Modeling of the groundwater flow system in excavated areas of an abandoned mine
Introduction
Acid mine drainage (AMD) or acid rock drainage (ARD) fluids are generally characterized by low pH and very high concentrations of sulfate, heavy metals (e.g., copper (Cu), lead (Pb), zinc (Zn), and cadmium (Cd)), and toxic metalloids (e.g., arsenic (As) and selenium (Se)), which pose serious threats when released without appropriate treatment, not only to the environment, but also to residents living around the affected areas (Tabelin et al., 2018). AMD is typically associated with many active, closed, and abandoned mines (Younger, 2001; Gault et al., 2005; Molson et al., 2005; Boularbah et al., 2006; Kimball et al., 2007; Lim et al., 2008; Hien et al., 2012; Hierro et al., 2014; Skierszkan et al., 2016; Zhao et al., 2017). ARD is also a major problem in many underground and tunnel construction projects when the excavated debris and/or waste rocks contain pyrite, which is the most abundant sulfide mineral in nature and the primary cause of acidic leachate formation (Tabelin and Igarashi, 2009; Tabelin et al., 2012a, Tabelin et al., 2012b, Tabelin et al., 2013, Tabelin et al., 2017a, Tabelin et al., 2017b; Tatsuhara et al., 2012; Andrea et al., 2014; Anawar, 2015).
The most common mitigation strategy for AMD–ARD is chemical neutralization, which is a method whereby the acidic effluents are mixed with alkaline materials like limestone, lime, or caustic soda to raise the pH and precipitate most of the heavy metals and metalloids (Iakovleva et al., 2015). Although effective, the long-term suitability of this approach depends on the volume and geochemical properties of the AMD–ARD being treated, as well as the total duration of treatment required at a specific site. As such, the treatment becomes costly and unsustainable because AMD–ARD contain high concentrations of heavy metals and are generated over a long period (>100 yr). The generation of large amounts of sludge that must be properly managed is also an important issue. Several advanced pyrite passivation techniques have been recently proposed for more sustainable management of AMD–ARD, such as carrier microencapsulation (Satur et al., 2007; Park et al., 2018a, Park et al., 2018b; Li et al., 2019) and galvanic microencapsulation (Tabelin et al., 2017c), which show selectivity for pyrite and arsenopyrite, even in the presence of silicates. However, these techniques are still in the experimental stages and remain untested under in situ conditions. Given this, the sustainability of existing AMD–ARD management strategies should be improved until more effective and sustainable methods are developed.
One potential way to improve the sustainability of current AMD–ARD treatment strategies is to limit the formation of acidic leachates at source by the application of hydrogeological methods (e.g., ground-sealing with low permeability materials) that reduce the amount of water in contact with pyrite-rich waste or wall rocks. In addition, covers with oxygen-barrier effects have been proposed as a viable option to minimize AMD, whilst also controlling gas flow and oxygen supply (Bussière et al., 2003; Lahmira et al., 2016). Some of these approaches could reduce the capacity of current water treatment plants or wetland-based treatment methods, by not only reducing the volume, but also improving the quality of AMD for treatment. However, for this approach to work, it is crucial to first understand both the sources and flow patterns of AMD–ARD within the mining area. The sources of AMD–ARD are typically identified by detailed geochemical and isotopic surveys of the target mine, which have been conducted in Japan (Iwatsuki and Yoshida, 1999; Okumura, 2003; Mahara et al., 2006) and in other countries (Razowska, 2001; Hazen et al., 2002; Sracek et al., 2004; Lee and Chon, 2006; Leybourne et al., 2006; Hubbard et al., 2009; Gammons et al., 2010; Galván et al., 2016; Cánovas et al., 2017; Migaszewski et al., 2018). In addition, AMD–ARD flow patterns can be predicted by combining numerical models with on-site hydrological surveys.
Once the sources of AMD are known and its flow patterns established, various countermeasures to limit the flow of AMD or retard the movement of contaminants could be evaluated using numerical models calibrated to on-site data. When properly calibrated, numerical models are capable of reproducing the groundwater flow in and around the mine site and changes in these flow patterns in response to various environmental parameters (e.g., changes in rainfall intensity). For example, several studies have successfully predicted both the geochemical evolution of groundwater (Bain et al., 2000; Molson et al., 2005; Castendyk and Webster-Brown, 2007; Yamaguchi et al., 2015; Pabst et al., 2018) and its movement through natural geological media using numerical modeling techniques (Wunsch et al., 1999; Sracek et al., 2004; Tomiyama et al., 2010a, Tomiyama et al., 2010b, Tomiyama et al., 2016; Bahrami et al., 2016; Ethier et al., 2018; Ramasamy et al., 2018).
One of the biggest challenges in the simulation of groundwater flow in closed and/or abandoned underground mines is how to consider a number of large excavated spaces and tunnels in the numerical model. For example, in a study of the Kamaishi mine (a skarn-type deposit), the modeling approach assumed that the excavated areas were hollow spaces with groundwater discharge by setting the walls as a fixed total head boundary (Japan Nuclear Cycle Development Institute, 1999). Modeling of underground oil storage in Japan by Yamaishi et al. (1998) calculated the groundwater discharge by assuming that the permeability values of rock tanks (i.e., empty spaces) were equal to infinity in the numerical model. In both of these previous studies, the groundwater was discharged outside the numerical model domain and excluded from the numerical calculation after discharge. This was a reasonable assumption because groundwater percolating into the excavated areas was pumped up to the ground surface in both the Kamaishi mine and underground oil storage facility.
However, such an assumption for large, empty underground spaces in numerical modeling is not applicable to the excavated areas of many metal sulfide underground mines, because these spaces are often back-filled with sand slime, tailings, and sludge/precipitates formed by neutralization, which means these are no longer empty spaces. Tomiyama et al. (2019) showed that AMD formed in the old mine workings (i.e., drift and shaft) of a closed mine flowed through and interacted with the back-filled materials into the empty spaces, and that the geochemical properties of AMD fluids were dramatically changed. Based on this previous study, it is probably more appropriate to consider old mine workings as porous media where water flow is governed by Darcy's law rather than as free discharging boundaries.
In this study, we evaluated the assumption that back-filled excavated areas of old mine workings can be modeled as porous media, where groundwater flow is governed by Darcy's law. The site selected for this study was the Yatani mine, which is a closed underground mine located in Yamagata Prefecture, Japan, where several different mining methods were adopted and detailed drawings of the excavated areas are available. A numerical model was constructed based on the topography of the mine and mining methods, as well as the geological and hydrological properties of the site. The model calibration was based on hydraulic conductivities (k) of volcanic rocks and faults. In addition, we compared and verified the results of the model with actual AMD fluxes measured on-site, and we then used the verified model to evaluate possible countermeasures to reduce AMD flux from the closed mine.
Section snippets
Geology and history of the mine
The Yatani mine is located 28 km southwest of Yonezawa City, within the Bandai–Asahi National Park (Fig. 1). It is situated in the green tuff area of northeast Japan, and comprises granitic basement overlain by Neogene volcanic and sedimentary rocks. Surface mining began in 1870 after the discovery of gold (Au) ore outcrops. When these were exhausted, mining continued underground for several decades. Taihei Mining Co. Ltd. acquired mining rights to the area in 1952 and began mining Pb and Zn
Sample collection and analysis
To identify sources of heavy metals and evaluate possible countermeasures to reduce AMD flux, mine drainage samples were collected and analyzed on 1 August and 9 October 2007, on the same dates as the flux measurements, from the locations shown in Fig. 4. Samples S-1 to S-10 were collected from prominent parts of the drainage tunnel (−2 L level) where significant discharge of groundwater was observed (e.g., from cracks in drainage tunnel walls). Samples M-1 to M-5 were also collected from the
Contaminants in the water samples
The geochemical properties of water samples collected from the drainage tunnel are summarized in Table 5, and spatial variations in pH and EC are shown in Fig. 10. The pH, EC, and temperature of the seepage water samples were in the ranges of 6.72–7.31, 21.5–91.1 mS m–1, and 11.6°C–17.4°C (1 August 2007), and 6.64–7.37, 21.5–108.6 mS m–1, and 12.1°C–17.5°C (9 October 2007), respectively. Sample S-1 had the highest pH and lowest EC. The sampling site of S-1 was at a NE–SW-trending fault (Sato et
Conclusions
Groundwater flow patterns in the Yatani mine area located in Yamagata Prefecture, Japan, were elucidated using a numerical model. The model was constructed based on the topography around the mine, detailed geological maps of the area, and measured and/or estimated hydrological properties of the various deposits and geological strata under saturated–unsaturated conditions. The model calibration was performed using hydraulic conductivities of volcanic rocks and faults, with the best-matched case
Declaration of competing interest
None
Acknowledgements
The authors thank the editor and anonymous reviewers for their constructive comments that improved this manuscript. The authors thank the staff of Mitsubishi Materials Corporation, Eco-Management Corporation, and Mitsubishi Materials Techno Corporation for their help, advice, and cooperation during this study.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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