Session Lead: Jianyong Wu (College of Public Health, Division of Environmental Health Sciences, The Ohio State University)

Co-Lead(s): Dongmei Alvi, Efeturi Oghenekaro

Session Format: Oral presentations

Session Description: 

As concerns about waterborne diseases and emerging contaminants grow, understanding water quality dynamics becomes crucial for protecting public health. This special session will focus on how advanced technologies can be applied to model, monitor, and manage water quality in the Chesapeake Bay region and beyond. By integrating advancements in machine learning, artificial intelligence (AI), Geographic Information Systems (GIS), and remote sensing, this session highlights innovative approaches to tackling public health challenges posed by contaminants in water.

Participants will examine how machine learning algorithms can improve the prediction and monitoring of water quality parameters, enabling the timely identification of pollution events and hotspots. The session will also showcase GIS-based methods for spatial analysis and visualization of health risks associated with emerging contaminants, providing actionable insights for water resource managers and public health officials. Additionally, the use of remote sensing technologies for large-scale monitoring of water quality and emerging contaminants will be discussed, emphasizing high-resolution, timely data collection to support restoration and management efforts in dynamic environments.


This session invites contributions that demonstrate the integration of these novel tools with traditional monitoring approaches, highlighting interdisciplinary applications and “team science” strategies. Case studies and practical examples of data-driven approaches to water quality monitoring and public health protection in the Chesapeake Bay or similar ecosystems are especially encouraged. Discussions will also consider challenges associated with interpreting complex datasets, managing misinformation, and communicating results effectively to stakeholders in a rapidly changing natural and human environment.


By convening managers, scientists, and stakeholders, this session aims to foster dialogue around the next generation of tools for water quality assessment and their implications for public health. Topics may include, but are not limited to:
• Application of machine learning algorithms for water quality prediction and monitoring.
• GIS-based spatial analysis and visualization of health risks from emerging contaminants.
• Remote sensing approaches for large-scale water quality monitoring.
• Integration of AI and mechanistic models for forecasting water quality dynamics.
• Interdisciplinary and team science approaches for addressing emerging water quality challenges.

***Note: Session lead Jianyang Wu is a guest editor for “Water,” an open-access, peer-reviewed journal, that is now accepting research papers through October 20th. Check out the Call for Papers for more information.

Presentations (Session 19 Abstracts):

  1. Emily H. Majcher: Status of PFAS in surface waters of the Chesapeake Bay Watershed and associations with sources and landscape characteristics
  2. Yanni Cao: Environmental Indicators of PFAS in an Urban Watershed Revealed by Explainable Machine Learning
  3. Benjamin Schelling, Margaret Mulholland: The Effects of Tidal Flooding on the Transport of Per- & Polyfluoroalkyl Substances (PFAS) into the Lafayette and Elizabeth Rivers
  4. Dongmei Alvi: Explainable AI Illuminates Microbial Contributions in a Highly Urbanized Watershed
  5. Anna Van Dongen: Public Health and Water Quality: Investigating the Relationship between Bacteria and Colored Dissolved Organic Matter
  6. Veronica Manka’a Tangiri: Analysis of the Health and Population of Benthic Macroinvertebrates with Increasing development: Quantico Creek Watershed, Prince William County, Virginia