A comparison between ion characteristics observed by the POLAR and DMSP spacecraft in the high-latitude magnetosphere

We study here the injection and transport of ions in the convection-dominated region of the Earth’s magnetosphere. The total ion counts from the CAMMICE MICS instrument aboard the POLAR spacecraft are used to generate occurrence probability distributions of magnetospheric ion populations. MICS ion spectra are characterised by both the peak in the differential energy flux, and the average energy of ions striking the detector. The former permits a comparison with the Stubbs et al. (2001) survey of He2+ ions of solar wind origin within the magnetosphere. The latter can address the occurrences of various classifications of precipitating particle fluxes observed in the topside ionosphere by DMSP satellites (Newell and Meng, 1992). The peak energy occurrences are consistent with our earlier work, including the dawn-dusk asymmetry with enhanced occurrences on the dawn flank at low energies, switching to the dusk flank at higher energies. The differences in the ion energies observed in these two studies can be explained by drift orbit effects and acceleration processes at the magnetopause, and in the tail current sheet. Near noon at average ion energies of _1 keV, the cusp and open LLBL occur further poleward here than in the Newell and Meng survey, probably due to convectionrelated time-of-flight effects. An important new result is that the pre-noon bias previously observed in the LLBL is most likely due to the component of this population on closed field lines, formed largely by low energy ions drifting earthward from the tail. There is no evidence here of mass and momentum transfer from the solar wind to the LLBL by nonreconnection coupling. At higher energies (_2–20 keV), we observe ions mapping to the auroral oval and can distinguish between the boundary and central plasma sheets. We show that ions at these energies relate to a transition from dawnward to duskward dominated flow, this is evidence of how ion drift orbits in the tail influence the location and behaviour of the plasma populations in the magnetosphere.

The economics of enhanced landfill mining: private and societal performance drivers

This paper addresses the economics of Enhanced Landfill Mining (ELFM) both from a private point of view as well as from a society perspective. The private potential is assessed using a case study for which an investment model is developed to identify the impact of a broad range of parameters on the profitability of ELFM. We found that especially variations in Waste-to-Energy (WtE efficiency, electricity price, CO2-price, WtE investment and operational costs) and ELFM support explain the variation in economic profitability measured by the Internal Rate of Return. To overcome site-specific parameters we also evaluated the regional ELFM potential for the densely populated and industrial region of Flanders (north of Belgium). The total number of potential ELFM sites was estimated using a 5-step procedure and a simulation tool was developed to trade-off private costs and benefits. The analysis shows that there is a substantial economic potential for ELFM projects on the wider regional level. Furthermore, this paper also reviews the costs and benefits from a broader perspective. The carbon footprint of the case study was mapped in order to assess the project’s net impact in terms of greenhouse gas emissions. Also the impacts of nature restoration, soil remediation, resource scarcity and reduced import dependence were valued so that they can be used in future social cost-benefit analysis. Given the complex trade-off between economic, social and environmental issues of ELFM projects, we conclude that further refinement of the methodological framework and the development of the integrated decision tools supporting private and public actors, are necessary.

EISCAT/CRRES observations: nightside ionospheric ion outflow and oxygen-rich substorm injections

We present combined observations made near midnight by the EISCAT radar, all-sky cameras and the combined released and radiation efects satellite (CRRES) shortly before and during a substorm. In particular, we study a discrete, equatorward-drifting auroral arc, seen several degrees poleward of the onset region. The arc passes through the field-aligned beam of the EISCAT radar and is seen to be associated with a considerable upflow of ionospheric plasma. During the substorm, the CRRES satellite observed two major injections, 17 min apart, the second of which was dominated by O+ ions. We show that the observed are was in a suitable location in both latitude and MLT to have fed O+ ions into the second injection and that the upward flux of ions associated with it was sufficient to explain the observed injection. We interpret these data as showing that arcs in the nightside plasma-sheet boundary layer could be the source of O+ ions energised by a dipolarisation of the mid- and near-Earth tail, as opposed to ions ejected from the dayside ionosphere in the cleft ion fountain.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

Assessment and improvement of biotransfer models to cow’s milk and beef used in exposure assessment tools for organic pollutants

The aim of this study was to assess and improve the accuracy of biotransfer models for the organic pollutants (PCBs, PCDD/Fs, PBDEs, PFCAs, and pesticides) into cow’s milk and beef used in human exposure assessment. Metabolic rate in cattle is known as a key parameter for this biotransfer, however few experimental data and no simulation methods are currently available. In this research, metabolic rate was estimated using existing QSAR biodegradation models of microorganisms (BioWIN) and fish (EPI-HL and IFS-HL). This simulated metabolic rate was then incorporated into the mechanistic cattle biotransfer models (RAIDAR, ACC-HUMAN, OMEGA, and CKow). The goodness of fit tests showed that RAIDAR, ACC-HUMAN, OMEGA model performances were significantly improved using either of the QSARs when comparing the new model outputs to observed data. The CKow model is the only one that separates the processes in the gut and liver. This model showed the lowest residual error of all the models tested when the BioWIN model was used to represent the ruminant metabolic process in the gut and the two fish QSARs were used to represent the metabolic process in the liver. Our testing included EUSES and CalTOX which are KOW-regression models that are widely used in regulatory assessment. New regressions based on the simulated rate of the two metabolic processes are also proposed as an alternative to KOW-regression models for a screening risk assessment. The modified CKow model is more physiologically realistic, but has equivalent usability to existing KOW-regression models for estimating cattle biotransfer of organic pollutants.

Emergence dynamics of barnyardgrass and jimsonweed from two depths when switching from conventional to reduced and no-till conditions

A cylinder experiment was conducted in northern Greece during 2005 and 2006 to assess emergence dynamics of barnyardgrass (Echinochloa crus-galli (L.) Beauv.) and jimsonweed (Datura stramonium L.) in the case of a switch from conventional to conservation tillage systems (CT). Emergence was surveyed from two burial depths (5 and 10 cm) and with simulation of reduced tillage (i.e. by soil disturbance) and no-till conditions. Barnyardgrass emergence was significantly affected by burial depth, having greater emergence from 5 cm depth (96%) although even 78% of seedlings emerged from 10 cm depth after the two years of study. Emergence of barnyardgrass was stable across years from the different depths and tillage regimes. Jimsonweed seeds showed lower germination than barnyardgrass during the study period, whereas its emergence was significantly affected by soil disturbance having 41% compared to 28% without disturbance. A burial depth x soil disturbance interaction was also determined, which showed higher emergence from 10 cm depth with soil disturbance. Jimsonweed was found to have significantly higher emergence from 10 cm depth with soil disturbance in Year 2. Seasonal emergence timing of barnyardgrass did not vary between the different burial depth and soil disturbance regimes, as it started in April and lasted until end of May in both years. Jimsonweed showed a bimodal pattern, with first emergence starting end of April until mid-May and the second ranging from mid-June to mid-August from 10 cm burial depth and from mid-July to mid-August from 5 cm depth, irrespective of soil disturbance in both cases.