Next Article in Journal
Micro/Nanoplastic Exposure on Placental Health and Adverse Pregnancy Risks: Novel Assessment System Based upon Targeted Risk Assessment Environmental Chemicals Strategy
Previous Article in Journal / Special Issue
Associations between Ethylene Oxide Exposure and Liver Function in the US Adult Population
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Consumption of Native Fish Associated with a Potential Carcinogenic Risk for Indigenous Communities in the Peruvian Amazon

by
Magaly Alejandra Brousett-Minaya
1,*,
Fred William Chu-Koo
1,
Juvenal Napuchi-Linares
1,
Cynthia Elizabeth Zambrano Panduro
1,
Juan Amilcar Reyes-Larico
2,
Adriana Edith Larrea-Valdivia
2 and
Ivan Edward Biamont-Rojas
3,4
1
Faculty of Sciences, National Autonomous University of Alto Amazonas (UNAAA), Prolongación Libertad 1220-1228, Yurimaguas 16501, Loreto, Peru
2
Faculty of Natural Sciences, National University of San Agustin—Arequipa (UNSA), Santa Catalina No. 117, Arequipa 04000, Arequipa, Peru
3
Institute of Science and Technology, State University of São Paulo (UNESP), Av. Três de Março 511, Alto da Boa Vista, Sorocaba 18087-180, SP, Brazil
4
Oceanographic Institute, University of São Paulo (USP), Praça do Oceanográfico, 191, São Paulo 05508-120, SP, Brazil
*
Author to whom correspondence should be addressed.
Toxics 2024, 12(8), 552; https://doi.org/10.3390/toxics12080552
Submission received: 21 June 2024 / Revised: 22 July 2024 / Accepted: 25 July 2024 / Published: 30 July 2024
(This article belongs to the Special Issue Environmental Contaminants and Human Health)

Abstract

:
Aquatic environments, such as fluvial environments, play an important role in the transport of material from throughout the basin, and this material partially sediments along the way. The objective of this study was to analyze, from an ecotoxicological point of view, the concentrations of arsenic and heavy metals in sediment and the muscle of native fish, to correlate their interaction and to evaluate the potential risk to public health using carcinogenic risk indices in four rivers of the Peruvian Amazon. There were 27 sampling sites where sediment and fish (except for five points) samples were collected. A sampling pool was created with fish muscles from all species collected at each sampling site. Concentrations of As, Cd, Cr, Cu, Ni, Pb, Zn, and Hg were analyzed in both sediment and fish muscle, in duplicate. The results indicate the presence of concentrations higher than those recommended by international guidelines for sediment and food. Mercury (Hg) concentrations in the Tigre, Morona, and Pastaza rivers are up to six times higher than the recommended value for daily consumption. The carcinogenic risk due to the regular consumption of native species in the indigenous communities living on the banks of the four studied rivers is high.

1. Introduction

The minerals found in the basins are transported to water bodies due to natural and artificial actions. Some of these elements are essential for the dynamics in aquatic environments, and for life itself [1,2]. A significant part of the rivers in the Amazon basin originates in the Peruvian and Ecuadorian Andes, draining their waters through steep and rocky volcanic terrain, carrying metals or soluble chemical compounds in their path [3]. Minerals, because of their polar properties, are adsorbed to particles (e.g., organic matter, dead cells, etc.), making them heavier and causing them to sediment [4,5,6]. This results in a natural sink for all of the material derived from natural or anthropic sources in the basin [7]. The sedimented material can easily be resuspended due to physical phenomena (e.g., wind, precipitation, current alteration, etc.), which increases their negative impact on the aquatic environment [2].
When metals and metalloids, as part of the minerals, are found in high concentrations in different environmental compartments, such as sediment, they become an environmental threat [8,9] to the aquatic system and to public health. The Amazon Basin is susceptible to these fluctuations, especially for human communities settled in riverside zones that culturally have a major food dependency on aquatic ecosystems for fishing and drinking water [10].
Artisanal mining is one of the main activities in the upper basin area (Peru and Ecuador). In this type of mining, chemicals such as mercury and cyanide 8 are frequently employed without supervision. Additionally, mining activity in the Ecuadorian Amazon tripled in size from 2015 to 2021 (7495 ha) across various provinces (Chinchipe, Napo, Morona Santiago, and Sucumbíos) [11,12,13,14]. The major concern is that not just mining activities but also petroleum extraction in the area jeopardizes indigenous communities in the Pastaza, Corrientes, Tigre, and Marañón rivers [15,16]. Both activities endanger the ichthyological diversity in the Amazonian rivers, considered the world’s highest diversity (2406 species) [17].
The persistence of minerals in aquatic bodies leads to their accumulation in living organisms. Some of these elements are difficult to metabolize and, therefore, remain in the organism, a phenomenon known as bioaccumulation. Another issue to consider is the process of accumulation within the food web itself, which can be related to each species’ feeding habits (detritivores, carnivores, benthonic, etc.) [18,19]. When some species feed on contaminated organisms, there is an increasing concentration of a particular metal or metalloid in the predator, a process known as biomagnification. These hydrobiological resources, many of which are essential in the diet of Amazonian people, are increasingly exposed to higher concentrations of elements in fluvial and lacustrine environments.
People of the Peruvian Amazon fish a wide variety of species (approximately 60 species), and fish consumption in this area is almost three times the national average, reaching 50 kg/person/year [10]. Indigenous people (105,900 inhabitants) [20] live in this area. Ethnic groups such as the Kichwa, Achuar, Wampis, Shapra, and Kandozi are mainly settled along the sides of the Pastaza, Morona, Tigre, and Corrientes rivers, with a traditional dependence on fish.
In this context, the presence of metals or metalloids in fluvial sediments, due to physical disturbances, can resuspend and increase bioaccumulation and biomagnification processes that affect fish, turning them into potential carriers of toxic substances in the food web. As far as is known, this is the first study in the Peruvian Amazon that relates the presence of metals and metalloids in fish muscles to their concentrations in fluvial sediments in areas affected by mining and petroleum activities. This study makes a significant contribution to environmental toxicology and geochemistry in Amazonian fluvial environments. Finally, the results explore the probable carcinogenic and non-carcinogenic risk occurrences due to fish consumption in human communities settled along four rivers in the Peruvian Amazon.
Therefore, the objective was to analyze the arsenic and heavy metal concentrations in both the sediment and native fish muscles from the ecotoxicological perspective, to correlate their interaction and assess their potential risk to public health, employing carcinogenic risk indices in four rivers of the Peruvian Amazon (Tigre, Morona, Corrientes, and Pastaza).

2. Material and Methods

During February and March 2023, sediment and fish samples were collected from four rivers in two provinces: Loreto (Corrientes and Tigre rivers) and Datém del Marañón (Pastaza and Morona rivers), both in the department of Loreto, Peru. The collections coincided with the time of high water, so all of the rivers were navigable, with considerable daily fluvial transit among rural areas where small indigenous communities are settled. These native communities are established along the riversides, with fish being their main food source.
Sediments were collected from 27 sites along the Tigre (T1–T7), Corrientes (C1–C6), Pastaza (P1–P6), and Morona (M1–M7) rivers (Figure 1 and Table 1). Petroleum storage reservoirs were observed at some sampling sites, which belong to PetroPeru. Fish samples were collected at the same sites as the sediments, from a total of 22 sites in fishing zones, except at C2, C5, T2, T3, and T5. Sediment and fish sampling sites are represented by orange dots, while sediment-only sampling sites are represented by green dots (Figure 1).

2.1. Fish Sampling

Fish were collected from different fishing areas located in the four rivers. These fish included carnivorous, omnivorous, and detritivore species (Table 2). The collection followed the protocols established by the Peruvian Ministry of Environment [21]. This method involved setting up fishing nets that remained in place for 24 h. These nets were periodically checked to ensure that predator fish did not damage the captured samples, and to prevent major vertebrates (reptiles, aquatic mammals, etc.) from being accidentally trapped. A total of 404 individuals were collected from the four rivers. Approximately 500 to 800 g of fish muscle was collected at the different sampling sites, according to the number of individuals captured (Table 1).
Fish muscle extraction was performed according to the Peruvian Technical Standard 700.002 (NTP, 2021). A sampling pool was created with fish muscles from all species collected at each sampling site. Sample pooling is a method where units that were supposed to be measured individually are processed together, making separate analyses impossible, because human residents of the indigenous communities did not consume only one species of fish per day but a variety of species existing in their catches. Sampling pool analyses were carried out, since the indigenous people do not have a preference for a particular fish species, nor do they select them in their daily diet for reasons of the place they occupy in the water column or because of the fish’s diet. The sample pools were stored at −20 °C until their evaluation in the laboratory.

2.2. Sediment Sampling

A total of 50 samples were collected from the Tigre (12 samples), Corrientes (12 samples), Pastaza (12 samples), and Morona (14 samples) (Figure 1). Samples were extracted from the upper layer (0–10 cm), extracted using a Van Veen sediment sampler (1000 cm3). The sediment was stored in plastic flasks (300 cm3) in the dark and kept at 4 °C until analysis in the laboratory.

2.3. Daily Fish Intake

To calculate the daily fish intake in riverside indigenous communities, census data from the National Institute of Statistics and Informatics [22] were used, indicating a total of 29,084 people living in the areas of interest. With this total number, and using finite population sampling (p = 0.05) [23], a representative sample of 1356 people from different indigenous communities was calculated and interviewed.

2.4. Metal Concentration Analysis

Fish muscle: The biological sample analysis was performed at the National University of San Agustín, following the method mentioned by Qin et al. (2015) [24], with partial modifications. This process took 0.5 g of the homogenized samples (epaxial muscle without skin), which were then subjected to microwave digestion in Teflon vessels (Milestone, Sorisole, Italy, Ethos Easy model). In the vessels, 10 mL of a HNO3:H2O2 mixture (at an 8:2 ratio) received the homogenized sample, which was heated to 280 °C in a thirty-minute ramp. The resulting sample was filtered through a PVDF membrane filter (Whatman, Maidstone, UK, 0.45 µm), and then it was diluted with Milli-Q water up to 50 mL and stored at 4 °C. Metals, except mercury, were analyzed via inductively coupled plasma mass spectrometry (ICP-MS) (PerkinElmer, Waltham, MA, USA, NexION 2000C model). Mercury was analyzed directly in an automatized mercury analyzer (Milestone, DMA-80 evo model).
Sediment: Eleven elements were studied (As, Cd, Cr, Cu, Ni, Pb, Al, Ba, Fe, Zn, and Hg). Metals, except Hg, were analyzed using inductively coupled plasma optical spectrometry (ICP-OES). This procedure requires a previous digestion of the sample, following the method of SW 846 US EPA 3050 [25]. It consisted of acid digestion, with the addition of HNO3, H2O2, and HCl in 4.5 h at 90 °C. In the case of mercury (Hg), it was analyzed via cold vapor atomic absorption spectroscopy (CVAAS), following the USEPA 7471B method [26], where the sample must be digested by a sequence of reactions employing H2SO4, HNO3, and KMnO4 in 2 h. Metal and metalloid data are expressed as milligrams per kilogram of dry weight (mg/kg dw).

2.5. QA/QC

Quality control was performed through the evaluation of the precision and accuracy of the methodology, as follows: Analytical-grade reagents (obtained from PerkinElmer) were used in all of the analyses. All glassware items and equipment used to store and process the samples for metal assessment were left in 10% nitric acid for at least 24 h, followed by rinsing with ultrapure water.
Fish muscle: Blank and control samples were analyzed in a 10-sample batch, assuring the standards. The results were between 80% and 120%, which is the recommended range [25]. During calibration, we obtained an R2 > 0.999, which indicates a strong correlation of the signal. The quantification limits for the metals analyzed were as follows: Cr (0.216 mg/kg), Ni (0.016 mg/kg), Cu (0.066 mg/kg), Zn (0.712 mg/kg), As (0.007 mg/kg), Cd (0.001 mg/kg), Hg (0.01 mg/kg), and Pb (0.016 mg/kg).
Sediments: Recovery control varied between 93.9% and 116%, which is in the recommended range [25]. The linearity and strong correlation of the signal were assured, with an R2 > 0.999. The quantification limits for the elements were as follows: Cr (1.0 mg/kg), Ni (1.0 mg/kg), Cu (0.8 mg/kg), Zn (0.6 mg/kg), As (3.6 mg/kg), Cd (0.3 mg/kg), Hg (0.01 mg/kg), and Pb (3.0 mg/kg), Fe (2.5 mg/kg), Al (3.0 mg/kg), and Ba (0.3 mg/kg).

2.6. Risk Assessment

2.6.1. Estimated Daily Intake (EDI)

The daily intake was calculated for each element (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) using the following equation:
E D I = F I R × C i B W
where FIR is the fish intake ratio (g/person/day, wet weight), Ci is the mean concentration of the element in fish muscle (mg/kg), and BW is the mean corporal weight of an adult (70 kg in this study) [27]. For adults living in rural areas, such as the ones in this study (Datém del Marañón and Loreto), the FIR was calculated for each particularly small town or village in the study, employing the data collected from the interviews, to present a more reliable measurement. This was merely to highlight that, since these small towns or villages are located far from major urban centers (1–2 days of navigating), fish intake represents a major percentage of the diet compared to other kinds of meat.

2.6.2. Non-Carcinogenic Risk

The target hazard quotient (THQ) was employed to measure the non-carcinogenic risk. The THQ is the ratio of EDI to the oral reference dose (RfD, mg/kg·day); this RfD was established by the US Environmental Protection Agency (U.S. EPA 2023). This quotient was calculated as follows:
T H Q = E D I R f D
On this basis, there are three scenarios according to Hossain et al. (2018) [28] and Huang et al. (2019) [29]:
THQ < 1, the population will not experience any adverse health hazards.
THQ = 1, the receptors of concern may experience non-carcinogenic health effects.
THQ > 1, there is an increasing probability of adverse health hazard occurrences.

2.6.3. Carcinogenic Risk (CRI)

This index is used to assess potential carcinogenic risks of fish consumption [30]. This calculation was performed only for Cr, As, and Cd, using the following equation:
C R I = F I R × E F × E D B W × T A × S F i × C i
where EF is the exposure frequency (365 day per year), ED is the exposure duration (70 years), TA is the average time (365 × ED d), and SFi is the oral slope factor (mg/kg·day) for a single element. The oral intake of carcinogenic slope factors for Cr, As, and Cd was 0.50, 0.38, and 1.50, respectively [29].
This brings three classifications based on the outcomes, according to [30]:
CRI < 10−6, the compound is safe for humans.
10−6 < CRI < 10−4, there are potential carcinogenic risks to the exposed population.
CRI > 10−4, the exposed people would encounter excess carcinogenic risk.

2.7. Statistical Analysis

Descriptive statistics was used to analyze the fish muscle and sediment concentrations. A principal component analysis (PCA) was performed to identify the relations among sample sites and the assessed elements, for both fish muscle and sediments. PCA reduces all datasets to a lower number of variables (usually the first two components) for plotting purposes. The graphical biplot correlation in the PCA was based on [31]; this analysis was performed using the free statistical software Paleontological Statistics (PAST) Version 4.03. Data analyzed in PCA were previously log-transformed; subsequently, the PCA was performed. A Spearman correlation was performed (p < 0.05) between fish muscle and sediments [32].

3. Results

3.1. Metal(loid)s in Fish Muscles

Regarding the element outcomes in fish (Table 3), the mean concentrations of Ni, Cu, Zn, As, Cd, and Pb demonstrated the presence of these metals at all sampling sites, with Ni, Cd, Pb, and Hg showing higher variation in at least one of the four rivers assessed. On the other hand, zinc is the metal that presented the highest mean concentrations in all four rivers, reaching a maximum of 9.88 mg/kg in the Tigre River (Table 3). Although the mean Zn concentrations were higher, they did not surpass the limits established by the FAO and ANVISA. However, the mean chromium concentrations registered in all rivers were higher than the WHO/FAO, EU, and ANVISA guidelines [27,33,34,35,36] (Table 4).
Even though the mean Hg concentrations did not surpass the guideline values in the Tigre and Pastaza rivers (0.31 and 0.28 mg/kg, respectively), the maximum values showed concentrations that are close to or even higher than the probable harmful concentration according to international guidelines. The percentage of the near and upper samples of Hg in the Tigre River was 40%, and in the Pastaza it was 16.7%.
The PCA for fish muscle (Figure 2) shows that there are no clear differences among the assessed rivers; however, the relationship of the sample sites to certain metals can be observed. There is a group composed of sample sites from the Pastaza (P1, P4, P5, and P6) and Tigre (T4a) rivers demonstrating a positive relation to mercury (Hg). Additionally, sample sites in the Corrientes, Tigre, and Morona rivers (C4, T6, and M5, respectively) show a positive relation to cadmium (Cd), while P2, P3, M1, and M2 (Pastaza and Morona rivers) are related to lead (Pb). Sample sites in the Morona (M3 and M7) exhibit a positive relation to arsenic (As), whereas sites in the Corrientes (C1, C3, and C6) and Tigre (T4b) show a relation to copper (Cu) and iron (Fe).
The correlation analysis among fish muscle samples indicates positive relationships between Cu and Zn, Ni and Zn, and Cr and Ni. On the other hand, there is an inverse relationship of Hg with Pb and Fe. A weak relationship between As and Cd can also be observed.

3.2. Sediments

Among all elements assessed in the four basins (Table 5), the highest mean concentrations of Cr, Ni, and Cu (17.85, 47.10, and 9.31 mg/kg, respectively) were observed in the Morona River. Additionally, the Morona River showed the highest mean concentration of Zn (52.19 mg/kg), with a maximum value reaching 99.80 mg/kg. In contrast, the Tigre River presented the lowest mean concentrations of Cr, Ni, Cu, and Zn (7.53, 7.00, 4.24, and 16.89 mg/kg, respectively).
Regarding the PCA (Figure 3), differences among the four assessed rivers were observed, with Morona River showing the clearest distinction, where the sample sites were related to Al, Ba, and Zn. The Pastaza River exhibited correlations with Ni and Cr. The Tigre and Corrientes rivers showed some similarities in certain sample sites; generally, these rivers were inversely related to Fe and Cr.
According to the Spearman correlation analyses performed, the metalloid contents obtained from the analyzes of fish and sediments showed significantly high ratios of Ni (Corrientes River) and Zn (Morona and Pastaza rivers), moderate ratios of Cr and Ni (Río Pastaza), and a low ratio of Cr (Río Morona) (Figure 4).

3.3. Carcinogenic and Non-Carcinogenic Analysis

The oral reference doses (RfD) for Ni, Cu, Zn, Cr, As, Cd, and Hg were 20, 40, 300, 3, 0.3, 1 and 0.3 µg/kg·d, respectively [37]. Following the European Food Safety Authority’s guidelines, the RfD for Pb was set to 1.5 µg/kg·d [38].
Table 6 shows the results for RfD, EDI, THQ, and CRI for the analyzed elements in different fish muscles collected from the four river basins. Concerning THQ, Hg is the only metal that exceeds the USEPA-recommended levels in the Tigre, Morona, and Pastaza rivers (1.92, 1.83, and 1.50, respectively). When examining the CRI, Cr is the only metal that poses a potential carcinogenic risk in all four basins, with maximum values even higher in the Tigre and Morona rivers.

4. Discussion

The findings of this study highlight a concerning scenario regarding potential carcinogenic risks associated with mercury (Hg) and chromium (Cr) for indigenous communities in the Peruvian Amazon. The high concentrations of metals and metalloids found in the Tigre, Pastaza, and Morona rivers imply risks to public health and the well-being of communities residing in these areas. It is crucial to recognize that remote communities, situated far from urban centers, rely heavily on local resources for their livelihoods.
However, the disruption caused by human activities directly impacts aquatic environments, leading to scenarios of bioaccumulation and biomagnification.

4.1. Fish Muscle Analysis

It is evident that the more remotely a community is located, the higher its fish consumption, as shown in Table 1 and Figure 1. This trend can be observed at sites like T1 and C1 in Loreto, or M2 and P2 in Datém del Marañón. The prevalence of fish in the diets of these communities is notable not only on the Peruvian side, but also in Ecuador, where it reaches up to 623 g/day in the Pastaza River [39]. Regarding the metal and metalloid concentrations in fish muscle, chromium (Cr) exceeds the established reference concentrations set out by the WHO, FAO, EU, and ANVISA in all four rivers (Table 4). Although chromium naturally originates from rocks in the basin, its presence is also linked to human activities such as petroleum extraction [40]. Additionally, the concentration of Cr can vary depending on its geological source [41], and its use in the paint industry (Cr2O3) provides chemical resistance to abrasion [42]. This type of paint is commonly used on boats in these areas.
When comparing data from the Tigre River with other global rivers (Table 7), it is evident that the maximum Cr values are higher than those observed in the Sinos and Northeast China rivers [29,43], and similar to those found in the Paraopeba [44] and Piracicaba [45] rivers in Brazil, where significant anthropogenic pollution has been reported. In contrast, the outcomes for the Tigre and Pastaza rivers are considerably lower than those observed in the Houjin River [46].
A similar pattern was observed with Cu, the levels of which were comparable to the Yangtze River [47] but much lower than the Paraopeba River. Regarding Zn, the Tigre and Pastaza rivers showed lower concentrations compared to the Paraopeba, Sinos [43], and Northeast China rivers [29], possibly due to agricultural activities. Mercury in the Tigre and Pastaza Rivers presents maximum concentrations that exceed the values reported in the Northeast China [29] and Gomti [28] Rivers. In the case of Peru, fish in other Amazonian rivers have registered higher values than the FEPA and WHO guidelines [50].

4.2. Sediment Analysis

The outcomes indicate that the Cr, Ni, and Zn concentrations surpass the NOAA, ISQG, and EPA international guidelines (Table 5). According to the statistical analysis, significant correlations between sediment and fish muscle were identified for Ni, Zn, and Cr (Figure 4). This phenomenon could be related to bioaccumulation occurrences, particularly noticeable in the case of Cr.

4.3. Human Health Risks

The findings of this study highlight a concerning scenario regarding possible carcinogenic risks related to mercury (Hg) and chromium (Cr) for indigenous communities in the Peruvian Amazon. The recommended daily intake limits for metals [33,51] are 0.2 mg/day for Cr, 1.5 mg/day for Cu, 0.002 mg/day for As, 0.007 mg/day for Cd, and 0.0001 mg/day for Hg per person. It is evident from the findings that the EDI calculated for metals (except Hg) in this study did not exceed the recommended values. However, the Hg concentrations in the Tigre River (0.00057 mg/kg·person), Morona River (0.00055 mg/kg·person), and Pastaza River (0.00045 mg/kg·person) are 5 to 6 times higher than the recommended values.
Regarding the risks to human health, it is noteworthy that, among the areas assessed in this study, three basins are more likely to pose negative health effects due to high THQ values for Hg and Cr. Sampling sites such as T4, M1, M3, and P3 have THQ values above 1 for Hg, while T1, T6, M2, P2, and P4 reach values of almost 3, indicating an increased probability of adverse health effects. Of particular concern is sampling site M3, which reports a THQ of 6.43, indicating a substantial potential health impact. Similar studies have also shown high THQ values in Amazonian fish species, ranging from 1.04 to 4.18 [52,53], categorizing them as unsuitable for human consumption.
Regarding the mean THQ value for Cr, it did not exceed the reference value in any of the four basins. However, it is important to note that M1 and M2 registered 1.18 and 1.44, respectively. This is a health alert for the Morona River Basin, as well as for the Shapra and Wampis indigenous communities, which should be continuously monitored by the appropriate authorities. Additionally, the CRI values for Cr were higher than the reference values in all four basins, indicating that fish consumption may expose people to excess carcinogenic risk. The Morona River Basin shows the highest CRI values, with sites like M1, M2, and M4 having CRI values of 0.0018, 0.0022, and 0.001, respectively.
In the case of Hg, THQ’s potential risks to the environment are probably the result of human activities developed in the watershed, which includes Ecuadorian territory. In Peru, there are no official reports of legal gold exploitation in the three basins; however, a metallogenic gold map was recently published in Peru [54]. This map clearly establishes that certain Amazonian rivers (Santiago River, the Marañón, the middle part of the Morona River, and the lower part of the Pastaza River are favorable for placer, alluvial, and moraine-type gold deposits. Therefore, the possible presence of informal or illegal dredgers furtively exploiting gold with unsafe techniques and acting as potential sources of Hg in these rivers cannot be ruled out. On the other hand, studies indicate the presence of mining activities, mainly illegal gold mining, including the upper basin of the Morona and Pastaza rivers [11,12,55].

5. Conclusions

The sediment and fish muscle assessment in the four rivers revealed the actual concentrations of metals in the environment. In the case of sediments, Cr, Ni, and Zn exceeded the concentrations of international standards (NOAAA and ISQG). Similarly, high concentrations of Cr and Hg were found in fish muscle in the Tigre, Morona, and Pastaza rivers.
On the other hand, the non-carcinogenic risk analysis indicates that Hg is considered to be a health threat, especially in the Tigre, Morona, and Pastaza rivers, which means that there is an increasing likelihood of adverse health events related to this metal. Meanwhile, the carcinogenic risk assessment shows that Cr is a potential driver in all four rivers, so exposed people would face an excess carcinogenic risk due to this metal. Thus, the evidence found regarding the high concentrations of metals (Hg, Cr) could become chronic accumulation in human organisms, resulting in a public health problem.
The results presented in this study provide information that will serve as a basis for decision-making in future management plans that would help maintain or restore the riparian environment in the Department of Loreto.

Author Contributions

M.A.B.-M. performed conceptualization, formal analysis, investigation, methodology, project administration, supervision, writing—original draft, writing—review and editing, and fund acquisition; F.W.C.-K., formal analysis, investigation, data curation, writing—original draft, writing—review and editing; C.E.Z.P., investigation; J.N.-L., conceptualization, investigation, methodology; A.E.L.-V., investigation, methodology; J.A.R.-L., investigation, methodology; I.E.B.-R., conceptualization, methodology, investigation, formal analysis, writing—original draft, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Autonomous University of Alto Amazonas through the Research Institute (Resolución Presidencial No. 292-2022-UNAAA/P).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available on request due to ethical restrictions.

Acknowledgments

The authors would like to thank to Hans Marbel Pashanase Melo and Carlos Javier Valenzuela Huillca, who helped in the field and laboratory phases; We also wish to thank that Programa de Estudantes-Convênio de Pós-Graduação (PEC-PG), CAPES/CNPq—Brasil (process 190216/2017-4), for having provided a scholarship to Ivan Edward Biamont-Rojas.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zoroddu, M.A.; Aaseth, J.; Crisponi, G.; Medici, S.; Peana, M.; Nurchi, V.M. The Essential Metals for Humans: A Brief Overview. J. Inorg. Biochem. 2019, 195, 120–129. [Google Scholar] [CrossRef] [PubMed]
  2. Biamont-Rojas, I.E.; Cardoso-Silva, S.; Alves de Lima Ferreira, P.; Alfaro-Tapia, R.; Figueira, R.; Pompêo, M. Chronostratigraphy Elucidates Environmental Changes in Lacustrine Sedimentation Rates and Metal Accumulation. Environ. Sci. Pollut. Res. 2023, 30, 72430–72445. [Google Scholar] [CrossRef]
  3. McClain, M.E.; Naiman, R.J. Andean Influences on the Biogeochemistry and Ecology of the Amazon River. Bioscience 2008, 58, 325–338. [Google Scholar] [CrossRef]
  4. Biamont-Rojas, I.E.; Cardoso-Silva, S.; Pompêo, M. Heterogeneidade Espacial e Ecotoxicidade de Metais No Sedimento Em Três Reservatorios Paulistas Aplicando Um Enfoque Geoestatístico. In Aspectos da Ecotoxicidade em Ambientes Aquáticos; Pompêo, M., Moschini-Carlos, V., López-Doval, J.C., Eds.; Instituto de Biociências, Universidade de São Paulo: São Paulo, Brazil, 2022; pp. 43–57. [Google Scholar]
  5. Martins, T.; Ferreira, K.; Rani-Borges, B.; Biamont-Rojas, I.; Cardoso-Silva, S.; Moschini-Carlos, V.; Pompêo, M. Land Use, Spatial Heterogeneity of Organic Matter, Granulometric Fractions and Metal Complexation in Reservoir Sediments. Acta Limnol. Bras. 2021, 33, e23. [Google Scholar] [CrossRef]
  6. Biamont-Rojas, I.E.; Mamani Villalba, B.A. Mercurio Derivado de La Actividad Minera Artesanal Evidencia Un Alto Potencial Tóxico En Una Subcuenca Del Lago Titicaca, Perú. In Minería, Mercurio y Salud. Aportes de la Comunidad Académica; Cabrera Jaramillo, A., Arango Ruiz, Á., Eds.; Editorial Lasallista: Caldas, Colombia, 2022; pp. 43–75. [Google Scholar]
  7. Khan, R.; Saxena, A.; Shukla, S. Evaluation of Heavy Metal Pollution for River Gomti, in Parts of Ganga Alluvial Plain, India. SN Appl. Sci. 2020, 2, 1451. [Google Scholar] [CrossRef]
  8. Brousett-Minaya, M.A.; Rondan-Sanabria, G.G.; Chirinos-Marroquín, M.; Biamont-Rojas, I. Impacto de La Minería En Aguas Superficiales de La Región Puno—Perú. Fides Ratio—Rev. Difusión Cult. Científica Univ. Salle Boliv. 2021, 21, 187–208. [Google Scholar]
  9. Biamont-Rojas, I.E.; Cardoso-Silva, S.; Figueira, R.C.L.; Kim, B.S.M.; Alfaro-Tapia, R.; Pompêo, M. Spatial Distribution of Arsenic and Metals Suggest a High Ecotoxicological Potential in Puno Bay, Lake Titicaca, Peru. Sci. Total Environ. 2023, 871, 162051. [Google Scholar] [CrossRef]
  10. Heilpern, S.A.; Sethi, S.A.; Barthem, R.B.; Batista, V.D.S.; Doria, C.R.C.; Duponchelle, F.; Vasquez, A.G.; Goulding, M.; Isaac, V.; Naeem, S.; et al. Biodiversity Underpins Fisheries Resilience to Exploitation in the Amazon River Basin. Proc. R. Soc. B Biol. Sci. 2022, 289, 20220726. [Google Scholar] [CrossRef]
  11. Passarelli, I.; Villacis Verdesoto, M.V.; Jiménez-Oyola, S.; Flores Huilcapi, A.G.; Mora-Silva, D.; Anfuso, G.; Esparza Parra, J.F.; Jimenez-Gutierrez, M.; Carrera Almendáriz, L.S.; Avalos Peñafiel, V.G.; et al. Analysis of Mercury in Aquifers in Gold Mining Areas in the Ecuadorian Amazon and Its Associated Risk for Human Health. Toxics 2024, 12, 162. [Google Scholar] [CrossRef]
  12. Mestanza-Ramón, C.; Cuenca-Cumbicus, J.; D’Orio, G.; Flores-Toala, J.; Segovia-Cáceres, S.; Bonilla-Bonilla, A.; Straface, S. Gold Mining in the Amazon Region of Ecuador: History and a Review of Its Socio-Environmental Impacts. Land 2022, 11, 221. [Google Scholar] [CrossRef]
  13. Sánchez-Vázquez, L.; Espinosa-Quezada, M.G.; Eguiguren-Riofrío, M.B. “Golden Reality” or the “Reality of Gold”: Artisanal Mining and Socio-Environmental Conflict in Chinapintza, Ecuador. Extr. Ind. Soc. 2016, 3, 124–128. [Google Scholar] [CrossRef]
  14. Villacís, S.; Ochoa, J.; Borja, M.O.; Josse, C.; Finer, M.; Mamani, N. MAAP #151: Minería Ilegal En La Amazonía Ecuatoriana. Available online: https://www.maaproject.org/2022/mineria-ecuador/ (accessed on 10 July 2024).
  15. Ulfe, M.E.; Vergara, R. Measuring Incommensurability: Compensations in Judicial Processes of Oil Spills in Northern Peruvian Amazon. Tapuya Lat. Am. Sci. Technol. Soc. 2022, 5, 2144004. [Google Scholar] [CrossRef]
  16. MINSA. Ministerio de Salud; Instituto Nacional de Salud. Niveles y Factores de Riesgo de Exposición a Metales Pesados e Hidrocarburos En Los Habitantes de Las Comunidades de Las Cuencas de Los Ríos Pastaza, Tigre, Corrientes y Marañón Del Departamento de Loreto; Ministerio de Salud: Lima, Peru, 2017.
  17. Jézéquel, C.; Tedesco, P.A.; Bigorne, R.; Maldonado-Ocampo, J.A.; Ortega, H.; Hidalgo, M.; Martens, K.; Torrente-Vilara, G.; Zuanon, J.; Acosta, A.; et al. A Database of Freshwater Fish Species of the Amazon Basin. Sci. Data 2020, 7, 96. [Google Scholar] [CrossRef] [PubMed]
  18. Al-Homaidan, A.A.; Al-Otaibi, T.G.; El-Sheikh, M.A.; Al-Ghanayem, A.A.; Ameen, F. Accumulation of Heavy Metals in a Macrophyte Phragmites Australis: Implications to Phytoremediation in the Arabian Peninsula Wadis. Environ. Monit. Assess. 2020, 192, 202. [Google Scholar] [CrossRef] [PubMed]
  19. Basallote, M.D.; Zarco, V.; Macías, F.; Cánovas, C.R.; Hidalgo, P.J. Metal Bioaccumulation in Spontaneously Grown Aquatic Macrophytes in Fe-Rich Substrates of a Passive Treatment Plant for Acid Mine Drainage. J. Environ. Manag. 2023, 345, 118495. [Google Scholar] [CrossRef] [PubMed]
  20. INEI. Censos Nacionales 2017. Resultados Definitivos de los Censos Nacionales 2017—Puno. Available online: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1563/ (accessed on 30 July 2022).
  21. MINAM; Ministerio del Ambiente. Métodos de Colecta, Identificación y Análisis de Comunidades Biológicas: Plancton, Perifiton, Bentos (Macroinvertebrados) y Necton (Peces) En Aguas Continentales Del Perú; Zona Comunicaciones S.A.C.: Lima, Peru, 2014.
  22. INEI. Perú: Proyecciones de Población, Según Departamento, Provincia y Distrito, 2021-2022; McGraw-Hill Interamericana: Lima, Peru, 2020.
  23. López-Alvarenga, J.C.; Reding-Berrnal, A. Cálculo Del Tamaño de La Muestra: Enfoque Práctico de Sus Elementos Necesarios. In Introducción a la Metodología de la Investigación en Ciencias de la Salud; McGraw-Hill Interamericana: Ciudad de México, Mexico, 2011; pp. 67–76. [Google Scholar]
  24. Qin, D.; Jiang, H.; Bai, S.; Tang, S.; Mou, Z. Determination of 28 Trace Elements in Three Farmed Cyprinid Fish Species from Northeast China. Food Control 2015, 50, 1–8. [Google Scholar] [CrossRef]
  25. U.S. EPA. Method 3050B: Acid Digestion of Sediments, Sludges, and Soils, Revision 2; U.S. EPA: Washington, DC, USA, 1996.
  26. U.S. EPA. Method: 7471B Mercury in Solid or Semisolid Waste (Manual Cold-Vapor Technique); U.S. EPA: Washington, DC, USA, 2007.
  27. FAO. Heavy Metals Regulations Legal Notice No 66/2003; FAO: Rome, Italy, 2003. [Google Scholar]
  28. Hossain, M.B.; Ahmed, A.S.S.; Sarker, M.S.I. Human Health Risks of Hg, As, Mn, and Cr through Consumption of Fish, Ticto Barb (Puntius Ticto) from a Tropical River, Bangladesh. Environ. Sci. Pollut. Res. 2018, 25, 31727–31736. [Google Scholar] [CrossRef] [PubMed]
  29. Huang, X.; Qin, D.; Gao, L.; Hao, Q.; Chen, Z.; Wang, P.; Tang, S.; Wu, S.; Jiang, H.; Qiu, W. Distribution, Contents and Health Risk Assessment of Heavy Metal(Loid)s in Fish from Different Water Bodies in Northeast China. RSC Adv. 2019, 9, 33130–33139. [Google Scholar] [CrossRef]
  30. U.S. EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories, 3rd ed.; United States Environmental Protection Agency: Washington, DC, USA, 2000; Volume 2.
  31. Legendre, P.; Legendre, L. Numerical Ecology, 2nd ed.; Elsevier: Amsterdam, The Netherlands, 1998. [Google Scholar] [CrossRef]
  32. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023; Available online: https://www.R-project.org/ (accessed on 9 July 2024).
  33. WHO. Evaluation of Certain Food Additives and Contaminants: Thirty-Third Report of the Joint FAO/WHO Expert Committee on Food Additives; WHO: Geneva, Switzerland, 1989.
  34. GB2762-2017; National Food Safety Standard: Limits of Contaminants in Food. Office of Agricultural Affairs: Beijing, China, 2017.
  35. EU. Commission Regulation EC No. 629/2008. Setting Maximum Levels for Certain Contaminants in Foodstuffs; EU: Brussels, Belgium, 2008. [Google Scholar]
  36. ANVISA. Agência Nacional de Vigilância Sanitária. Decreto No. 55.871. Modifica o Decreto No 50.040, de 24 de Janeiro de 1961, Referente a Normas Reguladoras do Emprêgo de Aditivos para Alimentos, Alterado Pelo Decreto No 691, de 13 de Março de 1962; Presidencia da República: Brasília, Brazil, 1965.
  37. U.S. EPA. Regional Screening Level (RSL)—Generic Tables; U.S. EPA: Washington, DC, USA, 2024.
  38. EFSA. Scientific Opinion on Lead in Food. EFSA J. 2010, 8, 1570. [Google Scholar] [CrossRef]
  39. Vasco, C.; Sirén, A. Determinants of Wild Fish Consumption in Indigenous Communities in the Ecuadorian Amazon. Soc. Nat. Resour. 2019, 32, 21–33. [Google Scholar] [CrossRef]
  40. Islam, M.A.; Angove, M.J.; Morton, D.W. Recent Innovative Research on Chromium (VI) Adsorption Mechanism. Environ. Nanotechnol. Monit. Manag. 2019, 12, 100267. [Google Scholar] [CrossRef]
  41. Ullah, S.; Liu, Q.; Wang, S.; Jan, A.U.; Sharif, H.M.A.; Ditta, A.; Wang, G.; Cheng, H. Sources, Impacts, Factors Affecting Cr Uptake in Plants, and Mechanisms behind Phytoremediation of Cr-Contaminated Soils. Sci. Total Environ. 2023, 899, 165726. [Google Scholar] [CrossRef] [PubMed]
  42. Bolelli, G.; Steduto, D.; Kiilakoski, J.; Varis, T.; Lusvarghi, L.; Vuoristo, P. Tribological Properties of Plasma Sprayed Cr2O3, Cr2O3–TiO2, Cr2O3–Al2O3 and Cr2O3–ZrO2 Coatings. Wear 2021, 480–481, 203931. [Google Scholar] [CrossRef]
  43. Weber, P.; Behr, E.R.; Knorr, C.D.L.; Vendruscolo, D.S.; Flores, E.M.M.; Dressler, V.L.; Baldisserotto, B. Metals in the Water, Sediment, and Tissues of Two Fish Species from Different Trophic Levels in a Subtropical Brazilian River. Microchem. J. 2013, 106, 61–66. [Google Scholar] [CrossRef]
  44. Savassi, L.A.; Paschoalini, A.L.; Arantes, F.P.; Rizzo, E.; Bazzoli, N. Heavy Metal Contamination in a Highly Consumed Brazilian Fish: Immunohistochemical and Histopathological Assessments. Environ. Monit. Assess. 2020, 192, 542. [Google Scholar] [CrossRef] [PubMed]
  45. Meche, A.; Martins, M.C.; Lofrano, B.E.S.N.; Hardaway, C.J.; Merchant, M.; Verdade, L. Determination of Heavy Metals by Inductively Coupled Plasma-Optical Emission Spectrometry in Fish from the Piracicaba River in Southern Brazil. Microchem. J. 2010, 94, 171–174. [Google Scholar] [CrossRef]
  46. Vu, C.T.; Lin, C.; Yeh, G.; Villanueva, M.C. Bioaccumulation and Potential Sources of Heavy Metal Contamination in Fish Species in Taiwan: Assessment and Possible Human Health Implications. Environ. Sci. Pollut. Res. 2017, 24, 19422–19434. [Google Scholar] [CrossRef] [PubMed]
  47. Yi, Y.J.; Zhang, S.H. The Relationships between Fish Heavy Metal Concentrations and Fish Size in the Upper and Middle Reach of Yangtze River. Procedia Environ. Sci. 2012, 13, 1699–1707. [Google Scholar] [CrossRef]
  48. Ferreira da Silva, S.; de Oliveira Lima, M. Mercury in Fish Marketed in the Amazon Triple Frontier and Health Risk Assessment. Chemosphere 2020, 248, 125989. [Google Scholar] [CrossRef]
  49. Basta, P.C.; de Vasconcellos, A.C.S.; Hallwass, G.; Yokota, D.; Pinto, D.d.O.d.R.; de Aguiar, D.S.; de Souza, C.C.; Oliveira-da-Costa, M. Risk Assessment of Mercury-Contaminated Fish Consumption in the Brazilian Amazon: An Ecological Study. Toxics 2023, 11, 800. [Google Scholar] [CrossRef]
  50. Rengifo, P.D.; Reyes, L.W. Presencia de Mercurio En La Amazonía Peruana, Río Napo. In Memoria del Primer Encuentro de Investigadores Ambientales; Ministerio del Ambiente, Dirección General de Investigación e Información Ambiental: Iquitos, Peru, 2012; pp. 1–6. [Google Scholar]
  51. National Research Council (US). Subcommittee on the Tenth Edition of the Recommended Dietary Allowances, 10th ed.; National Academies Press (US): Washington, DC, USA, 1989. [Google Scholar]
  52. da Silva Costa, M.; Viana, L.F.; Lima Cardoso, C.A.; Gonar Silva Isacksson, E.D.; Silva, J.C.; Florentino, A.C. Landscape Composition and Inorganic Contaminants in Water and Muscle Tissue of Plagioscion Squamosissimus in the Araguari River (Amazon, Brazil). Environ. Res. 2022, 208, 112691. [Google Scholar] [CrossRef] [PubMed]
  53. Souza-Araujo, J.; Souza-Junior, O.G.; Guimarães-Costa, A.; Hussey, N.E.; Lima, M.O.; Giarrizzo, T. The Consumption of Shark Meat in the Amazon Region and Its Implications for Human Health and the Marine Ecosystem. Chemosphere 2021, 265, 129132. [Google Scholar] [CrossRef] [PubMed]
  54. Instituto Geológico, Minero y Metalúrgico (INGEMMET). Mapa Metalogenético del oro del Perú: Operaciones y Proyectos Mineros; Instituto Geológico, Minero y Metalúrgico (INGEMMET): Lima, Peru, 2023; Available online: https://hdl.handle.net/20.500.12544/3839 (accessed on 19 July 2024).
  55. Mestanza-Ramón, C.; Jiménez-Oyola, S.; Montoya, A.V.G.; Vizuete, D.D.C.; D’Orio, G.; Cedeño-Laje, J.; Straface, S. Assessment of Hg Pollution in Stream Waters and Human Health Risk in Areas Impacted by Mining Activities in the Ecuadorian Amazon. Environ. Geochem. Health 2023, 45, 7183–7197. [Google Scholar] [CrossRef] [PubMed]
Figure 1. The Morona, Pastaza, Corrientes, and Tigre rivers are located in the northeast of Peru, in the department of Loreto. These rivers have their headwaters in Ecuadorian territory; from there, they flow into the Peruvian Amazon, joining the Marañón River, which, in confluence with the Ucayali River, forms the Amazon River. Sediment and fish sampling sites are in orange; sediment-only sampling sites are in green.
Figure 1. The Morona, Pastaza, Corrientes, and Tigre rivers are located in the northeast of Peru, in the department of Loreto. These rivers have their headwaters in Ecuadorian territory; from there, they flow into the Peruvian Amazon, joining the Marañón River, which, in confluence with the Ucayali River, forms the Amazon River. Sediment and fish sampling sites are in orange; sediment-only sampling sites are in green.
Toxics 12 00552 g001
Figure 2. Principal component analysis of fish muscle.
Figure 2. Principal component analysis of fish muscle.
Toxics 12 00552 g002
Figure 3. Principal component analysis for sediment.
Figure 3. Principal component analysis for sediment.
Toxics 12 00552 g003
Figure 4. Spearman correlation analyses for sediments and fish tissues.
Figure 4. Spearman correlation analyses for sediments and fish tissues.
Toxics 12 00552 g004
Table 1. Sampling sites, geographical information, and daily fish intake for the Corrientes, Tigre, Morona, and Pastaza rivers. Data on daily fish consumption were obtained through interviews with residents.
Table 1. Sampling sites, geographical information, and daily fish intake for the Corrientes, Tigre, Morona, and Pastaza rivers. Data on daily fish consumption were obtained through interviews with residents.
SiteSmall Town or VillageNo. of Fish Specimens per PoolRiverDistrict/ProvinceHuman Daily Fish Intake (g/Person/Day)
C1Nueva Valencia18CorrientesTrompeteros/Loreto103.00
C3Pucacuro19CorrientesTrompeteros/Loreto103.23
C4Trompeteros17CorrientesTrompeteros/Loreto82.94
C6Providencia31CorrientesTrompeteros/Loreto60.00
T1Paiche Playa16TigreTigre/Loreto146.14
T4aIntuto19TigreTigre/Loreto122.45
T4b28 de Julio28TigreTigre/Loreto175.68
T6Piura11TigreTigre/Loreto134.46
T7Nueva York30TigreTigre/Loreto56.62
M1Inka Roca-Tigreyacu10MoronaMorona/Datém del Marañón302.36
M2Caballito-Tipishcacocha19MoronaMorona/Datém del Marañón383.00
M3Shoroya nuevo10MoronaMorona/Datém del Marañón382.11
M4Pinshacocha17MoronaMorona/Datém del Marañón63.01
M5San Martin41MoronaMorona/Datém del Marañón62.53
M6Puerto Alegría13MoronaMorona/Datém del Marañón63.15
M7Puerto América15MoronaMorona/Datém del Marañón64.24
P1Nuevo Soplín13PastazaMorona/Datém del Marañón60.00
P2Loboyacu18PastazaMorona/Datém del Marañón302.37
P3Sungache18PastazaMorona/Datém del Marañón100.00
P4San Fernando10PastazaMorona/Datém del Marañón101.20
P5Musakarusha12PastazaMorona/Datém del Marañón82.41
P6Nueva Alianza8PastazaMorona/Datém del Marañón84.43
Table 2. Basic biological information of fish collected from the four Amazonian rivers in this study.
Table 2. Basic biological information of fish collected from the four Amazonian rivers in this study.
Scientific NameNameRiverTotalFeeding
Regime
Habitat
CorrientesTigreMoronaPastaza
Acestrorhynchus falcirostrisBig-eyed cachorro12 14PiscivorousBenthopelagic
Adontosternarchus balaenopsGhost knifefish 4 4CarnivorousBenthopelagic
Ageneiosus inermisManduba1 214PiscivorousPelagic
Amblydoras affinisRego rego 7 7OmnivoreDemersal
Ancistrus dolichopterusBushymouth catfish 1 1OmnivoreDemersal
Ancistrus temminckiiCarachama 4 4OmnivoreDemersal
Anodus elongatusYulilla13 4OmnivorePelagic
Astronatus ocellatusOscar 22OmnivoreBenthopelagic
Biotodoma cupidoGreenstreaked eartheater 123OmnivoreBenthopelagic
Brachyplatytoma vaillantiiLaulao catfish 11PiscivorousDemersal
Brycon amazonicusSábalo 13 4OmnivoreBenthopelagic
Bunocephalus coraoideusBanjo catfish 1 1OmnivoreDemersal
Calophysus macropterusZamurito 11CarnivorousDemersal
Chaetobranchus flavescensBujurqui vaso 151521OmnivoreBenthopelagic
Cheirodon interruptusUruguay tetra 2 2OmnivoreBenthopelagic
Chilodus punctatusSpotted headstander 5 5OmnivorePelagic
Cichla monoculusPeacock bass1 23CarnivorousBenthopelagic
Cichla ocellarisPeacock cichlid 1 1CarnivorousBenthopelagic
Corydoras arcuatusSkunk corydoras 1 1DetritivoreDemersal
Curimatella meyeriYahuarachi 11DetritivoreBenthopelagic
Cynodon gibbusChambira 11CarnivorousPelagic
Hemiodus microlepisYulilla 11OmnivoreBenthopelagic
Hemisorubim platyrhynchosPorthole shovelnose catfish1 1CarnivorousDemersal
Heros efasciatusBujurqui acha vieja 11OmnivoreBenthopelagic
Heros severusBanded cichlid 1 1OmnivoreBenthopelagic
Hoplerythrinus unitaeniatusAimara 21912CarnivorousPelagic
Hoplias malabaricusTrahira357722CarnivorousBenthopelagic
Hoplosternum littoraleAtipa 44DetritivoreDemersal
Hyphessobrycon copelandiWira mojara 5 5OmnivoreBenthopelagic
Hypophthalmus edentatusHighwaterman catfish161 17HerbivorePelagic
Hypselecara temporalisEmerald cichlid 55OmnivoreBenthopelagic
Lamontichthys stibarosShitari1 1DetritivoreDemersal
Leporinus fridericiThreespot leporinus 6 6OmnivoreBenthopelagic
Megaleporinus trifasciatusLisa11 2OmnivoreBenthopelagic
Myleus rubripinnisRedhook myleus1071624OmnivoreBenthopelagic
Pimelodus blochiiBloch’s catfish121 4OmnivoreBenthopelagic
Pinirampus pirinampuFlatwhiskered catfish1 1CarnivorousDemersal
Potamorhina altamazonicaYahuarachi6225437DetritivoreBenthopelagic
Potamotrygon magdalenaeMagdalena river stingray 2 2DetritivoreBenthopelagic
Prochilodus nigricansBlack prochilodus111211245DetritivoreBenthopelagic
Psalidodon fasciatusBanded astyanax 1 1OmnivoreBenthopelagic
Psectrogaster amazonicaChio Chio11264 41DetritivoreBenthopelagic
Pseudorinelepis genibarbisCarachama negra 1 1DetritivoreBenthopelagic
Pterodoras granulosusGranulated catfish 22 4OmnivoreDemersal
Pterygoplichthys pardalisAmazon sailfin catfish 16 16DetritivoreDemersal
Pygocentrus nattereriRed piranha 415CarnivorousPelagic
Rhaphiodon vulpinusBiara 1 1CarnivorousPelagic
Rhytiodus microlepisLisa 1 1HerbivorousBenthopelagic
Roeboides myersiiDentón21418CarnivorousBenthopelagic
Satanoperca jurupariDemon eartheater 55CarnivorousBenthopelagic
Schizodon fasciatusCharacin 549OmnivoreBenthopelagic
Semaprochilodus insignisKissing prochilodus12 25DetritivoreBenthopelagic
Serrasalmus rhombeusRed-eye piranha1 10314CarnivorousBenthopelagic
Squaliforma emarginataCarachama blanca 2 2DetritivoreDemersal
Trachelyopterus galeatusNovia cunchi2 248OmnivoreDemersal
Triportheus angulatusSardina334717OmnivoreBenthopelagic
Table 3. Metal(loid)s’ descriptive statistics in fish muscle (mg/kg wet weight); mean, standard deviation, range, and coefficient of variation (expressed in %) for the four rivers assessed.
Table 3. Metal(loid)s’ descriptive statistics in fish muscle (mg/kg wet weight); mean, standard deviation, range, and coefficient of variation (expressed in %) for the four rivers assessed.
ElementTigre (n = 104)Morona (n = 125)Corrientes (n = 85)Pastaza (n = 79)
Mean ± SDRangeCVMean ± SDRangeCVMean ± SDRangeCVMean ± SDRangeCV
Cr0.73 ± 0.340.32–1.2951.950.46 ± 0.240.23–0.9353.20.53 ± 0.100.43–0.6719.010.40 ± 0.160.29–0.7240.88
Ni0.22 ± 0.190.04–0.6494.740.10 ± 0.030.04–0.1332.070.19 ± 0.090.08–0.3245.80.06 ± 0.050.02–0.1392.2
Cu0.54 ± 0.190.22–0.8933.360.46 ± 0.130.22–0.6728.890.47 ± 0.110.32–0.6824.310.41 ± 0.130.28–0.5932.74
Zn6.30 ± 1.763.91–9.8825.195.34 ± 0.964.37–7.4215.796.58 ± 0.855.60–7.6412.985.49 ± 1.324.17–7.7224.03
As0.02 ± 0.02ND–0.07109.670.02 ± 0.02ND–0.07104.790.02 ± 0.01ND–0.0360.610.01 ± 0.002ND–0.0121.02
Cd0.006 ± 0.0020.002–0.0140.530.006 ± 0.010.001–0.0294.680.005 ± 0.0020.003–0.0131.110.01 ± 0.0040.004–0.0132.6
Pb0.05 ± 0.020.03–0.0731.910.04 ± 0.020.01–0.0754.980.06 ± 0.040.02–0.1270.60.06 ± 0.030.03–0.1353.29
Hg0.31 ± 0.100.20–0.4333.430.18 ± 0.100.10–0.3954.90.11 ± 0.010.10–0.1311.240.28 ± 0.140.12–0.5250.02
ND = not detected; n: the number of samples of fish muscle.
Table 4. Limit concentrations of metals in fish muscle according to international guidelines (mg/kg).
Table 4. Limit concentrations of metals in fish muscle according to international guidelines (mg/kg).
Guideline/ReferenceCrNiCuPbZnHgAsCd
FAO [27] 0.2500.5 0.05
FAO/WHO [33]0.058.97300.05 0.015
China QS [34]2.0 0.5 0.50.1
European Union [35]0.5 0.3 0.5 0.05
ANVISA [36]0.15.030 500.50.5
Table 5. Descriptive statistical data of the metal(loid)s found in sediments. Standard deviation and range (min–max) values in mg/kg, and coefficient of variation (CV) expressed in %.
Table 5. Descriptive statistical data of the metal(loid)s found in sediments. Standard deviation and range (min–max) values in mg/kg, and coefficient of variation (CV) expressed in %.
ElementTigre (n = 12)Morona (n = 14)Corrientes (n = 12)Pastaza (n = 12)Guidelines
Mean ± SDRangeCVMean ± SDRangeCVMean ± SDRangeCVMean ± SDRangeCVNOAAAISQGSQG-EPA
Cr7.53 ± 1.771.90–16.0063.3412.74 ± 1.518.30–21.8035.4311.58 ± 2.233.20–35.3079.6517.85 ± 3.0612.50–24.0022.771337.243.4
Ni7 ± 1.283.60–10.4032.6310.83 ± 2.397.20- 18.031.2810.57 ± 5.773.10–21.1054.5947.10 ± 8.6727.5–95.1039.629.91822.7
Cu4.24 ± 0.492.20–8.4058.677.48 ± 1.251.90–21.0096.954.58 ± 1.982.10–8.7065.059.31 ± 1.955.80–18.0042.422535.731.6
Zn16.89 ± 2.525.40–38.4062.2652.19 ± 13.5829.70–99.8045.1820.78 ± 2.328.90–37.6044.8631.94 ± 3.1925.0–39.5013.1338123121
Al2523.78 ± 378.2626.6–7447.079.459489.71 ± 1239.44467.0–21,160.062.982988.03 ± 206.5941.4–736563.715714.92 ± 379.23404.0–8674.025.762600
Fe5671.50 ± 424.11911.0–12,585.062.9414,114.14 ± 2921.68664.0–23,352.034.657160.2 ± 322.22366–14,37556.6915,543.0 ± 1077.512,993.0–19,773.012.9918,000 20,000
Ba26.80 ± 2.18.60–71.871.4889.93 ± 11.5552.7–158.138.8635.9 ± 2.8816.9–51.735.8834.97 ± 7.119.40–47.7022.580.70
As<3.600.00<3.600<3.600<3.6001.15.9<3
Cd<0.300.00<0.300<0.300<0.3000.30.60.99
Pb<3.000.00<3.000<3.000<3.000173535.8
Hg0.02 ± 0.010.01–0.0670.460.04 ± 0.020.01–0.0755.220.02 ± 0.010.01–0.0350.650.02 ±0.010.01–0.0483.850.050.170.18
n: the number of samples of sediment. NOAA: National Oceanic and Atmospheric Administration (USA). ISQG: Interim Sediment Quality Guidelines (Canada). SQG-EPA: Small Quantity Generator U.S. Environmental Protection Agency (USA).
Table 6. Carcinogenic and non-carcinogenic risk analysis of elements in fish consumed in Loreto, and carcinogenic risk index for Cr (10−4), As, and Cd (10−6). RfDs (µg/kg·day), CRI range (min–max).
Table 6. Carcinogenic and non-carcinogenic risk analysis of elements in fish consumed in Loreto, and carcinogenic risk index for Cr (10−4), As, and Cd (10−6). RfDs (µg/kg·day), CRI range (min–max).
ElementsRfDs TigreMoronaCorrientesPastaza
EDITHQCRIEDITHQCRIEDITHQCRIEDITHQCRI
Cr11.330.446.6
(1.3–13.1)
1.580.537.8
(1.01–21.5)
0.680.233.3
(1.8–4.8)
0.720.243.5
(1.3–9)
Ni200.400.02 0.260.01 0.220.01 0.140.01
Cu400.970.02 1.380.03 0.570.01 0.670.02
Zn30011.310.04 14.660.05 8.110.03 9.080.03
As* 0.30.050.1671.6
(8.6–183.8)
0.050.1778.4
(2.99–312.9)
0.020.0835.5
(5.47–64.4)
0.020.0730.3
(14.9–85.2)
Cd10.010.014.6
(0.7–7.6)
0.010.014.8
(0.8–14.6)
0.010.012.2
(1.7–2.7)
0.020.028.2
(1.4–24.1)
Pb1.50.090.06 0.100.07 0.090.06 0.110.07
Hg** 0.30.571.92 0.551.83 0.140.45 0.451.50
* Inorganic arsenic; ** elemental mercury, and mercury compounds.
Table 7. Comparison of metal(loid) concentrations (mg/kg ww) in fish among different riverine environments.
Table 7. Comparison of metal(loid) concentrations (mg/kg ww) in fish among different riverine environments.
RiverCountryCrNiCuZnAsCdPbHgReferences
TigrePeru0.32–1.290.04–0.490.28–0.754.14–8.280.003–0.060.002–0.010.03–0.070.20–0.41This study
PastazaPeru0.29–0.720.02–0.130.28–0.594.17–7.72ND–0.010.004–0.010.03–0.130.12–0.52This study
Abaeté and ParaopebaBrazil0.42–1.7 5.62–32.28.88–21.1 0.02–0.330.17–2.11 [44]
PiracicabaBrazil0.05- 1.110.05–2.830.03–4.18 0.73–0.930.02–0.920.66–3.25 [45]
SinosBrazil0.09–0.38-1.43–5.422.9–34.61.47–8.790.07–0.81.2–2.5-[43]
Northeast of ChinaChinaND–0.49ND–0.780.067–0.972.49–51.38ND–0.39ND–0.009ND–0.70ND–0.82[29]
YangtzeChina0.10–0.24-0.77–1.222.8–7.55-0.046–0.120.21–0.81-[47]
HoujinTaiwanND–83.44ND–34.59ND–380ND–107ND–3.11ND–14.5ND–13.5ND–0.38[46]
GomtiBangladesh0.004---0.002BDLBDL0.006[28]
Alto SolimõesBrazil 0.081–0.33[48]
Rio Amazonas–ManaosBrazil 0.0–2.18[49]
Bajo Napo–LoretoPerú 0.04–1.94[50]
ND: not detected; BDL: below detectable level.
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

Brousett-Minaya, M.A.; Chu-Koo, F.W.; Napuchi-Linares, J.; Zambrano Panduro, C.E.; Reyes-Larico, J.A.; Larrea-Valdivia, A.E.; Biamont-Rojas, I.E. Consumption of Native Fish Associated with a Potential Carcinogenic Risk for Indigenous Communities in the Peruvian Amazon. Toxics 2024, 12, 552. https://doi.org/10.3390/toxics12080552

AMA Style

Brousett-Minaya MA, Chu-Koo FW, Napuchi-Linares J, Zambrano Panduro CE, Reyes-Larico JA, Larrea-Valdivia AE, Biamont-Rojas IE. Consumption of Native Fish Associated with a Potential Carcinogenic Risk for Indigenous Communities in the Peruvian Amazon. Toxics. 2024; 12(8):552. https://doi.org/10.3390/toxics12080552

Chicago/Turabian Style

Brousett-Minaya, Magaly Alejandra, Fred William Chu-Koo, Juvenal Napuchi-Linares, Cynthia Elizabeth Zambrano Panduro, Juan Amilcar Reyes-Larico, Adriana Edith Larrea-Valdivia, and Ivan Edward Biamont-Rojas. 2024. "Consumption of Native Fish Associated with a Potential Carcinogenic Risk for Indigenous Communities in the Peruvian Amazon" Toxics 12, no. 8: 552. https://doi.org/10.3390/toxics12080552

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