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R.H. Johnson

Publications and source records attributed to R.H. Johnson.

5 recordsLinked to original sources
USGS2008

Geochemical characterisation of seepage and drainage water quality from two sulphide mine tailings impoundments: Acid mine drainage versus neutral mine drainage

Seepage water and drainage water geochemistry (pH, EC, O 2 , redox, alkalinity, dissolved cations and trace metals, major anions, total element concentrations) were studied at two active sulphide mine tailings impoundments in Finland (the Hitura Ni mine and Luikonlahti Cu mine/talc processing plant). The data were used to assess the factors influencing tailings seepage quality and to identify constraints for water treatment. Changes in seepage water quality after equilibration with atmospheric conditions were evaluated based on geochemical modelling. At Luikonlahti, annual and seasonal changes were also studied. Seepage quality was largely influenced by the tailings mineralogy, and the serpentine-rich, low sulphide Hitura tailings produced neutral mine drainage with high Ni. In contrast, drainage from the high sulphide, multi-metal tailings of Luikonlahti represented typical acid mine drainage with elevated contents of Zn, Ni, Cu, and Co. Other factors affecting the seepage quality included weathering of the tailings along the seepage flow path, process water input, local hydrological settings, and structural changes in the tailings impoundment. Geochemical modelling showed that pH increased and some heavy metals were adsorbed to Fe precipitates after net alkaline waters equilibrated with the atmosphere. In the net acidic waters, pH decreased and no adsorption occurred. A combination of aerobic and anaerobic treatments is proposed for Hitura seepages to decrease the sulphate and metal loading. For Luikonlahti, prolonged monitoring of the seepage quality is suggested instead of treatment, since the water quality is still adjusting to recent modifications to the tailings impoundment.

Mine Water and the Environment↗
USGS2007

Insights into the use of time-lapse GPR data as observations for inverse multiphase flow simulations of DNAPL migration

Perchloroethylene (PCE) saturations determined from GPR surveys were used as observations for inversion of multiphase flow simulations of a PCE injection experiment (Borden 9??m cell), allowing for the estimation of optimal bulk intrinsic permeability values. The resulting fit statistics and analysis of residuals (observed minus simulated PCE saturations) were used to improve the conceptual model. These improvements included adjustment of the elevation of a permeability contrast, use of the van Genuchten versus Brooks-Corey capillary pressure-saturation curve, and a weighting scheme to account for greater measurement error with larger saturation values. A limitation in determining PCE saturations through one-dimensional GPR modeling is non-uniqueness when multiple GPR parameters are unknown (i.e., permittivity, depth, and gain function). Site knowledge, fixing the gain function, and multiphase flow simulations assisted in evaluating non-unique conceptual models of PCE saturation, where depth and layering were reinterpreted to provide alternate conceptual models. Remaining bias in the residuals is attributed to the violation of assumptions in the one-dimensional GPR interpretation (which assumes flat, infinite, horizontal layering) resulting from multidimensional influences that were not included in the conceptual model. While the limitations and errors in using GPR data as observations for inverse multiphase flow simulations are frustrating and difficult to quantify, simulation results indicate that the error and bias in the PCE saturation values are small enough to still provide reasonable optimal permeability values. The effort to improve model fit and reduce residual bias decreases simulation error even for an inversion based on biased observations and provides insight into alternate GPR data interpretations. Thus, this effort is warranted and provides information on bias in the observation data when this bias is otherwise difficult to assess. ?? 2006 Elsevier B.V. All rights reserved.

Journal of Contaminant Hydrology↗
USGS2005

Iterative use of the Bruggeman-Hanai-Sen mixing model to determine water saturations in sand

The accuracy of the Bruggeman-Hanai-Sen (BHS) mixing model has been previously demonstrated for two-material mixtures during BHS model development. Using permittivities determined from modeling ground-penetrating radar (GPR) data, the BHS model has been iteratively applied to three-material mixtures of water, sand, and a dense, nonaqueous-phase liquid (DNAPL). However, the accuracy of this application has not been verified. A 10-cm air-line system driven by a network analyzer is used to measure bulk permittivitities when the water saturations in a sand are varied (frequency range of 20 to 200 MHz). Through iterative use of the BHS mixing model, the measured permittivities are used to calculate water saturations, which are compared to known saturation values. An iterative BHS mixing model for an air/water/sand system must consider which two-material end member (air/sand or water/sand) represents the matrix term in the original two-material BHS model. An air/sand matrix provides the best accuracy for low water saturations, and a water/sand matrix provides the best accuracy for high water saturations; thus, a new weighted model is developed. For a given porosity and a measured bulk permittivity, water saturation is most accurately determined by proportionally weighting the water saturation values determined using air/sand as the matrix and water/sand as the matrix in the BHS model. ?? 2005 Society of Exploration Geophysicists. All rights reserved.

Geophysics↗
USGS2002

Residual bias in a multiphase flow model calibration and prediction

When calibrated models produce biased residuals, we assume it is due to an inaccurate conceptual model and revise the model, choosing the most representative model as the one with the best-fit and least biased residuals. However, if the calibration data are biased, we may fail to identify an acceptable model or choose an incorrect model. Conceptual model revision could not eliminate biased residuals during inversion of simulated DNAPL migration under controlled conditions at the Borden Site near Ontario Canada. This paper delineates hypotheses for the source of bias, and explains the evolution of the calibration and resulting model predictions.

Acta Universitatis Carolinae, Geologica↗