A Community-Centric Intelligent Cyberinfrastructure for Addressing Nitrogen Pollution using Web Systems and Conversational AI

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Samrat Shrestha, Jerry Mount, Gabriel Vald, Muhammed Yusuf Sermet, Dinesh Jackson Samuel, Chelsea Bryant, Ana Peralta Brichtova, Marcus Beck , Steven Meyers, Frank E. Muller-Karger, David Cwiertny, Ibrahim Demir

Abstract

The Blue-Green Action Platform (BlueGAP) information system (IS) is an intelligent cyberinfrastructure framework designed to support large-scale water quality assessments in the context of demographic statistics and community stories about water issues. The system prioritizes collaboration with interested parties in three pilot watersheds with test cases implemented in US locations including Iowa, Tampa, and the U.S. Virgin Islands. The BlueGAP IS leverages Artificial Intelligence (AI) technologies with large language models based on regional nutrient management issues and community knowledge to provide access to water quality information. The current focus of the system is on nitrate in drinking water, rivers, and waterways, and can be expanded to incorporate other water quality information. BlueGAP identifies possible partnerships and promotes collaborations among diverse stakeholders to facilitate effective evaluation of nitrogen-related analytes, guide action to address possible pollution, and outline sustainable water management practices. The BlueGAP IS also emphasizes its educational mission by connecting water quality data with inclusive and accessible educational content through AI technology. By integrating nitrogen data and water quality issues into educational resources, BlueGAP fosters a deeper understanding of water quality issues across diverse communities, empowering users to make informed decisions and contribute to sustainable water management practices.

DOI

https://doi.org/10.31223/X5K11D

Subjects

Agricultural Science, Agriculture, Environmental Studies, Marine Biology, Pharmacology, Toxicology and Environmental Health

Keywords

web systems, Information Systems, AI agents, nitrogen pollution management, water quality, Large Language Model, conversational AI

Dates

Published: 2024-09-13 00:36

Last Updated: 2024-09-13 04:36

License

CC BY Attribution 4.0 International