Appendices to an Aqueous Environmental Simulation Model for Mid-south Lakes and Reservoirs

The program simulates some of the major physical, chemical and biological processes occuring within the aqueous phase of lakes and reservoirs. The program was developed to study the eutrophic development of these water bodies

An Aqueous Environmental Simulation Model for Mid-South Lakes and Reservoirs

Quantitative relationships and associated computer program has been developed to simulate some of the major physical, chemical and biological processes occuring within the aqueous phase of lakes and reservoirs. The model was developed, in part, to study the eutrophic development of these water bodies. Emphasis is upon lakes in the Mid-South U.S.A. The physical model reflects the general environment in this region and includes a single stratified period. The chemical subsystem includes nitrogen, phosphorus, oxygen and carbon. The biological subsystem includes phytoplankton, zooplankton, omnivorous fish, carnivorous fish and aerobic bacteria. The model differential equations are solved numerically with the IBM Continuous System Modeling Program (CSMP). The output results (graphical or numerical) of critical eutrophic parameters can be obtained as a function of time (Julian Day), depth and distance down-lake. The model has been adjusted to field data from Beaver Reservoir in Northwest Arkansas. A comparison of the adjusted simulation and the field data is presented along with examples of use of the model for predictive purposes. The final completion report includes an appendix that contains the program listing, documentation and case studies

Modelling interactions of acid–base balance and respiratory status in the toxicity of metal mixtures in the American oyster Crassostrea virginica

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology 155 (2010): 341-349, doi:10.1016/j.cbpa.2009.11.019.Heavy metals, such as copper, zinc and cadmium, represent some of the most common and serious pollutants in coastal estuaries. In the present study, we used a combination of linear and artificial neural network (ANN) modelling to detect and explore interactions among low-dose mixtures of these heavy metals and their impacts on fundamental physiological processes in tissues of the Eastern oyster, Crassostrea virginica. Animals were exposed to Cd (0.001 – 0.400 μM), Zn (0.001 – 3.059 μM) or Cu (0.002 – 0.787 μM), either alone or in combination for 1 to 27 days. We measured indicators of acid-base balance (hemolymph pH and total CO2), gas exchange (Po2), immunocompetence (total hemocyte counts, numbers of invasive bacteria), antioxidant status (glutathione, GSH), oxidative damage (lipid peroxidation; LPx), and metal accumulation in the gill and the hepatopancreas. Linear analysis showed that oxidative membrane damage from tissue accumulation of environmental metals was correlated with impaired acid-base balance in oysters. ANN analysis revealed interactions of metals with hemolymph acid-base chemistry in predicting oxidative damage that were not evident from linear analyses. These results highlight the usefulness of machine learning approaches, such as ANNs, for improving our ability to recognize and understand the effects of sub-acute exposure to contaminant mixtures.This study was supported by NOAA’s Center of Excellence in Oceans and Human Health at HML and the National Science Foundation

Chemodynamics : Environmental movement of chemical in air, water, and soil

xxiii, 501 p. : il; 22 c

Chemodynamics: environmental movement of chemicals in air, water, and soil

xv+97hlm.;21c