A Method to Assess Analytical Uncertainties Over Large Concentration Ranges With Reference to Volatile Organics in Water

The uncertainty associated with a volatile organic concentration measurement is a function of variability and bias introduced at the various levels of sample handling: collection, storage, and analysis. During the past decade, sampling materials and the development and/or improvement of sampling protocols have been the subject of considerable research activity. As a result, in cases of samples properly handled, the analytical variability can be the dominant source of uncertainty in a given concentration value. Here analytical variability refers to any error that might arise during analysis, including the detector response error and any sample handling errors common to both standards and samples. This can be a particular concern for field analyses by gas chromatography (GC), Well-established statistical methods are available to estimate analytical uncertainty from linear calibration curves, but these methods are poorly suited for the analysis of volatile organics because organic samples frequently require instrument calibration (usually GC) over several orders of magnitude in concentration. If a single linear calibration curve is used to determine sample concentrations and uncertainties, then unrealistically large uncertainties may be assigned to low concentration samples. However, the methods can be adopted for extended concentration range calibration curves by breaking the overall calibration line down into smaller sub-calibration lines that span smaller ranges. These can then be examined and used selectively to determine concentrations with more appropriate uncertainties attached. The method of multiple callbration line analysis described here is suitable for programming with any high level computer language. It can be used to calculate meaningful analytical uncertainty values for any substance analyzed over a wide range in concentrations (i.e., an order of magnitude or more).     

A spreadsheet method of estimating best-fit hydraulic gradients using head data from multiple wells

Hydraulic gradients from planar water tables, or piezometric surfaces, and horizontal flow regimes can be quickly and conveniently calculated from data sets involving numerous wells. The matrix-solving functions of a modem spreadsheet program (Excel) were used to determine the equation of a water-table plane, Ax + By + Cz - D = 0, and the equation coefficients were then used to determine the magnitude of the hydraulic gradient, according to gradient = square root of A2 + B2/C2, and its direction, according to alpha = arctan B/A, where alpha is the angle measured from the x-axis. A pre-prepared Excel file constructed to handle data from up to 20 wells at once is available for free downloading at www.geo.ku.edu/hydro/KUHydro.html.     

Setting nutrient thresholds to support an ecological assessment based on nutrient enrichment, potential primary production and undesirable disturbance

The EU Water Framework Directive recognises that ecological status is supported by the prevailing physico-chemical conditions in each water body. This paper describes an approach to providing guidance on setting thresholds for nutrients taking account of the biological response to nutrient enrichment evident in different types of water. Indices of pressure, state and impact are used to achieve a robust nutrient (nitrogen) threshold by considering each individual index relative to a defined standard, scale or threshold. These indices include winter nitrogen concentrations relative to a predetermined reference value; the potential of the waterbody to support phytoplankton growth (estimated as primary production); and detection of an undesirable disturbance (measured as dissolved oxygen). Proposed reference values are based on a combination of historical records, offshore (limited human influence) nutrient concentrations, literature values and modelled data. Statistical confidence is based on a number of attributes, including distance of confidence limits away from a reference threshold and how well the model is populated with real data. This evidence based approach ensures that nutrient thresholds are based on knowledge of real and measurable biological responses in transitional and coastal waters.     

Trace elements and the panspermia hypotheses

The modal concentrations of elements in four representative classes of organisms, namely bacteria, fungi, plants, and land animals, are compared with the concentrations of the elements in sea water. A strong correlation is found between these concentrations, and this correlation reduces to an expected linear concentration law when only “trace” elements are considered. Deviations from strict linearity are shown to arise from the chemical natures of the elements Apart from suggesting an oceanic genesis for terrestrial life, the data are strongly against a nonterrestrial origin of life as proposed by the panspermia hypotheses.     

A Comparison of Tools and Methods for Estimating Groundwater-Surface Water Exchange

A comparison of tools for measuring discharge rates in a sandy streambed was conducted along a transect near the north bank of the Grindsted Å (stream), Denmark. Four tools were evaluated at six locations spaced 3 m apart in the stream: mini-piezometers, streambed point velocity probes (SBPVPs), temperature profilers, and seepage meters. Comparison of the methods showed that all identified a similar trend of low to high groundwater discharges moving westward along the transect. Furthermore, it was found that the differences between discharges estimated from Darcy calculations (using the mini-pizometers), and SBPVPs were not statistically different from zero, at the 90% confidence level. Seepage meter estimates were consistently lower than those of the other two methods, but compared more reasonably with the application of a correction factor of 1.7, taken from the literature. In contrast, discharges estimated from temperature profiling (to a depth of 40 cm) were found to be about an order of magnitude less than those determined with the other methods, possibly due to interferences from horizontal hyporheic flow. Where the various methods produced statistically different discharge estimations at the same location, it is hypothesized that the differences arose from method-specific sources of bias, including installation depths. On the basis of this work, practitioners interested in measuring flow across the groundwater-surface water interface achieve the least variability with seepage meters and the SBPVP. However the accuracy of the seepage meter depended on a calibrated correction factor while that of the SBPVP did not.