CAREER: Community-Engaged, Sensor Network for Identifying Air Pollution Sources

职业：社区参与的传感器网络，用于识别空气污染源

• 批准号: 1943413
• 负责人: Kerry Kelly
• 金额: $ 50万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943413&HistoricalAwards=false
• 关键词: CAREER; Community; Engaged; Sensor; Network

Formaldehyde is a key air pollutant and known human carcinogen. Each year over 25 million Americans are exposed to formaldehyde at levels exceeding the US Environmental Protection Agency (EPA) cancer risk threshold. This is a particularly urgent problem in northern Utah because formaldehyde levels are 50 times greater than the cancer risk threshold on average. Combating this problem requires controlling emission sources, which are largely unknown in northern Utah. Identifying emission source(s) requires a large number of samples over space and time. The expense of performing these measurements limits data collection, making it nearly impossible to identify the source(s). This CAREER project will address this issue by developing lower cost nanofiber sensors. These sensors will be deployed in dense arrays in northern Utah using advanced analytical techniques and a community-based network. The results are anticipated to lead to identification of sources for eventual control and improvement of air quality. Technological developments resulting from this project will be broadly applicable nationwide for communities, policy makers, and researchers to identify and address pollutant sources to protect human health. This project will also develop a rich framework for attracting and retaining engineering students to enhance the Nation’s STEM workforce. This effort will be focused particularly on students from under-represented populations through the development and delivery of engaging, place-based educational activities.The goal of this CAREER project is to provide innovative solutions to reduce the expense of collecting time-resolved, chemically speciated, air-quality measurements. This will be achieved through the development of cost-effective carbon nanofiber sensors, with complementary data analytics and community-engaged measurements to solve critical air-quality issues. The sensors will be deployed in a high-resolution sensor network capable of geolocating air pollution sources. Formaldehyde, a human carcinogen, is the focus of the project because it is an urgent problem in northern Utah due to local ambient levels that are 50 times greater than US EPA’s cancer risk threshold. The objectives of the research project are to develop: (1) A broadly applicable set of techniques and principles for identifying a pollutant source that leverages a distributed network of validated, cost-effective, carbon-nanofiber-based, air-quality sensors; (2) Cost-effective techniques to differentiate between primary and secondary pollutant sources; and (3) Versatile data analytics for integrating meteorological information with measurements of key chemical species. Data analytics will include multivariate data analysis techniques, forward and back trajectory modeling, conditional bivariate probability functions, and cluster analysis. Together, these novel developments will be applied to a low-cost sensor dataset to detect formaldehyde source(s) in northern Utah. The proposed research will be broadly impactful as the general nature of the approach will work across numerous domains, including smart and connected cities, community-engaged science, chemical threat detection, indoor-air quality, environmental justice, and education. This project also provides a rich framework for STEM education and sustainable opportunities for attracting and retaining engineering students, particularly those from under-represented populations through the development and delivery of engaging, place-based educational activities.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.

甲醛是一种主要的空气污染物，也是已知的人类致癌物质。每年有超过2500万美国人暴露在超过美国环境保护局(EPA)癌症风险阈值的甲醛中。这在犹他州北部是一个特别紧迫的问题，因为甲醛水平平均比癌症风险阈值高出50倍。解决这个问题需要控制排放源，而犹他州北部的排放源在很大程度上是未知的。确定排放源(S)需要大量的空间和时间样本。执行这些测量的费用限制了数据收集，使得几乎不可能确定来源(S)。这个职业项目将通过开发成本更低的纳米纤维传感器来解决这个问题。这些传感器将部署在犹他州北部的密集阵列中，使用先进的分析技术和基于社区的网络。预计结果将导致确定最终控制和改善空气质量的来源。该项目产生的技术发展将广泛适用于全国范围内的社区、政策制定者和研究人员，以识别和解决污染源，以保护人类健康。该项目还将开发一个丰富的框架，以吸引和留住工程专业的学生，以加强国家的STEM劳动力。这一努力将特别侧重于来自代表性不足人群的学生，通过开发和提供引人入胜的、以地点为基础的教育活动。这一职业项目的目标是提供创新的解决方案，以减少收集时间分辨的、化学分类的空气质量测量的费用。这将通过开发具有成本效益的碳纳米纤维传感器、补充数据分析和社区参与的测量来解决关键的空气质量问题来实现。传感器将部署在能够对空气污染源进行地理定位的高分辨率传感器网络中。甲醛是一种人类致癌物质，是该项目的重点，因为它是犹他州北部的一个紧迫问题，因为当地环境水平比美国环保局的癌症风险阈值高出50倍。该研究项目的目标是：(1)一套广泛适用的确定污染源的技术和原则，利用经验证的、具有成本效益的、以碳纳米纤维为基础的空气质量传感器的分布式网络；(2)具有成本效益的技术，以区分主要和次要污染源；以及(3)将气象信息与关键化学物质的测量相结合的通用数据分析。数据分析将包括多变量数据分析技术、向前和向后轨迹建模、条件双变量概率函数和聚类分析。总之，这些新的发展将应用于一个低成本的传感器数据集，以检测犹他州北部的甲醛来源(S)。拟议的研究将产生广泛的影响，因为该方法的一般性将在许多领域发挥作用，包括智能和互联城市、社区参与的科学、化学威胁检测、室内空气质量、环境正义和教育。该项目还为STEM教育提供了丰富的框架，并为吸引和留住工科学生提供了可持续的机会，特别是通过发展和提供引人入胜的、以地点为基础的教育活动来吸引和留住那些来自代表性不足人口的学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。