Methane and nitrous oxide emissions from a subtropical coastal embayment (Moreton Bay, Australia)

Surface water methane (CH₄) and nitrous oxide (N₂O) concentrations and fluxes were investigated in two subtropical coastal embayments (Bramble Bay and Deception Bay, which are part of the greater Moreton Bay, Australia). Measurements were done at 23 stations in seven campaigns covering different seasons during 2010-2012. Water-air fluxes were estimated using the Thin Boundary Layer approach with a combination of wind and currents-based models for the estimation of the gas transfer velocities. The two bays were strong sources of both CH₄ and N₂O with no significant differences in the degree of saturation of both gases between them during all measurement campaigns. Both CH₄ and N₂O concentrations had strong temporal but minimal spatial variability in both bays. During the seven seasons, CH₄ varied between 500% and 4000% saturation while N₂O varied between 128 and 255% in the two bays. Average seasonal CH₄ fluxes for the two bays varied between 0.5 ± 0.2 and 6.0 ± 1.5 mg CH₄/(m²·day) while N₂O varied between 0.4 ± 0.1 and 1.6 ± 0.6 mg N₂O/(m²·day). Weighted emissions (t CO₂-e) were 63%-90% N₂O dominated implying that a reduction in N₂O inputs and/or nitrogen availability in the bays may significantly reduce the bays' greenhouse gas (GHG) budget. Emissions data for tropical and subtropical systems is still scarce. This work found subtropical bays to be significant aquatic sources of both CH₄ and N₂O and puts the estimated fluxes into the global context with measurements done from other climatic regions.     

An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination

An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also provides more realistic support for remediation-related decisions.     

Incorporating layer- and local-scale heterogeneities in numerical simulation of unsaturated flow and tracer transport

This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty assessment of unsaturated flow and tracer transport in the unsaturated zone of Yucca Mountain, USA. The layer-scale heterogeneity is specific to hydrogeologic layers with layerwise properties, while the local-scale heterogeneity refers to the spatial variation of hydraulic properties within a layer. A Monte Carlo method is used to estimate mean, variance, and 5th, and 95th percentiles for the quantities of interest (e.g., matrix saturation and normalized cumulative mass arrival). Model simulations of unsaturated flow are evaluated by comparing the simulated and observed matrix saturations. Local-scale heterogeneity is examined by comparing the results of this study with those of the previous study that only considers layer-scale heterogeneity. We find that local-scale heterogeneity significantly increases predictive uncertainty in the percolation fluxes and tracer plumes, whereas the mean predictions are only slightly affected by the local-scale heterogeneity. The mean travel time of the conservative and reactive tracers to the water table in the early stage increases significantly due to the local-scale heterogeneity, while the influence of local-scale heterogeneity on travel time gradually decreases over time. Layer-scale heterogeneity is more important than local-scale heterogeneity for simulating overall tracer travel time, suggesting that it would be more cost-effective to reduce the layer-scale parameter uncertainty in order to reduce predictive uncertainty in tracer transport.     

The effect of recycling price uncertainty on municipal waste management choices

This paper analyzes the effect of price uncertainty and irreversible investment on the decision of municipalities to switch from landfill waste disposal to recycling by developing a model to predict recycling adoption behavior and applying it to empirical data. It is shown that uncertainty regarding the price of recycled materials may induce a risk neutral municipality to prefer landfill disposal, even when recycling is less expensive. A model is developed to describe the switching process and estimate its parameters using empirical data from 79 municipalities in Israel. The model is then used to predict municipalities' recycling adoption decisions under various assumptions regarding price uncertainty. The results support the hypothesis that price uncertainty is a major obstacle for recycling. Finally, several options for price stabilization are sketched and it is argued that these policies may be effective in establishing viable recycling markets.     

An analysis of the likely success of policy actions under uncertainty: Recovery from acidification across Great Britain

In the context of wider debates about the role of uncertainty in environmental science and the development of environmental policy, we use a Generalised Likelihood Uncertainty Estimate (GLUE) approach to address the uncertainty in both acid deposition model predictions and in the sensitivity of the soils to assess the likely success of policy actions to reduce acid deposition damage across Great Britain. A subset of 11, 699 acid deposition model runs that adequately represented observed deposition data were used to provide acid deposition distributions for 2005 and 2020, following a substantial reduction in SO₂ and NO_x emissions. Uncertain critical loads data for soils were then combined with these deposition data to derive estimates of the accumulated exceedance (AE) of critical loads for 2005 and 2020. For the more sensitive soils, the differences in accumulated exceedance between 2005 and 2020 were such that we could be sure that they were significant and a meaningful environmental improvement would result. For the least sensitive soils, critical loads were largely met by 2020, hence uncertainties in the differences in accumulated exceedance were of little policy relevance. Our approach of combining estimates of uncertainty in both a pollution model and an effects model, shows that even taking these combined uncertainties into account, policy-makers can be sure that the substantial planned reduction in acidic emissions will reduce critical loads exceedances. The use of accumulated exceedance as a relative measure of environmental protection provides additional information to policy makers in tackling this ‘wicked problem’.     

Policy and Science Implications of the Framing and Qualities of Uncertainty in Risks: Toxic and Beneficial Fish from the Baltic Sea

Policy and research issues in the framing and qualities of uncertainties in risks are analyzed, based on the assessments of dioxin-like compounds (DLCs) and other ingredients in Baltic Sea fish, a high-profile case of governance. Risks are framed broadly, to then focus on dioxins and beneficial fatty acids, fish consumption, human health, and science-management links. Hierarchies of uncertainty (data, model, decision rule, and epistemic) and ambiguity (of values) are used to identify issues of scientific and policy contestation and opportunities for resolving them. The associated complexity of risks is illustrated by risk–benefit analyses of fish consumption and by evaluations of guideline values, highlighting value contents and policy factors in presumably scientific decision criteria, and arguments used in multi-dimensional risk and benefit comparisons. These comparisons pose challenges to narrow assessments centered, for e.g., on toxicants or on food benefits, and to more many-sided and balanced risk communication and management. It is shown that structured and contextualized treatment of uncertainties and ambiguities in a reflexive approach can inform balances between wide and narrow focus, detail and generality, and evidence and precaution.     

A method for ranking components importance in presence of epistemic uncertainties

Importance Measures (IMs) are used to rank the contributions of components or basic events to the system performance, e.g. its reliability or risk. Most times, IMs are calculated without due account of the uncertainties in the model of the behavior of the system. The objective of this work is to investigate how uncertainties can influence IMs and to develop a method for giving them due account in the corresponding ranking of the components or basic events. The uncertainties considered in this work affect the model parameters values and are assumed to be described by probability density functions. The method for ranking the contributors to the system performance measure is applied to the auxiliary feedwater system of a nuclear pressurized water reactor.     

Uncertainties in QRA: Analysis of losses of containment from piping and implications on risk prevention and mitigation

Quantitative Risk Assessment (QRA) is commonly used in the chemical industry to support decision-making. Common practices are based on standard methods, such as fault tree, event tree, etc.; in this frame, risk is a function of frequency of events (probability) and associated consequences (negative outcomes), but relevant uncertainties often are not properly taken into account in the derived results. This paper presents the application of an extended risk analysis of loss of containments for a case-study with the following aims: firstly, the uncertainties related to the results of the analysis, which derive from assumption in the application of the standard models, are qualitatively assessed; secondly the application allows evaluating the impact of the uncertainties on the trustworthiness of the results and, finally, commenting about their use in the risk prevention and mitigation.     

Multi-criteria decision-making considering risk and uncertainty in physical asset management

In this work we present a method for risk-informed decision-making in the physical asset management context whereby risk evaluation and cost-benefit analysis are considered in a common framework. The methodology uses quantitative risk measures to prioritize projects based on a combination of risk tolerance criteria, cost-benefit analysis and uncertainty reduction metrics. There is a need in the risk and asset management literature for a unified framework through which quantitative risk can be evaluated against tolerability criteria and trade-off decisions can be made between risk treatment options. The methodology uses quantitative risk measures for loss of life, loss of production and loss of property. A risk matrix is used to classify risk as intolerable, As Low As Reasonably Practicable (ALARP) or broadly tolerable. Risks in the intolerable and ALARP region require risk treatment, and risk treatment options are generated. Risk reduction benefit of the treatment options is quantified, and cost-benefit analysis is performed using discounted cashflow analysis. The Analytic Hierarchy Process is used to derive weights for prioritization criteria based on decision-maker preferences. The weights, along with prioritization criteria for risk reduction, tolerance criteria and project cost, are used to prioritize projects using the Technique for Order Preference by Similarity to Ideal Solution. The usefulness of the methodology for improved decision-making is illustrated using a numerical example.     

Extensions to minimum relative entropy inversion for noisy data

Minimum relative entropy (MRE) and Tikhonov regularization (TR) were compared by Neupauer et al. [Water Resour. Res. 36 (2000) 2469] on the basis of an example plume source reconstruction problem originally proposed by Skaggs and Kabala [Water Resour. Res. 30 (1994) 71] and a boxcar-like function. Although Neupauer et al. [Water Resour. Res. 36 (2000) 2469] were careful in their conclusions to note the basis of these comparisons, we show that TR does not perform well on problems in which delta-like sources are convolved with diffuse-groundwater contamination response functions, particularly in the presence of noise. We also show that it is relatively easy to estimate an appropriate value for epsilon, the hyperparameter needed in the minimum relative entropy solution for the inverse problem in the presence of noise. This can be estimated in a variety of ways, including estimation from the data themselves, analysis of data residuals, and a rigorous approach using the real cepstrum and the Akaike Information Criterion (AIC). Regardless of the approach chosen, for the sample problem reported herein, excellent resolution of multiple delta-like spikes is produced from MRE from noisy, diffuse data. The usefulness of MRE for noisy inverse problems has been demonstrated.