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

A Eutrophication Model of the White River Basin Above Beaver Reservoir in Northwest Arkansas

With national interest focused on man’s ever increasing degradation of the waters in this nation, it is clearly evident that an accurate assessment of all parameters influencing water quality needs to be made. Moreover, nutrient levels and budgets reflecting eutrophication trends are important parameters in the overall factors effecting water quality in lakes and reservoirs. The ability to predict future eutrophication levels will greatly enhance the retardation of the eutrophication process. Through mathematical simulation of this process, eutrophication can be analyzed and intelligent decisions regarding water quality management can be made

Simulation of Observed PCBs and Pesticides in the Water Column during the North Atlantic Bloom Experiment

The dynamics of persistent organic pollutants in the oceans are not well constrained, in particular during a bloom formation and collapse. Polychlorinated biphenyls (PCBs) and some pesticides were measured in air, water, and zooplankton tracking the North Atlantic Bloom in May 2008. Lower weight PCBs were entering the water column from the atmosphere during the main bloom period but reached equilibrium after the bloom collapsed. The PCBs in the lipids of zooplankton Calanus were in equilibrium with those in the dissolved phase. A Lagrangian box model was developed to simulate the dissolved phase PCBs and pesticides by including the following processes: air–water exchange, reversible sorption to POC, changes in mixed layer depth, removal by sinking particles, and degradation. Results suggest that sorption to (sinking) POC was the dominant removal process for hydrophobic pollutants from seawater. Statistical test suggested simulated results were not significantly different from observed values for hydrophobic pollutants (p,p’-DDE)

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