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向消费者通报水安全、查明水源问题以及促进讨论公共卫生和饮用水环境至关重要。该项目的总体目标是建立一个可靠和及时检测饮用水污染的框架，以建立可持续和相互联系的社区。它关注的是那些使用私人水井取水而没有中央公用设施监测水质的社区。它让社区参与进来，利用数据分析方面的进步，探索技术和社会层面，以回答一个公共卫生问题：社区的饮用水安全吗？该项目促进了公众参与性科学研究（也称为公民科学）在数据收集中的作用。它利用机器学习和统计学的方法开发新的推理模型，以提高通过公众参与收集的数据的准确性、可靠性、可信度和价值。它提高了对在不同社区类型中影响公众参与和数据质量的关键社会人口因素的理解。它展示了公民科学在引发行为变化方面的潜在作用，以及它如何影响规划和监管实践，例如，在本关于健康和可持续社区的地下水质量的研究中。该项目为印第安纳州研究水质的社区开发的这个框架被称为智能水群体感知（SWC）框架，应该成为全国寻求社区公众参与研究当地公共卫生问题的社区的典范。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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