Geochemistry of Flooded Underground Mine Workings Influenced by Bacterial Sulfate Reduction

Unlike the majority of the water in the flooded mine complex of Butte Montana, which includes the highly acidic Berkeley pit lake, groundwater in the flooded West Camp underground mine workings has a circum-neutral pH and contains at least 8 μM aqueous sulfide. This article examines the geochemistry and stable isotope composition of this unusual H₂S-rich mine water, and also discusses problems related to the colorimetric analysis of sulfide in waters that contain FeS_(aq) cluster compounds. The West Camp mine pool is maintained at a constant elevation by continuous pumping, with discharge water that contains elevated Mn (90 μM), Fe (16 μM), and As (1.3 μM) but otherwise low metal concentrations. Dissolved inorganic carbon in the mine water is in chemical and isotopic equilibrium with rhodochrosite in the mineralized veins. The mine water is under-saturated with mackinawite and amorphous FeS, but is supersaturated with Cu- and Zn-sulfides. However, voltammetry studies show that much of the dissolved sulfide and ferrous iron are present as FeS_(aq) cluster molecules: as a result, the free concentration of the West Camp water is poorly constrained. Concentrations of dissolved sulfide determined by colorimetry were lower than gravimetric assays obtained by AgNO₃ addition, implying that the FeS_(aq) clusters are not completely extracted by the Methylene Blue reagent. In contrast, the clusters are quantitatively extracted as Ag₂S after addition of AgNO₃. Isotopic analysis of co-existing aqueous sulfide and sulfate confirms that the sulfide was produced by sulfate-reducing bacteria (SRB). The H₂S-rich mine water is not confined to the immediate vicinity of the extraction well, but is also present in flooded mine shafts up to 3 km away, and in samples bailed from mine shafts at depths up to 300 m below static water level. This illustrates that SRB are well established throughout the southwestern portion of the extensive (>15 km³) Butte flooded mine complex.     

Predicting financial barriers to formal healthcare utilisation among poor older people under the Livelihood Empowerment Against Poverty Programme in Ghana

GeoJournal - Poor older people surviving under the Livelihood Empowerment Against Poverty (LEAP) programme continue to experience financial difficulties to formal healthcare utilisation despite the...     

Hydrogen Cyanide Accumulation and Transformations in Non-polluted Salt Marsh Sediments

While cyanide is known to be produced by many organisms, including plants, bacteria, algae, fungi and some animals, it is generally thought that high levels of cyanide in aquatic systems require anthropogenic sources. Here, we report accumulation of relatively high levels of cyanide in non-polluted salt marsh sediments (up to 230 μmol kg^?1). Concentrations of free cyanide up to 1.92 μmol L^?1, which are toxic to aquatic life, were detected in the pore-waters. Concentration of total (free and complexed) cyanide in the pore-waters was up to 6.94 μmol L^?1. Free cyanide, which is released to the marsh sediments, is attributed to processes associated with decomposition of cord grass, Spartina alterniflora, roots and possibly from other sources. This cyanide is rapidly complexed with iron and adsorbed on sedimentary organic matter. The ultimate cyanide sink is, however, associated with formation of thiocyanate by reaction with products of sulfide oxidation by Fe(III) minerals, especially polysulfides. The formation of thiocyanate by this pathway detoxifies two poisonous compounds, polysulfides and hydrogen cyanide, preventing release of free hydrogen cyanide from salt marsh sediments into overlying water or air.     

Locational analysis of slums and the effects of slum dweller’s activities on the social,economic and ecological facets of the city: insights from Kumasi in Ghana

GeoJournal - Slum development has become a major urban planning and management problem due to the challenges they pose to the larger urban environment. Activities of slum dwellers are...     

Sulfur cycling in a stratified euxinic lake with moderately high sulfate: Constraints from quadruple S isotopes

We present a 3-year study of concentrations and sulfur isotope values (δ³⁴S, Δ³³S, and Δ³⁶S) of sulfur compounds in the water column of Fayetteville Green Lake (NY, USA), a stratified (meromictic) euxinic lake with moderately high sulfate concentrations (12-16 mM). We utilize our results along with numerical models (including transport within the lake) to identify and quantify the major biological and abiotic processes contributing to sulfur cycling in the system. The isotope values of sulfide and zero-valent sulfur across the redox-interface (chemocline) change seasonally in response to changes in sulfide oxidation processes. In the fall, sulfide oxidation occurs primarily via abiotic reaction with oxygen, as reflected by an increase in sulfide δ³⁴S at the redox interface. Interestingly, S isotope values for zero-valent sulfur sampled at this time still reflect production and recycling by phototrophic S-oxidation. In the spring, sulfide S isotope values suggest an increased input from phototrophic oxidation, consistent with a more pronounced phototroph population at the chemocline. This trend is associated with smaller fractionations between sulfide and zero-valent sulfur, suggesting a metabolic rate control on fractionation similar to that for sulfate reduction. Comparison of our data with previous studies indicates that the S isotope values of sulfate and sulfide in the deep waters are remarkably stable over long periods of time, with consistently large fractionations of up to 58‰ in δ³⁴S. Models of the δ³⁴S and Δ³³S trends in the deep waters (considering mass transport via diffusion and advection along with biological processes) require that these fractionations are a consequence of sulfur compound disproportionation at and below the redox interface in addition to large fractionations during sulfate reduction. The large fractionations during sulfate reduction appear to be a consequence of the high sulfate concentrations and the distribution of organic matter in the water column. The occurrence of disproportionation in the lake is supported by profiles of intermediate sulfur compounds and by lake microbiology, but is not evident from the δ³⁴S trends alone. These results illustrate the utility of including minor S isotopes in sulfur isotope studies to unravel complex sulfur cycling in natural systems.     

Introduction to pareto optimal mine planning

Summary Recent advances in mathematical optimization have resulted in the development of superior techniques for solving realistic decision making problems. The technique called PARETO OPTIMAL SERIAL DYNAMIC PROGRAMMING is presented here as a tool for rational mine planning. This approach always enables the identification of a set of decision alternatives considered superior to the remaining feasible, usually numerous, decision alternatives, when a number of conflicting, noncommensurable, objectives are simultaneously optimized. It is further noted that the decision makers' truly preferred decision is always one of the members identified as the superior set.Notation [A] Number of initial decision alternatives for production stage 1 - [A ] Accumulated pareto optimal stage objective vector at stage for the remainder of the stagesN – - [A] Accumulated non-pareto optimal stage objective vector at stage for the remainder of the stagesN – - [B] Number of initial decision alternatives for production stage 2 - [C] Number of initial decision alternatives for production stageN - [COG _k,] Cutoff grade for decision,k, at production stage, - [D] Number of initial pareto solutions for production stage 1 - [D ] Decision at stage - [E] Number of initial pareto solutions for production stage 2 - [F] Number of initial pareto solutions for production stageN - [j] A random objective function - [J] Number of objective functions - [LIFE _k,] Minelife for decision,k, at production stage, - [] An arbitrary production stage - [N] Final production stage - [NPV] Expected net present value - [NPV _k,] Project net present value for decision,k, at production stage - [OPR _k,] Ore production rate for decision,k, at production stage, - [S ] State of the system in stage - [ _n] Immediate state objective vector at stage