U.S. Environmental Protection Agency

The Indices of Environmental Status and Trend

Research Project

The Indices of Environmental Status and Trend project was part of a consortium including American, George Mason, Maryland-Baltimore County and Penn State that was organized by the EPA's Center of Environmental Information and Statistics. NISS worked on the protocols and case studies for the public release and use of environmental data. NISS identified sources of air quality and meteorological data, and obtained the data required for the analysis. This process revealed both strengths and weaknesses in the resources that are currently readily accessible.

Mechanisms for Rapid Response to Environmental Statistics Problems

Research Project

Under a cooperative agreement with the EPA, NISS provided the EPA rapid access to the national statistics community, which in turn furnished statistical input and commentary on environmental issues in a timely basis to policy and decision makers, other officials and researchers. This project provided rapid, accurate, and informed responses by statistical experts to inquiries about issues important to the EPA.

Technical Report(s):

Statistical Strategies for Environmental Modeling and Monitoring

Case Study


Outcomes & Results

Research Project

The Environmental Protection Agency and scientists from several universities across the United States and Canada worked on network design for environmental monitoring; space-time models, data fusion, health and pharmacokinetics; risk assesment of toxicants; and data representation and reporting.

Statistical Analysis and Predictive Modeling for OMICS Technology

Research Project

Emerging OMICS technologies such as microarray and highthroughput screening have been adopted across a broad spectrum of applications as these technologies are capable of simultaneous measurements of very large numbers of channels. Thus they enable rapid experimental turnaround with an accompanying promise to extract the small number of relevant elements or factors from a massively larger number of inert ones.