MyEcoReporter: a prototype for artificial intelligence-facilitated pollution reporting.

IF 4.1 3区医学 Q2 ENVIRONMENTAL SCIENCES

Journal of Exposure Science and Environmental Epidemiology Pub Date : 2025-01-20 DOI:10.1038/s41370-025-00747-5

Weihsueh A Chiu, Galen Newman, Garett Sansom, Xinyue Ye, Andriy Rusyn, Haotian Wu, Tom Winckelman, Ivan Rusyn

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引用次数: 0

Abstract

Background: Many chemical releases are first noticed by community members, but reporting these concerns often involves considerable hurdles. Artificial Intelligence (AI)-enabled technologies, especially large language models (LLMs), can potentially reduce these barriers.

Objective: We hypothesized that AI-powered chatbots can facilitate reporting of pollution incidents through text messaging.

Methods: We created an AI-powered chatbot, "MyEcoReporter," that enables communities to report environmental incidents to government authorities. Eschewing traditional web-based forms, users text concerns via SMS to the LLM-powered application, engaging in a natural conversation through which required information is collected. The application was built using Python, AWS Lambda, DynamoDB, and Twilio, and deployed via Serverless.

Results: This architecture allowed rapid customization for various use cases, which successfully facilitated conversations and stored structured data for formal submission.

Impact statement: MyEcoReporter showcases the potential of Artificial Intelligence/Large Language Models to create user-friendly tools that translate community environmental concerns into actionable information for reporting to government authorities.

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MyEcoReporter：人工智能辅助污染报告的原型。

背景：许多化学物质的释放首先是由社区成员注意到的，但是报告这些问题往往涉及相当大的障碍。人工智能（AI）技术，特别是大型语言模型（llm），可以潜在地减少这些障碍。目的：我们假设人工智能聊天机器人可以通过短信报告污染事件。方法：我们创建了一个人工智能聊天机器人“MyEcoReporter”，使社区能够向政府当局报告环境事件。避开传统的基于web的表单，用户通过SMS向llm驱动的应用程序发送关注的内容，参与自然对话，通过对话收集所需的信息。该应用程序使用Python、AWS Lambda、DynamoDB和Twilio构建，并通过Serverless进行部署。结果：该体系结构允许对各种用例进行快速定制，从而成功地促进了对话并存储了用于正式提交的结构化数据。影响声明：MyEcoReporter展示了人工智能/大型语言模型的潜力，可以创建用户友好的工具，将社区环境问题转化为可操作的信息，以便向政府当局报告。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Exposure Science and Environmental Epidemiology 医学-毒理学

CiteScore

8.90

自引率

6.70%

发文量

审稿时长

3 months

期刊介绍： Journal of Exposure Science and Environmental Epidemiology (JESEE) aims to be the premier and authoritative source of information on advances in exposure science for professionals in a wide range of environmental and public health disciplines. JESEE publishes original peer-reviewed research presenting significant advances in exposure science and exposure analysis, including development and application of the latest technologies for measuring exposures, and innovative computational approaches for translating novel data streams to characterize and predict exposures. The types of papers published in the research section of JESEE are original research articles, translation studies, and correspondence. Reported results should further understanding of the relationship between environmental exposure and human health, describe evaluated novel exposure science tools, or demonstrate potential of exposure science to enable decisions and actions that promote and protect human health.