965 resultados para Thermal pollution of rivers, lakes, etc


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During 28-29, September 2005, water was drawn from Hanjiang River and Houguan Lake to the Yangzi River via Sanjiao Lake and Nantaizi Lake in Wuhan in order to provide favorable conditions for ecosystem restoration. To evaluate the feasibility and validity of drawing water as a means of ecosystem restoration, zooplankton populations were studied 3 times (before, immediately after finishing and a month after drawing water) at seven locations from 27 Sept. 2005 to 2 Nov. 2005. Water quality in the lakes was mostly improved and zooplankton species richness decreased as soon as drawing water had finished but increased a month after drawing water. Zooplankton density and biomass was reduced in the lakes by drawing water but was increased at the entrance to Sanjiao Lake because of landform geometry change. Before drawing water, most species in Sanjiao Lake e.g., Brachionus sp. and Keratella sp. were tolerant of contamination. After drawing water oligotrophic-prone species such as Lecane ludwigii and Gastropus stylifer emerged. We conclude that drawing water could be important for improving water quality and favour ecosystem restoration. Dilution of nutrient concentrations may be an important role in the effect.

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A one-dimensional surface energy-balance lake model, coupled to a thermodynamic model of lake ice, is used to simulate variations in the temperature of and evaporation from three Estonian lakes: Karujärv, Viljandi and Kirjaku. The model is driven by daily climate data, derived by cubic-spline interpolation from monthly mean data, and was run for periods of 8 years (Kirjaku) up to 30 years (Viljandi). Simulated surface water temperature is in good agreement with observations: mean differences between simulated and observed temperatures are from −0.8°C to +0.1°C. The simulated duration of snow and ice cover is comparable with observed. However, the model generally underpredicts ice thickness and overpredicts snow depth. Sensitivity analyses suggest that the model results are robust across a wide range (0.1–2.0 m−1) of lake extinction coefficient: surface temperature differs by less than 0.5°C between extreme values of the extinction coefficient. The model results are more sensitive to snow and ice albedos. However, changing the snow (0.2–0.9) and ice (0.15–0.55) albedos within realistic ranges does not improve the simulations of snow depth and ice thickness. The underestimation of ice thickness is correlated with the overestimation of snow cover, since a thick snow layer insulates the ice and limits ice formation. The overestimation of snow cover results from the assumption that all the simulated winter precipitation occurs as snow, a direct consequence of using daily climate data derived by interpolation from mean monthly data.

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The thermal structure, heat content and stability were studied in Lakes Dom Helvécio and Carioca during an annual cycle. It was found that the maximum heat content, stability and work of the wind in Lake Dom Helvécio correspond to two, four and four times, respectively, the values for the Lake Carioca. These difference can be attributed to morphometric differences in the lakes. A long-term record of heat content and stability for lake Carioca is also presented. Diel variations were studied in summer and winter. The tropicality of the lakes is discussed and compared with other lacustrine systems. © 1989 Kluwer Academic Publishers.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Incluye Bibliografía

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A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.