Occurrence of uraniferous iron and manganese oxides in biotite granite North East Gabal El Sela area, South Eastern Desert, Egypt

Optical microscopy, X-ray diffraction (XRD), and back-scattered electron imaging (BSE) have been used to determine the mineralogical composition of the uraniferous iron and manganese oxides and the associated U-minerals hosted in biotite granite that occurred north east Gabal El Sela area south Eastern Desert, Egypt. These mineralizations were found as veinlets fractures filling associated with strongly kaolinitic alteration of the coarse-grained biotite granite. XRD determined that the geothite mineral form the main constituent of uraniferous iron oxide in addition to tapiolite, and kaolinite minerals, where as uraniferous manganese oxide composed of pyrolusite, ramsdellite, and cryptomelane. BSE confirmed that the associated uranium minerals represented by uranothorite, kazolite, and zentime in addition to columbite-bearing minerals. Uranothorite and columbite-bearing minerals are the most abundant minerals in this mineralization. Petrographically, biotite granite is composed mainly of quartz, in addition to K-feldspars, biotite and muscovite with minor zircon, garnet, apatite, uranium-rich thorite and iron oxide. Petrochemical studies and tectonic discrimination diagrams for this granite reveal that they are classified as granite to alkali feldspar granite, originated from calc-alkaline magma having peraluminous nature and developed in within-plate tectonic environment. Field radiometric measurements revealed the localization of two high radiometric anomalies associated with iron and manganese oxides, within this anomaly uranium content range from 65 to 85 ppm. Alpha Track-etch Detectors of radon gas registrations revealed high track density reach up to 15,448.7 Bq/m³.     

A novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains of Arkansas,USA

This research deploys a novel mass wasting susceptibility modeling approach for cases where temporal information is unavailable and circumstances are prejudiced to merit applying traditional susceptibility modeling strategies. Conventional models typically employ approaches deemed problematic for this study, e.g., biased weighted input; a “more is better” approach pertaining to voluminous inputs; neglecting geologic structural influence; and establishing temporal linkages between cause (trigger) and effect (failure) with a trigger being defined as a catalyst for failure, such as timed events like earthquakes or precipitation as well as physical changes like vegetation removal or slope disturbance. Road bias may also influence modeling dramatically when event data are derived from observations of road-related failures, which become unreliable at predicting susceptibility in regions with no roads. However, a triggerless approach can extrapolate naturally occurring susceptibility via priori knowledge of local topography and structural geology factors. Two models are then created for comparison: One model has integrated empirical Bayesian kriging and fuzzy logic considering basically local topography and structural geology, while the second model has employed a standard implementation of a weighted overlay using all available (8) input data layers. Statistical comparisons show that the first model has identified ~ 83%, compared to only ~ 28% for the latter model, of the 47 documented mass wasting events in the selected study site. These results demonstrate that the introduced triggerless approach is efficiently capable of modeling mass wasting susceptibility in areas lacking temporal datasets, which in turn can help in mitigating future geohazards.     

Performance of multi-model ensembles for the simulation of temperature variability over Ontario,Canada

Climate ensembles utilize outputs from multiple climate models to estimate future climate patterns. These multi-model ensembles generally outperform individual climate models. In this paper, the performance of seven global climate model and regional climate model combinations were evaluated for Ontario, Canada. Two multi-model ensembles were developed and tested, one based on the mean of the seven combinations and the other based on the median of the same seven models. The performance of the multi-model ensembles were evaluated on 12 meteorological stations, as well as for the entire domain of Ontario, using three temperature variables (average surface temperature, maximum surface temperature, and minimum surface temperature). Climate data for developing and validating the multi-model ensembles were collected from three major sources: the North American Coordinated Regional Downscaling Experiment, the Digital Archive of Canadian Climatological Data, and the Climactic Research Unit’s TS v4.00 dataset. The results showed that the climate ensemble based on the mean generally outperformed the one based on the median, as well as each of the individual models. Future predictions under the Representative Concentration Pathway 4.5 (RCP4.5) scenario were generated using the multi-model ensemble based on the mean. This study provides credible and useful information for climate change mitigation and adaption in Ontario.     

Impact of biochar,bentonite, and compost on physical and chemical characteristics of a sandy soil

In this study, bentonite (Ben), compost (Com), and biochar (Bio) were used as soil amendments to enhance sandy soil physical properties. A soil column experiment was conducted in a laboratory. Application rates were 3% (weight/weight) of Bio (T₁), Ben (T₂), and Com (T₃). Furthermore, mixtures 1.5% and 1.5% of Bio and Ben (T₄), Ben and Com (T₅), and Bio and Com (T₆), and a mixture 1%, 1%, and 1% of Bio, Ben, and Com (T₇) in addition to control treatment were adopted. The mixtures of amendments and sandy soil were concentrated at the top 10 cm of columns. Results revealed that the cumulative evaporation was reduced by 2.3% and 5.7% as a result of using T₃ and T₅, respectively. However, the remaining treatments enhanced the cumulative evaporation. The application of amendments increased the capacity of the soil to maintain water by 35.4%, 24.4%, 13.3%, and 10.2%, for soils treated with T₅, T₃, T₇, and T₄, respectively. The water content at field capacity had the highest increase in the top 10 cm when treatment T₃ was used. Although T₃ (compost) was the most efficient for enhancing soil physical properties, this study recommends T₅ and T₇ to improve hydraulic properties of sandy soils. This is due to the fact that biochar and bentonite remain in the soil for a longer period and resist biodegradation while compost overcomes the negative impact of soil chemical properties as a result of biochar and bentonite additions.     

Effects of land-cover and watershed protection futures on sustainable groundwater management in a heavily utilized aquifer in Hawai‘i (USA)

Hydrogeology Journal - Groundwater sustainability initiatives, including sustainable yield and watershed policy protection policies, are growing globally in response to increasing demand for...     

Expansion formulae in canonical transformations depending on a small parameter

The theory of perturbation based on Lie transforms is considered. Deprit's equation is reduced to a form which enables us to generate simplified general recursion formulae. These expansions are then modified to speed up the implementation of such perturbation theory in the computerized symbolic manipulation.