Accurately defining the reference condition for summary biotic metrics: A comparison of four approaches

Protocols for bioassessment often relate changes in summary metrics that describe aspects of biotic assemblage structure and function to environmental stress. Biotic assessment using multimetric indices now forms the basis for setting regulatory standards for stream quality and a range of other goals related to water resource management in the USA and elsewhere. Biotic metrics are typically interpreted with reference to the expected natural state to evaluate whether a site is degraded. It is critical that natural variation in biotic metrics along environmental gradients is adequately accounted for, in order to quantify human disturbance-induced change. A common approach used in the IBI is to examine scatter plots of variation in a given metric along a single stream size surrogate and a fit a line (drawn by eye) to form the upper bound, and hence define the maximum likely value of a given metric in a site of a given environmental characteristic (termed the 'maximum species richness line' - MSRL). In this paper we examine whether the use of a single environmental descriptor and the MSRL is appropriate for defining the reference condition for a biotic metric (fish species richness) and for detecting human disturbance gradients in rivers of south-eastern Queensland, Australia. We compare the accuracy and precision of the MSRL approach based on single environmental predictors, with three regression-based prediction methods (Simple Linear Regression, Generalised Linear Modelling and Regression Tree modelling) that use (either singly or in combination) a set of landscape and local scale environmental variables as predictors of species richness. We compared the frequency of classification errors from each method against set biocriteria and contrast the ability of each method to accurately reflect human disturbance gradients at a large set of test sites. The results of this study suggest that the MSRL based upon variation in a single environmental descriptor could not accurately predict species richness at minimally disturbed sites when compared with SLR's based on equivalent environmental variables. Regression-based modelling incorporating multiple environmental variables as predictors more accurately explained natural variation in species richness than did simple models using single environmental predictors. Prediction error arising from the MSRL was substantially higher than for the regression methods and led to an increased frequency of Type I errors (incorrectly classing a site as disturbed). We suggest that problems with the MSRL arise from the inherent scoring procedure used and that it is limited to predicting variation in the dependent variable along a single environmental gradient.

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.

Stability of hydrotalcites formed from Bayer refinery environmental control processes

Bauxite refinery residues (red mud) are derived from the Bayer process by the digestion of crushed bauxite in concentrated sodium hydroxide at elevated temperatures and pressures. This slurry residue, if untreated, is unsuitable for discharge directly into the environment and is usually stored in tailing dams. The liquid portion has the potential for discharge, but requires pre-treatment before this can occur. The seawater neutralisation treatment facilitates a significant reduction in pH and dissolved metal concentrations, through the precipitation of hydrotalcite-like compounds and some other Mg, Ca, and Al hydroxide and carbonate minerals. The hydrotalcite-like compounds, precipitated during seawater neutralisation, also remove a range of transition metals, oxy-anions and other anionic species through a combination of intercalation and adsorption reactions: smaller anions are intercalated into the hydrotalcite matrix, while larger molecules are adsorbed on the particle surfaces. A phenomenon known as ‘reversion’ can occur if the seawater neutralisation process is not properly controlled. Reversion causes an increase in the pH and dissolved impurity levels of the neutralised effluent, rendering it unsuitable for discharge. It is believed that slow dissolution of components of the red mud residue and compounds formed during the neutralisation process are responsible for reversion. This investigation looked at characterising natural hydrotalcite (Mg6Al2(OH)16(CO3)∙4H2O) and ‘Bayer’ hydrotalcite (synthesised using the seawater neutralisation process) using a variety of techniques including X-ray diffraction, infrared and Raman spectroscopy, and thermogravimetric analysis. This investigation showed that Bayer hydrotalcite is comprised of a mixture of 3:1 and 4:1 hydrotalcite structures and exhibited similar chemical characteristic to the 4:1 synthetic hydrotalcite. Hydrotalcite formed from the seawater neutralisation of Bauxite refinery residues has been found not to cause reversion. Other components in red mud were investigated to determine the cause of reversion and this investigation found three components that contributed to reversion: 1) tricalcium aluminate, 2) hydrocalumite and 3) calcium hydroxide. Increasing the amount of magnesium in the neutralisation process has been found to be successful in reducing reversion.

The mineral tooeleite Fe6(AsO3)4SO4(OH)4⋅4H2O – An infrared and Raman spectroscopic study-environmental implications for arsenic remediation

The mineral tooeleite Fe6(AsO3)4SO4(OH)4�4H2O is secondary ferric arsenite sulphate mineral which has environmental significance for arsenic remediation because of its high stability in the regolith. The mineral has been studied by X-ray diffraction (XRD), infrared (IR) and Raman spectroscopy. The XRD result indicates tooeleite can form more crystalline solids in an acid environment than in an alkaline environment. Infrared spectroscopy identifies moderately intense band at 773 cm�1 assigned to AsO3� 3 symmetric stretching vibration. Raman spectroscopy identifies three bands at 803, 758 and 661 cm�1 assigned to the symmetric and antisymmetric stretching vibrations of AsO3� 3 and As-OH stretching vibration respectively. In addition, the infrared bands observed at 1116, 1040, 1090, 981 and 616 cm�1, are assigned to the m3, m1 and m4 modes of SO2� 4 . The same bands are observed at 1287, 1085, 983 and 604 cm�1 in the Raman spectrum. As3d band at binding energy of 44.05 eV in XPS confirms arsenic valence of tooeleite is +3. These characteristic bands in the IR and Raman spectra provide useful basis for identifying the mineral tooeleite.

An experimental based assessment of the deviation of the bearing characteristic frequencies

Slippage in the contact roller-races has always played a central role in the field of diagnostics of rolling element bearings. Due to this phenomenon, vibrations triggered by a localized damage are not strictly periodic and therefore not detectable by means of common spectral functions as power spectral density or discrete Fourier transform. Due to the strong second order cyclostationary component, characterizing these signals, techniques such as cyclic coherence, its integrated form and square envelope spectrum have proven to be effective in a wide range of applications. An expert user can easily identify a damage and its location within the bearing components by looking for particular patterns of peaks in the output of the selected cyclostationary tool. These peaks will be found in the neighborhood of specific frequencies, that can be calculated in advance as functions of the geometrical features of the bearing itself. Unfortunately the non-periodicity of the vibration signal is not the only consequence of the slippage: often it also involves a displacement of the damage characteristic peaks from the theoretically expected frequencies. This issue becomes particularly important in the attempt to develop highly automated algorithms for bearing damage recognition, and, in order to correctly set thresholds and tolerances, a quantitative description of the magnitude of the above mentioned deviations is needed. This paper is aimed at identifying the dependency of the deviations on the different operating conditions. This has been possible thanks to an extended experimental activity performed on a full scale bearing test rig, able to reproduce realistically the operating and environmental conditions typical of an industrial high power electric motor and gearbox. The importance of load will be investigated in detail for different bearing damages. Finally some guidelines on how to cope with such deviations will be given, accordingly to the expertise obtained in the experimental activity.

In Situ Observation of the Thermal Decomposition of Weddelite by Heating Stage Environmental Scanning Electron Microscopy

The morphological and chemical changes occurring during the thermal decomposition of weddelite, CaC2O4·2H2O, have been followed in real time in a heating stage attached to an Environmental Scanning Electron Microscope operating at a pressure of 2 Torr, with a heating rate of 10 °C/min and an equilibration time of approximately 10 min. The dehydration step around 120 °C and the loss of CO around 425 °C do not involve changes in morphology, but changes in the composition were observed. The final reaction of CaCO3 to CaO while evolving CO2 around 600 °C involved the formation of chains of very small oxide particles pseudomorphic to the original oxalate crystals. The change in chemical composition could only be observed after cooling the sample to 350 °C because of the effects of thermal radiation.