Next Article in Journal
Improving Sustainable Forest Management of Pinus halepensis Mill. Mid-Aged Stands in a Context of Rural Abandonment, Climate Change, and Wildfires
Previous Article in Journal
Spatial Downscaling of Forest Above-Ground Biomass Distribution Patterns Based on Landsat 8 OLI Images and a Multiscale Geographically Weighted Regression Algorithm
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 3
10.3390/f14030528
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effect of Pb Stress on Ionome Variations and Biomass in Rhus chinensis Mill

by
Wenxiang He
1,2,
Shufeng Wang
1,
Yangdong Wang
1,
Mengzhu Lu
2 and
Xiang Shi
1,*
1
Key Laboratory of Tree Breeding of Zhejiang Province, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China
2
State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(3), 528; https://doi.org/10.3390/f14030528
Submission received: 2 February 2023 / Revised: 3 March 2023 / Accepted: 4 March 2023 / Published: 7 March 2023
(This article belongs to the Section Forest Ecophysiology and Biology)
Browse Figures
Review Reports Versions Notes

Abstract

:
This study examined changes in the ionome of Rhus chinensis in response to Pb stress so as to understand Pb phytotoxicity-related processes and provide theoretical support for improving the efficiency of this plant in remediating heavy metal-polluted soils. Rhus chinensis seedlings were cultured in pots with soil. The concentrations of 12 elements in the roots, stems, and leaves of the seedlings under treatments of 0, 500, and 1000 mg·kg−1 Pb(NO3)2 were determined. Principal component analysis (PCA), correlation analysis, and partial least squares path modeling (PLS-PM) were used to analyze the contributions of the affected elements in the ionomes of different organs of the seedlings under Pb stress. PCA showed that 74% of the total ionome variation was caused by the difference in organ types. N, Mg, K, Cu, Ca, and Zn were the important elements contributing to the variation of the ionome. Pb disturbed the ionome of different organs at varying levels, and the order of the interference levels between the organs was: stem > root > leaf. Correlation analysis showed that biomass had a significant positive correlation with N and K and a significant negative correlation with Pb, Mn, and the C:N ratio. Stem biomass had a significant positive correlation with Ca, Cu, and the N:P ratio. Root biomass had a significant negative correlation with the C:P ratio. PLS-PM analysis indicated that Pb stress had a major, direct, and inhibitory effect on biomass. The variation of ionomic profiles caused by Pb stress was mainly caused by the difference in organ types; the variation of the ionomic profiles of each organ was mainly caused by Pb stress. The elements that caused the variation of ionomic profiles varied with organ types, and the plant biomass was directly affected by a strong Pb poisoning effect and indirectly affected by a weak ionomic profile variation effect.

1. Introduction

Pb is the second most harmful heavy metal element after As (Arsenic) among all elements [1]. Industrial activities, mining, smelting, and the use of Pb-containing pigments and gasoline have caused the rapid spread of Pb pollution [2]. Excessive Pb in the environment has harmful effects on plants and animals and threatens human health throughout the food chain [3]. Therefore, Pb pollution is considered the most serious form of heavy metal pollution [4].
Phytoremediation is an effective, low-cost, environmentally friendly, and sustainable technique for the reduction or removal of heavy metal elements [5,6]. Phytoremediation efficiency largely depends on the ability of the plants to accumulate heavy metal elements and their biomass [7]. Under the stress caused by high concentrations of heavy metals, there is often a contradiction between plant growth and heavy metal accumulation [8]. Minerals are essential for plant growth, and affect many physiological processes and metabolic activities in plants [9]. Maintaining homeostasis is an important strategy plants use to cope with heavy metal elements [10]. Due to the complexity of the mechanism regulating the balance of various elements, it is difficult to achieve a comprehensive insight into the interactive network of plant mineral elements using traditional research approaches that focus only on a single element or a small number of elements [11,12].
Ionomics is a powerful method to study the interactive network of mineral elements [13]. The ionomic profile is the result of biological processes and plant–environment interactions [13,14]. Therefore, the variation of the ionomic profile can be used as an indicator to reflect the physiological state of a plant in a specific environment [15]. In addition, ecological stoichiometry (part of the ionomics approach) is a tool for studying the multiple balances between elements (mainly C, N, and P) that are required by organisms and can influence ecosystem productivity and nutrient cycling [16]. It has been suggested that stoichiometry plays an important role in predicting the functional characteristics of organisms and the ecological strategies they adopt [17]. Studies have shown that under heavy metal stress plants may adapt to adverse conditions by changing the composition and content of the elements in the ionome [18]. A number of researchers have studied the effect of heavy metal stress on the ionome of model plants and crops [9,19,20,21,22]. For example, ionomic and transcriptomic analyses confirmed that the main barriers to Cd and As transport into brown rice were the root and node [9]. Generally, even very low concentrations of Pb can result in disordered seed germination and retarded seedling growth, which may be due to the antagonism between the uptake of mineral elements and Pb [2]. This suggested that plant growth is inhibited when the supply of mineral elements is insufficient or unbalanced [23]. However, some studies showed that Pb stress did not cause significant changes in macro and micro elements in Zygophyllum fabago [2] and Cucumis sativus [24]. Usually, under the stress of different Pb concentrations, the changes in the concentrations of macro and micro elements in different plants vary significantly [25,26], which might be due to the varied strategies or efficiencies of different plants in the detoxification of Pb [2]. Hence, studying both the phytotoxicity and nutritional state of plants may provide a better understanding of plant growth [27]. The application of the ionomics approach can not only clarify the correlation between heavy metal elements and minerals in plants but can also help to better understand the process of heavy metal phytotoxicity. Therefore, related studies may provide theoretical support for the use of mineral elements to reduce the toxicity of heavy metal elements.
Rhus chinensis is a small deciduous tree of the Anacardiaceae family. Due to its fast growth and strong adaptability to adversity, R. chinensis is a pioneer plant in the restoration of contaminated sites [28]. Previous studies have shown that R. chinensis has noticeable tolerance to Pb, as well as the ability to absorb and accumulate Pb, and thus is an important resource among Pb-tolerant plants [29,30]. To date, most studies have focused on analyzing the relationship between a single mineral element and Pb tolerance, and the relationship between the ionomic profile of minerals and the biomass of this plant has not been explored [29,30]. In addition, the adaptation strategies of R. chinensis under Pb stress were rarely explored in terms of C:N:P stoichiometry. To address this knowledge gap, this study investigated the relationship between the ionomic profile and biomass and obtained an insight into Pb phytotoxicity-related process through principal component analysis (PCA), correlation analysis, and partial least squares path modeling (PLS-PM) analysis, which provided theoretical support for improving the efficiency of R. chinensis in remediating heavy metal element-polluted soils.

2. Materials and Methods

2.1. Plant Materials and Growth Conditions

Seed collection and plant culture methods are detailed in a previous paper [29]. When the shoots and root systems of seedlings developed well, seedlings with a height of 30–40 cm and a ground diameter of 0.3 cm were selected for the experiment.

2.2. Experimental Method

Red soil (0–30 cm) was collected from Fuyang District, Hangzhou (30°03′43″ N, 119°57′12″ E). This soil was air-dried, sieved, and then used in the subsequent pot experiments. Based on the results of preliminary tests, R. chinensis can grow in soils contaminated with different Pb concentrations, and the toxicity symptoms would appear under high concentrations of Pb stress (≥1000 mg kg−1). Hence, soil Pb concentrations of 0 mg kg−1 (T0, control), 500 mg kg−1 (T500, low Pb concentration), and 1000 (T1000, high Pb concentration) mg kg−1 were designed. Pb(NO3)2 solution was prepared and sprayed on the soil, and then the soil was equilibrated for one month. The final Pb; available nitrogen, phosphorus, and potassium concentrations; and pH are listed in Table 1. In May 2020, uniform seedlings were transplanted into rectangular containers (36 cm width × 45 cm length × 20 cm height) with 10 kg of soil, and each pot contained 6 seedlings. Each treatment was replicated three times, and each replicate consisted of two rectangular containers, for a total of 36 seedlings per treatment. The experiment lasted 90 days.

2.3. Biomass Measurements

After harvesting, the roots were immersed in 20 mmol/L Na2–EDTA for 15 min to remove metals adhering to the root surface. Then, the leaves, stems, and roots were separated. Biomass measurement and calculation of tolerance index (TI) are detailed in a previous paper [29].

2.4. Analysis of Elemental Concentrations of R. chinensis

Dried plant samples were ground into powder, and each dried powdered sample (0.2 g) was transferred to a Teflon tube and digested in the Mars 6 microwave system (CEM, Matthews, NC, USA). The mixture (v/v) is 3 mL HNO3 (69%) and 2 mL H2O2 (30%) as the use of HClO4 in a microwave digestion system can cause an explosion. P, K, Ca, Mg, S, Fe, Mn, Cu, Zn, and Pb concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS, NexION300D, PerkinElmer Inc., Shelton, CT, USA).
The C concentration was determined by the potassium dichromate-sulfuric acid oxidation method. Each dried powdered sample (0.03 g) was transferred to a test tube, and 10 mL of 0.8 mol potassium dichromate and 10 mL of H2SO4 were added accurately. The tubes were placed in a graphite furnace digestion instrument heated to 180 °C. Timing was started when the solution in the tubes was slightly boiling, and the wire cage was removed after 8 min. After cooling, the digestion solution was transferred to a 150 mL triangular flask and the inner wall of the test tube was washed several times; then, the entire wash solution was poured into the triangular flask. When the volume of the solution reaches 60–70 mL, the color of the solution should be orange or pale yellow. Then, 4 drops of O-phenanthroline indicator were added and titrated with 0.2 mol standard solution of ferrous sulfate (FeSO4). The end point is when the solution changes from yellow to green, grayish blue to brownish red, and the ferrous sulfate is recorded.
Each dried powdered sample (0.2 g) was digested using the H2SO4-H2O2 method [31], and the N concentration was determined by an automated Kjeldahl analyzer (Kjeltec 8400, FOSS, Copenhagen, Denmark).
Certified reference materials (Garlic powder, GBW10022) were used to ensure the quality of analyses. Analyses of the element concentrations in the plant material were performed at the Quality Testing Center for Edible Forest Products of the State Forestry Administration (Hangzhou, China).
The bioconcentration factor (BCF) and the translocation factor (TF) are two important indicators for evaluating the efficiency of phytoremediation. BCF = Atissues/Asoil, where Atissues (mg kg−1) is the total Pb accumulated in roots or shoots, and Asoil (mg kg−1) is the Pb concentration in the soil. TF = As/Ar, where As and Ar are total elements accumulated in shoots and in roots, respectively (both in mg kg−1) [32].

2.5. Statistical Analysis of Data

Statistical analysis was performed using SPSS 26.0 software. Duncan’s method was used to test the significance of differences (p < 0.05). Origin 2022 software was used for plotting. Hierarchical clustering analysis (HCA) based on the Euclidean distance and grouped by k-means clustering. The heatmap was performed with the R package ‘pheatmap’. PCA was used to visualize the ionomic profile of different organs of R. chinensis plants under Pb treatment. The Mantel test was performed between 12 elements and the biomass in different organs of R. chinensis plants with R package ‘linkET’. PLS-PM was used to analyze the direct and indirect effects of different concentrations of Pb, macro and micro elements, and the stoichiometric pattern of C, N, and P on the biomass of R. chinensis plants. PLS-PM analysis was performed using the R package ‘plspm’. To satisfy the normality assumptions, all selected data were log-transformed prior to running the PLS-PM. Four latent variables were selected: macro-nutrients (C, N, P, K, Ca, S, and Mg concentration in different organs), micro-nutrients (Fe, Mn, Cu, and Zn concentration in different organs), plant stoichiometry (C:N, C:P and N:P in different organs), and biomass. Finally, the latent variables were linked to the measured variable (Pb treatment), representing the external or measurement model.

3. Results

3.1. Biomass and Tolerance Index

The results showed that all R. chinensis seedlings could survive in Pb-contaminated soils after 3 months. There were statistically significant differences in biomass among Pb treatments (Table 2, p < 0.05). The biomass of leaves, stems, and roots of R. chinensis seedlings in the T500 treatment were reduced by 26.53%, 20.42%, and 27.46%, respectively, compared to T0. Meanwhile, in the T1000 treatment, the biomass of R. chinensis seedlings decreased by 1.40 (leaves), 0.59 (stems), and 0.58 (roots) g · plant−1, respectively, and the tolerance index was 0.60, which was lower than that of 0.75 in the T500 treatment.

3.2. Accumulation and Translocation of Pb

Table 3 shows that the Pb concentrations in different organs were increased with increasing Pb dose. The Pb concentration in different organs decreased in the order roots > stems > leaves (Pb treatment). Meanwhile, only a small amount of Pb was translocated to shoots and the BCF of shoots and TF values of R. chinensis seedlings were less than 0.2 and 0.3, respectively (Pb treatment, Table 3). However, the BCF of roots values under T500 and T1000 treatments were 0.67 and 0.81, respectively.

3.3. Stoichiometric Patterns of C, N, and P

Variance analysis showed that there were no significant differences in terms of C concentration under different Pb treatments (Figure 1). However, under Pb stress, the mean N and P concentrations of the leaves, stems, and roots of R. chinensis seedlings were lower than those of the control (except for P in the stems in T500), decreasing by 2.4%–12.5% (leaf N), 32.9%–63.8% (stem N), 28.4%–41.6% (root N), 11.0%−13.7% (leaf P), −0.8%–8.3% (stem P), and 18.0%–22.2% (root P), respectively (Figure 1). The concentration of N and P in the different organs decreased in the order leaves > roots > stems. Under the stress of different concentrations of Pb, the C:N and C:P ratios of the organs of R. chinensis seedlings were higher, and the N:P ratio was lower (except for in the leaves) to varied extents compared to the control (Figure 1).

3.4. Concentration of the Elements in R. chinensis Plants

Overall, with the increase in the Pb concentration the concentrations of K, Mg, Ca, and Cu in R. chinensis seedlings gradually decreased, while under the stress of a low concentration of Pb, the concentrations of Mg and Ca in leaves increased compared with control (Figure 2). In addition, the concentrations of S and Fe increased first and then decreased with the increase in the Pb concentration (except for in the leaves, Figure 2). The concentration of Zn in the plants under the stress of different Pb concentrations was lower compared with that of the control, while the concentration of Mn increased with the increase in Pb concentration (Figure 2). Due to the reduction in biomass, the contents of elements in R. chinensis seedlings decreased significantly (except for Mn and Fe in the stems, Table S1). Under Pb stress, the TF values of N, P, K, and Cu increased while those of Fe and S decreased compared with the control. The TF values of C, Mg, Ca, Zn, and Mn increased first and then decreased with the increase in Pb concentration (Figure S1). However, analysis of variances showed that the TF value of each element had no significant difference between treatments (except for Zn). Under Pb stress, the homeostasis of the contents of the elements in leaves was maintained (except for Ca, Zn, and Pb, Figure S2). However, Pb stress caused the imbalanced uptake of different minerals in stems and roots (Figure S2).
The accumulation of elements in different types of organs showed diverse responses to Pb stress. Based on hierarchical clustering analysis (Figure 3), Mn and Pb were clustered into the first group, and they had the most similar distribution in all organs. In stems and roots, S and Fe were clustered into the second group, and other elements were clustered into the third group (except for C). In leaves, P, K, Cu, and Zn were clustered into the second group, and other elements were clustered into the third group. The elements in the first group exhibited an antagonistic relationship with the elements in the other two groups. Correlation analysis also indicated that Pb and Mn usually coordinated with each other in different organs, showing a significant positive correlation (p < 0.05, Figure 4). Pb had a significant positive correlation with the C:N and C:P ratios (except for in stems) and a negative correlation with N, K, Ca, and Cu (p < 0.05, except for Ca and Cu in leaves, Figure 4). Correlation analysis confirmed that biomass had a significant positive correlation with N and K and a significant negative correlation with Pb, Mn, and the C:N ratio (Figure 4). Stem biomass had a significant positive correlation with Ca, Cu, and the N:P ratio. There was a significant negative correlation between root biomass and the C:P ratio (Figure 4).

3.5. Changes of Ionomic Profiles in Different Types of Organs of R. chinensis Plants under Pb Stress

PCA was used to reveal the effects of different treatments on the ionomic profiles of different types of organs of R. chinensis seedlings (Figure 5). The ionomic profiles of the roots, stems, and leaves of seedlings were significantly disturbed under the stress of different Pb concentrations. The cumulative contributions of the first principal component (PC1) and the second principal component (PC2) reached 74.0%. PC1 and PC2 clearly distinguished three types of organ samples (leaves, stems, and roots), and the samples treated with different concentrations of Pb were also clearly distinguished in the principal components. The ionomic data of stems and roots were more scattered compared to the ionomic data of leaves, indicating that Pb stress interfered more with the ionomes of stems and roots than with the ionome of leaves. Among the 12 elements, N, Mg, K, Cu, Ca, and Zn had greater contributions to PC1, while Pb, S, and Fe had greater contributions to PC2. The stoichiometric pattern had a greater contribution to PC1. For the ionomic profile of leaves, the data of the treatment groups were distinctly separated from the data of the control group. PC1 and PC2 explained 40.9% and 22.7% of the total variation, respectively (Figure S3). For the ionomic profile of stems, PC1 and PC2 explained 60.9% and 16.2% of the total variation, respectively (Figure S3). For the ionomic profile of roots, PC1 and PC2 explained 49.6% and 20.7% of the total variation, respectively (Figure S3). The level of Pb interference with the ionomic profiles of different organs was in the following order: stem > root > leaf.
PLS-PM was used to evaluate the effects of Pb stress, macro and micro elements, and the stoichiometric pattern on the biomass of R. chinensis seedlings in different organs (Figure 6). PLS-PM is a type of structural equation modeling that can be used to construct an interactive network. The constructed model indicated that Pb stress only had a significant negative effect on leaf biomass (path coefficient = −0.974, R2 = 0.95). In addition, Pb stress showed strong negative effects on both macro and micro elements (except for the micro elements in leaves). Pb stress also imposed indirect, weak, and negative effects on leaf biomass through affecting elements and imposed indirect, weak, and positive effects on the biomass of stems and roots. In addition, Pb stress positively regulated the stoichiometric pattern and imposed indirect, weak, and positive effects on biomass through affecting the stoichiometric pattern.

4. Discussion

4.1. Change of the Ionome of R. chinensis Plants under Pb Stress

One of the ways in which heavy metal elements harm plants is by damaging the uptake of nutrients, which disrupts the balance of nutrients and metabolism in plants [23,33]. Plants may adapt to heavy metal stress by changing the composition and content of the elements in the ionome [14]. Studies have shown that plant ionomic profiles are jointly determined by genetic factors and environmental conditions. In the present study, PCA showed that PC1 and PC2 distinguished the samples of different organ types and explained 74.0% of the total variation, indicating that the variation of the ionome of R. chinensis seedlings was mainly determined by genetic factors. Further division of the contributions of the ionomic profiles of different organs indicated that the ionome of stems and roots was more susceptible to the interference of Pb stress, while the leaf ionome was relatively stable. The ionome of different types of organs differed in the response to environmental factors, which might have been due to the varied physiological functions and active states of different organs [14]. This phenomenon was also found in C. sinensis [34] and Vicia faba [35].
The main elements contributing to ionomic variations were N, K, Ca, Cu, Mn, and Pb in roots; N, K, Mg, Ca, Zn, Cu, Mn, and Pb in stems; and N, K, Mn, and Pb in leaves (Figure S3). Under the stress of low concentrations of Pb, the concentration of N (in roots and stems) and Zn (in leaves and roots) decreased by more than 20% compared with the control. However, the concentrations of Mg and Ca in leaves increased by 12.7% and 13.4%, respectively, compared with control (Figure 2). This might be because Pb stress reduced biomass but did not prevent the roots from absorbing and transferring nutrient elements, which caused the accumulation of nutrient elements. Under the stress of high concentrations of Pb, the concentrations of N, P, K, Ca, and Cu in roots; N, Mg, S, Ca, and Zn in stems; and Ca in leaves decreased by more than 20% compared with the control. This phenomenon of a decrease in the concentration of nutrient elements in plants under heavy metal stress has been observed in Zea mays [21] and Oryza sativa [36]. These results also indicated that the stress of low concentrations of Pb mainly disturbed the ionic balance, while the stress of high concentrations of Pb not only disturbed the ionic balance but also inhibited the absorption of minerals. This phenomenon is also found in Vallisneria natans [25]. Under the stress of low concentrations of Pb, the TF value of each element (except for S and Fe) was higher than that of the control, while under the stress of high concentrations of Pb, only the TF values of N, P, K, and Cu were higher than that of the control. This result indicated that under the stress of low concentrations of Pb, the roots of R. chinensis seedlings were able to actively uptake and accumulate a large amount of minerals and transport them to aboveground parts to maintain growth, showing a certain level of resistance to Pb stress. Those results also indicated that there were different mechanisms controlling the response of the ionomic profile under the stress of different Pb concentrations.
Compared with the results from previous short-term and hydroponic experiments [29,30], in this study, the concentration of macro elements in the root system was significantly lower under the stress of high concentrations of Pb, which might have been due to differences in the experimental conditions and the experimental duration. The results also indicated that long-term Pb stress harmed plants by damaging their physiological functions, resulting in a decline in their ability to uptake macro elements. In addition, the uptake of essential nutrient elements by plants is usually completed by specific carriers [20]. Under heavy metal stress, heavy metal ions compete with essential elements for binding sites on the carriers, thereby reducing the uptake of essential elements [9,37,38]. In addition, the concentration of Mn in the organs was higher compared with that of the control. C. sinensis also showed similar performance under F stress [22]. In contrast, in our previous hydroponic experiment, the concentration of Mn in leaves was gradually decreased [29]. Mn is a key element in plant development and an enzyme cofactor in various biochemical pathways. In addition, Mn helps to reduce the toxicity of heavy metals [39]. The results also indicated that the balance of ions in the plants under the stress of high concentrations of Pb was heavily disturbed, but the plants were still able to retain minerals that helped to reduce heavy metal stress damage to maintain life activities [40].
The mean leaf N content of the control in this experiment was 23.2 mg g−1, which was higher than the geometric mean of leaf N content of 129 deciduous tree species in China (22.2 mg g−1) [41]. The mean leaf P content (1.41 mg g−1) was higher than the geometric mean of leaf P content of 189 deciduous tree species in China (1.30 mg g−1) [41]. In this study, the leaf C:N ratio of the control was 17.8, significantly lower than the 22.5 of plants on a global scale, and the C:P ratio was 293.2, higher than the 232.0 of plants on a global scale [42]. Under the stress of different Pb concentrations, the leaf C:N and C:P ratios of R. chinensis seedlings were higher than those of the control. These results indicated that the nutrient utilization strategies of R. chinensis were different from those of the control under Pb stress. In this study, the leaf N:P mass ratio of R. chinensis was greater than 16, indicating that the growth of R. chinensis is mainly limited by P [43].

4.2. Major Factors Regulating Biomass

PCA is one of the most widely used dimensionality reduction algorithms, but the correlation information it relies on may limit research results [27]. In contrast, the structural equation modeling approach may obtain deeper insight into the causality [27]. This approach was used in this study and could lead to a better understanding of the Pb phytotoxicity-related process. Generally, a high concentration of Pb in plants weakens photosynthesis, reduces the activity of intracellular enzymes, and damages cell membranes, leading to retarded development, hindered root growth, and reduced biomass [44]. This phenomenon was also found in R. chinensis [29,30]. For example, Zhou et al. reported that lead treatment significantly changed the R. chinensis cell structure of the root tip [30]. According to the results of the PLS-PM, the present study further indicated that Pb had a direct and negative effect on biomass, especially the biomass of leaves. Moreover, in the present study, the plant ionome was strongly affected by Pb stress, which indirectly affected the biomass of stems and roots by negatively regulating macro and micro elements. These results may have been found because the reduction of the macro element content in stems and roots caused by Pb stress made the plants unable to obtain sufficient nutrients, which led to the disordered synthesis and metabolism of important substances, such as proteins and nucleic acids [45,46,47]. The reduction of or increase in micro elements, important components of plant growth and metabolism [23], may have disrupted the balance of ions and also reduced biomass. PLS-PM analysis showed that Pb stress had an indirect, weak, and negative effect on leaf biomass by affecting mineral element content. PCA also indicated that the leaf ionome was relatively stable under Pb stress. Further analysis showed that the indirect effect of Pb stress on biomass by affecting the ionome was very weak. This might have been because the plants changed their root structure to actively absorb and accumulate minerals so as to maintain the balance of the elements under Pb stress [48]. PLS-PM analysis showed that Pb stress had a significant positive effect on the stoichiometric pattern. The C:N and C:P ratios indicate the ability of plants to assimilate C while uptaking N and P, which reflects the nutrient use efficiency of plants [33]. In the present study, the C:N and C:P ratios of the leaves in the treatment groups were higher than those of the control, which indicated that R. chinensis plants could improve their tolerance to the poisoning caused by heavy metal elements by adjusting their stoichiometric pattern [31]. Therefore, the addition of an appropriate amount of compound fertilizer can be applied to mitigate the toxic effects of Pb [49,50]. The results described above indicate that the relationship between Pb, mineral elements, and the stoichiometric pattern is highly complicated and needs to be studied further at the molecular level.

5. Conclusions

Under Pb stress, changes in the ionome of R. chinensis seedlings occurred and varied with organ types. The ionomic profile of different types of organs differed. The ionome of stems and roots was the most sensitive to Pb, followed by the ionome of leaves. For each type of organ, the distinct change in the ionomic profile was caused by Pb treatments. The biomass of R. chinensis seedlings was closely correlated with macro elements and divalent cations, such as Mn, Ca, and Cu. PLS-PM analysis showed that the biomass of R. chinensis seedlings was mainly affected by the direct poisoning effect of Pb and was indirectly affected by the weak effect of ionomic change and the stoichiometric pattern.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14030528/s1, Figure S1: The translocation factor (TF) of elements in R. chinensis under different treatments; Figure S2: Balance (%) of ion groups in leaf, stem and root of R. chinensis; Figure S3: Principal component analysis of the ionomics in leaf (A), stem (B) and root (C) for R. chinensis; Table S1: The elements content (mg plant−1) in R. chinensis under different treatments.

Author Contributions

W.H.: Investigation, formal analysis, original draft preparation. S.W.: Methodology. Y.W. and M.L.: Reviewing and Editing. X.S.: Conceptualization, Methodology, funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Funds of China (No. 31870583).

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare that they have no conflict of interest.

References

  1. Zulfiqar, U.; Farooq, M.; Hussain, S.; Maqsood, M.; Hussain, M.; Ishfaq, M.; Anjum, M.Z. Lead toxicity in plants: Impacts and remediation. J. Environ. Manag. 2019, 250, 109557. [Google Scholar] [CrossRef]
  2. Ferrer, M.A.; Cimini, S.; López-Orenes, A.; Calderón, A.A.; De Gara, L. Differential Pb tolerance in metallicolous and non-metallicolous Zygophyllum fabago populations involves the strengthening of the antioxidative pathways. Environ. Exp. Bot. 2018, 150, 141–151. [Google Scholar] [CrossRef]
  3. Gupta, D.K.; Huang, H.G.; Corpas, F.J. Lead tolerance in plants: Strategies for phytoremediation. Environ. Sci. Pollut. Res. 2013, 20, 2150–2161. [Google Scholar] [CrossRef]
  4. Hanumanth Kumar, G.; Pramoda Kumari, J. Heavy metal lead influative toxicity and its assessment in phytoremediating plants—A review. Water Air Soil Pollut. 2015, 226, 324. [Google Scholar] [CrossRef]
  5. Zeng, P.; Guo, Z.; Xiao, X.; Peng, C.; Liu, L.; Yan, D.; He, Y. Physiological stress responses, mineral element uptake and phytoremediation potential of Morus alba L. in cadmium-contaminated soil. Ecotoxicol. Environ. Saf. 2020, 189, 109973. [Google Scholar] [CrossRef]
  6. Yang, W.; Liu, D.; Wang, Y.; Hussain, B.; Zhao, F.; Ding, Z.; Yang, X.; Dawood, M. Variations in phytoremediation potential and phytoavailability of heavy metals in different Salix genotypes subjected to seasonal flooding. J. Environ. Manag. 2021, 299, 113632. [Google Scholar] [CrossRef]
  7. Cai, X.; Fu, J.; Li, X.; Peng, L.; Yang, L.; Liang, Y.; Jiang, M.; Ma, J.; Sun, L.; Guo, B.; et al. Low-molecular-weight organic acid-mediated tolerance and Pb accumulation in centipede grass under Pb stress. Ecotoxicol. Environ. Saf. 2022, 241, 113755. [Google Scholar] [CrossRef]
  8. Wang, S.; Volk, T.A.; Xu, J. Variability in growth and cadmium accumulation capacity among willow hybrids and their parents: Implications for yield-based selection of Cd-efficient cultivars. J. Environ. Manag. 2021, 299, 113643. [Google Scholar] [CrossRef]
  9. Feng, X.; Han, L.; Chao, D.; Liu, Y.; Zhang, Y.; Wang, R.; Guo, J.; Feng, R.; Xu, Y.; Ding, Y.; et al. Ionomic and transcriptomic analysis provides new insight into the distribution and transport of cadmium and arsenic in rice. J. Hazard. Mater. 2017, 331, 246–256. [Google Scholar] [CrossRef] [Green Version]
  10. Leitenmaier, B.; Küpper, H. Compartmentation and complexation of metals in hyperaccumulator plants. Front. Plant. Sci. 2013, 4, 374. [Google Scholar] [CrossRef] [Green Version]
  11. Qin, X.; Xia, Y.; Hu, C.; Yu, M.; Shabala, S.; Wu, S.; Tam, Q.; Xu, S.; Sun, X. Ionomics analysis provides new insights into the co-enrichment of cadmium and zinc in wheat grains. Ecotoxicol. Environ. Saf. 2021, 223, 112623. [Google Scholar] [CrossRef]
  12. Zhang, Y.; Wang, Y.; Ding, Z.; Wang, H.; Song, L.; Jia, S.; Ma, D. Zinc stress affects ionome and metabolome in tea plants. Plant Physiol. Biochem. 2017, 111, 318–328. [Google Scholar] [CrossRef]
  13. Huang, X.Y.; Salt, D.E. Plant Ionomics: From elemental profiling to environmental adaptation. Mol. Plant 2016, 9, 787–797. [Google Scholar] [CrossRef] [Green Version]
  14. Baxter, I.; Dilkes, B.P. Elemental profiles reflect plant adaptations to the environment. Science 2012, 336, 1661–1663. [Google Scholar] [CrossRef] [Green Version]
  15. Baxter, I. Should we treat the ionome as a combination of individual elements, or should we be deriving novel combined traits? J. Exp. Bot. 2015, 66, 2127–2131. [Google Scholar] [CrossRef] [Green Version]
  16. Filipiak, M.; Filipiak, Z.M. Application of ionomics and ecological stoichiometry in conservation biology: Nutrient demand and supply in a changing environment. Biol. Conserv. 2022, 272, 109622. [Google Scholar] [CrossRef]
  17. Fernández-Martínez, M. From atoms to ecosystems: Elementome diversity meets ecosystem functioning. New Phytol. 2022, 234, 35–42. [Google Scholar] [CrossRef]
  18. Mleczek, M.; Gąsecka, M.; Waliszewska, B.; Magdziak, Z.; Szostek, M.; Rutkowski, P.; Kaniuczak, J.; Zborowska, M.; Budzyńska, S.; Mleczek, P.; et al. Salix viminalis L.—A highly effective plant in phytoextraction of elements. Chemosphere 2018, 212, 67–78. [Google Scholar] [CrossRef]
  19. Fan, W.; Xia, Z.; Liu, C.; Ma, S.; Liu, S.; Wu, Y.; Zhu, B.; Xu, C.; Zhao, A. Ionomics, transcriptomics and untargeted metabolomics analyses provide new insights into the Cd response and accumulation mechanisms of mulberry. Environ. Exp. Bot. 2022, 196, 104821. [Google Scholar] [CrossRef]
  20. Huang, Q.; Wang, Y.; Qin, X.; Zhao, L.; Liang, X.; Sun, Y.; Xu, Y. Soil application of manganese sulfate effectively reduces Cd bioavailability in Cd-contaminated soil and Cd translocation and accumulation in wheat. Sci. Total Environ. 2022, 814, 152765. [Google Scholar] [CrossRef]
  21. Mišúthová, A.; Slováková, Ľ.; Kollárová, K.; Vaculík, M. Effect of silicon on root growth, ionomics and antioxidant performance of maize roots exposed to As toxicity. Plant Physiol. Biochem. 2021, 168, 155–166. [Google Scholar] [CrossRef] [PubMed]
  22. Peng, C.; Xu, X.; Zhu, H.; Ren, Y.; Niu, H.; Hou, R.; Wan, X.; Cai, H. Metabolics and ionomics responses of tea leaves (Camellia sinensis (L.) O. Kuntze) to fluoride stress. Plant Physiol. Biochem. 2021, 158, 65–75. [Google Scholar] [CrossRef] [PubMed]
  23. Rahman, S.U.; Nawaz, M.F.; Gul, S.; Yasin, G.; Hussain, B.; Li, Y.; Chen, H. State-of-the-art OMICS strategies against toxic effects of heavy metals in plants: A review. Ecotoxicol. Environ. Saf. 2022, 242, 113952. [Google Scholar] [CrossRef]
  24. Cseh, E.; Fodor, F.; Varga, A.; Záray, G. Effect of lead treatment on the distribution of essential elements in cucumber. J. Plant Nutr. 2000, 23, 1095–1105. [Google Scholar] [CrossRef]
  25. Wang, C.; Lu, J.; Zhang, S.; Wang, P.; Hou, J.; Qian, J. Effects of Pb stress on nutrient uptake and secondary metabolism in submerged macrophyte Vallisneria natans. Ecotoxicol. Environ. Saf. 2011, 74, 1297–1303. [Google Scholar] [CrossRef] [PubMed]
  26. Kısa, D.; Öztürk, L.; Doker, S.; Gökçe, İ. Expression analysis of metallothioneins and mineral contents in tomato (Lycopersicon esculentum) under heavy metal stress. J. Sci. Food Agric. 2017, 97, 1916–1923. [Google Scholar] [CrossRef]
  27. Mondaca, P.; Valenzuela, P.; Quiroz, W.; Valdenegro, M.; Abades, S.; Celis-Diez, J.L. Environmental conditions and plant physiology modulate Cu phytotoxicity in field-contaminated soils. Ecotoxicol. Environ. Saf. 2022, 246, 114179. [Google Scholar] [CrossRef]
  28. Shi, X.; Chen, Y.T.; Wang, S.F.; Li, J.C. Pb, Zn accumulation and nutrient uptake of 15 plant species grown in abandoned mine tailings. Environ. Sci. 2012, 33, 2021–2027. [Google Scholar]
  29. Shi, X.; Wang, S.; Wang, D.; Sun, H.; Chen, Y.; Liu, J.; Jiang, Z. Woody species Rhus chinensis Mill. seedlings tolerance to Pb: Physiological and biochemical response. J. Environ. Sci. 2019, 78, 63–73. [Google Scholar] [CrossRef]
  30. Zhou, C.; Huang, M.; Ren, H.; Yu, J.; Wu, J.; Ma, X. Bioaccumulation and detoxification mechanisms for lead uptake identified in Rhus chinensis Mill. seedlings. Ecotoxicol. Environ. Saf. 2017, 142, 59–68. [Google Scholar] [CrossRef]
  31. Cao, Y.; Ma, C.; Tong, R.; Chen, J.; Li, X.; Chen, G. Flooding alleviates copper stress on Salix: Evidence from stoichiometric patterns among plant tissues. Plant Soil 2022, 478, 545–558. [Google Scholar] [CrossRef]
  32. Li, Y.; Wang, Y.; Khan, M.A.; Luo, W.; Xiang, Z.; Xu, W.; Zhong, B.; Ma, J.; Ye, Z.; Zhu, Y.; et al. Effect of plant extracts and citric acid on phytoremediation of metal-contaminated soil. Ecotoxicol. Environ. Saf. 2021, 211, 111902. [Google Scholar] [CrossRef] [PubMed]
  33. Monaci, F.; Ancora, S.; Paoli, L.; Loppi, S.; Franzaring, J. Differential elemental stoichiometry of two Mediterranean evergreen woody plants over a geochemically heterogeneous area. Perspect. Plant Ecol. 2022, 55, 125672. [Google Scholar] [CrossRef]
  34. Yamashita, H.; Fukuda, Y.; Yonezawa, S.; Morita, A.; Ikka, T. Tissue ionome response to rhizosphere pH and aluminum in tea plants (Camellia sinensis L.), a species adapted to acidic soils. Plant-Environ. Interact. 2020, 1, 152–164. [Google Scholar] [CrossRef]
  35. Lai, J.; Liu, Z.; Luo, X. A metabolomic, transcriptomic profiling, and mineral nutrient metabolism study of the phytotoxicity mechanism of uranium. J. Hazard. Mater. 2020, 386, 121437. [Google Scholar] [CrossRef] [PubMed]
  36. Raghuvanshi, R.; Raut, V.V.; Pandey, M.; Jeyakumar, S.; Verulkar, S.; Suprasanna, P.; Srivastava, K.A. Arsenic and cadmium induced macronutrient deficiencies trigger contrasting gene expression changes in rice. Environ. Pollut. 2022, 300, 118923. [Google Scholar] [CrossRef]
  37. Qin, S.; Liu, H.; Nie, Z.; Rengel, Z.; Gao, W.; Li, C.; Zhao, P. Toxicity of cadmium and its competition with mineral nutrients for uptake by plants: A review. Pedosphere 2020, 30, 168–180. [Google Scholar] [CrossRef]
  38. Borišev, M.; Pajević, S.; Nikolić, N.; Orlović, S.; Župunski, M.; Pilipović, A.; Kebert, M. Magnesium and iron deficiencies alter Cd accumulation in Salix viminalis L. Int. J. Phytorem. 2016, 18, 164–170. [Google Scholar] [CrossRef]
  39. Wang, Y.; Xu, Y.; Liang, X.; Wang, L.; Sun, Y.; Huang, Q.; Qin, X.; Zhao, L. Soil application of manganese sulfate could reduce wheat Cd accumulation in Cd contaminated soil by the modulation of the key tissues and ionomic of wheat. Sci. Total Environ. 2021, 770, 145328. [Google Scholar] [CrossRef]
  40. Upadhyay, R.; Panda, S.K. Zinc reduces copper toxicity induced oxidative stress by promoting antioxidant defense in freshly grown aquatic duckweed Spirodela polyrhiza L. J. Hazard. Mater. 2010, 175, 1081–1084. [Google Scholar] [CrossRef]
  41. Han, W.; Fang, J.; Guo, D.; Zhang, Y. Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytol. 2005, 168, 377–385. [Google Scholar] [CrossRef] [PubMed]
  42. Elser, J.J.; Fagan, W.F.; Denno, R.F.; Dobberfuhl, D.R.; Folarin, A.; Huberty, A.; Interlandi, S.; Kilham, S.S.; McCauley, E.; Schulz, K.L.; et al. Nutritional constraints in terrestrial and freshwater food webs. Nature 2000, 408, 578–580. [Google Scholar] [CrossRef] [PubMed]
  43. Koerselman, W.; Meuleman, A.F. The vegetation N: P ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 1996, 33, 1441–1450. [Google Scholar] [CrossRef]
  44. Collin, S.; Baskar, A.; Geevarghese, D.M.; Ali, M.N.V.S.; Bahubali, P.; Choudhary, R.; Lvov, V.; Tovar, G.I.; Senatov, F.; Koppala, S.; et al. Bioaccumulation of lead (Pb) and its effects in plants: A review. J. Hazard. Mater. Lett. 2022, 3, 100064. [Google Scholar] [CrossRef]
  45. Muratore, C.; Espen, L.; Prinsi, B. Nitrogen uptake in plants: The plasma membrane root transport systems from a physiological and proteomic perspective. Plants 2021, 10, 681. [Google Scholar] [CrossRef]
  46. Chen, X.; Liu, Y.; Zhao, Q.; Cao, W.; Chen, X.; Zou, C. Health risk assessment associated with heavy metal accumulation in wheat after long-term phosphorus fertilizer application. Environ. Pollut. 2020, 262, 114348. [Google Scholar] [CrossRef]
  47. de Bang, T.C.; Husted, S.; Laursen, K.H.; Persson, D.P.; Schjoerring, J.K. The molecular-physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants. New Phytol. 2021, 229, 2446–2469. [Google Scholar] [CrossRef]
  48. Wang, S.; Shi, X.; Sun, H.; Chen, Y.; Pan, H.; Yang, X.; Rafiq, T. Variations in metal tolerance and accumulation in three hydroponically cultivated varieties of Salix integra treated with lead. PLoS ONE 2014, 9, e108568. [Google Scholar] [CrossRef]
  49. Eissa, M.A.; Roshdy, N.M.K. Nitrogen fertilization: Effect on Cd-phytoextraction by the halophytic plant quail bush [Atriplex lentiformis (Torr.) S. Wats]. S. Afr. J. Bot. 2018, 115, 126–131. [Google Scholar] [CrossRef]
  50. Kerdsomboon, K.; Techo, T.; Limcharoensuk, T.; Tatip, S.; Auesukaree, C. Low phosphate mitigates cadmium-induced oxidative stress in Saccharomyces cerevisiae by enhancing endogenous antioxidant defence system. Environ. Microbiol. 2022, 24, 707–720. [Google Scholar] [CrossRef]
Forests 14 00528 g001 550
Figure 1. The N, P, and K concentration and their stoichiometric pattern in R. chinensis under different treatments. Each value represents the mean of three replicates ± SE. Different letters indicate significant difference between the treatments derived from the same organ (p < 0.05).
Figure 1. The N, P, and K concentration and their stoichiometric pattern in R. chinensis under different treatments. Each value represents the mean of three replicates ± SE. Different letters indicate significant difference between the treatments derived from the same organ (p < 0.05).
Forests 14 00528 g001
Forests 14 00528 g002 550
Figure 2. The element concentration in R. chinensis under different treatments. Each value represents the mean of three replicates ± SE. Different letters indicate significant difference between the treatments derived from the same organ (p < 0.05).
Figure 2. The element concentration in R. chinensis under different treatments. Each value represents the mean of three replicates ± SE. Different letters indicate significant difference between the treatments derived from the same organ (p < 0.05).
Forests 14 00528 g002
Forests 14 00528 g003 550
Figure 3. Clustered heatmap of averaged element concentrations (rows) in R. chinensis under different treatments (columns).
Figure 3. Clustered heatmap of averaged element concentrations (rows) in R. chinensis under different treatments (columns).
Forests 14 00528 g003
Forests 14 00528 g004 550
Figure 4. Mantel test of plant biomass and element concentration. The distance matrix of the element concentration was calculated based on Euclidean distance, and the plant biomass was based on Bray–Curtis distance. The color and width of the edge denote statistical significance and Mantel’s r-statistic for the corresponding distance correlation.
Figure 4. Mantel test of plant biomass and element concentration. The distance matrix of the element concentration was calculated based on Euclidean distance, and the plant biomass was based on Bray–Curtis distance. The color and width of the edge denote statistical significance and Mantel’s r-statistic for the corresponding distance correlation.
Forests 14 00528 g004
Forests 14 00528 g005 550
Figure 5. Principal component analysis of ion contents and stoichiometric pattern in different organs of R. chinensis. Clusters of the organs (ellipses significance = 0.95). The lines originating from central point of biplots indicate positive or negative correlations of different variables.
Figure 5. Principal component analysis of ion contents and stoichiometric pattern in different organs of R. chinensis. Clusters of the organs (ellipses significance = 0.95). The lines originating from central point of biplots indicate positive or negative correlations of different variables.
Forests 14 00528 g005
Forests 14 00528 g006 550
Figure 6. Partial least-squares path modeling (PLS−PM) of Pb effects on the plant elements, plant stoichiometry and biomass production in leaf, stem, and root of R. chinensis. Measured variables (Pb treatment) are indicated by rounded rectangles and potential variables (macro element, micro element, stoichiometric ratio, biomass) are indicated by ovals. Black and red arrows indicate positive and negative effects, respectively, and the width of the line indicates the intensity of the direct effect. The numbers near the solid and dashed arrows indicate significant and nonsignificant normalized pathways, respectively, and the numbers in parentheses indicate the path coefficients of indirect effects. Goodness of fit (GoF) statistical superiority was used to evaluate the model; the GoF for leaf, stem, and root were 0.63, 0.80, and 0.77, respectively.
Figure 6. Partial least-squares path modeling (PLS−PM) of Pb effects on the plant elements, plant stoichiometry and biomass production in leaf, stem, and root of R. chinensis. Measured variables (Pb treatment) are indicated by rounded rectangles and potential variables (macro element, micro element, stoichiometric ratio, biomass) are indicated by ovals. Black and red arrows indicate positive and negative effects, respectively, and the width of the line indicates the intensity of the direct effect. The numbers near the solid and dashed arrows indicate significant and nonsignificant normalized pathways, respectively, and the numbers in parentheses indicate the path coefficients of indirect effects. Goodness of fit (GoF) statistical superiority was used to evaluate the model; the GoF for leaf, stem, and root were 0.63, 0.80, and 0.77, respectively.
Forests 14 00528 g006
Table
Table 1. Chemical characteristics of test soil. Each value represents the mean of three replicates ± SD. The same below.
Table 1. Chemical characteristics of test soil. Each value represents the mean of three replicates ± SD. The same below.
Pb/(mg kg−1)Available Pb/(mg kg−1)Total Pb
/(mg kg−1)		Hydrolysable
Nitrogen/(mg kg−1)		Available
Phosphorus/(mg kg−1)		Available
Potassium/(mg kg−1)		pH
024.0 ± 4.740.4 ± 1.156.3 ± 2.53.53 ± 0.06124.3 ± 0.65.59 ± 0.02
500254.7 ± 9.9409 ± 32.592.7 ± 2.15.62 ± 0.03130.0 ± 1.05.66 ± 0.02
1000630.3 ± 32.5867.7 ± 19.176.8 ± 1.05.51 ± 0.02135.0 ± 3.05.39 ± 0.02
Table
Table 2. Mean harvest dry weights (g) of the component parts, the reduction ratio, and the tolerance index (TI) in R. chinensis under different treatments. Each value represents the mean of three replicates ± SD; Different letters indicate significant difference between the treatments (p < 0.05).
Table 2. Mean harvest dry weights (g) of the component parts, the reduction ratio, and the tolerance index (TI) in R. chinensis under different treatments. Each value represents the mean of three replicates ± SD; Different letters indicate significant difference between the treatments (p < 0.05).
LeafReduction%StemReduction%RootReduction%TI
T02.75 ± 0.07 a 1.81 ± 0.04 a 1.82 ± 0.04 a
T5002.02 ± 0.22 b26.531.44 ± 0.15 b20.421.32 ± 0.13 b27.460.75
T10001.35 ± 0.16 c51.111.22 ± 0.14 b32.681.24 ± 0.21 b31.690.60
Table
Table 3. The Pb concentration (mg kg−1) and the bioconcentration factor (BCF) and translocation factor (TF) of Pb in R. chinensis under different treatments. Each value represents the mean of three replicates ± SD; Different letters indicate significant difference between the treatments (p < 0.05).
Table 3. The Pb concentration (mg kg−1) and the bioconcentration factor (BCF) and translocation factor (TF) of Pb in R. chinensis under different treatments. Each value represents the mean of three replicates ± SD; Different letters indicate significant difference between the treatments (p < 0.05).
T0T500T1000
Pb-Leaf14.71 ± 1.62 c50.84 ± 5.15 b64.70 ± 1.36 a
Pb-Stem7.23 ± 1.00 b78.22 ± 14.97 a86.67 ± 3.92 a
Pb-Root28.53 ± 1.72 c270.76 ± 11.34 b699.50 ± 51.10 a
BCF-shoot0.291 ± 0.028 a0.154 ± 0.021 b0.087 ± 0.003 c
BCF-root0.706 ± 0.043 b0.669 ± 0.028 b0.806 ± 0.059 a
TF0.411 ± 0.018 a0.230 ± 0.031 b0.108 ± 0.005 c
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

He, W.; Wang, S.; Wang, Y.; Lu, M.; Shi, X. Effect of Pb Stress on Ionome Variations and Biomass in Rhus chinensis Mill. Forests 2023, 14, 528. https://doi.org/10.3390/f14030528

AMA Style

He W, Wang S, Wang Y, Lu M, Shi X. Effect of Pb Stress on Ionome Variations and Biomass in Rhus chinensis Mill. Forests. 2023; 14(3):528. https://doi.org/10.3390/f14030528

Chicago/Turabian Style

He, Wenxiang, Shufeng Wang, Yangdong Wang, Mengzhu Lu, and Xiang Shi. 2023. "Effect of Pb Stress on Ionome Variations and Biomass in Rhus chinensis Mill" Forests 14, no. 3: 528. https://doi.org/10.3390/f14030528

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop