4 resultados para Equation of Motion

em Aquatic Commons


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During October, 1972 the Patuxent River Estuary was monitored intensively and synoptically over two tidal cycles to determine the spatial and temporal patterns of various hydrodynamic, chemical and biological features. Forty-one depths at eleven stations along nine transects were sampled simultaneously at hourly intervals for salinity, temperature, dissolved oxygen, chlorohyll a, particulate nitrogen, nitrate, nitrite, total kjeldahl nitrogen, ammonia, particulate carbohydrate, dissolved organic carbon, total hydrolizable phosphorous, dissolved inorganic phosphorous, suspended sediment, particle size distribution, and zooplankton. Tidal velocity was continuously monitored at each depth by recording current meters. Riverine input and meteorological conditions were relatively stable for two weeks preceeding the deployment. This communication describes the calculation of the intrinsic rates of change of the observed variables from their measured distributions in the Estuary. The steady-state, one-dimensional equation of species continuity is employed to separate the advection and tidal dispersion of a hydrodynamically passive substance frbm its intrinsic rate of change at point. A new spatial transform is introduced for the purpose of interpolation and extrapolation of data.The intrinsic rate of change profiles reveal a region of heavy bloom activity in the upper estuary and a secondary bloom near the point in the River that most of the suspended material settles out. The changes in ammonia and nitrates are highly correlated to the productivity patterns. Phosphorous rates are less closely correlated to productivity. The perturbations that the Chalk Point steam electric power plant have on the heat and oxygen balances are easily discernible.

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The biomass yields of duck week (Lemna minor(L) was monitored in hydroponic media prepared by variously extracting 0.50, 1.00 and 2.00g of dried chicken manure per liter of city water (tap water) supply. The culture media consisting of aqueous extract of the various manure treatments were made up to 12 liters in all cases with tap water as control. Plastic baths of 25 liters capacity with 0.71 super(m2) surface area were used as culture facility. Each bath was stocked at a density of 30g super(m-2) with fresh weed samples (i.e 21.30g/bath). Maximum yields were obtained at all treatment levels and control on day 3 and based on the highest yield of 0.37gm super(-2)d super(-1) (dry matter) obtained at 1.00gL manure treatment which was however not significantly higher (P>0.05) than the 0.36gm super(-2)d super(-1) (dry matter) at 0.05gl super(-1) media manure content, an average manure level of 0.75l super(-1) was selected and used to determine the operational plant density. Thus fresh weights of 30 to 300gm super(-2) was grown in triplicate at 30g intervals for a period of 3 days. A regression equation of Y=2.6720+0.0021x with a corresponding maximum density or operational plant density of 266gm super(-2) and yield of 0.98gm super(-2), d super(-1) (dry matter) were obtained. Further growth trials were carried out at the operational density and manure levels of 0.50, 0.75, 1.00, 1.25, 1.50, 1.75 and 2.00gl super(-1) media manure concentration giving a significantly higher yield (P<0.05) of 17gm super(-2), d super(-1) (dry matter). This yield was however doubled to between 2.21 and 2.24gm super(-2) d super(-1) (equivalent to 7.96 to 8.06mt.ha-1, Yr-1 dry matter on extrapolation) if 25% and 75% respectively of the total weed cover were harvested daily within the experimental period. The role of some dissolved plant nutrients (DPN) were also discussed

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This research work involves the determination and modelling of water parameter such as pH, temperature, turbidity, chloride, hardness. The result of the analysis was used as important operating variables to generate a model equation of pH, hardness, temperature, turbidity and chloride. The values obtained from the model equation were compared with those from experiment. On an average bases the values were close. These parameters can be used to monitor the extent of pollution of pond water and to monitor stress and diseases of fish. The experimental data of pH was in the range of 6.7 to 6.9 while the modelled result was also between 6.7 to 7.0. The turbidity experimental value was close to the modelled value also. The chloride value for the experimental data was in the range of 25.32 to 35.0. The total hardness value ranges between 4.5 to 65.1 mg/l while the modelled result ranges between 11.025 to 68.402 mg/l. The result was within the acceptable limit of world health organization standard on water quality parameter.

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The age and growth dynamics of the spinner shark (Carcharhinus brevipinna) in the northwest Atlantic Ocean off the southeast United States and in the Gulf of Mexico were examined and four growth models were used to examine variation in the ability to fit size-at-age data. The von Bertalanffy growth model, an alternative equation of the von Bertalanffy growth model with a size-at-birth intercept, the Gompertz growth model, and a logistic model were fitted to sex-specific observed size-at-age data. Considering the statistical criteria (e.g., lowest mean square error [MSE], high coefficient-of-determination, and greatest level of significance) we desired for this study, the logistic model provided the best overall fit to the size-at-age data, whereas the von Bertalanffy growth model gave the worst. For “biological validity,” the von Bertalanffy model for female sharks provided estimates similar to those reported in other studies. However, the von Bertalanffy model was deemed inappropriate for describing the growth of male spinner sharks because estimates of theoretical maximum size (L∞) indicated a size much larger than that observed in the field. However, the growth coefficient (k= 0.14/yr) from the Gompertz model provided an estimate most similar to that reported for other large coastal species. The analysis of growth for spinner shark in the present study demonstrates the importance of fitting alternative models when standard models fit the data poorly or when growth estimates do not appear to be realistic.