Modal parameter estimation in the presence of non-linearities

This paper investigates the use of time-frequency techniques to assist in the estimation of power system modes which are resolvable by a Digital Fourier Transform (DFT). The limitations of linear estimation techniques in the presence of large disturbances which excite system non-linearities, particularly the swing equation non-linearity are shown. Where a nonlinearity manifests itself as time varying modal frequencies the Wigner-Ville Distribution (WVD) is used to describe the variation in modal frequencies and construct a window over which standard linear estimation techniques can be used. The error obtained even in the presence of multiple resolvable modes is better than 2%.

Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA)

Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

Modelling bus lost time : an additional parameter influencing bus dwell time and station platform capacity at a BRT station platform

The common approach to estimate bus dwell time at a BRT station is to apply the traditional dwell time methodology derived for suburban bus stops. In spite of being sensitive to boarding and alighting passenger numbers and to some extent towards fare collection media, these traditional dwell time models do not account for the platform crowding. Moreover, they fall short in accounting for the effects of passenger/s walking along a relatively longer BRT platform. Using the experience from Brisbane busway (BRT) stations, a new variable, Bus Lost Time (LT), is introduced in traditional dwell time model. The bus lost time variable captures the impact of passenger walking and platform crowding on bus dwell time. These are two characteristics which differentiate a BRT station from a bus stop. This paper reports the development of a methodology to estimate bus lost time experienced by buses at a BRT platform. Results were compared with the Transit Capacity and Quality of Servce Manual (TCQSM) approach of dwell time and station capacity estimation. When the bus lost time was used in dwell time calculations it was found that the BRT station platform capacity reduced by 10.1%.

Influence of hydrocarbon contamination on clay soil microstructure

Microstructural (fabric, forces and composition) changes due to hydrocarbon contamination in a clay soil were studied using Scanning Electron Microscope (micro-fabric analysis), Atomic Force Microscope (forces measurement) and sedimentation bench test (particle size measurements). The non-polluted and polluted glacial till from north-eastern Poland (area of a fuel terminal) were used for the study. Electrostatic repelling forces for the polluted sample were much lower than for the non-polluted sample. In comparison to non-polluted sample, the polluted sample exhibited lower electric charge, attractive forces on approach and strong adhesion on retrieve. The results of the sedimentation tests indicate that clay particles form larger aggregates and settle out of the suspension rapidly in diesel oil. In non-polluted soil, the fabric is strongly aggregated – densely packed, dominate the face-to-face and edge-to-edge types of contacts, clay film tightly adheres to the surface of larger grains and interparticle pores are more common. In polluted soil, the clay matrix is less aggregated – loosely packed, dominate the edge-to-face types of contacts and inter-micro-aggregate pores are more frequent. Substantial differences were observed in the morphometric and geometrical parameters of pore space. The polluted soil micro-fabric proved to be more isotropic and less oriented than in non-polluted soil. The polluted soil, in which electrostatic forces were suppressed by hydrocarbon interaction, displays more open porosity and larger voids than non-polluted soil, which is characterized by occurrence of the strong electrostatic interaction between clay particles.

Effect of biochar amendment on the soil-atmosphere exchange of greenhouse gases from an intensive subtropical pasture in northern New South Wales, Australia

We assessed the effect of biochar incorporation into the soil on the soil-atmosphere exchange of the greenhouse gases (GHG) from an intensive subtropical pasture. For this, we measured N2O, CH4 and CO2 emissions with high temporal resolution from April to June 2009 in an existing factorial experiment where cattle feedlot biochar had been applied at 10 t ha-1 in November 2006. Over the whole measurement period, significant emissions of N2O and CO2 were observed, whereas a net uptake of CH4 was measured. N2O emissions were found to be highly episodic with one major emission pulse (up to 502 µg N2O-N m-2 h 1) following heavy rainfall. There was no significant difference in the net flux of GHGs from the biochar amended vs. the control plots. Our results demonstrate that intensively managed subtropical pastures on ferrosols in northern New South Wales of Australia can be a significant source of GHG. Our hypothesis that the application of biochar would lead to a reduction in emissions of GHG from soils was not supported in this field assessment. Additional studies with longer observation periods are needed to clarify the long term effect of biochar amendment on soil microbial processes and the emission of GHGs under field conditions.

Thermal stability of the soil minerals destinezite and diadochite Fe3+2(PO4)(SO4)(OH)·6H2O - implications for soils in bush fires

Thermogravimetry combined with evolved gas mass spectrometry has been used to ascertain the stability of the soil minerals destinezite and diadochite. These two minerals are identical except for their morphology. Diadochite is amorphous whereas destinezite is crystalline. Both minerals are found in soils. It is important to understand the stability of these minerals because soils are subject to bush fires especially in Australia. The thermal analysis patterns of the two minerals are similar but not identical. Subtle differences are observed in the DTG patterns. For destinezite, two DTG peaks are observed at 129 and 182°C attributed to the loss of hydration water, whereas only a broad peak with maximum at 84°C is observed for diadochite. Higher temperature mass losses at 685°C for destinezite and 655°C for diadochite, based upon the ion current curves, are due to sulphate decomposition. This research has shown that at low temperatures the minerals are stable but at high temperatures, as might be experienced in a bush fire, the minerals decompose.

Raman spectroscopic study of the minerals diadochite and destinezite Fe3+2(PO4,SO4)2(OH)•6H2O – implications for soil science

The two minerals diadochite and destinezite of formula Fe2(PO4,SO4)2(OH)•6H2O have been characterised by Raman spectroscopy and complimented with infrared spectroscopy. These two minerals are both found in soils and are identical except for their morphology. Diadochite is amorphous whereas destinezite is highly crystalline. The spectra of diadochite are broad and ill-defined, whereas the spectra of destinezite are intense and well defined. Bands are assigned to phosphate and sulphate stretching and bending modes. Two symmetric stretching modes for both the phosphate and sulphate symmetric stretching modes support the concept of non-equivalent phosphate and sulphate units in the mineral structure. Multiple water bending and stretching modes imply that non-equivalent water molecules in the structure exist with different hydrogen bond strengths.

How can soil monitoring networks be used to improve predictions of organic carbon pool dynamics and CO2 fluxes in agricultural soils?

Abstract As regional and continental carbon balances of terrestrial ecosystems become available, it becomes clear that the soils are the largest source of uncertainty. Repeated inventories of soil organic carbon (SOC) organized in soil monitoring networks (SMN) are being implemented in a number of countries. This paper reviews the concepts and design of SMNs in ten countries, and discusses the contribution of such networks to reducing the uncertainty of soil carbon balances. Some SMNs are designed to estimate country-specific land use or management effects on SOC stocks, while others collect soil carbon and ancillary data to provide a nationally consistent assessment of soil carbon condition across the major land-use/soil type combinations. The former use a single sampling campaign of paired sites, while for the latter both systematic (usually grid based) and stratified repeated sampling campaigns (5–10 years interval) are used with densities of one site per 10–1,040 km². For paired sites, multiple samples at each site are taken in order to allow statistical analysis, while for the single sites, composite samples are taken. In both cases, fixed depth increments together with samples for bulk density and stone content are recommended. Samples should be archived to allow for re-measurement purposes using updated techniques. Information on land management, and where possible, land use history should be systematically recorded for each site. A case study of the agricultural frontier in Brazil is presented in which land use effect factors are calculated in order to quantify the CO2 fluxes from national land use/management conversion matrices. Process-based SOC models can be run for the individual points of the SMN, provided detailed land management records are available. These studies are still rare, as most SMNs have been implemented recently or are in progress. Examples from the USA and Belgium show that uncertainties in SOC change range from 1.6–6.5 Mg C ha−1 for the prediction of SOC stock changes on individual sites to 11.72 Mg C ha−1 or 34% of the median SOC change for soil/land use/climate units. For national SOC monitoring, stratified sampling sites appears to be the most straightforward attribution of SOC values to units with similar soil/land use/climate conditions (i.e. a spatially implicit upscaling approach). Keywords Soil monitoring networks - Soil organic carbon - Modeling - Sampling design

Soil carbon sequestration rates and associated economic costs for farming systems of south-eastern Australia

Soil organic carbon (C) sequestration rates based on the Intergovernmental Panel for Climate Change (IPCC) methodology were combined with local economic data to simulate the economic potential for C sequestration in response to conservation tillage in the six agro-ecological zones within the Southern Region of the Australian grains industry. The net C sequestration rate over 20 years for the Southern Region (which includes discounting for associated greenhouse gases) is estimated to be 3.6 or 6.3 Mg C/ha after converting to either minimum or no-tillage practices, respectively, with no-till practices estimated to return 75% more carbon on average than minimum tillage. The highest net gains in C per ha are realised when converting from conventional to no-tillage practices in the high-activity clay soils of the High Rainfall and Wimmera agro-ecological zones. On the basis of total area available for change, the Slopes agro-ecological zone offers the highest net returns, potentially sequestering an additional 7.1 Mt C under no-tillage scenario over 20 years. The economic analysis was summarised as C supply curves for each of the 6 zones expressing the total additional C accumulated over 20 years for a price per t C sequestered ranging from zero to AU$200. For a price of $50/Mg C, a total of 427 000 Mg C would be sequestered over 20 years across the Southern Region, <5% of the simulated C sequestration potential of 9.1 Mt for the region. The Wimmera and Mid-North offer the largest gains in C under minimum tillage over 20 years of all zones for all C prices. For the no-tillage scenario, for a price of $50/Mg C, 1.74 Mt C would be sequestered over 20 years across the Southern Region, <10% of the simulated C sequestration potential of 18.6 Mt for the region over 20 years. The Slopes agro-ecological zone offers the best return in C over 20 years under no-tillage for all C prices. The Mallee offers the least return for both minimum and no-tillage scenarios. At a price of $200/Mg C, the transition from conventional tillage to minimum or no-tillage practices will only realise 19% and 33%, respectively, of the total biogeochemical sequestration potential of crop and pasture systems of the Southern Region over a 20-year period.

Bounded parameter Markov Decision Processes with average reward criterion

Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, the notion of an optimal policy for a BMDP is not entirely straightforward. We consider two notions of optimality based on optimistic and pessimistic criteria. These have been analyzed for discounted BMDPs. Here we provide results for average reward BMDPs. We establish a fundamental relationship between the discounted and the average reward problems, prove the existence of Blackwell optimal policies and, for both notions of optimality, derive algorithms that converge to the optimal value function.