Assessing the Environmental Hazard of Using Seawater for Ore Processing at the Lasail Mine Site in the Sultanate of Oman

The Lasail mining area (Sultanate of Oman) was contaminated by acid mine drainage during the exploitation and processing of local and imported copper ore and the subsequent deposition of sulphide-bearing waste material into an unsealed tailings dump. In this arid environment, the use of seawater in the initial stages of ore processing caused saline contamination of the fresh groundwater downstream of the tailings dump. After detection of the contamination in the 1980s, different source-controlled remediation activities were conducted including a seepage water collection system and, in 2005, surface sealing of the tailings dump using an HDPE-liner to prevent further infiltration of meteoric water. We have been assessing the benefits of the remediation actions undertaken so far. We present chemical and isotopic (δ18O, δ 2H, 3H) groundwater data from a long-term survey (8–16 years) of the Wadi Suq aquifer along a 28 km profile from the tailings dump to the Gulf of Oman. Over this period, most metal concentrations in the Wadi Suq groundwater decreased below detection limits. In addition, in the first boreholes downstream of the tailings pond, the salinity contamination has decreased by 30 % since 2005. This decrease appears to be related to the surface coverage of the tailings pond, which reduces flushing of the tailings by the sporadic, but commonly heavy, precipitation events. Despite generally low metal concentrations and the decreased salinity, groundwater quality still does not meet the WHO drinking water guidelines in more than 90 % of the Wadi Suq aquifer area. The observations show that under arid conditions, use of seawater for ore processing or any other industrial activity has the potential to contaminate aquifers for decades.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

A catalog of high-impact windstorms in Switzerland since 1859

In recent decades, extremely hazardous windstorms have caused enormous losses to buildings, infrastructure and forests in Switzerland. This has increased societal and scientific interest in the intensity and frequency of historical high-impact storms. However, high-resolution wind data and damage statistics mostly span recent decades only. For this study, we collected quantitative (e.g., volumes of windfall timber, losses relating to buildings) and descriptive (e.g., forestry or insurance reports) information on the impact of historical windstorms. To define windstorm severity, normalized and declustered quantitative data were processed by extreme value statistics. Descriptive information was classified using a conceptual guideline. Validation with independent damage information, as well as comparison with wind measurements and a reanalysis, indicates that the most hazardous winter storms are captured, while too few moderate windstorms are detected. Strong storms in the wind measurements and reanalysis are thus added to the catalog. The final catalog encompasses approximately 240 high-impact windstorms in Switzerland since 1859. It features three robust severity classes and contains eight extreme windstorms. Evidence of high winter storm activity in the early and late 20th century compared to the mid-20th century in both damage and wind data indicates a co-variability of hazard and related damage on decadal timescales.