Advertisement

Controls of uncertainty in acid rock drainage predictions from waste rock piles examined through Monte-Carlo multicomponent reactive transport

  • Daniele PedrettiEmail author
  • K. Ulrich Mayer
  • Roger D. Beckie
Original Paper
  • 33 Downloads

Abstract

Heterogeneity in waste rock piles (WRPs) determines uncertainty in acid mine drainage (ARD) predictions from these deposits. Numerical modeling based on a novel and efficient stochastic framework to evaluate influential heterogeneity-linked factors controlling such uncertainty. The analysis considers a representative WRP with a mean neutralization potential ratio \(\overline{NPR} = 2\). The heterogeneity-linked factors are: (1) Scale-dependent mineralogical variability. At the “local” scale, the variability within individual rock blocks in the waste rocks (10 s of cm) is measured through the correlation coefficient (\(\rho\)) between acid producing and acid consuming minerals, here considered a geogenic property of the site. For the analyzed conditions, as \(\rho \to 0\) WRPs tend generate a higher risk of ARD and higher variability among results, which can be explained by the increasing mineralogical mixing (blending) as \(\rho\) grows. At the “field” scale, the coefficient of variation (\(CV\)) is measured as the mineralogical variability of all rock blocks within the WRP. Since \(CV\) is an engineering design parameter of a WRP, the results suggest that building WRPs with lower \(CVs\) results in less uncertain predictions of long-term neutralization capacity of the piles. (2) Flow heterogeneity. The variance of solute travel times through a pile, here measured by \(\sigma_{w}^{2}\), can be used to characterize flow heterogeneity, where high variance means stronger preferential flow in the WRP. Simulated ARD mass loadings with strong flow heterogeneity (\(\sigma_{w}^{2} \ge 1\)) leads to significant differences to the homogeneous case, increasing the uncertainty in the estimation of the ARD risk. (3) Pore gas concentration. In well-ventilated WRPs the effect of mineralogical heterogeneity is enhanced (WRPs generate much higher risk than WRPs with diffusion-limited gas transport modalities. Gas diffusion limits the amount of acidity produced in sulfidic-rich zones, thus attenuating the effect of mineralogical variability at the scale of the WRPs compared to well-ventilated piles.

Keywords

Stochastic modeling framework Mining waste rock piles Uncertainty Multicomponent reactive transport modeling Geostatistics Acid rock drainage 

Notes

Acknowledgements

All data used to perform the simulations are available after requested to the corresponding author (DP).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.

Supplementary material

477_2019_1756_MOESM1_ESM.pdf (608 kb)
Supplementary material 1 (PDF 608 kb)

References

  1. Amos RT, Blowes DW, Bailey BL, Sego DC, Smith L, Ritchie AIM (2014) Waste-rock hydrogeology and geochemistry. Appl Geochem.  https://doi.org/10.1016/j.apgeochem.2014.06.020 CrossRefGoogle Scholar
  2. Appels WM, Ireson AM, Barbour SL (2018) Impact of bimodal textural heterogeneity and connectivity on flow and transport through unsaturated mine waste rock. Adv Water Resour 112:254–265.  https://doi.org/10.1016/j.advwatres.2017.12.008 CrossRefGoogle Scholar
  3. Azam S, Wilson GW, Herasymuik G, Nichol C, Barbour LS (2007) Hydrogeological behaviour of an unsaturated waste rock pile: a case study at the Golden Sunlight Mine, Montana, USA. Bull Eng Geol Env 66(3):259–268CrossRefGoogle Scholar
  4. Bianchi M, Pedretti D (2018) An entrogram-based approach to describe spatial heterogeneity with applications to solute transport in porous media. Water Resour Res 54(7):4432–4448.  https://doi.org/10.1029/2018WR022827 CrossRefGoogle Scholar
  5. Binning PJ, Postma D, Russell TF, Wesselingh JA, Boulin PF (2007) Advective and diffusive contributions to reactive gas transport during pyrite oxidation in the unsaturated zone. Water Resour Res 43(2):W02414.  https://doi.org/10.1029/2005WR004474 CrossRefGoogle Scholar
  6. Blackmore S, Smith L, Ulrich Mayer K, Beckie RD (2014) Comparison of unsaturated flow and solute transport through waste rock at two experimental scales using temporal moments and numerical modeling. J Contam Hydrol 171:49–65.  https://doi.org/10.1016/j.jconhyd.2014.10.009 CrossRefGoogle Scholar
  7. Blackmore S, Pedretti D, Mayer KU, Smith L, Beckie RD (2018) Evaluation of single- and dual-porosity models for reproducing the release of external and internal tracers from heterogeneous waste-rock piles. J Contam Hydrol.  https://doi.org/10.1016/j.jconhyd.2018.05.007 CrossRefGoogle Scholar
  8. Corbella M, Ayora C, Cardellach E (2004) Hydrothermal mixing, carbonate dissolution and sulfide precipitation in Mississippi Valley-type deposits. Miner Deposita 39(3):344–357.  https://doi.org/10.1007/s00126-004-0412-5 CrossRefGoogle Scholar
  9. Day SJ (1994) Evaluation of acid generating rock and acid consuming rock mixing to prevent acid rock drainage. In: International land reclamation and mine drainage conference and the 3rd international conference on the abatement of acidic drainage, Pittsburgh, PAGoogle Scholar
  10. Destouni G (1991) Applicability of the steady state flow assumption for solute advection in field soils. Water Resour Res 27(8):2129–2140.  https://doi.org/10.1029/91WR01115 CrossRefGoogle Scholar
  11. Dold B (2017) Acid rock drainage prediction: A critical review. J Geochem Explor 172(Supplement C):120–132.  https://doi.org/10.1016/j.gexplo.2016.09.014 CrossRefGoogle Scholar
  12. Eriksson N, Destouni G (1997) Combined effects of dissolution kinetics, secondary mineral precipitation, and preferential flow on copper leaching from mining waste rock. Water Resour Res 33(3):471–483CrossRefGoogle Scholar
  13. Fahd F, Khan F, Hawboldt K, Abbassi R (2014) Developing a novel methodology for ecological risk assessment of thiosalts. Stoch Environ Res Risk Assess 28(2):383–391.  https://doi.org/10.1007/s00477-013-0758-2 CrossRefGoogle Scholar
  14. Fala O, Molson J, Aubertin M, Dawood I, Bussière B, Chapuis RP (2013) A numerical modelling approach to assess long-term unsaturated flow and geochemical transport in a waste rock pile. Int J Min Reclam Environ 27(1):38–55.  https://doi.org/10.1080/17480930.2011.644473 CrossRefGoogle Scholar
  15. Freeze RA (1975) A stochastic-conceptual analysis of one-dimensional groundwater flow in nonuniform homogeneous media. Water Resour Res 11(5):725–741.  https://doi.org/10.1029/WR011i005p00725 CrossRefGoogle Scholar
  16. Gerke HH, Molson JW, Frind EO (2001) Modelling the impact of physical and chemical heterogeneity on solute leaching in pyritic overburden mine spoils. Ecol Eng 17(2–3):91–101.  https://doi.org/10.1016/S0925-8574(00)00150-6 CrossRefGoogle Scholar
  17. Kefeni KK, Msagati TAM, Mamba BB (2017) Acid mine drainage: prevention, treatment options, and resource recovery: a review. J Clean Prod 151(Supplement C):475–493.  https://doi.org/10.1016/j.jclepro.2017.03.082 CrossRefGoogle Scholar
  18. Lahmira B, Lefebvre R, Aubertin M, Bussière B (2016) Effect of heterogeneity and anisotropy related to the construction method on transfer processes in waste rock piles. J Contam Hydrol 184(Supplement C):35–49.  https://doi.org/10.1016/j.jconhyd.2015.12.002 CrossRefGoogle Scholar
  19. Lahmira B, Lefebvre R, Aubertin M, Bussière B (2017) Effect of material variability and compacted layers on transfer processes in heterogeneous waste rock piles. J Contam Hydrol 204(Supplement C):66–78.  https://doi.org/10.1016/j.jconhyd.2017.07.004 CrossRefGoogle Scholar
  20. Lefebvre R, Hockley D, Smolensky J, Lamontagne A (2001) Multiphase transfer processes in waste rock piles producing acid mine drainage: 2. Applications of numerical simulation. J Contam Hydrol 52(1–4):165–186.  https://doi.org/10.1016/S0169-7722(01)00157-7 CrossRefGoogle Scholar
  21. Lorca ME, Mayer KU, Pedretti D, Smith L, Beckie RD (2016) Spatial and temporal fluctuations of pore-gas composition in sulfidic mine waste rock. Vadose Zone J.  https://doi.org/10.2136/vzj2016.05.0039 CrossRefGoogle Scholar
  22. Lottermoser B (2010) Mine wastes. Springer, BerlinCrossRefGoogle Scholar
  23. Madani N, Emery X (2017) Plurigaussian modeling of geological domains based on the truncation of non-stationary Gaussian random fields. Stoch Environ Res Risk Assess 31(4):893–913.  https://doi.org/10.1007/s00477-016-1365-9 CrossRefGoogle Scholar
  24. Malmström ME, Destouni G, Banwart SA, Strömberg BHE (2000) Resolving the scale-dependence of mineral weathering rates. Environ Sci Technol 34(7):1375–1378.  https://doi.org/10.1021/es990682u CrossRefGoogle Scholar
  25. Malmström ME, Destouni G, Martinet P (2004) Modeling expected solute concentration in randomly heterogeneous flow systems with multicomponent reactions. Environ Sci Technol 38(9):2673–2679.  https://doi.org/10.1021/es030029d CrossRefGoogle Scholar
  26. Mayer KU, Frind EO, Blowes DW (2002) Multicomponent reactive transport modeling in variably saturated porous media using a generalized formulation for kinetically controlled reactions. Water Resour Res 38(9):13-1–13-2.  https://doi.org/10.1029/2001WR000862 CrossRefGoogle Scholar
  27. Mehling PE, Day SJ, Sexsmith K (1997) Blending and layering waste rock to delay, mitigate or prevent acid generation: a case study review. In: Proceedings of the 4th international conference on acid rock drainage, vol 2, pp 953–970Google Scholar
  28. Miller S, Andrina J, Richards D (2003) Overburden geochemistry and ARD scale-up investigations at the Grasberg Mine, Papua Province, Indonesia. In: Proceedings of the 6th international conference on acid rock drainage (ICARD)Google Scholar
  29. Modis K, Vatalis K, Papantonopoulos G, Sachanidis Ch (2010) Uncertainty management of a hydrogeological data set in a greek lignite basin, using BME. Stoch Environ Res Risk Assess 24(1):47–56.  https://doi.org/10.1007/s00477-008-0298-3 CrossRefGoogle Scholar
  30. Morin KA, Hutt NM (2000) Discrete-zone mixing of net-acid-neutralizing and net-acid-generating rock: avoiding the argument over appropriate ratios. Paper presented at the proceedings from the 5th international conference on acid rock drainageGoogle Scholar
  31. Morin KA, Hutt NM (2008) Field study of unavailable neutralization potential in acidic rock. Unpublished web document. MDAG.com Internet Case Study 31Google Scholar
  32. Neuner M, Smith L, Blowes DW, Sego DC, Smith LJD, Fretz N, Gupton M (2013) The Diavik waste rock project: water flow through mine waste rock in a permafrost terrain. Appl Geochem 36:222–233.  https://doi.org/10.1016/j.apgeochem.2012.03.011 CrossRefGoogle Scholar
  33. Nichol C, Smith L, Beckie R (2005) Field-scale experiments of unsaturated flow and solute transport in a heterogeneous porous medium. Water Resour Res 41(5):W05018.  https://doi.org/10.1029/2004WR003035 CrossRefGoogle Scholar
  34. Pearce S, Scott P, Weber P (2015) Waste rock dump geochemical evolution: matching lab data, models and predictions with reality. In: Proceedings of the 10th international conference on acid rock drainage (ICARD)Google Scholar
  35. Pedretti D, Bianchi M (2018) Reproducing tailing in breakthrough curves: are statistical models equally representative and predictive? Adv Water Resour 113:236–248.  https://doi.org/10.1016/j.advwatres.2018.01.023 CrossRefGoogle Scholar
  36. Pedretti D, Mayer KU, Beckie RD (2016) Blending as an effective option to reduce the risk of water acidification from waste rock pile: a stochastic analysis. In: XXII international conference of computational methods in water resources (CMWR) Toronto, Canada, 67Google Scholar
  37. Pedretti D, Masetti M, Beretta GP (2017a) Stochastic analysis of the efficiency of coupled hydraulic-physical barriers to contain solute plumes in highly heterogeneous aquifers. J Hydrol 553(Supplement C):805–815.  https://doi.org/10.1016/j.jhydrol.2017.08.051 CrossRefGoogle Scholar
  38. Pedretti D, Mayer KU, Beckie RD (2017b) Stochastic multicomponent reactive transport analysis of low quality drainage release from waste rock piles: controls of the spatial distribution of acid generating and neutralizing minerals. J Contam Hydrol 201:30–38.  https://doi.org/10.1016/j.jconhyd.2017.04.004 CrossRefGoogle Scholar
  39. Pedretti D, Mayer KU, Beckie RD (2017c) Risk assessment of acidic drainage from waste rock piles using stochastic muticomponent reactive transport modeling. In: Wolkersdorfer C, Sartz L, Sillanpää M, Häkkinen A (eds) Mine water and circular economy. Lappeenranta University of Technology, Lappeenranta, pp 696–703Google Scholar
  40. Price WA (2009) Prediction manual for drainage chemistry from sulphidic geologic materials. MEND report 1.1 NRC CanadaGoogle Scholar
  41. Remy N, Boucher A, Wu J (2009) Applied geostatistics with SGeMS. A user’s guideGoogle Scholar
  42. Ren L, Yuan X, Liu R (2006) Research on method development during the strategic environmental assessment. Stoch Environ Res Risk Assess 21(2):151–157.  https://doi.org/10.1007/s00477-006-0052-7 CrossRefGoogle Scholar
  43. Rezaee H, Asghari O, Koneshloo M, Ortiz JM (2014) Multiple-point geostatistical simulation of dykes: application at Sungun porphyry copper system, Iran. Stoch Environ Res Risk Assess 28(7):1913–1927.  https://doi.org/10.1007/s00477-014-0857-8 CrossRefGoogle Scholar
  44. Rossi ME (2006) Geostatistical modeling of acid rock prediction uncertainty. In: Barnhisel RI (ed) 7th international conference on acid rock drainage (ICARD), 26–30 March 2006. Published by the American Society of Mining and Reclamation (ASMR), St. Louis, MO, pp 1717–1727Google Scholar
  45. Sapsford DJ, Bowell RJ, Dey M, Williams KP (2009) Humidity cell tests for the prediction of acid rock drainage. Miner Eng 22(1):25–36.  https://doi.org/10.1016/j.mineng.2008.03.008 CrossRefGoogle Scholar
  46. Shaw S, Samuels A (2009) An empirical comparison of humidity cell and field barrel data to inform scale-up factors for water quality predictions. In: 9th International Conference on Acid Rock Drainage (ICARD). Ottawa, Canada, 8Google Scholar
  47. Sherlock EJ, Lawrence RW, Poulin R (1995) On the neutralization of acid rock drainage by carbonate and silicate minerals. Environ Geol 25:43–54CrossRefGoogle Scholar
  48. Smith JL, Beckie RD (2003) Hydrologic and geochemical transport processes in mine waste rock. In: Jambor JL, Blowes DW, Ritchie AIM (eds) Environmental aspects of mine wastes. Mineralogical Association of Canada, Ottawa, pp 51–72Google Scholar
  49. Stockwell J, Smith JL, Jambor JL, Beckie RD (2006) The relationship between fluid flow and mineral weathering in heterogeneous unsaturated porous media: a physical and geochemical characterization of a waste-rock pile. Appl Geochem 21(8):1347–1361.  https://doi.org/10.1016/j.apgeochem.2006.03.015 CrossRefGoogle Scholar
  50. Strömberg B, Banwart S (1994) Kinetic modelling of geochemical processes at the Aitik mining waste rock site in northern Sweden. Appl Geochem 9(5):583–595.  https://doi.org/10.1016/0883-2927(94)90020-5 CrossRefGoogle Scholar
  51. Tartakovsky DM (2013) Assessment and management of risk in subsurface hydrology: a review and perspective. Adv Water Resour 51:247–260.  https://doi.org/10.1016/j.advwatres.2012.04.007 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di Scienze della Terra “A. Desio”Università degli Studi di MilanoMilanItaly
  2. 2.Earth, Ocean and Atmospheric SciencesUniversity of British ColumbiaVancouverCanada

Personalised recommendations