Towards science-driven designs of transportation green infrastructure

实现交通绿色基础设施的科学驱动设计

• 批准号: 1605407
• 负责人: Ke Zhang
• 金额: $ 32.59万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1605407&HistoricalAwards=false
• 关键词: Towards; science; driven; designs; transportation

1605407ZhangNear-road air pollution is a worldwide public health concern. A predictive tool (model) will be developed to analyze various atmospheric processes and their interactions in the roadway-vegetation environments. The overall research objective of this proposal is to test the hypothesis that science-driven designs of roadside green infrastructure such as vegetation barriers and vegetated noise barriers can effectively mitigate near-road air pollution. The predictive tool will be built on an environmental turbulent reacting flow modeling framework, Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry, developed by PI's research group. New components will be created to represent the turbulent mixing and atmospheric deposition processes due to roadside green infrastructure. In addition, a full gas-phase chemical mechanism will be incorporated to assess the role of biogenic volatile organic compound emissions on near-road air pollution. Furthermore, a novel method to couple turbulent mixing and multi-component aerosol dynamics will be created to enhance the accuracy in quantifying the effects of green infrastructure on evolution of particulate matter near roadways. Each process will be tested extensively against both laboratory and field measurement datasets. Working with ecosystem designers and scientists, the research team will apply the predictive tool to investigate how the advanced scientific understanding can improve real-world green infrastructure designs, i.e., testing the hypothesis, at two locations, Oakland, CA and Research Triangle Park, NC. Finally, the effects of green infrastructure will be parameterized by modifying Gaussian plume formulations using a novel multi-regime method based on the fundamental understanding of the flow structures before and behind roadside green infrastructure. The parameterizations will be incorporated into a highway dispersion model, CALINE4. Thus the modified CALINE4 becomes an assessment tool for the broad community. The specific objectives of the proposed project are to: 1) elucidate how the presence of green infrastructure affects the different atmospheric processes near roadways using an integrated experimental modeling approach, 2) evaluate how different green infrastructure designs affect their effectiveness in reducing near-road air pollutant levels, and, 3) create an assessment tool that allow planners and designers to evaluate different green infrastructure designs. The research products from this project will transform the transportation planning and community designing process. The current role of transportation planning in improving air quality is mostly "passive," meaning that air quality is treated as compliance. The research will encourage local communities to identify solutions to their local near-road air pollution problems, contributing to sustainable community development. Their modeling framework will become a platform for fostering interdisciplinary partnerships among different fields (e.g., air quality and transportation management, landscaping and urban planning) in solving transportation air quality problems. The collaborations with government agencies (i.e., EPA and Forestry Service), companies (i.e., Accent Environmental) and NGOs (i.e., Urban Biofilter and Breathe California of Sacramento) on this project will mark the first step towards this interdisciplinary partnership. Lastly, the objective of the education plan is to bring more underrepresented minority students into the research fields, built on PI's previous success in working with and recruiting underrepresented minority students. Besides the interdisciplinary training of a graduate student, the PI will partner with the Diversity Programs in Engineering at Cornell University to recruit underrepresented minority undergraduates to work on the Research Experiences for Undergraduates (REU) program associated with the proposed project.

近路空气污染是一个全球性的公共卫生问题。将开发一种预测工具（模型）来分析各种大气过程及其在道路-植被环境中的相互作用。本提案的总体研究目标是验证科学驱动的路边绿色基础设施（如植被屏障和植被噪声屏障）设计可以有效缓解近路空气污染的假设。该预测工具将建立在由PI研究小组开发的环境湍流反应流模型框架上，即综合湍流气溶胶动力学和气体化学。由于路边的绿色基础设施，将创建新的组件来代表湍流混合和大气沉积过程。此外，将纳入一个完整的气相化学机制，以评估生物源性挥发性有机化合物排放对道路附近空气污染的作用。此外，还将建立一种将湍流混合和多组分气溶胶动力学相结合的新方法，以提高量化绿色基础设施对道路附近颗粒物演化影响的准确性。每个过程将针对实验室和现场测量数据集进行广泛的测试。与生态系统设计师和科学家合作，研究团队将应用预测工具来研究先进的科学理解如何改善现实世界的绿色基础设施设计，即在加利福尼亚州奥克兰和北卡罗来纳州三角研究公园两个地点测试假设。最后，基于对路边绿色基础设施前后流动结构的基本认识，采用一种新的多状态方法，通过修正高斯羽流公式，对绿色基础设施的影响进行参数化。参数化将被纳入公路分散模型CALINE4。因此，修改后的CALINE4成为广泛社区的评估工具。拟议项目的具体目标是：1)利用综合实验建模方法阐明绿色基础设施的存在如何影响道路附近不同的大气过程，2)评估不同的绿色基础设施设计如何影响其减少道路附近空气污染物水平的有效性，以及3)创建一个评估工具，使规划师和设计师能够评估不同的绿色基础设施设计。该项目的研究成果将改变交通规划和社区设计过程。目前交通规划在改善空气质量方面的作用大多是“被动的”，这意味着空气质量被视为合规。这项研究将鼓励当地社区找出解决当地公路附近空气污染问题的办法，为可持续社区发展作出贡献。他们的建模框架将成为一个平台，促进不同领域（如空气质量和交通管理、景观美化和城市规划）之间的跨学科伙伴关系，以解决交通空气质量问题。与政府机构（即EPA和林业局）、公司（即Accent Environmental）和非政府组织（即城市生物过滤器和萨克拉门托的Breathe California）在这个项目上的合作将标志着这种跨学科合作关系的第一步。最后，教育计划的目标是将更多代表性不足的少数民族学生带入研究领域，这是基于PI之前在与代表性不足的少数民族学生合作和招募方面取得的成功。除了研究生的跨学科培训外，PI还将与康奈尔大学工程多样性项目合作，招募未被充分代表的少数民族本科生参与与拟议项目相关的本科生研究经验（REU）项目。