Preliminary Interdependency Analysis: An Approach to Support Critical Infrastructure Risk Assessment

We present a methodology, Preliminary Interdependency Analysis (PIA), for analysing interdependencies between critical infrastructure (CI). Consisting of two phases – qualitative analysis followed by quantitative analysis – an application of PIA progresses from a relatively quick elicitation of CI-interdependencies to the building of representative CI models, and the subsequent estimation of any resilience, risk or criticality measures an assessor might be interested in. By design, stages in the methodology are both flexible and iterative, resulting in interacting CI models that are scalable and may vary significantly in complexity and fidelity, depending on the needs and requirements of an assessor. For model parameterisation, one relies on a combination of field data, sensitivity analysis and expert judgement. Facilitated by dedicated software tool support, we illustrate PIA by applying it to a complex case-study of interacting Power (distribution and transmission) and Telecommunications networks in the Rome area. A number of studies are carried out, including: 1) an investigation of how “strength of dependence” between the CIs’ components affects various measures of risk and uncertainty, 2) for resource allocation, an exploration of different, but related, notions of CI component importance, and 3) highlighting the impact of model fidelity on the estimated risk of cascades

Integrated System Dynamic Study and Prognosis on Municipal Solid Waste Management for Northern New Jersey Urban Area

Ordinary effects from everyday human activity such as waste generation and management have currently graduated to a global problem, vexing the social, environmental, economic and political functions of the society at all spatial scales. The US has one of the highest per capita productions of waste in the world accounting for an average of 4.5 pounds per person per day. With approximately 8.7 million residents (US Census Bureau, 2006) New Jersey comprises the most densely populated state in the nation and thus has more than a passing problem to face in the area of waste management. Integrated waste management involves the prediction of waste generation of an area and analysis of its impact on the environment and general socio-economics involved of the process, for adopting better management procedures and achieving optimized results. This work tries to assess possible future scenarios at the city level for the state of New Jersey and seek to derive an optimal balance in municipal solid waste management (MSW) operations at different scales of human activity that contributes to it (from the individual to the state), incorporating environmental, social and economic parameters. The dissertation, thus, provides the results of three primary research objectives: The first objective involved estimating the possible future scenario of MSW generation at business-as-usual rate using a prognosis model build on a Java platform for a representative city in New Jersey and determining the limitations. The second objective was to explore the possibility of integrating systems dynamics methodology for a comprehensive assessment of waste management system and their impacts so as to provide integrated assessments though a systems perspective and contribute to state (or regional) scale planning. The increase in waste generation and related management costs, apart from relating it to general population growth, can also be related to geographic size and nature of urban development. The third objective was thus to assess the possibilities of developing alternate solutions for the issue of urban solid waste management incorporating geoprocessing methods using Geographic Information System. By the proposed evaluation procedure of current methods of management, it would be possible to make better decisions in administering and planning of urban waste resource functions to attenuate adverse future environmental, economic and social impacts

A Purview of Waste Management Evolution: Special Emphasis on USA

The generation of waste in urban regions over time is seen to impact the balance of anthropogenic and natural resources. Various national and international initiatives to manage urban solid waste are in place and has thus have evolved at present to form an assortment of different subcomponents involving environmental, administrative, regulatory, scientific, market, technology, and socio-economic factors, which has increasing bearing on the US due to its volume and nature of discards. This paper draws together the various aspects of municipal solid waste (MSW) management as it evolved, particularly in the American society through reviewing works and findings. In many parts of the country, waste management at present, primarily involves landfilling, incineration with and without energy recovery, recycling and composting. Legislation, nature of wastes and market trends continue to redefine management operations and its responsibilities and impacts. Complexities are added to it by the nature of urban development as well. New studies and concepts like 3Rs, cradle-to-cradle, industrial ecology, and integrated waste management are adding new dimensions for solving waste problems towards achieving sustainable resource use. Local initiatives, both public and private are in the forefront of adopting alternate waste management procedures. The assistance from various government and private bodies, supporting shifts in waste management approaches, have immense value, as according to the new paradigms, nothing goes to waste

A System Dynamic Modeling Approach for Evaluating Municipal Solid Waste Generation, Landfill Capacity and Related Cost Management Issues

As planning for sustainable municipal solid waste management has to address several inter-connected issues such as landfill capacity, environmental impacts and financial expenditure, it becomes increasingly necessary to understand the dynamic nature of their interactions. A system dynamics approach designed here attempts to address some of these issues by fitting a model framework for Newark urban region in the US, and running a forecast simulation. The dynamic system developed in this study incorporates the complexity of the waste generation and management process to some extent which is achieved through a combination of simpler sub-processes that are linked together to form a whole. The impact of decision options on the generation of waste in the city, on the remaining landfill capacity of the state, and on the economic cost or benefit actualized by different waste processing options are explored through this approach, providing valuable insights into the urban waste-management process

Advances in Mapping Woody Plant Canopies Using the NASA MISR Instrument on Terra

Red (672 nm) band reflectance estimates from the NASA Multi-angle Imaging Spectro-Radiometer (MISR) on the Earth Observing System Terra satellite were used to obtain maps of woody plant fractional crown cover, mean canopy height, and biomass for large parts of south-eastern Arizona and southern New Mexico (\u3e200,000 km2). MISR red band bidirectional reflectance estimates in nine views mapped to a 250 m grid were used to adjust the Simple Geometric-optical Model (SGM) that is based on the principles of Boolean geometry first exploited in LiStrahler geometric-optical (GO) models. The soil-understory background signal was decoupled a priori by exploiting relationships with the nadir camera reflectance data and the kernel weights of the LiSparse-RossThin kernel-driven bidirectional reflectance distribution function (BRDF) model. Maps of fractional crown cover, mean canopy height, and biomass were produced via retrievals of the mean crown radius and shape parameters from inversion of the SGM using the Praxis algorithm. The mean absolute error in randomly sampled and filtered estimates of fractional crown cover, mean canopy height, and woody biomass was 0.10, 2.2 meters, and 4.5 tons acre-1 (10.1 Mg ha-1), with RMS errors of 0.12, 3.3 and 6.2 (14.0), and coefficients of determination (R2) of 0.78, 0.69, and 0.81, respectively, significant at the 0.01 level (N=576)