<p>The Greater Manchester Brownfield Ground Risk Calculator (BGR_calc) is a Geographical Information System (GIS) spatial decision support tool designed to provide an early indication of potentially abnormal ground conditions and the indicative costs of mitigating them. This is important because abnormal ground conditions can affect the viability of the constructing of new homes on post-industrial brownfield sites. Multi-criteria decision analysis methods were used process and utilise over 30 input dataets. BGR_calc comprises four primary outputs, each represents a different set of ground risk or cost mitigation characteristics that occur within the Greater Manchester area, presented alongside their associated input data. Each output comprises risk scores (scored between 0 to 1) or risk mitigation cost estimates (&#163;) presented as 50 m grid cells and site based summaries for over 2000 individual sites. BGR_calc makes the assumption that all brownfield land evaluated will be used to develop two storey residential housing at a density of 30 houses per hectare. Ground risk scores reflect the nominal risk that soil and groundwater contamination and soil and rock hazards might pose to human health, controlled waters and the structural integrity of new homes. The scores are derived from data on sources of contamination or ground conditions resulting from previous land-uses and/or natural processes, the presence of exposure pathways and sensitive receptors (residents, water resources and homes). For there to be a risk, the source, pathway and receptor components must be linked. Risk mitigation cost estimates represent the amount that might need to be paid to develop a brownfield site over and above &#8216;normal&#8217; development costs.&#160; No allowance is made in BGR_calc for the financial benefits of pre-existing infrastructure, proximity to services and employment that brownfield land usually have but these ought to be considered within the overall economic evaluation of individual sites.</p>
In recent years, a number of high profile landslide events have caused disruption, derailments or damage to railway infrastructure in Great Britain. A landslide susceptibility model of the entire railway network was created, designed to give a national overview of potential landslide hazard originating from Outside Party Slopes. The current assessment was compiled using Geographic Information System (GIS) techniques and desktop modelling to apply a structured analysis of each buffered Earthwork Inspection 5 Chain ( c . 100 m; EI5C). Data analysed along the network included the BGS GeoSure instability model and newly updated national models for debris flow, earth flow and rock fall, supported by historical landslide data. In order to further focus the Outside Party Slope zone, a buffer of External Natural Geological Influence (BENGI) was created using a 5 m Digital Terrain Model. Landslide susceptibility for each EI5C was categorized using a ‘Classification of Hazards on Outside Party Slopes’ (CHOPS) score; representing the modelled potential for landslide hazard. The outputs were combined as a series of matrices to present the CHOPS and Network Rail Derailment Criticality Band interactions. This research will allow further focused analysis of the network, in order to prioritize and direct future investigation and policy decisions.
ABSTRACT: Earth scientists are often asked to establish or constrain the likely provenance of very small quantities of earth‐related material as part of a forensic investigation. We tested the independent and collective interpretations of four experts with differing analytical skills in the prediction of sample provenance for three samples from different environmental settings. The methods used were X‐ray diffraction, scanning electron microscopy, the assessment of pollen assemblages, and structural characterization of organic matter at the molecular level. Independent interpretations were less accurate than those where multiple techniques were combined. Collective interpretation was very effective in the assessment of provenance for two of the three sites where the mineralogy and plant communities were distinctive. At the other site, although the mineralogical analysis correctly identified the Triassic mudstone soil parent material, Carboniferous spores from domestic coal were initially interpreted as deriving directly from bedrock. Such an interpretation could be a common pitfall owing to anthropogenic redistribution of material such as coal.
Abody has been found in woodland. A suspect denies
having been to the site. There is little evidence apart from
a tiny amount of the victim’s blood – or is there? Could a
geological sleuth provide the answer from mud on the
defendant’s shoe? The British Geological Survey (BGS) hopes so
through its development of forensic geology techniques.
Sir Arthur Conan Doyle introduced the idea of forensic
geology in his first Sherlock Holmes story, A Study in Scarlet, in
1886: ‘Knowledge of Geology. – Practical, but limited. Tells at a
glance different soils from each other. After walks has shown me
splashes upon his trousers, and told me by their colour and
consistence in what part of London he had received them.’ In
forensic investigations, for example, where earth from a crime
scene has stuck to a car’s wheel arch, a suspect’s clothes or shoes,
the police may well ask where the
particles came from.
The British Geological Survey (BGS) has developed a multi-stage methodology for landslide mapping by augmenting traditional mapping techniques with new geospatial technologies. This allows better characterisation and understanding of the country's landslides: an essential requirement for landslide susceptibility modelling, risk assessment and resilient infrastructure planning. The BGS methodology has most recently been applied to the North York Moors National Park in northern England, UK: an area with steep slopes, landslide-prone lithologies and an exposed coastal section but few recorded landslide events. Over 550 landslides have now been identified and data on the characteristics and mechanisms of these have been used to inform hazard assessments and susceptibility modelling research including the National Landslide Database, the National Landslide Domains Map and the National Geohazard Assessment.
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