Three District Water Authorities cover the whole Hungarian stretch of the Danube. Since the formation of the monitoring network fifteen years ago, the water quality has been observed at fifteen sampling points. Throughout this time, sampling has been performed under different hydrometeorological conditions. Based on these investigations, it was found that the quality of the river was influenced equally by the local and the hydrometeorological conditions. Major pollution sources are: the sewage from Bratislava, Györ and Budapest; the paper pulp, chemical, and sugar beet factories in the Slovakian catchment; the Hungarian chemical, petro-chemical, and food industries; and non-point source pollution from agriculture. The effects of these sources depend on the degree of wastewater treatment, and on the mixing rate. The waste loads provide a continuous source of nutrients, giving rise to bacterial proliferation. The organic nitrate and phosphorus loads are increasing, which is compensated for by biodegradation. In the winter, when the water temperature falls below 10°C and solar radiation is low, saprobic conditions characterize the water quality. In the summer, when solar radiation and temperature increase, trophic conditions determine the water quality. Thus, in winter the ammonia content increases, but in the spring, nitrification starts to improve and, especially in the lower reaches, algal overproduction can be detected. This situation changes during flood periods, when the concentration of polluting material is decreased by dilution, and at the same time, the high level of suspended solids inhibits the growth of organisms needing solar radiation. The most unfavourable water quality conditions occur in the winter low-flow period, when problems may occur in drinking water supply if the water is chlorinated.
Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.
Some results of the joint Yugoslav-Hungarian investigations of the radioactivity of the Danube on the border profile in the period 1978 –1988 are presented. From the results of gross β, 90Sr, 3H activity and γ-spectroscopy measurements on the water, fish, sediment and algae samples obtained before and after the Chernobyl accident, the long term impact of that accident on the river ecosystem is determined. By the analysis of the data, it was concluded that the annual mean elimination rate of 106Ru, 137Cs and 134Cs from all media has the value 40% per year. The values of the annual equivalent doses from long-lived man-made nuclides in water and fish are estimated showing an increase by a factor of 5 in the µSv range due to the Chernobyl accident.
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