Next Article in Journal
Characterising Carbon Monoxide Household Exposure and Health Impacts in High- and Middle-Income Countries—A Rapid Literature Review, 2010–2024
Previous Article in Journal
WHOQOL-BREF in Measuring Quality of Life Among Sickle Cell Disease Patients with Leg Ulcers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 22
Issue 1
10.3390/ijerph22010109
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

The First Report of Environmental Exposure to Barium in 10 Localities Close to Industrial Areas and Ports in the Amazon

by
Brenda Rodrigues Chagas
1,*,
Volney de Magalhães Câmara
1,
Karytta Sousa Naka
1,
Thaís Karolina Lisboa de Queiroz
1,2,
Lorena de Cássia dos Santos Mendes
2,3,
Iracina Maura de Jesus
2,3,
Marcelo de Oliveira Lima
4 and
Armando Meyer
1
1
Programa de Pós-Graduação em Saúde Coletiva, Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro (IESC/UFRJ), Rio de Janeiro 21941-901, Brazil
2
Seção de Meio Ambiente (SAAMB), Instituto Evandro Chagas, Secretaria de Vigilância em Saúde e Ambiente, Ministério da Saúde (IEC/SVSA/MS), Ananindeua 67030-000, Brazil
3
Programa de Pós-Graduação em Epidemiologia e Vigilância em Saúde (PPGEVS/IEC/SVSA/MS), Instituto Evandro Chagas (IEC), Ananindeua 67030-000, Brazil
4
Associação Brasileira de Saúde Coletiva (ABRASCO), Rio de Janeiro 21040-900, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2025, 22(1), 109; https://doi.org/10.3390/ijerph22010109
Submission received: 8 November 2024 / Revised: 21 December 2024 / Accepted: 22 December 2024 / Published: 15 January 2025
(This article belongs to the Section Environmental Health)
Browse Figures
Versions Notes

Abstract

:
Environmental exposure to metallic contaminants such as barium (Ba) is a worldwide concern, as these metals can even be toxic to the human body. Data on different sources of exposure to Ba and possible routes of entry are important for preventing adverse health effects. Blood Ba levels were evaluated in 10 localities in the cities of Barcarena and Abaetetuba in the Amazon. Ba levels were quantified using induced coupled plasma mass spectrometry, and the data were stratified per epidemiological variables and lifestyle habits. The localities were divided into two groups: Group 1, localities wherein individuals had the lowest median levels (0.299–1.330 µg·L−1), and Group 2, localities wherein individuals had the highest median levels (8740–37,300 µg·L−1). Factors such as duration of residency, sex, age, smoking status, and alcohol consumption significantly contributed to the increase in exposure. The highest concentrations were associated with drinking water sources such as underground wells and local rivers, as well as the consumption of fish. This is the first study to record Ba exposure in individuals living in localities close to the industrial areas in the Amazon. These findings may facilitate the development of new health surveillance policies and the implementation of preventive measures.

1. Introduction

Industrial processes can release metallic elements into the environment in the form of ions or volatile compounds. Losses or inefficiencies in industrial processes result in the emission of atmospheric pollutants or the production of hazardous waste. Depending on the physical and chemical characteristics and the mobilization capacity of these contaminants, occupational or environmental exposure of humans to them may occur [1,2,3,4].
In the 1980s, large multinationals began industrial production activities in the city of Barcarena, Brazil, and were responsible for the release of pollutants, including toxic metals, and numerous environmental disasters in the area, such as contamination of surface and underground water bodies by effluents from kaolin processing; fish deaths; excess soot from industrial chimneys; rupture of pipelines with acidic effluents, reaching rivers; and leakage of waste from kaolin and bauxite processes [5].
These changes have modified ecosystems and increased the health risks to urban and traditional human populations (riverside communities, quilombolas, and indigenous peoples) living in this region [5].
Depending on the chemical composition of the raw materials or substances used during the industrial processing stages, these environmental impacts have resulted in the release of metallic contaminants into the environment, such as barium (Ba). Ba can be emitted through anthropogenic activities, such as the extraction and processing of minerals; data on the chemical composition of effluents generated by industrial processes in Barcarena have shown the presence of this metal in effluents discharged directly into the region’s rivers [3,6,7,8,9]. Ba is a biologically non-essential element and can accumulate through the food chain [10].
The main route of continued absorption of Ba would be through the consumption of contaminated water or food [11,12]. In the human body, this metal can cause blockages in potassium channels, a phenomenon known as hypokalemia, leading to metabolic disorders that cause damage to the cardiovascular, renal, and neurological systems. Ba may also be associated with a high incidence of Multiple Sclerosis [11,12,13].
In Barcarena, urban and traditional populations use river water for human consumption, recreation, and/or subsistence fishing. Considering these habits and the information indicating possible anthropogenic sources of exposure to Ba, toxicological analyses were performed, and the results were cross-referenced with epidemiological and lifestyle data to assess environmental exposure to this contaminant in individuals living in nine locations in Barcarena and one in Abaetetuba, a neighboring city of Barcarena.

2. Materials and Methods

2.1. Study Design

This was an epidemiological and cross-sectional study for quantifying blood Ba levels in populations exposed to industrial pollutants in nine localities in Barcarena, namely, Acuí (ACUI), Bairro Industrial (BIN), Vila de Itupanema (VIT), Comunidade Laranjal (CLA), Bairro Canaã (BCA), Vila do Conde (VCO), Dom Manoel (DMN), Curuperê (CUR), Ilha São João (ISJ), and one from Abaetetuba known as Vila de Beja (VBJ) (Figure 1).
The collection of blood samples and epidemiological surveys were conducted through home visits between February 2012 and February 2013. The city of Barcarena has a territory of approximately 1310 km2 and is located in the Amazon River Delta region in northern Brazil [14]. In the early 1980s, Barcarena had approximately 20,000 inhabitants (22.37/km2), and currently, the population is over 120,000 inhabitants (93.32/km2) [15]. Due to the local industrial activity, it is among the cities with the highest economic development indicators, with a per capita income of BRL 39,732.60.
Abaetetuba is a municipality located next to Barcarena, with an estimated area of 1610 km², a population of 141,000 inhabitants (87.61/km²), and a per capita income of BRL 8718.18. The selection of these study areas was motivated by geospatial factors and previous reports of risks to human health from the consumption of water contaminated by toxic metals and inhalation of particulate matter (soot) emitted by industries, as shown in Table 1 [16,17,18,19,20,21,22].
The districts of ACUI (N = 88), BIN (N = 188), VIT (N = 209), DMN (N = 43), CUR (N = 22), ISJ (N = 31), VCO (N = 230), and BCA (N = 75) were located up to 1 km from the site where industrial kaolin processing occurs, where there are reports indicating that there is atmospheric emission of particles from boiler chimneys and possible contamination of groundwater used for human consumption from settling basins of liquid effluents from kaolin processing in these districts.
The districts of CLA (N = 296) and VBJ (N = 295) were selected because these districts are relatively distant (3 to 7 km) from where these anthropogenic activities occur and would be parameters for comparing concentration levels by proximity. During the sampling, a portion of the total population of Barcarena, approximately 20,000 inhabitants, was used to calculate the sample size (Sample N = 1477) sufficient for statistical inference, 95% confidence interval for significance.

2.2. Epidemiology

The study was approved by the Ethics Committee of the Instituto Evandro Chagas (IEC) (0010/2009). Participants included in this study were individuals of both sexes who resided in the 10 districts studied. This study involved the application of an epidemiological survey and sampling of biological material (blood). Participation was voluntary, and after all individuals signed the Free and Informed Consent Form (TCLE), an epidemiological form was applied with sociodemographic data, habits, and lifestyle variables (schooling, gender, consumption of alcoholic beverages, and the use of cigarettes, or the habit of dyeing hair). In addition, dietary variables (daily consumption of fruits, vegetables, red meat, fish, and seafood or canned food) and variables related to the morbidities reported by the interviewees were included, type of housing, forms of drinking water treatment (the use of mineral water, boiling, coagulation, use of hypochlorite, filtration or the non-treatment of consumed water) and the origin of the water consumed by the interviewees were also included.

2.3. Blood Sampling

Blood samples were collected through venipuncture on the forearm (5 mL) using needles and vacuum tubes containing high-purity K2EDTA anticoagulant, indicated for metal analysis. After collection, the samples were immediately transported in thermal boxes. Following this, after thawing and homogenizing the samples, 200 μL of the whole blood was transferred to 15 mL polytetrafluorethylene (PFA) tubes, and 200 µL of Triton X-100 solution (0.1%) and 1% HNO3 were added. Subsequently, the sample solutions were stirred (VORTEX, QUIMIS, Diadema, Brazil) and left to stand for 5 min, following which they were adjusted to 4 mL with 1% HNO3 solution. The solutions were again homogenized and centrifuged at 6000 rpm for 10 min (Z206A, HERMLE, Gosheim, Germany). Finally, the supernatant was transferred to 15-mL PFA tubes [28]. The metals were quantified using an inductively coupled plasma mass spectrometer (ICP-MS, Bruker, Billerica, MA, USA, model 820-MS).
The operating conditions of the ICP-MS were 1400 W radio frequency power, 15 L min−1 argon flow, Meinhard nebulizer with 0.98 L min−1 nebulization flow, and a cyclonic chamber and Scan mode: peak hopping. All blood samples were analyzed in duplicate, with five readings per sample. The Ba isotope measured was Ba-137, and the concentration in the calibration curve varied between 0.600 and 9.600 µg·L−1. The quantification limit for Ba was 0.598 µg·L−1, and the detection limit was 0.179 µg·L−1. The mean recovery percentage of the reference materials used in the quantification of blood Ba was 80.10% for Whole Blood Lyophilized L-1 and 75.6% for Whole Blood Lyophilized L-2.
The samples were prepared, and readings were performed in a clean room environment (class 1000) to minimize the risk of contamination. All blood samples were analyzed in duplicates, with five readings per sample. As an internal standard, multi-element solutions of yttrium, gallium, lanthanum, rhenium, and rhodium were used at a concentration of 5 µg·L−1. The equipment ICP-MS was calibrated using the standard addition method with the multi-element solutions,

2.4. Statistical Analysis

The results obtained were organized and stored in Microsoft Excel 2013 spreadsheets. Statistical analysis was performed using the Minitab 17.0 and BioStat 5.2 software. Values below the LOQ were considered to be ½ LOQ, as used in other studies [29,30,31]. The Grubbs test was applied to verify the presence of outliers, and these were excluded. Ba concentrations were described using the arithmetic mean (AM), geometric mean (GM), and median (MED). The distribution of normality of variables by groups was determined using the Kolmogorov–Smirnov test (K–S).
The variables studied were sex, age group (>10, 10 to 19, 20 to 59, and ≥60 years), duration of residency in the study area (2 to 5, 6 to 9, and ≥10 years), drinking water source (well or general network and river), alcohol consumption (consumes or does not consume alcohol), smoking (smoker or nonsmoker), and eating habits related to food items (consumption of meat, chicken, fish, seafood, Brazil nuts, vegetables, and fruits), as they could be associated with blood Ba levels.
To compare blood Ba levels among two or more groups, an analysis of variance using the non-parametric Mann–Whitney and Kruskal–Wallis tests was performed. Dunn’s test was also used as a post-hoc test to identify the pairs different from samples with more than three groups.
To verify possible associations between Ba contents and predictive variables, Spearman’s Correlation Test was used. Variables that showed a significant correlation with Ba levels in the sequence were evaluated by simple regression analysis. The concentration of Ba was defined as a dependent variable, with the independent variables: proximity to impacted rivers (next door, near, and far), proximity to the kaolin industry (next door, near, and far), water consumption (deep wells, wells shallow, and river water consumption), and frequency of weekly fish consumption (<1 time, 2 to 4 times, and <4 times).
All tests were performed using a 95% confidence interval and a p-value of 0.05 for significance.

3. Results

The study involved 1477 individuals. Table 2 shows the general characteristics of the population assessed from each locality. Most individuals were women (54.91%), adults aged 20–59 years (47.33%), and had lived in the area for >5 years (81.04%). The location with the highest number of individuals evaluated was CMA (N = 296), and the one with the lowest participation was CUR (N = 22).
The K–S normality test for blood Ba levels of all evaluated individuals showed a skewed distribution (KS = 0.290, p < 0.010). Table 3 shows the AM, MED, GM, standard deviation, and percentage distributions of blood Ba levels for all localities. The results showed differences in the median concentration ranges among the localities, based on which they were divided into two distinct groups: Group 1, localities wherein the median blood Ba levels ranged from 0.299 to 1.330 μg·L−1 (ACUI, BIN, VIT, CLA, and VCO) and Group 2, localities wherein the median blood Ba levels ranged from 9.570 to 37.300 μg·L−1 (BCA, VBJ, DMN, CUR, and ISJ).
The Kruskal–Wallis test demonstrated that there were significant differences (H = 567.29, p = 0.000) in the median blood Ba levels among the 10 localities evaluated. In addition, Dunn’s test was used to determine which pairs of localities had similar median values (Table 4). These results showed that there were no significant differences in the median blood Ba levels between BIN and ACUI (p = 0.549) or between CLA and VCO (p = 0.918), with both pairs belonging to Group 1.
There were also no significant differences between the medians of BCA and DMN (p = 0.129), VBJ and DMN (p = 0.894), VBJ and CUR (p = 0.338), DMN and CUR (p = 0.469), and CUR and ISJ (p = 0.241). All localities in the second group had higher blood Ba levels. All individuals with the lowest median blood Ba concentrations resided in locations that were different from the locations of individuals with the highest concentrations, confirming that the categorization of groups was appropriate.
The Grubbs outlier test was performed to determine outliers for each location. The blood Ba level of each outlier, results of the Grubbs test, and the level of significance were as follows: ACUI (4.867 µg·L−1; G = 4.61, p > 0.001), BIN (50.451 µg·L−1, G = 13.07, p > 0.001), VIT (46.606 µg·L−1; G = 6.04; p > 0.001), CLA (113.698 µg·L−1; G = 5.44, p > 0.001), VCO (27.033 µg·L−1; G = 5.67; p > 0.001), BCA (84.266 µg·L−1; G = 5.32, p > 0.001), VBJ (55.653 µg·L−1; G = 4.71, p > 0.001), DMN (21.131 µg·L−1; G = 4.47; p > 0.001), CUR (62.269 µg·L−1; G = 3.26; p > 0.001), and ISJ (135.850 µg·L−1; G = 3.33; p > 0.001). After determining the outliers, these were removed to perform the statistical analysis for each locality.
Figure 2 shows the frequency of distribution of blood Ba levels over four concentration ranges: ≤1 µg·L−1, >1 to ≤10 µg·L−1, >10 to ≥50 µg·L−1, and >50 µg·L−1. The distribution frequencies of the blood Ba levels were different among the ranges mentioned above, based on which the 10 localities could be divided into two different groups: Group 1 showed a frequency of Ba blood levels with the lowest concentrations < 1 µg·L−1, mainly in ACUI and BIN, with concentrations ranging from >1 to ≤10 µg·L−1 in VIT, CLA, and VCO. In Group 2 (BCA, VBJ, DMN, CUR, and ISJ), the frequency with the highest concentrations for blood Ba levels ranged from >10 to ≥50 µg·L−1, with concentrations reaching > 50 µg·L−1.
Table 5 and Table 6 show data for all districts with Ba concentrations stratified per the co-variables studied (sex, age, duration of residency in the study area, alcohol consumption, smoking status, and source of drinking water).
The results showed significant differences among different sources of drinking water in BIN (p = 0.002), between male and female residents in CUR (p = 0.009), and among age groups in DMN (p = 0.041). Figure 3 depicts the trend in water consumption.
Table 7 and Table 8 show the data for all locations with Ba concentrations stratified per eating habits. Significant differences were found in individuals from VIT who consumed Brazil nuts (p = 0.035); individuals in CLA who consumed fish (p = 0.034); individuals from VCO who consumed meat (p = 0.050), fish (p = 0.020), seafood (p = 0.010), vegetables (p = 0.031), and fruits (p = 0.020); and individuals from VBJ who consumed Amazon nuts (p = 0.052) and fruits (p = 0.018), compared with the individuals who did not consume these food items.
Significant differences were found in blood Ba levels between individuals from CLA and VCO who consumed fish more than four times a week and individuals who did not. Figure 4 shows the associations between blood Ba levels and fish consumption for all locations.
It was noted that individuals in 8 of the 10 localities had average blood Ba concentrations above the normal reference values reported in the literature (Figure 5).
Statistical analysis in Table 9 demonstrates the Spearman correlation coefficients and linear regression to identify the influence between Ba and the independent variables, also considering statistical significance (p = 0.05). The correlation analysis showed that Ba concentrations in the blood were strongly affected in the vicinity of the kaolin factory by water consumption and the frequency of weekly fish consumption.

4. Discussion

This is the first study regarding environmental exposure to Ba in the Amazon in almost 50 years since the beginning of mineral mining and industrial development in the region. Blood is the most frequently used biological sample to measure exposure levels of metals in an organism. However, metal exposure can also be evaluated using other samples such as hair, bones, urine, and feces [9]. Previous studies have shown that the blood levels of metals tend to be higher in populations living close to industrial areas [12,17,32].
When comparing the results of the mean Ba levels in the blood of the study population with the results of studies of unexposed populations, we can observe that 8 of the 10 districts studied (VIT, CLA, VCO, VBJ, BCA, DMN, CUR and ISJ) exhibit higher concentrations than studies of unexposed populations such as Italy (AM = 1.2 µg·L−1; N = 590), Argentina (AM = 0.48 µg·L−1; N = 31) and Guinea in Africa (AM = 0.43 µg·L−1; N = 70) [22,33,34].
However, these levels were lower than those found in a population of mining workers in Russia (AM = 84.5 µg·L−1; N = 50) and occupationally exposed individuals [35]. These data indicate that most locations had greater environmental exposure to Ba than that reported in other countries and that living in industrial areas is a factor that contributes to increased blood Ba levels (Figure 5).
Among the localities in Group 1, most individuals from ACUI (91.95%), BIN (88.30%), VIT (71.29%), CLA (57.09%), and VCO (48.70%) had blood Ba levels in the range of normal reference values for environmentally unexposed populations. However, notably, in VIT, CLA, and VCO, a high percentage (28.71–51.30%) of individuals presented with higher concentration ranges, indicating that blood Ba levels of individuals in these three localities may be gradually increasing over time, and if exposure to the source continues, individuals with normal Ba levels may eventually present with the profile of environmentally exposed populations.
These observations correspond with the records of significant increases in industrial production in Barcarena in the last decades [26,36]. The increase in production generates a greater amount of waste, leading to a greater production of effluents and expanding the need for larger areas for waste deposition [3]. The data show that the areas marked for waste deposition have expanded in recent years. Additionally, the distance of these areas from residential localities in the region has gradually decreased [27,36].
In Group 2, most individuals in BCA (79.73%), VBJ (87.46%), DMN (100%), CUR (100%), and ISJ (96.77%) had blood Ba levels higher than the reference range for environmentally unexposed populations. The comparison tests between groups (Table 5 and Table 6) were indicative of a possible relationship between higher blood Ba levels and drinking water sources. Water sources are among the main sources of exposure to metallic elements [32,37].
BIN and VBJ showed blood cadmium (Cd) levels that were 1.5–3 times higher in individuals who consumed well water than in those who consumed general network water and also demonstrated higher blood lead (Pb) concentrations in consumers of well water than in those consuming network water in the DMN locality [17,38].
Ba is known to become naturally bioavailable in the environment from weathering processes, which are normally more intense in tropical regions with high temperatures and humidity [2,8,32,39]. However, Ba can also be introduced into the environment through effluent discharge and waste generated from industrial processes [9,22,40].
Although only the data for BIN indicated a significant association (p = 0.002) with drinking water sources, Figure 3 indicates a trend of increases in the median blood Ba levels that were associated with the main sources of drinking water in these localities. Notably, the levels were higher in localities where the main sources of drinking water were wells or rivers. Importantly, ISJ, CUR, and BCA, all in Group 2, are located on the banks of the Curuperê and Dendê rivers, and individuals here presented with higher than normal blood Ba levels.
The Curuperê and Dendê rivers were, for almost a decade, the final destination of untreated effluents and debris from countless dam ruptures in the basins where kaolin processing plants had been constructed close to the spring sources of these water bodies since the 1980s. Data from 2003 and 2007 showed that there was a continuous discharge of untreated effluent on the margins of these water bodies, and the concentrations of Ba in surface waters reached 744 µg·L−1 [3,23].
These levels were higher than those found in other rivers in the Amazon, for which surface water levels ranged from 3.1 to 56.1 µg·L−1 [41]. They were also lower than the results for drinking water in Santana in Amapá State (56–179 µg·L−1), where the drinking water sources were underground wells or rivers (Amazon River) [42]. Studies on the Murucupi River, also located in Barcarena, showed that the Ba levels varied from 14.6 to 76.7 µg·L−1, indicating that the levels in the Curuperê and Dendê rivers were higher than the natural levels in the region [18].
Only a study of the population of Kandal, a locality in the Mekong River basin in Cambodia with 1028 µg·L−1 of Ba in the drinking water, reported values higher than those in our findings [43]. Therefore, this information indicates that the higher blood Ba concentrations in the ISJ, CUR, and BCA localities may potentially be associated with the use of water from the Curuperê and Dendê rivers, which are water bodies that have received large amounts of effluent discharge and waste for years, without implementation of remediation measures [3].
ISJ is a riverside locality on the banks of the Dendê River, which the population uses for leisure, fishing, and transportation; CUR is located adjacent to the kaolin processing industry and uses the Curuperê River for fishing. The wells in this region are considered to be shallow [23,44]. In cases of man-made disasters such as effluent spills, there are reports of changes in the color and odor of the rivers in these locations, as well as increased fish mortality rates [3,23,25,42].
Owing to the deposition of contaminants in these rivers over the years, the highest levels of Ba in these localities may be associated with particular sources of drinking water and fish consumption. The correlation and regression tests reinforce this assertion: whoever lives next to or close to the kaolin industry and consumes water from shallow wells or rivers is directly linked to having higher concentrations of Ba in the blood. The higher frequency of weekly fish consumption is also directly associated with higher concentrations of Ba in the blood of the population.
Geological studies of the kaolin extracted in the Capim region in the Amazon showed Ba levels between 15.8 and 39.8 mg kg−1 in the ores [8,45]. In Botswana, up to 5806 mg kg−1 of Ba was found in samples of kaolin deposits [46], and in western Cameroon, there are reports of concentrations up to 699 mg kg−1 [47]. Similarly, the chemical profile of trace elements in the bauxite ore mining in Paragominas City, Pará State, showed BA levels in the range of 17 to 70 mg kg−1 [41]. Therefore, the presence of high Ba concentrations in the residues and effluents from kaolin and bauxite processing is expected.
The occurrence of the greater availability of Ba in drinking water has been shown in similar situations in Texas, Georgia, New Mexico, Kentucky, and Pennsylvania in the USA [48]. Ingestion of Ba, even for relatively short periods, can cause gastrointestinal disorders, nausea, vomiting, colic, and muscle weakness. Further, ingestion of high levels for a long time can result in cardiovascular, kidney, metabolic, neurological, and mental disorders [11,49,50].
Kaolin is also present in the geological layers of the subsoil in Barcarena and Abaetetuba. Georgia has the second-largest kaolin deposit worldwide [3,45]. However, this study showed that greater proximity to industrial areas and commercial ports contributes to high barium exposure, owing to the impact of large industries on the aquifers and nearby rivers. Among these industrial enterprises, kaolin processing and aluminum production have the greatest environmental impact.
However, BIN, which is also located adjacent to an industrial kaolin processing area, showed the lowest median blood Ba levels (median 0.299 µg·L−1 N = 120 for wells; median 0.299 µg·L−1 N = 67 for general network). In BIN after 2007, when the kaolin tailing basin ruptured, resulting in effluent contamination of the drinking wells, a deep well was built for water collection and subsequent distribution through the general network. At the time of this study, most residents no longer used shallow wells and did not normally have direct contact with the river [17,51]. Therefore, despite the proximity to the kaolin processing industries, the locality did not have high exposure levels. This finding indicates that the use of deep wells may reduce Ba exposure in the region.
ACUI (MED = 0.299 µg·L−1; N = 87) is a locality with an indigenous population that practices rural habits of the local cultivation of fruits and vegetables and animal husbandry in their backyards. However, this locality is situated at some distance from the industrial areas. On the other hand, VIT (MED = 0.299 µg·L−1 N = 114 for wells; MED = 0.299 µg·L−1 N = 92 for the general network) and CLA (MED = 0.320 µg·L−1 N = 215 for wells; MED = 0.300 µg·L−1 N = 80 for the general network) are more urbanized localities and less dependent on rivers in the region.
VCO (MED = 1.432 µg·L−1 N = 100 for wells; MED = 1.346 µg·L−1 N = 125 for the general network) is one of the oldest localities in the region. It is situated on the banks of the Pará River and next to large ports that are used daily for the transport of raw materials and manufactured products [52].
BCA (MED = 9.21 µg·L−1 N = 74 for wells) and DMN (MED = 8672 µg·L−1 N = 42 for wells) are located close to the kaolin processing industries and record high levels of atmospheric pollution and deposition of untreated effluents or industrial waste. However, most residents of these localities use water from shallow wells [53].
In VBJ (Median 13,381 µg·L−1 N = 156 for wells; 13,354 µg·L−1 N = 138 for the general network), there were no significant differences in the median Ba levels between individuals who consumed drinking water from wells and those who consumed water from the general network. The local sanitation plan of the locality comprises a small water collection and distribution system. A semi-artesian well supplies water to only 26.66% of the population, whereas the rest use other wells or rivers [54].
Although only data for CLA (p = 0.034) and VCO (p = 0.031) showed that there was a significant association of Ba levels with the weekly frequency of fish consumption (more than four times a week) (Figure 4), there were significant increments in the blood Ba level in individuals from other localities (Figure 2). Ba is known to accumulate in fish and other aquatic organisms [11,49]. Therefore, in communities with a high consumption of fish, this can be one of the main sources of exposure to toxic metals [13,55]. The sustained higher-than-normal Ba levels in surface waters may be indicative of the accumulation of this metal in the fish population, which can ultimately lead to high Ba levels in the local population.
Data for VIT and VBJ showed significant associations between Ba levels and consumption of Brazil nuts two to four times a week. This finding corroborates reports that show that Ba accumulates in Brazil nuts, fruits, and vegetables [11,13,56,57].
Although most participants were women, no significant associations were observed between blood Ba levels and sex, confirming that blood Ba levels depend mainly on possible sources of exposure relevant to the locality. In Cambodia, there were no significant associations of Ba concentrations in hair with age or sex, corroborating the assumption that these variables do not influence Ba exposure, and the main factor is the proximity of exposure sources [43].
Alcohol consumption and cigarette smoking were not associated with increased blood Ba levels, demonstrating that these habits are not exposure sources. Studies show that blood Ba levels are primarily associated with the proximity of exposure sources. Although Ba is known for its acute toxicity, epidemiological data for chronic exposure are still scarce. There are also important gaps in the current literature with respect to the identification of vulnerable populations and the factors that make some individuals susceptible to Ba toxicity [11,32,40].
We emphasize that the main effect of Environmental Exposure to Barium is associated with changes in the sodium–potassium pump that regulates the circulatory system and may be associated with possible changes in the blood pressure of exposed individuals. This information on blood pressure was not collected during this research, but it alerts health agencies to the need for better monitoring of cardiovascular diseases through a future biomonitoring program. Cardiovascular diseases are the main health indicators associated with exposure to barium.

5. Conclusions

This study provided data on the blood Ba levels in people living near the port and industrial areas in the Amazon and showed possible sources of human exposure. The average Ba levels found in individuals from all communities indicated continual exposure in the region; individuals from 8 of the 10 communities had blood concentration levels up to 50 times higher than the normal levels previously reported in the literature.
There is evidence that living near kaolin processing plants and drinking water from rivers directly increases Ba levels in the blood of this population. Populations living on the banks of rivers, such as the Curuperê and Dendê, may be at greater risk of adverse health effects.
In addition, greater exposure to Ba was observed in individuals with higher weekly fish consumption. Fish is part of the diet of riverside and quilombola populations, which may be evidence of the accumulation of Ba in the trophic chain of the fish consumed.
The results of this study provide relevant toxicological information regarding Ba exposure in the Amazon, and the results may be used for comparisons of future epidemiological studies in Brazil and other countries. There is a lack of studies on monitoring and systematic management of environmental exposure to metals in Brazil.
The need for these studies is based on laboratory, clinical, and epidemiological assessments. Therefore, we advise public health systems that these populations need to be monitored continuously through biomonitoring, especially for populations located near industrial production areas, to identify potential sources of environmental exposure.

Author Contributions

B.R.C., Conceptualization, formal analysis, writing—original draft preparation; V.d.M.C., writing—review and editing; K.S.N., methodology; T.K.L.d.Q., methodology; L.d.C.d.S.M., methodology; I.M.d.J., investigation; M.d.O.L., writing—review and editing; A.M., investigation, writing—review and editing, supervision, project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Evandro Chagas Institute (IEC) (protocol code 0010/2009 approved on 9 July 2009).

Informed Consent Statement

Written informed consent has been obtained from the participants to publish this paper.

Data Availability Statement

Available on request from the corresponding author due to some of this data is part of an ongoing study.

Acknowledgments

The authors are grateful for the financial support of the Evandro Chagas Institute (IEC), Adjustment of Terms of Conduct (TAC-Barcarena), the Coordination for the Improvement of Higher Education Personnel (CAPES), and the National Council for Scientific and Technological (CNPQ).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bass, D.A.; Hickok, D.; Quig, D.; Urek, K. Trace element analysis in hair: Factors determining accuracy, precision, and reliability. Altern. Med. Rev. 2001, 6, 472–481. [Google Scholar] [PubMed]
  2. Gonçalves, Í.G. Determinação da Concentração de Contaminantes no Caolim Através da Teoria de Kubelka-Munk. Master’s Thesis, Universidade Federal do Rio Grande do Sul, Farroupilha, Porto Alegre, Brazil, 2009. [Google Scholar]
  3. Lima, M.O. Impactos Ambientais na Bacia Hidrográfica do Rio Pará: Uma Abordagem Analítica E Quimiométrica. Ph.D Thesis, Universidade Federal Do Pará, Belém, Brazil, 2011. [Google Scholar]
  4. Santos, L.F.P. Avaliação dos Teores de Cádmio e Chumbo em Pescado Proveniente de São Francisco do Conde, Bahia. Master’s Thesis, Universidade Federal da Bahia, Salvador, Brazil, 2011. [Google Scholar]
  5. Lira, S.R.B.; Silva, M.L.M.; Pinto, R.S. Desigualdade e heterogeneidade no desenvolvimento da Amazônia no século XXI. Nova Econ. 2009, 19, 153–184. [Google Scholar] [CrossRef]
  6. Aguiar, M.R.M.P.; Novaes, A.C. Remoção de metais pesados de efluentes industriais por aluminossilicatos. Mol. Biol. 2002, 17, 683–704. [Google Scholar] [CrossRef]
  7. Porfirio, D.M.O. Intemperismo Tropical Como Agente de degradação de cadeias de Isoladores de Alta Tensão em Subestações na Amazônia oriental: Estudo de Caso em Barcarena-PA e São Luís-MA. Ph.D Thesis, Universidade Federal do Pará, Belém, Brazil, 2018. [Google Scholar]
  8. Sousa, D.J.L.; Varajão, A.F.D.C.; Yvon, J.; Costa, G.M. Mineralogical, micromorphological and geochemical evolution of the kaolin facies deposit from the Capim region (northern Brazil). Clay Miner. 2007, 42, 69–87. [Google Scholar] [CrossRef]
  9. Brabo, E.S.; Lima, M.O.; Santos, J.G. Avaliação do Impacto no rio Murucupi em Decorrência do Transbordo de Efluentes da Bacia de Rejeitos Sólidos da ALUNORTE no Município de Barcarena-Pa; IEC: Ananindeua, Brazil, 2003. [Google Scholar]
  10. Toxicological Profile for Barium and Barium Compounds; Agency for Toxic Substances and Disease Registry (US): Atlanta, GA, USA, 2007. [PubMed]
  11. Bhoelan, B.S.; Stevering, C.H.; van der Boog, A.T.J.; van der Heyden, M.A.G. Barium toxicity and the role of the potassium inward rectifier current. Clin. Toxicol. 2014, 52, 584–593. [Google Scholar] [CrossRef]
  12. Nordberg, G.F.; Fowler, B.A.; Nordberg, M. Handbook on the Toxicology of Metals; Elsevier: San Diego, CA, USA, 2007. [Google Scholar]
  13. Agency For Toxic Substances and Disease Registry (ATSDR). ToxGuide for Barium. Atlanta. 2007. Available online: https://www.atsdr.cdc.gov/toxprofiles/tp24.pdf (accessed on 5 February 2009).
  14. Carmo, M.B.S.; Costa, S.M.F. Os paradoxos entre os urbanos no município de Barcarena, Pará. Urbe. Rev. Bras. Gestão Urbana 2016, 8, 291–305. [Google Scholar] [CrossRef]
  15. Instituto Brasileiro De Geografia E Estatística (IBGE). Rio de Janeiro. 2019. Available online: https://cidades.ibge.gov.br/brasil/pa/barcarena/panorama (accessed on 5 February 2019).
  16. Barros, M.J.B. Mineração, Finanças Públicas e Desenvolvimento Local no Município de Barcarena-Pará. Master’s Thesis, Universidade Federal do Pará, Belém, Brazil, 2009. [Google Scholar]
  17. Naka, K.S.; Mendes, L.C.S.; Queiroz, T.K.L.; Costa, B.N.S.; Jesus, I.M.; Câmara, M.V.; Lima, O.M. A comparative study of cadmium levels in blood from exposed populations in an industrial area of the Amazon, Brazil. Sci. Total Environ. 2020, 698, 134309. [Google Scholar] [CrossRef]
  18. Pereira, S. Estudo químico ambiental do rio Murucupi–Barcarena, PA, Brasil, área impactada pela produção de alumínio. Ambiente Agua Interdiscip. J. Appl. Sci. 2006, 2, 62–82. [Google Scholar]
  19. Carneiro, B.S.; Faial, K.C.F.; Pinheiro, S.C.C.; Costa, V.B.; Sousa, E.B.; Moraes, L.L.C.S.; Mendes, R.A.; Vasconcelos Junior, N.T.; Santos, L.F.; Sousa, T.L.G.; et al. Relatório Iec-Semam N° 014/2016 Relatório Técnico Referente ao Monitoramento da Água Superficial da Área de Influência do Porto de Vila do Conde; IEC: Ananindeua, Brazil, 2016. [Google Scholar]
  20. Carneiro, B.S.; Faial, K.C.F.; Medeiros, A.C.; Vale, E.R. Relatório Iec-Samam 10/2008 Relatório Técnico Sobre a Avaliação da Qualidade da Água do Rio Arienga, Igarapé Bacabal e Poços Utilizados pela Comunidade Ribeirinha, Município de Abaetetuba Estado do Pará; IEC: Ananindeua, Brazil, 2008. [Google Scholar]
  21. Lima, M.O.; Mendes, L.C.S.; Costa, T.K.L.Q.; Souza, B.N.; Silva, M.M.C.; Ferreira, D.; Patrick, M.; Vasconcelos Júnior, N.T.; Carneiro, B.S.; Marques, L.C.A.; et al. Avaliação Dos Impactos Referente Ao Transbordo De Efluentes De Lama- Vermelha Na Cidade De Barcarena, Estado Do Pará. Available online: https://www.iec.gov.br/wp-content/uploads/2018/03/RELATÓRIO-TÉCNICO-002-2018-Final-Tabela-4-Modificada_SS.pdf (accessed on 2 September 2019).
  22. Lu, Y.; Kippler, M.; Harari, F.; Grandér, M.; Palm, B.; Nordqvist, H.; Vahter, M. Alkali dilution of blood samples for high throughput ICP-MS analysis-comparison with acid digestion. Clin. Biochem. 2015, 48, 140–147. [Google Scholar] [CrossRef]
  23. Santos, E.C.O.; Brabo, E.S.; Sá, L.L.C.; Lima, M.O.; Girard, R.P. Avaliação Técnica Sobre o Fenômeno da Mortandade de Peixes Ocorrido no dia 23/06/03 na Praia de Itupanema no Município de Barcarena, Estado do Pará; IEC: Ananindeua, Brazil, 2003. [Google Scholar]
  24. Santos, E.C.O.; Brabo, E.S.; Lima, M.O.; Santos, J.G. Relatório Técnico dos Impactos Sobre os Igarapés Curuperê e Dendê em Decorrência do Lançamento de Efluentes da Empresa Rio Capim Caulim (RCC) no Município de Barcarena–Estado do Pará; IEC: Ananindeua, Brazil, 2003. [Google Scholar]
  25. Carneiro, B.S.; Vale, E.R.; Lima, M.D.O. Atividades Industriais no Município de Barcarena, Pará: Os Impactos Ambientais nos Igarapés Curuperê e Dendê a Partir do Lançamento de Efluentes Ácidos do Processo de Beneficiamento do Caulim e Avaliação das Águas de Consumo das Comunidades do Bairro Industrial; IEC: Ananindeua, Brazil, 2007. [Google Scholar]
  26. Carmo, E.; Cravo, J.R.S.; Hazeu, M.; Fialho, N.; Gayoso, S. Barcarena Livre Informa 1 “37 Anos De Desastres Socioambientais Em Barcarena”. 2016. Available online: https://repositorio.ufpa.br/bitstream/2011/12066/1/Tese_PlanosDesenvolvimentoDesigualdade.pdf (accessed on 5 February 2009).
  27. Lima, M.O.; Mendes, L.C.S.; Costa, T.K.L.Q.; Souza, B.N.; Silva, M.M.C.; Ferreira, D.P.M.; Vasconcelos Júnior, N.T.; Carneiro, B.S.; Marques, L.C.A.; Faia, K.C.F. Avaliação Preliminar Dos Impactos Ambientais Referente Ao Transbordo E Lançamentos Irregulares De Efluentes De Lama Vermelha Na Cidade De Barcarena, Estado Do Pará; IEC: Belém, Brazil, 2018. Available online: www.iec.pa.gov.br (accessed on 27 February 2020).
  28. Kira, C.S. Determinação de Valores de Referência Para Chumbo, Cádmio, Mercúrio e Níquel em Sangue de Crianças e Adultos da Cidade de São Paulo. Ph. D. Thesis, Universidade de São Paulo, São Paulo, Brazil, 2014. [Google Scholar]
  29. Becker, K.; Schulz, C.; Kaus, S.; Seiwert, M.; Seifert, B. German environmental survey 1998 (GerES III): Environmental pollutants in the urine of the German population. Int. J. Hyg. Environ. Health 2003, 206, 15–24. [Google Scholar] [CrossRef]
  30. Kira, C.S.; Sakuma, A.M.; Capitani, E.M.; Freitas, C.U.; Cardoso, M.R.A.; Gouveia, N. Associated factors for higher lead and cadmium blood levels, and reference values derived from general population of São Paulo, Brazil. Sci. Total Environ. 2016, 543, 628–635. [Google Scholar] [CrossRef] [PubMed]
  31. Link, B.; Gabrio, T.; Piechotowski, I.; Zöllner, I.; Schwenk, M. Baden-Wuerttemberg Environmental Health Survey (BW-EHS) from 1996 to 2003: Toxic metals in blood and urine of children. Int. J. Hyg. Environ. Health 2007, 210, 357–371. [Google Scholar] [CrossRef] [PubMed]
  32. Pragst, F.; Stieglitz, K.; Runge, H.; Runow, K.D.; Quig, D.; Osborne, R.; Runge, C.; Ariki, J. High concentrations of lead and barium in hair of the rural population caused by water pollution in the Thar Jath oilfields in South Sudan. Forensic Sci. Int. 2017, 274, 99–106. [Google Scholar] [CrossRef] [PubMed]
  33. Minoia, C.; Sabbioni, E.; Apostoli, P.; Pietra, R.; Pozzoli, L.; Gallorini, M.; Nicolaou, G.; Alessio, L.; Capodaglio, E. Trace element reference values in tissues from inhabitants of the european community i. A study of 46 elements in urine, blood and serum of italian subjects. Sci. Total Environ. 1990, 95, 89–105. [Google Scholar] [CrossRef]
  34. Yedomon, B.; Menudier, A.; Etangs, F.L.D.; Anani, L.; Fayomi, B.; Druet-Cabanac, M.; Moesch, C. Biomonitoring of 29 trace elements in whole blood from inhabitants of Cotonou (Benin) by ICP-MS. J. Trace Elem. Med. Biol. 2017, 43, 38–45. [Google Scholar] [CrossRef]
  35. Ivanenko, N.B.; Ivanenko, A.A.; Solovyev, N.D.; Zeimal, A.E.; Navolotskii, D.V.; Drobyshev, E.J. Biomonitoring of 20 trace elements in blood and urine of occupationally exposed workers by sector field inductively coupled plasma mass spectrometry. Talanta 2013, 116, 764–769. [Google Scholar] [CrossRef]
  36. Silva, S.F.; Hazeu, M.T. O complexo industrial-portuário em Barcarena e a saúde de comunidades tradicionais na Amazônia brasileira. O Soc. Questão 2019, 21, 171–194. [Google Scholar]
  37. Kato, M.; Kumasaka, M.Y.; Ohnuma, S.; Furuta, A.; Kato, Y.; Shekhar, H.U.; Kojima, M.; Koike, Y.; Dinh Thang, N.; Ohgami, N.; et al. Comparison of barium and arsenic concentrations in well drinking water and in human body samples and a novel remediation system for these elements in well drinking water. PLoS ONE 2013, 8, 4–9. [Google Scholar] [CrossRef]
  38. Queiroz, T.K.L.; Naka, K.S.; Santos, M.L.C.; Costa, B.N.S.; Jesus, I.M.; Câmara, V.M.; Lima, M.O. Human blood lead levels and the first evidence of environmental exposure to industrial pollutants in the amazon. Int. J. Environ. Res. Public Health 2019, 16, 3047. [Google Scholar] [CrossRef]
  39. Martin, S.; Griswold, W. Human Health Effects of Heavy Metals. Environ. Sci. Technol. Briefs Citiz. 2009, 15, 5. [Google Scholar]
  40. Kravchenko, J.; Darrah, T.H.; Miller, R.K.; Lyerly, H.K.; Vengosh, A. A review of the health impacts of barium from natural and anthropogenic exposure. Environ. Geochem. Health 2014, 36, 797–814. [Google Scholar] [CrossRef] [PubMed]
  41. Marini, O.J.; Queiroz, E.T.; Ramos, B.W. Caracterização de Depósitos Minerais em Distritos Mineiros da Amazônia; DNPM: Brasilía, Brazil; ADIMB: Brasilía, Brazil, 2005. [Google Scholar]
  42. Lima, M.O.; Faial, K.R.F.; Brabo, E.S.; Santos, E.C.O.; Angélica, R.S.; Mendes, R.A.; Carneiro, B.S.; Sa, L.L.C.; Vale, E.R.; Jesus, I.M. Total arsenic, trace elements and bacteriologycal evaluation in drinking water at Elesbão district, Santana city, Amapa state, Brazil. Cad. Saude Coletiva 2007, 15, 467–482. [Google Scholar]
  43. Phan, K.; Phan, S.; Huoy, L.; Suy, B.; Wong, M.H.; Hashim, J.H.; Mohamed, Y.M.S.; Aljunid, S.M.; Sthiannopkao, S.; Kim, K.W. Assessing mixed trace elements in groundwater and their health risk of residents living in the Mekong River basin of Cambodia. Environ. Pollut. 2013, 182, 111–119. [Google Scholar] [CrossRef] [PubMed]
  44. Moura, E.A.F. Laranja sem laranjal: Condições de vida após a implementação de grandes projetos a Amazônia. In: Encontro Nacional De Estudos Populacionais, 7., Belo Horizonte, 1990. Belo Horizonte: ABEP/NEPO, 1990. p. 197–224. Available online: http://www.abep.org.br/publicacoes/index.php/anais/issue/view/27/showToc (accessed on 29 August 2019).
  45. Pruett, R.J. Kaolin deposits and their uses: Northern Brazil and Georgia, USA. Appl. Clay Sci. 2016, 131, 3–13. [Google Scholar] [CrossRef]
  46. Ekosse, G. Provenance of the Kgwakgwe kaolin deposit in Southeastern Bostwana and its possible utilization. Appl. Clay Sci. 2001, 20, 137–152. [Google Scholar] [CrossRef]
  47. Njoya, A.; Nkoumbou, C.; Grosbois, C.; Njopwouo, D.; Njoya, D.; Courtin-Nomade, A.; Yvon, J.; Martin, F. Genesis of Mayouom kaolin deposit (western Cameroon). Appl. Clay Sci. 2006, 32, 125–140. [Google Scholar] [CrossRef]
  48. Wones, R.G.; Stadler, B.L.; Frohman, L.A. Lack of effect of drinking water barium on cardiovascular risk factors. Environ. Health Perspect. 1990, 85, 355–359. [Google Scholar]
  49. Agency For Toxic Substances and Disease Registry (ATSDR). BARIUM. Atalnta. 2013. Available online: http://www.cdc.gov/exposurereport/pdf/FourthReport_UpdatedTables_Sep2012.pdf (accessed on 28 August 2019).
  50. Ghose, A.; Sayeed, A.A.; Hossain, A.; Rahman, R.; Faiz, A.; Haque, G. Mass barium carbonate poisoning with fatal outcome, lessons learned: A case series. Cases J. 2009, 2, 1–4. [Google Scholar] [CrossRef]
  51. Brasil Ministério das Minas e Energia. Desenvolvimento De Estudos Para Elaboração Do Plano Duodecenal (2010–2030) De Geologia, Mineração E Transformação Mineral; Ministério das Minas e Energia: Brasília, Brazil, 2009.
  52. Paz, A.C.; Frédou, L.F.; Frédou, T. Caracterização da atividade pesqueira em Vila do Conde (Barcarena, Pará), no estuário amazônico. Bol. Mus. Para. Emílio Goeldi Ciências Hum. 2011, 6, 307–318. [Google Scholar] [CrossRef]
  53. Faial, K.C.F.; Costa, A.C.G.; Souza, E.B.; Carneiro, B.S.; Pinheiro, S.C.C. Relatório Técnico Iec-Samam 018/2015 Avaliação de Impactos Ambientais no Furo do Arrozal Provenientes do Embarque de Soja, Município de Barcarena, Estado do Pará; IEC: Ananindeua, Brazil, 2015. [Google Scholar]
  54. Abaetetuba (PA). Prefeitura Municipal. Secretaria Municipal de Meio Ambiente. Plano Municipal De Saneamento Do Município De Abaetetuba–Diagnóstico. Available online: https://www.abaetetuba.pa.gov.br/arquivos/25/PMS_ABAETETUBA_MS_2017_DIAG_01.pdf (accessed on 18 February 2020).
  55. Silva, S.F.; Lima, M.O. Mercury in fish marketed in the Amazon triple frontier and health risk assessment. Chemosphere 2020, 248, 125989. [Google Scholar] [CrossRef]
  56. Łukasik-Głebocka, M.; Sommerfeld, K.; Hanć, A.; Grzegorowski, A.; Barałkiewicz, D.; Gaca, M.; Zielińska-Psuja, B. Barium determination in gastric contents, blood and urine by inductively coupled plasma mass spectrometry in the case of oral barium chloride poisoning. J. Anal. Toxicol. 2014, 28, 380–382. [Google Scholar] [CrossRef]
  57. World Health Organization (WHO). Concise international chemical assessment document 71. Barium Barium Compd. 2001, 71, 1–46. [Google Scholar]
Ijerph 22 00109 g001
Figure 1. The map of the studied districts.
Figure 1. The map of the studied districts.
Ijerph 22 00109 g001
Ijerph 22 00109 g002
Figure 2. The frequency of distribution of blood Ba levels (µg·L−1) by the communities evaluated.
Figure 2. The frequency of distribution of blood Ba levels (µg·L−1) by the communities evaluated.
Ijerph 22 00109 g002
Ijerph 22 00109 g003
Figure 3. The concentration of Ba (µg·L−1) in blood by drinking water sources.
Figure 3. The concentration of Ba (µg·L−1) in blood by drinking water sources.
Ijerph 22 00109 g003
Ijerph 22 00109 g004
Figure 4. Blood concentrations of Ba (µg·L−1) by weekly fish consumption.
Figure 4. Blood concentrations of Ba (µg·L−1) by weekly fish consumption.
Ijerph 22 00109 g004
Ijerph 22 00109 g005
Figure 5. Mean levels of Ba (μg·L−1) in exposed and unexposed populations and in the studied population.
Figure 5. Mean levels of Ba (μg·L−1) in exposed and unexposed populations and in the studied population.
Ijerph 22 00109 g005
Table 1. The history of environmental disasters in Barcarena.
Table 1. The history of environmental disasters in Barcarena.
YearEvent
2003Fish mortality [23]
2003Rupture of dams and discharge of untreated red mud effluents [9]
2003Discharge of untrated kaolin efluentes [24]
2004Emission of industrial soot [14]
2006Seaweed bloms [14]
2006Emission of industrial soot [14]
2006Dicharge of untread efluentes [14]
2007Discharge of untreated kaolin effluents [14]
2007Rupture of dams and discharge of untreated kaolin effluents [25]
2008Wreck of fuel cargo [14]
2009Rupture of dams and discharge of untrated red mud effluents [26]
2010Emission of industrial soot [19]
2011Discharge of untreated kaolin effluents [14]
2012Discharge of untreated kaolin effluents [14]
2014Discharge of untreated kaolin effluents [14]
2015Wreck of fuel cargo and 5.000 oxen [14]
2016Discharge of untreated kaolin effluents [21]
2018Discharge of untrated red mud effluents [27]
Table 2. The localities’ characteristics, Barcarena e Abaetetuba Cities, 2012–2013.
Table 2. The localities’ characteristics, Barcarena e Abaetetuba Cities, 2012–2013.
LOCALITYFemaleMaleAge Group Years (N)Residence Time Years (N)Total
N%N%0–1011–1920–59≥60<22–5>5
Acuí4955.683944.32202136115117288
Bairro Industrial9852.139047.87424391121841129188
Vila de Itupanema12258.378741.6344469623216191209
Comunidade Laranjal16756.4212943.584782145221630250296
Vila do Conde13357.839742.173665107221013207230
Bairro Canaã3850.673749.3323113656402975
Vila de Beja16355.2513244.755259140441436245295
Dom Manuel1944.192455.81119194643343
Curuperê940.911359.0942151*12122
Ilha São João1341.941858.0687142832031
General total81154.9166645.092873456991468519511971477
* Does not present any individual in the studied group.
Table 3. Statistical analysis of the blood Ba concentrations (μg·L−1) of the studied populations, Barcarena and Abaetetuba, 2012–2013.
Table 3. Statistical analysis of the blood Ba concentrations (μg·L−1) of the studied populations, Barcarena and Abaetetuba, 2012–2013.
LOCALITYNAMMedianGMSDRangeP5%P25%P75%P95%
Acuí880.5830.2990.3820.9100.299–4.8670.2990.2990.2992.444
Bairro Industrial1880.8900.2990.4243.7930.299–50.4510.2990.2990.2992.286
Vila de Itupanema2093.2470.2990.7787.1830.299–46.6060.2990.2992.62917.769
Comunidade Laranjal2968.5000.3101.32919.3500.300–113.7000.2990.3004.39061.278
Vila do Conde2302.7631.3301.1974.2820.299–27.0330.2990.2993.20212.118
Bairro Canaã7511.9809.5705.17913.5900.300–84.2700.2991.95015.46034.078
Vila do Beja29513.26913.5428.2399.0010.299–55.6530.2997.39417.72328.457
Dom Manuel439.1208.7408.9442.1967.156–21.1317.4717.8719.78610.930
Curuperê2216.92014.24012.10613.9001.980–62.3003.4054.94023.72032.592
Ilha São João3138.35037.30026.05329.3101.200–135.8505.07613.89059.75077.096
AM: Arithmetic Mean; GM: Geometric mean; SD: Standard deviation.
Table 4. The Dunn’s test results with the adjustment of p-values after Kruskal–Wallis H test.
Table 4. The Dunn’s test results with the adjustment of p-values after Kruskal–Wallis H test.
LOCALITYGroup 1Group 2
ACUIBINVITCLAVCOBCAVBJDMNCUR
BIN0.549--------
Group 1VITp < 0.05p < 0.05-------
CLAp < 0.05p < 0.05p < 0.05------
VCOp < 0.05p < 0.05p < 0.050.918-----
BCAp < 0.05p < 0.05p < 0.05p < 0.05p < 0.05----
Group 2VBJp < 0.05p < 0.05p < 0.05p < 0.05p < 0.05p < 0.05---
DMNp < 0.05p < 0.05p < 0.05p < 0.05p < 0.050.1290.894--
CURp < 0.05p < 0.05p < 0.05p < 0.05p < 0.05p < 0.050.3380.469-
ISJp < 0.05p < 0.05p < 0.05p < 0.05p < 0.05p < 0.05p < 0.05p < 0.050.241
Table 5. The description of the statistical analysis by localities (Group 1) with lower blood Ba levels (µg·L−1) by the epidemiological variables.
Table 5. The description of the statistical analysis by localities (Group 1) with lower blood Ba levels (µg·L−1) by the epidemiological variables.
GROUPSACUI BIN VIT CLA VCO
NAMMEDRANGEp **NAMMEDRANGEpNAMMEDRANGEp **NAMMEDRANGEp **NAMMEDRANGEp **
Gender
        Female490.5330.2990.299–4.7960.949970.7150.2990.299–11.8480.481212.7650.2990.299–37.0690.6631667.2600.3000.300–95.8100.6761322.6081.3550.299–23.1380.821
        Male380.5360.2990.299–3.365 900.5280.2990.299–4.773 873.4200.2990.299–35.784 1299.2900.3400.300–90.280 972.7231.2100.299–22.080
Age group (years)
        0 a 10200.3860.2990.299–2.0280.392420.5500.2990.299–2.9350.793443.3900.3000.30–37.0700.0784710.3402.0300.300–90.2800.403362.7221.5180.299–23.1380.839
        11 a 19210.5070.2990.299–2.495 420.6820.2990.299–4.772 451.3640.2990.299–21.08 829.6600.3000.300–86.270 642.4781.4060.299–19.085
        20 a 59350.5250.2990.299–3.767 910.6500.2990.299–11.848 963.3810.2990.299–32.548 1446.6100.3000.300–95.810 1072.9311.3710.299–22.080
        ≥60110.8840.2990.299–4.796 120.5030.2990.299–2.748 234.2100.3000.300–35.780 227.9102.5400.300–83.270 221.7381.1500.299–8.3020
Residence time (years)
        <250.2990.2990.299–0.2990.064180.4190.2990.299–1.5680.68323.2903.2900.300–6.2700.6481611.5801.4100.300–90.2800.465103.2502.4300.300–12.9100.644
        2 a 5110.2990.2990.299–0.299 410.6520.2990.299–2.936 163.8600.3000.300–29.450 309.3301.6200.300–83.270 131.7452.1950.299–3.859
        >5710.5870.2990.299–4.796 1280.6460.2990.299–11.848 1902.9670.2990.299–37.069 2497.7800.3000.300–95.810 2062.6851.2360.299–23.138
Alcohol
        Consumers410.4870.2990.299–3.7670.814980.6830.2990.299–11.8480.403963.2040.2990.299–35.7840.893116.0009.5300.3000.300–95.8100.887772.3621.1220.299–21.6380.362
        Non-consumers390.5660.2990.299–4.796 750.4730.2990.299–5.279 502.9800.2990.299–24.044 119.0005.7900.3400.300–73.280 712.9321.1770.299–22.080
Smoking habit
        Smokers71.6570.2990.299–4.796 270.4630.2990.299–2.1110.834133.0300.3000.300–10.3400.64361.0004.7200.5100.300–67.2800.909141.7830.2990.299–6.4430.213
        Nonsmokers730.4170.2990.299–3.7670.1081460.6160.2990.299–11.847 1313.1050.2990.299–35.784 176.0008.5800.3000.300–95.810 1352.8391.3140.299–22.080
Drinking water source
        Well870.5340.2990.299–4.796*1200.5160.2990.299–11.8480.0021143.4070.2990.299–37.0690.3952158.6400.3200.300–95.8100.2991003.0451.4320.299–23.1380.966
        General network**** 670.8210.2990.299–4.772 922.5100.2990.299–29.447 806.8100.3000.300–86.270 1252.4111.3460.299–19.085
        River**** **** **** **** ****
AM—arithmetic mean; MED—median; * Does not present any individual in the studied group; ** Comparisons between the studied variables: p ≤ 0.05.
Table 6. The description of the statistical analysis by location (Group 2) with higher levels of Ba (µg·L−1) by the variables studied.
Table 6. The description of the statistical analysis by location (Group 2) with higher levels of Ba (µg·L−1) by the variables studied.
GROUPSBCA VBJ DMN CUR ISJ
NAMMEDRANGEp **NAMMEDRANGEp **NAMMEDRANGEp **NAMMEDRANGEp **NAMMEDRANGEp **
Gender
        Female3810.8108.7000.300–45.4900.76216212.46712.0350.299–42.8390.113199.1278.8697.463–11.3780.16895.0008.5101.980–25.2600.0091228.76025.2201.20–64.7800.204
        Male3611.21010.7500.300–34.780 13213.93314.3670.299–50.740 238.5928.6047.156–10.617 1219.08019.4503.520–32.640 1839.32041.0603.480–84.610
Age group (years)
        0 a 102312.5009.5700.300–40.8200.9355211.47410.7270.299–36.0100.577118.7588.6047.669–10.4380.041417.21017.5404.100–31.6500.818841.20044.9006.700–84.6000.117
        11 a 191010.3105.9300.300–45.490 5913.14414.8260.299–25.463 99.2819.7867.542–10.617 215.00015.0004.800–25.300 739.57037.61015.710–60.270
        20 a 593610.5708.5300.300–34.780 14013.53314.0820.299–50.740 188.9548.8257.558–11.378 1412.52013.6701.980–32.640 1333.96027.8007.510–67.680
        ≥6058.65011.5602.78–13.640 4313.77015.0300.300–42.840 47.4977.4347.156–7.965 118.35718.35718.357–18.357 22.3402.3401.200–3.480
Residence time (years)
        <2616.86011.3700.300–34.7800.3141414.93014.9700.300–36.3000.57158.9598.747.669–10.6170.713****0.713838.51040.8401.200–69.5800.741
        2 a 53912.2008.5400.300–45.490 3612.04010.0100.300–38.210 49.159.1127.938–10.438 119.85219.85219.852–19.852 346.00045.2008.000–84.600
        >5298.1806.0600.300–22.940 24413.18113.9820.299–50.740 338.7778.6047.156–11.378 2013.66014.5101.980–32.640 1931.94027.0903.480–64.780
Alcohol
        Consumers410.4870.2990.299–3.7670.81417912.55512.5310.299–42.8390.313109.0198.6917.156–11.3780.429810.53013.4101.980–31.6500.5151737.18044.3801.200–84.6100.688
        Non-consumers390.5660.2990.299–4.796 11514.01314.3330.299–50.740 128.4158.0247.405–9.686 1313.92015.5903.520–32.640 1032.03027.4507.510–67.680
Smoking habit
        Smokers1010.1303.8800.300–29.9300.8924914.19014.9800.300–29.8000.17968.0637.9877.405–9.3620.106514.56014.9605.000–29.8100.804121.30821.30821.308–21.3080.108
        Nonsmokers6311.2309.8700.300–45.490 24512.91312.3340.299–50.74 368.9638.8257.156–11.378 1613.66014.7001.980–32.640 2635.81037.4501.200–84.610
Drinking water source
        Well7411.0109.2100.300–45.490*15612.95913.3810.299–36.2970.902428.8348.6727.156–11.378*2013.66014.5801.980–32.640******
        General network**** 13813.31313.3540.299–50.740 **** **** ****
        River**** **** **** **** 3035.10036.0101.200–84.610
AM—arithmetic mean; MED—median; * Does not present any individual in the studied group; ** Comparisons between the studied variables: p ≤ 0.05.
Table 7. Statistical analysis of eating habits by localities (Group 1) with lower blood Ba levels (µg·L−1) by the epidemiological variables.
Table 7. Statistical analysis of eating habits by localities (Group 1) with lower blood Ba levels (µg·L−1) by the epidemiological variables.
EATING HABITS (WEEK)ACUI BIN VIT CLA VCO
NMAMEDRANGEp **NMAMEDRANGEp **NMAMEDRANGEp **NMAMEDRANGEp **NMAMEDRANGEp **
Chicken
        ≤160.2990.2990.299–0.2990.728130.5430.2990.299–2.5060.761403.2200.3000.3–31.2200.702306.8100.5400.300–66.1700.772303.3461.6640.299–22.0800.452
        2 A 4650.5870.2990.299–4.796 1260.6860.2990.199–11.848 1443.1310.2990.299–37.069 2277.6100.3200.300–95.810 1762.5341.2360.299–23.138
        >4160.4070.2990.299–2.029 480.4730.2990.299–2.748 242.1500.3000.300–29.450 3810.0300.3000.300–68.990 232.5931.5270.299–11.459
Meat
        ≤1240.5210.2990.299–3.7670.619210.3040.2990.299–0.4050.280321.9280.2990.299–10.6010.919435.7100.7000.300–66.1700.368224.2812.9410.299–19.0850.005
        2 A 4610.5110.2990.299–4.795 1070.6740.2990.299–11.848 1403.0150.2990.299–37.069 2208.3900.3000.300–95.810 1822.6131.1660.299–23.138
        >421.4001.4000.3–2.49 590.6390.2990.199–2.789 364.1000.3000.300–32.550 326.8800.7300.300–68.990 251.4500.8240.299–7.007
Fish
        ≤1290.6580.2990.299–4.7960.8991070.7000.2990.199–11.8480.7771192.9440.2990.299–35.7840.8141697.0200.3100.300–95.8100.0341052.1080.8240.299–22.080.020
        2 A 4540.4850.2990.299–3.365 720.5140.2990.299–2.748 813.3240.2990.299–37.069 1017.7500.3000.300–86.270 1172.9921.6830.299–23.138
        >440.2990.2990.299–0.299 80.5330.2990.299–1.199 81.4660.2990.299–5.069 2513.7502.5600.300–68.990 74.9503.1601.350–17.590
Brazil nuts
        ≤1520.5650.2990.299–4.7960.9871750.5450.2990.199–5.2790.1092032.7740.2990.299–35.7840.0352828.0700.3100.300–95.8100.6922262.6471.2980.299–23.1380.830
        2 A 4170.4460.2990.299–2.348 111.8700.9100.300–11.850 321.96024.0404.770–37.070 103.6200.3000.300–26.670 32.6401.5200.300–6.100
        >4180.5290.2990.299–2.495 10.2990.2990.299–0.299 21.1741.1740.299–2.049 30.3780.3200.299–0.514 ****
Shellfish and crustaceans
        ≤1790.5580.2990.299–4.7960.5181680.6260.2990.199–11.8470.9061653.1250.2990.299–37.0690.4732718.0700.3000.300–95.8100.1851752.8121.6250.299–22.080.010
        2 A 480.2990.2990.299–0.299 120.5860.2990.299–2.748 263.3500.2990.299–21.080 133.4600.3000.300–26.670 481.8630.2990.299–23.138
        >4**** 70.5670.2990.299–1.631 171.6820.2990.299–7.718 117.3102.4000.300–53.240 64.0901.8000.530–12.810
Vegetable
        ≤1290.7760.2990.299–4.7960.632620.6990.2990.299–11.8480.902522.0860.2990.299–26.1980.560476.7900.3000.300–86.2700.306613.7912.0580.299–23.1380.031
        2 A 4440.4490.2990.299–3.365 890.6100.2990.199–4.7723 1232.8630.2990.299–35.784 1307.6700.7200.300–83.270 1222.1500.9090.299–22.080
        >4140.2990.2990.299–0.299 360.5160.2990.299–1.7012 335.1700.3000.300–37.070 1188.4400.4300.300–95.810 462.4451.5370.299–12.812
Fruit -
        ≤1870.5340.2990.299–4.7959*1870.6210.2990.199–11.847*632.3530.2990.299–19.5270.123405.8300.3000.300–66.1700.224533.7241.9730.299–23.1380.020
        2 A 4**** **** 555.0500.3000.300–37.070 1477.6300.3000.300–86.270 671.8780.6080.299–12.812
        >4**** **** 902.2800.2990.299–32.548 1088.8600.6800.300–95.810 1092.5951.1220.299–22.080
AM—arithmetic mean; MED—median; * Does not present any individual in the studied group; ** Comparisons between the studied variables: p ≤ 0.05.
Table 8. Statistical analysis of eating habits by localities (Group 2) with lower blood Ba levels (µg·L−1) by the epidemiological variables.
Table 8. Statistical analysis of eating habits by localities (Group 2) with lower blood Ba levels (µg·L−1) by the epidemiological variables.
EATING HABITS (WEEK)BCA VBJ DMN CUR ISJ
NMAMEDRANGEpNMAMEDRANGEp **NMAMEDRANGEp **NMAMEDRANGEp **NMAMEDRANGEp **
Chicken
        ≤129.9409.9400.300–19.5800.8285812.08012.1800.300–42.8400.462108.7818.6117.665–10.7730.984118.35718.35718.357–18.3570.6201434.33034.5103.480–64.7800.901
        2 A 47211.0309.2100.300–45.490 21613.39212.9640.299–50.740 288.8788.6717.156–11.378 2013.66014.5801.980–32.640 1635.77036.0101.200–84.6100
        >4*** 2013.14014.1700.300–24.150 48.6568.7827.558–9.503 **** ****
Meat
        ≤1811.4205.8000.300–33.7700.6293410.6708.1000.300–28.5300.087199.1399.2517.542–11.3780.132516.27015.2807.070–19.8500.6201434.33034.5103.480–64.7800.901
        2 A 46511.1009.8700.300–45.490 23713.24414.0010.299–42.839 228.6368.6037.156–10.947 1612.78014.6001.980–32.640 1635.77036.0101.200–84.610
        >411.4081.4081.408–1.408 2315.42016.3200.300–50.740 17.4057.4057.405–7.405 **** ****
Fish
        ≤13612.15010.1100.30–45.4900.19210912.71713.5420.299–28.4270.219288.7038.6037.542–10.7730.6311713.92015.0001.980–32.6400.796****0.553
        2 A 43410.6709.3600.300–33.770 17412.93412.4380.299–50.740 149.0969.1847.156–11.378 313.41012.2503.520–19.810 1833.58031.2601.200–84.610
        >443.5802.2200.300–9.570 1119.96018.2800.300–42.790 **** 118.35718.35718.357–18.357 1237.37043.9006.670–64.780
Brasil nuts
        ≤15712.2109.8700.300–45.4900.44525212.98812.5460.299–50.740.052428.8348.6727.156–11.378*817.31016.20050.000–29.8100.3392733.70034.7301.200–84.6100.284
        2 A 4126.9805.1700.300–15.220 3514.97015.0900.300–42.840 **** 13.5193.5193.519–3.519 ***
        >456.9505.3300.300–14.200 78.4905.0400.300–38.210 **** 1212.78014.7401.980–32.640 347.70064.8008.700–69.600
Shellfish and crustaceans
        ≤15311.6309.5700.300–45.4900.42523912.86612.7620.299–42.8390.542428.8348.6727.156–11.378*1913.92015.1601.980–32.6400.5491930.82027.8003.480–69.5800.468
        2 A 4219.4305.3300.300–29.930 5514.21014.1700.300–50.740 **** 210.94010.9403.520–18.360 641.60044.1001.200–84.600
        >4**** **** **** 543.50060.3008.700–64.800
Vegetable
        ≤1812.55013.5600.300–29.7800.9756214.04714.9000.299–42.8390.20158.3378.6047.156–9.5030.540129.81229.81229.812–29.8120.4091430.25025.8703.480–64.7800.582
        2 A 41311.0006.0600.300–40.820 19113.20713.1660.299–50.740 249.0058.8257.405–11.378 1313.41013.7601.980–32.640 1141.99044.5201.200–84.610
        >45310.7709.5700.300–45.490 4111.2908.3300.300–31.050 138.7108.1247.542–10.617 716.27014.4803.400–31.650 533.51037.30013.890–50.960
Fruit
        ≤17411.0009.2100.300–45.490*4111.9808.3300.300–28.4300.018108.2458.2387.156–9.1310.3152113.92014.7601.980–32.640*3035.10036.0101.200–84.610*
        2 A 4**** 18514.18114.9320.299–50.740 239.0298.8697.405–11.378 **** ****
        >4**** 6810.9059.1490.299–31.045 98.9909.1177.750–10.947 **** ****
AM—arithmetic mean; MED—median; * Does not present any individual in the studied group; ** Comparisons between the studied variables: p ≤ 0.05.
Table 9. The correlation test and linear regressions of Ba concentrations in relation to the independent variables.
Table 9. The correlation test and linear regressions of Ba concentrations in relation to the independent variables.
Variablersp-ValueR2 adjp Value
Proximity to impacted rivers0.0180.5000.00.310
Proximity to the kaolin industry0.144<0.0011.6<0.001
Water consumption0.103<0.0012.2<0.001
Weekly consumption of fish0.089<0.0011.5<0.001
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

Chagas, B.R.; de Magalhães Câmara, V.; Naka, K.S.; de Queiroz, T.K.L.; de Cássia dos Santos Mendes, L.; de Jesus, I.M.; de Oliveira Lima, M.; Meyer, A. The First Report of Environmental Exposure to Barium in 10 Localities Close to Industrial Areas and Ports in the Amazon. Int. J. Environ. Res. Public Health 2025, 22, 109. https://doi.org/10.3390/ijerph22010109

AMA Style

Chagas BR, de Magalhães Câmara V, Naka KS, de Queiroz TKL, de Cássia dos Santos Mendes L, de Jesus IM, de Oliveira Lima M, Meyer A. The First Report of Environmental Exposure to Barium in 10 Localities Close to Industrial Areas and Ports in the Amazon. International Journal of Environmental Research and Public Health. 2025; 22(1):109. https://doi.org/10.3390/ijerph22010109

Chicago/Turabian Style

Chagas, Brenda Rodrigues, Volney de Magalhães Câmara, Karytta Sousa Naka, Thaís Karolina Lisboa de Queiroz, Lorena de Cássia dos Santos Mendes, Iracina Maura de Jesus, Marcelo de Oliveira Lima, and Armando Meyer. 2025. "The First Report of Environmental Exposure to Barium in 10 Localities Close to Industrial Areas and Ports in the Amazon" International Journal of Environmental Research and Public Health 22, no. 1: 109. https://doi.org/10.3390/ijerph22010109

APA Style

Chagas, B. R., de Magalhães Câmara, V., Naka, K. S., de Queiroz, T. K. L., de Cássia dos Santos Mendes, L., de Jesus, I. M., de Oliveira Lima, M., & Meyer, A. (2025). The First Report of Environmental Exposure to Barium in 10 Localities Close to Industrial Areas and Ports in the Amazon. International Journal of Environmental Research and Public Health, 22(1), 109. https://doi.org/10.3390/ijerph22010109

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