cattle, 7th ed.; Update 2000; The National Academies Press: Washington, DC, USA, 2000. [Google Scholar]
  56. Yoon, J.; Cao, X.; Zhou, Q.; Ma, L.Q. Accumulation of Pb, Cu, and Zn in Native Plants Growing on a Contaminated Florida Site. Sci. Total Environ. 2006, 368, 456–464. [Google Scholar] [CrossRef]
  57. Fitz, W.J.; Wenzel, W.W. Arsenic Transformations in the Soil-Rhizosphere-Plant System: Fundamentals and Potential Application to Phytoremediation. J. Biotechnol. 2002, 99, 259–278. [Google Scholar] [CrossRef]
Figure 1. Map of the study area. Sampling sites numbered 1–12 on the map.
Figure 1. Map of the study area. Sampling sites numbered 1–12 on the map.
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Figure 2. HMs content (mg·kg−1) in plant species grown on the study area.
Figure 2. HMs content (mg·kg−1) in plant species grown on the study area.
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Figure 3. The data of the means ± standard error with one-way analysis of variance. Lowercase letters indicated that the mean content of HMs (mg·kg−1) in different plant species have statistically significant differences at the 0.05 probability level.
Figure 3. The data of the means ± standard error with one-way analysis of variance. Lowercase letters indicated that the mean content of HMs (mg·kg−1) in different plant species have statistically significant differences at the 0.05 probability level.
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Table 1. Chemical and physical properties of the study area.
Table 1. Chemical and physical properties of the study area.
ParametersMinimumMaximumMean (SD a)
Sand%556058.1 (1.4)
Silt%182622.4 (2.6)
Clay%162319.4 (2.1)
pH (1:1)7.48.78.2 (0.4)
EC b dsm−16.612.89.19 (2.0)
% O.M c0.430.980.74 (0.2)
CaCO3%28.542.235 (5.1)
a Standard deviation. b Electrical conductivity. c Soil organic matter.
Table 2. The species characteristic of the study areas.
Table 2. The species characteristic of the study areas.
FamilySpeciesClassification
ApocynaceaeCalotropis proceraPerennial
CucurbitaceaeCitrullus colocynthisAnnual
FabaceaeCassia italicaPerennial
CyperaceaeCyperus laevigatusPerennial
PapaveraceaeArgemone mexicanaAnnual
ApocynaceaeRhazya strictaPerennial
CombretaceaeConocarpus lancifoliusPerennial
ZygophyllaceaeZygophyllum simplexAnnual
AmaranthaceaeSalsola imbricataAnnual or perennial
MalvaceaeMalva parvifloraAnnual or perennial
BrassicaceaeSisymbrium irioAnnual or winter-annual
PoaceaePhrgmites australisAnnual or perennial
Table 3. Mean and standard deviation of heavy metal concentrations in soil of study areas (12 site, n = 36; mg·kg−1).
Table 3. Mean and standard deviation of heavy metal concentrations in soil of study areas (12 site, n = 36; mg·kg−1).
Sites No.Mean (SD a)
Heavy Metal
CdCrCuFePbNiZn
S11.7 (0.34)15.85 (0.32)14.6 (1.87)4035.5 (207.7)16.7 (1.12)26.65 (1.08)33.95 (0.11)
S222 (1.41)20.55 (2.38)11.1 (0.88)5218 (163.8)25.5 (5.12)21.7 (2.22)32.5 (0.40)
S33.1 (1.43)15.55 (15.55)18.7 (2.31)4848 (183.5)18.05(1.65)60.1 (5.94)20.1 (2.52)
S416 (0.62)1.25 (0.36)12.55 (2.67)655.5 (19.7)5.1 (1.28)72.8 (5.32)22.8 (4.05)
S512.9 (1.79)34.2 (3.37)16.95 (1.12)6478.5 (162.8)10.9 (1.53)45.0 (8.64)42.6 (2.13)
S65.5 (0.51)16.75 (1.15)22.4 (1.64)1660.8 (76.9)10.75 (1.54)48.8 (1.51)20.1 (2.56)
S715.1 (0.67)11.7 (0.57)12.4 (0.36)1303.5 (30.3)12.55 (1.52)66.5 91.16)21.15 (0.94)
S85.9 (0.57)4.5 (0.59)17.9 (0.23)6262 (186.6)22.55 (2.19)38.75 (0.32)14.35 (0.10)
S97.7 (0.96)18.1 (0.36)14.9 (0.23)4860 (60.8)3.35 (0.26)29.3 (0.93)21.7 (0.17)
S101.1 (0.08)5.7 (0.37)22.35 (0.32)2003.7 (7.57)16.25 (0.24)34.5 (1.08)11.55 (0.66)
S118.3 (90.36)2.75 (0.10)8.2 (0.29)1829.2 (117.1)22.55 (0.88)32.5 (0.50)15.25 (0.07)
S123.6 (0.22)1.6 (0.15)9.25 (0.07)3117.5 (181.0)25.5 (0.45)64.93 (2.12)16.65 (0.11)
a Standard deviation.
Table 4. The Pearson correlation matrix for HMs and soil physicochemical characteristics.
Table 4. The Pearson correlation matrix for HMs and soil physicochemical characteristics.
ZnPbNiFeCuCrCd
Sand%0.486−0.07−0.030.362−0.627 *0.2440.195
Silt%−0.5440.611 *−0.127−0.2340.103−0.373−0.194
Clay%0.326−0.753 **0.1930.0160.3730.2960.099
pH0.474−0.527−0.086−0.0950.4470.4620.071
EC−0.4860.4750.528−0.457−0.452−0.549−0.035
O.M%0.05−0.119−0.449−0.2410.1570.3070.188
CaCO3%0.126−0.030.3570.067−0.266−0.1880.02
* Correlation is significant at the 0.05 level. ** Correlation is significant at the 0.01 level.
Table 5. Percentage of contaminated samples according to Enrichment factor (EF) of the studied metals in sites (S1–S12).
Table 5. Percentage of contaminated samples according to Enrichment factor (EF) of the studied metals in sites (S1–S12).
Enrichment Factor (EF)CdCrCuPbZnNi
Deficiency to minimal enrichment066.6008.338.33
Moderate enrichment033.458.338.335033.33
Significant enrichment0033.3441.6633.3333.33
Very high enrichment008.3333.38.338.33
Extremely high enrichment100008.33016.66
Table 6. Enrichment factor (EF) between the studied metals of the studied metals in sites (S1–S12).
Table 6. Enrichment factor (EF) between the studied metals of the studied metals in sites (S1–S12).
Enrichment Factor (EF)
Soil SitesS1S2S3S4S5S6S7S8S9S10S11S12
Cd266267040515,45712612097733659610033472873731
Cr1.51.51.20.723.83.40.31.41.10.60.2
Cu4.62.74.924.23.317.1123.63.914.15.73.8
Pb15.718.614.129.66.424.636.613.72.630.846.831.1
Zn6.44.73.226.459.212.31.73.44.46.34.1
Ni6.3411.8105.56.627.948.55.95.716.416.919.8
Table 7. Mean of contamination factor (CF) and pollution load index (PLI) of the studied metals in sites (S1–S12).
Table 7. Mean of contamination factor (CF) and pollution load index (PLI) of the studied metals in sites (S1–S12).
Site IDContamination Factor (CF)Pollution Load Index (PLI)
CdCrCuFePbZnNi
S128.330.160.490.111.670.680.670.78
S2366.670.210.370.142.550.650.541.19
S351.670.160.620.131.810.41.50.95
S4266.670.010.420.020.510.461.820.52
S52150.340.570.171.090.851.131.33
S691.670.170.750.041.080.41.220.83
S7251.670.120.410.031.260.421.660.87
S898.330.050.60.162.260.290.970.83
S9128.330.180.50.130.340.430.730.77
S1018.330.060.750.051.630.230.860.54
S11138.330.030.270.052.260.310.810.60
S12600.020.310.082.550.331.620.62
Range18.3–366.70.01–0.340.27–0.750.02–0.170.34–2.550.23–0.850.54–1.820.52–1.33
Table 8. Summary of heavy metal contents (mg·kg−1) in native plants (n = 36).
Table 8. Summary of heavy metal contents (mg·kg−1) in native plants (n = 36).
Heavy MetalsMinimum (mg·kg−1)Maximum (mg·kg−1)Mean (mg·kg−1)
Zn6.622.112.75
Pb0.722.15.4
Ni7.52812.9
Fe125.92308544.8
Cu4.06327.6
Cr1.27.84.271
Cd0.22.41.12
Table 9. Mean and SD of heavy metals concentrations (mg·kg−1) in native plants (n = 36).
Table 9. Mean and SD of heavy metals concentrations (mg·kg−1) in native plants (n = 36).
Plant SpeciesMean (SD a)
CdCrCuFeNiPbZn
Calotropis procera1.2 (0.80)5 (1.41)n.d.445.98 (43.17)15.1 (0.22)5.5 (0.62)17.6 (1.61)
Citrullus colocynthis1.9 (0.22)4.9 (0.45)13 (1.41)629.73 (86.93)9.7 (1.84)9.6 (1.81) 10.7 (1.37)
Cassia italica1.7 (0.22)7.8 (1.23) n.d.578.07 (31.96)11.8 (2.65)1.1 (0.65)6.9 (0.92)
Phragmite australis0.97 (0.16)1.2 (0.22)9.4 (0.78)379.47 (27.69)8.7 (1.44)0.7 (0.33)12.8 (2.57)
Cyperus laevigatus2.4 (0.22)3.2 (0.75)10.5 (1.22)171.25 (10.15)14.5 (1.31)3 (0.82)22.1 (1.56)
Argemone mexicana0.7 (0.14)5.8 (0.67)5.8 (2.19)156.47 (4.94)13.3 (1.63)2.3 (0.29)15.2 (2.62)
Rhazya stricta1.3 (0.29)5.3 (0.16)7.2 (0.11)125.98 (4.02)28 (1.08)22.1 (0.43)18.9 (0.29)
Conocarpus lancifolius1.1 (0.22)2.41 (0.13)14.13 (0.29)242.21 (917.18)11.40 (0.45)7.51 (1.63)8.9 (0.29)
Zygophyllum simplex0.3 (0.11)3.0 (0.36)4.76 (0.20)412.46 (23.17)7.46 (0.09)2.71 (0.19)12.1 (0.22)
Salsola imbricata0.2 (0.01)4.1 (0.16)17.25 (0.33)543.3 (16.7)8.8 (0.26)6.3 (0.28)6.6 (0.15)
Malva parviflora1.25 (0.20)3.2 (0.21)22.6 (0.49)2308 (221.5)12.92 (0.07)3.3 (0.24)11.1 (0.62)
Sisymbrium irio0.46 (0.05)1.4 (0.14)14.2 (0.11)936.8 (18.26)6.7 (0.12)5.2 (0.03)10.1 (0.15)
a Standard deviation. n.d. Not determined.
Table 10. Bioconcentration factor (BF) of HMs (mg·kg−1) in plants (n = 36) in the study area.
Table 10. Bioconcentration factor (BF) of HMs (mg·kg−1) in plants (n = 36) in the study area.
Plant SpeciesSitesBioconcentration Factor (BF)
CdCrCuFePbZnNi
Calotropis proceraS10.710.32n.d.0.110.330.520.57
Citrullus colocynthisS20.090.241.170.120.380.330.45
Cassia italicaS30.550.5n.d.0.120.060.340.20
Phragmite australisS40.060.960.750.580.140.560.12
Cyperus laevigatusS50.190.090.620.030.280.520.32
Argemone mexicanaS60.130.350.260.090.210.760.27
Rhazya strictaS70.090.450.580.100.600.890.42
Conocarpus lancifoliusS80.190.540.790.040.980.620.29
Zygophyllum simplexS90.040.170.320.080.810.560.25
Salsola imbricataS100.180.720.770.270.390.570.26
Malva parvifloraS110.151.162.761.260.150.730.40
Sisymbrium irioS120.130.881.540.300.200.610.10
Range0.04–0.710.09–1.160.26–2.760.03–1.260.06–0.980.33–0.890.1–0.57
n.d. Not determined.
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Aloud, S.S.; Alotaibi, K.D.; Almutairi, K.F.; Albarakah, F.N. Assessment of Heavy Metals Accumulation in Soil and Native Plants in an Industrial Environment, Saudi Arabia. Sustainability 2022, 14, 5993. https://doi.org/10.3390/su14105993

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Aloud SS, Alotaibi KD, Almutairi KF, Albarakah FN. Assessment of Heavy Metals Accumulation in Soil and Native Plants in an Industrial Environment, Saudi Arabia. Sustainability. 2022; 14(10):5993. https://doi.org/10.3390/su14105993

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Aloud, Saud S., Khaled D. Alotaibi, Khalid F. Almutairi, and Fahad N. Albarakah. 2022. "Assessment of Heavy Metals Accumulation in Soil and Native Plants in an Industrial Environment, Saudi Arabia" Sustainability 14, no. 10: 5993. https://doi.org/10.3390/su14105993

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