Projects

We are all familiar with the concept of the electrical grid. Images of a distributed network of power plants, transmission lines, and end point consumers come to mind. We draw a mental picture of operators in control rooms, monitoring the grid in real time. As we contemplate the electrical grid of the future, we think of it as being smart, with ubiquitous sensors and information flows from producers to consumers and back. We think of it as being green and sustainable, with electricity produced from renewable, nonfossil fuel sources. We even envision a globe‐spanning electrical Supergrid. Similarly, we can think of water systems as a HydroGrid that encompasses surface water and groundwater, natural environment, and built infrastructure. We can draw analogies and learn from the knowledge base of the electrical grid to better understand and sustainably manage the HydroGrid.   

This project seeks to minimize economic and human losses from future urban flooding in the United States. 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. We use 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 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.

Even as a waterborne pathogens present significant problems in both developed and developing worlds, their complete eradication is not feasible for technical or economic reasons. Surface waters are particularly vulnerable because they do not have the benefit of natural filtration through soil. As many municipalities worldwide use surface waters as a major source of potable water, their impairment is of significant concern. A modeling strategy that takes into account the inherent randomness of the occurrence and transport of pathogens in surface water is important for accurate risk assessment and prediction of water contamination events. We have developed a stochastic Markov model of microorganism transport, with distinct states of microorganism behavior capturing the microbial partitioning between solid and aqueous phases in runoff and soil surface, including the partitioning among soil particles of various sizes. We are working on extension of this model to different types of biological and chemical contaminants. This model is also a basis for stochastic fate & transport analysis in complex systems such as watersheds.

Operating in an uncertain environment is one of the biggest challenges in both civil and military settings. The natural environment often serves as a medium for biological and chemical threat regardless of its origin. For threat reduction, we are developing a dynamic multi-scale decision framework based on (1) situational awareness using Geographic Information Systems (GIS); (2) strategic and optimized bio-surveillance leading to establishment of a dynamic observatory; and (3) multi-scale data analysis and predictive models of environmental contamination, exposure and disease outbreaks.  This effort will result in a comprehensive, systems-based decision making resource that could be used to help prevent infectious disease outbreaks due to waterborne pathogens, and to provide rapid response during such events. It will help identify sources of contamination, the origin of pathogens, and predict the scale of ongoing outbreaks.

Many contaminant interact with soil particles as they rest on soil surface or travel in overland flow, therefore there is often clear partitioning of contaminants in soil/water systems. We conduct lab experiments to measure it. This involves studies of contaminant distribution in soil sediments of varying sizes and of contaminant-soil particle aggregate behavior. The measurements are used in inference of fate&transport model parameters.

The extent of water quality alteration due to the changing climate is not easy to measure for two main reasons.  First, monitoring and sample testing to obtain comprehensive data on water quality is difficult; and second, because climate change has an indirect impact on water quality. Rather, it is the complex interplay between soil, land surface and, most of all, hydrological parameters, which determines the contaminant loads and transport in surface and ground waters. We study correlations and spatiotemporal patterns of climate and water quality parameters based on large datasets. 

Impact of climate change on water quality

Impact of climate change on water quality

Interactions of HIV with the immune system have been studied experimentally and using mathematical models. Epidemiological studies provided insight into HIV dynamics in human populations. The importance of social network topology in epidemics became apparent as research on structure of technological networks progressed. We have developed a mathematical model that bridges the scales between in-host processes and HIV transmission in human sexual networks. Each individual goes through a sequence of health states reflecting HIV status and treatment. Associated with each state is an in-host model of HIV-immune system interactions. At the same time, each individual is a node in a sexual network. We investigate the effect of a variety of medical care criteria and decisions on both the health state of the individual, and on the spread of disease in populations with different network topologies. This model is currently being extended to describe waterborne disease.