Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation. Results for simulated percolation were similar for the two soils. Predictions of water volumes percolated were found to be only marginally affected by changes in input parameters and the most influential parameter was the water content defining the boundary between micropores and macropores in this dual-porosity model. In contrast, predictions of pesticide losses were found to be dependent on the scenarios considered and to be significantly affected by variations in input parameters. In most scenarios, predictions for pesticide losses by MACRO were most influenced by parameters related to sorption and degradation. Under specific circumstances, pesticide losses can be largely affected by changes in hydrological properties of the soil. Since parameters were varied within ranges that approximated their uncertainty, a first-step assessment of uncertainty for the predictions of pesticide losses was possible. Large uncertainties in the predictions were reported, although these are likely to have been overestimated by considering a large number of input parameters in the exercise. It appears desirable that a probabilistic framework accounting for uncertainty is integrated into the estimation of pesticide exposure for regulatory purposes.     

The Detection Rate of Enteric Viruses and <Emphasis Type="Italic">Clostridium difficile</Emphasis> in a Waste Water Treatment Plant Effluent

Waste water treatment plant (WWTP) is considered as an important source of surface water contamination by enteric pathogens. In this study, we describe the occurrence of enteric viruses (group A rotaviruses, noroviruses, astroviruses, sapoviruses, hepatitis A virus, and hepatitis E virus) and Clostridium difficile in the effluent of a wastewater treatment plant during a 1-year period. Enteric viruses were simultaneously and efficiently concentrated in a single step using methacrylate monolithic chromatographic support. Rotaviruses, noroviruses (genogroup I and II), and sapoviruses were detected in all 12 concentrated samples, whereas astroviruses were not detected in August and September and hepatitis A and E viruses were not detected at all. Clostridium difficile was detected in all samples and altogether 121 strains were isolated and grouped into 32 different ribotypes of which 014/020 and 010 were most prevalent. Pathogens detected in WWTP effluent partially reflect the epidemiological situation of enteric viruses and C. difficile in human population and open the discussion on implementation of possible techniques for virus and bacteria removal from WWTP effluent prior to release into the surface water system.     

Risk-based standards: integrating top–down and bottom–up approaches

In response to rapidly changing threats posed to increasingly complex socio-technical systems, many in the government and private sector have called for protection through risk-based standards. However, given the nature of these dynamic and uncertain threats, traditional risk assessment techniques may not be sufficient. Instead, there is a critical need for an integrated approach in which decision analytic techniques are used to assess evidence-based data with the values and preferences of decision makers. We point to three examples in the fields of nuclear power regulation, nanotechnology, and cybersecurity, where risk-based approaches (bottom–up) have been combined with decision analysis (top–down) to guide decision makers toward risk management policies that manifest both the best available evidence and the plurality of values within a society.     

Risk and resilience lessons from Venice

In the history of disasters in Venice, there are implications for modern times in terms of complex systems management and emerging threats, in particular from examples of risk management and resilience achieved by the Venetian state during outbreaks of the plague. In fourteenth century Venice, risk assessment the way we practice it today would fail to provide meaningful recommendations to reduce the casualty rate of the plague epidemic because the cause and transmission of the disease was not understood. Instead, a set of systemic actions across the social, economic, and transportation networks of the city taken by officials and doctors eventually slowed and arguably stopped the spread of the disease. These latter actions are an early example of what is now considered resilience management. Resilience management improves a complex system’s ability to prepare, absorb, recover, and adapt to unexpected threats and does so by address the capabilities at a system, rather than component, level. Resilience management can be a guide to addressing current issues of population growth and rising sea level in modern day Venice and across the globe. This paper calls for integration of resilience assessment in comprehensive risk and resilience management framework.     

Climate change mitigation policy paradigms—national objectives and alignments

The aim of this paper is to assess how policy goals in relation to the promotion of green growth, energy security, pollution control and greenhouse gas (GHG) emissions reductions have been aligned in policies that have been implemented in selected countries during the last decades as a basis for discussing how a multi objective policy paradigm can contribute to future climate change mitigation. The paper includes country case studies from Brazil, Canada, China, the European Union (EU), India, Japan, Mexico, Nigeria, South Africa, South Korea and the United States covering renewable energy options, industry, transportation, the residential sector and cross-sectoral policies. These countries and regions together contribute more than two thirds of global GHG emissions. The paper finds that policies that are nationally driven and that have multiple objectives, including climate-change mitigation, have been widely applied for decades in both developing countries and industrialised countries. Many of these policies have a long history, and adjustments have taken place based on experience and cost effectiveness concerns. Various energy and climate-change policy goals have worked together in these countries, and in practice a mix of policies reflecting specific priorities and contexts have been pursued. In this way, climate-change mitigation has been aligned with other policy objectives and integrated into broader policy packages, though in many cases specific attention has not been given to the achievement of large GHG emission reductions. Based on these experiences with policy implementation, the paper highlights a number of key coordination and design issues that are pertinent to the successful joint implementation of several energy and climate-change policy goals.     

Application of non-linear automatic optimization techniques for calibration of HSPF.

Development of TMDLs (total maximum daily loads) is often facilitated by using the software system BASINS (Better Assessment Science Integrating point and Nonpoint Sources). One of the key elements of BASINS is the watershed model HSPF (Hydrological Simulation Program Fortran) developed by USEPA. Calibration of HSPF is a very tedious and time consuming task, more than 100 parameters are involved in the calibration process. In the current research, three non-linear automatic optimization techniques are applied and compared, as well an efficient way to calibrate HSPF is suggested. Parameter optimization using local and global optimization techniques for the watershed model is discussed. Approaches to automatic calibration of HSPF using the nonlinear parameter estimator PEST (Parameter Estimation Tool) with its Gauss-Marquardt-Levenberg (GML) method, Random multiple Search Method (RSM), and Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA) are presented. Sensitivity analysis was conducted and the most and the least sensitive parameters were identified. It was noted that sensitivity depends on number of adjustable parameters. As more parameters were optimized simultaneously--a wider range of parameter values can maintain the model in the calibrated state. Impact of GML, RSM, and SCE-UA variables on ability to find the global minimum of the objective function (OF) was studied and the best variables are suggested. All three methods proved to be more efficient than manual HSPF calibration. Optimization results obtained by these methods are very similar, although in most cases RSM outperforms GML and SCE-UA outperforms RSM. GML is a very fast method, it can perform as well as SCE-UA when the variables are properly adjusted, initial guess is good and insensitive parameters are eliminated from the optimization process. SCE-UA is very robust and convenient to use. Logical definition of key variables in most cases leads to the global minimum.