1. Introduction

2. Material and methods

Table 1. Search Syntax(CH3Hg⁺ also termed as MeHg).

3. Results

Figure 1. Flowchart of literature search according to PRISMA 2020 guidelines.

Figure 1. Flowchart of literature search according to PRISMA 2020 guidelines.

3.1. Study characteristics

The 105 reports selected by PRISMA were processed using VosViewer to generate 1,383 keywords based on titles and abstracts of the articles. This bibliometric visualization was based on the density of eight occurrences of related keywords. Error! Reference source not found. Presents a network of keyword co-occurrences in the articles included in the review.

Figure 2. VosViewer keyword analysis with relevance in the year of article acceptance by color coding.

Figure 2. VosViewer keyword analysis with relevance in the year of article acceptance by color coding.

Results indicate a predominance of studies reporting on fish muscle tissues contaminated by Hg caused by Artisanal Small Scale Gold Mining (ASGM) (the only pollution activity mentioned), presenting risk factors through fish consumption, and studies predominantly carried out in the Brazilian Amazon region. Additionally, total Hg and methylmercury are measured in human hair and fish muscle tissues as well as the reproductive population. Moreover, the color classification in Error! Reference source not found. Shows the highest occurrence of relevant keywords in studies between 2020 and 2021, with the largest single volume of studies published in 2018. Error! Reference source not found. Shows the distribution of the number of articles published on this topic by year and country.

Figure 3. Article occurrences: (a) valid for each year; (b) valid for each Amazonian country (those not represented are grayed out).

Figure 3. Article occurrences: (a) valid for each year; (b) valid for each Amazonian country (those not represented are grayed out).

Brazil is the most covered country by relevant research about mercury contamination in the Amazon, with a total of 25 published articles. Peru and Colombia recorded 13 and 6 articles, respectively; French Guiana, Guyana, and Bolivia have 2 articles each, and Suriname has 1 article. Ecuador and Venezuela were not represented. Among the studies analyzed, the pie charts in Error! Reference source not found. shows that the most commonly used biomarker was muscle in animal tissues, and in studies conducted with human data, the most commonly used biomarker was hair, apart from blood and breast milk, each of which was presented in a single article.

Figure 4. Predominant tissue samples in datasets: (a) non-human and (b) human after screening.

Figure 4. Predominant tissue samples in datasets: (a) non-human and (b) human after screening.

Among the studies that analyzed the presence of THg in animals, analysis of different fish species prevailed in addition to categorizing these feeding guilds (Error! Reference source not found.).

Figure 5. Main data sources obtained: (a) species and (b) trophic levels.

Figure 5. Main data sources obtained: (a) species and (b) trophic levels.

3.2. Overview of THg levels in human research

The results of these studies demonstrated that ecotoxicological research on Hg is predominantly in aquatic ecosystems, mostly comprising fish species, considering exposure to indigenous and riverside communities in major rivers and tributaries. Therefore, to facilitate a more effective statistical overview, records that used fish muscle as a THg biomarker were reviewed separately.

Accordingly, the predominance of fish muscle tissue could be plotted and categorized into predatory and non-predatory feeding guilds. The boxplots for fish indicate the distribution of THg into the following categories: all fish muscle (median 0.440 µg/g and Interquartile Range (IQR) 0.820 µg/g), predatory fish muscle (median 0.690 µg/g and IQR 0.900 µg/g), and non-predatory muscle (median 0.210 µg/g and IQR 0.300 µg/g). Predatory fish, classified as carnivores, exhibit THg ranges above the guidelines established by WHO at a level of 1 µg/g and 0.5 µg/g, respectively for predatory and non-predatory fish. In contrast, the non-predatory group, which included omnivores and other species, displayed significantly lower THg levels, suggesting a safe mean level (Error! Reference source not found.).

Figure 6. Representations of THg ranges in fish muscle tissue from animal research compared to WHO and MERCOSUR guidelines.

Figure 6. Representations of THg ranges in fish muscle tissue from animal research compared to WHO and MERCOSUR guidelines.

Accordingly, the highest frequencies of studies in the main tributaries of the Amazon River were for Plagioscion squamosis Yesus, Serrasalmus rhombeus, and Cichla spp. The carnivorous guild had the highest THg values ranging from 0.086 to 5.920 μg/g, with a mean of 1.058 μg/g, followed by omnivores (0.029 μg/g to 0.700 μg/g) with a mean of 0.316 μg/g, and detritivores (0.064 μg/g to 0.370 μg/g) with a mean of 0.180 μg/g (Error! Reference source not found.).

Table 2. Highest mean or median THg value reported in the muscle records of fish grouped by basin location with indication of fish feeding habits and whether mining activities were part of the study.

Table 2. Highest mean or median THg value reported in the muscle records of fish grouped by basin location with indication of fish feeding habits and whether mining activities were part of the study.

Hydrographic basin *	Mentioned mining	Country	Study	Species/feeding guild	THg (μg/g) based on mean/median †
A	No	Brazil	[66,67]**	Hoplias malabaricus, carnivore	0.947†
A	No	Brazil	[68]	Carcharhinus acronotus, carnivore	1.120
A, T	Yes	Brazil	[69]	Arapaima sp., carnivore	0.375
A,T	No	Brazil	[70]	Plagioscion squamosisYesus, carnivore	1.510
A	Yes	Brazil	[71]	Pseudoplatystoma tigrinum, carnivore	0.920
A	No	Brazil	[72]	Ageneiosus inermis, carnivore	0.691
A	No	Brazil	[73]	Acestrorhyncus falcirostris, carnivore	1.490
A	No	Brazil	[74]	Colomesus asellus, omnivore	0.350
T	Yes	Brazil	[75]	Carnivores Group: Cichla monoculus , Plagioscion squamosisYesus , Serrasalmus calmoni.	0.668
T	Yes	Brazil	[76]	Plagioscion squamosisYesus, carnivore	0.730
T	Yes	Brazil	[77]	Cichla pinima, carnivore	1.172
T	No	Brazil	[78]	Serrasalmus rhombeus, carnivore	0.088
M	No	Brazil	[79]	Carnivorous Group: Plasgioscion squamosisYesus, Calophysus macropterus, Cichla pleiozona and Hoplias malabaricus.	0.970
M	No	Brazil	[80]	Prochilodus nigricans, detritivore	0.064
M	No	Brazil	[81]	Serrasalmus rhombeus, carnivore	0.263
M	Yes	Brazil	[82]	Triportheus angulatus, omnivore	0.290
M	No	Brazil	[83]	Triportheus albus, omnivore	0.029
M	No	Brazil	[84]	Pinirampus pirinampu, benthivore	0.060
M	No	Brazil	[85]	Arapaima gigas, carnivore	0.153
M	No	Brazil	[86]	Serrasalmus rhombeus, carnivore	1.640
M	No	Brazil	[87]	Plagioscion squamosisYesus, carnivore and Colossoma macropomum, omnivore	0.086
M	No	Brazil	[88]	Brachyplatystoma filamentosum, carnivore	0.402
M	No	Brazil	[89]	Calophysus maropterus, carnivore	1.400
M	No	Peru/ Brazil (Border)	[90]	Calophysus maropterus, carnivore	0.229
M	No	Brazil	[91]	Cichla spp., carnivore	0.128
M	No	Brazil	[92]	Semaprochilodus spp.(Jaraqui), detritivore	0.132
M	No	Brazil	[93]	Serrasalmus rhombeus, carnivore	0.268
M	No	Brazil	[94]	Cichla spp. (Tucunaré), carnivore	0.435
M	Yes	Brazil	[95]	Serrasalmus rhombeus, carnivore	0.417
X	No	Brazil	[96]	Hemiodus unimaculatus, omnivore	0.480
Tp	No	Brazil	[97]	Serrasalmus rhombeus, carnivore	0.304
Tp	No	Brazil	[98]	Brycon falcatus, omnivore	0.052
Ar	Yes	Brazil	[12]	Carnivorous Group: Ageneiosus inermis, Boulengerella cuvieri, Cichla monoculus, and Hoplias aimara	0.580
Ar	Yes	Brazil	[99]	Curimata incompta, detritivore	0.370
Ar	Yes	Brazil	[100]	Plagioscion squamosisYesus, carnivore	0.320†
J	No	Brazil	[101]	Plagioscion squamosisYesus, carnivore	1.090
MD	Yes	Peru	[102]	Serrasalmus spp., carnivore	0.280
MD	Yes	Peru	[103]	Serrasalmus spp., carnivore	3.720
B	No	Bolivia	[104]	Brycon amazonicus, omnivore	0.700
Br	Yes	Brazil	[105]	Pinirampus pirinampu, carnivore	0.869
Br	Yes	Brazil	[33]	Pygocentrus nattereri, carnivore	1.215
M	Yes	Brazil	[106]	Serrasalmus rhombeus, carnivore	0.283
Su	Yes	Suriname	[107]	Multiple, high THg in carnivores: Acestrorhynchus microlepsis, Hoplias malabaricus, Cichla ocellaris, Serrasalmus rhombeus,and Pristobrycon eigenmanni.	2.528
C	Yes	French Guiana	[108]	Hoplias aimara and Boulengerella cuvieri, carnivore	2.900
Mz	Yes	Guyana	[109]	Ageneiosus ucayalensis, carnivore	5.920

* Main Amazon River: A, Tapajós River: T, Madeira River: M, Xingu River: X, Teles Pirés River: Tp, Jiparaná River: J, Madre de Dios River; MD, Beni River: B, Rio Branco: Br, Suriname River: Su, Camopi River: C; Mazaruni River: Mz. ** These studies used the same fish muscle tissue collection and mean THg.

Additionally, studies evaluating fish in hydroelectric reservoirs and dam regions showed that Puruzinho and Catalão Lakes presented higher mercury concentrations of S. rhombeus (mean 0.789 μg/g, and range of 0.029 to 1.640 μg/g) [73,83,86]. On the other hand, the reservoirs of Belo Monte and Jirau Hydroelectric Plants showed high concentrations of mercury in S. rhombeus (0.480 μg/g) and lower concentrations in Triportheus albus (0.132 μg/g) [92,96].

Error! Reference source not found. presents the remaining records, which neither analyzed fish muscle tissue for THg nor were sampled at Amazonian basin locations. The measured THg included muscle tissue samples from fish along the Atlantic coast, hepatic tissue of fish, and tissues from species other than fish. These animal tissues show a mean THg value of 3.850 μg/g with a range from 0.011 to 17.900 μg/g with the highest THg value in the liver of Arapaima gigas.

Table 3. Tissue THg in fish, birds, mammals, and crustaceans according to location in the Amazon region and mining area coverage.

Table 3. Tissue THg in fish, birds, mammals, and crustaceans according to location in the Amazon region and mining area coverage.

Location	Mentioned mining	Study	Species	Tissue	Mean THg (μg/g)
Madeira River, Rondônia, Brazil	No	[110]	Arapaima gigas, fish, carnivore	hepatic	17.420
Araguari River, Amapá, Brazil	No	[111]	Anodus orinocensis, fish, omnivore	hepatic	0.500
Madeira River, Brazil	No	[112]	Macrobrachium amazonicum, shrimp, omnivore	muscle	0.610
Atlantic Coast, Ilha dos Caranguejos, Brazil	No	[113]	Sciades herzbergii, fish, omnivore	muscle	0.033
Mamirauá, Amazonas, Brazil	No	[114]	Panthera onca, mammal, carnivore	pelage	17.900
Guaporé River, Brazil	No	[115]	Inia boliviensis, mammal, piscivore	adipose	1.323
Beni River, Bolívia	No	[116]	Caiman yacare, reptile, piscivore	muscle	0.150
Madeira River, Brazil	No	[117]	Ardea cocoi, bird, carnivore	feather	4.046
Biological Station Cocha Cashu, Peru	No	[118]	Rhynconycteris naso, mammal, carnivore	pelage	7.440
Madre de Dios, Peru	Yes	[119]	Phyllostomus elongatus, mammal, carnivore	pelage	0.660
Figueiredo, Amazonas, Brazil	No	[120]	Trichechus inunguis, mammal, herbivore	muscle	0.059
Arauca River and Orinoco River, Colombia	Yes	[121]	Inia sp. and Sotalia sp., mammal, piscivore	muscle	0.870
Itapuru mirim Lagoon, Brazil	No	[122]	Podocnemis unifilis, reptile, herbivore	muscle	0.011
Xingu and Teles Pires’ Rivers, Brazil	No	[123]	Podocnemis unifilis, reptile, herbivore	muscle	0.134
Uatumã River, Balbina Brazil	No	[124]	Podocnemis expansa, reptile, omnivore	muscle	0.109
Teles Pires, Brazil	No	[125]	Chloroceryle amazona, bird, piscivore	feather	11.570
Teles Pires, Brazil	No	[126]	Chloroceryle amazona, bird, piscivore	feather	4.000
Madeira River, Brazil	No	[127]	Macrobrachium depresYesanum, Macrobrachium jelskii, shrimp, omnivore	muscle	0.022

Variation from 0.010 to 17.900 µg/g of THg across ecoregions can be observed. Most sampling points are recorded in the southern Amazon forests, with a mean mercury level of 0.890 µg/g (range 0.016 to 11.570 µg/g), followed by the western Amazon forests, with a mean mercury level of 1.700 µg/g (range 0.150 to 17.420 µg/g). The least abundant points were recorded in the Guyana ecoregion, with a mean mercury level of 1.570 µg/g (range 0.165 to 5.920 µg/g).

The Brazilian territory has the highest concentration of scientific reports, including the Amazon River and its major tributaries, with emphasis on the Madeira and Tapajós Rivers. Specifically, the Madeira River basin, which acts as a boundary between bioregions, has a mean mercury level of 0.470 µg/g in fish (range 0.014 to 4.046 µg/g). Also in Brazil Rondônia, near the region of Porto Velho, and the Tapajós River have a mean of 0.69 µg/g (range 0.0740 to 1.510 µg/g). Additionally, the Madre de Dios River basin in Peru has a mean of 0.720 µg/g of mercury (range 0.200 to 3.720 µg/g), while the mean mercury level in fish of the Amazon River was 0.673 µg/g (range 0.050 to 1.490 µg/g) (Error! Reference source not found.).

Figure 7. Spatial distribution of animal studies is categorized by THg levels in fish in relation to the permissible reference concentration of 1.0 µg/g in the human diet, as established by WHO.

Figure 7. Spatial distribution of animal studies is categorized by THg levels in fish in relation to the permissible reference concentration of 1.0 µg/g in the human diet, as established by WHO.

The highest levels of THg reported in the Amazon are in the skin tissues of Panthera onca (17.900 µg/g) and the hepatic tissue of Arapaima gigas (17.400 µg/g). In Brazil, the highest value of mercury in predatory fish was found in the black piranha (S. rhombeus), a carnivore with 1.640 µg/g [86]. The highest muscle mercury concentration in countries outside Brazil was found in a study carried out at Oxbow Lakes, a natural protected area in Madre de Dios Province, Peru, at 3.170 µg/g, and along the Mazaruni River in Guyana, at 5.920 µg/g, both in areas close to mining activities [103,109].

3.3. Overview of THg levels in human research

In a comparative analysis of indigenous populations (impacted or not by mining), and non-indigenous populations, this systematic review provided evidence that THg found in the hair of indigenous populations was marginally higher (median of 10.900 µg/g and IQR 4.900 µg/g) than that found among non-indigenous populations (median of 9.150 µg/g and IQR 7.490 µg/g). Studies that examined (small-scale) mining areas had a comparatively larger sampling range (IQR 10.652 µg/g) but lower median (8.275 µg/g) within its population.

All categories analyzed exceeded the WHO guidelines for Hg in hair, suggesting that indigenous populations are exposed to higher levels of Hg, which may have implications for the health of these populations (Error! Reference source not found.).

Figure 8. Comparison of THg concentrations in human hair in different groups. The figure points out data for overall samples, indigenous population and mining areas.

Figure 8. Comparison of THg concentrations in human hair in different groups. The figure points out data for overall samples, indigenous population and mining areas.

Total hair THg in humans ranged from 1.030 to 27.620 µg/g. Notably, we can distinguish the following groups: children, who have a mean of 8.81 µg/g (range 1.030 to 22.380 µg/g); indigenous population with a mean of 13.190 µg/g (range 2.060 to 34.900 µg/g); women of reproductive age, including mothers, pregnant and lactating, who have a mean of 7.710 µg/g (range 2.120 to 12.80 µg/g); riverside populations with mean concentrations of 10.610 µg/g (range 3.070 to 22.380 µg/g); and, finally, a mean of 11.930 µg/g (range 1.740 to 34.90 µg/g) reported for individuals living in mining areas. Error! Reference source not found. shows a list of human studies with significant findings related to THg concentrations in human tissues.

Table 4. Total THg in human hair of Amazonian populations.

Table 4. Total THg in human hair of Amazonian populations.

Hydrographic basin *	Mining mentioned	Country	Study	Community/Population	THg (μg/g) in mean /median †
To	Yes	Brazil	[128]	Adults (18-70), fished as a staple food, near the reservoir of Tucuruí Dam.	10.900
U	Yes	Brazil	[129]	Age groups and comparison of various villages. Yanomami indigenous reserve, with a high diet of fish and mining activity.	15.500†
M	Yes	Brazil	[130]	Age groups of adults ranging from 17 to 92 years.	26.030
To, Ta	Yes	Brazil	[131]	Adult riverside dwellers only (18 to 60 years old).	4.500
Ta	Yes	Brazil	[132]	Riverside dwellers only; adult women (13 to 53 years old).	9.150
Ta	Yes	Brazil	[133]	Munduruku Indigenous Reserve. Comparison between villages.	7.400
M, Ta	No	Brazil	[76]	Pregnant women (18 to 40 years old). Fish Diet.	6.070
MD	Yes	Peru	[64]	Urban and rural demographic comparison with a focus on fish diets.	1.740
A	Yes	Colombia	[71]	Indigenous community in Puerto Nariño. Mean age ~ 35 years. Diet rich in fish.	5.310
Ta	No	Brazil	[11]	Munduruku Indigenous Reserve. Comparison between villages and age categorization with juvenile, childbearing age, and other adults	11.500
Ta	No	Brazil	[134]	Munduruku Indigenous Reserve. Comparison between villages and the exclusively juvenile population.	11.800
Ta	No	Brazil	[10]	Munduruku Indigenous Reserve. Comparison between villages. Ages > 12 years.	7.400
A, Cg, Ta	Yes	Brazil	[135]	Youth and adults in riverside communities.	12.700
MD	Yes	Peru	[136]	Matsigenka Indigenous community (years 1 to 65).	11.830
MD	No	Peru	[137]	Riverside communities. High fish diet.	4.800
MD	No	Peru	[138]	Comparison of various dwellings in the Amarakaeri Reserve, age categorization (under 5 years old, and 5 to 11 years old).	1.030†
MD	Yes	Peru	[139]	Comparison of various dwellings in the Amarakaeri Reserve.	4.150
A	Yes	Brazil	[140]	Riverside population. Prenatal exposure, women of childbearing age (15 to 49 years)	6.490
To, Ta	No	Brazil	[141]	Riverside communities with ages between 19 and 70 years (high THg)	15.900†
M, N	Yes	Brazil	[142]	Lactating women.	2.120
To	No	Brazil	[143]	Riverine populations in the Tucuruí Dam reservoir area.	8.120†
CP	Yes	Colombia	[26]	Indigenous communities in Tarapacá village.	17.800†
MD	Yes	Peru	[144]	Women of child-bearing age.	5.500
Ta, To	No	Brazil	[145]	Children from riverside villages; born to women aged between 25 and 40. Fish-rich diet and primary exposure to Hg.	22.380
M	No	Brazil	[146]	Children/adolescents aged 6 to 14 along the Madeira River.	3.070
MD	Yes	Peru	[147]	Comparison of various dwellings in the Amarakaeri Reserve. Indigenous Native Children (6 to 15 years old)	2.060
RK	Yes	Guyana	[148]	Indigenous people from the Rupununi region (15 to 78 years old). High fish diet. Comparison of mining area and control area.	27.620
Ta, To	Yes	Brazil	[149]	Riverine men and miners and THg among them. High fish diet. Itaituba and Serra Pelada.	20.000
MD	Yes	Peru	[150]	Comparison of various dwellings in the Amarakaeri Reserve. Women of childbearing age (15 to 49 years).	3.500†
M	Yes	Brazil	[151]	Riverine, rural, mining and urban communities. Women of childbearing age.	12.220†
To	No	Brazil	[152]	Adults (18–70 years), Riverine populations in the Tucuruí Dam reservoir area.	7.900†
Ap	Yes	Colombia	[153]	Population in different locations in mining regions. High fish diet.	14.920
Ta	No	Brazil	[154]	Munduruku indigenous reserve. Comparison between villages and categorization. Diet rich in fish. Age over 12 years old.	8.500
Ap	Yes	Colombia	[15]	Indigenous population of the Yaigojé Apaporis National Natural Park.	34.900
Ta	No	Brazil	[55]	Adult riverside residents only (18 to 60 years old). High consumption of fish.	10.800
Co	Yes	French Guiana	[155]	Only pregnant women, ethnic groups considered (15 to 41 years old); tribal and indigenous communities of Wayana.	12.800
M	No	Brazil	[156]	Mothers and children in childbirth after pregnancy at 6, 24 and 59 months of age.	11.610

* Madre de Dios River: MD, Tapajós River: Ta, Cotuhe and Putumayo Rivers: CP, Campina Grande River: Cg, Tocantins River: To, Apapóris River: Ap, Uiaiacás River: U, Courantyne River: Co, Rupununi and Kuyuwini Rivers (Guyana): RK.

Only two studies do not consider total human mercury concentrations in hair. One reported data in breast milk (mean 0.010 µg/g THg) of lactating women to estimate exposure to neurotoxic metals influenced by small-scale mining in infants in Porto Velho, Rondônia, Brazil [157] Another reported blood levels (mean 0.021 µg/g THg) in Amazonian juvenile populations in Cuniã, Brazil [158].

It should be noted that the indigenous community is well represented, especially in the Munduruku reserve/community in the Tapajós basin and in studies in the Madre de Dios region in Peru, as part of cohort studies [139,147,150], where comparisons were made between native demographics and mining hotspots, together with respective controls around the Amarakaeri Communal Reserve. Pregnant women, women of reproductive age, and juveniles received particular attention owing to their heightened vulnerability to mercury exposure. Interestingly, one study revealed that occupational Hg exposure from mining remained lower than levels observed in riverside communities, as exemplified by the riverside population of Itaituba (20 µg/g THg) compared to the mining region of Serra Pelada (1 µg/g THg) [149].

No statistical differences (p < 0.05) in THg concentration were found between the groups analyzed (Children, Indigenous, Maternity, Riverside, and those directly affected by mining) using MLG models. According to the summary in Error! Reference source not found., no parameters were strongly correlated with concentrations in human hair. Logistic regression showed better model fit by Akaike information criterion (AIC), but all P values were > 0.05, i.e., no demonstrated connection.

Table 5. General linear model (GLM) and logistic regression of the effect of THg on mining-related parameters and demographic classes in human studies.

Table 5. General linear model (GLM) and logistic regression of the effect of THg on mining-related parameters and demographic classes in human studies.

		GLM	Logistic regression
	AIC	257.26	53.867
	parameters	P-value	P-value
THg	Children	0.6055	0.416
	Indigenous	0.4201	0.273
	Maternity	0.2364	0.661
	Riverside	0.6108	0.287
	Mining	0.2413	0.774

The spatial representation of human research is highest in the southern forest bioregion with 34 records. In this area, the highest density was recorded, in the Tapajós basin, followed by western Amazon ecoregions with 28 records. The remaining studies were conducted in the other three regions.

Human data presented spatial trends, nearly identical to studies concentrated in the Tapajós and Madeira River basins in Brazil and Madre de Dios in Peru. Evidence of three classes of THg gradients, along with several high-Hg points, can be visualized as deep red dots scattered across the map. This feature has also been observed in the northern and western areas of the Amazon Forest, including studies conducted in the state of Amazonas and, Roraima in Brazil, and Nariño in the Colombian Amazon. These observations are illustrated in Error! Reference source not found..

Figure 9. Spatial distribution of human studies by THg levels was categorized by permissible (1.8 µg/g), dangerous (10 µg/g), and above the danger level (> 10 µg/g).

Figure 9. Spatial distribution of human studies by THg levels was categorized by permissible (1.8 µg/g), dangerous (10 µg/g), and above the danger level (> 10 µg/g).

3.3. Mining

Studies have reported on Hg in regions with mining activities in floodplain areas, including all classes of Amazonian floodplains, grouped according to Dinerstein et al. [58]. It was possible to observe tributaries with intense mining activity near the sampling points, which are grouped on the map into classes by distance, i.e., of 0-20 km, 20-50 km, and above 50 km, relative to mining polygons. The mining polygons (orange scheme) are a combination of datasets consisting of global mining footprints [60] with a layer of illegal mining, as adapted from RAISG [46].

Floodplain areas were specifically included in the analysis to represent zones of high water retention and accumulation of mining tailings, which are factors that can contribute to Hg concentrations in the environment [86,159]. Notably, the extent of illegal mining is more widely recognized by initiatives such as RAISG, the data from which indicate an overlap of these activities with the studied areas. In particular, these illegal operations are prevalent in the western regions of the Napo River, an important tributary of the Amazon River basin, affecting the sample collection areas situated in the border zones that separate Peru, Colombia, and Brazil. Moreover, illegal mining activity is intense along the Madre de Dios River basin in Peru, with operations extending close to designated sample collection areas. Certain sample points with close proximity classes (up to 20km, and 20km to 50km) intersect with mining zones.

Highlights for animal studies from these points are in the Mazaruni River basin region in Guyana and the downstream Suriname River region, despite the low representation of study articles. Regions with significant research overlap and mining activity in the interior include the Tapajós basin, close to the Munduruku Reserve and Itaituba, and the Itacaiúnas River Basin, situated between the Xingu and Tocantins River basins in Brazil.

Based on distance classification, mercury concentrations within a 20 km radius of the georeferenced mining sites (closest) had a mean of 0.870 µg/g (range 0.033–5.920 µg/g) for fish, and Serrasalmus spp. are the most frequently mentioned. The approximation category between 20 and 50 km had a mean of 0.506 µg/g (range 0.022 to 1.510 µg/g), and within this distance category, Plagioscion squamosisYesus was the most cited fish species. Although higher, the mean muscle THg concentrations in fish closest to mining are still slightly below the 1 µg/g limit, indicating a permissible level for predatory fish, but it does exceed the permitted dietary reference of Hg set at 0.50 µg/g for their non-predatory counterparts. Error! Reference source not found. shows the point data for animal records, which are mapped alongside polygons that indicate small-scale mining.

Figure 10. Spatial distribution of animal studies relative to mining polygons. Distance classes, as indicated by the 20 km and 50 km markers, were defined by the difference in point size.

Figure 10. Spatial distribution of animal studies relative to mining polygons. Distance classes, as indicated by the 20 km and 50 km markers, were defined by the difference in point size.

Research on human tissues has also been mapped along with the geospatial information of mining and indigenous territories. The results of human studies show that the highest number of studies are close to the illegal mining polygons of RAISG in the Madre de Dios and Tapajós basins. The Madeira River basin is also the subject of a high number of studies, but with less illegal mining activity. This is because illegal mining follows river flow, likely the result of alluvial mining activities. Consequently, the cities of Itaituba and Marabá, both in the Tapajós Basin, are among the most frequent sample areas near illegal mining activities reported in high numbers.

Despite limited spatial representation in the north, some studies have highlighted the presence of mining activities. Among these, small-scale mining has been reported in the mountainous region of Marudi, which is located in Guyana [[148]. Another point of interest is the Lawa River Basin situated in French Guiana [129]. Additionally, the Yanomami indigenous region in Roraima, Brazil, which is in the Uraricoera River Basin, is also mentioned [155]. Although limited in number, these studies hold significant value because of their proximity to mining areas in the Guiana Shield.

The human samples closest to mining areas up to 20 km had a mean THg of 4.301 µg/g (1 to 11.61 µg/g), while human samples at a distance of 20 km to 50 km had a mean of 6.782 µg/g (0.01 to 12.80 µg/g). Notably, all studies conducted within a 20 km radius of mining areas were also within a 17 km radius of indigenous territories. Error! Reference source not found. shows human studies related to mining polygons.

Figure 11. Spatial distribution of human studies relative to mining polygons. Distance classes, as indicated by the 20 km and 50 km markers, were defined by the difference in point size. Native/indigenous territories were included as darker overlays.

Figure 11. Spatial distribution of human studies relative to mining polygons. Distance classes, as indicated by the 20 km and 50 km markers, were defined by the difference in point size. Native/indigenous territories were included as darker overlays.

4. Discussion

5. Conclusions

References

1. Paiva, P.F.P.R.; de Lourdes Pinheiro Ruivo, M.; da Silva Júnior, O.M.; de Nazaré Martins Maciel, M.; Braga, T.G.M.; de Andrade, M.M.N.; dos Santos Junior, P.C.; da Rocha, E.S.; de Freitas, T.P.M.; da Silva Leite, T.V.; et al. Deforestation in Protect Areas in the Amazon: A Threat to Biodiversity. Biodiversity and Conservation 2020, 29, 19–38. [Google Scholar] [CrossRef]
2. Garcia, B. The Amazon from an International Law Perspective. Cambridge University Press, 2011; 1-139-49668-9. [Google Scholar]
3. Antonelli, A.; Zizka, A.; Carvalho, F.A.; Scharn, R.; Bacon, C.D.; Silvestro, D.; Condamine, F.L. Amazonia Is the Primary Source of Neotropical Biodiversity. Proceedings of the National Academy of Sciences 2018, 115, 6034–6039. [Google Scholar] [CrossRef] [PubMed]
4. Nunes, C.A.; Berenguer, E.; França, F.; Ferreira, J.; Lees, A.C.; Louzada, J.; Sayer, E.J.; Solar, R.; Smith, C.C.; Aragão, L.E.O.C.; et al. Linking Land-Use and Land-Cover Transitions to Their Ecological Impact in the Amazon. Proceedings of the National Academy of Sciences 2022, 119, 1–9. [Google Scholar]
5. Matricardi, E.A.T.; Skole, D.L.; Costa, O.B.; Pedlowski, M.A.; Samek, J.H.; Miguel, E.P. Long-Term Forest Degradation Surpasses Deforestation in the Brazilian Amazon. Science 2020, 369, 1378–1382. [Google Scholar] [CrossRef] [PubMed]
6. Giljum, S.; Maus, V.; Kuschnig, N.; Luckeneder, S.; Tost, M.; Sonter, L.J.; Bebbington, A.J. A Pantropical Assessment of Deforestation Caused by Industrial Mining. Proc. Natl. Acad. Sci. U.S.A. 2022, 119, e2118273119. [Google Scholar] [CrossRef]
7. Albert, J.S.; Carnaval, A.C.; Flantua, S.G.A.; Lohmann, L.G.; Ribas, C.C.; Riff, D.; Carrillo, J.D.; Fan, Y.; Figueiredo, J.J.P.; Guayasamin, J.M.; et al. Human Impacts Outpace Natural Processes in the Amazon. Science 2023, 379. [Google Scholar] [CrossRef]
8. Vega, C.; Orellana, J.; Oliveira, M.; Hacon, S.; Basta, P. Human Mercury Exposure in Yanomami Indigenous Villages from the Brazilian Amazon. IJERPH 2018, 15, 1051. [Google Scholar] [CrossRef] [PubMed]
9. Siqueira-Gay, J.; Sánchez, L.E. The Outbreak of Illegal Gold Mining in the Brazilian Amazon Boosts Deforestation. Regional Environmental Change 2021, 21, 1–5. [Google Scholar] [CrossRef]
10. Achatz, R.W.; De Vasconcellos, A.C.S.; Pereira, L.; Viana, P.V.D.S.; Basta, P.C. Impacts of the Goldmining and Chronic Methylmercury Exposure on the Good-Living and Mental Health of Munduruku Native Communities in the Amazon Basin. IJERPH 2021, 18, 8994. [Google Scholar] [CrossRef]
11. Basta, P.C.; De Sousa Viana, P.V.; De Vasconcellos, A.C.S.; Santos Périssé, A.R.; Hofer, C.B.; Paiva, N.S.; Kempton, J.W.; De Andrade, D.C.; De Oliveira, R.A.A.; Achatz, R.; et al. Mercury Exposure in Munduruku Indigenous Communities from Brazilian Amazon: Methodological Background and an Overview of the Principal Results. International Journal of Environmental Research and Public Health 2021, 18. [Google Scholar] [CrossRef]
12. Hacon, S.D.S.; Oliveira-da-Costa, M.; Gama, C.D.S.; Ferreira, R.; Basta, P.C.; Schramm, A.; Yokota, D. Mercury Exposure through Fish Consumption in Traditional Communities in the Brazilian Northern Amazon. IJERPH 2020, 17, 5269. [Google Scholar] [CrossRef]
13. Vasconcellos, A.C.S. de; Hallwass, G.; Bezerra, J.G.; Aciole, A.N.S.; Meneses, H.N. de M.; Lima, M. de O.; Jesus, I.M. de; Hacon, S. de S.; Basta, P.C. Health Risk Assessment of Mercury Exposure from Fish Consumption in Munduruku Indigenous Communities in the Brazilian Amazon. International Journal of Environmental Research and Public Health 2021, 18, 7940. [Google Scholar] [CrossRef]
14. Pestana, I.A.; De Rezende, C.E.; Almeida, R.; De Lacerda, L.D.; Bastos, W.R. Let’s Talk about Mercury Contamination in the Amazon (Again): The Case of the Floating Gold Miners’ Village on the Madeira River. The Extractive Industries and Society 2022, 11, 101122. [Google Scholar] [CrossRef]
15. Valdelamar-Villegas, J.; Olivero-Verbel, J. High Mercury Levels in the Indigenous Population of the Yaigojé Apaporis National Natural Park, Colombian Amazon. Biol Trace Elem Res 2020, 194, 3–12. [Google Scholar] [CrossRef]
16. Calvimontes, J.; Massaro, L.; Araujo, C.H.X.; Moraes, R.R.; Mello, J.; Ferreira, L.C.; De Theije, M. Small-Scale Gold Mining and the COVID-19 Pandemic: Conflict and Cooperation in the Brazilian Amazon. The Extractive Industries and Society 2020, 7, 1347–1350. [Google Scholar] [CrossRef]
17. Velásquez Ramírez, M.G.; Barrantes, J.A.G.; Thomas, E.; Gamarra Miranda, L.A.; Pillaca, M.; Tello Peramas, L.D.; Bazán Tapia, L.R. Heavy Metals in Alluvial Gold Mine Spoils in the Peruvian Amazon. Catena 2020, 189, 104454. [Google Scholar] [CrossRef]
18. Adler Miserendino, R.; Guimarães, J.R.D.; Schudel, G.; Ghosh, S.; Godoy, J.M.; Silbergeld, E.K.; Lees, P.S.J.; Bergquist, B.A. Mercury Pollution in Amapá, Brazil: Mercury Amalgamation in Artisanal and Small-Scale Gold Mining or Land-Cover and Land-Use Changes? ACS Earth and Space Chemistry 2018, 2, 441–450. [Google Scholar] [CrossRef]
19. Bullock, E.L.; Woodcock, C.E.; Souza, C.; Olofsson, P. Satellite-based Estimates Reveal Widespread Forest Degradation in the Amazon. Global Change Biology 2020, 26, 2956–2969. [Google Scholar] [CrossRef]
20. Gallay, M.; Mora, A.; Martinez, J.; Gardel, A.; Laraque, A.; Sarrazin, M.; Beaucher, E.; Doudou, J.; Lagane, C. Dynamics and Fluxes of Organic Carbon and Nitrogen in Two Guiana Shield River Basins Impacted by Deforestation and Mining Activities. Hydrological Processes 2018, 32, 17–29. [Google Scholar] [CrossRef]
21. Mestanza-Ramón, C.; Mora-Silva, D.; D’Orio, G.; Tapia-Segarra, E.; Gaibor, I.D.; Esparza Parra, J.F.; Chávez Velásquez, C.R.; Straface, S. Artisanal and Small-Scale Gold Mining (ASGM): Management and Socioenvironmental Impacts in the Northern Amazon of Ecuador. Sustainability 2022, 14, 6854. [Google Scholar] [CrossRef]
22. Veiga, M.M.; Gunson, A.J. Gravity Concentration in Artisanal Gold Mining. Minerals 2020, 10, 1026. [Google Scholar] [CrossRef]
23. Dethier, E.N.; Sartain, S.L.; Lutz, D.A. Heightened Levels and Seasonal Inversion of Riverine Suspended Sediment in a Tropical Biodiversity Hot Spot Due to Artisanal Gold Mining. Proceedings of the National Academy of Sciences of the United States of America 2019, 116, 23936–23941. [Google Scholar] [CrossRef]
24. Esdaile, L.J.; Chalker, J.M. The Mercury Problem in Artisanal and Small-Scale Gold Mining. Chemistry - A European Journal 2018, 24, 6905–6916. [Google Scholar] [CrossRef]
25. Espejo, J.C.; Messinger, M.; Román-Dañobeytia, F.; Ascorra, C.; Fernandez, L.E.; Silman, M. Deforestation and Forest Degradation Due to Gold Mining in the Peruvian Amazon: A 34-Year Perspective. Remote Sensing 2018, 10, 1–17. [Google Scholar] [CrossRef]
26. Alcala-Orozco, M.; Caballero-Gallardo, K.; Olivero-Verbel, J. Mercury Exposure Assessment in Indigenous Communities from Tarapaca Village, Cotuhe and Putumayo Rivers, Colombian Amazon. Environ Sci Pollut Res 2019, 26, 36458–36467. [Google Scholar] [CrossRef]
27. Cosio, C. Special Issue on Bioconversion, Bioaccumulation and Toxicity of Mercury in a Changing World. Applied Sciences 2020, 10, 6548. [Google Scholar] [CrossRef]
28. Teixeira, R.A.; Pereira, W.V.D.S.; Souza, E.S.D.; Ramos, S.J.; Dias, Y.N.; Lima, M.W.D.; De Souza Neto, H.F.; Oliveira, E.S.D.; Fernandes, A.R. Artisanal Gold Mining in the Eastern Amazon: Environmental and Human Health Risks of Mercury from Different Mining Methods. Chemosphere 2021, 284, 131220. [Google Scholar] [CrossRef]
29. Fujimura, M.; Usuki, F. Cellular Conditions Responsible for Methylmercury-Mediated Neurotoxicity. International Journal of Molecular Sciences 2022, 23, 7218. [Google Scholar] [CrossRef]
30. Vasconcellos, A.C.S.D.; Hallwass, G.; Bezerra, J.G.; Aciole, A.N.S.; Meneses, H.N.D.M.; Lima, M.D.O.; Jesus, I.M.D.; Hacon, S.D.S.; Basta, P.C. Health Risk Assessment of Mercury Exposure from Fish Consumption in Munduruku Indigenous Communities in the Brazilian Amazon. IJERPH 2021, 18, 7940. [Google Scholar] [CrossRef]
31. Crespo-Lopez, M.E.; Augusto-Oliveira, M.; Lopes-Araújo, A.; Santos-Sacramento, L.; Yuki Takeda, P.; Macchi, B. de M.; do Nascimento, J.L.M.; Maia, C.S.F.; Lima, R.R.; Arrifano, G.P. Mercury: What Can We Learn from the Amazon? Environment International 2021, 146. [Google Scholar] [CrossRef]
32. Crespo-López, M.E.; Augusto-Oliveira, M.; Santos-Sacramento, L.; Lopes-Araújo, A. Mercúrio Na Amazônia Uma Breve Contextualizaçao Do Problema. 2021.
33. De Oliveira, D.F.; De Castro, B.S.; Do Nascimento Recktenvald, M.C.N.; Da Costa Júnior, W.A.; Da Silva, F.X.; De Menezes Alves, C.L.; Froehlich, J.D.; Bastos, W.R.; Ott, A.M.T. Mercury in Wild Animals and Fish and Health Risk for Indigenous Amazonians. Food Additives & Contaminants: Part B 2021, 14, 161–169. [Google Scholar] [CrossRef] [PubMed]
34. Hacon, S. de S.; Oliveira-Da-costa, M.; Gama, C. de S.; Ferreira, R.; Basta, P.C.; Schramm, A.; Yokota, D. Mercury Exposure through Fish Consumption in Traditional Communities in the Brazilian Northern Amazon. International Journal of Environmental Research and Public Health 2020, 17, 1–15. [Google Scholar] [CrossRef] [PubMed]
35. Baturin, G.; Gordeev, V. Geochemistry of Suspended Matter in the Amazon River Waters. Geochemistry International 2019, 57, 197–205. [Google Scholar] [CrossRef]
36. Kasper, D.; Forsberg, B.R.; do Amaral Kehrig, H.; Amaral, J.H.F.; Bastos, W.R.; Malm, O. Mercury in Black-Waters of the Amazon; 2018; ISBN 978-3-319-90121-3.
37. Fisher, J.A.; Schneider, L.; Fostier, A.-H.; Guerrero, S.; Guimarães, J.R.D.; Labuschagne, C.; Leaner, J.J.; Martin, L.G.; Mason, R.P.; Somerset, V.; et al. A Synthesis of Mercury Research in the Southern Hemisphere, Part 2: Anthropogenic Perturbations. Ambio 2023, 52, 918–937. [Google Scholar] [CrossRef] [PubMed]
38. Brito, B.C.; Forsberg, B.R.; Kasper, D.; Amaral, J.H.F.; De Vasconcelos, M.R.R.; De Sousa, O.P.; Cunha, F.A.G.; Bastos, W.R. The Influence of Inundation and Lake Morphometry on the Dynamics of Mercury in the Water and Plankton in an Amazon Floodplain Lake. Hydrobiologia 2017, 790, 35–48. [Google Scholar] [CrossRef]
39. Salazar-Camacho, C.; Salas-Moreno, M.; Marrugo-Madrid, S.; Marrugo-Negrete, J.; Díez, S. Dietary Human Exposure to Mercury in Two Artisanal Small-Scale Gold Mining Communities of Northwestern Colombia. Environment International 2017, 107, 47–54. [Google Scholar] [CrossRef] [PubMed]
40. Diringer, S.E.; Berky, A.J.; Marani, M.; Ortiz, E.J.; Karatum, O.; Plata, D.L.; Pan, W.K.; Hsu-Kim, H. Deforestation Due to Artisanal and Small-Scale Gold Mining Exacerbates Soil and Mercury Mobilization in Madre de Dios, Peru. Environmental Science and Technology 2019. [Google Scholar] [CrossRef] [PubMed]
41. Santos-Sacramento, L.; Arrifano, G.P.; Lopes-Araújo, A.; Augusto-Oliveira, M.; Albuquerque-Santos, R.; Takeda, P.Y.; Souza-Monteiro, J.R.; Macchi, B.M.; do Nascimento, J.L.M.; Lima, R.R.; et al. Human Neurotoxicity of Mercury in the Amazon: A Scoping Review with Insights and Critical Considerations. Ecotoxicology and Environmental Safety 2021, 208. [Google Scholar] [CrossRef]
42. Miguel V. M., Neto; Darlan, Q. Brito Mercury (Hg) Researches in Brazilian Biomes: A Scientometric Analysis between the Years 1991 and 2018. J Veterina Sci Res 2021, 01–13. [Google Scholar] [CrossRef]
43. Basta, P.C.; De Vasconcellos, A.C.S.; Hallwass, G.; Yokota, D.; Pinto, D.D.O. d’El 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]
44. Moulatlet, G.M.; Yacelga, N.; Rico, A.; Mora, A.; Hauser-Davis, R.A.; Cabrera, M.; Capparelli, M.V. A Systematic Review on Metal Contamination Due to Mining Activities in the Amazon Basin and Associated Environmental Hazards. Chemosphere 2023, 339, 139700. [Google Scholar] [CrossRef] [PubMed]
45. Gimenes, T.C.; Penteado, J.O.; dos Santos, M.; da Silva Júnior, F.M.R. Methylmercury in Fish from the Amazon Region—a Review Focused on Eating Habits. Water, Air, and Soil Pollution 2021, 232. [Google Scholar] [CrossRef]
46. RAISG Amazon in Numbers 2022.
47. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. Systematic Reviews 2021, 10, 1–11. [Google Scholar] [CrossRef] [PubMed]
48. Cochrane Cochrane Handbook for Systematic Reviews of Interventions; Higgins, J., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M., Welch, V., Eds.; 2nd ed.; John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England: Chichester (UK), 2019.
49. Kirby, A. Exploratory Bibliometrics: Using VOSviewer as a Preliminary Research Tool. Publications 2023, 11, 10. [Google Scholar] [CrossRef]
50. Cohen Hubal, E.A.; Frank, J.J.; Nachman, R.; Angrish, M.; Deziel, N.C.; Fry, M.; Tornero-Velez, R.; Kraft, A.; Lavoie, E. Advancing Systematic-Review Methodology in Exposure Science for Environmental Health Decision Making. Journal of Exposure Science and Environmental Epidemiology 2020, 30, 906–916. [Google Scholar] [CrossRef] [PubMed]
51. FAO. WHO GENERAL STANDARD FOR CONTAMINANTS AND TOXINS IN FOOD AND FEED. CODEX ALIMENTARIUS 2019, 1–13. [Google Scholar]
52. WHO Chapter 6.9 Mercury General Description. Air Quality Guidelines 2000, 1–15.
53. Ministério da Saúde RESOLUÇÃO - RDC No 42, DE 29 DE AGOSTO DE 2013 2013.
54. Poulin, J.; Gibb, H.; Prüss-Üstün, A. World Health Organization Mercury: Assessing the Environmental Burden of Disease at National and Local Levels. 2008.
55. Costa Junior, J.M.F.; da Silva, C.I.M.; Lima, A.A. da S.; Rodrigues Júnior, D.; Silveira, L.C. de L.; Souza, G. da S.; Pinheiro, M. da C.N. Teores de Mercúrio Em Cabelo e Consumo de Pescado de Comunidades Ribeirinhas Na Amazônia Brasileira, Região Do Tapajós. Ciencia e Saude Coletiva 2018, 23, 805–812. [Google Scholar] [CrossRef] [PubMed]
56. Wang, Y.; Li, L.; Yao, C.; Tian, X.; Wu, Y.; Xie, Q.; Wang, D. Mercury in Human Hair and Its Implications for Health Investigation. Current Opinion in Environmental Science and Health 2021, 22, 100271. [Google Scholar] [CrossRef]
57. QGIS Association QGIS.Org 2023.
58. Dinerstein, E.; Olson, D.; Joshi, A.; Vynne, C.; Burgess, N.D.; Wikramanayake, E.; Hahn, N.; Palminteri, S.; Hedao, P.; Noss, R.; et al. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience 2017, 67, 534–545. [Google Scholar] [CrossRef]
59. Southern America | Realm & Subrealms | One Earth. Available online: https://www.oneearth.org/realms/southern-america/ (accessed on 16 November 2023).
60. Tang, L.; Werner, T.T. Author Correction: Global Mining Footprint Mapped from High-Resolution Satellite Imagery. Commun Earth Environ 2023, 4, 163. [Google Scholar] [CrossRef]
61. Yevugah, L.L.; Darko, G.; Bak, J. Does Mercury Emission from Small-Scale Gold Mining Cause Widespread Soil Pollution in Ghana? Environmental Pollution 2021, 284, 116945. [Google Scholar] [CrossRef] [PubMed]
62. Goix, S.; Maurice, L.; Laffont, L.; Rinaldo, R.; Lagane, C.; Chmeleff, J.; Menges, J.; Heimbürger, L.-E.; Maury-Brachet, R.; Sonke, J.E. Quantifying the Impacts of Artisanal Gold Mining on a Tropical River System Using Mercury Isotopes. Chemosphere 2019, 219, 684–694. [Google Scholar] [CrossRef] [PubMed]
63. Gerson, J.R.; Szponar, N.; Zambrano, A.A.; Bergquist, B.; Broadbent, E.; Driscoll, C.T.; Erkenswick, G.; Evers, D.C.; Fernandez, L.E.; Hsu-Kim, H. Amazon Forests Capture High Levels of Atmospheric Mercury Pollution from Artisanal Gold Mining. Nature communications 2022, 13, 559. [Google Scholar] [CrossRef] [PubMed]
64. Feingold, B.J.; Berky, A.; Hsu-Kim, H.; Rojas Jurado, E.; Pan, W.K. Population-Based Dietary Exposure to Mercury through Fish Consumption in the Southern Peruvian Amazon. Environmental Research 2020, 183, 108720. [Google Scholar] [CrossRef]
65. R Core Team R: A Language and Environment for Statistical Computing 2023.
66. Silva, S.F.D.; Oliveira, D.C.; Pereira, J.P.G.; Castro, S.P.; Costa, B.N.S.; Lima, M.D.O. Seasonal Variation of Mercury in Commercial Fishes of the Amazon Triple Frontier, Western Amazon Basin. Ecological Indicators 2019, 106, 105549. [Google Scholar] [CrossRef]
67. 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] [PubMed]
68. 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]
69. Albuquerque, F.E.A.; Minervino, A.H.H.; Miranda, M.; Herrero-Latorre, C.; Barrêto Júnior, R.A.; Oliveira, F.L.C.; Sucupira, M.C.A.; Ortolani, E.L.; López-Alonso, M. Toxic and Essential Trace Element Concentrations in Fish Species in the Lower Amazon, Brazil. Science of The Total Environment 2020, 732, 138983. [Google Scholar] [CrossRef]
70. Lino, A.S.; Kasper, D.; Guida, Y.S.; Thomaz, J.R.; Malm, O. Mercury and Selenium in Fishes from the Tapajós River in the Brazilian Amazon: An Evaluation of Human Exposure. Journal of Trace Elements in Medicine and Biology 2018, 48, 196–201. [Google Scholar] [CrossRef] [PubMed]
71. Alcala-Orozco, M.; Caballero-Gallardo, K.; Olivero-Verbel, J. Biomonitoring of Mercury, Cadmium and Selenium in Fish and the Population of Puerto Nariño, at the Southern Corner of the Colombian Amazon. Arch Environ Contam Toxicol 2020, 79, 354–370. [Google Scholar] [CrossRef] [PubMed]
72. Da Silva, S.F.; Pereira, J.P.G.; Oliveira, D.C.; Lima, M.D.O. Methylmercury in Predatory and Non-Predatory Fish Species Marketed in the Amazon Triple Frontier. Bull Environ Contam Toxicol 2020, 104, 733–737. [Google Scholar] [CrossRef] [PubMed]
73. De Castro Paiva, T.; Dary, E.P.; Pestana, I.A.; Amadio, S.A.; Malm, O.; Kasper, D. Flood-Pulse and Trophic Position Modulate Mercury Concentrations in Fishes from an Amazon Floodplain Lake. Environmental Research 2022, 215, 114307. [Google Scholar] [CrossRef]
74. Finoto Viana, L.; Damasceno De Souza, D.C.; Batista Da Silva, E.; Kummrow, F.; Lima Cardoso, C.A.; De Lima, N.A.; Crispim, B.D.A.; Barufatti, A.; Florentino, A.C. Bioaccumulation of Metals and Genotoxic Effects in Females of Colomesus Asellus Collected in an Amazon River Estuary, Amapá, Brazil. Limnetica 2023, 42, 1. [Google Scholar] [CrossRef]
75. Lino, A.S.; Kasper, D.; Guida, Y.S.; Thomaz, J.R.; Malm, O. Total and Methyl Mercury Distribution in Water, Sediment, Plankton and Fish along the Tapajós River Basin in the Brazilian Amazon. Chemosphere 2019, 235, 690–700. [Google Scholar] [CrossRef] [PubMed]
76. Oliveira, R.B.D.; Silva, D.M.D.; Franco, T.S.B.S.; Vasconcelos, C.R.S.; Sousa, D.J.D.A.D.; Sarrazin, S.L.F.; Sakamoto, M.; Bourdineaud, J.-P. Fish Consumption Habits of Pregnant Women in Itaituba, Tapajós River Basin, Brazil: Risks of Mercury Contamination as Assessed by Measuring Total Mercury in Highly Consumed Piscivore Fish Species and in Hair of Pregnant Women. Archives of Industrial Hygiene and Toxicology 2022, 73, 131–142. [Google Scholar] [CrossRef]
77. De Souza Azevedo, J.; Hortellani, M.A.; De Souza Sarkis, J.E. Organotropism of Total Mercury (THg) in Cichla Pinima, Ecological Aspects and Human Consumption in Fish from Amazon Region, Brazil. Environ Sci Pollut Res 2019, 26, 21363–21370. [Google Scholar] [CrossRef]
78. Da Silva Montes, C.; Ferreira, M.A.P.; Giarrizzo, T.; Amado, L.L.; Rocha, R.M. The Legacy of Artisanal Gold Mining and Its Impact on Fish Health from Tapajós Amazonian Region: A Multi-Biomarker Approach. Chemosphere 2022, 287, 132263. [Google Scholar] [CrossRef]
79. Azevedo, L.S.; Pestana, I.A.; Da Costa Nery, A.F.; Bastos, W.R.; Souza, C.M.M. Variation in Hg Accumulation between Demersal and Pelagic Fish from Puruzinho Lake, Brazilian Amazon. Ecotoxicology 2019, 28, 1143–1149. [Google Scholar] [CrossRef]
80. Cavecci-Mendonça, B.; Cavalcante de Souza Vieira, J.; Monteiro de Lima, P.; Leite, A.L.; Buzalaf, M.A.R.; Zara, L.F.; de Magalhães Padilha, P. Study of Proteins with Mercury in Fish from the Amazon Region. Food Chemistry 2020, 309, 125460. [Google Scholar] [CrossRef] [PubMed]
81. de Queiroz, J.V.; Cavecci-Mendonça, B.; Vieira, J.C.S.; Martins, R.A.; de Almeida Assunção, A.S.; Cavallini, N.G.; dos Santos, F.A.; de Magalhães Padilha, P. Metalloproteomic Strategies for Identifying Proteins as Biomarkers of Mercury Exposure in Serrasalmus Rhombeus from the Amazon Region. Biological Trace Element Research 2021, 199, 712–720. [Google Scholar] [CrossRef]
82. Soares, J.M.; Gomes, J.M.; Anjos, M.R.; Silveira, J.N.; Custódio, F.B.; Gloria, M.B.A. Mercury in Fish from the Madeira River and Health Risk to Amazonian and Riverine Populations. Food Research International 2018, 109, 537–543. [Google Scholar] [CrossRef] [PubMed]
83. Azevedo, L.S.; Pestana, I.A.; Nery, A.F.D.C.; Bastos, W.R.; Souza, C.M.M. Influence of the Flood Pulse on Mercury Accumulation in Detritivorous, Herbivorous and Omnivorous Fish in Brazilian Amazonia. Ecotoxicology 2019, 28, 478–485. [Google Scholar] [CrossRef]
84. Vieira, J.C.S.; De Oliveira, G.; Cavallini, N.G.; Braga, C.P.; Adamec, J.; Zara, L.F.; Buzalaf, M.A.R.; De Magalhães Padilha, P. Investigation of Protein Biomarkers and Oxidative Stress in Pinirampus Pirinampu Exposed to Mercury Species from the Madeira River, Amazon-Brazil. Biol Trace Elem Res 2022, 200, 1872–1882. [Google Scholar] [CrossRef] [PubMed]
85. Da Cunha Bataglioli, I.; De Queiroz, J.V.; Vieira, J.C.S.; Cavalline, N.G.; Braga, C.P.; Buzalaf, M.A.R.; Zara, L.F.; Adamec, J.; De Magalhães Padilha, P. Mercury Metalloproteomic Profile in Muscle Tissue of Arapaima Gigas from the Brazilian Amazon. Environ Monit Assess 2022, 194, 705. [Google Scholar] [CrossRef]
86. Azevedo, L.S.; Pestana, I.A.; Almeida, M.G.; Ferreira Da Costa Nery, A.; Bastos, W.R.; Magalhães Souza, C.M. Mercury Biomagnification in an Ichthyic Food Chain of an Amazon Floodplain Lake (Puruzinho Lake): Influence of Seasonality and Food Chain Modeling. Ecotoxicology and Environmental Safety 2021, 207, 111249. [Google Scholar] [CrossRef]
87. Bittarello, A.C.; Vieira, J.C.S.; Braga, C.P.; De Paula Araújo, W.L.; Da Cunha Bataglioli, I.; Da Silva, J.M.; Buzalaf, M.A.R.; Fleuri, L.F.; De Magalhães Padilha, P. Characterization of Molecular Biomarkers of Mercury Exposure to Muscle Tissue of Plagioscion Squamosissimus and Colossoma Macropomum from the Amazon Region. Food Chemistry 2019, 276, 247–254. [Google Scholar] [CrossRef] [PubMed]
88. De Queiroz, J.V.; Vieira, J.C.S.; De Oliveira, G.; Braga, C.P.; Da Cunha Bataglioli, I.; Da Silva, J.M.; De Paula Araújo, W.L.; De Magalhães Padilha, P. Identification of Biomarkers of Mercury Contamination in Brachyplatystoma Filamentosum of the Madeira River, Brazil, Using Metalloproteomic Strategies. Biol Trace Elem Res 2019, 187, 291–300. [Google Scholar] [CrossRef]
89. Mussy, M.H.; De Almeida, R.; De Carvalho, D.P.; Lauthartte, L.C.; De Holanda, I.B.B.; Almeida, M.G.D.; De Sousa-Filho, I.F.; De Rezende, C.E.; Malm, O.; Bastos, W.R. Evaluating Total Mercury and Methylmercury Biomagnification Using Stable Isotopes of Carbon and Nitrogen in Fish from the Madeira River Basin, Brazilian Amazon. Environ Sci Pollut Res 2022, 30, 33543–33554. [Google Scholar] [CrossRef]
90. Reis, P.A.; Ozório, R.O.A.; Rodriguez, A.F.R.; Faria, F.S.E.D.V.; Furtado, C.M.; Ribeiro, R.A. Mercury Distribution in Two Commercial Fish Species (Pimelodus Maculatus and Calophysus Macropterus) - Case Study of River Acre (Acre State, Brazilian Amazon). Human and Ecological Risk Assessment 2020, 26, 1439–1448. [Google Scholar] [CrossRef]
91. Vieira, J.C.S.; Braga, C.P.; Queiroz, J.V.D.; Cavecci-Mendonça, B.; Oliveira, G.D.; Freitas, N.G.D.; Fernandes, A.A.H.; Fernandes, M.D.S.; Buzalaf, M.A.R.; Adamec, J.; et al. The Effects of Mercury Exposure on Amazonian Fishes: An Investigation of Potential Biomarkers. Chemosphere 2023, 316, 137779. [Google Scholar] [CrossRef] [PubMed]
92. Vieira, J.C.S.; Braga, C.P.; De Oliveira, G.; Padilha, C.D.C.F.; De Moraes, P.M.; Zara, L.F.; Leite, A.D.L.; Buzalaf, M.A.R.; Padilha, P.D.M. Mercury Exposure: Protein Biomarkers of Mercury Exposure in Jaraqui Fish from the Amazon Region. Biol Trace Elem Res 2018, 183, 164–171. [Google Scholar] [CrossRef]
93. De Queiroz, J.V.; Vieira, J.C.S.; Da Cunha Bataglioli, I.; Bittarello, A.C.; Braga, C.P.; De Oliveira, G.; Do Carmo Federici Padilha, C.; De Magalhães Padilha, P. Total Mercury Determination in Muscle and Liver Tissue Samples from Brazilian Amazon Fish Using Slurry Sampling. Biol Trace Elem Res 2018, 184, 517–522. [Google Scholar] [CrossRef]
94. Vieira, J.C.S.; Cavecci, B.; Queiroz, J.V.; Braga, C.P.; Padilha, C.C.F.; Leite, A.L.; Figueiredo, W.S.; Buzalaf, M.A.R.; Zara, L.F.; Padilha, P.M. Determination of the Mercury Fraction Linked to Protein of Muscle and Liver Tissue of Tucunaré (Cichla Spp.) from the Amazon Region of Brazil. Arch Environ Contam Toxicol 2015, 69, 422–430. [Google Scholar] [CrossRef]
95. da Silva Montes, C.; Pantoja Ferreira, M.A.; Giarrizzo, T.; Amado, L.L.; Rocha, R.M. Evaluation of Metal Contamination Effects in Piranhas through Biomonitoring and Multi Biomarkers Approach. Heliyon 2020, 6, e04666. [Google Scholar] [CrossRef]
96. Souza-Araujo, J.; Andrades, R.; Hauser-Davis, R.A.; Lima, M.O.; Giarrizzo, T. Before the Dam: A Fish-Mercury Contamination Baseline Survey at the Xingu River, Amazon Basin Before the Belo Monte Dam. Bull Environ Contam Toxicol 2022, 108, 861–866. [Google Scholar] [CrossRef]
97. De Matos, L.S.; Silva Correa, A.S.A.; Da Silva, S.A.A.; Muniz, C.C.; Alves Ignacio, A.R. Mercury Concentrations in Fish and Human Health Assessment in Preflood Phase of a Hydro Dam in Teles Pires River, Southern Brazilian Amazon. Elementa 2021, 9, 1–13. [Google Scholar] [CrossRef]
98. Matos, L.S.D.; Silva, J.O.S.; Kasper, D.; Carvalho, L.N. Assessment of Mercury Contamination in Brycon Falcatus (Characiformes: Bryconidae) and Human Health Risk by Consumption of This Fish from the Teles Pires River, Southern Amazonia. Neotrop. ichthyol. 2018, 16. [Google Scholar] [CrossRef]
99. Viana, L.F.; Kummrow, F.; Cardoso, C.A.L.; De Lima, N.A.; Do Amaral Crispim, B.; Barufatti, A.; Florentino, A.C. Metal Bioaccumulation in Fish from the Araguari River (Amazon Biome) and Human Health Risks from Fish Consumption. Environ Sci Pollut Res 2023, 30, 4111–4122. [Google Scholar] [CrossRef]
100. 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). Environmental Research 2022, 208, 112691. [Google Scholar] [CrossRef]
101. Costa, I.D.D.; Nascimento, E.L.D.; Faccheti, M.S.D.A.; Nunes, N.N.D.S.; Gomes, J.P.D.O.; Almeida, R.D.; Bastos, W.R. Mercury in Muscle and Liver of Plagioscion Squamosissimus (Acanthuriformes: Sciaenidae) from the Machado River, Brazilian Amazon. Acta Amaz. 2022, 52, 60–68. [Google Scholar] [CrossRef]
102. Martinez, G.; McCord, S.; Driscoll, C.; Todorova, S.; Wu, S.; Araújo, J.; Vega, C.; Fernandez, L. Mercury Contamination in Riverine Sediments and Fish Associated with Artisanal and Small-Scale Gold Mining in Madre de Dios, Peru. IJERPH 2018, 15, 1584. [Google Scholar] [CrossRef] [PubMed]
103. Barocas, A.; Vega, C.; Alarcon Pardo, A.; Araujo Flores, J.M.; Fernandez, L.; Groenendijk, J.; Pisconte, J.; Macdonald, D.W.; Swaisgood, R.R. Local Intensity of Artisanal Gold Mining Drives Mercury Accumulation in Neotropical Oxbow Lake Fishes. Science of The Total Environment 2023, 886, 164024. [Google Scholar] [CrossRef] [PubMed]
104. Rodriguez-Levy, I.E.; Van Damme, P.A.; Carvajal-Vallejos, F.M.; Bervoets, L. Trace Element Accumulation in Different Edible Fish Species from the Bolivian Amazon and the Risk for Human Consumption. Heliyon 2022, 8, e11649. [Google Scholar] [CrossRef] [PubMed]
105. De Vasconcellos, A.C.S.; Ferreira, S.R.B.; De Sousa, C.C.; De Oliveira, M.W.; De Oliveira Lima, M.; Basta, P.C. Health Risk Assessment Attributed to Consumption of Fish Contaminated with Mercury in the Rio Branco Basin, Roraima, Amazon, Brazil. Toxics 2022, 10, 516. [Google Scholar] [CrossRef]
106. Borges, A.C.; Da Silva Montes, C.; Barbosa, L.A.; Ferreira, M.A.P.; Berrêdo, J.F.; Martins Rocha, R. Integrated Use of Histological and Ultrastructural Biomarkers for Assessing Mercury Pollution in Piranhas (Serrasalmus Rhombeus) from the Amazon Mining Region. Chemosphere 2018, 202, 788–796. [Google Scholar] [CrossRef]
107. Vreedzaam, A.; Ouboter, P.; Hindori-Mohangoo, A.D.; Lepak, R.; Rumschlag, S.; Janssen, S.; Landburg, G.; Shankar, A.; Zijlmans, W.; Lichtveld, M.Y.; et al. Contrasting Mercury Contamination Scenarios and Site Susceptibilities Confound Fish Mercury Burdens in Suriname, South America. Environmental Pollution 2023, 336, 122447. [Google Scholar] [CrossRef]
108. Laffont, L.; Menges, J.; Goix, S.; Gentès, S.; Maury-Brachet, R.; Sonke, J.E.; Legeay, A.; Gonzalez, P.; Rinaldo, R.; Maurice, L. Hg Concentrations and Stable Isotope Variations in Tropical Fish Species of a Gold-Mining-Impacted Watershed in French Guiana. Environ Sci Pollut Res 2021, 28, 60609–60621. [Google Scholar] [CrossRef]
109. Montaña, C.G.; Liverpool, E.; Taphorn, D.C.; Schalk, C.M. The Cost of Gold: Mercury Contamination of Fishes in a Neotropical River Food Web. Neotrop. ichthyol. 2021, 19, e200155. [Google Scholar] [CrossRef]
110. Cunha Bataglioli, I.D.; Souza Vieira, J.C.; Vitor De Queiroz, J.; Da Silva Fernandes, M.; Bittarello, A.C.; Braga, C.P.; Rabelo Buzalaf, M.A.; Adamec, J.; Zara, L.F.; Magalhães Padilha, P.D. Physiological and Functional Aspects of Metal-Binding Protein Associated with Mercury in the Liver Tissue of Pirarucu (Arapaima Gigas) from the Brazilian Amazon. Chemosphere 2019, 236, 124320. [Google Scholar] [CrossRef]
111. Viana, L.F.; Cardoso, C.A.L.; Lima-Junior, S.E.; Súarez, Y.R.; Florentino, A.C. Bioaccumulation of Metal in Liver Tissue of Fish in Response to Water Toxicity of the Araguari-Amazon River, Brazil. Environmental Monitoring and Assessment 2020, 192. [Google Scholar] [CrossRef] [PubMed]
112. Albuquerque, F.E.A.; Minervino, A.H.H.; Miranda, M.; Herrero-Latorre, C.; Barrêto Júnior, R.A.; Oliveira, F.L.C.; Dias, S.R.; Ortolani, E.L.; López-Alonso, M. Toxic and Essential Trace Element Concentrations in the Freshwater Shrimp Macrobrachium Amazonicum in the Lower Amazon, Brazil. Journal of Food Composition and Analysis 2020, 86, 103361. [Google Scholar] [CrossRef]
113. Pinheiro-Sousa, D.B.; da Costa Soares, S.H.; Torres, H.S.; de Jesus, W.B.; de Oliveira, S.R.S.; Bastos, W.R.; de Oliveira Ribeiro, C.A.; Carvalho-Neta, R.N.F. Sediment Contaminant Levels and Multibiomarker Approach to Assess the Health of Catfish Sciades Herzbergii in a Harbor from the Northern Brazilian Amazon. Ecotoxicology and Environmental Safety 2021, 208. [Google Scholar] [CrossRef] [PubMed]
114. Lopes, M.C.B.; de CARVALHO, G.O.; Bernardo, R.R.; Macedo, J.; Lino, A.S.; Ramalho, E.E.; Kasper, D.; Meire, R.O.; Torres, J.P.M.; Malm, O. Total Mercury in Wild Felids Occurring in Protected Areas in the Central Brazilian Amazon. Acta Amazonica 2020, 50, 142–148. [Google Scholar] [CrossRef]
115. Barbosa, M.S.; Carvalho, D.P.; Gravena, W.; De Almeida, R.; Mussy, M.H.; Sousa, E.A.; Holanda, I.B.B.; De Sousa-Filho, I.F.; Bastos, W.R. Total Mercury and Methylmercury in River Dolphins (Cetacea: Iniidae: Inia Spp.) in the Madeira River Basin, Western Amazon. Environ Sci Pollut Res 2021, 28, 45121–45133. [Google Scholar] [CrossRef] [PubMed]
116. Salazar-Pammo, A.C.; Achá, D.; Miranda-Chumacero, G. Preferential Liver Accumulation of Mercury Explains Low Concentrations in Muscle of Caiman Yacare (Alligatoridae) in Upper Amazon. Bull Environ Contam Toxicol 2021, 106, 264–269. [Google Scholar] [CrossRef] [PubMed]
117. Dias Dos Santos, A.N.; Recktenvald, M.C.N. do N.; De Carvalho, D.P.; Bortoleto Puerta, E.L.; De Sousa-Filho, I.F.; Dórea, J.G.; Bastos, W.R. Mercury in Birds (Aquatic and Scavenger) from the Western Amazon. Environmental Research 2021, 201, 111574. [Google Scholar] [CrossRef] [PubMed]
118. Kumar, A.; Divoll, T.J.; Ganguli, P.M.; Trama, F.A.; Lamborg, C.H. Presence of Artisanal Gold Mining Predicts Mercury Bioaccumulation in Five Genera of Bats (Chiroptera). Environmental Pollution 2018, 236, 862–870. [Google Scholar] [CrossRef]
119. Moreno-Brush, M.; Portillo, A.; Brändel, S.D.; Storch, I.; Tschapka, M.; Biester, H. Mercury Concentrations in Bats (Chiroptera) from a Gold Mining Area in the Peruvian Amazon. Ecotoxicology 2018, 27, 45–54. [Google Scholar] [CrossRef]
120. Da Silva Júnior, F.J.T.M.; Ribeiro, J.D.N.; Da Silva, H.L.A.; Da Silva Carneiro, C.; De Jesus, E.F.O.; De Araújo, U.B.; Lazzarini, S.M.; Souza, A.R.; Simões, J.S.; Lopes, R.T.; et al. Study of Inorganic Elements in Different Organs and Tissues of Amazonian Manatee (Trichechus Inunguis) from Brazil. Environ Sci Pollut Res 2022, 29, 30486–30495. [Google Scholar] [CrossRef] [PubMed]
121. Mosquera-Guerra, F.; Trujillo, F.; Parks, D.; Oliveira-da-Costa, M.; Van Damme, P.A.; Echeverría, A.; Franco, N.; Carvajal-Castro, J.D.; Mantilla-Meluk, H.; Marmontel, M.; et al. Mercury in Populations of River Dolphins of the Amazon and Orinoco Basins. EcoHealth 2019, 16, 743–758. [Google Scholar] [CrossRef] [PubMed]
122. Borges, Â.O.; Erickson, J.; Silva, L.A.D.; Fantin, C.; Domingos-Moreira, F.X.V. Mercury Bioaccumulation, Genotoxic and Biochemical Biomarkers Reveal the Health Status of Yellow-Spotted Amazon River Turtles (Podocnemis Unifilis) in an Environmental Protection Area in the Amazon. Acta Amaz. 2022, 52, 254–263. [Google Scholar] [CrossRef]
123. Pignati, M.; Pezzuti, J.; Souza, L.; Lima, M.; Pignati, W.; Mendes, R. Assessment of Mercury Concentration in Turtles (Podocnemis Unifilis) in the Xingu River Basin, Brazil. IJERPH 2018, 15, 1185. [Google Scholar] [CrossRef] [PubMed]
124. Targino, F.J.; Ribeiro, J.D.D.N.; Simões, J.S.; Carneiro, C.S.; Lazzarini, S.M.; Souza, A.R.; Ferreira, M.D.S.; Mano, S.B.; Mársico, E.T. Total Mercury Content in the Tissues of Freshwater Chelonium (Podocnemis Expansa) and a Human Health Risk Assessment for the Amazon Population in Brazil. IJERPH 2023, 20, 6489. [Google Scholar] [CrossRef] [PubMed]
125. Oliveira, E.; Ignácio, A.R.A.; Lázaro, W.L.; Díez, S.; Guimarães, J.R.D.; Santos-Filho, M. Green Kingfishers as Sentinel Species for Mercury Contamination in Amazon. Arch Environ Contam Toxicol 2023, 85, 34–45. [Google Scholar] [CrossRef] [PubMed]
126. Hurtado, T.C.; De Medeiros Costa, G.; De Carvalho, G.S.; Brum, B.R.; Ignácio, Á.R.A. Mercury and Methylmercury Concentration in the Feathers of Two Species of Kingfishers Megaceryle Torquata and Chloroceryle Amazona in the Upper Paraguay Basin and Amazon Basin. Ecotoxicology 2023. [Google Scholar] [CrossRef] [PubMed]
127. Galvão, R.C.F.; Holanda, I.B.B.; De Carvalho, D.P.; Almeida, R.; Souza, C.M.M.; Lacerda, L.D.; Bastos, W.R. Freshwater Shrimps (Macrobrachium Depressimanum and Macrobrachium Jelskii) as Biomonitors of Hg Availability in the Madeira River Basin, Western Amazon. Environ Monit Assess 2018, 190, 77. [Google Scholar] [CrossRef]
128. Arrifano, G.P.F.; Martín-Doimeadios, R.C.R.; Jiménez-Moreno, M.; Ramírez-Mateos, V.; Da Silva, N.F.S.; Souza-Monteiro, J.R.; Augusto-Oliveira, M.; Paraense, R.S.O.; Macchi, B.M.; Do Nascimento, J.L.M.; et al. Large-Scale Projects in the Amazon and Human Exposure to Mercury: The Case-Study of the Tucuruí Dam. Ecotoxicology and Environmental Safety 2018, 147, 299–305. [Google Scholar] [CrossRef]
129. Vega, C.; Orellana, J.; Oliveira, M.; Hacon, S.; Basta, P. Human Mercury Exposure in Yanomami Indigenous Villages from the Brazilian Amazon. IJERPH 2018, 15, 1051. [Google Scholar] [CrossRef]
130. Feitosa-Santana, C.; Souza, G.D.S.; Sirius, E.V.P.; Rodrigues, A.R.; Cortes, M.I.T.; Silveira, L.C.D.L.; Ventura, D.F. Color Vision Impairment with Low-Level Methylmercury Exposure of an Amazonian Population – Brazil. NeuroToxicology 2018, 66, 179–184. [Google Scholar] [CrossRef]
131. Dos Santos Freitas, J.; Da Costa Brito Lacerda, E.M.; Da Silva Martins, I.C.V.; Rodrigues, D.; Bonci, D.M.O.; Cortes, M.I.T.; Corvelo, T.C.O.; Ventura, D.F.; De Lima Silveira, L.C.; Da Conceição Nascimento Pinheiro, M.; et al. Cross-Sectional Study to Assess the Association of Color Vision with Mercury Hair Concentration in Children from Brazilian Amazonian Riverine Communities. NeuroToxicology 2018, 65, 60–67. [Google Scholar] [CrossRef]
132. Costa Junior, J.M.F.; Lima, A.A.D.S.; Rodrigues Junior, D.; Khoury, E.D.T.; Souza, G.D.S.; Silveira, L.C.D.L.; Pinheiro, M.D.C.N. Manifestações Emocionais e Motoras de Ribeirinhos Expostos Ao Mercúrio Na Amazônia. Rev. bras. epidemiol. 2017, 20, 212–224. [Google Scholar] [CrossRef]
133. Oliveira, R.A.A.D.; Pinto, B.D.; Rebouças, B.H.; Ciampi De Andrade, D.; Vasconcellos, A.C.S.D.; Basta, P.C. Neurological Impacts of Chronic Methylmercury Exposure in Munduruku Indigenous Adults: Somatosensory, Motor, and Cognitive Abnormalities. IJERPH 2021, 18, 10270. [Google Scholar] [CrossRef]
134. Perini, J.A.; Silva, M.C.; Vasconcellos, A.C.S.D.; Viana, P.V.S.; Lima, M.O.; Jesus, I.M.; Kempton, J.W.; Oliveira, R.A.A.; Hacon, S.S.; Basta, P.C. Genetic Polymorphism of Delta Aminolevulinic Acid Dehydratase (ALAD) Gene and Symptoms of Chronic Mercury Exposure in Munduruku Indigenous Children within the Brazilian Amazon. IJERPH 2021, 18, 8746. [Google Scholar] [CrossRef]
135. Vianna, A.D.S.; Câmara, V.D.M.; Barbosa, M.C.D.M.; Santos, A.D.S.E.; Asmus, C.I.R.F.; Luiz, R.R.; Jesus, I.M.D. Exposição Ao Mercúrio e Anemia Em Crianças e Adolescentes de Seis Comunidades Da Amazônia Brasileira. Ciênc. saúde coletiva 2022, 27, 1859–1871. [Google Scholar] [CrossRef] [PubMed]
136. Silman, A.K.; Chhabria, R.; Hafzalla, G.W.; Giffin, L.; Kucharski, K.; Myers, K.; Culquichicón, C.; Montero, S.; Lescano, A.G.; Vega, C.M.; et al. Impairment in Working Memory and Executive Function Associated with Mercury Exposure in Indigenous Populations in Upper Amazonian Peru. IJERPH 2022, 19, 10989. [Google Scholar] [CrossRef] [PubMed]
137. Wyatt, L.; Ortiz, E.; Feingold, B.; Berky, A.; Diringer, S.; Morales, A.; Jurado, E.; Hsu-Kim, H.; Pan, W. Spatial, Temporal, and Dietary Variables Associated with Elevated Mercury Exposure in Peruvian Riverine Communities Upstream and Downstream of Artisanal and Small-Scale Gold Mining. IJERPH 2017, 14, 1582. [Google Scholar] [CrossRef]
138. Weinhouse, C.; Ortiz, E.J.; Berky, A.J.; Bullins, P.; Hare-Grogg, J.; Rogers, L.; Morales, A.-M.; Hsu-Kim, H.; Pan, W.K. Hair Mercury Level Is Associated with Anemia and Micronutrient Status in Children Living Near Artisanal and Small-Scale Gold Mining in the Peruvian Amazon. The American Journal of Tropical Medicine and Hygiene 2017, 97, 1886–1897. [Google Scholar] [CrossRef]
139. Koenigsmark, F.; Weinhouse, C.; Berky, A.; Morales, A.; Ortiz, E.; Pierce, E.; Pan, W.; Hsu-Kim, H. Efficacy of Hair Total Mercury Content as a Biomarker of Methylmercury Exposure to Communities in the Area of Artisanal and Small-Scale Gold Mining in Madre de Dios, Peru. IJERPH 2021, 18, 13350. [Google Scholar] [CrossRef]
140. Vasconcellos, A.C.S.D.; Barrocas, P.R.G.; Ruiz, C.M.V.; Mourão, D.D.S.; Hacon, S.D.S. Burden of Mild Mental Retardation Attributed to Prenatal Methylmercury Exposure in Amazon: Local and Regional Estimates. Ciênc. saúde coletiva 2018, 23, 3535–3545. [Google Scholar] [CrossRef] [PubMed]
141. De Paula Fonseca Arrifano, G.; Del Carmen Rodriguez Martin-Doimeadios, R.; Jiménez-Moreno, M.; Augusto-Oliveira, M.; Rogério Souza-Monteiro, J.; Paraense, R.; Rodrigues Machado, C.; Farina, M.; Macchi, B.; Do Nascimento, J.L.M.; et al. Assessing Mercury Intoxication in Isolated/Remote Populations: Increased S100B mRNA in Blood in Exposed Riverine Inhabitants of the Amazon. NeuroToxicology 2018, 68, 151–158. [Google Scholar] [CrossRef] [PubMed]
142. Cerbino, M.R.; Vieira, J.C.S.; Braga, C.P.; Oliveira, G.; Padilha, I.F.; Silva, T.M.; Zara, L.F.; Silva, N.J.; Padilha, P.M. Metalloproteomics Approach to Analyze Mercury in Breast Milk and Hair Samples of Lactating Women in Communities of the Amazon Basin, Brazil. Biol Trace Elem Res 2018, 181, 216–226. [Google Scholar] [CrossRef] [PubMed]
143. Arrifano, G.P.F.; Martín-Doimeadios, R.C.R.; Jiménez-Moreno, M.; Fernández-Trujillo, S.; Augusto-Oliveira, M.; Souza-Monteiro, J.R.; Macchi, B.M.; Alvarez-Leite, J.I.; Do Nascimento, J.L.M.; Amador, M.T.; et al. Genetic Susceptibility to Neurodegeneration in Amazon: Apolipoprotein E Genotyping in Vulnerable Populations Exposed to Mercury. Front. Genet. 2018, 9, 285. [Google Scholar] [CrossRef] [PubMed]
144. Gonzalez, D.J.X.; Arain, A.; Fernandez, L.E. Mercury Exposure, Risk Factors, and Perceptions among Women of Childbearing Age in an Artisanal Gold Mining Region of the Peruvian Amazon. Environmental Research 2019, 179, 108786. [Google Scholar] [CrossRef] [PubMed]
145. Freitas, J.S.; Lacerda, E.M.C.B.; Rodrigues Júnior, D.; Corvelo, T.C.O.; Silveira, L.C.L.; Pinheiro, M.D.C.N.; Souza, G.S. Mercury Exposure of Children Living in Amazonian Villages: Influence of Geographical Location Where They Lived during Prenatal and Postnatal Development. An. Acad. Bras. Ciênc. 2019, 91, e20180097. [Google Scholar] [CrossRef] [PubMed]
146. Santos-Lima, C.D.; Mourão, D.D.S.; Carvalho, C.F.D.; Souza-Marques, B.; Vega, C.M.; Gonçalves, R.A.; Argollo, N.; Menezes-Filho, J.A.; Abreu, N.; Hacon, S.D.S. Neuropsychological Effects of Mercury Exposure in Children and Adolescents of the Amazon Region, Brazil. NeuroToxicology 2020, 79, 48–57. [Google Scholar] [CrossRef] [PubMed]
147. Reuben, A.; Frischtak, H.; Berky, A.; Ortiz, E.J.; Morales, A.M.; Hsu-Kim, H.; Pendergast, L.L.; Pan, W.K. Elevated Hair Mercury Levels Are Associated With Neurodevelopmental Deficits in Children Living Near Artisanal and Small-Scale Gold Mining in Peru. GeoHealth 2020, 4, e2019GH000222. [Google Scholar] [CrossRef]
148. Watson, L.C.; Hurtado-Gonzales, J.L.; Chin, C.J.; Persaud, J. Survey of Methylmercury Exposures and Risk Factors Among Indigenous Communities in Guyana, South America. Journal of Health and Pollution 2020, 10, 200604. [Google Scholar] [CrossRef]
149. Lacerda, E.M.D.C.B.; Souza, G.D.S.; Cortes, M.I.T.; Rodrigues, A.R.; Pinheiro, M.C.N.; Silveira, L.C.D.L.; Ventura, D.F. Comparison of Visual Functions of Two Amazonian Populations: Possible Consequences of Different Mercury Exposure. Front. Neurosci. 2020, 13, 1428. [Google Scholar] [CrossRef]
150. Weinhouse, C.; Gallis, J.A.; Ortiz, E.; Berky, A.J.; Morales, A.M.; Diringer, S.E.; Harrington, J.; Bullins, P.; Rogers, L.; Hare-Grogg, J.; et al. A Population-Based Mercury Exposure Assessment near an Artisanal and Small-Scale Gold Mining Site in the Peruvian Amazon. J Expo Sci Environ Epidemiol 2021, 31, 126–136. [Google Scholar] [CrossRef] [PubMed]
151. Bello, T.C.S.; Buralli, R.J.; Cunha, M.P.L.; Dórea, J.G.; Diaz-Quijano, F.A.; Guimarães, J.R.D.; Marques, R.C. Mercury Exposure in Women of Reproductive Age in Rondônia State, Amazon Region, Brazil. IJERPH 2023, 20, 5225. [Google Scholar] [CrossRef] [PubMed]
152. Lopes-Araújo, A.; Arrifano, G.P.; Macchi, B.M.; Augusto-Oliveira, M.; Santos-Sacramento, L.; Rodríguez Martín-Doimeadios, R.C.; Jiménez-Moreno, M.; Martins Filho, A.J.; Alvarez-Leite, J.I.; Oriá, R.B.; et al. Hair Mercury Is Associated with Dyslipidemia and Cardiovascular Risk: An Anthropometric, Biochemical and Genetic Cross-Sectional Study of Amazonian Vulnerable Populations. Environmental Research 2023, 229, 115971. [Google Scholar] [CrossRef] [PubMed]
153. Suárez-Criado, L.; Rodríguez-González, P.; Marrugo-Negrete, J.; García Alonso, J.I.; Díez, S. Determination of Methylmercury and Inorganic Mercury in Human Hair Samples of Individuals from Colombian Gold Mining Regions by Double Spiking Isotope Dilution and GC-ICP-MS. Environmental Research 2023, 231, 115970. [Google Scholar] [CrossRef] [PubMed]
154. Silva, M.C.D.; Oliveira, R.A.A.D.; Vasconcellos, A.C.S.D.; Rebouças, B.H.; Pinto, B.D.; Lima, M.D.O.; Jesus, I.M.D.; Machado, D.E.; Hacon, S.S.; Basta, P.C.; et al. Chronic Mercury Exposure and GSTP1 Polymorphism in Munduruku Indigenous from Brazilian Amazon. Toxics 2023, 11, 138. [Google Scholar] [CrossRef]
155. Pignoux, R.; Gourves, P.-Y.; Sow, M.; Maury-Brachet, R. Imprégnation mercurielle des femmes enceintes de Guyane (Haut Maroni) : étude et prévention. Toxicologie Analytique et Clinique 2019, 31, 37–48. [Google Scholar] [CrossRef]
156. Cunha, M.; Marques, R.; Dórea, J. Influence of Maternal Fish Intake on the Anthropometric Indices of Children in the Western Amazon. Nutrients 2018, 10, 1146. [Google Scholar] [CrossRef] [PubMed]
157. Bastos, W.R.; Vieira, S.M.; Manzatto, Â.G.; Dórea, J.G.; Rubira, M.C.; De Souza, V.F.P.; Da Costa Junior, W.A.; Souza Bastos, M.T. Heterogeneity of Multimedia Exposures to Neurotoxic Elements (Al, As, Cd, Pb, Mn, and Hg) in Breastfed Infants from Porto Velho, Brazil. Biol Trace Elem Res 2018, 184, 7–15. [Google Scholar] [CrossRef]
158. Carvalho, L.V.B.; Hacon, S.S.; Vega, C.M.; Vieira, J.A.; Larentis, A.L.; Mattos, R.C.O.C.; Valente, D.; Costa-Amaral, I.C.; Mourão, D.S.; Silva, G.P.; et al. Oxidative Stress Levels Induced by Mercury Exposure in Amazon Juvenile Populations in Brazil. International Journal of Environmental Research and Public Health 2019, 16. [Google Scholar] [CrossRef]
159. Oliveira, A.T. de; Rodrigues, P. de A.; Ramos Filho, A.M.; Gomes, M.F. da S.; Liebl, A.R. da S.; de Pinho, J.V.; Aride, P.H.R.; Conte-Junior, C.A. Levels of Total Mercury and Health Risk Assessment of Consuming Freshwater Stingrays (Chondrichthyes: Potamotrygoninae) of the Brazilian Amazon. International Journal of Environmental Research and Public Health 2023, 20, 6990. [Google Scholar]
160. Kumar, V.; Umesh, M.; Shanmugam, M.K.; Chakraborty, P.; Duhan, L.; Gummadi, S.N.; Pasrija, R.; Jayaraj, I.; Dasarahally Huligowda, L.K. A Retrospection on Mercury Contamination, Bioaccumulation, and Toxicity in Diverse Environments: Current Insights and Future Prospects. Sustainability 2023, 15, 13292. [Google Scholar] [CrossRef]
161. Casagrande, G.C.R.; Dambros, J.; de Andrade, E.A.; Martello, F.; Sobral-Souza, T.; Moreno, M.I.C.; Battirola, L.D.; de Andrade, R.L.T. Atmospheric Mercury in Forests: Accumulation Analysis in a Gold Mining Area in the Southern Amazon, Brazil. Environmental Monitoring and Assessment 2023, 195, 477. [Google Scholar] [CrossRef] [PubMed]
162. Esteban-López, M.; Arrebola, J.P.; Juliá, M.; Pärt, P.; Soto, E.; Cañas, A.; Pedraza-Díaz, S.; González-Rubio, J.; Castaño, A. Selecting the Best Non-Invasive Matrix to Measure Mercury Exposure in Human Biomonitoring Surveys. Environmental Research 2022, 204, 112394. [Google Scholar] [CrossRef] [PubMed]
163. Santos Serrão De Castro, N.; De Oliveira Lima, M. Hair as a Biomarker of Long Term Mercury Exposure in Brazilian Amazon: A Systematic Review. IJERPH 2018, 15, 500. [Google Scholar] [CrossRef]
164. Pestana, I.A.; Almeida, M.G.; Bastos, W.R.; Souza, C.M.M. Total Hg and Methylmercury Dynamics in a River-Floodplain System in the Western Amazon: Influence of Seasonality, Organic Matter and Physical and Chemical Parameters. Science of the Total Environment 2019, 656, 388–399. [Google Scholar] [CrossRef] [PubMed]
165. Torres, C.; Verschoor, G. Re-Imagining Environmental Governance: Gold Dredge Mining vs Territorial Health in the Colombian Amazon. Geoforum 2020, 117, 124–133. [Google Scholar] [CrossRef] [PubMed]
166. Queiroz, J.; Gasparinetti, P.; Bakker, L.B.; Lobo, F.; Nagel, G. Socioeconomic Cost of Dredge Boat Gold Mining in the Tapajós Basin, Eastern Amazon. Resources Policy 2022, 79, 103102. [Google Scholar] [CrossRef]
167. Junk, W.J.; Wittmann, F.; Schöngart, J.; Piedade, M.T.F. A Classification of the Major Habitats of Amazonian Black-Water River Floodplains and a Comparison with Their White-Water Counterparts. Wetlands Ecology and Management 2015, 23, 677–693. [Google Scholar] [CrossRef]