Norwegian Geotechnical Institute (NGI) Digital Archive
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A Simple Model for the Variability of Release Area Size
Get PDFThe hazard mapping tool NAKSIN estimates the release probability of potential release areas (PRAs) by testing a stability criterion based on the infinite-slope approximation with a large sample of synthetic weather situations. The release area is thus assumed to comprise the entire PRA, which is unrealistic for avalanches with return periods shorter than 100–300 y. To remedy this, a stability criterion is proposed that accounts for stabilizing forces across the slab perimeter and so is sensitive to the slab extent. The criterion is applied to a sequence of subareas of the PRA with increasing minimum slope angle to find the subarea with maximum release probability. The method is described and formulated mathematically. Also, tools for coding it are suggested but implementation in NAKSIN and testing are left for future work.NVE (Norges vassdrags- og energidirektorat
WP1 - Container experiments - Yearly report 2021
Get PDFContainer experiments investigating leaching from alum shale under different conditions were started autumn 2020 as a part of the Under Oslo project at NGI (SP13). Furthermore, leaching from alum shale mixed with different amounts of rhomb porphyry was investigated, giving a total of 14 containers. The black shale in these containers were taken out from a road cut by E16 at Kleggerud autumn 2020. Additionally, 5 containers set up by the Norwegian Public Road Authorities (Statens vegvesen, SVV) in 2014 and 2015 to investigate leaching from alum shale and galgeberg shale originating from the construction of the tunnel at Gran, Rv. 4, were taken over by NGI and measurements of leachate quality were resume
Limited access to oxygen reduces the release of harmful trace elements from submerged alum shale debris
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Modelling Plume Development with Annual Pulses of Contaminants Released from an Airport Runway to a Layered Aquifer, Evaluation of an In Situ Monitoring System
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Can big data and random forests improve avalanche runout estimation compared to simple linear regression?
Get PDFAccurate prediction of snow avalanche runout-distances in a deterministic sense remains a challenge due to the complexity of all the physical properties involved. Therefore, in many locations including Norway, it has been common practice to define the runout distance using the angle from the starting point to the end of the runout zone (α-angle). We use a large dataset of avalanche events from Switzerland (N = 18,737) acquired using optical satellites to calculate the α-angle for each avalanche. The α-angles in our dataset are normally distributed with a mean of 33° and a standard deviation of 6.1°, which provides additional understanding and insights into α-angle distribution. Using a feature importance module in the Random Forest framework, we found the most important topographic parameter for predicting α-angles to be the average gradient from the release area to the β-point. Despite the large dataset and a modern machine learning (ML) method, we found the simple linear regression model to yield a higher performance than our ML attempts. This means that it is better to use a simple linear regression in an operational contextpublishedVersio
Towards a more accurate characterization of granular media 2.0: Involving AI in the process
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Passive-Sampler-Derived PCB and OCP Concentrations in the Waters of the World─First Results from the AQUA-GAPS/MONET Network
No full textPersistent organic pollutants (POPs) are recognized as pollutants of global concern, but so far, information on the trends of legacy POPs in the waters of the world has been missing due to logistical, analytical, and financial reasons. Passive samplers have emerged as an attractive alternative to active water sampling methods as they accumulate POPs, represent time-weighted average concentrations, and can easily be shipped and deployed. As part of the AQUA-GAPS/MONET, passive samplers were deployed at 40 globally distributed sites between 2016 and 2020, for a total of 21 freshwater and 40 marine deployments. Results from silicone passive samplers showed α-hexachlorocyclohexane (HCH) and γ-HCH displaying the greatest concentrations in the northern latitudes/Arctic Ocean, in stark contrast to the more persistent penta (PeCB)- and hexachlorobenzene (HCB), which approached equilibrium across sampling sites. Geospatial patterns of polychlorinated biphenyl (PCB) aqueous concentrations closely matched original estimates of production and use, implying limited global transport. Positive correlations between log-transformed concentrations of Σ7PCB, ΣDDTs, Σendosulfan, and Σchlordane, but not ΣHCH, and the log of population density (p < 0.05) within 5 and 10 km of the sampling sites also supported limited transport from used sites. These results help to understand the extent of global distribution, and eventually time-trends, of organic pollutants in aquatic systems, such as across freshwaters and oceans. Future deployments will aim to establish time-trends at selected sites while adding to the geographical coverage.acceptedVersio
