Application of a Bayesian Inference Method to Reconstruct Short-Range Atmospheric Dispersion Events

In the event of an accidental or intentional release of chemical or biological (CB) agents into the atmosphere, first responders and decision makers need to rapidly locate and characterize the source of dispersion events using limited information from sensor networks. In this study the stochastic event reconstruction tool (SERT) is applied to a subset of the Fusing Sensor Information from Observing Networks (FUSION) Field Trial 2007 (FFT 07) database. The inference in SERT is based on Bayesian inference with Markov chain Monte Carlo (MCMC) sampling. SERT adopts a probability model that takes into account both positive and zero-reading sensors. In addition to the location and strength of the dispersion event, empirical parameters in the forward model are also estimated to establish a data-driven plume model. Results demonstrate the effectiveness of the Bayesian inference approach to characterize the source of a short range atmospheric release with uncertainty quantification

Improved Source-Reconstruction Through the Exploitation of Dose-Response Models

The emergency actions following an accidental or intentional release of a hazardous material (HazMat) are strongly influenced by what is known of the HazMat event and its evolution (e.g. mass flowrate of the release, location of the release, and the duration of the release). Thus, the availability of such information in a timely and accurate manner is of great importance for emergency crews both on-site and in the areas in the proximity of the site. This information aids in predicting the fate of the release and mitigating the possible threats imposed by it on human health. Characterizing the source of an atmospheric contaminant is typically regarded as an inverse problem, where one has to infer the source characteristics of the released HazMat from concentration or deposition measurements. The inverse solution is obtained by combining atmospheric dispersion models and concentration measurements in an optimal way, where dispersion models are used to produce concentration predictions in space and time using as input initial guesses of the source information. Furthermore, estimating the source parameters needed for atmospheric transport and dispersion modeling requires the application of deterministic and stochastic inversion techniques, such as the Bayesian frames employing Monte Carlo methods. Locating the source and determining its release rate based on downwind concentration measurements, however, is only viable for some cases like industrial accidents, where measurement data from monitoring stations are typically obtained on-site. For other release cases, such as transport accidents or malevolent attacks, there is a lack of input data (i.e. immediate concentration measurements). Hence, there is a necessity to develop new approaches for real cases where this information is probably not available to the required extent or at all. This work presents the development, application and assessment of computational algorithms used in reconstructing the source characteristics following an accidental release of an airborne hazard. A promising methodology is the utilization of the resulting health symptoms from exposure as indirect input for emergency response systems. In this work, a total of six scenarios were constructed and analyzed in terms of their ability to reconstruct the source rate in addition to the source location. The results revealed that the source term information can be identified with good agreement with the true source parameters when using the new scheme. However, the complexity of the information used as input was reflected on the minimum requirement of input data needed to reconstruct the source term. The results also revealed the necessity to explore other sophisticated sampling and inversion techniques

Information based mobile sensor planning for source term estimation of a non-continuous atmospheric release

This paper presents a method to estimate the original location and the mass of an instantaneous release of hazardous material into the atmosphere. It is formulated as an inverse problem, where concentration observations from a mobile sensor are fused with meteorological information and a Gaussian puff dispersion model to characterise the source. Bayes’ theorem is used to estimate the parameters of the release taking into account the uncertainty that exists in the dispersion parameters and meteorological variables. An information based reward is used to guide an unmanned aerial vehicle equipped with a chemical sensor to the expected most informative measurement locations. Simulation results compare the performance between a single mobile sensor with various amounts of static sensors

A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research

On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research.close

Locating and quantifying gas emission sources using remotely obtained concentration data

We describe a method for detecting, locating and quantifying sources of gas emissions to the atmosphere using remotely obtained gas concentration data; the method is applicable to gases of environmental concern. We demonstrate its performance using methane data collected from aircraft. Atmospheric point concentration measurements are modelled as the sum of a spatially and temporally smooth atmospheric background concentration, augmented by concentrations due to local sources. We model source emission rates with a Gaussian mixture model and use a Markov random field to represent the atmospheric background concentration component of the measurements. A Gaussian plume atmospheric eddy dispersion model represents gas dispersion between sources and measurement locations. Initial point estimates of background concentrations and source emission rates are obtained using mixed L2-L1 optimisation over a discretised grid of potential source locations. Subsequent reversible jump Markov chain Monte Carlo inference provides estimated values and uncertainties for the number, emission rates and locations of sources unconstrained by a grid. Source area, atmospheric background concentrations and other model parameters are also estimated. We investigate the performance of the approach first using a synthetic problem, then apply the method to real data collected from an aircraft flying over: a 1600 km^2 area containing two landfills, then a 225 km^2 area containing a gas flare stack

Information-based search for an atmospheric release using a mobile robot: algorithm and experiments

Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring, thus it has been an active research area for several years known as source term estimation. This paper presents a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately. The estimation is performed recursively using Bayes’ theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with a low-cost gas sensor responds to the hazardous source stimulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for source term estimation based on a mobile robot and a low-cost sensor

Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk

Assessment of the impact of climate change on hydroelectric power

Global climate change is one of the greatest challenges of the twenty-first century. Rising temperatures and alteration of weather patterns are anticipated to result from increased atmospheric concentrations of greenhouse gases, caused, in part, by the use of fossil fuels for electricity generation. Climate change is predicted to have major impacts on many aspects of human society from agriculture to water supply. The process of limiting the extent of climatic change began with the Kyoto Protocol, committing industrialised nations to modest cuts in their emissions. To achieve these and in the longer term, much greater cuts, electricity production must reduce its reliance on fossil fuels, by the increased use of renewable resources. Hydropower is currently the only major renewable source contributing to energy supply, and its future contribution is anticipated to increase significantly. However, the successful expansion of hydropower is dependent on the availability of the resource and the perceptions of those financing it. Increased evaporation, as a result of higher temperatures, together with changes in precipitation patterns may alter the timing and magnitude of river flows. This will affect the ability of hydropower stations to harness the resource, and may result in reduced energy production, implying lower revenues and poorer financial returns. The continuing liberalisation of the electricity industry implies that, increasingly, profitability and the level of risk will drive investment decision-making. As such, investors will be concerned with processes, such as climatic change, that have the potential to alter the balance of risk and reward. This thesis describes a methodology to assess the potential impact of climatic change on hydropower investment, and details the implementation of a technique for quantifying changes in profitability and risk. A case study is presented as an illustration, the results of which are analysed with respect to the implications for future provision of hydropower, as well as our ability to limit the extent of climatic change