984 resultados para radioactive pollution


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Successive applications of pig litter to the soil surface can increase the phosphorus (P) content and alter its adsorption, promoting P transfer to surface or subsurface waters. The purpose of this study was to evaluate P accumulation and the pollution potential of a soil after application of pig litter. In March 2010, eight years after the installation of an experiment in Braço do Norte, Santa Catarina, SC, Brazil, on a Typic Hapludult, soil was sampled (layers 0-2.5, 2.5-5, 5-10, 10-15, 15-20 and 20-30 cm) after the following fertilization treatments: no pig litter fertilization, pig slurry application and pig manure application. In this period, 694 and 1,890 kg P2O5 ha-1 were applied in the treatments with pig slurry and pig manure, respectively. The P content was determined, based on Mehlich-1, anion exchange resin (AER), 0.01 mol L-1 CaCl2 and total P in the samples. The adsorption isotherm parameters were also determined by the Langmuir and Koski-Vähälä & Hartikainem models in the layers 0-2.5 and 20-30 cm. The application of 1,890 kg P2O5 ha-1 in the form of pig manure led to P accumulation, as evidenced by Mehlich-1, down to a depth of 15 cm, by AER and 0.01 mol L-1 CaCl2 down to 20 cm and by total P to 30 cm. After application of 1,890 kg P2O5 ha-1 in the form of pig manure, the values of maximum P adsorption capacity were lowest in the deepest layer (20-30 cm), indicating the occupation of part of the adsorption sites of the particles. The application of swine manure to the soil over eight years increased the P quantity in the soil solution of the surface layer, indicating environmental contamination risk for surface and subsurface waters.

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Audit report on the Iowa Water Pollution Control Works Financing Program and the Iowa Drinking Water Facilities Financing Program, joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources for the year ended June 30, 2011

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Audit report on the Iowa Water Pollution Control Works Financing Program and the Iowa Drinking Water Facilities Financing Program, joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources for the year ended June 30, 2012

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[spa] En este artículo aplicamos un modelo input-output ampliado medioambientalmente para analizar un aspecto específico de la hipótesis de la curva de Kuznets ambiental. El propósito del estudio es analizar si las estructuras de consumo de los hogares con una mejor ‘posición económica’ pueden tener un efecto positivo para reducir las presiones medioambientales. Para ello combinamos información de diferentes bases de datos para analizar el impacto de la contaminación atmosférica del consumo de diferentes hogares españoles en el año 2000. Consideramos nueve gases, i.e. los seis gases de efecto invernadero (CO2, CH4, N2O, SF6, HFCs, y PFCs) y otros tres gases (SO2, NOx, y NH3). Clasificamos los hogares en quintiles de gasto per capita y quintiles de gasto equivalente. Los resultados obtenidos muestran que hay una relación positiva y elevada entre el nivel de gasto y las emisiones directas e indirectas generadas por el consumo de los hogares; sin embargo, las intensidades de emisión tienden a disminuir con el nivel de gasto para los diferentes gases, con la excepción de SF6, HFCs, y PFCs.

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Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

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Astonishing as it may seem, one organism's waste is often ideal food for another. Many waste products generated by human activities are routinely degraded by microorganisms under controlled conditions during waste-water treatment. Toxic pollutants resulting from inadvertent releases, such as oil spills, are also consumed by bacteria, the simplest organisms on Earth. Biodegradation of toxic or particularly persistent compounds, however, remains problematic. What has escaped the attention of many is that bacteria exposed to pollutants can adapt to them by mutating or acquiring degradative genes. These bacteria can proliferate in the environment as a result of the selection pressures created by pollutants. The positive outcome of selection pressure is that harmful compounds may eventually be broken down completely through biodegradation. The downside is that biodegradation may require extremely long periods of time. Although the adaptation process has been shown to be reproducible, it remains very difficult to predict.

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Audit report on the Iowa Water Pollution Control Works Financing Program and the Iowa Drinking Water Facilities Financing Program, joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources for the year ended June 30, 2013

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This report discusses the asphalt pavement recycling project designated Project HR-188 in Kossuth County, Iowa. Specific objectives were: (a) to determine the effectiveness of drum mixing plant modifications designed to control air pollution within limits specified by the Iowa Department of Environmental Quality; (b) to assess the impact of varying the proportions of recycled and virgin aggregates, (c) to assess the impact of varying the production rate of the plant, and (d) to assess the impact of varying the mixing temperature. The discussion includes information on the proposed use of research funds, project location and description, the project planning conference, plan development, bid letting, asphalt plant configuration, actual plant operation, why this method is successful, probable process limitations, pollution results, recycled pavement test results, and the cost of virgin vs. recycled asphalt pavements.

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Audit report on the Iowa Water Pollution Control Works Financing Program and the Iowa Drinking Water Facilities Financing Program, joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources, for the year ended June 30, 2014

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The incorporation of space allows the establishment of a more precise relationship between a contaminating input, a contaminating byproduct and emissions that reach the final receptor. However, the presence of asymmetric information impedes the implementation of the first-best policy. As a solution to this problem a site specific deposit refund system for the contaminating input and the contaminating byproduct are proposed. Moreover, the utilization of a successive optimization technique first over space and second over time enables definition of the optimal intertemporal site specific deposit refund system

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Audit report on the Iowa Water Pollution Control Works Financing Program (Clean Water Program) and the Iowa Drinking Water Facilities Financing Program (Drinking Water Program), joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources, for the year ended June 30, 2005