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Article

Mercury Contamination and Human Health Risk by Artisanal Small-Scale Gold Mining (ASGM) Activity in Gunung Pongkor, West Java, Indonesia

1
Graduate School of Environmental and Symbiotic Sciences, Prefectural University of Kumamoto, Tsukide 3-1-100, Kumamoto 862-8502, Japan
2
Research Center for Environmental and Clean Technology, National Research and Innovation Agency, Science and Technology Complex (KST) B.J. Habibie, South Tangerang 15314, Indonesia
3
Research Centre for Limnology and Water Resources, National Research and Innovation Agency, Science and Technology Complex (KST) Ir. Soekarno, Bogor 16911, Indonesia
*
Author to whom correspondence should be addressed.
Earth 2025, 6(3), 67; https://doi.org/10.3390/earth6030067
Submission received: 23 May 2025 / Revised: 19 June 2025 / Accepted: 28 June 2025 / Published: 1 July 2025

Abstract

Artisanal small-scale gold mining (ASGM) is the largest source of global mercury (Hg) emissions. This study investigated Hg contamination in water, soil, sediment, fish, and cassava plants around ASGM sites in Gunung Pongkor, West Java, Indonesia. Hg concentration ranged from 0.06 to 4.49 µg/L in water; 0.420 to 144 mg/kg dw in soil; 0.920 to 150 mg/kg dw in sediment; 0.259 to 1.23 mg/kg dw in fish; 0.097 to 5.09 mg/kg dw in cassava root; and 0.350 to 8.84 mg/kg dw in cassava leaf. Geo-accumulation index (Igeo) analysis revealed moderate to heavy soil contamination upstream, likely due to direct ASGM input. In contrast, sediment Igeo values indicated heavy contamination downstream, suggesting Hg transport and sedimentation. Bioconcentration factors (BCFs) in fish were predominantly high in downstream and midstream areas, indicating enhanced Hg bioavailability. Bioaccumulation factors (BAFs) in cassava were higher in upstream areas. Health risk assessment, based on the Hazard Quotient (HQ) and Hazard Index (HI), identified ingestion as the primary exposure route, with children exhibiting significantly higher risks than adults. These findings highlight the significant Hg contamination associated with ASGM in Gunung Pongkor and emphasize the need for targeted mitigation strategies to protect human and environmental health.

1. Introduction

Mercury (Hg) is a persistent environmental contaminant released from natural sources (e.g., volcanic eruptions, weathering rocks) and anthropogenic activities, notably mining. Atmospheric mercury can travel globally before deposition [1]. As a heavy metal (density > 5 g/cm3), Hg returns to the geosphere through sedimentation and soil formation. However, residual mined sediment can increase background concentrations and act as a persistent pollutant. The ability of Hg to bioaccumulate and biomagnify in food chains, particularly its transformation to methylmercury (CH3Hg) in aquatic systems and subsequent accumulation in fish and shellfish [2], poses significant health risks. Hg is highly toxic, accumulating in organs such as the brain, kidneys, bones, and liver [3].
Artisanal small-scale gold mining (ASGM) is a major source of global Hg emissions. A 2018 UNEP report estimated that ASGM contributed approximately 38% of global emissions of Hg in 2015, averaging 838 tons annually. ASGM is particularly prevalent in South America, Sub-Saharan Africa, and East and Southeast Asia, providing livelihoods for many, especially in developing countries. Using Hg to extract gold is a simple and inexpensive amalgamation process that results in substantial environmental contamination through atmospheric release and discharge into water bodies, posing health risks to miners and communities [4].
Indonesia, a signatory to the Minamata Convention, has implemented a National Action Plan (NAP) since 2017 to reduce Hg use in ASGM, including regulations on its use, import, export, and trade [5]. ASGM is widespread in Indonesia [6], notably in Kalimantan [7], Sulawesi [8,9], Sumatra [10], and Java [11,12,13,14,15], where Hg has been used extensively. This activity has resulted in widespread Hg contamination and human health risk to the surrounding inhabitants. For instance, in Kalimantan, ASGM has caused severe river pollution, impacting aquatic life and downstream communities [7]. Similar contamination of soil and water sources occurs in Sulawesi and Sumatra, posing environmental and public health risks [9,10]. However, there is still a need to carry out a comprehensive evaluation of Hg pollution on various media and their potential health risk in other locations of Indonesia, which lacks information.
Gunung Pongkor in West Java is considered one of Indonesia’s largest ASGM sites, comprising 850 hotspots operated by over 150,000 miners [12]. These ASGM sites are typically located near rice fields and residential zones in upland areas, facilitating Hg transport to lowland areas. Traditional amalgamation methods in Gunung Pongkor result in significant Hg loss, with approximately 19 g lost to tailings and 1 g to the atmosphere for every gram of gold produced [16]. This high volume of Hg released, particularly in tailings, can cause widespread contamination of water, sediment, biota, soil, and plants [16]. Cassava is a staple crop widely cultivated by smallholder farmers, frequently intercropped with other essential subsistence crops. Its tolerance to poor soil conditions and minimal input requirements, combined with its importance as a food source in the Gunung Pongkor region, contribute to its common cultivation near ASGM areas. For these reasons, cassava was selected for this study. Furthermore, cassava’s capacity to uptake Hg from contaminated soils and water renders it an effective bioindicator for evaluating Hg accumulation in the local food chain and assessing potential health risks to surrounding communities [17].
Several fragmentary studies have indicated Hg contamination and potential risk from Hg from ASGM in Gunung Pongkor. For instance, Hg contamination has been observed in several kinds of environmental matrices associated with ASGM in Gunung Pongkor, such as water from various rivers, including the most concerning ones in the Cikaniki River [13,14,18,19,20,21,22], Cisarua River [22,23], Cibuluh River [11], Cidikit River [13], and Cianten River [22]; sediment from the Cikaniki River [20]; and soil, including rice from agricultural land [20] and the atmosphere [24,25]. Furthermore, a study of the hair of 40 ASGM workers in Cisarua Village showed higher Hg levels, with 60% of them exceeding the WHO limit of 2 ppm Hg [26], indicating potential health impacts on the population.
Although the studies conducted above indicate significant Hg contamination in certain environmental compartments and their potential impacts on the health of local residents, to date, there has been no comprehensive and integrated assessment conducted simultaneously on abiotic and biotic media across the landscape of Gunung Pongkor. The above studies have also not fully detailed how Hg is distributed and moves through various environments, including water, soil, and sediment. Furthermore, how it accumulates in local food sources, such as fish and cassava, is very important to observe as a pathway for community exposure. The focus on linking environmental pollution to health risk assessments is also inadequate there because the evaluation framework is dependent upon the resources located in a particular place.
Taking the limitations of previous studies into account, this study comprehensively investigates the distribution and contamination of Hg in various biotic and abiotic samples such as water, soil, sediment, fish, and cassava plants around the ASGM site in Gunung Pongkor, from upstream to downstream, with the objectives of (1) identifying the distribution and transport of Hg in abiotic (water, soil, sediment) and biotic (fish, cassava plants) samples; (2) assessing the level of Hg accumulation; and (3) evaluating the potential health risks due to Hg exposure. This study will provide a first significant and integrated understanding of the impacts of ASGM on the environment and human health in Gunung Pongkor.

2. Materials and Methods

2.1. Study Area

Investigation and sampling were conducted within the Cikaniki watershed in Gunung Pongkor District of Bogor, West Java, Indonesia. This region is known for its rich gold deposits. The area has a wet tropical climate with high humidity and significant rainfall, ranging from 30.3 to 671 mm [27]. The terrain is deeply dissected, with altitudes ranging from 375 to 850 m above sea level, and is predominantly covered by lateritic soil. The Gunung Pongkor deposit is located within Tertiary igneous rocks (tuff breccia, lapilli, and intrusive andesite) surrounded by Quaternary volcanic breccia [28].

2.2. Sampling

According to Figure 1, the study area was divided into upstream, midstream, and downstream sections of the Cikaniki watershed, spanning approximately 14 km. ASGM activities are concentrated in the upstream area, which potentially has elevated Hg levels in this section. The Cikaniki River has a flow originating from the headwaters in the Mount Halimun Salak National Park, West Java, and flows to the Java Sea via the Cisadane River in West Java and Banten Provinces. The average river water discharge ranges from 5.36 to 11.48 m3/s with an average flow travel time of 14.7–35.7 min per kilometer. Meanwhile, the Cikaniki River Basin Area is 199.6 km2. This river plays a crucial role in the agricultural sector, as well as in tourism and daily life, but is currently facing the issue of declining water quality, particularly due to illegal gold mining activities [29].
A total of 44 water (rivers, ponds, and groundwater), 22 soil (open land, residential, and agricultural, including cassava cultivation areas), 20 sediments (riverbeds and pond bottoms), 29 individual of tilapia fish species (Oreochromis niloticus) (2–5 individuals from each of 12 ponds), and 14 cassava plants (leaf and root from upstream to downstream) were collected in September 2022 and April 2024. This sampling strategy aimed to comprehensively assess Hg pollution across various environmental media in the Gunung Pongkor area, informing management and mitigation efforts. All samples were meticulously collected and handled according to standardized protocols to ensure data reliability and accuracy in assessing the environmental impact of ASGM activities.
Water samples (∼100 mL) were collected in polyethylene bottles. The samples were preserved at pH < 2 with HNO3 to prevent metal adsorption and microbial activity, ensuring Hg stability before analysis. This acidic condition also ensures that Hg remains in a soluble form, facilitating accurate measurement during analysis. Physico-chemical parameters (pH, temperature, dissolved oxygen (DO), total dissolved solids (TDS), turbidity, salinity, and electrical conductance (EC)) were analyzed on-site by using a multi-parameter water quality checker HORIBA U-50 (Kyoto, Japan). Samples were stored at a temperature of less than −4 °C until analysis.
A scoop was used to collect soil and sediment samples (∼200 g) at 10–15 cm depth into zipper-locked plastic bags. A fishing net was used for sampling the fish. The weight (Wt) and length (Lt) of all specimens were determined before excising the axial muscles, which were then placed in zipper-locked plastic bags and frozen for storage until transport to the Prefectural University of Kumamoto, Japan. Cassava Samples were prepared from the roots and leaves of the plant. Cassava roots were sampled from 15–20 cm below top topsoil with a stainless-steel scoop from the depth.

2.3. Analytical Procedure

2.3.1. Sample Preparation

Water samples were filtered in the laboratory shortly after collection using a 0.2 µm syringe filter. Soil and sediment were freeze-dried within 1 day after separating out all the roots, stones, wood, and other rubbish. The freeze-dried samples were then manually sieved through a 2 mm mesh. The frozen fish muscle tissues and cassava root and leaf were freeze-dried for 24 h and ground into powder afterward before preparation for analysis. The edible part of cassava roots was peeled, washed, and cut into pieces. Similar procedures were performed on leaf samples, which were first cleaned with tap water to remove soil and dust contamination and then rinsed with distilled water.

2.3.2. Analysis of Hg

Mercury concentration in water samples was determined according to the standardized procedure specified in Method 245.1 of the United States Environmental Protection Agency (USEPA) [30], using a cold vapor atomic absorption spectrometer (CVAAS) (MA-3000, Nippon Instruments Corporation, Tokyo, Japan). This method involves the reduction of Hg (Hg2+) to elemental Hg (Hg0) using stannous chloride (SnCl2). The amalgamated Hg was then released by heating the collector tube and detected at a wavelength of 253.7 nm. The method has a detection limit of 0.026 µg/L, enabling the accurate measurement of trace levels of Hg in water samples. Mercury concentration in soil, sediment, fish, and cassava samples was determined by thermal decomposition using a mercury analyzer (MA-3000), following the USEPA Method 7473. In this method, samples are thermally decomposed in an oxygenated furnace, and Hg is selectively trapped in an amalgamator. Upon heating, Hg vapor is released and carried by oxygen to a spectrophotometer, where it is measured at 253.7 nm using atomic absorption. The method has a detection limit of 0.001 mg/kg, allowing for the accurate detection of Hg in soil, sediment, fish, and cassava.

2.3.3. Quality Assurance and Quality Control for Hg Analysis

To prevent contamination, all were washed adequately with detergent, rinsed with tap water, sonicated with an ultrasonic for 10 min, soaked in 10% HNO3 for at least 24 h, rinsed five times with ultrapure water, and dried in a dry machine overnight.
Spike recovery experiments were carried out for water samples, and the recovery rate ranged from 97 to 108%. Certified reference materials (CRMs) from the National Metrology Institute of Japan (NMIJ), a division of the National Institute of Advanced Industrial Science and Technology (AIST), were used for analysis. NMIJ CRM 7302-a (developed for trace elements in marine sediment) and NMIJ CRM 7403-a (swordfish tissue, for trace elements, arsenobetaine, and methylmercury) were employed in the study. The recovery rates were 93.8–97% and 92.3–95.0%, respectively. The coefficient of variation (CV) was below 5% in both cases.

2.4. Geo-Accumulation Index (Igeo)

The geo-accumulation index (Igeo) was used to assess Hg contamination in soil and sediment by evaluating metal enrichment relative to background values [3]. The Igeo was computed according to Equation (1) [31], with Igeo classes as shown in Table S1.
I geo = log 2 Cn 1.5   ×   Bn
where
Cn: Concentration of Hg in soil/sediment;
Bn: Background concentration of Hg in soil (0.020 mg/kg [32] and sediment (0.023 mg/kg [33]), acquired from previous studies conducted in relatively uncontaminated areas in Indonesia.
1.5: The corrective factor adjusts for lithologic variations to avoid overestimating pollution from natural element fluctuations.

2.5. Bioconcentration Factor (BCF)

The bioconcentration factor (BCF) determines Hg’s accumulation tendency in aquatic organisms. The BCF is calculated as the ratio of the concentration of Hg in the fish tissue to that in the surrounding water, using Equation (2) [34]. Classes of BCF [35] are shown in Table S2.
B C F = C   in   Fish C   in   Water
where
C in Fish: Concentration of Hg in fish (mg/kg ww);
C in Water: Concentration of Hg in water (mg/L).

2.6. Bioaccumulation Factor (BAF)

The bioaccumulation factor (BAF) was used to determine the ability of cassava roots to accumulate Hg relative to its concentration in the soil. BAF was evaluated using Equation (3) [36].
B A F = C   in   Cassava   Roots C   in   Soil
where
C in Cassava Roots: Concentration of Hg in cassava roots (mg/kg dw);
C in Soil: Concentration of Hg in soil (mg/kg dw).

2.7. Health Risk Assessment

This study employed the health risk assessment methodology for chemical substances proposed by the United States Environmental Protection Agency (USEPA) [37] to evaluate health risks associated with heavy metals, particularly Hg. There are various pathways through which Hg can enter the body: (a) direct ingestion; (b) inhalation of resuspended particles; (c) dermal absorption; and (d) inhalation of vapor. As an outcome of utilizing the USEPA’s criteria, health risks have been assessed from a chronic daily intake (CDI) [38]. The CDI was estimated using Equations (4)–(6), with all variables defined in Table S3.
C D I i n g = C   ×   IR w / s / sd / f / cr / cl   ×   ED   ×   EF BW   ×   AT
C D I i n h = C   ×   InhR   ×   ED   ×   EF BW   ×   AT   ×   PEF
C D I d e r = C   ×   ED   ×   EF   ×   SA   ×   AF   ×   ABS   ×   10 6 BW   ×   AT
where
CDIing is the CDI via ingestion;
CDIinh is the CDI via inhalation;
CDIder is the CDI via dermal.
The Hazard Quotient (HQ) (Equation (7)) assesses potential mercury exposure relative to a reference dose (RfD). An HQ greater than 1 indicates a potential for non-carcinogenic health effects, while an HQ less than or equal to 1 suggests a negligible risk. When multiple exposure pathways were considered, the Hazard Index (HI) was calculated (Equation (8)). An HI greater than 1 indicates a potential for adverse health effects, while an HI less than or equal to 1 suggests a negligible risk.
H Q i n g / i n h / d e r = CDI i n g / i n h / d e r   RfD i n g / i n h / d e r
HI = HQ i n g + HQ i n h + HQ d e r
Parameter values and probability distributions used in the human health risk assessment are presented in Table S3.

2.8. Statistical Analysis

The data analysis for this study was conducted using a combination of specialized software packages, including IBM SPSS Statistics version 26 (IBM Corp., Armonk, NY, USA). For spatial visualization and mapping, ArcGIS Pro (Environmental Systems Research Institute (ESRI), Redlands, United States America (USA)) was used. This integrated approach allowed for comprehensive data processing, from statistical evaluation to risk assessment and geographical representation. The Kolmogorov–Smirnov test was employed to assess normality, revealing a non-normal distribution of the data. Consequently, a non-parametric analysis was conducted using Spearman’s rank correlation coefficient to examine the relationships between variables. This test evaluates the strength and direction of monotonic relationships based on ranked data and does not assume a normal distribution, making it well-suited for environmental datasets. In this study, a p-value of less than 0.05 was deemed statistically significant. Additionally, the Smirnov–Grubbs test was implemented to identify outlier data, while the Mann–Whitney U test was conducted to compare different groups.

3. Results and Discussion

3.1. Concentration of Hg in Water

This study analyzed Hg concentration and key water quality parameters across different water types in the study area, comparing the findings to Indonesian government standards (Indonesia Minister of Environment and Forestry Regulation No. 22 of 2021). Parameters like pH (6.37–8.39) and turbidity (0.93–31.2 NTU) were within acceptable ranges, while total dissolved solids (TDS) varied (18.3–356 mg/L), and temperatures in pond and river water (24.13–33.0 °C) occasionally exceeded the Indonesian water quality standard limit of 30 °C. Dissolved oxygen (DO) was highest in pond water (1.83–9.14 mg/L) (Table S4).
River water revealed a clear trend of increasing Hg concentration along the river’s course (Table 1), while median concentrations were similar in the upstream and midstream areas (0.28 µg/L). A notable increase to 1.65 µg/L was observed downstream. Nonetheless, statistical analysis revealed no significant differences among the three locations (p = 0.095). Figure 2A illustrates the geographical distribution of Hg concentration in river water surrounding the Gunung Pongkor area. Elevated levels of Hg were detected not only in the upstream area but also in both the midstream and downstream areas. Spearman’s rank analysis of Hg concentration in river water showed no significant correlation with distance from ASGM sites 1 (r = −0.065, p = 0.792) and 2 (r = −0.070, p = 0.775) (Table S5). The analysis also revealed no significant correlation between Hg levels in river water and sediment (r = 0.082, p = 0.0789) and a relationship between Hg in river water and soil (r = 0.322, p = 0.308). This result suggests that Hg contamination is not limited to areas immediately adjacent to the ASGM sites, but rather, ASGM activities and their impacts are distributed along the entire Cikaniki River, affecting all segments of the river system.
While previous studies, such as Hiola et al. [39], have reported that Hg concentrations in the Hulawa River are typically higher near ASGM locations and similar patterns have been observed in the Tano River Basin in Ghana [40], the situation in the Cikaniki River appears to be different. Here, widespread and possibly informal ASGM activities, combined with hydrological processes such as sediment transport and erosion, likely contribute to a more uniform distribution of Hg along the river. Rainwater and soil erosion can wash Hg-laden sediments from multiple points along the riverbank into the water, and these sediments are then transported downstream [41]. As a result, Hg pollution is not confined to areas near the main ASGM sites but is dispersed throughout the river system. Therefore, while proximity to mining sites often plays a significant role in Hg distribution, the diffuse nature of ASGM activities and river dynamics in the Cikaniki River lead to a more widespread impact.
In contrast, pond water and groundwater exhibit significantly lower median Hg concentrations, ranging from 0.08 to 0.14 µg/L and 0.09 to 0.16 µg/L, respectively, indicating less exposure to pollution sources. Figure 2B,C illustrates the spatial distribution of Hg levels in ponds and groundwater, respectively. In the context of these water types, the distribution patterns in each subdomain are relatively homogeneous except for the influence of ASGM, which is hydrologically interconnected. Overall, despite the impact of ASGM operations on Hg levels, variations in Hg contamination patterns suggest the influence of several environmental factors beyond proximity to mining sites.
Based on a comparison between Hg in water with the standards established by Indonesia [42], the levels allow up to 2.0 µg/L of Hg in river water and 1.0 µg/L in drinking water. Alarmingly, 26% of river water samples exceeded the threshold limits, while pond and groundwater samples were below the threshold limits. This result highlights the greater susceptibility of river water to Hg pollution derived from ASGM activities, considering the intensive use of Hg and the direct discharge of waste into river systems.
The correlation between Hg concentration and various physicochemical parameters was assessed using Spearman’s rank correlation coefficient across different water types given in Table 2. A significant positive correlation existed between Hg concentration and turbidity in all samples (r = 0.665, p < 0.01) and river water (r = 0.778, p < 0.01), indicating that higher turbidity levels are associated with increased Hg concentrations, especially in river water. Turbidity often contains colloidal particles that are sufficiently small to remain suspended in water. Mercury can bind to these colloids, which may not be completely eliminated during filtration, leading to a higher measured concentration of Hg [43]. In natural waters, colloidal Hg plays a significant role in its transport, bioavailability, and environmental fate [44].
A moderate positive correlation was also observed between Hg and pH in all samples (r = 0.432, p < 0.01), suggesting that higher pH levels may influence Hg solubility and mobility by promoting the formation of more mobile and bioavailable dissolved Hg complexes [45]. At high pH, Hg will be more likely to form soluble complexes, e.g., Hg(OH)2 and other hydroxylated species [45]. These complexes are more stable and stay in solution rather than precipitating or adsorbing to sediments. Alkaline conditions could also lower the binding affinity of Hg to organic matter and particles and render it more mobile and potentially more bioavailable. Thus, Hg may remain in the water column for longer and become more readily transported or taken up by aquatic organisms, increasing the environmental and health risks. In aquatic environments, Hg primarily exists as inorganic Hg (Hg2+) and methylmercury (MeHg), a highly bioavailable, neurotoxic compound that is persistent in biological systems [46]. While this study did not directly measure MeHg, the observed pH and turbidity conditions may facilitate its formation and mobility.
To contextualize the results, our study’s Hg concentration is compared to those of other impacted and reference sites both within Indonesia and internationally (Table 1). Within our study area, previous research on the Cikaniki River in Bogor [13,14,18,19,20,21,22,23] reported a wider range of Hg concentration, with an upper limit of 606 μg/L [22], exceeding the levels observed in this study. This difference likely reflects the 2017 ban on Hg use in ASGM in Indonesia [47], suggesting a potential decrease in Hg contamination in recent years. Other Indonesian rivers impacted by ASGM have also exhibited higher Hg concentrations, including the Hulawa River in North Gorontalo [39] and, notably, the Bone River in Gorontalo [48], which reported extreme values. In contrast, lower mean Hg levels were found in Lebak Situ-Banten [49] and Krueng Sabee-Aceh [50]. As expected, reference sites in Mandailing, Natal-North Sumatra, considered uncontaminated, showed very low Hg concentrations [10]. Gunung Pongkor stands out for showing signs of environmental improvement, likely due to regulatory actions and improved ASGM practices. This makes it an important case study for evaluating the effectiveness of Hg reduction efforts in ASGM regions.
Globally, Hg concentration in water sources near ASGM sites, such as the Insingile River in Tanzania [51] and the Anikoko River in Ghana [52], is generally higher than that observed in this study. However, some locations, like the Tapajos River in the Brazilian Amazon [53], have reported extremely low Hg levels, comparable to uncontaminated sites. Overall, our results indicate that Hg concentration in the Gunung Pongkor area is elevated compared to uncontaminated reference sites but lower than those observed in other heavily polluted ASGM areas in Indonesia and internationally, highlighting that pollution influences not only current ASGM practices but also the legacy of historical contamination.

3.2. Hg Concentration in Soil

Table 3 illustrates the Hg concentration in soil along a river system and residential area, revealing a clear decreasing trend from upstream (5.51 mg/kg dw) to midstream (4.45 mg/kg) and downstream (0.62 mg/kg). This strong upstream-to-downstream gradient (Figure 2D) suggests a point source of pollution near the upstream area, potentially from intensive land use practices like ASGM activities. This pattern aligns with previous studies that documented elevated levels of Hg in samples collected near mining operations [10,14,54]. The main environmental concern with Hg accumulation in soil is its potential transformation into methylmercury (CH3Hg), a highly toxic and bioavailable form. Inorganic Hg (Hg2+) binds to organic matter and mineral surfaces, with its mobility and speciation influenced by environmental factors such as pH, redox conditions, and temperature. Acidic soils favor the persistence of Hg2+, while oxygen-rich conditions can enhance its solubility. Moreover, atmospheric deposition of Hg (e.g., HgO) can contribute to soil contamination through wet and dry deposition. Rainfall and flooding can further mobilize Hg, increasing its distribution in surrounding environments [55].
Table S5 provides a Spearman’s rank correlation for various parameters associated with distance to ASGM 1, distance to ASGM 2, and Hg concentration. Analysis showed no relationship among the parameters with Hg concentration. The observed decrease in Hg levels downstream can likely be attributed to processes such as dilution, sedimentation, or changes in soil composition affecting Hg retention. All soil samples showed Hg concentration above the standard set (0.3 mg/kg) by the Indonesian Minister of Environment and Forestry Regulation [42]. Notably, Hg concentration in the downstream areas was above this standard, possibly due to naturally high background levels. Furthermore, agricultural practices and ASGM contribute to the mobilization of Hg-enriched soils.
Contextualizing these findings within the broader Indonesian and global landscape provides crucial insights (Table 3). Within Indonesia, Gunung Pongkor’s upstream soils showed a higher Hg concentration than forest soils in Bogor [21] and paddy soils in North Sumatra [10], underscoring the severe impact of ASGM. However, the contamination levels in Gunung Pongkor were significantly lower than the extreme cases reported in Bombana-Sulawesi [56]. Globally, comparisons with other ASGM-affected areas further illuminate the extent of Hg contamination in ASGM-affected areas. Soils from Tanzania [51], Mauritania [57], South Africa [58], and Ghana (mean: 2.17 mg/kg) [17] generally showed lower Hg concentration than those observed in Gunung Pongkor’s upstream sites.

3.3. Hg Concentration in Sediment

The median concentration of Hg in river and pond sediment samples exhibited an unusual increasing trend (Table 4) from upstream (10.0 mg/kg dw), midstream (6.00 mg/kg dw), and downstream (64.4 mg/kg dw) (Figure 2E). This downstream increase, also observed in previous studies [59], suggests multiple point sources of Hg pollution along the river system, particularly in the downstream reaches. The extreme variability in Hg concentrations, especially downstream, further indicates localized pollution sources, likely related to current or historical gold mining activities [59]. Correlation analysis demonstrated no significant correlation between the Hg concentration in sediment and each parameter (Table S5). This lack of correlation, along with the observed downstream increase in Hg, suggests that other factors may contribute to Hg accumulation. One such factor could be the abundance of organic matter, which is often higher in soil than in river sediment. Organic matter can strongly bind Hg, reducing its mobility and promoting its retention in the soil. This binding is primarily due to the formation of stable complexes between Hg and organic compounds, particularly humic substances, which inhibit Hg release and enhance its spatial stability [60].
Because the Indonesian government lacks specific regulations for sediment quality for Hg, the Hong Kong Interim Sediment Quality Guidelines (IHSG) (0.15 mg/kg) [61] were utilized to assess the results. All sediment samples exceeded these guidelines.
Comparing this study’s findings with other reports from Indonesia is necessary to interpret our findings, as shown in Table 4. This study’s median Hg concentration downstream was higher than that of the previous research in Bogor-West Java [14], Banyumas-Central Java [62], Ciujung-Banten [63], and Lolayan-Bolaang Mongondow. However, they were lower than those reported in Bone Bolango-Gorontalo [48], an area known to be heavily polluted by Hg from ASGM.
Compared with studies in other countries, Hg concentration in this study was higher than those in Gauteng-South Africa [58], Brazilian Amazon [53], Ghana [40], Anka-Northwest Nigeria [3], Northwest China [64], and Central Brazil [65]. These findings underscore the critical need for comprehensive environmental monitoring, the stringent regulation of ASGM practices, and targeted remediation efforts in heavily impacted areas like Gunung Pongkor.

3.4. Hg Concentration in Fish

Concentration of Hg in fish from the Gunung Pongkor area is shown in Table 5. Median Hg concentrations in fish from ponds were 0.755 mg/kg dw in the upstream area, 0.700 mg/kg dw in the midstream area, and 0.392 mg/kg dw in the downstream area.
Some variation in Hg concentrations was observed among the ponds from the upstream–downstream areas (Figure 2F, statistical analysis revealed no significant differences (p > 0.05), suggesting that spatial variability was limited under current conditions. Likewise, Hg concentration in pond water (p = 0.112) and fish length (p = 0.182) did not differ significantly between sites, suggesting that these factors were relatively uniform across sampling sites. However, a significant difference was observed in fish weight (p = 0.03), implying that fish weight varied meaningfully among the sites, even though Hg exposure and other measured environmental parameters did not show significant spatial trends. This spatial variability aligns with findings from previous studies. For example, Lavoie et al. [66] discussed how local geochemistry and food web structure influence Hg bioaccumulation in fish, explaining the observed variations across sampling sites. Eagles-Smith et al. [67] highlighted the complexity of factors (fish species, fish size, and habitats) affecting Hg concentrations in freshwater fish.
Correlation analysis revealed significant positive correlations between Hg concentrations in fish muscle and both pond water (r = 0.421, p < 0.05) and pond sediment (r = 0.520, p < 0.05), indicating that both are important sources of Hg exposure for fish in the Gunung Pongkor area. Fish can absorb heavy metals, including Hg, through their gills and skin via ion exchange, consuming contaminated food, or through adsorption into their tissues [68]. No significant correlation was found between the Hg concentration in fish tissue and either fish length (r = 0.202) or fish weight (r = 0.262).
At a wet weight basis, the median concentrations of Hg in tilapia from upstream (0.156 mg/kg ww), midstream (0.140 mg/kg ww), and downstream (0.082 mg/kg ww) were all below the threshold level of 0.5 mg/kg ww according to the Indonesian Food and Drug Administration Regulation No. 09 [69]. This result indicates that the fish product has a permitted level of Hg pollutant.
A comparison of Hg concentration in fish from Gunung Pongkor with other studies in Indonesia and internationally is shown in Table 5. Within Indonesia, this study exhibited a higher median Hg level compared to similar species in Gorontalo [70] and Lolayan-Bolaang Mongondow [71], indicating a higher degree of Hg contamination. However, these levels were substantially lower than those found in West Sumbawa’s Tilapia fish [72]. The stark contrast with the minimal contamination (0.0942 mg/kg ww) observed in the reference site at Tatelu-North Sulawesi [73] underscores the impact of human activities, likely ASGM, on the Hg level in Gunung Pongkor. Internationally, the level of Hg found in fish from Gunung Pongkor was relatively low when compared to the high levels of Hg measured in Ghana from the study of 13 fish species, including tilapia from rivers surrounding gold mining operations [74]. Additionally, Hg levels in tilapia from fish ponds in Migori County in Kenya were higher than those from Gunung Pongkor [75]. Conversely, Hg levels in Gunung Pongkor exceed those observed in Piaractus brachypomus from Madre de Dios, Peru [76]. Notably, the range of Hg concentration in Gunung Pongkor falls within the broader spectrum documented in Puerto Narino, Southern Colombia, for 24 species of fish from the Amazon River [77], highlighting the global variability in fish Hg contamination. These comparisons emphasize the influence of local factors such as geology, anthropogenic activities, and environmental conditions on Hg bioaccumulation in fish. The findings underscore the critical need for continued monitoring and region-specific assessments to inform public health guidelines and develop targeted environmental management strategies to mitigate Hg pollution in aquatic ecosystems.
Table 1. Comparison of Hg concentration (µg/L) in water with other studies around ASGM areas.
Table 1. Comparison of Hg concentration (µg/L) in water with other studies around ASGM areas.
LocationRemarksnMeanRangeMedianReference
Indonesia
Gunung Pongkor—Bogor, West Java This study
UpstreamRiver water (Ciguha—Cikaniki River)70.950.10–2.480.28
Midstream 71.630.06–4.490.28
Downstream 51.640.08–4.351.65
Upstream 30.160.11–0.230.14
MidstreamPond water70.090.06–0.140.08
DownstreamGroundwater30.100.08–0.120.10
Upstream30.160.09–0.230.16
Midstream30.080.07–0.100.09
Downstream60.110.09–0.170.10
West Java2001—Cikaniki River33.75 *2.74–4.863.63 *[13]
2002—Cikaniki River30.74 *0.37–1.130.72 *
Bogor—West Java UpstreamCikaniki River10.44--[19]
Midstream 10.30--
Bogor—West Java UpstreamCikaniki River10.50--[18]
Bogor—West JavaCikaniki River2-0.119–0.218-[20]
Bogor—West JavaCikaniki River53.46 *0.09–9.070.36 *[14]
Bogor—West JavaCikaniki River3-1.35–1.57-[23]
Bogor—West JavaCikaniki River93.49 *0.40–9.600.66 *[21]
Bogor—West JavaCikaniki River [22]
March 2013134.60 *0.002–17.40.95 *
March 2014910.3 *1.20–24.08.80 *
September 2014114.31 *0.003–12.02.32 *
December 2014124.40 *0.12–15.54.44 *
August 20151187.7 *0.36–6069.88 *
December 201591.81 *0.24–9.870.63 *
November 201671.17 *0.43–1.941.07 *
November 201782.76 *0.41–7.431.40 *
North Gorontalu—GorontaloHulawa River58.260.10–21.31.30 *[39]
Bone Bolango—GorontaloBone River1134516.0–208071.0[48]
Banyumas—Central JavaTajum River71003100–1900-[62]
Lebak situ—BantenRice Field Water120.1420.009–0.927-[49]
Aceh Jaya—AcehKrueng Sabee, Panga, and Teunom River180.0970.007–0.3330.056[50]
Jayapura—PapuaJabawi, Kleblow, and Komba River441.322.0–55.044.0[78]
Nauli and Simalagi villages—Mandailing Natal, North SumatraGroundwater160.620.45–1.3-[10]
Drinking water160.590.45–0.75-
Reference sites—Mandailing Natal, North SumatraDrinking water—uncontaminated area30.04--[10]
Groundwater—uncontaminated area30.06--
Other Countries
Northwest ChinaXiyu River171.340.07–7.59-[64]
Bono, Bono East, Ahafo—GhanaRiver Tano Basin90.045BDL–0.190.014[40]
Pestea Huni Valley—GhanaAnikoko, Abodwesh, Ankobra, Amenkime, Dinyame, Anfoe, Woawora, Benyan, and Mansi River70-132–866-[52]
Randfontein, Gauteng—South AfricaTailing dams7-0.032–0.067-[58]
Streams--0.004–0.068-
Wetlands--0.007–0.012-
Abu Hamad—SudanNile River7-0.27–3.26-[79]
River Nile State, Darmali—SudanGroundwater and tap water are drawn from the Nile River80.26--[80]
Rwamagasa, Geita—TanzaniaInsingile River as a water source2447.8<1.0–92020[51]
Brazilian AmazonUnfiltered water of Tapajos River470.0050.007–0.024-[53]
Central BrazilAraguaia River Floodplain980.0020.0001–0.004-[65]
BDL: Below detection limit. -: Not available. *: Average/median was obtained from raw data.
Table 2. Spearman rank correlation coefficient for Hg with the physicochemical parameters of the water.
Table 2. Spearman rank correlation coefficient for Hg with the physicochemical parameters of the water.
pHTemperatureDOTDSTurbiditySalinityEC
River water0.061−0.4110.293−0.0970.778 **0.074−0.027
Pondwater0.1350.029−0.1860.424na0.2580.450
Groundwater0.5340.432−0.2430.036na0.1880.358
All sample0.432 **−0.1920.086−0.0320.665 **0.0160.167
**: The correlation coefficient is significant (two-tailed) at the 1% level. na: not available.
Table 3. Comparison of Hg concentration (mg/kg dw) in soil with other studies around ASGM areas.
Table 3. Comparison of Hg concentration (mg/kg dw) in soil with other studies around ASGM areas.
LocationRemarksnMeanRangeMedianReference
Indonesia
Gunung Pongkor—Bogor, West Java This study
UpstreamResidential areas and riverbank soil1318.00.439–1445.51
Midstream53.601.18–6.044.45
Downstream41.100.420–2.780.62
Bogor—West JavaForest soil341.89 *0.11–9.35-[14]
Paddy field1515.3 *1.03–73.0-
Bogor—West JavaForest soil70.69 *0.11–2.22-[21]
Paddy field soil99.000.40–24.9
Lebak Situ—BantenRice field soil150.1240.212–2.47-[49]
Bombana—SulawesiASGM area839012.0–250063.0[56]
Mining commercial area1213.00.00–45.02.40
Nagan Raya—AcehSoil riverbank30.2780.271–0.328-[81]
Nauli and Simalagi village, Mandailing Natal—North SumatraPaddy soil205.600.26–5.80-[10]
Farm soil2019.00.18–100-
Reference site—Banjarbaru, South KalimantanAgriculture soil—uncontaminated area60.020.01–0.04-[32]
Other Countries
Obuasi, Ashanti—GhanaFarmland soil92.172.10–2.25-[82]
Soil tailing-0.8550.853–0.858
Obuasi—GhanaSoil around Tweapease33.683.54–3.88-[17]
Soil around Nyamebekyere32.032.01–2.05-
Soil around Ahansonyewodea31.331.29–1.39-
Migori, Transmara—KenyaSoil riverbank940.140.02–1.100.10[83]
Nouakchott, Chami Town—MauritaniaNatural soils, soil from residential areas, and soil from the ASGM area180-0.002–9.3-[57]
Anka—Northwest NigeriaSoil420.85--[3]
Randfontein, Gauteng—South AfricaTailing dams11-0.89–6.76-[58]
Community soil--0.43–0.97-
Garden soil--0.47–1.02-
Darmali—SudanAgricultural soil18--0.057[80]
Residential area10--0.044
Tailing6--9.60
Mbogwe, Geita—TanzaniaAgricultural soil120.0170.008–0.026-[84]
Rwamagasa, Geita—TanzaniaSoil920.0580.01–1.76-[51]
-: Not available. *: Average/median was obtained from raw data.
Table 4. Comparison of Hg concentration (mg/kg dw) in sediment with other studies around ASGM areas.
Table 4. Comparison of Hg concentration (mg/kg dw) in sediment with other studies around ASGM areas.
LocationRemarksnMeanRangeMedianReference
Indonesia
Gunung Pongkor—Bogor, West Java This study
UpstreamCiguha River to Cikaniki River712.90.920–31.710.0
Midstream916.11.17–67.36.00
Downstream470.32.60–15064.4
Bogor—West JavaCikaniki River10.10--[18]
West JawaCikaniki River2-0.83–1.07-[20]
Bogor—West JavaCikaniki River819.7 *0.093–85.2-[14]
Bogor—West JavaCikaniki River [22]
March 2013951.0 *29.0–79.243.4 *
September 201495.56 *0.20–15.84.67 *
December 20141221.4 *8.50–40.720.0 *
December 2015834.0 *9.50–71.822.0 *
November 2016736.1 *9.70–93.423.3 *
November 2017923.8 *7.00–49.922.9 *
Bone Bolango—GorontaloBone River11186BDL–790-[48]
Banyumas—Central JavaTajum River79.756.90–11.8-[62]
Ciujung—BantenCiujung Watershed110.610.02–0.910.62[63]
Lolayan—Bolaang MongondowBakan River33.26--[71]
Reference site—Buru, MalukuUpstream of Waelata River—uncontaminated area30.0230.021–0.025-[33]
Other Countries
Northwest ChinaXiyu River172.690.27–9.16-[64]
Bono, Bono East, Ahafo—GhanaRiver Tano Basin91.24BDL–4.801.00[40]
Anka—Northwest NigeriaStream sediment222.12--[3]
Randfontein, Gauteng—South AfricaTailing dams7-0.65–1.99-[58]
Streams--0.60–1.36-
Wetlands--0.68–1.36-
Brazilian AmazonSuperficial sediment of Tapajos River270.0740.019–0.155-[53]
Central BrazilAraguaia River Floodplain980.0440.010–0.107-[65]
BDL: Below detection limit. -: Not available. *: Average/median was obtained from raw data.
Table 5. Comparison of Hg concentration (mg/kg dw) in fish with other studies around ASGM areas.
Table 5. Comparison of Hg concentration (mg/kg dw) in fish with other studies around ASGM areas.
LocationRemarksnMeanRangeMedianReference
Indonesia
Gunung Pongkor—Bogor, West Java This study
UpstreamTilapia (Orechromis niloticus) from fish ponds40.6930.260–1.0000.755
Midstream30.6700.259–1.050.700
Downstream50.5220.268–1.230.392
GorontaloTilapia (Oreochromis niloticus) from Limboto Lake60.283 *0.0471 *–0.424 *0.283 *[70]
Lolayan—Bolaang MongondowTilapia (Oreochromis niloticus) from Bakan river30.382 *--[71]
West SumbawaTilapia (Oreochromis niloticus) from Rawa Taliwang Lake43.44 *3.06 *–3.82 *-[72]
Reference—Tatelu, North SulawesiFreshwater fish from Toldano River–uncontaminated area60.0942 *--[73]
Other Countries
Ghana13 species of fish from rivers in gold mining areas171.18 *--[74]
Migori County—KenyaOreochromis niloticus from fish ponds102.07 *0.848 *–4.33 *1.79 *[75]
Madre de Dios—PeruPiaractus brachypomus from farmed fish1110.236 *0.0471 *–1.083 *-[76]
Puerto Narino—Southern Colombia24 species of fish from the Amazon River1020.942 *0.0471 *–6.59 *-[77]
BDL: Below detection limit. -: Not available. *: Concentration on dw was calculated based on the assumption that the fish water content is 79%.

3.5. Hg Concentration in Cassava

The Hg concentration in cassava roots and leaves from various sampling locations are depicted in Table 6 and Table 7, respectively. The median Hg concentrations in cassava roots were 0.488 mg/kg dw upstream, 1.05 mg/kg dw midstream, and 0.108 mg/kg dw downstream. Cassava leaves show median Hg concentrations of 5.40 mg/kg dw upstream, 1.98 mg/kg dw midstream, and 0.860 mg/kg dw downstream.
The geographical distribution of Hg in cassava roots and leaves (Figure 2G,H) showed a high concentration for both in the upstream area (5.09 mg/kg dw in roots and 8.84 mg/kg dw in leaves), likely due to the proximity of ASGM workplaces. A statistical test revealed significant differences in Hg among areas for cassava roots (p = 0.017); in contrast, no significant differences were found for cassava leaves. Variability was observed within each area, particularly in upstream samples, which may be attributed to aerial Hg sources, consistent with observations by Adjorlolo-Gasokpoh et al. [85]. Of particular interest is the marked variability observed across upstream samples, especially among certain root samples, which displayed low concentrations. This observation underscores the complex dynamics of Hg uptake and distribution in cassava plants. The generally lower Hg concentration in downstream samples suggests a dilution effect or reduced Hg bioavailability further from the potential source [85].
On a wet weight basis, the concentration of Hg in cassava roots from upstream (0.193 mg/kg ww), midstream (0.415 mg/kg ww), and downstream (0.043 mg/kg ww) samples exceeded the threshold level of 0.03 mg/kg ww according to Indonesian Food and Drug Administration Regulation No. 9 [69]. All cassava leaves from upstream (1.38 mg/kg ww), midstream (0.522 mg/kg ww), and downstream (0.263 mg/kg ww) exceeded the threshold.
Mercury concentration in cassava leaf (n = 10) was significantly higher than in cassava root (n = 10) (p < 0.001) (Figure 3A). This pattern, consistent with findings from other gold mining-affected regions [51,85], can be attributed to both atmospheric Hg0 uptake through stomata [86] and soil-to-plant transfer [87]. Soil characteristics, such as organic matter (OM), pH, and microbial activity, can influence Hg speciation and bioavailability to plants [88]. Although the atmospheric Hg concentration plays a key role in leaf accumulation, particularly under elevated ambient concentration, soil Hg content also contributes to total Hg uptake [87]. Approximately 10% of the Hg concentration in the soil can be transported to the leaves, especially when atmospheric Hg levels are low [87]. This study found no significant correlation between the soil and leaf Hg concentrations. This indicates that atmospheric deposition may be the primary source of Hg in cassava leaves.
The comparison of the median Hg concentration in cassava roots in Gunung Pongkor with other regions in Indonesia and internationally reveals significant variations in contamination levels associated with ASGM activities (Table 6). Within Indonesia, Gunung Pongkor’s median Hg level was lower than those in Sukabumi-West Java [89] but higher than those in Palu-Central Sulawesi [90]. In the international context, Gunung Pongkor’s contamination level was generally higher than those reported in Ghana [17,87], Tanzania [51], and Uganda [91]. Conversely, Hg concentration found in the downstream area of this study was lower than that recorded in El Bagre, Colombia [92]. Notably, Hg levels in both the upstream and downstream areas of Gunung Pongkor were higher than those observed in El Bagre. These comparisons highlight how ASGM activities affect Hg contamination in cassava roots differently across regions. Local assessments and targeted strategies are essential to address Hg pollution. The data from Gunung Pongkor shows the serious environmental challenges of ASGM in Indonesia, stressing the importance of effective pollution control measures.
Meanwhile, a comparison of the median Hg concentration in cassava leaves in Gunung Pongkor with other places in Indonesia and around the world is revealed in Table 7. Mercury concentration upstream was lower than that found in Bombana, Sulawesi, around the ASGM area [56], yet higher than those near commercial mining areas, as well as in Sukabumi and Mandailing Natal [10,89]. In contrast, the midstream and downstream levels at Gunung Pongkor were lower than those observed in Mandailing Natal, North Sumatra [10]. On a global scale, the Hg concentration at Gunung Pongkor, particularly in upstream areas, was markedly elevated compared to Bogoso, Ghana [85], Mbogwe, and Rwamagasa in Geita, Tanzania [51,84], as well as Eastern Uganda [91]. These findings underscore the significant effects of ASGM activities on Hg accumulation in the environment, especially in areas where ASGM is located in Sukabumi.
Table 6. Comparison of Hg concentration (mg/kg dw) in cassava roots with other studies around ASGM areas.
Table 6. Comparison of Hg concentration (mg/kg dw) in cassava roots with other studies around ASGM areas.
LocationRemarksnMeanRangeMedianReference
Indonesia
Gunung Pongkor—Bogor, West Java This study
UpstreamResidential area and riverbank61.750.139–5.090.488
Midstream41.180.343–2.281.05
Downstream40.1060.0973–0.1100.108
Sukabumi—West JavaBall mill area231.1--[89]
Palu—Central SulawesiAgricultural area40.33--[90]
Other Countries
Bogoso—GhanaRiver Bogo riverbank10.079--[85]
Obuasi—GhanaTweapease, farmland30.3310.321–0.345-[17]
Nyamebekyere, farmland30.2430.236–0.248
Ahansonyewodea, farmland30.1150.100–0.130
Rwamagasa, Geita—TanzaniaResidential area140.0030.001–0.008-[51]
Bugiri, Busia, and Namayingo—UgandaFarmland area30.020.004–0.042-[91]
El Bagre, Bajo Cauca—ColombiaChards or cropped areas close to local inhabitants’ homes120.39--[92]
-: Not available.
Table 7. Comparison of Hg concentration (mg/kg dw) in cassava leaves with other studies around ASGM areas.
Table 7. Comparison of Hg concentration (mg/kg dw) in cassava leaves with other studies around ASGM areas.
LocationRemarksnMeanRangeMedianReference
Indonesia
Gunung Pongkor—Bogor, West Java This study
UpstreamResidential area and riverbank64.840.750–8.845.40
Midstream42.160.650–4.021.98
Downstream40.9450.350–1.710.860
Bombana—SulawesiASGM89.901.50–25005.90[56]
Mining commercial area63.200.00–45.02.20
Sukabumi—West JavaBall mill area74.61--[89]
Mandailing Natal—North SumatraAgricultural area62.00--[10]
Other Countries
Bogoso—GhanaRiver Bogo riverbank10.136--[85]
Mbogwe, Geita—TanzaniaFarmland area40.150.08–0.34-[84]
Rwamagasa, Geita—TanzaniaResidential area140.0610.008–0.167-[51]
Bugiri, Busia, and Namayingo—Eastern UgandaFarmland area30.110.05–0.15-[91]
-: Not available.

3.6. Geo-Accumulation Index (Igeo) in Soil and Sediment

The Igeo index (Figure 4) was used to assess Hg contamination in soil across sampling points from upstream to downstream, with values ranging from 1.15 (class 2) to 3.68 (class 4), indicating varying degrees of contamination. Upstream sites exhibited Igeo values between 1.17 and 3.68, categorized as moderately to heavily contaminated. Specifically, 31% of upstream sampling points were classified as moderately contaminated (class 2), 62% as moderately to heavily contaminated (class 3), and 8% as heavily contaminated (class 4). Notably, sampling point S7 (upstream) recorded the highest Igeo value (3.68), suggesting severe contamination likely due to nearby ASGM activities. In the midstream area, Igeo values ranged from 1.59 to 2.30, with 40% of sites moderately contaminated and 60% moderately to heavily contaminated. Downstream Igeo values ranged from 1.15 to 1.97, and all sampling points fell into class 2, indicating moderate contamination. This spatial pattern reveals a pollution gradient, with higher contamination in the upstream sample closely associated with ASGM activity, and decreasing levels downstream, likely due to dilution and/or dispersion processes. The Igeo value also appears to be influenced by land use, where areas dominated by ASGM activities, particularly upstream (Figure S3A), showed higher contamination indices compared to midstream and downstream zones that included more agricultural and residential land uses.
Similar to soil, Igeo values in sediment (Figure 4) indicated varying Hg enrichment levels across sampling locations, ranging from 1.43 to 3.64. Upstream sediment had Igeo values ranging from 1.43 to 3.24, with 43% classified as moderately contaminated (class 2) and 57% as moderately to heavily contaminated (class 3). Midstream Igeo values ranged from 1.53 to 3.29, with sites classified as 33% moderately contaminated (class 2), 44% moderately to heavily contaminated (class 3), and 22% heavily contaminated (class 4). Downstream Igeo values ranged from 1.88 to 3.64, with 50% classified as moderately contaminated (class 2) and 50% as heavily contaminated (class 4). This distribution suggests that sediment contamination is influenced by both intrinsic factors and anthropogenic activities. Some midstream and downstream areas showed higher contamination than that expected from upstream sources alone, potentially due to localized pollution events or pollutant transport and sedimentation patterns.

3.7. Bioconcentration Factor (BCF) in Fish

The bioconcentration factor (BCF) analysis of fish samples from different areas of a water system revealed varying levels of accumulation, with BCF values ranging from 31 to 4279 (Figure S1). The distribution of BCF categories was as follows: 41% of samples were categorized as high accumulation (BCF > 1000), predominantly from downstream and midstream areas; 45% exhibited medium accumulation (BCF 100–1000) across all areas; and 14% exhibited low accumulation (BCF < 100) as seen in downstream samples. This distribution suggests a gradient of bioaccumulation potential, with higher concentrations generally found in downstream and midstream areas (Figure S3C), affected by hydrological conditions (e.g., stagnant vs. flowing water), which influence bioavailability. It means that the availability of Hg in downstream and midstream areas is high. The accumulation of heavy metals, including Hg, in the body of organisms depends on the concentration of Hg in the context of the water/environment, temperature, pH, and dissolved oxygen [93]. Consequently, organisms inhabiting these regions face Hg accumulation, which could have implications for ecosystem health and human exposure through the food chain.

3.8. Bioaccumulation Factor (BAF) in Cassava Root

A statistically significant positive correlation was found between Hg concentration in soil and cassava root (r = 0.556, p < 0.05) (Figure 3B), indicating that Hg accumulation occurs from soil to root. This finding aligns with previous research demonstrating cassava’s ability to accumulate Hg from contaminated soil near mining activities [85].
The BAF measures the level of absorption and accumulation of the contaminant from the soil by cassava plants. Most samples fall within a moderate accumulation range of 0.1 to 0.5 (Figure S2), highlighting the varying levels of risk associated with Hg exposure through cassava consumption. The wide range of BAF values (Figure S3D) suggests that factors such as land use, which can affect organic matter input and nutrient status, alter uptake dynamics in plants. Samples with BAF values exceeding 1 are particularly concerning as they indicate a significant Hg concentration. These data highlight the need for specific assessments to evaluate health risks associated with Hg exposure through cassava root consumption and the importance of understanding factors that contribute to Hg accumulation in these crops.

3.9. Health Risk Assessment

To assess the health risks associated with Hg exposure from various environmental samples (water, soil, sediment, fish, and cassava plants), we applied the USEPA model to calculate the HQ (Figure 5) and HI (Figure 6), considering multiple exposure pathways including ingestion, dermal, and inhalation for both children and adults. An HQ or HI value exceeding one indicates the potential health risks, while a value below one suggests acceptable exposure levels. The analysis of 44 water samples showed generally low risk, with HQ values below one. However, some locations, particularly those near ASGM activities, showed elevated HQ for children. This discrepancy highlights that HQ values are influenced by exposure assumptions (e.g., ingestion rate, duration, body weight) and that low HQ can occur even when Hg concentrations exceed regulatory limits. Importantly, water quality standards are designed to be highly protective, so exceedances signal the need for monitoring rather than an immediate health threat.
In terms of soil exposure, children were notably at risk, primarily through ingestion, accounting for 10% of cases. In contrast, adults showed no significant risk, as all HQ values remained below one. The HI presented varying risk levels across the sampled sites, with particular areas, especially those adjacent to ASGM activities (upstream area), displaying high-risk levels for children. Whereas, downstream regions indicated minimal risk. Sediment exposure analysis pointed to significant health risks, predominantly through ingestion. Several sites recorded high HQ and HI values for children, with adults also exhibiting marginally elevated values, necessitating further attention. By examining fish from 12 locations, it was observed that children faced significantly greater risks compared to adults, with 79% of sites exceeding safe HQ levels for children, in contrast to 45% for adults. Certain fish samples demonstrated extraordinarily high HQ values for children, underscoring their heightened vulnerability. The evaluation of cassava roots predominantly indicated low risk, with HQ values remaining below one. Nonetheless, specific areas reported HQ values surpassing safety thresholds for children and slightly exceeding them for adults. Conversely, a considerable portion of cassava leaves exhibited HQ values that exceeded safe limits, thereby posing risks for both demographics, accounting for 57% of samples that exceeded acceptable thresholds for children.

3.10. Limitations of the Study

The study primarily concentrated on Hg, which may have led to the oversight of other significant contaminants. In the absence of a designated reference site, contamination levels were assessed by comparing the results to the national environmental quality standards, particularly those set by Indonesian regulations. While this provides a snapshot of contamination levels at a specific point in time, it does not account for long-term trends or seasonal variations. Additionally, the lack of air sampling restricted the assessment of atmospheric Hg deposition onto cassava leaf.
To overcome the limitations of the current study, future research will incorporate long-term monitoring to capture temporal variations in Hg contamination and to better evaluate its bioavailability and chemical speciation across different environmental matrices. Understanding Hg speciation is essential, as it directly influences the metal’s toxicity, mobility, and persistence in the environment, particularly in areas affected by ASGM. Future investigations will also include comparative analyses with data from a designated reference site to strengthen the interpretation of contamination levels. In addition, exploring the pathways through which Hg is released from ASGM activities will help clarify its transport mechanisms. A more detailed assessment of Hg accumulation in various cassava tissues will further reveal the plant’s uptake processes and the potential exchange between atmospheric Hg and plant surfaces. Collectively, these approaches will contribute to a more comprehensive understanding of Hg dynamics and its environmental and health impacts in ASGM-impacted regions.

4. Conclusions

This study revealed significant Hg contamination in samples such as water, soil, sediment, fish, and cassava surrounding Gunung Pongkor, West Java, Indonesia, due to ASGM activities, posing substantial risks to both the environment and human health. Mercury concentration in river water frequently exceeded regulatory limits, indicating potential pollution sources along the Cikaniki River. Soil samples showed Hg levels far above national standards, particularly in upstream areas, highlighting ASGM as a significant pollution source. Sediment samples also exhibited high Hg concentration, surpassing international guidelines, with variability suggesting multiple contamination sources. Fish samples remained within the threshold and showed positive correlations between Hg level in fish tissue and both pond water and sediment, indicating that both sources are significant contributors to Hg exposure for fish. All cassava leaf samples and 21% of root samples exceeded safety thresholds, posing a significant dietary risk, particularly for children, who exhibited substantially higher HQ values. Health risk assessments revealed concerning HI values, especially for children exposed to soil, sediment, and fish in certain areas, highlighting the vulnerability of this population. This study highlights the urgent need for targeted environmental management and health risk assessments to reduce Hg exposure, particularly among vulnerable groups such as children. The findings stress the importance of continuous monitoring and effective intervention strategies to protect both environmental and human health in areas affected by ASGM activities. In line with the Minamata Convention on Hg, of which Indonesia is a signatory, and national regulations, strengthened implementation and active field enforcement are essential. This includes continuous environmental monitoring and the promotion of Hg-free mining alternatives. While the study provides valuable insights, it is based on a single time-point and does not capture long-term trends or seasonal variations. We also recognize the ethical responsibility in conducting research involving potentially toxic elements (PTEs) and emphasize the need for the clear communication of results to local communities and stakeholders to raise awareness about Hg-related health risks. In collaboration with local authorities, we recommend implementing mitigation strategies to reduce the environmental impact and protect public health. Future studies will focus on extended monitoring and a deeper investigation of Hg behavior in the environment to provide a more comprehensive risk assessment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/earth6030067/s1, Table S1: the Igeo indexes and level of contamination; Table S2: the BCF classes of contamination; Table S3: parameters used in the risk assessment [94]; Table S4: data on physicochemical parameters of Hg in water; Table S5: spearman rank correlation coefficient for each index in soil and sediment; Figure S1: BCF of Hg in fish; Figure S2: BAF of Hg in cassava root; Figure S3: Geographical distribution of Igeo in soil (A), Igeo in sediment (B), BCF in fish (C), and BAF in cassava root (D).

Author Contributions

The manuscript was authored by T.A. (Tia Agustiani). Data collection was conducted by T.A. (Tia Agustiani), A.S. and B.K. Data curation was conducted by T.A. (Tia Agustiani), S.S. and T.A. (Tetsuro Agusa). Visualization was conducted by T.A. (Tia Agustiani) and S.S. Critical revisions of the manuscript were provided by S.S., A.S., P.A.P., J.K., A.E. and T.A. (Tetsuro Agusa). Supervision was carried out by Y.I., Y.A. and T.A. (Tetsuro Agusa). All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the International Postgraduate Scholarship for Mercury Research of the Kumamoto Prefecture Government and the Heiwa Nakajima Foundation.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to acknowledge the support and commitment of the Kumamoto Prefecture Government, the Prefectural University of Kumamoto, and the National Research and Innovation Agency (BRIN) of the Republic of Indonesia to this study. They also thank Syaiful Habib, Edy Ayuba, Arif Rahman Saleh, Rohyan, Asep, and Dulah from PT. Antam UBPE Pongkor as well as Suherman and Onig for helping during the sampling campaign in Gunung Pongkor.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Moreno-Brush, M.; McLagan, D.S.; Biester, H. Fate of Mercury from Artisanal and Small-Scale Gold Mining in Tropical Rivers: Hydrological and Biogeochemical Controls. A Critical Review. Crit. Rev. Environ. Sci. Technol. 2020, 50, 437–475. [Google Scholar] [CrossRef]
  2. Goix, S.; Maurice, L.; Laffont, L.; Rinaldo, R.; Lagane, C.; Chmeleff, J.; Menges, J.; Heimbürger, L.E.; Maury-Brachet, R. Quantifying the Impacts of Artisanal Gold Mining on a Tropical River System Using Mercury Isotopes. Chemosphere 2019, 219, 684–694. [Google Scholar] [CrossRef] [PubMed]
  3. Adewumi, A.J.; Laniyan, T.A. Contamination, Sources and Risk Assessments of Metals in Media from Anka Artisanal Gold Mining Area, Northwest Nigeria. Sci. Total Environ. 2020, 718, 137235. [Google Scholar] [CrossRef] [PubMed]
  4. United Nations Environment Programme (UNEP). Global Mercury Assessment 2018; UN Environment Programme, Chemicals and Health Branch: Geneva, Switzerland, 2019; ISBN 978-92-807-3744-8. Available online: www.unep.org/resources/publication/global-mercury-assessment-2018 (accessed on 11 September 2024).
  5. United Nations Environment Programme (UNEP). Minamata Convention on Mercury: Text and Annexes; UNEP: Geneva, Switzerland, 2019; Available online: https://minamataconvention.org/sites/default/files/2021-06/Minamata-Convention-booklet-Sep2019-EN.pdf (accessed on 11 September 2024).
  6. Ismawati, Y. Policy Brief: ASGM in Indonesia; BaliFokus: Denpasar, Indonesia, 2010. [Google Scholar]
  7. Elvince, R.; Inoue, T.; Tsushima, K.; Takayanagi, R.; Ardianor; Darung, U.; Gumiri, S.; Dohong, S.; Nagafuchi, O.; Kawakami, T.; et al. Assessment of Mercury Contamination in the Kahayan River, Central Kalimantan. J. Water Environ. Technol. 2008, 6, 103–112. [Google Scholar] [CrossRef]
  8. Mallongi, A.; Pataranawat, P.; Parkpian, P. Mercury emission from artisanal buladu gold mine and its bioaccumulation in rice grains, Gorontalo Province, Indonesia. Adv. Mater. Res 2014, 931–932, 744–748. [Google Scholar] [CrossRef]
  9. Limbong, D.; Kumampung, J.; Rimper, J.; Arai, T.; Miyazaki, N. Emissions and environmental implications of mercury from artisanal gold mining in north Sulawesi, Indonesia. Sci. Total Environ. 2003, 302, 227–236. [Google Scholar] [CrossRef]
  10. Arrazy, S.; Addai-arhin, S.; Jeong, H.; Novirsa, R.; Wispriyono, B.; AGUSA, T.; Ishibashi, Y.; Kobayashi, J. Spatial Distribution and Human Health Risks of Mercury in the Gold Mining Area of Mandailing Natal District, Indonesia. Environ. Monit. Contam. Res. 2023, 3, 33–42. [Google Scholar] [CrossRef]
  11. Iqbal, R.; Inoue, T. Mercury Pollution in Java Island: Past and Present. J. Ecotechnol. Res. 2011, 16, 51–57. [Google Scholar] [CrossRef]
  12. Ismawati, Y.; Digangi, J.; Petrlik, J. Mercury Hotspot in Indonesia, ASGM Site: Poboya and Sekotong in Indonesia; BaliFokus: Denpasar, Indonesia, 2013; Available online: www.ipen.org/hgmonitoring/pdfs/indonesia-report-en.pdf (accessed on 11 September 2024).
  13. Yustiawati; Syawal, M.S.; Terashima, M.; Tanaka, S. Speciation analysis of mercury in river water in West Java, Indonesia. Tropics 2006, 15, 425–428. [Google Scholar] [CrossRef]
  14. Tomiyasu, T.; Kono, Y.; Kodamatani, H.; Hidayati, N.; Rahajoe, J.S. The distribution of mercury around the small-scale gold mining area along the Cikaniki river, Bogor, Indonesia. Environ. Res. 2013, 125, 12–19. [Google Scholar] [CrossRef]
  15. Harianja, A.H.; Saragih, G.S.; Fauzi, R.; Hidayat, M.Y.; Syofyan, Y.; Tapriziah, E.R.; Kartiningsih, S.E. Mercury Exposure in Artisanal and Small-Scale Gold Mining Communities in Sukabumi, Indonesia. J. Health Pollut. 2020, 10, 201209. [Google Scholar] [CrossRef] [PubMed]
  16. Agrawal, S.; Susilorini, B. National Overview of the Indonesian ASGM Sector; Basel and Stockholm Convention Regional Center for Southeast Asia (BCRC-SEA): Jakarta, Indonesia, 2020; p. 128. [Google Scholar]
  17. Addai-Arhin, S.; Novirsa, R.; Jeong, H.H.; Phan, Q.D.; Hirota, N.; Ishibashi, Y.; Shiratsuchi, H.; Arizono, K. Potential Human Health Risk of Mercury-contaminated cassavas—Preleminary studies. Fundam. Toxicol. Sci. 2022, 9, 61–69. [Google Scholar] [CrossRef]
  18. Hidayati, N.; Juhaeti, T.; Syarif, F. Mercury and Cyanide Contaminations in Gold Mine Environment and Possible Solution of Cleaning Up by Using Phytoextraction. HAYATI J. Biosci. 2009, 16, 88–94. [Google Scholar] [CrossRef]
  19. Kido, M.; Yustiawati; Syawal, M.S.; Sulastri; Hosokawa, T.; Tanaka, S.; Saito, T.; Iwakuma, T.; Kurasaki, M. Comparison of General Water Quality of Rivers in Indonesia and Japan. Environ. Monit. Assess. 2009, 156, 317–329. [Google Scholar] [CrossRef]
  20. Yasuda, M.; Yustiawati; Suhaemi Syawal, M.; Sikder, M.T.; Hosokawa, T.; Saito, T.; Tanaka, S.; Kurasaki, M. Metal Concentrations of River Water and Sediments in West Java, Indonesia. Bull. Environ. Contam. Toxicol. 2011, 87, 669–673. [Google Scholar] [CrossRef]
  21. Tomiyasu, T.; Kodamatani, H.; Hamada, Y.K.; Matsuyama, A.; Imura, R.; Taniguchi, Y.; Hidayati, N.; Rahajoe, J.S. Distribution of Total Mercury and Methylmercury around the Small-Scale Gold Mining Area along the Cikaniki River, Bogor, Indonesia. Environ. Sci. Pollut. Res. 2017, 24, 2643–2652. [Google Scholar] [CrossRef]
  22. Tomiyasu, T.; Hamada, Y.K.; Kodamatani, H.; Hidayati, N.; Rahajoe, J.S. Transport of Mercury Species by River from Artisanal and Small-Scale Gold Mining in West Java, Indonesia. Environ. Sci. Pollut. Res. 2019, 26, 25262–25274. [Google Scholar] [CrossRef]
  23. Pratama Yoga, G.; Lumbanbatu, D.T.; Riani, E.; Wardiatno, Y. Secondary Production of the Net-Spinning Cadisfly, Cheumatopsyche spp. (Trichoptera: Hydropsychidae) in Mercuric Contaminated River. J. Trop. Biol. Conserv. 2014, 11, 1–12. [Google Scholar]
  24. Kono, Y.; Rahajoe, J.S.; Hidayati, N.; Kodamatani, H.; Tomiyasu, T. Using native epiphytic ferns to estimate the atmospheric mercury levels in a small-scale gold mining area of West Java, Indonesia. Chemosphere 2012, 89, 241–248. [Google Scholar] [CrossRef]
  25. Kono, Y.; Tomiyasu, T. Quantitative Evaluation of Real-time Measurements of Atmospheric Mercury in a Mercury contaminated Area. J. Environ. Saf. 2013, 4, 153–157. [Google Scholar]
  26. Sumantri, A.; Laelasari, E.; Junita, N.R.; Nasrudin, N. Logam Merkuri pada Pekerja Penambangan Emas Tanpa Izin. J. Kesehat. Masy. Nas. 2014, 8, 398–403. [Google Scholar] [CrossRef]
  27. Indonesia Central Bureau of Statistics (ICBS). Average Weekly Per Capita Consumption of Several Types of Important Food Ingredients, 2007–2023. Available online: https://www.bps.go.id/id/statistics-table/1/OTUwIzE=/rata-rata-konsumsi-per-kapita-seminggu-beberapa-macam-bahan-makanan-penting--2007-2022.html (accessed on 11 September 2024).
  28. Basuki, A.; Sumanagara, D.A.; Sinambela, D. The Gunung Pongkor Gold-Silver Deposit, West Java, Indonesia. J. Geochem. Explor. 1994, 50, 371–391. [Google Scholar] [CrossRef]
  29. Azizah, M.; Anen, N. Water quality status of Cikaniki River, Bogor Regency based on pollution index and macrofauna diversity. Florea J. Biol. Its Learn. 2019, 6, 79–87. [Google Scholar]
  30. U.S. EPA. Method 245.1: Determination of Mercury in Water by Cold Vapor Atomic Absorption Spectrometry: Revision 3.0; U.S. EPA: Cincinnati, OH, USA, 1994.
  31. Muller, G. Index of Geoaccumulation in Sediments of the Rhine River. GeoJournal 1969, 2, 108–118. [Google Scholar]
  32. Sudarningsih, S.; Fahruddin, F.; Lailiyanto, M.; Noer, A.A.; Husain, S.; Siregar, S.S.; Wahyono, S.C.; Ridwan, I. Assessment of Soil Contamination by Heavy Metals: A Case of Vegetable Production Center in Banjarbaru Region, Indonesia. Pol. J. Environ. Stud. 2022, 32, 249–257. [Google Scholar] [CrossRef]
  33. Heumasse, H.; Omar, S.B.A.; Demmallino, E.B. Mercury (Hg) contamination on water, sediment and macrozoobenthos in Waelata River, Wamsait Village Waelata Sub-district, Buru District, Maluku Province. J. Phys. Conf. Ser. 2019, 1341, 092019. [Google Scholar] [CrossRef]
  34. OECD. Test No. 305: Bioaccumulation in Fish: Aqueous and Dietary Exposure, OECD Guidelines for the Testing of Chemicals, Section 3; OECD Publishing: Paris, France, 2012. [Google Scholar] [CrossRef]
  35. LaGrega, M.D.; Buckingham, P.L.; Evans, J.C. Hazardous Waste Management, 2nd ed.; McGraw-Hill Publications: New York, NY, USA, 2001. [Google Scholar]
  36. Udiba, U.U.; Udofia, U.U.; Akpan, E.R.; Antai, E.E. Assessment of lead (Pb) uptake and hazard potentials of the cassava plant (Manihot esculentus Cranz), Dareta Village, Zamfara, Nigeria. Int. Res. J. Public Environ. Health 2019, 6, 115–126. [Google Scholar]
  37. U.S. EPA. Exposure Factors Handbook: 2011 Edition; National Center for Environmental Assessment: Washington, DC, USA, 2011. Available online: https://cfpub.epa.gov/ncea/efp/recordisplay.cfm?deid=236252 (accessed on 11 September 2024).
  38. U.S. EPA. Exposure Factors Handbook—General Factors (No. EPA/540/R-95/128); Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency: Washington, DC, USA, 1997.
  39. Hiola, R. Mercury levels in the river water and urine of traditional gold miners in Hulawa village east Sumalata district north Gorontalo regency. Res. J. Med. Sci. 2017, 11, 89–94. [Google Scholar]
  40. Nyantakyi, A.J.; Akoto, O.; Fei-Baffoe, B. Seasonal Variations in Heavy Metals in Water and Sediment Samples from River Tano in the Bono, Bono East, and Ahafo Regions, Ghana. Environ. Monit. Assess. 2019, 191, 570. [Google Scholar] [CrossRef]
  41. Hellal, J.; Schäfer, J.; Vigouroux, R.; Lanceleur, L.; Laperche, V. Impact of Old and Recent Gold Mining Sites on Mercury Fluxes in Suspended Particulate Matter, Water and Sediment in French Guiana. Appl. Sci. 2020, 10, 7829. [Google Scholar] [CrossRef]
  42. Indonesia Minister of Environment and Forestry (IMEF). Minister of Environment and Forestry Regulation No. 6 of 2021 Concerning Procedures and Requirements for Managing Hazardous and Toxic Waste; Department of Environmental Affairs of Indonesia: Jakarta, Indonesia, 2021. Available online: https://jdih.menlhk.go.id/new/uploads/files/2021pmlhk006_menlhk_06082021104752.pdf (accessed on 11 September 2024).
  43. Santoro, A.; Terzano, R.; Medici, L.; Beciani, M.; Pagnoni, A.; Blo, G. Colloidal Mercury (Hg) Distribution in Soil Samples by Sedimentation Field-Flow Fractionation Coupled to Mercury Cold Vapour Generation Atomic Absorption Spectroscopy. J. Environ. Monit. 2012, 14, 138–145. [Google Scholar] [CrossRef] [PubMed]
  44. Buffle, J.; Wilkinson, K.J.; Van Leeuwen, H.P. Chemodynamics and Bioavailability in Natural Waters. Environ. Sci. Technol. 2009, 43, 7170–7174. [Google Scholar] [CrossRef] [PubMed]
  45. Hsu, C.Y.; Lin, T.H.; Wang, M.K.; Liu, C.C. Effects of pH on the Distribution of Mercury in Freshwater Systems. Water 2009, 43, 3777–3785. [Google Scholar]
  46. Kim, M.K.; Zoh, K.D. Fate and Transport of Mercury in Environmental Media and Human Exposure. J. Prev. Med. Public Health 2012, 45, 335–343. [Google Scholar] [CrossRef]
  47. Meutia, A.A.; Lumowa, R.; Sakakibara, M. Indonesian Artisanal and Small-Scale Gold Mining—A Narrative Literature Review. Int. J. Environ. Res. Public Health 2022, 19, 3955. [Google Scholar] [CrossRef]
  48. Gafur, N.A.; Sakakibara, M.; Sano, S.; Sera, K. A Case Study of Heavy Metal Pollution in Water of Bone River by Artisanal Small-Scale Gold Mine Activities in Eastern Part of Gorontalo, Indonesia. Water 2018, 10, 1507. [Google Scholar] [CrossRef]
  49. Novirsa, R.; Quang, P.D.; Jeong, H.; Fukushima, S.; Dinh, Q.P.; Ishibashi, Y.; Wispriyono, B.; Arizono, K. The Evaluation of Mercury Contamination in Upland Rice Paddy Field around Artisanal Small-Scale Gold Mining Area, Lebaksitu, Indonesia. J. Environ. Saf. 2019, 10, 119–125. [Google Scholar] [CrossRef]
  50. Suhud, K.; Wahidah, S.; Maulana, I.; Idroes, R.; Fudholi, A.; Suprayitno, L.; Fudholi, A. Mercury Analysis with Principal Component Analysis for Water, Sediment, And Biota Samples in Aceh, Indonesia. ARPN J. Eng. Appl. Sci. 2020, 15, 1764–1770. [Google Scholar]
  51. Nyanza, E.C.; Dewey, D.; Thomas, D.S.K.; Davey, M.; Ngallaba, S.E. Spatial Distribution of Mercury and Arsenic Levels in Water, Soil and Cassava Plants in a Community with Long History of Gold Mining in Tanzania. Bull. Environ. Contam. Toxicol. 2014, 93, 716–721. [Google Scholar] [CrossRef]
  52. Obiri, S.; Yeboah, P.O.; Osae, S.; Adu-Kumi, S.; Cobbina, S.J.; Armah, F.A.; Ason, B.; Antwi, E.; Quansah, R. Human Health Risk Assessment of Artisanal Miners Exposed to Toxic Chemicals in Water and Sediments in the Presteahuni Valley District of Ghana. Int. J. Environ. Res. Public Health 2016, 13, 139. [Google Scholar] [CrossRef]
  53. 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]
  54. Krisnayanti, B.D.; Anderson, C.W.N.; Utomo, W.H.; Feng, X.; Handayanto, E.; Mudarisna, N.; Ikram, H.; Khususiah. Assessment of environmental mercury discharge at a four year-old artisanal gold mining area on Lombok Island, Indonesia. J. Environ. Monit. 2012, 14, 2598–2607. [Google Scholar] [CrossRef] [PubMed]
  55. Ignatavičius, G.; Unsal, M.H.; Busher, P.E.; Wołkowicz, S.; Satkūnas, J.; Šulijienė, G.; Valskys, V. Geochemistry of mercury in soils and water sediments. AIMS Environ. Sci. 2022, 9, 277–297. [Google Scholar] [CrossRef]
  56. Basri; Sakakibara, M.; Sera, K. Mercury in Soil and Forage Plants from Artisanal and Small-Scale Gold Mining in the Bombana Area, Indonesia. Toxics 2020, 8, 15. [Google Scholar] [CrossRef]
  57. Maha, M.M.; Matsuyama, A.; Arima, T.; Sainoki, A. Assessment of Total Mercury Levels Emitted from ASGM into Soil and Groundwater in Chami Town, Mauritania. Sustainability 2024, 16, 7992. [Google Scholar] [CrossRef]
  58. Malehase, T.; Daso, A.P.; Okonkwo, J.O. Determination of Mercury and Its Fractionation Products in Samples from Legacy Use of Mercury Amalgam in Gold Processing in Randfontein, South Africa. Emerg. Contam. 2016, 2, 157–165. [Google Scholar] [CrossRef]
  59. Aji, T.B.; Setiawan, Y.; Abidin, Z.; Tarno, S. Spatial distribution of mercury pollution in the Mempawah River Watershed, West Kalimantan—Indonesia. Int. J. Chem. Mater. Sci. 2024, 7, 1–10. [Google Scholar] [CrossRef]
  60. He, M.; Tian, L.; Braaten, H.F.V.; Wu, Q.; Luo, J.; Cai, L.M.; Meng, J.H.; Lin, Y. Mercury–Organic Matter Interactions in Soils and Sediments: Angel or Devil? Bull. Environ. Contam. Toxicol. 2019, 102, 621–627. [Google Scholar] [CrossRef]
  61. Hong Kong Environmental Protection Department (EPD). Hong Kong Interim Sediment Quality Guidelines; EPD: Hong Kong, China, 1999.
  62. Budianta, W.; Fahmi, F.L.; Arifudin; Warmada, I.W. The Distribution and Mobility of Mercury from Artisanal Gold Mining in River Sediments and Water, Banyumas, Central Java, Indonesia. Environ. Earth Sci. 2019, 78, 90. [Google Scholar] [CrossRef]
  63. Nugraha, W.C.; Ishibashi, Y.; Arizono, K. Assessment of Heavy Metal Distribution and Contamination in the Sediment of the Ciujung Watershed, Banten Province, Indonesia. J. Mater Cycles Waste Manag. 2023, 25, 2619–2631. [Google Scholar] [CrossRef]
  64. Luo, Y.; Wang, N.; Liu, Z.; Sun, Y.; Lu, N. Characteristics and Risk Assessment of Potentially Toxic Elements Pollution in River Water and Sediment in Typical Gold Mining Areas of Northwest China. Sci. Rep. 2024, 14, 12715. [Google Scholar] [CrossRef] [PubMed]
  65. Monteiro, L.C.; Vieira, L.C.G.; Bernardi, J.V.E.; Bastos, W.R.; de Souza, J.P.R.; Recktenvald, M.C.N.d.N.; Nery, A.F.d.C.; Oliveira, I.A.d.S.; Cabral, C.d.S.; Moraes, L.d.C.; et al. Local and Landscape Factors Influencing Mercury Distribution in Water, Bottom Sediment, and Biota from Lakes of the Araguaia River Floodplain, Central Brazil. Sci. Total Environ. 2024, 908, 168336. [Google Scholar] [CrossRef] [PubMed]
  66. Lavoie, R.A.; Jardine, T.D.; Chumchal, M.M.; Kidd, K.A.; Campbell, L.M. Biomagnification of Mercury in Aquatic Food Webs: A Worldwide Meta-Analysis. Environ. Sci. Technol. 2013, 47, 13385–13394. [Google Scholar] [CrossRef] [PubMed]
  67. Eagles-Smith, C.A.; Ackerman, J.T.; Willacker, J.J.; Tate, M.T.; Lutz, M.A.; Fleck, J.A.; Stewart, A.R.; Wiener, J.G.; Evers, D.C.; Lepak, J.M.; et al. Spatial and temporal patterns of mercury concentrations in freshwater fish across the Western United States and Canada. Sci. Total Environ. 2016, 568, 1171–1184. [Google Scholar] [CrossRef]
  68. Mustafa, S.A.; Al-Rudainy, A.J.; Salman, N.M. Effect of Environmental Pollutants on Fish Health: An Overview. Egypt J. Aquat. Res. 2024, 50, 225–233. [Google Scholar] [CrossRef]
  69. Indonesia Food and Drug Administration (IFDA). Indonesia Food and Drug Administration Regulation No. 9 of 2021 Concerning Requirements for Heavy Metal Contaminants in Processed Food; Indonesia Food and Drug Administration: Jakarta, Indonesia, 2022. Available online: https://standarpangan.pom.go.id/dokumen/peraturan/202x/logam_2022.pdf (accessed on 10 August 2024).
  70. Nakoe, M.R.; Ardian, Y.; Ruhardi, A.; Dwinugroho, F.; Yudhastuti, R.; Sulistyorini, L.; Azizah, R.; Indriani, D. Risk assessment exposure of mercury (Hg) at people who consuming Nila fish (Oreochromis niloticus) from Limboto lake of Gorontalo province. Res. J. Pharm. Biol. Chem. Sci. 2014, 5, 1420–1427. [Google Scholar]
  71. Maddusa, S.S.; Asrifuddin, A.; Mantjoro, E.M. Public Health Risk Analysis Due to Consuming Tilapia (Oreochromis niloticus) Containing Heavy Metals in Bakan Village, Lolayan District, Bolaang Mongondow Regency. Int. J. Community Med. Public Health 2022, 10, 45. [Google Scholar] [CrossRef]
  72. Mulyani, I.; Yamin, M.; Khairuddin, K. Analysis of Mercury (Hg) Content in Tilapia Fish (Oreochromis mossambicus) from Rawa Taliwang Lake to Enrich the Course Materials on Ecotoxicology. J. Penelit. Pendidik. IPA 2023, 9, 4679–4684. [Google Scholar] [CrossRef]
  73. Castilhos, Z.C.; Rodrigues-Filho, S.; Rodrigues, A.P.C.; Villas-Bôas, R.C.; Siegel, S.; Veiga, M.M.; Beinhoff, C. Mercury Contamination in Fish from Gold Mining Areas in Indonesia and Human Health Risk Assessment. Sci. Total Environ. 2006, 368, 320–325. [Google Scholar] [CrossRef]
  74. Doke, D.A.; Gohike, J.M. Estimation of human health risk from exposure to methylmercury via fish consumption in Ghana. J. Health Pollut. 2014, 4, 18–25. [Google Scholar] [CrossRef]
  75. Kola, S.; Kanja, L.W.; Mbaria, J.M.; Maina, J.G.; Okumu, M.O. Levels of Mercury in Nile Tilapia (Oreochromis niloticus), Water, and Sediment in the Migori Gold Mining Belt, Kenya, and the Potential Ramifications to Human Health. F1000Research 2019, 8, 1244. [Google Scholar] [CrossRef]
  76. Langeland, A.L.; Hardin, R.D.; Neitzel, R.L. Mercury Levels in Human Hair and Farmed Fish near Artisanal and Small-Scale Gold Mining Communities in the Madre de Dios River Basin, Peru. Int. J. Environ. Res. Public Health 2017, 14, 302. [Google Scholar] [CrossRef] [PubMed]
  77. 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]
  78. Tanjung, R.H.R.; Yonas, M.N.; Suwito Maury, H.K.; Sarungu, Y.; Hamuna, B. Analysis of surface water quality of four rivers in Jayapura Regency, Indonesia: CCME-WQI Approach. J. Ecol. Eng. 2022, 23, 73–82. [Google Scholar] [CrossRef]
  79. Elwaleed, A.; Jeong, H.H.; Abdelbagi, A.H.; Quynh, N.T.; Agusa, T.; Ishibashi, Y.; Arizono, K. Human Health Risk Assessment from Mercury-Contaminated Soil and Water in Abu Hamad Mining Market, Sudan. Toxics 2024, 12, 112. [Google Scholar] [CrossRef]
  80. Elwaleed, A.; Jeong, H.; Abdelbagi, A.H.; Thi Quynh, N.; Nugraha, W.C.; Agusa, T.; Ishibashi, Y.; Arizono, K. Assessment of Mercury Contamination in Water and Soil from Informal Artisanal Gold Mining: Implications for Environmental and Human Health in Darmali Area, Sudan. Sustainability 2024, 16, 3931. [Google Scholar] [CrossRef]
  81. Nisah, K.; Muslem, M.; Ashari, T.M.; Afkar, M.; Iqhrammullah, M. Distribution of Mercury in Soil, Water, and Vegetable Fern in a Former Gold Mining Area—Evidence from Nagan Raya Regency, Aceh Province, Indonesia. J. Ecol. Eng. 2022, 23, 30–39. [Google Scholar] [CrossRef]
  82. Addai-Arhin, S.; Novirsa, R.; Jeong, H.H.; Phan, Q.D.; Hirota, N.; Ishibashi, Y.; Shiratsuchi, H.; Arizono, K. The Human health risks assessment of mercury in soils and plantains farms in selected artisanal and small-scale gold mining communities around Obuasi, Ghana. J. Appl. Toxicol. 2021, 42, 258–273. [Google Scholar] [CrossRef]
  83. Odumo, B.O.; Carbonell, G.; Angeyo, H.K.; Patel, J.P.; Torrijos, M.; Rodríguez Martín, J.A. Impact of Gold Mining Associated with Mercury Contamination in Soil, Biota Sediments and Tailings in Kenya. Environ. Sci. Pollut. Res. 2014, 21, 12426–12435. [Google Scholar] [CrossRef]
  84. Sanga, T.R.; Maseka, K.K.; Ponraj, M.; Tungaraza, C.; Mng’ong’o, M.E.; Mwakalapa, E.B. Accumulation and Distribution of Mercury in Agricultural Soils, Food Crops and Associated Health Risks: A Case Study of Shenda Gold Mine-Geita Tanzania. Environ. Chall. 2023, 11, 100697. [Google Scholar] [CrossRef]
  85. Adjorlolo-Gasokpoh, A.; Golow, A.A.; Kambo-Dorsa, J. Mercury in the Surface Soil and Cassava, Manihot Esculenta (Flesh, Leaves and Peel) Near Goldmines at Bogoso and Prestea, Ghana. Bull. Environ. Contam. Toxicol. 2012, 89, 1106–1110. [Google Scholar] [CrossRef] [PubMed]
  86. Yang, Y.; Yanai, R.D.; Driscoll, C.T.; Montesdeoca, M.; Smith, K.T. Concentrations and Content of Mercury in Bark, Wood, and Leaves in Hardwoods and Conifers in Four Forested Sites in the Northeastern USA. PLoS ONE 2018, 13, e0196293. [Google Scholar] [CrossRef] [PubMed]
  87. Marrugo-Negrete, J.; Marrugo-Madrid, S.; Pinedo-Hernández, J.; Durango-Hernández, J.; Díez, S. Screening of Native Plant Species for Phytoremediation Potential at a Hg-Contaminated Mining Site. Sci. Total Environ. 2016, 542, 809–816. [Google Scholar] [CrossRef] [PubMed]
  88. He, L.; Peng, X. Content and Bioavailability of Hg in a Soil–Tea Plant System in Anxi Area, Southeast China. Water 2023, 15, 179. [Google Scholar] [CrossRef]
  89. Saragih, G.S.; Tapriziah, E.R.; Syofyan, Y.; Masitoh, S.; Pandiangan, Y.S.H. Andriantoro Mercury Contamination in Selected Edible Plants and Soil from Artisanal and Small-Scale Gold Mining in Sukabumi Regency, Indonesia. Makara J. Sci. 2021, 25, 222–228. [Google Scholar] [CrossRef]
  90. Ramlan; Basir-Cyio, M.; Napitupulu, M.; Inoue, T.; Anshary, A.; Mahfudz; Isrun; Rusydi, M.; Golar; Sulbadana; et al. Pollution and Contamination Level of Cu, Cd, and Hg Heavy Metals in Soil and Food Crop. Int. J. Environ. Sci. Technol. 2022, 19, 1153–1164. [Google Scholar] [CrossRef]
  91. Ssenku, J.E.; Naziriwo, B.; Kutesakwe, J.; Mustafa, A.S.; Kayeera, D.; Tebandeke, E. Mercury Accumulation in Food Crops and Phytoremediation Potential of Wild Plants Thriving in Artisanal and Small-Scale Gold Mining Areas in Uganda. Pollutants 2023, 3, 181–196. [Google Scholar] [CrossRef]
  92. Muñoz, N.C.; González-Álvarez, D.; Jaramillo, A.C.; Soto-Ospina, A.; Ruiz, Á.A. Toxicological Risk in Individuals Exposed to Methylmercury and Total Mercury through Daily-Consumed Foodstuffs in One of the Mining Regions of Bajo Cauca, Antioquia, Colombia. Emerg. Contam. 2023, 9, 100226. [Google Scholar] [CrossRef]
  93. Astuti, L.P.; Warsa, A.; Nurfiarini, A.; Tjahjo, D.W.H. Bioaccumulation of Non-Essential Heavy Metals in Fish in Ir H. Djuanda Reservoir, West Java. In Proceedings of the International Symposium on Aquatic Sciences and Resources Management, Bogor, Indonesia, 16–17 November 2020; IOP Conference Series: Earth and Environmental Science. IOP Publishing Ltd.: Bristol, UK, 2021; Volume 744, p. 012004. [Google Scholar] [CrossRef]
  94. Fahimah, N.; Salami, I.R.S.; Oginawati, K.; Susetyo, S.H.; Tambun, A.; Ardiwinata, A.N.; Sukarjo. The assessment of water quality and human health risk from pollution of chosen heavy metals in the Upstream Citarum River, Indonesia. J. Water Land Dev. 2023, 56, 153–163. [Google Scholar] [CrossRef]
Figure 1. Sampling sites in Gunung Pongkor.
Figure 1. Sampling sites in Gunung Pongkor.
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Figure 2. Geographical distribution of Hg concentration in river water (A), pond water (B), groundwater (C), soil (D), sediment (E), fish (F), cassava root (G), and cassava leaf (H).
Figure 2. Geographical distribution of Hg concentration in river water (A), pond water (B), groundwater (C), soil (D), sediment (E), fish (F), cassava root (G), and cassava leaf (H).
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Figure 3. Difference in concentration of Hg between cassava root and cassava leaf (A) and relationship between concentration of Hg in soil and cassava root (B).
Figure 3. Difference in concentration of Hg between cassava root and cassava leaf (A) and relationship between concentration of Hg in soil and cassava root (B).
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Figure 4. Igeo value for Hg in soil and sediment.
Figure 4. Igeo value for Hg in soil and sediment.
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Figure 5. Hazard quotient (HQ) for Hg by various pathways.
Figure 5. Hazard quotient (HQ) for Hg by various pathways.
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Figure 6. Contribution of Hg exposure to HI.
Figure 6. Contribution of Hg exposure to HI.
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Agustiani, T.; Sulistia, S.; Sudaryanto, A.; Kurniawan, B.; Poku, P.A.; Elwaleed, A.; Kobayashi, J.; Ishibashi, Y.; Anan, Y.; Agusa, T. Mercury Contamination and Human Health Risk by Artisanal Small-Scale Gold Mining (ASGM) Activity in Gunung Pongkor, West Java, Indonesia. Earth 2025, 6, 67. https://doi.org/10.3390/earth6030067

AMA Style

Agustiani T, Sulistia S, Sudaryanto A, Kurniawan B, Poku PA, Elwaleed A, Kobayashi J, Ishibashi Y, Anan Y, Agusa T. Mercury Contamination and Human Health Risk by Artisanal Small-Scale Gold Mining (ASGM) Activity in Gunung Pongkor, West Java, Indonesia. Earth. 2025; 6(3):67. https://doi.org/10.3390/earth6030067

Chicago/Turabian Style

Agustiani, Tia, Susi Sulistia, Agus Sudaryanto, Budi Kurniawan, Patrick Adu Poku, Ahmed Elwaleed, Jun Kobayashi, Yasuhiro Ishibashi, Yasumi Anan, and Tetsuro Agusa. 2025. "Mercury Contamination and Human Health Risk by Artisanal Small-Scale Gold Mining (ASGM) Activity in Gunung Pongkor, West Java, Indonesia" Earth 6, no. 3: 67. https://doi.org/10.3390/earth6030067

APA Style

Agustiani, T., Sulistia, S., Sudaryanto, A., Kurniawan, B., Poku, P. A., Elwaleed, A., Kobayashi, J., Ishibashi, Y., Anan, Y., & Agusa, T. (2025). Mercury Contamination and Human Health Risk by Artisanal Small-Scale Gold Mining (ASGM) Activity in Gunung Pongkor, West Java, Indonesia. Earth, 6(3), 67. https://doi.org/10.3390/earth6030067

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