Comparison of info-gap and robust optimisation methods for integrated water resource management under severe uncertainty

This paper evaluates two established decision making methods and analyses their performance and suitability within an Integrated Water Resource Management (IWRM) problem. The methods under comparison are Info-Gap decision theory (IG) and Robust Optimisation (RO), with particular regard to two key issues: (a) a local vs global measure of water supply robustness and (b) a pre-specified vs optimisation method of generating intervention strategies. Solutions are compared with plans proposed from current industry practice especially in regard to employing a longer planning horizon. The results reveal the impact of using alternative methodologies and analysis parameters on the final intervention strategies selected

Evaluation Of Decision Making Methods For Integrated Water Resource Management Under Uncertainty

Water companies and utilities in the UK are required to produce Water Resource Management Plans (WRMPs) every five years that outline their future strategies for maintaining a secure water supply to meet anticipated demand levels. Regulatory frameworks differ around the world but in most countries similar plans are developed under the auspices of Integrated Water Resources Management (IWRM) programmes. The plans justify new demand management and water supply infrastructure needed and validate management decisions. One of the greatest problems now facing decision makers in the water industry are the increasing uncertainties in the variables used in estimating the supply and demand balance due to increased levels of climate change. WRMPs in the future will need to deliver plans that can adapt water systems to face a widening variation of possible future states; with increased consideration to uncertain water availability, resource deterioration and demand levels. This paper reviews several established decision making methods and analyses their performance and suitability within an IWRM problem. The methods include Info-Gap decision theory, Robust Optimisation, Minimax Regret, Laplace theory and Maximin theory. These methods have been designed to aid decision making under severe uncertainty but differences exist in their approach and attitude to risk. For example, the Info-Gap methodology offers solutions that provide robustness of sufficing over a wide range of uncertainty, but is highly dependent on initial parameter settings. Robust Optimisation concentrates on optimising for robustness over a set of objective functions instead of satisfying a set of constraints. Laplace, Maximin and Minimax Regret are all classical decision methods that implicitly reflect a particular attitude to risk when dealing with severe uncertainty. These methods were applied to a case study resembling the Sussex North region in England, assessing their applicability at improving the IWRM problem and highlighting the strengths and weaknesses of each method

An assessment of uncertainties in the analysis of the impact of climate change on flooding

This thesis aims to address the role of uncertainty in climate change impact studies, with particular focus on the impacts of climate change on UK flooding. Methods are developed to quantify the uncertainty associated with climate variability, hydrological model parameters and flood frequency estimation. Each is evaluated independently, before being combined to assess the relative importance of the different sources of uncertainty in the ‘top down’ impact study framework over multiple time horizons. The uncertainty from climate variability is addressed through the creation of a resampling methodology to be applied to global climate model outputs. Through resampling model precipitation, the direction of change for both mean monthly flows and flood quantiles are found to be uncertain with large possible ranges. Hydrological model parameter uncertainty is quantified using Monte Carlo methods to sample the model parameter space. Through sensitivity experiments, individual hydrological model parameters are shown to influence the magnitude of simulated flood quantile changes. If a larger number of climate scenarios are used, hydrological model parameter uncertainty is small only contributing up to 5% to the total range of impacts. The uncertainty in estimating design standard flood quantiles is quantified for the Generalised Pareto distribution. Flood frequency uncertainty is found to be most important for nearer time horizons, contributing up to 50% to the total range of climate change impacts. In catchments where flood estimation uncertainty is less important, global climate models are found to contribute the largest uncertainty in the nearer term, between 40% and 80% of the total range, with emissions scenarios becoming increasingly important from the 2050s onwards

Nitric oxide and superoxide mediate diesel particle effects in cytokine-treated mice and murine lung epithelial cells — implications for susceptibility to traffic-related air pollution

Abstract Background Epidemiologic studies associate childhood exposure to traffic-related air pollution with increased respiratory infections and asthmatic and allergic symptoms. The strongest associations between traffic exposure and negative health impacts are observed in individuals with respiratory inflammation. We hypothesized that interactions between nitric oxide (NO), increased during lung inflammatory responses, and reactive oxygen species (ROS), increased as a consequence of traffic exposure ─ played a key role in the increased susceptibility of these at-risk populations to traffic emissions. Methods Diesel exhaust particles (DEP) were used as surrogates for traffic particles. Murine lung epithelial (LA-4) cells and BALB/c mice were treated with a cytokine mixture (cytomix: TNFα, IL-1β, and IFNγ) to induce a generic inflammatory state. Cells were exposed to saline or DEP (25 μg/cm2) and examined for differential effects on redox balance and cytotoxicity. Likewise, mice undergoing nose-only inhalation exposure to air or DEP (2 mg/m3 × 4 h/d × 2 d) were assessed for differential effects on lung inflammation, injury, antioxidant levels, and phagocyte ROS production. Results Cytomix treatment significantly increased LA-4 cell NO production though iNOS activation. Cytomix + DEP-exposed cells incurred the greatest intracellular ROS production, with commensurate cytotoxicity, as these cells were unable to maintain redox balance. By contrast, saline + DEP-exposed cells were able to mount effective antioxidant responses. DEP effects were mediated by: (1) increased ROS including superoxide anion (O2˙-), related to increased xanthine dehydrogenase expression and reduced cytosolic superoxide dismutase activity; and (2) increased peroxynitrite generation related to interaction of O2˙- with cytokine-induced NO. Effects were partially reduced by superoxide dismutase (SOD) supplementation or by blocking iNOS induction. In mice, cytomix + DEP-exposure resulted in greater ROS production in lung phagocytes. Phagocyte and epithelial effects were, by and large, prevented by treatment with FeTMPyP, which accelerates peroxynitrite catalysis. Conclusions During inflammation, due to interactions of NO and O2˙-, DEP-exposure was associated with nitrosative stress in surface epithelial cells and resident lung phagocytes. As these cell types work in concert to provide protection against inhaled pathogens and allergens, dysfunction would predispose to development of respiratory infection and allergy. Results provide a mechanism by which individuals with pre-existing respiratory inflammation are at increased risk for exposure to traffic-dominated urban air pollution.</p

Characterization of mouse macrophage differentiation antigens by monoclonal antibodies

Monoclonal rat antibodies to mouse macrophage antigens were prepared. For immunization phagocytic cells in the spleens of mice recovering from sublethal irradiation were used. Specificities of the monoclonal antibodies obtained were determined on cells of normal mouse cell populations as well as on cells of a panel of mouse cell lines. In an attempt to monitor expression of differentiation-related antigens two models of in vitro-induced macrophage differentiation were used: differentiation of cells of the myeloblast line M1; CSF-1 -induced differentiation of bone marrow cells. The results obtained clearly show that during maturation from undifferentiated to highly differentiated cells of the macrophage lineage expression of antigens recognized by the MIV 38, MIV 55, MV 87, and MV 114 monoclonal antibodies is enhanced. At the same time, expression of antigens recognized by the MIV 52, MIV 113, and MIV 116 monoclonal antibodies diminishes at a similar rate. The suitability of these monoclonal antibodies for the characterization of differentiation states of mouse macrophages is discussed