Ranji Ranjithan

Per- and polyfluoroalkyl substances (PFAS) are emerging as a major public health problem in North Carolina and across the United States. PFAS comprise a class of over 5,000 compounds. Their unique chemical properties have been harnessed to make consumer and industrial products more water, stain, and grease resistant; they are found in products as diverse as cosmetics and flame-retardants. PFAS are resistant to degradation, move easily through the environment, and accumulate in living organisms. Exposure to PFAS has been associated with health effects including cancer and toxicity to the liver, reproductive development, and thyroid and immune systems. Despite widespread detection in the environment and evidence of increasing human exposure, understanding about PFAS toxicity, its bioaccumulative potential in dietary sources such as aquatic organisms, and effective remediation remain notably understudied. The recent discovery by this proposed Center������������������s Deputy Director, Dr. Detlef Knappe, of widespread PFAS contamination in the Cape Fear River watershed in NC underscores that these compounds are in need of immediate investigation.. The goal of our Center is to advance understanding about the environmental and health impacts of PFAS. To meet this goal we are employing a highly trans-disciplinary approach that will integrate leaders in diverse fields (epidemiology, environmental science and engineering, biology, toxicology, immunology, data science, and advanced analytics); all levels of biological organization (biomolecule, pathway, cell, tissue, organ, model organism, human, and human population); state-of-the-art analytical technologies; cutting-edge data science approaches; a recognized track record in interdisciplinary, environmental health science (EHS) training; and well-established partnerships with government and community stakeholders.

Recent surveys of the national water industry warn of looming costs for capital improvements for drinking water systems in the coming decades [1,2]. One AWWA report estimates that over $1 trillion are needed over the next 25 years, and $1.7 trillion over the next 40 years; about half of this investment would cover the renewal and replacement of aging pipes, and half would pay for system expansions to accommodate population changes [1]. At the same time, SCADA and sensor systems, monitoring and modeling software are facilitating real-time operational decision making in water utilities as never before. These short-term operational decisions, as well as capital improvements such as system expansions, equipment or technology upgrades, and price structures, affect system performance with respect to long-term master planning goals such as system resilience, cost and resource sustainability. Currently, no systematic decision support methodologies exist to optimize the timing of the needed investments over the 25 or 40 year horizon, assessing the criticality of these decisions for overall system vulnerability and resource optimization. The proposed project aims to build a resilience modeling framework using a water utility������������������s in-house data and models to bridge the gap between short-term operations and the medium- and long-term decisions that influence master planning objectives such as system resilience. The objectives of this research are: develop a general purpose resilience modeling framework that integrates computational tools and data to expand the optimization capacity of available data and infrastructure component models from short-term operational to long-term planning horizons; build, demonstrate and test the modeling framework using a case study water utility������������������s data and models to provide in-house decision support for optimal timing and balance between short-term performance and long-term objectives; and develop an open-source water system resilience library that is compatible with a standard resilience modeling language and water infrastructure system modeling tools.

The overall objective of the proposed project is to evaluate the environmental impacts (including greenhouse gas emissions) for three options for managing Montgomery County���s residual municipal solid waste (MSW). Option 1: Disposal at an out-of-state landfill, with transportation by truck, rail, or both. Option 2: Disposal at a new in-county landfill to be constructed on the County���s Site 2 properties in Dickerson, MD with transportation by rail, truck, or both. Option 3: Management at the County���s existing mass burn waste-to-energy (WtE) facility in Dickerson, Montgomery Count, Maryland with transport by rail.

Bridge girder cross-sections continue to become regional in nature, with many state DOTs adopting their own unique sections at either the state or regional level. Typically, girders are developed without consideration of a formal ���������������optimization������������������ of cross-section shape, or when any optimization was employed, the process of optimization and hence its outcome posed several limitations. For example, in many cases, the optimization focused on a single girder without considering any deck on it, whereas the lateral spacing of girders and thickness of the overhead deck are design variables which should be considered while optimizing the girder. Also, such optimization was often based on local search algorithms that do not guarantee global optimality, especially when the solution space is multi-dimensional and highly nonlinear. In most instances, optimization process only included ���������������quantifiable������������������ factors like material cost, volume or weight, labor cost, and formwork cost, etc. But solutions that are mathematically optimal with respect to the quantified factors are not necessarily and readily acceptable when considering non-quantifiable factors and preferences pertaining to practical and field implementation issues. Hence, it is important to extend the optimization procedure to enable outcomes from a formal optimization to be integrated with important subjective considerations. The objective of this research is to develop and apply contemporary meta-heuristic global search procedures for optimizing pre-tensioned decked bulb-tee girders for systematically identifying new optimized cross-section shapes. It is envisioned that, for a specific girder span length and a number of lateral girders, several maximally different alternative cross-sections with competitive structural and cost performance will be first identified; this will be repeated for different combinations of girder spans and numbers of girders to analyze and develop structural and cost performance characteristics variation with girder span length. Then in consultation with AKDOT and precast manufacturers, the alternative optimized cross-section shapes will be screened and fine-tuned based on practical considerations to identify a small set of ���������������best feasible������������������ cross-section shapes. The cross sections to be explored here will be compared for structural performance and material savings against existing ���������������optimized������������������ sections as well as various legacy sections used by Alaska DOT (existing decked-bulb-tee section), and sections employed elsewhere around the US (i.e., AASHTO girders, PCI Bulb-Tees). Preliminary exploratory analysis will be conducted to study the effects of optimized cross-section shapes on extending the girder spans and reducing the number of spans, and therefore the approximate (empirically estimated) net life-cycle cost savings of the bridge system considering the number of piers, foundations, abutments, etc. We expect the outcomes of this project will potentially benefit the AKDOT in improving the ability to span longer distances, reducing overall bridge construction cost, and using resources more efficiently.

Leakage in bulk water pipelines is a major problem for many water utilities as it leads to significant economic losses and cause service disruptions threatening public safety. Most utilities currently employ intrusive methods based on acoustic or infrared signals that can be expensive, time consuming, and require trained personnel. Non-intrusive methods that currently exist require significant computational resources and are not suitable for real-time application. We have developed extremely fast leakage detection algorithms that are suitable for real-time application. These methods rely on a hydraulic model and routine pressure measurements and were developed as part of an NSF project funded from 2011-2016. In partnership with DC Water, Lakewood City (CA), TAGO (an Internet of Things (IoT) company based in Raleigh, NC), and Citilogics (a smart water analytics company based in Cincinnati, OH) we will conduct a proof-of-concept study for isolated sections of each utilities������������������ water networks. Data from existing pressure sensors as well as new sensors installed at strategic locations in the network will be used to validate our algorithms using experimentally simulated leakage scenarios. Citilogics will provide necessary expertise and software for processing the real time data suitable for inputs into the hydraulic model from AMI, SCADA, and CMMS systems. TAGO will build an IoT framework for acquiring the pressure data (from sensors) in real-time and for integrating our algorithms and associated filters on cloud resources. Partner utilities will install the pressure sensors as needed and execute the experiment. NCSU will design the experiment, build the hydraulic model and the leak detection analytics, test the IoT architecture, and evaluate the potential for commercialization.

Floods impact a series of interconnected urban systems (referred to in this project as the Urban Multiplex) that include the power grid and transportation networks, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams, and other system, all of which are intertwined with the socioeconomic and public health sectors. This project uses a convergent approach to integrate these multiple interconnected systems and merges state-of-the-art practices in hydrologic and hydraulic engineering; systems analysis, optimization and control; machine learning, data and computer science; epidemiology; socioeconomics; and transportation and electrical engineering to develop an Urban Flood Open Knowledge Network (UF-OKN). The UF-OKN will be built by bringing together academic and non-academic researchers from engineering, computer science, social science, and economics. The UF-OKN is envisioned to empower decision makers and the general public by providing information not just on how much flooding may occur from a future event, but also to show the cascading impact of a flood event on natural and engineered infrastructure of an urban area, so that more effective planning and decision-making can occur.

The last decade saw a series of catastrophic floods due to hurricanes and storms of increased intensity, and news headlines like Cities Swimming in Raw Sewage After Storm Expose Flaws in System became commonplace. These events exposed the fault lines in flood management. Perhaps more importantly, they also revealed the complex and interconnected nature of engineered, natural and social systems that form the fabric of modern cities. These systems can be conceptualized as a network of networks, or a multiplex, that includes the power grid and transportation network, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams, intertwined with the socioeconomic and public health sectors. Under external pressures and improper management, failures propagating across the Urban Multiplex became obvious - as if viewed under a magnifying glass. Extreme weather is a primary driver of flooding. Its consequences however depend on the interconnectedness of the multiplex components that are, unfortunately, typically designed and/or analyzed independently of one another. For example, a power outage may lead to failure of a storm water network designed to carry the maximum flow during floods, resulting in raw sewage overflow into streets and exposing humans to pathogens. At the same time, storm water overflows could flood critical segments of road networks designed to meet traffic needs, preventing timely evacuation of vulnerable populations. Thus, it is impossible to effectively handle increasingly frequent urban floods by managing these components independently from one another, and ignoring the inherent interconnections of the urban multiplex. Rather, a convergent approach that integrates all interconnected systems and merges state-of-the-art in hydrological and hydraulic engineering; systems analysis, optimization and control; artificial intelligence, data and computer science; epidemiology; socioeconomics; transportation and electrical engineering is proposed in this research.

The primary goal of this proposed Science Across Virtual Institutes (SAVI) effort is to establish a self-sustaining virtual institute to enhance research, education, and outreach related to life-cycle assessment (LCA) of solid waste management (SWM) systems.

The US EPA is interested in developing a next generation tool for sustainable materials management as an update to the current Municipal Solid Waste Decision Support Tool (MSW DST). As part of this task, RTI International (RTI) will conduct a review the current SWOLF software tool being developed at North Carolina State University (NCSU) and assess the work required to convert the tool into a stand-alone desktop application, similar to the MSW DST.

NC State's EFRI PSBR program will model, develop, implement, and evaluate a scalable photosynthetic biorefinery (PSBR) that uses transformational nutrient recycle processes and supports efficient conversion of CO2 to lipid (oil) in a marine microalgae-based system. Algal oils are an ideal feedstock for biofuels production, offering high production density and the ability to use marginal water (municipal wastewater, brackish water, etc.) and reuse CO2 in flue gases. However, there are a number of technical challenges associated with culturing algae in current generation PSBRs. Using a tightly coupled synergistic approach employing both Engineers and Biologists, the team will: a) genetically engineer a marine microalgae species (Dunaliella spp.) with enhanced CO2 uptake/fixation and the capability to recycle N and P from microalgal biomass; b) design a small-scale PSBR informed by our kinetic model which will be used to develop a scalable dynamic reactor model based on computational fluids dynamic simulation of the PSBR; c) develop innovative, scalable approaches for algal harvesting and lipid extraction; and d) develop an analytical framework for the LCA of our microalgal PSBR system to include creation of flexible and scalable cost and LCI process models that will ultimately lead to generation of a robust PSBR life-cycle decision tool that can be applied to this and other PSBR systems. Intellectual Merit New technologies developed as a result of this project for scalable, sustainable culturing of phototrophic marine microalgae for optimized algal oil production will broaden scientific discovery and create the framework, synergy and momentum for biologists and engineers to further explore rational design and operation of PSBRs. Genetic enhancement, reactor modeling, and LCA will be used to optimize production of algal biomass and lipids in our PSBR. Exploration of innovative and efficient means for algal CO2 uptake/fixation, cell harvesting, lipid extraction, and nutrient and water recycle, will transform the scientific development of algae-based biorefineries. Demonstration of novel Lagrangian microsensors that can assess accumulation of light radiation in proportion to its exposure during transport through the reactor will significantly aid in the modeling and testing of PSBR operation in response to light. PSBR design optimization enabled by our experiment-informed kinetic and CFD modeling and LCA will advance knowledge in rational microalgal-based PSBR design and operation, ultimately leading to development of fully scalable and sustainable biofuel feedstock production systems. Broader Impacts The development of truly scalable and sustainable PSBRs offers tremendous economic and environmental impact by reducing the transportation sector?s reliance on fossil fuels. This increases the prospect of finally being able to fully exploit the promise of algae as a biofuels feedstock, given that production of algal-oil derived biofuels that are fully compatible with all existing infrastructure has been demonstrated. Innovative and transformative enabling-technologies that will permit robust production of marine microalgae biomass and lipids in scalable and sustainable PSBRs will bring significant environmental and economic benefits to the nation through the development of an efficient, high-yield alternative energy feedstock production platform. This interdisciplinary research among engineers, microbiologists, molecular biologists and plant physiologists provides unique training opportunities for high school, undergraduate, graduate and postdoctoral scholars to bridge traditional disciplines and become the new generation of scientists and engineers to develop renewable energy for future generations.

The overall objective of the proposed project is to assist the Wake County Solid Waste Division with long-term planning for SWM. SWOLF, a solid waste life-cycle model developed at NCSU, will be utilized to model the county������������������s current solid waste system and to explore and evaluate future alternatives in consideration of appropriate and county-specific preferences and constraints.

NCSU������������������s CCEE department will work with Sensus and Town of Cary (ToC) in designing and executing a series of experiments to evaluate NCSU optimization algorithms (Phases 1-3). Phases 1-3 are expected to take 1 year and will require 1 graduate student from the NCSU CCEE department. Depending on the success of these experiments, additional phases may be carried out with the goal of developing software modules for efficient management and real time operation and control of urban water distribution systems. Activities beyond the experimental phase will be contingent on developing a successful intellectual property agreement between NCSU and Sensus.

Urban water distribution systems (WDS) are prone to leaks, deterioration, and contaminant intrusion. The risks associated with these conditions can be spatially diverse due to flow conditions, network characteristics, and external factors. The key to long term sustainability of these systems is to identify high risk regions and develop sound operational procedures and preventive maintenance plans. This project will develop an adaptive leak detection and contaminant intrusion framework that utilizes real time pressure, flow, and water quality data driven by a high performance simulation optimization engine. The research will also develop a risk analysis capability that could be used to analyze economic and public health risks associated with gradual leaks and contaminant intrusion occurring in day to day operations. In a recently completed project funded by NSF, the research team has developed an adaptive high performance simulation-optimization engine and associated optimization methodologies for contaminant source identification and sampling design in water distribution systems. While the proposed work will build on this work to an extent, a number of new developments are planned including: (a) a new simulation-based leak detection and contaminant intrusion detection methodology that uses routinely measured pressure, flow and water quality measurements (e.g., from a SCADA system), (b) a methodology for incorporating spatially varying macro indicators (e.g., pipe attributes) to improve the search process, (c) a methodology for incorporating demand uncertainty in leak detection, and (d) a risk assessment methodology that targets economic losses from leaks and public health risks from contaminant intrusion. The developed framework could be used by decision makers to make operational decisions and to develop long term maintenance and expansion plans. The system can also be used by decision makers to evaluate the risk reduction potential of different response and maintenance actions. In collaboration with a local utility, the leak detection and risk assessment framework developed through this research will be applied and validated using data from a mid-sized urban area. The major objectives are to: (a) develop a parallel simulation-optimization leak detection and contaminant intrusion methodology that uses routinely measured pressure and water quality data; (b) develop a Markov Chain Monte Carlo (MCMC) methodology to incorporate prior information and demand uncertainty into the leak and contaminant intrusion detection framework; (c) develop a methodology for evaluating economic and public health risks associated with leaks and contaminant intrusion; (d) test and evaluate the computational framework and the associated components for a mid- sized urban area; and (e) integrate and disseminate the research results in classroom teaching for college students, as well as continuing education and short courses for practitioners. Intellectual Merit. While complementing other related NSF funded efforts, the proposed research hinges on a number of paradigm shifting and transformational developments, including the conjunctive use of routine operational measurements and external factors for generating leak and contaminant intrusion maps in real time, incorporating uncertainties in a probabilistic framework, and the use of high performance computing technologies to enable real time computation. Broader Impact. Besides addressing one of the nation?s critical infrastructure security priorities in a real setting, the project contributes in general to the development of adaptive simulation methods and optimization algorithms that can harness high end computing resources. The computational framework developed through this proposal will set the stage for using emerging automated data collection systems for real time characterization.

The goal of the proposed research is to investigate the cost and environmental implications of emerging greenhouse gas (GHG) reduction policies on the solid waste management (SWM) sector, and to outline alternative ways in which municipal solid waste (MSW) managers may optimally respond through changes to material flows and choice of process-technologies in SWM systems. This will be accomplished through the following research objectives: 1. Evaluate the changes over time in technology choices for individual MSW processes (e.g., collection, landfills, and composting facilities) that would most cost-effectively respond to climate change mitigation policies. For example, there are multiple technologies available for aerobic composting and landfill gas collection with varying levels of GHG emissions, and their cost-effectiveness will vary in response to climate change mitigation policies. 2. Evaluate the changes in integrated SWM strategies (i.e., waste flows and process choices) that would most effectively respond to different climate change mitigation policies. For example, as the price of energy and GHG emissions increases, the cost-effectiveness of each MSW process may change by varying degrees, affecting cost-effective combinations of integrated SWM programs designed to meet climate change mitigation goals. 3. Evaluate and determine the effects of climate change policies on other environmental emissions and impacts associated with integrated SWM strategies responding to climate mitigation policies. For example, a SWM program that responds to GHG emissions reduction policies may adversely impact other environmental considerations such as acidification potential, water consumption, and ozone depletion. 4. Evaluate and determine the interdependent effects of specific policies designed to influence SWM in a carbon regulated environment. For example, waste combustion is banned in some jurisdictions yet this encourages an inferior technology (landfills) with respect to GHG emissions.

NCSU has yet to tap existing on-campus resources to build our capacity to be a leader in urban sustainability. Capitalizing on NCSU’s present strengths in the College of Design, the College of Natural Resources, the College of Engineering, and the College of Humanities and Social Sciences,this funding will establish a Sustainable Cities Consortium to allow the scattered expertise and interest at NCSU to become more relevant and powerful, solidifying NCSU’s position as a leader to address the growing emphasis on both opportunities and challenges in urbanized and rapidly urbanizing areas. A series of workshops at NCSU, engaging 30-50 NCSU faculty and staff, will be focused on establishing a well-documented network to address funding opportunities, as well as determining interdisciplinary topics for white papers and proposals. A collaborative project site will also be established through a reputable online tool to more quickly identify appropriate collaborators through their expertise areas to streamline collaborative responses to funding opportunities.

The goal of the proposed research is to develop a life-cycle assessment (LCA) model capable of analyzing solid waste management (SWM) performance ? at both the individual process and integrated system levels ? taking into account implications of greenhouse gas (GHG) mitigation policies and competing SWM objectives (e.g., costs, emissions, and diversion targets). An integrated life-cycle optimization model will be developed to estimate the costs, energy use, emissions, and environmental impacts associated with the processes (e.g., collection, separation, waste-to-energy [WTE], composting, anaerobic digestion, landfilling) that constitute the SWM system. The model will be used to meet the following objectives: 1. Quantify the increased costs associated with various SWM processes due to different GHG mitigation policies including anticipated energy price changes induced by these policies. 2. Evaluate changes in integrated SWM strategies (i.e., waste flows and process choices) that most effectively respond to different GHG mitigation policies. 3. Quantify the effects of GHG mitigation policies and related energy price changes on other SWM-related environmental impacts (e.g., smog formation and acidification).

The purpose of this proposal is to establish a graduate research fellowship program to train students to be future leaders in the area of engineering of resilient civil infrastructure systems for coastal regions considering natural hazards. This program will be conducted in coordination with the ongoing DHS Center of Excellence on Natural Disasters, Coastal Infrastructure and Emergency Management.

Ensuring sustainability of water resources is often challenging owing to the range of time scales over which the water management problems span. In addition, limited scope for developing (expanding) new (existing) water supply infrastructure systems and use of conservative operational guidelines based on observed information only increase the vulnerability of existing systems to continually changing supply and demand variations. Though these issues in managing water supply systems (reservoirs, lakes) are common in many regions of the country, what is lacking is a systematic integrated approach that improves sustainability of existing systems in ensuring the desired reliability and resilience even under these changing conditions. Commonly employed strategies, e.g., water use restrictions and supply augmentation, are often invoked retroactively resulting in increased vulnerability on system management. We propose to research, develop and demonstrate methods that promote proactive and prognostic water management plans conditioned on near-term and short-term streamflow forecasts, which are developed based on weather and climatic information, respectively. By improving the operation of the reservoir systems adaptively using multiple time scale forecasts, we envision that such a prognostic approach will result in better system performance over the long-term and potentially reduce investments associated with additional structural measures or inter basin transfers to meet the supply and demand variations.

NCSU through the GIT student will provide research and analysis to SAS as set forth in this Agreement. Such research and analysis shall include, but is not limited to, research, generation, testing, and documentation of operations research software. GIT Student will provide such services for SAS' offices in Cary, North Carolina, at such times as have been mutally agreed upon by the parties.

While global interest in sustainable buildings has driven engineering innovations in building-related technology, considerably less attention has been paid to the effectiveness of the prevailing building design process. Computer simulations of building performance to assess the impact of design choices on energy, water, and material consumption as well as emissions are only loosely coupled to the architectural design process. Further, building simulation models only provide point estimates of performance, relying on users to make manual changes to inputs and rerun the model. The result is a design process driven by informed trial-and-error rather than quantitative building-specific performance data, which makes design for sustainability difficult. We hypothesize that the availability of targeted building performance data through the full breadth of the architectural design process will lead to innovative designs that improve the use of energy, water, and materials during both building construction and operation. The goal of this research is to evaluate a human-computer joint cognitive design process, allowing architects to couple building information models (BIM) with building performance analysis to create a progressive decision-making framework for building design. To meet this goal, we will test an approach to find good configurations of building options to meet performance objectives specified at any stage of the design process. This search will explore an array of alternatives that underscore the multiplicity of design possibilities available to match the design objectives.

The remainder of the work involves the parallel implementation of the Bayesian methods. The full remaining tasks are given below. Specifically, a parallel implementation of the Markov chain Monte Carlo method is to be developed and the results are to be disseminated; there is expected to be ultimate integration into the grid computing framework. To date, the testing of the methods have been implemented on a single networked computer. With this parallel implementation, it will be possible to further relax the assumptions so that an additional and rich set of problem features can be incorporated (alternate contaminant source profiles, multiple sensors, and additional sensor types). The work will be pursued as outlined in the original proposal; the key computational unit?an evaluation of the likelihood of the sensor data?will be distributed across multiple computers that are called by multiple computers running parallel MCMC chains. In addition, as specified in the proposal, local computer and tolerances for stopping criterion will be controlled to balance the tradeoff between accuracy and run times; the accuracy in identifying the best subsequent sampling location will be balanced against the need to quickly zero in on the contaminant source characterization. The work will then be integrated into the overall project codes/software in the Fall of 2008 with close coordination with project partners.

Effective threat management of urban water distribution systems, a critical civil infrastructure, is a challenging problem due to the dynamic nature of data and management requirements. Recent home-land security concerns have underscored the need for a dynamic feedback and control system that will automatically adapt to ever changing conditions and also devise optimal threat management strategies. Effective threat management requires that the source be characterized and contained and strategic shutdown and decontamination procedures implemented. An automated sensor network and an infrastructure that can use this data and control the data collection process is an important if not essential component in this process. How these systems are managed on a day to day basis and spontaneous threat management strategies will have profound socio-economic implications that may affect local city authorities as well as consumers. Adaptive management strategies will enable dynamic control of valves and pumps to control flow to meet demand conditions as well as to respond to accidents such as leaks and breakages. This in turn will lead to reliable and secure systems with increased operational life of various components (pipes, pumps, valves, sensors etc.). While some current systems have a level of automation in the way the major valves and pumps are controlled, they mostly rely on data provided by static sensor networks and do not provide any feed back to the sensors. This multi-disciplinary DDDAS proposal will attempt to develop a cyberinfrastructure system that will both adapt and control changing data, model, computer resources, and management needs.

We propose to do the following for Wake County, with intermediate targets and deliverables. - Review existing data provided by Wake County on: -- waste characterization by several cities in Wake County -- engineering design reports on composting and waste-to-energy combustion facilities that have been prepared for the county Working in coordination with the county solid waste staff, this review would be conducted to obtain as much site-specific data as possible for input to the SWM-LCI (Solid Waste Management Life Cycle Inventory) model. Based on this information, we will develop input data and a strategy to represent Wake County in the SWM-LCI model. This will take advantage of the existing model's flexibility to represent a site-specific scenario.

The objective of this research is to estimate the environmental benefits of the recycling and reuse of commercial, industrial and agricultural wastes generated in the State of Delaware.

The supplement will increase minority participation in the project by supporting an african american PhD student. This student will develop parallel hybrid optimization approaches to enable realtime processing of environmental characterization problems. This student will also participate in various instructional and outreach activities to reinforce diversity as a resource for enriching education in environmental engineering.

The objective of this proposal is to complete a report on the application of life-cycle analysis to solid waste management planning in the State of Delaware.