We received outstanding applications from eight colleges and universities in response to the 2015 TRI University Challenge, and we’ll be working with three of these academic partners for the 2015-2016 academic year.

Descriptions of each of the project proposals are below. Please note that expected project outcomes may change. As the projects continue throughout the school year, we will provide progress updates and post deliverables.  

Indiana University-Purdue University-Indianapolis – IU Fairbanks School of Public Health

Primary Researcher: 

• Dr. Yi Wang, Associate Professor of Environmental Health Sciences

Expected Project Outcomes: 

• Develop the Multi-layer Data Community Action Tool (MDCAT) web portal
• Increase information sharing and enhancement of environmental and public health initiatives
• Demonstrate how environmental data can empower communities 

Mercyhurst University – School of Health Professionals and Public Health

Primary Researcher:

• Dr. Thomas Cook, Assistant Professor of Public Health

Expected Project Outcomes: (Note: this is a 2-year project)

• Produce county profiles with a standardized core set of cross-sectional measures and trend data
• Develop a pilot dataset for Pennsylvania, including a databased code book, coding manual, methodology for extracting data sources, source code for integrating data sources, and a manualized protocol and criteria for inclusion
• Develop a replication pilot database for Puerto Rico with a different set of environmental health priorities and health-related indicators
• Publish a series of case studies in both English and Spanish based on the databases from Pennsylvania and Puerto Rico
• Hold a series of workshops for local health departments, environmental health organizations, and public health students

University of North Carolina, Charlotte – Department of Computer Science 

Primary Researcher: 

• Dr. Aidong Lu, Associate Professor of Computer Science
• Dr. Kalpathi Subramanian, Associate Professor of Computer Science

Expected Project Outcomes:

• Develop interactive visualization and analytical capabilities for studying data correlations
• Design a methodology for disseminating data and its underlying knowledge to the widest extend possible through a variety of venues
• Publish tools and methodology on a project website for public use

In addition to these three new projects, we will continue our partnerships with three multi-year projects selected in the 2014 Challenge: 

Southeastern Louisiana University – Computer Science and Industrial Technology (OSHE Program)

Primary Researchers:

• Dr. Ephraim Massawe, Assistant Professor of Occupational Safety, Health and Environment;
• Dr. John Burris, Assistant Professor of Computer Science

Expected Project Outcomes:

• Conduct research to determine whether a relationship exists between asthma and other respiratory illnesses, daily weather parameters, and TRI data
• If a relationship exists, develop mathematical relationships and models to improve understanding of the connection between TRI data and asthma outbreaks
• Create visualization tools that display outcomes of asthma and other respiratory illnesses using data and functionality from Google maps, CDX, TRI, NOAA, and Louisiana Asthma Coalition

Tennessee State University – Geographic Information Sciences Laboratory

Primary Researcher: 

• Dr. David A. Padgett, Associate Professor of Geography

Expected Project Outcomes:

• Train student team in the use of GIS, GPS, and TRI mapping tools in air quality assessment
• Develop Bucket Brigade air sampling modules for community stakeholders
• Lead community stakeholders in Bucket Brigade air sampling and produce maps displaying results
• Collect stakeholder input on the Bucket Brigade air sample mapping project

University of South Carolina – Department of Geography

Primary Researcher: 

• Dr. Diansheng Guo, Associate Professor of Geography

Expected Project Outcomes:

• Estimate exposure surfaces for different chemicals from TRI point sources
• Map multiple exposures across space and time
• Identify population at risk to certain combinations or levels of toxics
• Link derived data with public health data to create visualizations that display potential correlations, spatially and temporally