Development of Iron Speciation Reference Materials for Palaeoredox Analysis

The development and application of geochemical techniques to identify redox conditions in modern and ancient aquatic environments has intensified over recent years. Iron (Fe) speciation has emerged as one of the most widely used procedures to distinguish different redox regimes in both the water column and sediments, and is the main technique used to identify oxic, ferruginous (anoxic, Fe(II) containing) and euxinic (anoxic, sulfidic) water column conditions. However, an international sediment reference material has never been developed. This has led to concern over the consistency of results published by the many laboratories that now utilise the technique. Here, we report an interlaboratory comparison of four Fe speciation reference materials for palaeoredox analysis, which span a range of compositions and reflect deposition under different redox conditions. We provide an update of extraction techniques used in Fe speciation and assess the effects of both test portion mass, and the use of different analytical procedures, on the quantification of different Fe fractions in sedimentary rocks. While atomic absorption spectroscopy and inductively coupled plasma‐optical emission spectrometry produced comparable Fe measurements for all extraction stages, the use of ferrozine consistently underestimated Fe in the extraction step targeting mixed ferrous–ferric minerals such as magnetite. We therefore suggest that the use of ferrozine is discontinued for this Fe pool. Finally, we report the combined data of four independent Fe speciation laboratories to characterise the Fe speciation composition of the reference materials. These reference materials are available to the community to provide an essential validation of in‐house Fe speciation measurements.     

Multiobjective analysis of a public wellfield using artificial neural networks

As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods.     

Application of Artificial Neural Networks to Complex Groundwater Management Problems

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models.     

The COPAS’08 expedition to the Patagonian Shelf: Physical and environmental conditions during the 2008 coccolithophore bloom

Here we present observations of the hydrography of the Patagonian Shelf, shelf break and offshore waters, with reference to the environmental conditions present during the period of peak coccolithophore abundance. Analysis of a hydrographic dataset collected in December 2008 (austral spring/summer), as part of the Coccolithophores of the Patagonian Shelf (COPAS) research cruise, identified 5 distinct surface water masses in the region between 37°S and 55°S. These water masses, identified through salinity gradients, displayed varying mixed layer depths, macronutrient inventories and chlorophyll-a fluorescence. Subantarctic Shelf Water (SSW), located to the north of the Falkland Islands and extending north along the shelf break, was also host to a large coccolithophore bloom. The similarities between the distribution of calcite, as seen in remote sensing data, and SSW indicate that the coccolithophore bloom encountered conditions conducive to bloom development within this water mass. Analysis of chemical and environmental data also collected during the COPAS cruise revealed that many of the commonly cited conditions for coccolithophore bloom development were present within SSW (e.g. low N:P ratio, high N:Si ratio, shallow mixed layer depth). In the other water masses present on the Patagonian Shelf greater variability in these same parameters may explain the more diffuse concentration of calcite, and the smaller size of possible coccolithophore blooms. The distribution of SSW is strongly influenced by the latitudinal variation in shelf break frontal width, which varies from 20 to 200 km, and consequently strong hydrographic controls underlie the position of the coccolithophore bloom during austral summer.     

Predicting conductance due to upconing using neural networks

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.     

A neural network model for predicting aquifer water level elevations

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.     

A revised scheme for the reactivity of iron (oxyhydr)oxide minerals towards dissolved sulfide

The reaction between dissolved sulfide and synthetic iron (oxyhydr)oxide minerals was studied in artificial seawater and 0.1 M NaCl at pH 7.5 and 25°C. Electron transfer between surface-complexed sulfide and solid-phase Fe(III) results in the oxidation of dissolved sulfide to elemental sulfur, and the subsequent dissolution of the surface-reduced Fe. Sulfide oxidation and Fe(II) dissolution kinetics were evaluated for freshly precipitated hydrous ferric oxide (HFO), lepidocrocite, goethite, magnetite, hematite, and Al-substituted lepidocrocite. Reaction kinetics were expressed in terms of an empirical rate equation of the form: