Grundwasserbelastung durch Rüstungsaltlast-Emissionen Nachweis von Arsenkampfstoffen und weiteren kampfmittelspezifischen Rückst?nden

Die Ergebnisse der Untersuchung von Wasserproben aus dem Einflu?ereich der Rüstungsaltlast „Dethlinger Teich“ bei Munster/Niedersachsen werden dargestellt und bewertet. Von mehr als 50 untersuchten spezifischen Substanzen wurden Arsenkampfstoffe als Hauptkontaminanten erkannt. Im Rahmen der Gef?hrdungsabsch?tzung wird hier erstmals eine Analysenmethode zur Unterscheidung zwischen anorganisch und organisch gebundenem Arsen vorgestellt. Groundwater analyses in the area of the former munitions depository (World War II) „Dethlinger Teich“ near Munster/Niedersachsen were evaluated. Results from 50 different chemical substances showed that arsenic compounds are the main contaminating chemicals. A new method has been developed to distinguish between morganically- and organically bound compounds. This differentiation is required for risk assessment and evaluation. Also, the relationship of chemical structure and physiological effects in organo arsenic compounds is described.     

A natural analogue of high-pH cement pore waters from the Maqarin area of northern Jordan: Comparison of predicted and observed trace-element chemistry of uranium and selenium

Current design concepts for low-/intermediate-level radioactive waste disposal in many countries involve emplacement underground in a cementitious repository. The highly alkaline groundwaters at Maqarin, Jordan, are a good analogue for the cementitious pore waters that will be present within such a repository. A geochemical modelling study of these groundwaters has been carried out in order to test the applicability of equilibrium models in geochemical programs and their associated thermodynamic databases in such hyperalkaline conditions. This was achieved by comparison of elemental solubilities and speciations predicted by the programs with observations in the natural system. Five organisations took part in the study: AEA Technology, U.K.; Chalmers University of Technology, Sweden; MBT Tecnología Ambiental, Spain; Nagra, Switzerland; and SKB, Sweden. The modelling study was coordinated by the University of Berne.The results of the study showed good agreement between the predictions of the programs employed. Comparison of the observed solids with those predicted by the models has allowed limited validation of the databases. The results for U and Se are presented here.     

Prediction of ozone concentrations in Oporto city with statistical approaches

The performance of three statistical methods: time-series, multiple linear regression and feedforward artificial neural networks models were compared to predict the daily mean ozone concentrations. The study here reported was based on data from one urban site with traffic influences and one rural background site. The studies were performed for the year 2002 and the respective four trimesters separately. In the multiple linear regression and feedforward artificial neural network models, the concentrations of ozone, the concentrations of its precursors (nitrogen oxides) and some meteorological variables for one and two days before the prediction day were used as predictors. For the application of these models in the validation step, the inputs of ozone concentration for one and two days before were replaced by the ozone concentrations predicted by the models. The results showed that time-series modelling was not profitable. In the development step, similar performances were obtained with multiple linear regression and feedforward artificial neural network. Better performance indexes were achieved with feedforward artificial neural network models in validation step. Concluding, feedforward artificial neural network models were more efficient to predict ozone concentrations.     

Bagasse-assisted bioremediation of ammonia from shrimp farm wastewater.

Development of new economically feasible ecofriendly products from agricultural wastes or byproducts for shrimp farm wastewater treatment is the objective of our continued research. Ammonia is a nitrogenous toxicant, which is commonly found in wastewater from shrimp farms. In the present study, we explored the possibility of the use of simply and inexpensively prepared bagasse products so that this abundant crop byproduct could be used to remove ammonia from shrimp farm wastewater. Bagasse, a natural highly fibrous lignocellulosic byproduct of sugarcane, was converted into five different products. Experimental results have shown that ammonia is efficiently removed from wastewater by four bagasse products with a dose of 1 to 6 g/L within 24 hours. The effect of bagasse products on other water quality parameters and growth kinetics of biofilm bacteria onto bagasse fiber have also been studied. Efficacies of products were compared by using statistical analysis. Products developed from bagasse are useful and economical.     

Contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area, Portugal since the 19th century

The main purpose of this study was to evaluate the contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area (Portugal) since the 19th century. The study was based on pre-industrial and recent data series, the results being analyzed according to the atmospheric chemistry. The treatment of ozone and meteorological data was performed by classical statistics and by time-series analysis. It was concluded that in the 19th century the ozone present in the troposphere was not of photochemical origin, being possible to consider the respective concentrations as reference values. For recent data a cycle of 8h for ozone concentrations could be related to traffic. Compared to the 19th century, the current concentrations were 147% higher (252% higher in May) due to the increased photochemical production associated with the increased anthropogenic emissions.     

Uranium transport around the reactor zone at Bangombé and Okélobondo (Oklo): examples of hydrogeological and geochemical model integration and data evaluation

The sites at Bangombé and Okélobondo (Oklo) in Gabon provide a unique opportunity to study the behaviour of products from natural nuclear reactions in the vicinity of reactor zones which were active around two billion years ago. The Commission of the European Communities initiated the Oklo Natural Analogue Programme. One of the principal aims was to study indications of present time migration of elements from the reactor zones under ambient conditions. The hydrogeological and hydrochemical data from the Oklo sites were modelled in order to better understand the geochemical behaviour of radionuclides in the natural system, by using independent models and by comparing the modelling outcome. Two modelling approaches were used: M3 code (hydrochemical mixing and mass balance model), developed by the Swedish Nuclear Fuel and Waste Management Company (SKB) and HYTEC (reactive transport model) developed by Ecole des Mines de Paris. Two different reactor zones were studied: Bangombé, a shallow site, the reactor being at 11 m depth, and OK84 at Okélobondo, situated at about 450 m depth, more comparable with a real repository location. This allowed the validation of modelling tools in two different sedimentary environments: one shallow, with a more homogeneous layering situated in an area of meteoric alteration, and the other offering the opportunity to study radionuclide migration from the reaction zone over a distance of 450 m through very heterogeneous sedimentary layers. The modeling results indicate that the chemical reactions retarding radionuclide transport are very different at the two sites. At Bangombé, the decomposition of organic material consumes oxygen and at Okélobondo the oxygen is consumed by inorganic reactions resulting, in both cases, in uranium retardation. Both modelling approaches (statistic with M3 code and deterministic with HYTEC code) could describe this situation. The goal of this exercise is to test codes which can help to describe and understand the processes taking place at the sites, validate the models with in situ data, and thus build confidence in the tools used for future site characterization. Ultimately, this allows identifying and selecting processes and parameters that can be used as input into repository performance assessment calculations and modelling exercises.