SCC: Smart Water Crowdsensing: Examining How Innovative Data Analytics and Citizen Science Can Ensure Safe Drinking Water in Rural Versus Suburban Communities

SCC：智能水群体感知：研究创新数据分析和公民科学如何确保农村和郊区社区的安全饮用水

• 批准号: 2140999
• 负责人: Dong Wang
• 金额: $ 146.64万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140999&HistoricalAwards=false
• 关键词: SCC; Smart; Water; Crowdsensing; Examining

Monitoring drinking water contamination is vitally important to inform consumers about water safety, identify source water problems, and facilitate discussion of public health and the environment of our drinking water. The overall goal of this project is to develop a framework for reliable and timely detection of drinking water contamination to build sustainable and connected communities. It focuses on communities that use private wells for drinking water without the benefit of a central utility to monitor water quality. It engages the community to participate, leveraging advances in data analytics, exploring the technological and social dimensions to answer a public health question: Is the drinking water in the community safe?This project advances the role of public participatory scientific research, also referred to as citizen science, in data gathering. It develops new inference models using approaches from machine learning and statistics to improve accuracy, reliability, trustworthiness and value of the data, gathered through public participation. It improves understanding of key socio-demographic factors that influence public participation and data quality in contrasting community types. It demonstrates the potential role of citizen science in eliciting changes in behavior, and how that influences programmatic and regulatory practices, e.g., in this study of groundwater quality for healthy and sustainable communities. This framework, known as the Smart Water Crowdsensing (SWC) framework, developed by this project for communities in Indiana studying water quality, should serve as an exemplar for communities nationwide seeking community public participation in studying local public health questions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

监测饮用水污染对于告知消费者水安全，识别水的问题并促进公共卫生和饮用水环境的讨论至关重要。该项目的总体目标是为可靠，及时检测饮用水污染以建立可持续和互联社区的框架开发一个框架。 它专注于使用私人水井进行饮用水的社区，而无需中央公用事业以监测水质。它吸引了社区参与，利用数据分析的进步，探索技术和社会方面以回答公共卫生问题：社区中的饮用水是否安全？公民科学，在数据收集中。它使用机器学习和统计数据的方法开发了新的推理模型，以提高通过公众参与收集的数据的准确性，可靠性，可信赖性和数据价值。 它提高了对影响公众参与和数据质量在对比的社区类型中的关键社会人口统计学因素的理解。它证明了公民科学在引起行为变化中的潜在作用，以及这如何影响程序和监管实践，例如，在这项针对健康和可持续社区的地下水质量的研究中。 该项目由该项目针对印第安纳州研究水质开发的该项目开发的智能水拥挤框架（SWC）框架，应作为全国社区的典范，以寻求社区公众参与研究当地公共健康问题。这一奖项反映了NSF的法规奖使命，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估值得支持的。

项目成果

CrowdWaterSens: An uncertainty-aware crowdsensing approach to groundwater contamination estimation

• DOI: 10.1016/j.pmcj.2023.101788
• 发表时间: 2023-04
• 期刊: Pervasive Mob. Comput.
• 作者: Lanyu Shang;Yang Zhang;Quanhui Ye;Shannon L. Speir;Brett Peters;Ying Wu;Casey J. Stoffel;D. Bolster;J. Tank;Danielle Wood;Na Wei;Dong Wang

Solutions to Current Challenges in Widespread Monitoring of Groundwater Quality via Crowdsensing

• DOI: 10.1111/gwat.13150
• 发表时间: 2021-12-04
• 期刊: GROUNDWATER
• 影响因子: 2.6
• 作者: Speir，Shannon L.;Shang，Lanyu;Wang，Dong
• 通讯作者: Wang，Dong