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An update on the current research and development of the treatment technologies, which utilize natural processes or passive components in wastewater treatment, is provided in this paper. The main focus is on wetland systems and their applications in wastewater treatment (as an advanced treatment unit or decentralized system), nutrient and pollutant removal (metals, industrial and emerging pollutants including pharmaceutical compounds). A summary of studies involving the effects of vegetation, wetland design and modeling, hybrid and innovative systems, storm water treatment and pathogen removal is also included. [ABSTRACT FROM AUTHOR]

We evaluate and compare the performance of Bayesian Monte Carlo ( BMC), Markov chain Monte Carlo ( MCMC), and the Generalized Likelihood Uncertainty Estimation ( GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling ( HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space. [ABSTRACT FROM AUTHOR]

This paper provides a review of the treatment technologies, which utilize natural processes or passive components in wastewater treatment. In particular, this paper primarily focuses on wetland systems and their applications in wastewater treatment (as an advanced treatment unit or decentralized system), nutrient and pollutant removal (single and multiple pollutants, and metals), and emerging pollutant removal (pharmaceuticals). A summary of studies involving the plant (vegetation) effects, wetland design and modeling, hybrid and innovative systems, storm water treatment and pathogen removal is also included. [ABSTRACT FROM AUTHOR]

The steady-state response matrix has historically proved a valuable tool in computing the water quality response to loadings and in providing insight into the relative impact of individual loading sources. The insight obtained may be is particularly useful in modern applications of increasingly complex water quality models to problems involving multiple point and nonpoint sources, such as in the assessment of total maximum daily loads (TMDLs). Where appropriate and the underlying equations linear, the steady-state response matrix can be used to synthesize the results of more complicated models and present them in a way easily understood by poi- icy makers. A straightforward method is presented for generating the response matrix using complex models, and example applications discussed. Example applications include a simple demonstration; incorporation of the method into the Mississippi Department of Environmental Quality's STREAM model used in TMDL development; a TMDL modeling study of the Grand Calumet River and Indiana Harbor Canal, Indiana, using CE- QUAL-ICM; and a TMDL modeling study of the Big Sunflower River, Mississippi, using the Water Analysis Simulation Program model. [ABSTRACT FROM AUTHOR]

The authors examined the relationship between depression, maladaptive perfectionism, and shame. Regression analyses were used to replicate a model in which maladaptive perfectionism was negatively associated with self-esteem and positively associated with symptoms of depression, with self-esteem mediating the effects of maladaptive perfectionism on depressive symptoms. Additional models showed that the path from maladaptive perfectionism to depression was partially mediated by shame, although these paths differed for men and women. [ABSTRACT FROM AUTHOR]

This article reports that perfectionism has gained popularity among researchers and authors in the professional literature in recent years. Recent research supports the conceptualization of perfectionism as a multidimensional construct including both healthy and unhealthy dimensions. A number of theoretical explanations have been offered for the distinction between normal and neurotic perfectionism. A Rogerian/humanistic conceptualization of multidimensional perfectionism offers the advantage of explaining both the maladaptive and the adaptive dimensions of perfectionism. From a Rogerian perspective, maladaptive, or neurotic, perfectionists might be seen as lacking in organismic valuing, holding to a fixed system of introjected values characterized by "oughts" and "shoulds." A Rogerian/humanistic conceptualization of adaptive perfectionism not only incorporates the absence of negative concomitants such as lower self-esteem or a greater propensity for depression but also acknowledges the presence of positive factors, including the tendency of an organism toward self-actualization.

BSTRACT: A major contaminant monitoring and modeling study is underway for Green Bay, Lake Michigan. Monitoring programs in support of contaminant modeling of large waterbodies, such as for Green Bay, are expensive and their extent is often limited by budget limitations, laboratory capacity, and logistic constraints. Therefore, it is imperative that available resources be used in the most efficient manner possible. This use, or allocation of resources, may be aided through the application of readily available models in the planning stages of projects. To aid in the planning effort for the Green Bay project, a workshop was held and studies designed to aid in the allocation of resources for a year-long intensive field study. Physical/chemical and food chain models were applied using historical data to aid in project planning by identifying processes having the greatest impact on the predictive capability of mass balance models. Studies were also conducted to estimate errors in computed tributary loadings and in-Bay concentrations and contaminant mass associated with different sampling strategies. The studies contributed to the overall project design, which was a collaborative effort with many participants involved in budgeting, field data collection, analysis, processing of data, quality assurance, data management and modeling activities. [ABSTRACT FROM AUTHOR]