Os métodos simples que usam fórmulas práticas para a previsão do coeficiente de dispersão longitudinal em pequenos cursos d'água naturais são discutidos neste artigo. Dez fórmulas práticas de previsão do coeficiente são comparadas. A base de comparação são os resultados de 22 estudos de campo em que o coeficiente é obtido diretamente com o uso de traçadores ambientalmente neutros. A técnica de obtenção direta do coeficiente apóia-se em metodologia derivada de modelo clássico, tipo Fickiano. A análise dimensional é utilizada para a obtenção dos grupos adimensionais relevantes no processo de transporte de massa e para deduzir um modelo matemático alternativo para o coeficiente de dispersão longitudinal. Este modelo é ajustado pela técnica de regressão múltipla e, ao final, para a sua validação, é aplicado a novos dados de estudos de campo realizados no trecho urbano de um curso d'água natural.

Measurements are presented for the rate of atmospheric reaeration in open-channel flow under different conditions of depth and bed roughness. A 2k factorial design was employed for the estimation of both depth and bed resistance factor effects upon stream reaeration. Consistent results indicated that stream reaeration is primarily controlled by water shear, but that different modes of instability generated by a combination of depth and strip roughness levels might prevail for different boundary roughness conditions, and as a consequence, differently affect the reaeration coefficient.

Abstract:Mathematical modeling of water quality in rivers is an important tool for the planning and management of water resources. Nevertheless, the available models frequently show structural and functional limitations. With the objective of reducing these drawbacks, a new model has been developed to simulate water quality in rivers under unsteady conditions; this model runs on the Vensim PLE ® software and can also be operated for steady-state conditions. The following eighteen water quality variables can be simulated: DO, BOD c , organic nitrogen (N o ), ammonia nitrogen (N a ), nitrite (N i ), nitrate (N n ), organic and inorganic phosphorus (F o and F i , respectively), inorganic solids (Si), phytoplankton (F), zooplankton (Z), bottom algae (A), detritus (D), total coliforms (TC), alkalinity (Al.), total inorganic carbon (TIC), pH, and temperature (T). Methane as well as nitrogen and phosphorus compounds that are present in the aerobic and anaerobic layers of the sediment can also be simulated. Several scenarios were generated for computational simulations produced using the new model by using the QUAL2K program, and, when possible, analytical solutions. The results obtained using the new model strongly supported the results from the QUAL family and analytical solutions.

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