Ecological Risk Evaluation of Heavy Metals in Soils near a Water Dam in Baljurashi, KSA, and Their Accumulation in Dodonaea viscosa

Abstract

Soil’s contamination with heavy metals is a serious issue, and assessing their levels, regulating their sources, and finding cost-effective technology to limit their toxic effect and soil cleanup are of high priority. The focus of this study was to find out the extent of priority hazard heavy metal contamination in soils close to the Al-Janabeen water reservoir dam in Baljurashi, KSA, and the extent of their accumulation in the Dodonaea viscosa Tacq plant. Soil and plant samples were analyzed for heavy metal concentration using inductively coupled plasma spectroscopy (ICPS). Quantitative single and integrated contamination indices were used to characterize and evaluate the degree of heavy metal contamination in soils. The biological concentration factor (BCF) and the translocation factor (TF) were utilized to determine the extent of transfer and accumulation of heavy metals in the D. viscosa plant. Soil analysis showed the presence of Mn, Cu, Zn, Co, Cr, and Pb among the prioritized heavy metals with Cu, Zn, and Pb values being higher than those in natural background levels. The mean concentration order was Fe > Mn > Zn > Cu > Pb > Cr > Ni > Cr > Co. The single contamination indices’ quantification indicated marked contamination due to Mn, Zn, Cu, Co, Cr, and Pb elements. While integrated contamination factors’ computation showed low ecological risk due to accumulative metals of concern, Nemerow’s index (NI) showed that Cu, Zn, and Pb were the three most abundant pollutants in the examined soils. Analysis of plant parts showed the presence of Mn, Cu, Ni, Zn, Co, Cr, Cd, and Pb at varying quantities in various plant sections. Both BCF and TF factors were less than one, indicating that the D. viscosa plant was limited in its ability to phytoextract and accumulate the metals from the soil. This study suggests more research to locate suitable native plants for phytoremediation and soil cleaning is required in addition to the appropriate planning and management of landfills in order to ensure agricultural sustainability.

1. Introduction

Sustainable soil management is one of the parts of sustainable agriculture that is mostly dependent on soil quality in order to provide appropriate nutrients for the plants. Soil should be preserved from contamination, developed, and its level of fertility should be increased in order to satisfy the expanding agricultural needs in a healthy manner, as soil is heavily required for good and healthy foods.

Soils are thought to be important sinks of heavy metals and metalloids released into the environment as a result of anthropogenic activities such as the disposal of metal-rich waste [1,2]. Heavy metal contamination in soil is a worldwide concern due to the risks posed to the ecosystem, biological toxicity [3,4,5], difficulty of degradation, and its ability to accumulate in the food chain [6,7]. Heavy metals have also been shown to hinder organic pollutant biodegradation and impact degradation rates [8]. Furthermore, the heavy metal contamination of soils has an impact on the ability of the land to be used for agricultural production, threatening food security due to their potential bioaccumulation and biomagnification in the food chain [9].

According to the Agency of Toxic Substances and Disease Registry (ATSDR), metals such as As, Pb, Cr, Cd, Hg, Cu, Ni, and Zn are among those on the priority list, which are determined to pose potential ecological harm and threat to human health due to their known toxicity, and chronic exposure to them has carcinogenic and non-carcinogenic consequences [10]. The risk assessment of these hazardous metals can provide information about heavy metal speciation, bioavailability, and, as a result, the selection of appropriate remedial techniques such as soil washing, immobilization [11], or phytoremediation [12].

Single and integrated pollution indices are regarded as valuable quantitative techniques for assessing heavy metals in soils and are of critical importance in predicting future environmental sustainability, particularly for agricultural purposes [13]. The increase in research on heavy metal pollution and their risk assessment in agricultural soil has attracted researchers from all around the world [14]. Alzahrani et al. [15] recently published a research paper on the environmental assessment of heavy metals in soils around the Al-Janabeen dam in southwest Saudi Arabia, in which the authors claimed the analysis of the enrichment of the tested soils with Cu, Pb, Co, Cr, Ni, and Zn revealed anthropogenic effects for Cu, Zn, Cd, and Pb with a low risk of exposure to these heavy metals in the soils. Alarifi et al. [16] completed another investigation to analyze the health risks of potentially harmful substances in agricultural soil in the Al-Ammariah region of northwest Riyadh, Saudi Arabia. The study indicated that ingestion and skin route exposure to the analyzed soils associated with the observed hazardous elements posed no health risk.

The density and transit of a heavy metal in soil are heavily influenced by its chemical form and speciation, and it is critical to understand its speciation, bioavailability, and remedial options [17,18]. Heavy metals can speciate, changing their chemical forms and bioavailability [19], allowing them to be easily absorbed by plants and stored in plant parts [20]. Plant uptake, also known as phytoremediation, is thought to be one of the regulating techniques for heavy metal distribution in soils [21]. Plants that grow in heavy metal-contaminated soils can have unique features that make them suitable for phytoremediation [22,23,24]. Phytoremediation is an effective and generally accepted low-cost approach for treating or lowering the concentration of hazardous contaminants in contaminated soil and water through phytoextraction and phytostabilization mechanisms [25]. This is an alternative technology that uses natural biological mechanisms to make heavy metal pollutants harmless [26,27]. It is worth noting that the public’s interest in purchasing damaged lands and repairing them at a cheaper cost has been piqued by economically viable phytoremediation. Apart from its effectiveness in reducing pollution, numerous other aspects such as habitat restoration, the creation of greener spaces, landscaping benefits, and an increase in flora and faunal richness could explain why phytoremediation is widely perceived as a sustainable strategy. Other advantages of phytoremediation over older approaches include lower costs, less secondary waste, and the preservation of soil fertility [28]. For this reason, naturally occurring inedible plants can be a useful option [29,30]. The most effective phytoremediation technique is to use native plants that can thrive and grow in their natural environment [31].

Dodonaea viscosa Tacq is a plant native to Saudi Arabia [32] and is an inedible evergreen shrub that was observed growing naturally in the targeted soil near the Al-Janabeen water reservoir dam in Baljurashi, Saudi Arabia. This plant species was observed growing in contaminated regions with heavy metals. Castaneda-Espinoza et al. [33] recently investigated it as a phytoremediator for soils affected by heavy metals in abandoned mines. Their research was conducted in a greenhouse setting. The study found that D. viscosa plants have the ability to phytoextract (Cd, Cu, and Zn) and phytostabilize (Pb, Zn, Cd, Cu, and Fe) metals from polluted soils, making them an attractive choice for metal-polluted soil phytoremediation.

Aloud et al. [31] conducted a study in Riyadh, Saudi Arabia, to investigate the pollution of heavy metals in soils and their accumulation in native plant species in an industrial context. The study addressed the accumulation of heavy metals in 12 native plant species growing in the study area. The results revealed that the heavy metals content in the soil was in the following order: (Fe > Ni > Zn > Pb > Cu> Cr > Cd). The single enrichment factors (EF and CF) results indicated that the soil samples tested were particularly rich in Cd, Ni, and Pb, which was attributed to the industrial activities in the investigated area. Furthermore, the study found that plant species Cyperus laevigatus and Citrullus colocynthis accumulate much more Cd, Pb, and Ni than other investigated species, and the study concluded that annuals or perennials plants are potential species for phytoextraction and remediation based on the BF values observed [31].

For the current study, we chose a study site near a non-compliant open garbage dump and beside the Al-Janabeen water reservoir dam in Baljurashi, KSA. We proposed exposing the agricultural soil in the chosen location to heavy metals-rich waste residues from the nearby landfill. Furthermore, there is a scarcity of knowledge on the use of endemic plant species in this region for heavy metal phytoremediation. As a result, the primary goals of this research are as follows: (i) to conduct an ecological risk assessment of priority heavy metals in the chosen soils in order to provide insight into the degree of contamination and the associated potential risk, and (ii) to investigate the possible phytoaccumulation of heavy metals onto the commonly grown naturally shrub plant D. viscosa in the specified soil site and the possibility of using it for cleaning up the soils from hazardous heavy metals. Furthermore, the data will help policymakers, society, and environmental and agricultural engineers choose appropriate and feasible land to assure human safety and agricultural sustainability.

2. Materials and Methods

2.1. Site Description

The focus of the investigation pertains to the vicinity proximate to the Al-Jjanabeen water reservoir dam situated in Baljurashi (41°38′ E and 19°53′ N), which is located in the southwestern region of the Kingdom of Saudi Arabia (Figure 1). In close proximity to the designated study site, there was a non-compliant open garbage dump 500 m in distance (Figure 2) that did not adhere to the landfill’s waste-acceptance criteria, and its elevation exceeded that of the research location. The municipality closed the landfill fifteen years ago, yet there is still a potential environmental impact. During the rainy season, the soil washes away, carrying garbage residues with it and harming agricultural land in neighboring areas. Owing to the geographical position of the site, it is anticipated that water runoff will have a considerable impact on the research location through the waste that it carries with it, which is rich in heavy metals and other pollutants.

2.2. Soil Sampling

The approach used to collect soil samples from the study site was similar to that published by Orellana-Mendoza et al. [34]. Top soil samples (n = 10) weighing 0.5 kg were gathered randomly at a depth of nearly 30 cm using a small stainless-steel shovel, placed in clean plastic bags, carefully labeled, and sent to the laboratory. The samples were cleaned of stones, pebbles, trash, and other waste materials before being dried at room temperature (nearly 24 °C) for three days and then in an oven at 70–75 °C for 24 h before being pulverized to a homogenous analytical fineness powder using a mechanical pulverizer. The materials were then sieved through a custom 20 mesh lab sieve (0.9 mm diameter) stainless steel wire cloth. The samples were kept in clean, sealed sampling bottles until they were chemically examined.

2.3. Chemical Analysis of Heavy Metals

After digestion and adequate solution preparation, soil samples were examined for the presence of heavy metals and nonmetal concentrations in solution utilizing Thermo Scientific ICP-7000 (ThermoFisher Scientific, Waltham, MA, USA) plus Series ICP-OS inductively coupled plasma–atomic emission spectroscopy (ICP-AES). Digestion was achieved with the help of the microwave-assisted acid sediment digestion technique (EPA-200.7) described below, which is a consolidated of current methods for solids and appropriate to the targeted heavy metal analytes [35].

Soil samples were examined for heavy metal and nonmetal concentrations in solution using Thermo Scientific ICP-7000 plus Series ICP-OS inductively coupled plasma–atomic emission spectroscopy (ICP-AES) after digestion and sample preparation.

2.4. Soil Sample Preparation for Metal Analysis

For sample processing, an aliquot of a well-mixed, homogenous soil solid sample is carefully weighed (0.5 g) and digested using aqua regia. The analytes are initially solubilized by moderate refluxing with 3 mL nitric and 9 mL hydrochloric acids for a period of two hours for the total recoverable analysis of soil samples. Following cooling, the sample is made up to volume (20 mL) and allowed to rest overnight before analysis. To evaluate dissolved analytes in a filtered aqueous sample aliquot, the sample is prepared for analysis by adding the required amount of nitric acid; then, it is diluted with deionized water to a specified volume (50 mL) and mixed before analysis.

2.5. Plant Sampling

The locations for D. viscosa (Figure 3) sampling are aligned with those for soil sampling. In February 2023, a total of ten plant samples were gathered from the research location. The various components of the plant, including the roots, stem, and leaves, were individually separated and thoroughly cleansed by means of water rinse. Subsequently, the specimens underwent natural drying within the laboratory facility under exposed atmospheric conditions (25 °C) for 30 days. Once the parts were dried, they were ground into a consistent powder, sifted through a stainless-steel sieve with 0.15 mm holes, securely sealed in labeled containers, and appropriately stored for subsequent chemical evaluation.

2.6. Analysis of Heavy Metals in Plant Materials

First, 1.0 g of dry-milled samples from various plant parts was subjected to a soaking procedure in concentrated nitric acid (25 mL) within a conical flask for a duration of approximately 20 h. Following that, the flask was placed on a hot plate and heated for 60 min at an approximate temperature of 100 °C. Upon cooling, the sample was extracted using 7 mL of perchloric acid. Heating was continued until the solution became colorless, transparent, and finally evaporated to a volume of 2 mL. In the end, two drops of concentrated nitric acid were added into the respective solution. The ICP-AES method was used to determine the heavy metal content of the samples.

2.7. Quantification of Soil Enrichment by Heavy Metals

The degree of heavy metal enrichment in soils close to the Al-Janabeen water reservoir area has been assessed using various single and integrated pollution indices: enrichment factor (EF), contamination factor (CF), index of geo-accumulation (I_geo), modified degree of contamination (mCd), Pollution Load Index (PLI), Nemerow’s index (NI), and potential ecological risk index (PERI). The following equations were used for the calculation of each index [36,37]:

CF = Csoil/Cbackground

(1)

$$\mathrm{E}\mathrm{F}=\frac{{\left(\frac{\mathrm{M}}{\mathrm{F}\mathrm{e}}\right)}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}}{{\left(\frac{\mathrm{M}}{\mathrm{F}\mathrm{e}}\right)}_{\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}}}$$

(2)

where ${\left(\frac{\mathrm{M}}{\mathrm{F}\mathrm{e}}\right)}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}$ is the ratio of the soil sample’s metal and Fe concentration, and ${\left(\frac{\mathrm{M}}{\mathrm{F}\mathrm{e}}\right)}_{\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}}$ is the ratio of metal and Fe concentration of a background.

$${\mathrm{I}}_{\mathrm{g}\mathrm{e}\mathrm{o}}=\mathrm{l}\mathrm{o}{\mathrm{g}}_2\left(\frac{{\mathrm{C}}_{\mathrm{n}}}{1.5{\mathrm{B}}_{\mathrm{n}}}\right)$$

(3)

where C_n is the measured concentration of heavy metal (n) in soil samples, whereas B_n is the geochemical background value of element (n) in average shale. To take into consideration both minor anthropogenic influences and naturally occurring oscillations brought on by lithogenic processes, a background matrix adjustment factor of 1.5 was selected [37,38,39].

$$\mathrm{N}\mathrm{I}=\frac{\sqrt{({\left({\mathrm{I}}_{\mathrm{g}\mathrm{e}\mathrm{o}\mathrm{m}\mathrm{x}}\right)}^2+{\left({\mathrm{I}}_{\mathrm{g}\mathrm{e}\mathrm{o}\mathrm{m}\mathrm{n}}\right)}^2)}}{2}$$

(4)

where I_geomx = the value for maximum heavy metal concentration in the tested samples and I_geomn = the value for the arithmetic mean concentration of the heavy metal in the examined samples.

PLI = (CF1 × CF2 × CF3 × …. CFn)1/n

(5)

where n = number of metals.

$$\mathrm{mCd}=\frac{\sum CF}{n}$$

(6)

where n = number of metals.

PERI = Σ PERF

(7)

where PERI stands for the risk index for a group of metals, while PERF stands for the risk index for a single heavy metal that is calculated as:

PERF = CF × TRF

(8)

TRF denotes the toxic-response factor of a single heavy metal, whereas CF denotes the contamination factor for a given heavy metal, which can indicate the pollution character of the researched region but does not show the ecological impacts and hazards.

2.8. Accumulation and Translocation of Heavy Metals in Different Plant Parts

To investigate the accumulation ability and transportation properties of the metals of concern in the tissues of the targeted plant, the biological concentration factor (BCF) and translocation factor (TF) were determined in the soil–plant system [40]. The following equations were used to determine BCF and TF:

$$\begin{gathered} \mathrm{B}\mathrm{C}\mathrm{F}=(\mathrm{Conc}.\mathrm{of}\mathrm{metal}\mathrm{in}\mathrm{different}\mathrm{tissue})/(\mathrm{Conc}.\mathrm{of}\mathrm{metal}\mathrm{in}\mathrm{soil}) \\ \mathrm{B}\mathrm{C}\mathrm{F}=\frac{{\mathrm{C}}_{\mathrm{p}}}{{\mathrm{C}}_{\mathrm{S}}} \end{gathered}$$

(9)

$$\begin{gathered} \mathrm{TF}=(\mathrm{Conc}.\mathrm{of}\mathrm{metal}\mathrm{in}\mathrm{one}\mathrm{part})/(\mathrm{Conc}.\mathrm{of}\mathrm{metal}\mathrm{in}\mathrm{former}\mathrm{part}) \\ \mathrm{T}\mathrm{F}=\frac{{\mathrm{C}}_{\mathrm{P}\mathrm{i}}}{{\mathrm{C}}_{\mathrm{P}\mathrm{i}\mathrm{i}}} \end{gathered}$$

(10)

2.9. Statistical Analysis

The experimental data were analyzed using Microsoft Office Excel software (16.0) to determine the descriptive statistics of heavy metal concentrations in soil and plant material as well as pollution indices. Furthermore, Pearson correlation statistical analysis was used to evaluate the contamination link between the metal pairs in the soil samples and the most common source of contamination.

3. Results and Discussion

Heavy metals are among the several potentially dangerous substances that are commonly present in landfills. Under the right circumstances, these substances can be released into the environment and alter the geochemical properties of the exposed medium [41]. The selected study site in this study is close to a former landfill where, during rainfall, a torrent of water brings trash from the landfill site to the neighbors’ sites and creates an accumulation of wastes rich in heavy metals in the soils. This investigation provides information on understanding the risk of the hazardous metals that contaminate the agricultural soils due to human waste materials. The findings of this study have to be seen in the light of some limitations:

• This study is limited to the ecological risk assessment of priority hazard metals according to ATSDR.
• The study is specifically conducted in a soil site near the Al-Janabeen water reservoir dam at Baljurashi, southwest of Saudi Arabia, and near an old open landfill that does not meet standard criteria, with an assumption that wastes rich in heavy metals from the landfill reach the targeted site easily.
• The study especially examined whether the common plant (D. viscosa), which grows naturally at the study site, has the capacity to phyto-uptake heavy metals and may be an appropriate option for phytoremediation.

For comparison, we used the findings of a recent study by Alzahrani et al. [15] on the environmental assessment of heavy metals in soils, which was conducted in the same area but at a different site. Also, we used the recent published work conducted by Castañeda-Espinoza et al. [33] that used the same plant species (D. viscosa) as a phytoremediator for heavy metal-contaminated soil. Comparison also was made with the environmental assessment study of Aloud et al. [31] conducted at an industrial area in Riyadh, Saudi Arabi, which examined different native plants for the possible phytoremediation of heavy metals.

3.1. Soil Screening

The pH in the collected soil samples was measured and was in the range 6.23 to 8.43 with mean values 6.97 and was generally neutral to mildly alkaline.

Table 1. Mean concentrations of priority heavy metals detected in soils.

Heavy Metal	Minimum Concentration (mg/kg)	Maximum Concentration (mg/kg)	Mean Concentration (mg/kg)	International Level Values (mg/kg)
Fe	18,790	33,217	24,325.67	14,000 *
Mn	536.2	658.8	579.56	583
Cu	265	315	295.53	22.6
Ni	23.46	30.82	28.21	26.9
Zn	698	815.9	738.20	74.2
Co	7.732	15.4	10.77	12.7
Cr	45.48	58.79	50.29	61
As	ND	ND	ND	-
Cd	ND	ND	ND	-
Pb	101.1	155	128.05	20

* This is the background regional value, ND: not detected.

shows the mean concentrations (mg/kg) of heavy metals found in soil samples. The detected heavy metals include Mn, Cu, Ni, Zn, Co, Cr, and Pb, which are among the prioritized list of heavy metals that pose the most significant potential threat to human health based on a combination of their frequency, toxicity, and potential for human exposure [10]. We attempted to analyze for As and Cd, but they were not found in any of the samples gathered. The measured mean metal concentration was in the order of Fe > Mn > Zn > Cu > Pb > Cr > Ni > Cr > Co.

The identified heavy metals’ mean values were compared to the international reported reference levels (Table 1) [36]. We observed that the concentrations of Cu, Zn, and Pb exceeded the natural background level, Mn, Ni, and Co were almost equal, while Cr was less than the background value. Furthermore, these heavy metal international background levels in soils were employed as a baseline for evaluating various contamination risk factors.

When comparing our screening results to those reported by Alzahrani et al. [15], they found that the average concentration of heavy metals was in the following order: Fe > Mn > Cr > Cu > Zn > Ni > Co > Pb > Cd, which is nearly the same trend with a minor variance in the order. Although Alzahrani et al. [15] examined soils that had severe Cu and Pb enrichments as well as moderate levels of Co, Ni, Cr, and Zn, the stated concentration values fell below the international sediment quality guidelines (ISQGs). Compared to the international background values, our data showed that Cu, Zn, and Pb levels were greater. Additionally, our screening examination revealed that none of the gathered samples included any Cd element. This could be explained by the fact that our study site was close to a landfill, where wastes high in Cu, Zn, and Pb were more likely to reach the site’s soil and become accumulated in it.

3.2. Heavy Metals Correlation and Relationship Analysis in Soils

According to the literature review, there are complicated interactions between heavy metals in soils, and a variety of factors always regulate their abundance [37]. These include (i) the original heavy metal contents of the parent materials (rocks, soils, etc.); (ii) the process of soil formation; and (iii) pollution brought on by human activity and other anthropogenic processes.

We used statistical analysis with Pearson correlation analysis to assess the relationship between heavy metal pairs in soils and principal component analysis in order to identify the major sources of pollution. Heavy metal correlation analysis determines the similarity of their origins, since metals with a high coefficient of correlation are likely to have similar sources, whereas metals with a small correlation coefficient or a negative correlation may have different sources. In the Pearson correlation computation, the average level of heavy metals found in soils was used, and with normalized data, the Pearson correlation coefficients between the various metals identified in the soil samples were calculated.

The Pearson correlation coefficient matrix shown in

Table 2. Pearson correlation coefficient between different priority heavy metals detected in the soil samples (n = 10).

	Mg	Fe	Mn	Cu	Ni	Zn	Co	Cr	Al	Pb
Mg	1	0.776 *	0.365	0.135	−0.385	−0.268	0.620	−0.828	−0.180	−0.641
Fe		1	0.139	0.210	0.758	0.250	0.927 **	−0.533	−0.198	−0.865
Mn			1	0.467	0.134	0.108	0.176	0.606	0.161	0.166
Cu				1	−0.513	−0.624	0.121	−0.478	−0.325	−0.431
Ni					1	0.335	−0.745	0.476	0.104	0.686
Zn						1	0.085	0.539	0.912	0.671
Co							1	−0.326	0.151	−0.650
Cr								1	0.281	0.540
Al									1	0.645
Pb										1

Note: * p < 0.05; ** p < 0.01.

revealed a non-significant large positive relationship between Mg and Fe pair only at confidence level p < 0.05 (p = 0.07), and only Fe with Co pairs has a significant association at the confidence level p < 0.01 (p = 0.008). The levels of these two heavy metals were near to the equivalent background values, implying that they came primarily from natural sources. The other heavy metal couples tested exhibited a non-significant weak positive association or a very minor negative link at a high confidence level (p > 0.05). This can be explained by the fact that different degrees of pollution of these heavy metals may have distinct sources of contamination. This source could be owing to various human wastes that came from the nearby landfill and were placed into the path of water flow during the rainy season. In contrast, Alzahrani et al. [15] determined that there were natural sources for Cr, Mn, Fe, Co, and Ni and anthropogenic influences for Cu, Zn, Cd, and Pb. Both studies showed relatively similar findings for Fe and Co metals, but there was a significant difference for other metals, as our correlation analysis revealed that contamination from the remainder of the metals could be attributable to human waste materials.

3.3. Ecological Assessment of Soils Quality

Several methodologies and pollution indices have been developed throughout the years to assess soil quality based on expected total heavy metal concentrations. This study evaluated the soils of the intended site utilizing CF, EF, and I_geo as single indices to measure the contamination of specific metals in the soils of the targeted site. PLI, mCd, PERI, and NI were used as integrated indices to examine and describe the quality of the soils as a result of contamination by all detected elements.

3.3.1. Calculations of Single Pollution Indices (CF, EF and I_geo)

The extent of heavy metal enrichment caused by anthropogenic activities in the soils of the targeted study site, which is located near the Al-Janabeen water reservoir area, has been assessed using various quantitative pollution indices and their respective characterization standards. We solely look at the metals of concern (i.e., Cu, Zn, and Pb) that exceeded the international background levels.

The contamination factor (CF) is considered a single pollution index, which is the ratio of the sample’s mean single heavy metal concentration to the same heavy metal background concentration value (Equation (1)). In this analysis, we employed previously stated background values as a local study [36]. The levels of contamination with respect to CF values are as follow: CF < 1 indicates low contamination, 1 ≤ CF < 3 indicates moderate contamination, 3 ≤ CF < 6 indicates considerable contamination, and CF > 6 indicates very high contamination, as quoted by Alghamdi et al. [36].

The results of the CF calculations are shown in

Table 3. Calculated single heavy metal indices, contamination factor (CF), enrichment factor (EF) and geo-accumulation factor (I_geo).

Metal	Average Metal Concentration in Studied Soil Samples (mg/kg)	International Average Metal Concentration in Soils (mg/kg)	CF	EF	Igeo
Fe	24,325.67	14,000 *	0.58	1.00	0.21
Mn	579.56	583	1.01	0.57	−0.6
Cu	295.53	22.6	0.08	7.53	3.12
Ni	28.21	26.9	0.95	0.60	−0.52
Zn	738.20	74.2	0.1	5.7	2.73
Co	10.77	12.7	1.18	0.48	−0.8
Cr	50.29	61	1.21	0.47	−0.86
Pb	128.05	20	0.2	2.83	2.09
As	ND	-	-	-	-
Cd	ND	-	-	-	-

* This is the background regional value, ND: not detected.

. The metals Mn, Zn, Co, and Cr had CF values 1 ≤ CF < 3, indicating moderate contamination, while the remaining metals had values less than 1, indicating low contamination. This could be attributed to there being a higher waste content of materials containing Mn, Zn, Co, and Cr that reaches the soil under study. Furthermore, the difference in CF values for the detected metals confirms the heterogeneity of the distribution of metal content in the soil. The CF index, on the other hand, serves as the foundation for calculating the complicated indices group and the degree of soil pollution with all assessed heavy metals.

The enrichment factor (EF) is a widely used metric for determining how much an element’s concentration in a sampling medium has increased as a result of human activity in comparison to its typical natural abundance and for assisting in the design and implementation of appropriate remediation techniques [36,41]. The contamination is classified and described into five groups based on enrichment factor values: no enrichment if EF < 2; intermediate enrichment if 2 ≤ EF ˂ 5; serious enrichment if 5 ≤ EF ˂ 20; very high enrichment if 20 ≤ EF ˂ 40; and EF > 40 exceptionally high enrichment [36,42].

Table 3 shows the results of the EF calculations. The observed EF values in Table 3 revealed that the metals Cu and Zn have EF values ranging from 5 ≤ EF ˂ 20, indicating severe enrichment of these metals. Pb has an EF value of 2.83, which is in the range 2 ≤ EF ˂ 5, indicating moderate enrichment, whereas the remaining metals had EF values less than 2, indicating no enrichment. These data, specifically for Cu, Zn, and Pb, imply that waste residues rich in these metals reach the study site in greater quantities.

Another geochemical criterion utilized for assessing metal pollution in soils is the geo-accumulation index (I_geo). The I_geo is divided into seven descriptive grades or groups that range from unpolluted to severely contaminated [43]: >5 extremely contaminated; 4–5 strongly to extremely contaminated; 3–4 strongly contaminated; 2–3 moderately to strongly contaminated; 1–2 moderately contaminated; 0–1 uncontaminated to moderately contaminated; and >0 uncontaminated.

Table 3 displays the computed values of I_geo. The estimated geo-accumulation index revealed that the I_geo values for Cu, Zn, and Pb were in the range 2–3, suggesting moderate to strongly contamination from these metals, but the I_geo values for the other metals studied were less than 1, indicating uncontaminated status. These Igeo values show that more wastes including Cu, Zn, and Pb reach the study site and deposit in the soils.

The values of single contamination factors EF, CF and I_geo in the Alzahrani et al. [15] study showed different values and hence different enrichment quality from our study results. In the study of Alzahrani et al. [15], CF values indicated that the studied soils were very severely enriched with Cd, moderately severely enriched with Cu and Pb, and moderately enriched with Co, Cr, Ni and Zn. The different quality of the sources of these metals, whether natural or as a result of human activities, may account for the variation in single metal contamination between the two studies.

Similarly, Aloud et al. [31] observed a slightly different pattern of heavy metal contamination (Fe > Ni > Zn > Pb > Cu> Cr > Cd) in the investigated soils, which were notably high in Cu, Ni, and Pb. The results were interpreted as this being due to the industrial activities in that area.

It is evident that most industrial activities or industrial wastes that reach soils contain more Cu, Pb, Zn and Ni metals, including but not limited to various types of batteries, paints and varnish, heavy metal sludge, wires, and some home trash rich in metals.

3.3.2. Calculations of Integrated Indices (mCd, PLI, PERI and NI)

A scientific way for detecting if soils have truly been chemically enriched is the use of pollution indicators. Individual indices were utilized for estimating the degree of soil pollution caused by each of the metals studied, while integration indices such as the pollution load index (PLI), modified degree of contamination (mCd), potential ecological risk index (PERI), and Nemerow’s index (NI) were used to calculate the overall extent of heavy metal pollution. The integrated indicators group provides detailed specification of heavy metal contamination levels. To calculate each of the integrated indices, total concentrations of all heavy metals tested in soils and (in some circumstances) individual values of the generated indices were used.

The pollution load index is an excellent tool for assessing ecological geochemistry and has been used frequently to determine the relative degree of heavy metal contamination in soils. Depending on soil metal concentrations, the PLI method can be used to calculate the level of soil pollution for every single metal. Based on a review of the literature, PLI was categorized into four grade levels: PLI < 1 unpolluted; 1 ≤ PLI < 2 slight pollution; 2 ≤ PLI < 3 medium pollution; PL ≥ 3 severe pollution; and significant contamination is indicated [44].

According to the findings of this investigation, the pollutant load index has been calculated from CF values obtained (Table 3) using the equation mentioned before as follows:

PLI = (CFFe × CFMn × CFCu × CFNi × CFZn × CFCo × CFCr × CFPb) 1/8;

PLI = (0.58 × 1.01 × 0.08 × 0.95 × 1.01 × 1.18 × 1.21 × 0.2) 1/8;

PLI = (0.013) 1/8 = 0.58.

The computed result (PLI = 0.58), which is little and less than one, suggests that the observed heavy metals have caused no major contamination in the soil of the investigated site. This finding (PL < 1) was identical to that obtained by the Alzahrani research group [15], who stated that their study location was uncontaminated with the analyzed heavy metals.

mCd is a worldwide contamination index that assesses soil contamination by incorporating all hazardous metals analyzed in the ecosystem. A modified degree of contamination (mCd) is calculated by summing up all contamination factors for each metal detected divided by the number of hazardous metals found in the study site and indicates the quality of the soil. The soil considered uncontaminated if mCd ≤ 1.5, low contaminated if 1.5 < mCd ≤ 2, moderately contaminated if 2 < mCd ≤ 4, highly contaminated if 4 < mCd ≤ 8, very highly polluted if 8  <  mCd ≤ 16, extremely contaminated if 16  < mCd ≤ 32 and ultra-highly polluted if mCd > 32 [39]. In our case, the value of mCd is mCd = (0.58 × 1.01 × 0.08 × 0.95 × 1.01 × 1.18 × 1.21 × 0.2)/8 = 0.664. This value indicated that the soil of the study site is uncontaminated.

Dangerous metals found in soil might pose major environmental as well as health problems since they may penetrate the body via a variety of exposure routes [34]. The excessive heavy metal deposition in soils used for farming may have an impact on the safety and quality of food as well as raise the morbidity of serious illnesses. All across the world, monitoring soil heavy metal contamination and treating associated soil pollution are crucial components of ensuring the safety of food. The evaluation of potential ecological problems is required in garbage collection sites with elevated levels of heavy metals exposure.

The potential ecological risk index (PERI) technique has been used to quantify the level of ecological and environmental harm caused by heavy metal toxicity in soils [45]. This method is regarded as the most scientific and thorough way for assessing heavy metal pollution in soils. By combining the PERF for various heavy metals, PERI for particular heavy metals in soils was generated. It is worth noting that the PERI method is the only one that takes into account both heavy metal concentrations and hazardous response variables.

Table 4. Toxic-response coefficient for the identified heavy metals.

Element	Co	Cu	Cr	Fe	Mn	Ni	Pb	Zn
Toxic-response factor (TRF)	5	5	2	-	1	5	5	1
Contamination factor (CF)	1.18	0.08	1.21	0.58	1.01	0.95	0.2	0.1
Potential ecological risk factor (PERF)	5.9	0.4	2.42	-	1.01	4.75	1	0.1

shows the published toxic-response coefficients for a number of the metals studied in the present research [46,47]. Also, Table 4 displays the PERF value calculation results for each metal based on the CF values obtained (Table 3).

Likewise, by combining the PERF for various heavy metals, the PERI for particular heavy metals in soils was generated. The following is how PERI was calculated:

PERI = 5.9 + 0.4 + 2.42 + 1.01 + 4.75 + 1+ 0.1 = 15.58.

It is worth noting that the PERI method is the only one that takes into account both heavy metal concentrations and hazardous response variables.

The following scale rates the possible ecological risk of heavy metals [36]: PERF ˂ 40 indicates low pollution, 40 ≤ PERF ˂ 80 indicates moderate pollution, 80 ≤ PERF ˂ 160 indicates strong pollution, 160 ≤ PERF ˂ 320 indicates very strong pollution, and PERF ≥ 320 indicates extremely strong pollution. Furthermore, the following scale classifies index values: PERI ˂150 (low grade), 150 ≤ PERI ˂ 300 (moderate), 300 ≤ PERI ˂ 600 (severe), and 600 ≤ PERI (serious).

Our calculations (Table 4) revealed that all PERF values were below 40, implying an acceptable ecological risk factor owing to the single heavy metal found at the analyzed site, and the order of PERF was Co > Ni > Cr > Mn > Pb > Cu > Zn. Furthermore, the PERI value (15.58) is less than 150, indicating a low-grade degree of possible ecological harm caused by the heavy metals of concern found in the soil of the analyzed site. In comparison, Alzahrani et al. [15], who obtained average ecological risk values for Cd (Eri = 80.94), suggested a moderate risk of Cd, and for other heavy metals, the ecological risk was less than 40, which suggested no to low risk of exposure to these metals in the specified studied soils. As a result of both assessments, the quality of the analyzed soils has heavy metal accumulation values that are within a controlled range, and the overall quality of the researched soil sites is still at a low ecological risk level.

Because it may mitigate the impacts of rock resources and significant manmade influences on soil contamination from heavy metals, the geo-accumulation index is an appropriate instrument for evaluating a single heavy metal in waste and landfill locations. Nemerow’s index (NI) was established as a complete index approach to provide a reasonable estimation of the heavy metal contamination of each site as a whole [13,38]. The soil quality was classified as follows based on NI: 0 < NI ≤ 0.5 unpolluted; 0.5 < NI ≤ 1 uncontaminated to moderately polluted; 1 < NI ≤ 2 moderately polluted; 2 < NI ≤ 3 moderately polluted to heavily polluted; 3 < NI ≤ 4 seriously polluted; 4 < NI ≤ 5 seriously polluted to severely polluted; NI > 5 extremely polluted.

According to the Nemerow calculation results (

Table 5. Calculated Nemerow’s index (NI).

Element	Co	Cu	Cr	Fe	Mn	Ni	Pb	Zn
Igeomax	−0.60	3.22	−0.64	0.66	−0.40	−0.39	2.37	2.87
Igeo mean	−0.8	3.12	−0.80	0.21	0.60	−0.51	2.09	2.37
NI	0.75	4.50	1.03	0.74	0.68	0.62	3.16	3.99

), the NI index values for Co, Fe, Mn, and Ni are less than 1, which denotes class 1 contamination, which is the category for uncontaminated to moderately contaminated materials. The metals Zn and Pb displayed NI values that fall into the class 4 contamination category, which represents a heavily contaminated class, while Cu had the highest value (NI = 4.50), which is classed as heavily to extremely contaminated. As a result, Cu, Zn, and Pb metals are highly polluted in the examined soil.

Understanding the dangers of trace metal contamination of agricultural land because of the potential piling up of wastes rich in heavy metals from the nearby landfill sites during the rainy season is made possible by the information provided by this study. Mg, Fe, Mn, Cu, Ni, Zn, Mo, Co, Cr, Na, Al, and Pb were located in soil samples collected from the target site after examination as well as other metals and elements. We made an effort to analyze for As and Cd, but they were absent from all of the samples we had gathered and were not found.

The metals on the ATSDR [10] priority list that are deemed to be of great concern and may cause ecological and human harm upon prolonged exposure to them are the focus of our calculations for ecological risk assessment. Cu, Zn, and Pb were found to have amounts that were higher than the international background levels among the key metals that were measured. The results of the calculation of the single pollution indices EF and I_geo indicated moderate contamination caused by these three metals. This could be a sign of the kind of Cu, Zn, and Pb-rich wastes that are carried to the study site and contribute to soil pollution. Nemerow’s index results, which demonstrated that Cu, Zn, and Pb have a high level of pollution effect in the examined soils, supported this. Meanwhile, computations using the integrated indices PLI and PERI revealed minor ecological damage caused by these metals.

3.4. Distribution of Heavy Metals of Concern in Plant Parts

After obtaining samples of D. viscosa from the study location, the roots, stems, and leaves were subject to heavy metal analysis following proper treatment. It was observed that the various parts of the plant had varying levels of metal content.

Table 6. Heavy metal and other element content in various tissues (mg/kg DW).

Elements	Average in Roots	Average in Stems	Average in Leaves	Average in Soil
S	2335.33	1254.33	3419.67	4259.67
K	3047.00	4275.33	12,126.67	5504.00
Ca	9535.00	9061.33	6832.00	23,083.33
Mg	1114.17	993.70	1847.00	5793.33
Fe	967.70	900.33	432.70	24,325.67
Mn	40.40	37.17	38.66	579.57
Cu	200.76	16.23	23.95	295.53
Ni	9.30	4.87	3.31	28.21
Zn	133.67	18.67	5.09	738.20
Mo	-	-	-	-
Co	0.48	0.72	0.48	10.73
Cr	13.00	10.12	6.88	50.29
Na	2997.33	1179.57	4499.00	2344.67
Al	505.33	618.17	284.53	19,500.00
As	-	-	-	-
Cd	0.57	0.47	0.64	-
Pb	20.79	3.47	2.61	128.05

displays the mean concentrations (mg/kg DW) of metals, metalloids and non-metals that were detected.

Heavy metals (i.e., Mn, Cu, Ni, Zn, Co, Cr, Cd, and Pb) that pose a concern to human health due to a combination of their frequency, toxicity, and potential for human exposure have been found at varying quantities in various plant sections.

In general, the levels of the metals of concern are higher in the roots compared to the other parts. It was additionally found that the levels of Mn, Cu, and Cd were higher in the leaves compared to the stems, whereas the levels of Ni, Zn, Co, Cr, and Pb were higher in the stems compared to that in the leaves. These discrepancies could be attributable to the plant’s bioavailability of heavy metals and the varying accumulation capabilities of plant sections.

3.5. Accumulation and Translocation of Heavy Metals in Different Plant Parts

To investigate the accumulation ability and transportation properties of the metals of concern in the tissues of the targeted plant, the biological concentration factor (BCF) and translocation factor (TF) were determined in the soil–plant system [48]. Furthermore, BCF and TF can be utilized to assess the phytoextraction and phytostabilization capability of the selected plant species. BCF results greater than one indicate that the plant has the ability to phytoextract dangerous elements from soil. The results of BCF and TF calculations are displayed in

Table 7. BCF and TF values of heavy metals in the targeted plant parts.

Metals	Biological Concentration Factor (BCF)			Translocation Factor (TF)
	Roots	Stems	Leaves	Root/Soil	Stem/Root	Leaves/Stem
Fe	0.04	0.04	0.02	0.04	0.93	0.48
Mn	0.07	0.06	0.07	0.07	0.92	1.04
Cu	0.68	0.05	0.08	0.68	0.08	1.48
Ni	0.33	0.17	0.12	0.33	0.52	0.68
Zn	0.18	0.03	0.01	0.18	0.14	0.27
Co	0.04	0.07	0.04	0.04	1.50	0.67
Cr	0.26	0.20	0.14	0.26	0.78	0.68
Pb	0.16	0.03	0.02	0.16	0.17	0.75

. The BCF values of the heavy metals of concern (Mn, Cu, Ni, Zn, Co, Cr and Pb) were less than one, showing that the plant has no ability to accumulate any of these elements.

Table 7 also shows the results of TF calculations, and the calculated values were often less than 1, with the exception of Mn and Cu for the leaves/stem system and Co for the stem/root system. This suggests that leaves can absorb and translocate Mn, Cu, and Cd metals from stems to leaves, whereas stems can absorb and translocate Co metal from root to stem. However, the TF values are not statistically significant, indicating a minimal ability for the phytoextraction and phytostabilization of the observed metals of concern.

When compared to a recent study conducted by Castaneda-Espinoza et al. [33], which used the same plant species (D. viscosa) as a phytoremediator for heavy metal-contaminated soil, our investigation revealed the opposite finding of a low capacity to phytoextract and phytostabilize harmful metals. The mentioned study has limitations in that the D. viscosa plant was cultivated on mine tailing substrate under greenhouse conditions for 18 months. Their study demonstrated that D. viscosa has a high potential to bioaccumulate both necessary and non-essential metals [33]. In our investigation, the plant is grown naturally and exposed to contaminants for a longer period of time. Furthermore, the possibility of heavy metal availability for plants is small due to lower concentrations of these metals compared to the mandatory conditions of the experiment carried out by Castaneda-Espinoza et al. [33], in which they grew the plant in mine tailing leftovers rich in heavy metals.

In comparison, Aloud et al. [31] showed that the native plants C. laevigatus and C. colocynthis had the capacity to absorb Cd, Pb, and Ni considerably (p < 0.001) better than the D. viscosa plant in our examination. The variation could be attributed to plant species features, environmental conditions, the nature of the pollutants and their bioavailability, the pH value of the soil medium, and the presence or absence of various chelating agents.

Finally, wastes rich in hazardous metals directly damage the environment through serious contamination of the soils and other resources. Soil pollution affects agricultural yields and nutritional quality, contributing to the growing problem of food insecurity. It is critical to maintain good soils in order to provide a wide availability of nutritious and secure food. However, in future investigations, researchers should consider the following prospective directions: (a) developing effective ways for landfill and agricultural engineering; (b) developing ways that can successfully extract or permanently eliminate heavy metals from the soil; (c) concentrating on improving phytoremediation capability and finding new biochemical techniques for this purpose; and (d) understanding the immobilization of heavy metals in a complicated waste–soil interaction, and more advanced approaches for characterizing and understanding the influence of interaction of various soil ingredients on the speciation, changes and bioavailability of heavy metals in soils.

4. Conclusions

An ecological risk assessment analysis was performed to detect the presence of priority hazard metals that constitute a possible threat to humans in agricultural soils near the Al-Janabeen water reservoir dam in Baljurashi, KSA. Furthermore, the study looked at the occurrence of these metals in the D. viscosa shrub, which is a common plant in the field of the study. The following were the observed mean metal concentrations at the research site: Fe > Mn > Zn > Cu > Pb > Cr > Ni > Cr > Co, with Cu, Zn, and Pb being higher than natural background values.

Pearson’s correlation analysis indicated weak positive and very small negative relationship between pairs of the detected metals.

The computation of single metal indices yielded the following outcomes:

• CF calculations revealed considerable contamination due to Mn, Zn, Co and Cr.
• EF values revealed substantial enrichment due to Cu, Zn and Pb.
• The I_geo values for Cu, Zn and Pb suggested significant contamination.

Meanwhile, results of the computations of the integrated indices were as follows:

• The PLI and mCd values were low, indicating that the observed heavy metals caused no major contamination in the soil of the investigated site.
• The obtained PERF and PERI values indicated low ecological risk owing to single heavy metal and the accumulative metals found in the analyzed site.
• The NI calculations revealed that Cu, Zn and Pb are highly polluted in the examined soils.
• The calculated TF and BCF values for the investigated plant components were less than one, indicating a low ability to absorb and accumulate any of the metals of concern studied; hence, it is not a suitable candidate for phytoremediation of heavy metals.

Our findings suggested that despite the low ecological risk of heavy metal contamination in the studied site, relevant authorities, environmental and agricultural engineers ought to keep an eye on soil quality and develop appropriate management strategies to prevent the future contamination of ecosystems and biological communities in order to ensure the healthy use of these soil sites for agricultural sustainability.