Using Artificial Intelligence and Machine Learning to Better Understand Environmental Fate and Impacts of Contaminants
To gain a comprehensive understanding of processes affecting the environmental behaviors of various contaminants with the long-term goal of better informing risk assessment and policies to protect human health while minimizing the environmental burden
CALS Strategic Priority that best describes project goals:
Priority 1: Advance Excellence in Research, Teaching and Extension for the Commonwealth and Beyond
BENEFICIARIES: farmers and policy makers
METHODOLOGY USED: integrating field, lab and AI investiagtive approaches
Relation to Hatch or Hatch Multistate project: W4170, Beneficial Use of Residuals to Improve Soil Health and Protect Public, and Ecosystem Health, Greg Evanylo (PI)
External Funding Sources Associated with the project:
- USDA-NIFA, Can Manure Land Application Practices Designed for Best Nutrient Management Reduce the Flow of Antimicrobial Resistance Elements in Agro‐ecosystems and Enhance Overall Ecosystem Services?
- USDA-NIFA, Developing Computational Tools to Identify Critical Control Points for Mitigating the Spread of Antibiotic Resistance in Agro-ecosystem.