Research

On going projects:

Selected completed projects:

Using Bayesian Networks and satellite imagery to implement transparent decision-making and adaptive management

The project highlights the utility of satellite imagery in developing transparent regulatory decisions within a Bayesian framework. The work involves developing an watershed nutrient delivery model that predicts nitrogen loads in the Neuse River and that is capable of communicating to a dynamic chlorophyll multi-compartment estuary model that is also under development. The project will also be looking at ways to make better use of NASA's MODIS and SeaWiFS ocean color chlorophyll products in order to help in calibrating as well as updating the overall model, while also trying to optimize the existing monitoring network in the study area.

Water Quality Indicators: Nutrient Impacts on Chlorophyll a or Algae Species Composition
(2003-05)

Development of a Prototype Long Term Hydrologic Observatory (LTHO) on the Neuse Watershed
(2003-05)

Analysis of Regional Patterns and Trends in Atmospheric Deposition and EMAP Indicators
(1993-1996)

The Nicholas School of the Environment
Environmental Sciences and Policy Division

Saginaw Bay multiple stressors project

The project involves developing a Bayesian Network model that might effectively encapsulate the primary procedure leading to eutrophication in the Saginaw Bay, an embayment of Lake Huron in Michigan, while at the same time looking at other modulating factors, such as disturbances by invasive species which might also be responsible for altering the dynamics of eutrophication in the Bay.

Parameterizing the Biological Condition Gradient in the Northeast U.S. Using a Bayesian Network Approach

The Biological Condition Gradient (BCG) is a method of systematically defining levels of ecosystem health using characteristics of ecosystem structure and function that respond to increasing stress. This project translates the BCG conceptual framework into a Bayesian network of quantifiable nodes, relationships and probabilities to describe the effect of urbanization on macroinvertebrate biological condition in the Northeast U.S. This involves integrating Federal and State data with expert elicitation from experienced New England biologists to create a set of probabilistic linkages connecting urbanization metrics to interpretation of macroinvertebrate biological condition (via BCG Tiers). By incorporating not only information available from data but also expert knowledge, and uncertainty associated with both data and experts, these probabilistic linkages are able to thoroughly characterize the system of interest. Managers can interactively use this parameterized Bayesian network to calculate the probability of attaining desired aquatic ecosystem BCG tiers assuming different levels of urban stressors, environmental conditions and management options. This project is being conducted and funded through collaboration with USGS.

Saginaw Bay, MI

Bayesian Network Model for Fate and Effects of Hormones in Waste from Concentrated Animal Feeding Operations (CAFOs): Endocrine Disruptors

Natural steroidal estrogen hormones in wastes from Concentrated Animal Feeding Operations (CAFOs) have been arisen as potential pollutants to aquatic environments. The scientific understanding regarding the concentration, fate, and transport of the estrogenic compounds from the swine facilities and their environmental fate and impacts following land application is very limited. Therefore, a comprehensive model that takes into account building a total estrogen budget is needed to characterize the behavior of estrogens with respect to the physical, chemical, and biological factors influencing them, so that ultimately we can assess their ecological impacts on aquatic organisms. To address this issue, a total facility estrogen budget model has been developed to encompass the swine operation systems and spray fields. The model will try to relate production of estrogens at the facility to the impacts on groundwater and surface water using a Bayesian network (BN) model. The model will provide a mechanism to quantify the levels of three forms of estrogens and their activity throughout the swine facilities, while also helping to predict the overall contribution of estrogen compounds from each compartment of the system. The developed model will also assist in assessing the putative impact of estrogen compounds resulting from agricultural practices at the local watershed level, while incorporating uncertainty.

NANO-Ag BayesNet

Currently we are attempting to create a Bayesian Network to model emissions, fate/transport, uptake, and exposure to Nanomaterials in the environment, more specifically Nano-Ag. The idea is to create probabilistic models based on expert elicitation of causal maps that can be updated as experimental data becomes available, providing a useful framework for researchers and risk communicators.

The Reckhow Lab

The project aims at developing a Fecal Coliform TMDL for the Newport River and North River, North Carolina through the use of Bayesian Modeling and Novel Molecular Monitoring Techniques.

The project aims at developing a procedure to evaluate designated use attainment given current data limitations. The project is working on developing a novel method for setting eutrophication-related criteria using the predictive nutrient criteria method and the nutrient criteria utility analysis method. The use of these techniques provides a new and practical approach to select nutrient criteria that will help state agencies and the EPA to set nutrient criteria.

Fecal Coliform TMDL Assessment Project (2004-2009)

Development of a Plan to Achieve a CLEANER (Collaborative Large-scale Engineering Analysis Network for Environmental Research) Neuse River Basin in North Carolina: Nitrogen Pollution
(2004-06)

Developing Regional Scale Stressor-Response Models for use in Environmental Decision-making
(2003-06)

Evaluation of Nutrient Criteria for North Carolina Reservoirs
(2004-05)

Which Nutrient Criteria Should States and Tribes Choose to Determine Water Body Impairment? Using Science and Judgment to Inform Decision-Making (2004-2009)

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