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Using Artificial Intelligence and Machine Learning to Better Understand Environmental Fate and Impacts of Contaminants

Image from Xia Lab - Agricultural Practices

Project Overview

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.

ISSUE: Environmental Impacts of Agricultural Practices

WHAT WAS DONE TO ADDRESS THIS ISSUE: Multi-Scale and Multi-Process assessments of contaminants in agroecosystems

POTENTIAL IMPACTS: support environmentally sustainable agricultural practices

Project Team: 

Kang Xia
Professor, School of Plant and Environmental Sciences