Qualitätsanforderungen an Oberflächengewässer: Zielvorgaben,Qualitätsziele und chemische Gewässergüteklassifizierung

Quality Requirements for Fresh Waters: Water Quality Targets, Water Quality Objectives, and Chemical Water Quality Classification In the Federal Republic of Germany, water quality requirements for the protection of inland surface waters against hazardous substances are formulated on the basis of a quality targets derivation concept developed jointly by the Federal Government and the Federal States. The quality requirements were termed “water quality targets” in order to make it clear that the values derived are orientational values rather than legally binding limit values. The international comparison of quality requirements for surface waters shows that, on the whole, the national quality targets ensure a high level of protection. According to present scientific knowledge, impairments of uses, such as supply of drinking water, or risks to aquatic communities need not to be expected if the quality targets are complied with. A comparison of water quality data with the water quality targets makes it possible, on the one hand, to identify those substances whose inputs must be further reduced; on the other hand, it also shows that, for a number of substances, there is no need at present for concern over their adversely water quality. A further differentiation of the aquatic hazard potential of pollutants allows a water quality classification system to be developed on the basis of the quality targets derivation concept. The basic elements of this water quality classification system are presented, and its application is explained by way of examples.     

Evaluation of the photometric perturbations in close binary systems

In the present study the photometric perturbations arising from the mututal tides of the two components in a close binary system are calculated and discussed. The evaluation is made for each star separately as well as for both stars together in the case of occulation eclipses terminating in totality.     

Nonstationary pearl pulsations as a signature of magnetospheric disturbances

We analyse long-lasting (several hours) Pc1 pearl pulsations with decreasing, increasing or constant central frequencies. We show that nonstationary pearl events (those with either decreasing or increasing central frequency) are observed simultaneously with increasing auroral magnetic activity at the nightside magnetosphere while the stationary events (constant central frequency) correspond to quiet magnetic conditions. Events with decreasing central frequency are observed mostly in the late morning and daytime whereas events with increasing central frequency appear either early in the morning or in the afternoon. We explain the diurnal distribution of the nonstationary pearl pulsations in terms of proton drifts depending on magnetic activity, and evaluate the magnetospheric electric field based on the variation of the central frequency of pearl pulsations.     

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.     

Non-point source pollution in Indian agriculture: Estimation of nitrogen losses from rice crop using remote sensing and GIS

The paper presents a detailed understanding of nitrogenous fertilizer use in Indian agriculture and estimation of seasonal nitrogen loosses from rice crop in Indo-Gangetic plain region, the ‘food bowl’ of the Indian sub-continent. An integrated methodology was developed for quantification of different forms of nitrogen losses from rice crop using remote sensing derived inputs, field data of fertilizer application, collateral data of soil and rainfall and nitrogen loss coefficients derived from published nitrogen dynamics studies. The spatial patterns of nitrogen losses in autumn or ‘kharif’ and spring or ‘rabi’ season rice at 1 × 1 km grid were generated using image processing and GIS. The nitrogen losses through leaching in form of urea-N, ammonium-N (NH₄-N) and nitrate-N (NO₃-N) are dominant over ammonia volatilization loss. The study results indicate that nitrogen loss through leaching in kharif and rabi rice is of the order of 34.9% and 39.8% of the applied nitrogenous fertilizer in the Indo-Gangetic plain region. This study provides a significant insight to the role of nitrogenous fertilizer as a major non-point source pollutant from agriculture.     

Detection and extraction of signals from the epoch of reionization using higher-order one-point statistics

Detecting redshifted 21-cm emission from neutral hydrogen in the early Universe promises to give direct constraints on the epoch of reionization (EoR). It will, though, be very challenging to extract the cosmological signal (CS) from foregrounds and noise which are orders of magnitude larger. Fortunately, the signal has some characteristics which differentiate it from the foregrounds and noise, and we suggest that using the correct statistics may tease out signatures of reionization. We generate mock data cubes simulating the output of the Low Frequency Array (LOFAR) EoR experiment. These cubes combine realistic models for Galactic and extragalactic foregrounds and the noise with three different simulations of the CS. We fit out the foregrounds, which are smooth in the frequency direction, to produce residual images in each frequency band. We denoise these images and study the skewness of the one-point distribution in the images as a function of frequency. We find that, under sufficiently optimistic assumptions, we can recover the main features of the redshift evolution of the skewness in the 21-cm signal. We argue that some of these features – such as a dip at the onset of reionization, followed by a rise towards its later stages – may be generic, and give us a promising route to a statistical detection of reionization.     

Corundum- and kyanite-bearing anatexites from the Precambrian of tanzania

In the Precambrian near Morogoro (East Tanzania) there are anatectic gneisses which are composed of two different domains: (1) Medium grained gneiss with the assemblage (+ rutile + baddeleyite): albite (ab₉₅an₀₃or₀₂) + muscovite + phlogopite_ss + corundum, and albite (ab₈₈an₀₉or₀₃ + kyanite or sillimanite + phlogopite_ss. (2) Coarse grained nests of corundum and antiperthite with minor albite, muscovite, phlogopite, rutile, baddeleyite, and tourmaline. The bulk composition of the antiperthite id ab₇₅an₀₂or₂₃, the composition of the Na-phase ab₉₅an₀₃or₀₂, that of the K-phase ab₁₃an₀₀or₈₇. Phase relationship of the rock can be modelled in the Na-rich, Si-undersaturated part of the KNASH system. P-T conditions are close to the intersection of the melting curve muscovite_ss + H₂O = corundum + liquid with the kyanite-sillimanite transition, i.e. 7.7 kb/695°C assuming a_H2O = 1.