CAREER: Community-Engaged, Sensor Network for Identifying Air Pollution Sources
CAREER: Community-Engaged, Sensor Network for Identifying Air Pollution Sources
批准号:
1943413
负责人:
Kerry Kelly
金额:
$50.0万
依托单位:
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
项目状态:
未结题
起止时间:
2020-04-01 至 2025-03-31
中文摘要
甲醛是一种重要的空气污染物,也是已知的人类致癌物质。每年有超过2500万美国人暴露于甲醛,其水平超过美国环境保护署(EPA)的癌症风险阈值。这在北方犹他州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50倍。解决这个问题需要控制排放源,而北方犹他州在很大程度上还不知道这些排放源。识别排放源需要在空间和时间上进行大量采样。进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。这个CAREER项目将通过开发低成本的MEMS传感器来解决这个问题。这些传感器将使用先进的分析技术和基于社区的网络以密集阵列的形式部署在北方犹他州。预计研究结果将有助于确定污染源,以便最终控制和改善空气质量。该项目产生的技术发展将在全国范围内广泛适用于社区、决策者和研究人员,以确定和解决污染源,保护人类健康。该项目还将制定一个丰富的框架,吸引和留住工程专业的学生,以提高国家的STEM劳动力。这项工作将特别关注来自代表性不足人群的学生,通过开发和提供引人入胜的,基于场所的教育活动。该CAREER项目的目标是提供创新的解决方案,以减少收集时间分辨,化学物种,空气质量测量的费用。这将通过开发具有成本效益的碳排放传感器来实现,并通过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。这些传感器将部署在一个高分辨率的传感器网络中,能够对空气污染源进行地理定位。甲醛是一种人类致癌物质,是该项目的重点,因为它是北方犹他州的一个紧迫问题,因为当地环境水平是美国环保署癌症风险阈值的50倍。该研究项目的目标是:(1)开发一套广泛适用的技术和原则,用于确定污染源,利用经验证的、具有成本效益的、基于碳排放的空气质量传感器的分布式网络;(2)区分主要和次要污染源的具有成本效益的技术;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,前向和后向轨迹建模,条件二元概率函数和聚类分析。总之,这些新的发展将被应用到一个低成本的传感器数据集,以检测北方犹他州的甲醛源。拟议的研究将具有广泛的影响力,因为该方法的一般性质将适用于许多领域,包括智能和互联城市,社区参与科学,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,并提供了可持续的机会,以吸引和留住工程专业的学生,特别是那些来自代表性不足的人口,通过开发和提供引人入胜的,基于地方的教育活动。该奖项反映了NSF的法定使命,并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
英文摘要
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.
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会议论文
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依托单位:
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