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CAREER: Engaging Communities to Bridge the Local to Regional Gap in Air Pollution Exposure Assessment

职业：让社区参与弥合空气污染暴露评估中的地方与区域差距

基本信息

批准号：
1752231
负责人：
Kristina Wagstrom
金额：
$ 50万
依托单位：
University of Connecticut
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1752231&HistoricalAwards=false
关键词：
CAREER Engaging Communities Bridge Local

项目摘要

Over 19% of the United States population lives near major roads. This can negatively impact health and lead to lower life expectancy. This project will equip communities to obtain and understand information on local air pollution and advocate for solutions to local, near-road air quality concerns. Student involvement will lead to an increase in participation of underrepresented groups in engineering. The team will achieve these goals by combining air quality measurements, modeling, and community engagement. If successful, the results of this research will transform air pollution exposure assessment modeling and highlight the potential for productive collaborations between researchers and community members to solve air quality problems.The central research question for this project is how accounting for spatial and temporal variation in air pollutant concentrations impacts exposure estimates for socioeconomically disadvantaged populations and active individuals. The primary hypothesis is that the proposed hybrid modeling approach will yield better estimates of air pollutant concentrations than each model individually. The central education question is whether combining undergraduate service learning projects and community engagement increases retention and recruitment of underrepresented students in engineering and empowers community members around local air pollution concerns. This project pursues the following aims to address these questions: (1) Create a dataset of extensive, detailed, and accessible modeled air pollution concentrations and exposures needed for community planning, advocacy, and empowerment. (2) Partner with neighborhood associations to create a robust local dataset of measured air pollutant concentrations in Hartford, CT. (3) Engage engineering students in service learning projects using local air pollution assessment projects. The coupling of computational modeling, low-cost monitoring, community engagement, and service learning will transform the way air pollution researchers and impacted communities interact. This project moves beyond standard concentration estimates for exposure by addressing a large number of chemical species from all spatial scales. This project tests the effectiveness of high-resolution networks of low-cost ($1000) air pollution monitors for use in model evaluation. This contribution is significant because it uses an unprecedented combination of model development, community engaged research (citizen science), and service learning and will transform how policy makers, environmental justice researchers, urban planners, and epidemiologists estimate human exposures to air pollution and develop solutions to local concerns in a project that highlights engineering as an agent for societal change. Community engagement during this project increases the impacted public's literacy in areas pertaining to environmental science and air pollution. This work positively impacts society by facilitating the development of more effective and equitable air pollution policies, providing improved estimates of concentrations and exposures for epidemiological studies, informing urban planning, educating and empowering local communities and individuals around environmental concerns, and increasing retention and recruitment of underrepresented students in engineering.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.

超过19%的美国人口居住在主要道路附近。这可能会对健康造成负面影响，并导致预期寿命降低。该项目将使社区能够获得和了解有关当地空气污染的信息，并倡导解决当地近道路空气质量问题的解决方案。学生的参与将导致更多代表不足的群体参与工程学。该团队将通过结合空气质量测量、建模和社区参与来实现这些目标。如果成功，这项研究的结果将改变空气污染暴露评估模型，并突出研究人员和社区成员之间有效合作解决空气质量问题的潜力。该项目的中心研究问题是，考虑空气污染物浓度的时空变化如何影响社会经济弱势群体和活跃个人的暴露估计。主要的假设是，建议的混合建模方法将比单独使用每个模型产生更好的空气污染物浓度估计。教育的核心问题是，将本科生服务学习项目与社区参与结合起来，是否会增加工程学专业代表性不足的学生的留存和招聘，并使社区成员能够关注当地的空气污染问题。本项目致力于解决以下问题：(1)创建广泛、详细和可访问的模拟空气污染浓度和暴露的数据集，这些数据是社区规划、宣传和赋权所需的。(2)与社区协会合作，创建康涅狄格州哈特福德测量的空气污染物浓度的强大本地数据集。(3)利用当地的空气污染评估项目，让工科学生参与服务学习项目。计算建模、低成本监测、社区参与和服务学习的结合将改变空气污染研究人员和受影响社区的互动方式。该项目超越了暴露的标准浓度估计，解决了所有空间尺度上的大量化学物质。该项目测试用于模型评估的低成本(1000美元)空气污染监测仪的高分辨率网络的有效性。这一贡献意义重大，因为它史无前例地结合了模型开发、社区参与研究(公民科学)和服务学习，并将改变政策制定者、环境正义研究人员、城市规划者和流行病学家估计人类暴露在空气污染中的方式，并在一个突出工程作为社会变革推动者的项目中为当地关切制定解决方案。该项目期间的社区参与提高了受影响公众在环境科学和空气污染领域的识字能力。这项工作通过促进更有效和公平的空气污染政策的制定，为流行病学研究提供更好的浓度和暴露估计，为城市规划提供信息，围绕环境问题对当地社区和个人进行教育和授权，以及增加工程学专业未被充分代表的学生的保留和招生，对社会产生了积极影响。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。