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Article

Uptake Prediction of Eight Potentially Toxic Elements by Pistia stratiotes L. Grown in the Al-Sero Drain (South Nile Delta, Egypt): A Biomonitoring Approach

1
Biology Department, College of Science, King Khalid University, Abha 61321, Saudi Arabia
2
Botany Department, Faculty of Science, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt
3
Botany and Microbiology Department, Faculty of Science, Helwan University, Cairo 11790, Egypt
4
Botany and Microbiology Department, Faculty of Science, Damanhour University, Damanhour 22516, Egypt
5
Biology Department, Faculty of Science, Taif University, Taif 21944, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(9), 5276; https://doi.org/10.3390/su13095276
Submission received: 26 March 2021 / Revised: 1 May 2021 / Accepted: 5 May 2021 / Published: 8 May 2021
(This article belongs to the Special Issue Aquatic Plants as Bioindicators of Trace Metal Pollution)

Abstract

:
The potential to utilise the free-floating macrophyte Pistia stratiotes L. to survey contamination of the Al-Sero Drain in the South Nile Delta, Egypt, by eight potentially toxic elements (PTEs) was investigated in this study. This study considered the absorption of eight PTEs (Cd, Co, Cu, Fe, Mn, Ni, Pb, and Zn), and the evaluated P. stratiotes were located in three sampling locations along the Al-Sero Drain, with sampling conducted in both monospecific and homogenous P. stratiotes. Samples of both types of P. stratiotes and water were collected on a monthly basis between May 2013 and April 2014 at each location, utilising three randomly chosen 0.5 × 0.5 m quadrats. Regression models were designed to predict the concentration of the PTEs within the plant’s shoot and root systems. Elevated water Fe levels were correlated with a rise in shoot system Fe concentration, whereas higher Ni concentrations in the water led to a higher Ni concentration within the root system. The latter was also true for Pb. Water Cu levels had a negative association with the Cu concentration within the P. stratiotes shoot system. Raised Fe levels were also correlated with a diminished Fe level within the roots. For all PTEs, P. stratiotes was characterised by a bioconcentration factor of more than 1.0, and for the majority by a translocation factor of less than 1.0. The goodness of fit for most of the designed models, as indicated by high R2 values and low mean averaged errors, demonstrated the associations between actual and predicted PTE concentrations. Any disparity between measured and predicted parameters failed to reach significance with Student t-tests, reinforcing the predictive abilities of the designed models. Thus, these novel models have potential value for the prediction of PTE uptake by P. stratiotes macrophytes inhabiting the Al-Sero Drain. Furthermore, the macrophyte’s constituents indicate the long-term impact of water contamination; this supports the potential future use of P. stratiotes for biomonitoring the majority of the PTEs evaluated in this study.

1. Introduction

From the time of the industrial revolution, environmental pollution from potentially toxic elements (PTEs) has been increasing, with grave ecological consequences [1]. Globally, the effects of this pollution on the environment have generated a perilous situation due to the continued accelerated advancement of industrial endeavours [2]. Aquatic ecosystems are particularly at risk of contamination by PTEs. Their pollution is a major issue, since PTEs are persistent in the environment and become biomagnified as they pass through the food chain [3]. Not only are PTEs toxic, but they accumulate within the environment and are not biodegradable. They therefore represent a serious threat to aquatic ecosystems and to humans [4]. Their transfer through the food chain leads to their transference and accrual within animal organisms, including humans, damaging genetic material and leading to mutations and tumourigenesis [5,6].
Although PTEs are intrinsically present within the environment, commercial, artificial, and agronomic enterprises have all contributed to increased PTE levels [7]. PTEs are predominantly liberated into aqueous bodies from a number of activities, e.g., mining, urban wastewater, smelters, tanning industries, and the textile and chemical industries [8]. Thus, it is important to elucidate the mechanisms underlying transference of PTEs between water/soil and plants. Various models have been designed to understand these processes [9,10,11,12,13]. Furthermore, an appraisal of the amounts of PTEs taken up by vegetation is essential in order to quantify the hazard and for the purpose of environmental governance [14].
Drains are a man-made water collection system; excess surface water from nearby arable land and roads empties into these systems [15]. Drainage systems have been extended and adapted to suit different land use needs. These systems also act as channels for the removal of unsanitised human waste in areas without treatment plants [16]. Mitigating water pollution through the use of vegetated drains is generating considerable attention as an alternative to traditional water treatment processing [17]. Water treatment mechanisms utilising plants are cheap and are attractive to developing nations for wastewater recycling, particularly where PTEs are an issue [18]. The total areas of the Nile Delta and the Nile Valley are estimated to be 22,000 and 13,000 km2, respectively. The latter therefore forms nearly 63% of the agrarian area within Egypt [19]. The majority of worked Egyptian territories are irrigated via a mesh of channels that coalesce with a parallel connecting system of draining conduits [20]. The sum of the lengths of the channels from the two systems is over 47,000 km; irrigation canals comprise 31,000 km [21].
Pistia stratiotes L. (Araceae), a floating macrophyte, also referred to as water lettuce, proliferates vegetatively [22] and is in fact registered as an invasive species in the Global Invasive Species Database [23]. It is widely found within tropical and subtropical areas, but not in Antarctica [24]. Within Egypt, P. stratiotes can be observed within the slow-moving canals in the northern Nile Delta territory; in Embaba and in proximity to Cairo [25]; and in static and tranquil waters, particularly in the Fariskur area [22]. It has also been observed at a number of sites in Lake Mariut [26] and Lake Manzala [27] in the northern area of the Nile Delta. P. stratiotes is an invasive macrophyte that propagates rampantly, to the detriment of other vegetation such as Eichorrnia crassipes (C. Mart.) Solms and Lemna gibba L., within the drainage system in the Nile Delta [28]. It has been demonstrated that this species may play a key role in influencing water quality, as it has the capacity to uptake PTEs from wastewater [26,28].
A straightforward appraisal of the transfer factors involved could provide an approximate gauge of the spectrum of PTE transfer, although such as assessment would fail to appreciate precise site-specific properties [29]. However, regression models are mathematical strategies that could anticipate PTE concentrations in macrophyte vegetation by considering variables pertaining to water or soil, e.g., PTE concentration and pH [10,11,12,13]. They therefore represent a valuable tool for the assessment of PTE concentrations within macrophytes. Although P. stratiotes has been the subject of considerable recent phytoremediation research [28,30,31,32,33,34,35,36,37], there is a dearth of published prediction models for PTE uptake within the shoot and root systems of P. stratiotes growing in natural environments. Mathematical models describing PTE uptake by P. stratiotes grown on paper mill effluent in a lab scale phytoremediation experiment were developed by Kumar et al. [10]; however, these models cannot be used to predict PTE uptake in conditions other than those used in the experiment. Thus, the aim of the current research was to design a de novo regression model to predict PTE concentration within P. stratiotes shoot and root systems in a natural habitat (the Al-Sero Drain), considering water characteristics such as the PTE concentration and pH. Another goal was to discover how capable P. stratiotes could be as a biomonitor of eight PTE concentrations in the Al-Sero Drain, a site considered typical of the South Nile Delta drainage channels. Our hypothesis was that the PTE accumulation capabilities of P. stratiotes and its potential to serve as a biomonitor for PTE contamination could differ among populations grown under natural conditions and those grown under experimental conditions. This work will additionally be of value for the future utilisation of this form of vegetation in Egyptian phytoremediation research.

2. Materials and Methods

2.1. Study Area

The research location was in Giza Province, within the Egyptian South Nile Delta region (Figure 1). This territory is classified as hyperarid [38]. The yearly average climate parameters include precipitation in the region of 87 mm, maximum temperature of 30.0 °C and minimum of 14.8 °C, evaporation rate of 6.9 mm/day (Piche), relative humidity of 45.5%, and wind speed of 3.9 m/s [39].

2.2. Field and Laboratory

Three sampling locations were selected in relation to the Al-Sero Drain, which comprised monospecific and homogeneous stands of P. stratiotes (Figure 1). The site coordinates were (i) site 1: Lat. 30°03′18.88″ N, Long. 31°08′17.56″ E; (ii) site 2: Lat. 30°03′15.73″ N, Long. 31°08′28.20″ E; and (iii) site 3: Lat. 30°03′30.00″ N, Long. 31°08′14.00″ E. P. stratiotes biomass was sampled on a monthly basis between May 2013 and April 2014 at each site, utilising three randomly chosen 0.5 × 0.5 m quadrats. The entire population of P. stratiotes from each quadrat was harvested, stored in plastic bags, and then transported to the laboratory. The total biomass ranged between 29.9 g DM/m2 in May and 341.6 g DM/m2 in August. Detailed data on the biomass were presented in our previous paper [40].
The samples were divided into shoot and root systems and washed with tap water, and then cleaned with deionised water over a 4 mm mesh sieve to eliminate PTEs adsorbed on the tissue surface and to minimise material loss. In this way, only PTEs absorbed by the plant were determined, and then the bioaccumulation was assessed. The plant material was then reduced to a uniform mass by oven-drying at a temperature of 85 °C. A metal-free plastic mill (Philips HR2221/01, Philips, Shanghai, China) was used to pulverise the dried plant systems, which were then transferred and stored in a desiccator in sterile Ziploc bags. One composite sample from each quadrat from each P. stratiotes shoot and root systems at each of the three sampling sites per month was then utilised to assay cadmium (Cd), cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) levels. In total, 108 plant samples per each P. stratiotes shoot and root system (3 quadrats × 3 sampling locations × 12 sampling times (months)) were used to determine the uptake of the eight PTEs.

2.3. Water Sampling

Although the water PTE concentrations have not varied significantly in recent years [41], throughout this study, monthly samples were taken over a period of 12 months (May 2013–April 2014), which should have captured the variations in concentration in different months. Three water samples were gathered each month from the same sampling quadrats at each location. The samples were collected, utilising plastic bottles rinsed with deionised water, as coalesced composite samples from the water surface to a depth of 50 cm. At the laboratory, filtration was performed with Whatman membrane nylon filters (pore size 0.45 μm, diameter 47 mm), and then the samples were frozen at −20 °C, pending subsequent PTE analysis of Cd, Co, Cu, Fe, Mn, Ni, Pb, and Zn. This process has been detailed by the American Public Health Association [42].

2.4. Chemical Analysis

The eight PTEs under examination were subtracted from 0.5–1 g of the macrophyte’s shoot and root tissues by deploying a mixed-acid digestion technique, using HNO3/HClO4/HF, 1:1:2, v/v/v, in a microwave sample preparation system (PerkinElmer Titan MPS, PerkinElmer Inc., Waltham, Massachusetts, USA). The process was continued until the mixture lost its opacity. The plant digests were then filtered, and double deionised water was used to dilute the samples to 25 mL. Inductively coupled plasma optical emission spectrometry (ICP-OES) (Thermo Scientific iCAP 7000 Plus Series; Thermo Fisher Scientific, Waltham, MA, USA) was utilised for both P. stratiotes and the water samples in order to measure the PTE concentrations. Concentrations were given on the basis of dried matter, and deionised water was utilised at all times. Washed glassware and analytical grade reagents were employed appropriately. Instrument readouts were rectified utilising blank reagents. Standard solutions with established concentrations of Cd, Co, Cu, Fe, Mn, Ni, Pb, and Zn were used to calibrate the system. The instrument parameters and operating circumstances were set in keeping with the vendor’s operational guidelines. The PTE detection limits were Fe, Pb and Zn, 5.0 µg/L; Ni, 3.0 µg/L; Co and Cu, 0.5 µg/L; Mn, 0.3 µg/L; and Cd, 0.1 µg/L.

2.5. Quality Assurance and Quality Control

With the use of a certified reference material, SRM 1573a (tomato leaves), we confirmed the precision of the PTE test system. The reference material was digested and underwent the same analytical process as the shoot and root systems from the P. stratiotes samples on three replicates. The assayed concentrations were contrasted with the certified parameters, and then the percentage was calculated as an expression of accuracy. The spectrum of recovery rates was 96.5–104.3%.

2.6. Data Analysis

Student’s t-tests were used to analyse any variations in the PTE data between the shoot and root samples. The bioconcentration factor (BCF) was computed in order to establish the efficacy of PTE uptake from the water by P. stratiotes, where [43]
BCF = (PTE concentration (mg/kg) in the root system)/(PTE concentration (mg/L) in the water from the same site)
In order to assess the capacity of P. stratiotes to transport a particular PTE from its root to shoot system, we calculated the translocation factor (TF) [43]:
TF = (PTE concentration (mg/kg) in the shoot system)/(PTE concentration (mg/kg) in the root system)
Prior to conducting a one-way analysis of variance (ANOVA-1), we evaluated the BCF and TF data by using the Shapiro–Wilk W and Levene tests for the presence of a normal distribution and variance homogeneity. The data were then transformed into logs if necessary. An ANOVA-1 was performed on the BCF and TF results in order to identify any variation between the eight PTEs. Any significant variations between the means were established using Tukey’s HSD test at p < 0.05.
Water pH and its PTE concentration are the principal variables governing the PTE concentration in P. stratiotes [10]. The model’s general equation can be expressed as [10]
Cplant = a + (b × Cwater) + (c × pH)
where Cplant and Cwater represent a given PTE’s concentration in P. stratiotes tissue and water, respectively, and a, b, and c pertain to the regression coefficients.
There was little variation within the results from the three selected sampling areas (data not presented). In view of this, monthly gathered data from two of the sites (n = 72) were employed to establish the regression equations for the prediction of the PTE concentrations within P. stratiotes root and shoot tissues on the basis of the water indices of pH and the respective PTE concentrations as independent variables. The results from the remaining sampling location (n = 36) were kept as a validation dataset.
The determination coefficient, R2; model efficiency, ME; and model strength were used to appraise the quality of the model. Model strength was based on the mean normalised average error, MNAE. These parameters were computed according to the equations presented below [44]:
ME = 1 − {∑ (Cmodel − Cmeasured)2/∑(Cmeasured − Cmean)2}
MNAE = {∑ (Cmodel − Cmeasured)/(Cmeasured)}/n
where Cmodel, Cmeasured, and Cmean represent the model-predicted, measured, and mean of the measured concentrations of a given PTE, respectively, and n is the observation number.
The resulting regression equations were used to estimate the PTE concentrations of the validation. The deviations between the estimated and measured PTE concentrations relating to the same tissue were analysed utilising a Student’s t-test. The correlation between the PTE levels in the water and the BCF of the PTEs in the P. stratiotes root system was measured using non-linear regression. Statistica software, version 7.0 [45], was utilised for all data analysis.

3. Results

Chemical analysis of the water samples taken from the three locations along the Al-Sero Drain revealed a modestly alkaline water, with a mean pH of 7.5 (Table 1). The spectrum of PTE concentrations varied from Cd, 3.5 μg/L to Fe, 523.6 μg/L. The concentration level from high to low of each material was Fe > Pb > Mn > Ni > Zn > Co > Cu > Cd. Differences in concentrations of six of the PTEs, i.e., not Cd and Pb, between P. stratiotes shoot and root systems, were significant (Table 2). Furthermore, the majority of the PTEs were found in higher concentrations in the root system, as opposed to in the shoots. Within P. stratiotes, the mean PTE concentration ranges were as follows: Cd, 0.9–1.0 mg/kg; Co, 5.2–17.6 mg/kg; Cu, 10.0–55.5 mg/kg; Fe, 974.1–2511.0 mg/kg; Mn, 331.5–1160.7 mg/kg; Ni, 6.8–20.4 mg/kg; Pb, 39.8–42.0 mg/kg; and Zn, 37.1–48.2 mg/kg. The decreasing orders of PTE concentrations within the shoot and root systems were Fe > Mn > Cu > Pb > Zn > Ni > Co > Cd and Fe > Mn > Zn > Pb > Ni > Co > Cu > Cd, respectively.
The higher water Fe concentration was correlated with the Fe concentration of the shoots (r = 0.335, p < 0.001) (Figure 2, Table S1). An elevated concentration of Ni in the water was related to the root system’s Ni concentration (r = 0.212, p < 0.05). Elevated water and root system Pb quantities were also associated with each other (r = 0.294, p < 0.01). The water Cu concentration was negatively related to the Cu concentration within the shoot system (r = −0.589, p < 0.001). The elevated Fe concentration in the water was associated with reduced Fe in the roots (r = −0.287, p < 0.01).
A BCF > 1.0 was calculated for P. stratiotes for all the PTEs (Table 3). The values of the parameter were diverse, being generally higher for Mn, and then in descending order: Fe > Cu > Zn > Co > Cd > Ni > Pb. In this study, the TF values also differed according to the PTE under study (Table 3). A TF for the majority of the PTEs for P. stratiotes was computed to be <1.0. The TF ranking from root system to shoot system was as follows: Cu > Cd > Pb > Zn > Fe > Co > Ni > Mn. Figure 3 depicts the non-linear regression analysis conducted between the water concentration and the P. stratiotes BCF for these PTEs. The BCFs were noted to be maximal at lower water PTE concentrations; they demonstrated an exponential fall with rising PTE concentrations in the water. The R2 of these exponential equations varied from Pb 0.037 to Mn 0.974.
Regression models were designed to predict P. stratiotes root and shoot PTE concentrations on the basis of the latter’s water concentration and utilising the water pH as a cofactor. Table 4 illustrates the results from these models, as well as their predictive accuracy. Associations between measured and predicted PTE concentrations, together with high R2 and low mean averaged errors, provided an indication of the acceptability of most of the models. In addition, t-test values, which were utilised to analyse any difference between real and predicted concentrations for the eight PTEs in P. stratiotes root and shoot systems, were nonsignificant, highlighting the accuracy of the models. For all the models tested, R2 varied from 0.147 for Cu within the root system to 0.592 for Mn within the shoot system. ME parameters had a range between 0.367 for Cu within the root system and 0.811 for Mn within the shoot system. Furthermore, a low MNAE for the majority of the PTEs was observed in relation to the regression models, with a spectrum ranging from 0.179 for Mn within the shoot system to 0.628 for Cu within the root system. With respect to the shoot system, the model for Mn had the greatest R2 value (0.592) and was related to a high ME of 0.811 but a small MNAE of 0.179. In relation to the root system, the model for Pb demonstrated the highest R2 (0.405), with a high ME of 0.742 and the smallest MNAE of 0.248.

4. Discussion

This study demonstrated that the majority of PTE concentrations were notably elevated in P. stratiotes root systems, rather than in the shoot system. Numerous studies have reported similar findings [10,28,30,34,36,47,48]. This large PTE accumulation within the roots is likely to be a consequence of the PTEs forming complexes with sulphydryl residues, resulting in a lower concentration of free PTE to be transported into the shoots [49]. A number of publications have also described phytochelatin production; these compounds have the ability to sequester PTEs, again contributing to the retention of PTEs inside the roots [50]. Another reason for the higher root concentration is that the root system is the initial point of contact with the PTEs contained within the water [51]. The mean Cu and Pb concentrations recorded for the P. stratiotes shoot system in this study were within the phytotoxic ranges; the mean Cd, Co, Fe, Mn, Ni, and Zn concentrations were lower than the phytotoxic range [46]. The mean Co and Pb concentrations recorded for the P. stratiotes root system were within the phytotoxic ranges; the mean Cd, Cu, Ni, and Zn concentrations were lower than the phytotoxic range; and the mean Fe and Mn concentrations were higher than the phytotoxic range [46].
It has been shown that aquatic macrophytes are key actors in the extraction of PTEs from wastewater [52]. P. stratiotes functions in water pollution removal [28,30,31,32,33,34,36,37]; it is a relatively low-cost method, and in itself is environmentally sound [28]. P. stratiotes is typically utilised in constructing wetlands in order to improve the quality of water in water treatment systems [35]. Its advantages include its ability to propagate [53], as well as its PTEs assimilation capabilities [28]. Within the root and shoot systems of P. stratiotes, Fe, and then Mn, Zn, and Cu, were found in the highest concentrations, reflecting the straightforward underlying mechanisms for their uptake, as they are intrinsically necessary for the proliferation of most vegetation [54]. Similar findings were noted by Kumar et al. [10] for current species grown on paper mill effluent in a lab scale phytoremediation experiment, and by Eid et al. [55] for E. crassipes grown in irrigation canals in the North Nile Delta in Egypt. Fe is a critical minor nutrient for both vegetative and animal organisms. In the former, it is an essential component of chlorophyll; over 50% of a leaf’s Fe content is within the chloroplasts. This element additionally influences photosynthesis and biomass [56]. Fe and Mn are integrated within the complex of the enzyme nitrogenase, which is necessary for nitrogen fixation through symbiotic and non-symbiotic mechanisms [57]. Zn is also mandatory for both plants and animals, as it is related to numerous enzymes and specific proteins [58]. Both Mn and Zn act as part of the link between an enzyme and its substrate; Mn plays a role in nitrogen transformations in many plants and microorganisms.
Plants and animals also require Cu, which is again associated with enzyme function, especially those which trigger oxidative processes utilising molecular oxygen [59]. Cu is also a constituent of the photosynthesis pathway [60]. Despite the presence of high Pb concentrations within P. stratiotes samples, Pb per se is not necessary for plants survival but is carried into plants with other elements. Pb is toxic and is not associated with any notable biological function [61]. In contrast, there was a relatively low uptake of Cd into P. stratiotes, a result which reflected that of earlier publications [26,28,43]. Cd is extremely poisonous and is effectively a surplus waste substance discarded from metal refining and electroplating industries that contaminates the environment [58]. It impacts vegetative propagation, metabolism, and water status [62]. Furthermore, Cd acts as an inhibitor of enzymes within the chlorophyll biosynthesis pathway and thus decreases plant chlorophyll content [63].
Monitoring systems for evaluating the accumulation and effect of PTE contamination within aquatic ecosystems are often reliant on live organisms [64]. In this study, there were significant associations between the water concentration of several PTEs and the concentrations of these elements within P. stratiotes tissues, thus offering a measure of the amassed consequences of PTE pollution in drain water and a means by which to quantify the quality of the environment. This implies that P. stratiotes can act as an effective biomonitor of the presence of PTEs. Furthermore, vegetation containing notable concentrations of PTEs are now being viewed as possible measures of the availability of such elements [43]. It was also noted that some of the positive associations of water and P. stratiotes PTE concentrations failed to reach significance, implying that the macrophyte’s uptake of all the PTEs present was inconsistent. PTE absorption into P. stratiotes was therefore not dependent on the water concentration of the PTEs in every instance [65]. Similar data related to the association between the PTE concentration of the water and P. stratiotes have been published in previous studies [10,26,28].
PTE distributions within vegetative tissues are not generally uniform in plants from either aquatic or terrestrial ecosystems [26,66]. Their accumulation in various species occurs in accordance with multiple factors, including chemical speciation, water transport, plant species and accompanying phenology, physiology, vigour, propagation and age, climatic parameters, salinity, pH, and interchelating of the PTEs [43,51,67,68]. Calculating the BCF is a straightforward technique to measure the translocation of accessible PTEs from either the soil or water into a plant’s root system [69], whereas transport from the root to shoot system can be appraised utilising the TF. Yanqun et al. [70] published data indicating that, in plants, accrued PTEs have a BCF > 1.0, whereas in plants that exclude PTEs, the BCF < 1.0. The current research demonstrated a BCF > 1.0 for P. stratiotes in relation to all the PTEs tested, indicating the ability of this macrophyte to absorb PTEs within its root system, as well as its appropriateness for phytoremediation or rhizofiltration tasks. These data essentially mirrored work published by Galal et al. [28] and Kumar et al. [10]. The fact that P. stratiotes is recognised as being a possible candidate for phytoremediation reflects the view of Weis and Weis [71], who have also reported that PTEs can be accumulated by aquatic plant species through their root systems. Overall, Mn had the largest BCF, with lower values in descending order for Fe, Cu, Zn, Co, Cd, Ni, and Pb. Mn, Fe, Cu, and Zn exhibited a higher BCF as they are essential macronutrients for the macrophyte.
In the present study, non-linear regression was used to relate PTEs in P. stratiotes root system to the PTEs concentration in the water. The data demonstrated an exponential drop in BCF values for all the PTEs with rising water concentrations of these elements. In other words, the bioaccumulation of PTEs in root system decreased with an increase in PTE concentration in the water. A similar finding was noted by Prasad and Maiti [72] for E. crassipes growing in ponds from mining and non-mining areas in India, and Eid et al. [55] for E. crassipes grown in irrigation canals in the North Nile Delta in Egypt. A similar inverse relationship was recorded in another investigation in the terrestrial environment by Wang et al. [73] in four common vegetables (Chinese cabbage, spinach, celery, cole) grown on PTE-contaminated soils under field conditions in China. A potential mechanism to explain this is that the plants have a crucial ability to self-regulate PTE uptake into their root systems [74,75]. Additionally, the macrophytes tend to thrive less well in polluted water. This is particularly the case where the water is heavily contaminated; plants undergo blasting and may fail to survive, owing to the poisonous consequences of the water toxins [72]. In this situation, the poor quality of the habitat ameliorates the ability of the macrophytes to absorb PTEs, and thus the concentration of these PTEs within the root system is diminished [73]. The results therefore point to the fact that the concentration of the PTE is important for the availability of PTEs in water.
The TF is a measure of the effectiveness of PTE transfer from the macrophytes’ roots to their shoot systems. Calculation of this parameter for P. stratiotes revealed some differences between the varying PTEs; the value was < 1.0 for the majority of PTEs evaluated. P. stratiotes therefore has the capability to prevent some PTEs from reaching its physiologically active components, e.g., the leaves. The differences seen in the TF values could be associated with the interactions between the PTEs, which can originate from conflicting and synergetic processes [76,77]. Further factors to explain the differences in TF include physiological parameters relating to the plant, PTE solubility and availability factors, and governance pathways within the root and shoot systems which limit translocation to the latter [74,77].
Regression models can be used as mathematical strategies to facilitate the prediction of plant PTE concentrations utilising water parameters, e.g., the PTE concentration and pH [10,11]. Essential related concepts influencing plant absorption include PTE solubility and bioavailability [44]. pH acts as one of the most significant factors to determine the net metal ion availability in aqueous solutions, as well as their further absorption by plants [78]. Thus, the water pH is often involved in such models, as it impacts the bioavailability of the PTEs [10,11]. In the current study, the pH in the Al-Sero Drain ranged between 7.0 and 8.9. In a recently published study, the pH influence on the effectiveness of PTE absorption by the plant was reported as acidic > neutral > basic [29]. A study by Awuah et al. [79] showed that P. stratiotes was capable of growing at a minimum optimum pH of 4.4 when grown in ponds for wastewater treatment. Therefore, lowering the pH value of the Al-Sero Drain could enhance plant efficiency of the uptake of all selected PTEs. The results from this study demonstrated the ability of the models to estimate the quantity of PTEs within P. stratiotes root and shoot systems, according to parameters of model performance, i.e., R2, ME, MNAE, and t-values. In the designed models, satisfactory R2 parameters were calculated in some instances, within a spectrum extending from Cu, 0.147, in the root system, to Mn, 0.592, in the shoot system. The diversity observed indicates that P. stratiotes may exhibit some metal-specific uptake properties [10].
The data presented in this study are new, with respect to the generation of regression models, in terms of their use as predictive tools for PTE absorption in P. stratiotes grown in a natural environment. To the authors’ knowledge, no studies focused on this scenario have been published to date. Thus, the presented data have been contrasted with research conducted within a laboratory setting. Kumar et al. [10] described a range for R2 for Cd in P. stratiotes of 95.0–99.0% when the macrophyte was cultured in paper effluent within a laboratory sized phytoremediation model. This compares with an R2 for Cd of 29.4–29.9% measured in this study in a natural habitat. The R2 for Pb attained by Kumar et al. [10] was between 79.0% and 91.0%; in this study, the range was 18.6–40.5%. The higher values in the former work suggested minimal intersample diversity; the data were collected from macrophytes cultured in a uniform laboratory setting. In the current study, the lower R2 values may have been due to the fact that the samples were collected over a year, from May 2013 to April 2014, and any diversity in the water conditions and concentrations of PTEs became merged. Additionally, the smaller R2 parameters in this research may reflect a lack of model sophistication and its restricted ability to demonstrate complex natural PTE phenomena [80].

5. Conclusions

The current research was carried out in order to design new regression models for the prediction of eight PTE concentrations within the root and shoot systems of P. stratiotes, from the equivalent water elemental concentrations, utilising the water pH as a cofactor. P. stratiotes was characterised by a BCF > 1.0 for all eight PTEs evaluated in the study, and the TF of Cd, Cu, and Pb were > 1.0. This indicates that P. stratiotes is suitable for Cd, Cu, and Pb phytoextraction, as well as the exclusion of the remaining PTEs. Moreover, the high BAF and low TF of most investigated PTEs indicate the potential of P. stratiotes for phytostabilisation of these PTEs. The majority of the designed models for the prediction of PTE concentrations within the shoot and root systems of this plant were robust, offering a good fit, with high efficacy and minimal error. They could therefore be of use as predictors of PTE accretion within the plant components of P. stratiotes that inhabits drainage canals, with the exception of those with a low R2. These models represent new possibilities for environmental risk assessments and the creation of standards for PTE water quality. An extended field study may be needed for irrigation canals.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/su13095276/s1, Table S1: Pearson correlation coefficient (r-values, n = 108) between potentially toxic elements (PTEs) in Pistia stratiotes tissues and their concentrations in the water of the Al–Sero Drain (South Nile Delta, Egypt) over one year (May 2013–April 2014).

Author Contributions

Conceptualisation, T.M.G.; methodology, M.A.D., T.M.G., and L.M.H.; software, E.M.E.; formal analysis, M.A.D. and T.M.G.; investigation, E.M.E.; resources, E.M.E.; data curation, M.A.D., T.M.G., L.M.H., and E.M.E.; writing—original draft preparation, E.M.E.; writing—review and editing, M.A.D., T.M.G., L.M.H., and S.G.S.; visualisation, E.M.E.; supervision, L.M.H.; project administration, E.M.E.; funding acquisition, E.M.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Scientific Research Deanship at King Khalid University and the Ministry of Education in Saudi Arabia through the project number IFP-KKU-2020/3.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are contained within the article and Supplementary Materials File.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Leblebici, Z.; Kar, M. Accumulation of heavy metals in vegetables irrigated with different water sources and their daily intake in Nevsehir. J. Agric. Sci. Technol. 2018, 20, 401–415. [Google Scholar]
  2. Leblebici, Z.; Dalmis, E.; Andeden, E.E. Determination of the potential of Pistia stratiotes L. in removing nickel from the environment by utilizing its rhizofiltration capacity. Braz. Arch. Biol. Technol. 2019, 62, e19180487. [Google Scholar] [CrossRef]
  3. Charlesworth, S.; De Miguel, E.; Ordonez, A. A review of the distribution of particulate trace elements in urban terrestrial environments and its application to considerations of risk. Environ. Geochem. Health 2011, 33, 103–123. [Google Scholar] [CrossRef] [Green Version]
  4. Tchounwou, P.B.; Yedjou, C.G.; Patlolla, A.K.; Sutton, D.J. Heavy metal toxicity and the environment. Mol. Clin. Environ. Toxicol. 2012, 3, 133–164. [Google Scholar]
  5. Knasmuller, S.; Gottmann, E.; Steinkellner, H.; Fomin, A.; Pickl, C.; Paschke, A.; God, R.; Kundi, M. Detection of genotoxic effects of heavy metal contaminated soils with plant bioassay. Mutat. Res. 1998, 420, 37–48. [Google Scholar] [CrossRef]
  6. Gupta, S.; Satpati, S.; Nayek, S.; Garai, D. Effect of wastewater irrigation on vegetables in relation to bioaccumulation of heavy metals and biochemical changes. Environ. Monit. Assess. 2010, 165, 169–177. [Google Scholar] [CrossRef] [PubMed]
  7. Bonanno, G.; Vymazal, J. Compartmentalization of potentially hazardous elements in macrophytes: Insights into capacity and efficiency of accumulation. J. Geochem. Explor. 2017, 181, 22–30. [Google Scholar] [CrossRef]
  8. Chakravarty, P.; Sen Sarma, N.; Sarma, H.P. Biosorption of cadmium (II) from aqueous solution using heartwood powder of Areca catechu. Chem. Eng. J. 2010, 162, 949–955. [Google Scholar] [CrossRef]
  9. Yang, Y.; Zhang, F.S.; Li, H.F.; Jiang, R.F. Accumulation of cadmium in the edible parts of six vegetable species grown in Cd-contaminated soils. J. Environ. Manag. 2009, 90, 1117–1122. [Google Scholar] [CrossRef]
  10. Kumar, V.; Singh, J.; Kumar, P. Heavy metal uptake by water lettuce (Pistia stratiotes L.) from paper mill effluent (PME): Experimental and prediction modelling studies. Environ. Sci. Pollut. Res. 2019, 26, 14400–14413. [Google Scholar] [CrossRef]
  11. Kumar, V.; Singh, J.; Kumar, P. Regression models for removal of heavy metals by water hyacinth (Eichhornia crassipes) from wastewater of pulp and paper processing industry. Environ. Sustain. 2020, 3, 35–44. [Google Scholar] [CrossRef]
  12. Eid, E.M.; Shaltout, K.H.; Al-Sodany, Y.M.; Haroun, S.A.; Galal, T.M.; Ayed, H.; Khedher, K.M.; Jensen, K. Common reed (Phragmites australis (Cav.) Trin. ex Steudel) as a candidate for predicting heavy metal contamination in Lake Burullus, Egypt: A biomonitoring approach. Ecol. Eng. 2020, 148, 105787. [Google Scholar] [CrossRef]
  13. Eid, E.M.; Galal, T.M.; Shaltout, K.H.; El-Sheikh, M.A.; Asaeda, T.; Alatar, A.A.; Alfarhan, A.H.; Alharthi, A.; Alshehri, A.M.A.; Picó, Y.; et al. Biomonitoring potential of the native aquatic plant Typha domingensis by predicting trace metals accumulation in the Egyptian Lake Burullus. Sci. Total Environ. 2020, 714, 136603. [Google Scholar] [CrossRef] [PubMed]
  14. Soriano-Disla, J.M.; Gómez, I.; Navarro-Pedreno, J.; Jordán, M.M. The transfer of heavy metals to barley plants from soils amended with sewage sludge with different heavy metal burdens. J. Soil. Sediment. 2014, 14, 687–696. [Google Scholar] [CrossRef]
  15. Carluer, N.; Marsily, G. Assessment and modeling of the influence of man-made networks on the hydrology of a small watershed: Implications for fast flow components, water quality and landscape management. J. Hydrol. 2004, 285, 76–95. [Google Scholar] [CrossRef]
  16. Kumwimba, M.N.; Zhu, B.; Suanon, F.; Muyembe, D.K.; Dzakpasu, M. Long-term impact of primary domestic sewage on metal/loid accumulation in drainage ditch sediments, plants and water: Implications for phytoremediation and restoration. Sci. Total Environ. 2017, 581–582, 773–781. [Google Scholar] [CrossRef]
  17. Kumwimba, M.N.; Dzakpasu, M.; Zhu, B.; Muyembe, D.K. Uptake and release of sequestered nutrient in subtropical monsoon ecological ditch plant species. Water Air Soil Pollut. 2016, 227, 405. [Google Scholar] [CrossRef]
  18. Fawzy, M.A.; Badr, N.E.; El-Khatib, A.; Abo El-Kassem, A. Heavy metal biomonitoring and phytoremediation potentialities of aquatic macrophytes in River Nile. Environ. Monit. Assess. 2012, 184, 1753–1771. [Google Scholar] [CrossRef] [PubMed]
  19. Shaltout, K.H.; Galal, T.M.; El-Komi, T.M. Biomass, nutrients and nutritive value of Persicaria salicifolia Willd. in the water courses of Nile Delta, Egypt. Rend. Lincei 2014, 25, 167–179. [Google Scholar] [CrossRef]
  20. Shaltout, K.H.; Galal, T.M.; El-Komi, T.M. Phenology, biomass and nutrients of Imperata cylindrica and Desmostachya bipinnata along the water courses in Nile Delta, Egypt. Rend. Lincei 2016, 27, 215–228. [Google Scholar] [CrossRef]
  21. Shaltout, K.H.; Eid, E.M.; El-Komi, T.M. Phytomass and nutrient value of Potamogeton crispus L. in the water courses of Nile Delta, Egypt. Rend. Lincei 2016, 27, 251–259. [Google Scholar] [CrossRef]
  22. Täckholm, V. Students’ Flora of Egypt; Cooperative Printing Company: Beirut, Lebanon, 1974. [Google Scholar]
  23. GISD (Global Invasive Species Database). Species Profile: Pistia Stratiotes. Available online: http://www.iucngisd.org/gisd/speciesname/Pistia+stratiotes (accessed on 30 April 2020).
  24. Chapman, D.; Coetzee, J.; Hill, M.; Hussner, A.; Netherland, M.; Pescott, O.; Stiers, I.; van Valkenburg, J.; Tanner, R. Pistia stratiotes L. EPPO Bull. 2017, 47, 537–543. [Google Scholar]
  25. Boulos, L. Flora of Egypt: Volume 4, Monocotyledons (Altismataceae-Orchidaceae); Al-Hadara Publishing: Cairo, Egypt, 2005. [Google Scholar]
  26. Galal, T.M.; Farahat, E.A. The invasive macrophyte Pistia stratiotes L. as a bioindicator for water pollution in Lake Mariut, Egypt. Environ. Monit. Assess. 2015, 178, 701. [Google Scholar] [CrossRef]
  27. Galal, T.; Shaltout, K.; Hassan, L. The Egyptian Northern Lakes: Habitat Diversity, Vegetation and Economic Importance; LAP Lambert Academic Publishing: Saarbrücken, Germany, 2012. [Google Scholar]
  28. Galal, T.M.; Eid, E.M.; Dakhil, M.A.; Hassan, L.M. Bioaccumulation and rhizofiltration potential of Pistia stratiotes L. for mitigating water pollution in the Egyptian wetlands. Int. J. Phytoremediat. 2018, 20, 440–447. [Google Scholar] [CrossRef]
  29. Zeng, F.; Ali, S.; Zhang, H.; Ouyang, Y.; Qiu, B.; Wu, F.; Zhang, G. The influence of pH and organic matter content in paddy soil on heavy metal availability and their uptake by rice plants. Environ. Pollut. 2011, 159, 84–91. [Google Scholar] [CrossRef]
  30. Das, S.; Goswami, S.; Talukdar, A.D. A study on cadmium phytoremediation potential of water lettuce, Pistia stratiotes L. Bull. Environ. Contam. Toxicol. 2014, 92, 169–174. [Google Scholar] [CrossRef]
  31. Hanks, N.A.; Caruso, J.A.; Zhang, P. Assessing Pistia stratiotes for phytoremediation of silver nanoparticles and Ag(I) contaminated waters. J. Environ. Manag. 2015, 164, 41–45. [Google Scholar] [CrossRef]
  32. Victor, K.K.; Séka, Y.; Norbert, K.K.; Sanogo, T.A.; Celestin, A.B. Phytoremediation of wastewater toxicity using water hyacinth (Eichhornia crassipes) and water lettuce (Pistia stratiotes). Int. J. Phytoremediat. 2016, 18, 949–955. [Google Scholar] [CrossRef] [PubMed]
  33. Kumar, V.; Singh, J.; Pathak, V.V.; Ahmad, S.; Kothari, R. Experimental and kinetics study for phytoremediation of sugar mill effluent using water lettuce (Pistia stratiotes L.) and its end use for biogas production. Biotech 2017, 7, 330. [Google Scholar] [CrossRef] [PubMed]
  34. de Campos, F.V.; de Oliveira, J.A.; da Silva, A.A.; Ribeiro, C.; Farnese, F.S. Phytoremediation of arsenite-contaminated environments: Is Pistia stratiotes L. a useful tool? Ecol. Indic. 2019, 104, 794–801. [Google Scholar] [CrossRef]
  35. Kodituwakku, K.A.R.K.; Yatawara, M. Phytoremediation of industrial sewage sludge with Eichhornia crassipes, Salvinia molesta and Pistia stratiotes in batch fed free water flow constructed wetlands. Bull. Environ. Contam. Toxicol. 2020, 104, 627–633. [Google Scholar] [CrossRef] [PubMed]
  36. Tabinda, A.B.; Irfan, R.; Yasar, A.; Iqbal, A.; Mahmood, A. Phytoremediation potential of Pistia stratiotes and Eichhornia crassipes to remove chromium and copper. Environ. Technol. 2020, 41, 1514–1519. [Google Scholar] [CrossRef]
  37. Tang, K.H.D.; Awa, S.H.; Hadibarata, T. Phytoremediation of copper-contaminated water with Pistia stratiotes in surface and distilled water. Water Air Soil Pollut. 2020, 231, 573. [Google Scholar] [CrossRef]
  38. UNESCO. Map of the World Distribution of Arid Regions; MAB Technical Notes: Paris, France, 1977. [Google Scholar]
  39. NASA-POWER. Climatology Resource for Agroclimatology. NASA Prediction of Worldwide Energy. Available online: http://power.larc.nasa.gov/cgi-bin/cgiwrap/solar/agro.cgi (accessed on 28 April 2020).
  40. Galal, T.M.; Dakhil, M.A.; Hassan, L.M.; Eid, E.M. Population dynamics of Pistia stratiotes L. Rend. Lincei 2019, 30, 367–378. [Google Scholar] [CrossRef]
  41. Dakhil, M.; Galal, T.; Hassan, L. Population Dynamics and Nutrient Cycling of Pistia stratiotes L.; LAP LAMBERT Academic Publishing: Saarbrücken, Germany, 2016. [Google Scholar]
  42. APHA (American Public Health Association). Standard Methods for the Examination of Water and Wastewater; American Public Health Association: Washington, DC, USA, 1998. [Google Scholar]
  43. Eid, E.M.; Shaltout, K.H.; Moghanm, F.S.; Youssef, M.S.G.; El-Mohsnawy, E.; Haroun, S.A. Bioaccumulation and translocation of nine heavy metals by Eichhornia crassipes in Nile Delta, Egypt: Perspectives for phytoremediation. Int. J. Phytoremediat. 2019, 21, 821–830. [Google Scholar] [CrossRef] [PubMed]
  44. Novotná, M.; Mikeš, O.; Komprdová, K. Development and comparison of regression models for the uptake of metals into various field crops. Environ. Pollut. 2015, 207, 357–364. [Google Scholar] [CrossRef] [PubMed]
  45. Statsoft. Statistica Version 7.1; Statsoft Inc.: Tulsa, OK, USA, 2007. [Google Scholar]
  46. Kabata-Pendias, A. Trace Elements in Soils and Plants; CRC Press: Boca Raton, FL, USA, 2011. [Google Scholar]
  47. Lu, Q.; He, Z.L.; Graetz, D.A.; Stoffella, P.J.; Yang, X. Uptake and distribution of metals by water lettuce (Pistia stratiotes L.). Environ. Sci. Pollut. Res. 2011, 18, 978–986. [Google Scholar] [CrossRef]
  48. Kumar, V.; Sharma, A.; Kumar, R.; Bhardwaj, R.; Thukral, A.K.; Rodrigo-Comino, J. Assessment of heavy-metal pollution in three different Indian water bodies by combination of multivariate analysis and water pollution indices. Hum. Ecol. Risk Assess. 2018, 26, 1–16. [Google Scholar] [CrossRef]
  49. Singh, S.; Saxena, R.; Pandey, K.; Bhatt, K.; Sinha, S. Response of antioxidants in sunflower (Helianthus annuus L.) grown on different amendments of tannery sludge: Its metal accumulation potential. Chemosphere 2004, 57, 1663–1673. [Google Scholar] [CrossRef] [PubMed]
  50. Eid, E.M.; Shaltout, K.H. Monthly variations of trace elements accumulation and distribution in above- and below-ground biomass of Phragmites australis (Cav.) Trin. ex Steudel in Lake Burullus (Egypt): A biomonitoring application. Ecol. Eng. 2014, 73, 17–25. [Google Scholar] [CrossRef]
  51. Ouzounidou, G.; Ciamporova, M.; Moustakas, M.; Karataglis, S. Responses of maize (Zea mays L.) plants to copper stress I. Growth, mineral content and ultrastructure of roots. Environ. Exp. Bot. 1995, 35, 167–176. [Google Scholar] [CrossRef]
  52. Singha, K.T.; Sebatian, A.; Prasad, M.N.V. Iron plaque formation in the roots of Pistia stratiotes L.: Importance in phytoremediation of cadmium. Int. J. Phytoremediat. 2019, 21, 120–128. [Google Scholar] [CrossRef]
  53. Hanafiah, M.M.; Mohamad, N.H.S.M.; Abd Aziz, N.I.H. Salvinia molesta and Pistia stratiotes as phytoremediation agents in sewage wastewater treatment. Sains Malays. 2018, 47, 1625–1634. [Google Scholar]
  54. Lopes, C.; Herva, M.; Franco-Uría, A.; Roca, E. Multicorrelation models and uptake factors to estimate extractable metal concentrations from soil and metal in plants in pasturelands fertilized with manure. Environ. Pollut. 2012, 166, 17–22. [Google Scholar] [CrossRef] [PubMed]
  55. Eid, E.M.; Shaltout, K.H.; Almuqrin, A.H.; Aloraini, D.A.; Khedher, K.M.; Taher, M.A.; Alfarhan, A.H.; Picó, Y.; Barcelo, D. Uptake prediction of nine heavy metals by Eichhornia crassipes grown in irrigation canals: A biomonitoring approach. Sci. Total Environ. 2021, 782, 146887. [Google Scholar] [CrossRef] [PubMed]
  56. Nawab, J.; Khan, S.; Shah, M.T.; Gul, N.; Ali, A.; Khan, K.; Huang, Q. Heavy metal bioaccumulation in native plants in chromite impacted sites: A search for effective remediating plant species. Clean Soil Air Water 2016, 44, 37–46. [Google Scholar] [CrossRef]
  57. Brady, N.; Weil, R. The Nature and Properties of Soils, 11th ed.; Prentice-Hall International Inc.: Hoboken, NJ, USA, 1996. [Google Scholar]
  58. Allen, S.E. Chemical Analysis of Ecological Materials; Blackwell Scientific Publications: London, UK, 1989. [Google Scholar]
  59. Whitehead, D.C. Nutrient Elements in Grassland: Soil-Plant-Animal Relationships; CABI Publishing: New York, NY, USA, 2000. [Google Scholar]
  60. Marschner, H. Mineral Nutrition of Higher Plants; Academic Press: London, UK, 1995. [Google Scholar]
  61. Nawab, J.; Khan, S.; Shah, M.T.; Qamar, Z.; Din, I.; Mahmood, Q.; Gul, N.; Huang, Q. Contamination of soil, medicinal, and fodder plants with lead and cadmium present in mine-affected areas, Northern Pakistan. J. Environ. Monitor. 2015, 187, 605. [Google Scholar] [CrossRef] [PubMed]
  62. Divan Junior, A.M.; de Oliveira, P.L.; Perry, C.T.; Atz, V.L.; Azzarini-Rostirola, L.N.; Raya-Rodriguez, M.T. Using wild plant species as indicators for the accumulation of emissions from a thermal power plant, Candiota, South Brazil. Ecol. Indic. 2009, 9, 1156–1162. [Google Scholar] [CrossRef]
  63. Żurek, G.; Rybka, K.; Bogrzeba, M.; Krzyżak, J.; Prokopiuk, K. Chlorophyll α fluorescence in evaluation of the effect of heavy metal soil contamination on perennial grasses. PLoS ONE 2014, 9, e91475. [Google Scholar] [CrossRef]
  64. Tessier, A.; Turner, D.R. Metal Speciation and Bioavailability in Aquatic Systems; Wiley: London, UK, 1995. [Google Scholar]
  65. Singh, R.P.; Agrawal, M. Variations in heavy metal accumulation, growth and yield of rice plants grown at different sewage sludge amendment rates. Ecotoxicol. Environ. Saf. 2010, 73, 632–641. [Google Scholar] [CrossRef]
  66. Christou, A.; Theologides, C.; Costa, C.; Kalavrouziotis, I.; Varnavas, S. Assessment of toxic heavy metals concentrations in soils and wild and cultivated plant species in Limni abandoned copper mining site, Cyprus. J. Geochem. Explor. 2017, 178, 16–22. [Google Scholar] [CrossRef]
  67. Basta, N.T.; Ryan, J.A.; Chaney, R.L. Trace element chemistry in residual-treated soil: Key concepts and metal bioavailability. J. Environ. Qual. 2005, 34, 49–63. [Google Scholar] [CrossRef] [Green Version]
  68. Bonanno, G.; Vymazal, J.; Cirelli, G.L. Translocation, accumulation and bioindication of trace elements in wetland plants. Sci. Total Environ. 2018, 631–632, 252–261. [Google Scholar] [CrossRef]
  69. Branzini, A.; González, R.S.; Zubillaga, M. Absorption and translocation of copper, zinc and chromium by Sesbania virgata. J. Environ. Manag. 2012, 102, 50–54. [Google Scholar] [CrossRef]
  70. Zu, Y.; Li, Y.; Chen, J.; Chen, H.; Qin, L.; Christian, S. Hyperaccumulation of Pb, Zn and Cd in herbaceous grown on lead-zinc mining area in Yunnan, China. Environ. Int. 2005, 31, 755–762. [Google Scholar]
  71. Weis, J.S.; Weis, P. Metal uptake, transport and release by wetland plants: Implications for phytoremediation and restoration. Environ. Int. 2004, 30, 685–700. [Google Scholar] [CrossRef] [PubMed]
  72. Prasad, B.; Maiti, D. Comparative study of metal uptake by Eichhornia crassipes growing in ponds from mining and nonmining areas-a field study. Bioremediat. J. 2016, 20, 144–152. [Google Scholar] [CrossRef]
  73. Wang, X.-P.; Shan, X.-Q.; Zhang, S.-Z.; Wen, B. A model for evaluation of the phytoavailability of trace elements to vegetables under the field conditions. Chemosphere 2004, 55, 811–822. [Google Scholar] [CrossRef]
  74. Kim, I.S.; Kang, H.K.; Johnson-Green, P.; Lee, E.J. Investigation of heavy metal accumulation in Polygonum thunbergii for phytoextraction. Environ. Pollut. 2003, 126, 235–243. [Google Scholar] [CrossRef]
  75. Du Laing, G.; Van de Moortel, A.M.K.; Moors, W.; De Grauwe, P.; Meers, E.; Tack, F.M.G.; Verloo, M.G. Factors affecting metal concentrations in reed plants (Phragmites australis) of intertidal marshes in the Scheldt estuary. Ecol. Eng. 2009, 35, 310–318. [Google Scholar] [CrossRef]
  76. Weis, J.S.; Glover, T.; Weis, P. Interactions of metals affect their distribution in tissues of Phragmites australis. Environ. Pollut. 2004, 131, 409–415. [Google Scholar] [CrossRef] [PubMed]
  77. Yang, X.E.; Long, X.X.; Ye, H.B.; He, Z.L.; Calvert, D.V.; Stoffella, P.J. Cadmium tolerance and hyperaccumulation in a new Zn-hyperaccumulating plant species (Sedum alfredii Hance). Plant Soil. 2004, 259, 181–189. [Google Scholar] [CrossRef]
  78. Li, X.; Xi, H.; Sun, X.; Yang, Y.; Yang, S.; Zhou, Y.; Yang, Y. Comparative proteomics exploring the molecular mechanism of eutrophic water purification using water hyacinth (Eichhornia crassipes). Environ. Sci. Pollut. Res. 2015, 22, 8643–8658. [Google Scholar] [CrossRef]
  79. Awuah, E.; Lubberding, H.J.; Asante, K.; Gijzen, H.J. The effect of pH on enterococci removal in Pistia-, duckweed-and algae-based stabilization ponds for domestic wastewater treatment. Water Sci. Technol. 2002, 45, 67–74. [Google Scholar] [CrossRef]
  80. Ergönül, A.B.; Nassouhi, D.; Atasagun, S. Modeling of the bioaccumulative efficiency of Pistia stratiotes exposed to Pb, Cd, and Pd + Cd mixtures in nutrient-poor media. Int. J. Phytoremediat. 2020, 22, 201–209. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Satellite images of the study area, indicating the locations of the three sampling sites ( Sustainability 13 05276 i001).
Figure 1. Satellite images of the study area, indicating the locations of the three sampling sites ( Sustainability 13 05276 i001).
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Figure 2. The Pearson coefficient of correlation (r-values, n = 108) of potentially toxic elements in Pistia stratiotes over one year ((A): shoot system, (B): root system) and their concentration in the Al-Sero Drain waters (South Nile Delta, Egypt) (May 2013–April 2014).
Figure 2. The Pearson coefficient of correlation (r-values, n = 108) of potentially toxic elements in Pistia stratiotes over one year ((A): shoot system, (B): root system) and their concentration in the Al-Sero Drain waters (South Nile Delta, Egypt) (May 2013–April 2014).
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Figure 3. Scatter plot for the bioconcentration factor (BCF) values of eight potentially toxic elements in Pistia stratiotes root systems with respect to their concentrations in the water, from three sites in the Al-Sero Drain (South Nile Delta, Egypt), over one year (May 2013–April 2014).
Figure 3. Scatter plot for the bioconcentration factor (BCF) values of eight potentially toxic elements in Pistia stratiotes root systems with respect to their concentrations in the water, from three sites in the Al-Sero Drain (South Nile Delta, Egypt), over one year (May 2013–April 2014).
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Table 1. Potentially toxic element (PTE) concentrations and pH of the water from three sites in the Al-Sero Drain (South Nile Delta, Egypt), supporting the growth of Pistia stratiotes populations for one year (May 2013–April 2014).
Table 1. Potentially toxic element (PTE) concentrations and pH of the water from three sites in the Al-Sero Drain (South Nile Delta, Egypt), supporting the growth of Pistia stratiotes populations for one year (May 2013–April 2014).
ValuepHPTE Concentration (μg/L)
CdCoCuFeMnNiPbZn
Minimum7.01.03.01.062.04.07.0243.09.0
Maximum8.936.0100.022.0980.01160.0110.0461.0200.0
Mean (n = 108)7.53.520.08.2523.6234.747.3308.226.5
CV (%)5.7120.970.775.452.3112.934.69.295.1
CV: coefficient of variance.
Table 2. Shoot and root potentially toxic element (PTE) concentrations in Pistia stratiotes populations growing in the Al-Sero Drain, Egypt (South Nile Delta), over one year (May 2013–April 2014).
Table 2. Shoot and root potentially toxic element (PTE) concentrations in Pistia stratiotes populations growing in the Al-Sero Drain, Egypt (South Nile Delta), over one year (May 2013–April 2014).
TissueValuePTE Concentration (mg/kg)
CdCoCuFeMnNiPbZn
Shoot systemMinimum0.10.22.035.0105.70.11.218.9
Maximum3.513.5200.33272.0852.550.673.1113.9
Mean (n = 108)0.95.255.5974.1331.56.842.037.1
CV (%)87.165.793.889.559.3120.341.746.5
Root systemMinimum0.10.53.3288.0200.51.12.720.4
Maximum3.435.293.05637.01474.490.660.5124.7
Mean (n = 108)1.017.610.02511.01160.720.439.848.2
CV (%)66.555.8105.863.433.559.542.640.9
t-value0.4 ns12.5 ***8.8 ***8.4 ***26.2 ***11.0 ***0.9 ns4.4 ***
Phytotoxic range 5–3015–5020–100>1000400–100040–24630–300100–500
t-values represent Student’s t-test. ***: p < 0.001, ns: not significant (i.e., p > 0.05), CV: coefficient of variance. : Kabata–Pendias [46].
Table 3. Mean ± standard error (n = 108) of bioconcentration factors (BCFs), from the water to root system, and translocation factors (TFs), from the root to shoot system, of potentially toxic elements (PTEs) in Pistia stratiotes populations grown in the Al-Sero Drain (South Nile Delta, Egypt) over one year (May 2013–April 2014).
Table 3. Mean ± standard error (n = 108) of bioconcentration factors (BCFs), from the water to root system, and translocation factors (TFs), from the root to shoot system, of potentially toxic elements (PTEs) in Pistia stratiotes populations grown in the Al-Sero Drain (South Nile Delta, Egypt) over one year (May 2013–April 2014).
PTEBCFTF
Cd520.7 ± 52.3a2.1 ± 0.4b
Co1418.3 ± 151.6a0.6 ± 0.1a
Cu2990.3 ± 368.3a7.1 ± 0.8c
Fe8974.2 ± 1136.9a0.7 ± 0.1a
Mn39,642.5 ± 8247.3b0.3 ± 0.0a
Ni471.7 ± 24.6a0.4 ± 0.1a
Pb128.7 ± 5.2a1.9 ± 0.3b
Zn2316.3 ± 119.3a0.9 ± 0.1ab
F-value20.9 ***50.7 ***
F-values represent a one-way ANOVA, degrees of freedom = 7. Means in the same column followed by different letters are significantly different at p < 0.05 according to Tukey’s HSD test. ***: p < 0.001.
Table 4. Models of regression between potentially toxic elements in Pistia stratiotes (mg/kg) and potentially toxic elements in water (μg/L) and pH.
Table 4. Models of regression between potentially toxic elements in Pistia stratiotes (mg/kg) and potentially toxic elements in water (μg/L) and pH.
EquationR2MEMNAEStudent’s t-Test
t-Valuep
Shoot system
Cd = 7.74 − 0.14 × Cdwater − 0.83 × pH0.299 ***0.6640.3360.7380.465
Co = 29.70 + 0.18 × Cowater − 3.71 × pH0.325 ***0.7160.2540.7280.471
Cu = 257.25 − 4.77 × Cuwater − 21.58 × pH0.518 ***0.7480.2150.3780.708
Fe = 4531.40 + 0.67 × Fewater − 529.12 × pH0.279 ***0.6050.3820.9540.347
Mn = −1863.31 + 0.06 × Mnwater + 289.66 × pH0.592 ***0.8110.1790.1210.904
Ni = 20.94 + 0.09 × Niwater − 2.69 × pH0.225 ***0.5750.4721.3610.182
Pb = 113.52 − 0.20 × Pbwater − 1.07 × pH0.186 ***0.5690.5751.4330.161
Zn = −28.60 + 0.21 × Znwater + 8.05 × pH0.157 ***0.3860.5911.8000.080
Root system
Cd = 7.37 − 0.04 × Cdwater − 0.86 × pH0.294 ***0.6250.3510.9530.346
Co = −27.23 − 0.17 × Cowater + 6.53 × pH0.253 ***0.5810.4331.3290.192
Cu = 3.90 − 0.11 × Cuwater + 0.74 × pH0.147 ***0.3670.6281.9690.057
Fe = 8457.90 − 2.44 × Fewater − 628.29 × pH0.212 ***0.5710.5121.3870.173
Mn = −482.67 + 0.16 × Mnwater + 213.23 × pH0.173 ***0.4450.5851.7340.092
Ni = 9.66 + 0.23 × Niwater + 0.16 × pH0.177 ***0.4920.5841.5620.127
Pb = −157.31 + 0.29 × Pbwater + 14.22 × pH0.405 ***0.7420.2480.6630.512
Zn = −51.83 − 0.08 × Znwater + 13.50 × pH0.263 ***0.6000.4310.9600.343
R2: coefficient of determination, ME: model efficiency, MNAE: mean normalised average error, ***: p < 0.001. The estimated concentration of a potentially toxic element in a tissue was compared to the measured concentration of the same potentially toxic element using Student’s t-test.
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Eid, E.M.; Dakhil, M.A.; Hassan, L.M.; Salama, S.G.; Galal, T.M. Uptake Prediction of Eight Potentially Toxic Elements by Pistia stratiotes L. Grown in the Al-Sero Drain (South Nile Delta, Egypt): A Biomonitoring Approach. Sustainability 2021, 13, 5276. https://doi.org/10.3390/su13095276

AMA Style

Eid EM, Dakhil MA, Hassan LM, Salama SG, Galal TM. Uptake Prediction of Eight Potentially Toxic Elements by Pistia stratiotes L. Grown in the Al-Sero Drain (South Nile Delta, Egypt): A Biomonitoring Approach. Sustainability. 2021; 13(9):5276. https://doi.org/10.3390/su13095276

Chicago/Turabian Style

Eid, Ebrahem M., Mohammed A. Dakhil, Loutfy M. Hassan, Shaimaa G. Salama, and Tarek M. Galal. 2021. "Uptake Prediction of Eight Potentially Toxic Elements by Pistia stratiotes L. Grown in the Al-Sero Drain (South Nile Delta, Egypt): A Biomonitoring Approach" Sustainability 13, no. 9: 5276. https://doi.org/10.3390/su13095276

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