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Article

Exploratory Analysis on the Chemical Composition of Aquatic Macrophytes in a Water Reservoir—Rio de Janeiro, Brazil

by
Robinson Antonio Pitelli
1,*,
Rafael Plana Simões
2,*,
Robinson Luiz Pitelli
1,
Rinaldo José da Silva Rocha
3,
Angélica Maria Pitelli Merenda
1,
Felipe Pinheiro da Cruz
3,
Antônio Manoel Matta dos Santos Lameirão
3,
Arilson José de Oliveira Júnior
2 and
Ramon Hernany Martins Gomes
2
1
Ecosafe Agricultura e Meio Ambiente SS Ltda., 856 Rua Monteiro Lobato St., Jaboticabal 14870-410, SP, Brazil
2
School of Agriculture, São Paulo State University (UNESP), 3780 Universitária Avenue, Botucatu 18610-034, SP, Brazil
3
LIGHT Energia S.A., 168 Marechal Floriano Avenue, Rio de Janeiro 20080-002, RJ, Brazil
*
Authors to whom correspondence should be addressed.
Water 2025, 17(4), 582; https://doi.org/10.3390/w17040582
Submission received: 23 January 2025 / Revised: 13 February 2025 / Accepted: 14 February 2025 / Published: 18 February 2025
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Abstract

:
This study explores the chemical composition of different macrophyte species and infers their potential in extracting nutrients and some heavy metals from water as well as the use of macrophytes’ biomass as natural fertilizers. It used a dataset obtained from a previous study composed of 445 samples of chemical concentrations in the dried biomass of 16 macrophyte species collected from the Santana Reservoir in Rio de Janeiro, Brazil. Correlation tests, analysis of variance, and factor analysis of mixed data were performed to infer correspondences between the macrophyte species. The results showed that the macrophyte species can be grouped into three different clusters with significantly different profiles of chemical element concentrations (N, P, K+, Ca2+, Mg2+, S, B, Cu2+, Fe2+, Mn2+, Zn2+, Cr3+, Cd2+, Ni2+, Pb2+) in their biomass (factorial map from PCA). Most marginal macrophytes have a lower concentration of chemical elements (ANOVA p-value < 0.05). Submerged and floating macrophyte species presented a higher concentration of metallic and non-metallic chemical elements in their biomass (ANOVA p-value < 0.05), revealing their potential in phytoremediation and the removal of toxic compounds (such as heavy metal molecules) from water. A cluster of macrophyte species also exhibited high concentrations of macronutrients and micronutrients (ANOVA p-value < 0.05), indicating their potential for use as soil fertilizers. These results reveal that the plant’s location in the reservoir (marginal, floating, or submerged) is a relevant feature associated with macrophytes’ ability to remove chemical components from the water. The obtained results can contribute to planning the management of macrophyte species in large water reservoirs.

1. Introduction

Aquatic macrophytes are higher plants that have part of their tissue growing permanently or temporarily above or below the water surface [1]. These plants are crucial components of pristine aquatic ecosystems. However, in water bodies subjected to significant anthropogenic influence, some macrophyte populations may be favored over others, leading to extensive colonization and causing harmful interference in the multiple uses of the water and the water body [2]. The dense colonization of macrophytes can serve as a biological indicator of organic pollution and has been associated with adverse conditions in aquatic environments [3,4,5]. Studies have indicated that identifying the biological influences of these plants on aquatic ecosystems is highly complex [6,7]. This underscores the importance of researching to evaluate the role of macrophyte species in aquatic life [8].
Numerous studies have demonstrated that aquatic macrophytes play a mitigating role in extracting nutrients and heavy metals from the water through a process known as phytoremediation [3]. Macrophytes can degrade, remove, retain, or immobilize heavy metals, pesticides, excess mineral nutrients, and pharmaceuticals from the water [9,10]. However, much of this research has been conducted under controlled conditions in laboratories, greenhouses, or artificial floodplain systems [11,12]. There are also studies addressing mitigating actions against aquatic macrophytes, a topic of significant environmental and social importance that has garnered increasing interest within the scientific community. Most of this work focuses on tertiary systems for treating urban and industrial wastes [13,14].
In addition to their phytoremediation function, macrophytes have various applications, such as biomarkers, producers of valuable metabolites, or biomass sources for the production of feed, biofuels, pellets, or ceramics [15]. For instance, the use of freshwater macrophytes as dry biomass has shown significant benefits in different crops, promoting plant growth, increasing yield, reducing the use of chemical fertilizers, and decreasing the incidence of diseases [16]. Several studies have demonstrated that converting plant biomass into fertilizer and other bioproducts may support clean production and circular economy efforts [15,16,17].
Understanding the relationship between species composition and the phytoremediation effects on nutrients and heavy metals under natural reservoir conditions is highly relevant. The amount of accumulated nutrients depends on the physiological capacity for further uptake and biomass production of aquatic macrophytes, which varies among species and their phenotypic characteristics [18]. This study was carried out in Santana Reservoir (Piraí, RJ, Brazil), densely colonized with macrophytes, which are harvested and disposed of as organic waste in accordance with Brazilian legislation, using the argument of the presence of high concentrations of heavy metals, according to results obtained in macrophytes analysis of plants collected in artificial wetlands serving as tertiary treatment of polluted water. But, under natural conditions of lakes and reservoirs the accumulation of heavy metals is much lower, and the biomass can be used as fertilization for degraded soil recuperation and forest plantation [2].
This study explores the relationships between the chemical composition of macrophyte species in the Santana Reservoir, a eutrophic water reservoir in Brazil. The findings from this research can enhance our understanding of how the characteristics of various macrophyte species are associated with the absorption of chemical components in water, as well as inform management strategies and the utilization of these plants’ biomass.

2. Materials and Methods

2.1. Sample Collection and Analysis

The dataset utilized in this study comprises samples of macrophytes obtained from the Santana Reservoir, located in Piraí City, approximately 50 miles south of Rio de Janeiro (Piraí, RJ, Brazil: 43°49′08″ W, 22°31′56″ S). The reservoir hosts a large and highly diversified community of aquatic plants, particularly those with an emergent habitat [2]. In 2008, the Santana Reservoir was home to a community comprising 41 species of aquatic macrophytes. The families Poaceae, Pontederiaceae, and Cyperaceae exhibited the highest number of individuals throughout the year, with Salvinia herzogii and Egeria densa showing the highest colonization values [2,19]. The reservoir is fed by water pumped from the Paraíba do Sul River and the Piraí River, serving the purpose of transferring water from the Paraíba River to the Vigário Reservoir, which is located at a higher altitude and generates electric energy through a hydroelectric power plant.
The entire extent of the Santana Reservoir was surveyed using motorized boats. Aquatic plants were sampled and stored in plastic bags at three sites along the reservoir. The sampling procedure involved collecting plant parts, including leaves, flowers, stems, and roots (when feasible), simulating the mechanical harvesting that will compose the biomass that will be used as biofertilizers. The collected samples were transported to the laboratory, where they were rinsed with tap water to remove dirt, such as soil, organic matter, and fragments of other plants and algae. Subsequently, the plants were dried in an oven at 65 °C for five days or until they reached a constant weight and were then ground in a micromill. All materials were sent to a plant analysis laboratory for the assessment of macronutrients, micronutrients, and heavy metal content in their biomass. These collections took place between June 1998 and January 2018. A descriptive statistical analysis of the dataset used in this study has been previously published; however, the dataset has not been made public [2]. This study aims to re-evaluate these data using data mining techniques that have recently gained popularity for big data analysis [20]. These techniques facilitate the extraction of new information and interpretations from the data, thereby enabling the discovery of new scientific insights.
The dataset is based on the concentration values of 15 chemical elements: nitrogen (N), phosphorus (P), potassium (K+), calcium (Ca2+), magnesium (Mg2+), sulfur (S), boron (B), copper (Cu2+), iron (Fe2+), manganese (Mn2+), zinc (Zn2+), chromium (Cr3+), cadmium (Cd2+), nickel (Ni2+), and lead (Pb2+), from 16 different aquatic plant species: Urochloa subquadripara (BRASU), Echinochloa polystachya (ECHPO), Eichhornia azurea (EICAZ), Eichhornia crassipes (EICCR), Egeria densa (ELDDE), Enydra anagallis (ENYAN), Myriophyllum aquaticum (MYRAQ), Panicum rivulare (PANRI), Paspalum repens (PASRE), Pistia stratiotes (PIIST), Pontederia latifolia (POFCO), Polygonum ferrugineum (POLFE), Polygonum lapathifolium (POLLA), Sagittaria montevidensis (SAGMO), Salvinia auriculata (SAVAU), and Typha domingensis (TYHDO). The total number of samples was 445, but there was no uniformity in the number of samples for each evaluated species.

2.2. Statistical and Machine Learning Analysis

The data collected from samples were presented in the sequence, with each sample considered an instance within the dataset. The attributes of the dataset included the collection date and the concentration of chemical elements (as described in the previous section) are outlined in Table 1.
Descriptive analyses, including mean, median, standard deviation, and variance, as well as statistical inferences such as correlation tests and analysis of variance (ANOVA), were performed using R software (Version 3.5.1) and the Rcmdr library [21,22]. A p-value ≤ 0.05 was utilized to assess the significance of all statistical tests and inferences.
Principal component analysis (PCA), multiple correspondence analysis (MCA), and factor analysis of mixed data (FAMD) were conducted using R software and the FactoMineR library [23]. PCA and MCA are exploratory techniques suitable for large datasets. Their application results in graphical representations of the dataset in the same factorial plane (when using 2 principal components or dimensions) or in the same space (when using 3 principal components or dimensions). These techniques enable the inference of correspondence or association between data points based on Euclidean distances within these planes or spaces, resulting in groupings (or clusters) of corresponding information (attributes or instances) [24]. MCA is exclusively applied to qualitative or categorical data, while PCA is solely applied to continuous variables. For datasets containing both categorical and continuous data, factor analysis of mixed data (FAMD) can be employed [25]. In other words, factorial analyses can be applied as a dimensionality reduction tool, allowing datasets with many attributes (many variables) to be visualized in a two-dimensional plot, for example.
To compare the concentration of chemical elements across clusters of species identified from factorial analyses, each attribute value was normalized to a range between 0 and 1. Outlier instances were removed using the Quantile function from the Numpy library, implemented in the Python environment [26].

3. Results

The averages of nutrient and heavy metal concentrations (with standard deviation values) for each macrophyte species are shown in Table A1 and Table A2 (Appendix A). The results indicate that the macrophyte species evaluated in this study can be grouped into three distinct clusters (Figure 1).
The clusters obtained from PCA align with the phenotypic characteristics associated with nutrient absorption of the evaluated species. Clusterspecies1 (the most populated in this study) predominantly comprises marginal macrophyte species, with the exception of BRASU, which also exhibits epiphytic behavior. Clusterspecies2 predominantly includes floating species, except for ENYAN. Although ENYAN is classified as a marginal species, it has the capacity to float, which may explain its nutrient concentration profile similar to that of floating species. Floating macrophytes obtain nutrients from both the water and the riverbed/dam. Finally, Clusterspecies3 (the second most populated cluster) consists of marginal and submerged species. The instances representing marginal species in this cluster (PASRE, POLFE, SAGMO, SAVAU) are located close to Clusterspecies1 (which predominantly contains marginal species). However, these instances were grouped into a different cluster because they have relatively high concentrations of macro- and micronutrients, similar to the pattern observed for submerged species (ELDDE and MYRAQ) also allocated to Clusterspecies3.
The PCA variable map is shown in Figure 2. In this figure, the magnitude of the vector indicates the representativeness of the variable in the clustering and differentiation of the data presented in Figure 1. That is, the larger the vector magnitude, the greater the relevance of the variable in differentiating the macrophyte species. The direction and orientation of the vectors are associated with the similarity between variables, meaning that variables represented by vectors with similar direction and orientation are more likely to be positively correlated. The variable map shows that the groupings described in Figure 1 were defined by species with similar characteristics considering two different sets of chemical elements present in the plants. In other words, two groups of chemical composition allow for the differentiation (or discrimination) of the species under study. The first group predominantly comprises transition and post-transition metallic elements: Cd2+, Pb2+, Cu2+, P, Fe2+, Mn2+, Ni2+, Zn2+, and Cr3+, highlighted in blue in Figure 2. This group includes some heavy metals, such as Cd2+, Cu2+, Pb2+, Cr3+, and Ni2+, with Cd2+ and Pb2+ being particularly noted for their high toxicity to humans. These heavy metals are classified as “Class 1” elemental impurities in medicines according to the ICH-Q3D (International Council of Harmonization) guidelines. The second group is composed of alkaline, alkaline earth, and non-metallic elements: Ca2+, Mg2+, S, N, B, and K+, highlighted in green in Figure 2. Some of these elements, such as N and K+, are extremely important for soil fertilization (primary macronutrients) [27]. Finally, the attribute “month” (colored in red in Figure 2) did not show significant correspondence with the other attributes.
These results suggest that macrophyte species can potentially play an important role in removing chemical components from water, and there are differences in the nutrient absorption profiles among the different species studied. The violin plots in Figure 3 provide a better understanding of the nutrient concentration profile in each cluster shown in Figure 1. The violin plot combines the information of a box plot (quartile distribution within the plot) with a smoothed frequency distribution overlay (similar to histograms). These graphs are, therefore, excellent for representing and analyzing large datasets (population studies) and frequency distributions, which is the case for our dataset. However, we emphasize that mean values and standard deviations for each variable were provided in Table A1 and Table A2 (Appendix A). In this way, the violin plots illustrate the variance/distribution of the chemical composition of macrophytes, with the results grouped according to the chemical element clusters from Figure 2. In general, the results indicate that species in Clusterspecies2, predominantly consisting of floating macrophytes, presented a significantly higher concentration of alkaline, alkaline earth, and non-metallic elements compared to species in Clusterspecies1 (for all chemical elements) and species in Clusterspecies3 (for most chemical elements) (see Figure 3a and statistical inferences in Table 2). Additionally, species in Clusterspecies3 also presented higher concentrations of chemical elements in this class compared to species in Clusterspecies1, except for the macronutrients N and K+.
Finally, in the matrix of Spearman correlation (Figure 4a), it is still possible to observe that the variables (chemical elements) used to group the macrophyte species into clusters (factorial analysis) have a significant correlation, indicating agreement between the PCA and the correlation tests. These groupings’ positively correlated variables (Group 1 and Group 2) are highlighted in Figure 4b.

4. Discussion

Our results indicate that the species grouped in Clusterspecies2 may have great potential for their biomass to be used as fertilizer for agriculture since the elements in these chemical groups are frequently used for soil amendment, being divided into primary macronutrients (N, P, and K+) and secondary macronutrients (Ca2+, Mg2+, and S) [27,28]. Using aquatic macrophytes as organic fertilizer can be quite advantageous, as it can solve the problem of disposing of this material and restore nutrients to the soil [29]. Of all the species grouped in Clusterspecies2, PIIST stands out. The fact that it is further from the center of the factorial plane indicates that it has a different nutrient concentration profile compared to other species, with a higher concentration of macronutrients and micronutrients. This result is consistent with previous studies, which showed that the PIIST species can be used to enrich compost mixtures (straw), offering significant environmental and agricultural benefits [30,31,32].
The results presented in Table A1 provide support for and can be used in future studies on the use of macrophyte biomass for the preparation of nutrient-balanced fertilizers. This balancing can be achieved using techniques for solving linear systems of the form A·X = B. In this linear system, (i) matrix A corresponds to the average concentration values of elements in plant biomass (obtained directly from Table A1); (ii) vector B corresponds to the final chemical composition of the macrophyte mix (i.e., the desired balanced fertilizer); and (iii) vector X (the solution of the linear system) corresponds to the amounts of biomass of each species required to produce B. A popular application of this technique is in the study of nutrient balance in human and animal diets. Several examples of the use of this technique by different approaches are reported in the literature with extremely positive results and can be applied similarly to the production of fertilizers [33,34,35,36].
Considering all chemical elements evaluated in this study, it is also possible to observe that the macrophyte species in Clusterspecies1 presented lower concentration values. This result may indicate that the chemical elements in the water do not settle or accumulate in the marginal area. Additionally, these results denote that floating and submerged macrophyte species are more efficient than marginal species in removing chemical components from water, revealing the potential of these species for the phytoremediation process. These results are in agreement with a previous study in a controlled water system simulation that shows submerged and floating species have great potential for absorbing alkaline compounds from water [37]. Previous studies have also demonstrated that nutrient acquisition by root or shoot are the two dominant pathways for the growth of submerged and floating macrophytes, and experiments have shown that the nutrient absorption pathway can be altered by the nutrient levels in water [38]. Under an oligotrophic state, aquatic macrophytes mainly uptake nutrients from sediments during growth [38]. With nutrient enrichment, the relative contribution to nutrient absorption shifts away from roots and toward shoots [37]. These previous results, combined with our findings, allow us to infer that chemical compounds accumulate in higher concentrations at the bottom of water reservoirs than in marginal sediments.
Figure 3 and Table 2 show that the species in Clusterspecies3 have a different pattern of concentration of transition and post-transition metallic elements, with higher concentrations of nutrients than those in Clusterspecies1 (for all elements) and Clusterspecies2 (for the elements Fe2+, Mn2+, Cd2+, and Pb2+). This result reveals that the species in Clusterspecies3 have notable specificity for the absorption of transition and post-transition metallic elements. Of the species grouped in Clusterspecies3, ELDDE presented the highest relative concentration of metallic elements, which is consistent with previous studies reported in the literature [39]. Recent studies have also shown that ELDDE species have great potential for removing metals and heavy metals from water [40,41,42]. Another interesting fact is that submerged macrophyte species exhibited the highest concentration of metallic chemical elements. Indeed, previous studies have shown that certain macrophyte species (especially floating and submerged species) have great potential for removing heavy metals from aquatic reservoirs [43,44,45]. Evaluating exclusively the concentrations of heavy metals with more toxicity to the human body (Cd2+ and Pb2+), it can be observed that the species in Clusterspecies3 also presented higher concentrations. These results demonstrate the potential of Clusterspecies3 species in removing heavy metals from water and this is also consistent considering the fact that metallic elements tend to concentrate at the bottom of rivers [46].
Finally, Figure 2 also shows that the variable month was not relevant for discriminating the species under study. This finding occurs because species generally present a typical pattern of biomass variation throughout the year, which is typical for properties associated with climatic factors, as demonstrated in previous studies [47,48,49]. In fact, a Spearman correlation test (rank correlation) between the month and the other quantitative variables in this study (concentration of chemical elements) shows that the only variables correlated with the month (p-value ≤ 0.05) were the concentration of the chemical elements S, P, Fe2+, and Mn2+, but all of them with very low correlation coefficients. These results allow us to conclude that the plant’s location in the reservoir (marginal, floating, or submerged) is a more relevant feature than the month for removing chemical components from the reservoir.
It is important to clarify that the results presented here were obtained from an extensive reservoir (with an effective volume of approximately 11.55 hm3), and across different seasons, characterizing a sampling of environments and parameters with great diversity and variability, including water quality, contaminant concentration, water velocity/flow, depth, marginal vegetation, and climatic conditions, among others. Therefore, we believe that our results can represent general characteristics of chemical element concentrations in the different macrophyte species evaluated in this study. The discussions presented demonstrate that our findings are consistent with several other studies reported in the literature, as cited throughout this section. However, we acknowledge that the nutrient absorption and accumulation profiles of macrophyte species, as well as the prevailing species types, depend on environmental characteristics, including nutrient/chemical element concentrations in the water, as previously reported in the literature [50,51,52]. Thus, the chemical element concentration profiles of macrophyte species presented in this study may vary depending on environmental conditions.
As observed, macrophytes have a great ability to accumulate a variety of chemical elements in their biomass, including heavy metals, making them promising for the remediation of contaminated aquatic environments. However, the efficiency of this process depends not only on the plants but also on the microbial community present in their environment. Ijoma et al. (2024), in an extensive review of the role of macrophyte-associated microbial communities in bioremediation processes, discussed several studies indicating that the interaction between macrophytes and microorganisms, both endophytic and rhizospheric, can enhance their phytoremediation potential [53]. These microorganisms play essential roles, such as protecting plants against environmental stress and promoting their growth through the production of phytohormones and other bioactive substances.
In future studies, it will be important to consider the role of the microbiome in phytoremediation analyses to evaluate the individual impact of each variable—both plant-related and associated microorganisms. Analyzing the interactions between macrophytes and their microbiomes will allow for the identification of how these variables affect the efficiency of pollutant removal from water. Additionally, these studies can focus on identifying and designing optimal combinations of plant species and microorganisms to enhance phytoremediation processes, thereby developing more effective strategies for environmental decontamination. In this regard, predicting the limits and monitoring the environmental concentrations of contaminants are critical activities to maintain an ecosystem favorable to both macrophytes and the involved microorganisms, ensuring the effectiveness of phytoremediation processes. There are several standardized microbiological toxicity tests available to predict the toxicity of chemical compounds in aquatic systems. Recently, Strotmann et al. (2024) conducted an extensive review of these tests [54].
The results obtained in this study are significant because, in addition to helping to understand the process of absorption of compounds by macrophyte species, they can also provide support for strategies for managing and using the biomass from these species. It is worth highlighting that these results can be altered depending on climate patterns and the characteristics/chemical composition of the water where these species are collected. Additional studies must be carried out to verify whether the patterns identified in our results are repeated in environments with different characteristics. Considering the specific conditions of the Santana Reservoir, the dense submerged systems of aquatic macrophytes form a strong filtering network for suspended particles, which is paramount for water quality. However, literature data are not sufficient to determine a model for adequate management of macrophytes to optimize the phytoremediation process of the various species that make up macrophyte communities. This model must consider the significant differences in rates and times of plant growth, physiology, and tolerances to nutrient levels, in addition to water dynamics in the water circuit, which could be a topic for future studies [55].

5. Conclusions

The concentration profile of chemical compounds (N, P, K+, Ca2+, Mg2+, S, B, Cu2+, Fe2+, Mn2+, Zn2+, Cr3+, Cd2+, Ni2+, Pb2+) in dried biomass can be used to associate and discriminate different species of macrophytes from samples collected in a natural water reservoir.
The results showed that the macrophyte species can be grouped into three different clusters with significantly different profiles of chemical element concentrations in their biomass (factorial map from PCA).
The group predominantly formed by marginal macrophytes are those with the lowest concentration of chemical elements in their biomass (one-way ANOVA p-value < 0.05), and indirectly, this result allows us to infer that these chemical elements do not accumulate in relatively high quantities in the reservoir’s marginal sediments.
Submerged and floating macrophyte species have the highest concentration of metallic and non-metallic chemical elements in their biomass (one-way ANOVA p-value < 0.05), which reveals their essential role in phytoremediation and removal of toxic compounds in water reservoirs.
A cluster of macrophyte species exhibited high concentrations of macronutrients and micronutrients (one-way ANOVA p-value < 0.05), indicating their potential for use as soil fertilizers.
Additionally, the variable month cannot be used to discriminate the macrophyte species evaluated in this study. These results allow us to conclude that the plant’s location in the reservoir (marginal, floating, or submerged) is a more relevant feature than the variable month to evaluate the ability of macrophytes to remove chemical components from the water.

Author Contributions

Conceptualization, R.A.P., R.P.S., R.L.P. and A.M.P.M.; data curation, R.A.P., R.P.S., R.L.P., A.M.P.M. and R.H.M.G.; formal analysis, R.A.P. and R.P.S.; funding acquisition, R.A.P., R.J.d.S.R., A.M.M.d.S.L. and F.P.d.C.; investigation, R.A.P., R.P.S., R.L.P., A.M.P.M., A.J.d.O.J. and R.H.M.G.; methodology, R.A.P., R.P.S., R.L.P. and A.M.P.M.; project administration, R.A.P.; software, A.J.d.O.J.; supervision, R.A.P., R.J.d.S.R., A.M.M.d.S.L. and F.P.d.C.; validation, R.A.P. and R.P.S.; writing—original draft, R.A.P., R.P.S. and R.H.M.G.; writing—review and editing, R.A.P., R.P.S. and R.H.M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by resources regulated under the PDI ANEEL program, with project number PD 05161 0022/2022.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors due to privacy reasons.

Acknowledgments

The authors would like to thank the Brazilian Electricity Regulatory Agency (ANEEL) and its Research and Development Program for grant no. PD 05161 0022/202. The authors also thank Light Energia S.A. for the data and contributions.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Table A1. Average concentrations and standard deviation of chemical elements (N, P, K, Ca, Mg, S, and B) in the dried biomass of aquatic macrophytes.
Table A1. Average concentrations and standard deviation of chemical elements (N, P, K, Ca, Mg, S, and B) in the dried biomass of aquatic macrophytes.
SpeciesN (g/kg)P (g/kg)K (g/kg)Ca (g/kg)Mg (g/kg)S (g/kg)B (mg/kg)
BRASU24.1 ± 7.92.4 ± 0.844.3 ± 13.75.2 ± 3.92.7 ± 1.41.9 ± 0.55.5 ± 2
ECHPO13.2 ± 4.31.5 ± 0.646.6 ± 22.32.8 ± 1.12.2 ± 0.72.7 ± 0.8*
EICAZ21.1 ± 7.62.8 ± 0.964.3 ± 15.913.9 ± 4.23 ± 0.41.6 ± 0.618.7 ± 3.1
EICCR24.1 ± 5.82.4 ± 0.951.1 ± 11.920.5 ± 2.56.3 ± 0.72.6 ± 0.724.2 ± 5.8
ELDDE28.6 ± 6.74.1 ± 1.547.9 ± 20.615.9 ± 93.3 ± 1.12.9 ± 131.9 ± 5.9
ENYAN37.8 ± 1.63.2 ± 0.356.4 ± 0.721 ± 2.36.6 ± 0.83.2 ± 0.129 ± 4.7
MYRAQ24.5 ± 16.33.1 ± 1.244.9 ± 22.217.7 ± 5.23.9 ± 0.91.9 ± 0.7*
PANRI21.5 ± 8.12.4 ± 1.129.6 ± 12.72.2 ± 0.71.7 ± 0.42.1 ± 0.65.9 ± 2.5
PASRE24.2 ± 2.63.6 ± 176.2 ± 11.26.5 ± 23.7 ± 0.73.1 ± 0.1*
PIIST25.5 ± 5.22.8 ± 0.753.9 ± 13.734.8 ± 128.2 ± 22.8 ± 1.150.9 ± 13.6
POFCO20 ± 8.63.7 ± 1.149.8 ± 16.19.1 ± 2.42.3 ± 0.51.1 ± 0.2*
POLFE18.8 ± 5.52.6 ± 0.935.5 ± 15.215.5 ± 8.94.2 ± 12.2 ± 0.7*
POLLA26.8 ± 8.32.3 ± 0.949.3 ± 21.512.9 ± 4.24.5 ± 1.42 ± 0.524 ± 3.7
SAGMO19.1 ± 8.24.6 ± 1.466.3 ± 30.58.8 ± 3.13.2 ± 0.91.6 ± 0.7*
SAVAU21.7 ± 4.82.5 ± 0.820.4 ± 6.811.4 ± 4.93.8 ± 0.82.7 ± 0.542.2 ± 14.5
TYHDO16.7 ± 62.3 ± 0.838 ± 20.711.9 ± 1.93.3 ± 0.81.7 ± 0.7*
Note(s): * Unevaluated chemical element.
Table A2. Average concentrations and standard deviation of chemical elements (Cu, Fe, Mn, Zn, Cd, Ni, Cr, and Pb) in the dried biomass of aquatic macrophytes.
Table A2. Average concentrations and standard deviation of chemical elements (Cu, Fe, Mn, Zn, Cd, Ni, Cr, and Pb) in the dried biomass of aquatic macrophytes.
SpeciesCu (mg/kg)Fe (mg/kg)Mn (mg/kg)Zn (mg/kg)Cd (mg/kg)Ni (mg/kg)Cr (mg/kg)Pb (mg/kg)
BRASU14.8 ± 11.71455.4 ± 1963.9345.3 ± 450.967.2 ± 25.39.8 ± 9.75 ± 2.13.6 ± 3.515.5 ± 8.5
ECHPO27.4 ± 12.26433.9 ± 7343575.5 ± 602.376 ± 18.98.9 ± 10.19.7 ± 4.36.7 ± 6.427.1 ± 10.5
EICAZ25.7 ± 12.94153.7 ± 6347.1792.8 ± 455.3101 ± 35.18.7 ± 97.2 ± 4.23.6 ± 2.922.3 ± 9.9
EICCR14.9 ± 3.96062.9 ± 3886.22437.2 ± 2233.3175 ± 1221.9 ± 0.511.4 ± 59.3 ± 4.811 ± 5.2
ELDDE34.6 ± 119586.5 ± 8775.57968.7 ± 7291.4454.4 ± 293.732 ± 45.819.9 ± 7.910.4 ± 1329.2 ± 20.5
ENYAN21.3 ± 1.44659.3 ± 1766.91372.3 ± 480.8240 ± 31.61.2 ± 0.115.7 ± 1.812.8 ± 3.315.9 ± 1.8
MYRAQ30.1 ± 13.39004.3 ± 14,944.32039.3 ± 919.5127.5 ± 74.930.7 ± 57.712.5 ± 10.210.4 ± 11.137.6 ± 28.3
PANRI12.8 ± 11.31477.9 ± 1925255.8 ± 173.748.3 ± 17.412.2 ± 16.25.6 ± 3.25.8 ± 5.616.9 ± 11
PASRE39.5 ± 8.58400 ± 1449.61862.5 ± 506.9135.3 ± 32.513.8 ± 2.815.5 ± 2.32 ± 1.337.5 ± 8
PIIST18.2 ± 8.17054.2 ± 4021.91985.6 ± 1087.5183.6 ± 812.3 ± 2.514.3 ± 5.613.7 ± 415.6 ± 7.7
POFCO22.9 ± 10.51605.8 ± 13011401.1 ± 465.843.2 ± 19.810.7 ± 12.16.9 ± 45.2 ± 5.826.1 ± 17.2
POLFE28.4 ± 9.97857.7 ± 5709.41331.3 ± 726.9121.2 ± 43.927.4 ± 37.88.8 ± 58.2 ± 7.323.8 ± 10.3
POLLA15.7 ± 11.91585.9 ± 1911.8729.1 ± 569.293.8 ± 35.811.9 ± 20.45.7 ± 2.83.2 ± 2.217.4 ± 13.3
SAGMO28.4 ± 10.512,555.7 ± 12,360.51211 ± 562.5104.7 ± 40.815.8 ± 15.710.6 ± 85.8 ± 8.729.1 ± 15.5
SAVAU24.2 ± 6.618,513.7 ± 9903.92727.2 ± 1640169.8 ± 113.917.2 ± 34.718.2 ± 9.419.7 ± 12.624.3 ± 24.6
TYHDO28.4 ± 114745.7 ± 7746.71687.5 ± 1661.869.8 ± 40.932.5 ± 448.9 ± 5.91.5 ± 0.829.6 ± 18.8

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Water 17 00582 g001
Figure 1. Factorial map obtained using PCA grouping the instances by species. It was possible to determine three well-defined clusters, which were differentiated as follows: blue (Clusterspecies1), green (Clusterspecies2), and red (Clusterspecies3).
Figure 1. Factorial map obtained using PCA grouping the instances by species. It was possible to determine three well-defined clusters, which were differentiated as follows: blue (Clusterspecies1), green (Clusterspecies2), and red (Clusterspecies3).
Water 17 00582 g001
Water 17 00582 g002
Figure 2. Map of attributes (variables) for species discrimination obtained by PCA. The vectors module (represented by the size of each arrow) is associated with the representativeness of each attribute for discriminating the macrophyte species. The arrow colors indicate the cluster of attributes (or variables) to which it belongs. In this figure, for simplicity, metallic ions are represented solely by their corresponding chemical element symbols.
Figure 2. Map of attributes (variables) for species discrimination obtained by PCA. The vectors module (represented by the size of each arrow) is associated with the representativeness of each attribute for discriminating the macrophyte species. The arrow colors indicate the cluster of attributes (or variables) to which it belongs. In this figure, for simplicity, metallic ions are represented solely by their corresponding chemical element symbols.
Water 17 00582 g002
Water 17 00582 g003
Figure 3. Violin plots illustrating the distribution of normalized concentrations of chemical groups for the clustered macrophyte species (Clusterspecies1, Clusterspecies2, and Clusterspecies3): (a) alkaline metallic, alkaline earth, and non-metallic chemical elements; (b) transition and post-transition metallic chemical elements. In this figure, for simplicity, metallic ions are represented solely by their corresponding chemical element symbols.
Figure 3. Violin plots illustrating the distribution of normalized concentrations of chemical groups for the clustered macrophyte species (Clusterspecies1, Clusterspecies2, and Clusterspecies3): (a) alkaline metallic, alkaline earth, and non-metallic chemical elements; (b) transition and post-transition metallic chemical elements. In this figure, for simplicity, metallic ions are represented solely by their corresponding chemical element symbols.
Water 17 00582 g003
Water 17 00582 g004
Figure 4. (a) Illustration of the correlation between the quantitative variables evaluated in this study. The value in each matrix element represents the correlation coefficient from the Spearman test. Elements colored red indicate a negative correlation, and elements colored blue indicate a positive correlation. The symbol “×” on a cell denotes a non-significant correlation between the variables (p-value > 0.05). (b) Groupings of positively correlated variables derived from the correlation matrix. In this figure, for simplicity, metallic ions are represented solely by their corresponding chemical element symbols.
Figure 4. (a) Illustration of the correlation between the quantitative variables evaluated in this study. The value in each matrix element represents the correlation coefficient from the Spearman test. Elements colored red indicate a negative correlation, and elements colored blue indicate a positive correlation. The symbol “×” on a cell denotes a non-significant correlation between the variables (p-value > 0.05). (b) Groupings of positively correlated variables derived from the correlation matrix. In this figure, for simplicity, metallic ions are represented solely by their corresponding chemical element symbols.
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Table 1. Summary of the dataset used for statistical analysis and machine learning.
Table 1. Summary of the dataset used for statistical analysis and machine learning.
CharacteristicsDetails
Number of Instances445: Samples collected between 06/1998 and 01/2018
Number of Attributes17
Attribute TypesMacrophyte species: qualitative (nominal)
Collection date: date
N (g/kg): numeric
P (g/kg): numeric
K+ (g/kg): numeric
Ca2+ (g/kg): numeric
Mg2+ (g/kg): numeric
S (g/kg): numeric
B (mg/kg): numeric
Cu2+ (mg/kg): numeric
Fe2+ (mg/kg): numeric
Zn2+ (mg/kg): numeric
Mn2+ (mg/kg): numeric
Cd2+ (mg/kg): numeric
Ni2+ (mg/kg): numeric
Cr3+ (mg/kg): numeric
Pb2+ (mg/kg): numeric
Table 2. Statistical comparison of the chemical elements concentrations for the clusters that grouped the different species of macrophytes. Statistical inferences were performed using one-way ANOVA and multiple comparisons of means (Tukey contrasts). The letters a, b, and c indicate different variances, with c > b > a.
Table 2. Statistical comparison of the chemical elements concentrations for the clusters that grouped the different species of macrophytes. Statistical inferences were performed using one-way ANOVA and multiple comparisons of means (Tukey contrasts). The letters a, b, and c indicate different variances, with c > b > a.
Alkaline Metallic, Alkaline Earth, and Non-Metallic Elements
ElementANOVA p-ValueClusterspecies1Clusterspecies2Clusterspeciesi
N0.002 *aba
K+0.015 *aba
Ca2+<0.001 *acb
Mg2+<0.001 *acb
S<0.001 *acb
B<0.001 *abb
Transition and Post-Transition Metallic Elements
ElementANOVA p-ValueClusterspecies1Clusterspecies2Clusterspecies3
P<0.001 *aab
Cu2+<0.001 *aab
Fe2+<0.001 *abc
Mn2+<0.001 *abc
Zn2+<0.001 *abb
Cd2+<0.001 *abc
Ni2+<0.001 *abb
Cr3+<0.001 *abb
Pb2+<0.001 *bac
Note(s): * Statistically significant differences.
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Pitelli, R.A.; Simões, R.P.; Pitelli, R.L.; Rocha, R.J.d.S.; Merenda, A.M.P.; da Cruz, F.P.; Lameirão, A.M.M.d.S.; Oliveira Júnior, A.J.d.; Gomes, R.H.M. Exploratory Analysis on the Chemical Composition of Aquatic Macrophytes in a Water Reservoir—Rio de Janeiro, Brazil. Water 2025, 17, 582. https://doi.org/10.3390/w17040582

AMA Style

Pitelli RA, Simões RP, Pitelli RL, Rocha RJdS, Merenda AMP, da Cruz FP, Lameirão AMMdS, Oliveira Júnior AJd, Gomes RHM. Exploratory Analysis on the Chemical Composition of Aquatic Macrophytes in a Water Reservoir—Rio de Janeiro, Brazil. Water. 2025; 17(4):582. https://doi.org/10.3390/w17040582

Chicago/Turabian Style

Pitelli, Robinson Antonio, Rafael Plana Simões, Robinson Luiz Pitelli, Rinaldo José da Silva Rocha, Angélica Maria Pitelli Merenda, Felipe Pinheiro da Cruz, Antônio Manoel Matta dos Santos Lameirão, Arilson José de Oliveira Júnior, and Ramon Hernany Martins Gomes. 2025. "Exploratory Analysis on the Chemical Composition of Aquatic Macrophytes in a Water Reservoir—Rio de Janeiro, Brazil" Water 17, no. 4: 582. https://doi.org/10.3390/w17040582

APA Style

Pitelli, R. A., Simões, R. P., Pitelli, R. L., Rocha, R. J. d. S., Merenda, A. M. P., da Cruz, F. P., Lameirão, A. M. M. d. S., Oliveira Júnior, A. J. d., & Gomes, R. H. M. (2025). Exploratory Analysis on the Chemical Composition of Aquatic Macrophytes in a Water Reservoir—Rio de Janeiro, Brazil. Water, 17(4), 582. https://doi.org/10.3390/w17040582

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