CAREER: Leveraging Data Science & Policy to Promote Sustainable Development Via Resource Recovery

职业：利用数据科学

• 批准号: 2339025
• 负责人: Kevin Orner
• 金额: $ 54.89万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-01 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339025&HistoricalAwards=false
• 关键词: CAREER; Leveraging; Data; Science; &amp

The waste management sector is transitioning from an “out of site, out of mind” approach to a resource recovery approach in which valuable energy and fertilizer can be recovered. This resource recovery approach is driven by national and global challenges related to population growth, climate change, and resource scarcity. Rural agricultural regions are prime locations for resource recovery because they are typically abundant in organic waste streams such as animal manure and agricultural crop residues while also requiring fertilizer for crop production. Rural agricultural regions may generate so much organic waste that excess waste is shipped to other watersheds. Therefore, these regions could receive increased economic benefits and reduced environmental impacts if the excess waste was not shipped out and instead used as a feedstock for the recovery of energy and nutrients. However, such rural regions face challenges in implementing resource recovery technologies due to limited technical and economic resources, unavailable or inaccessible data, and lack of contextual policy support. Accordingly, the overarching goal of this CAREER project is to promote sustainable, context-sensitive resource recovery in rural regions. Successful completion of research and educational objectives on the topics of data science, life cycle modeling, policy, and stakeholder engagement will provide a data-driven, cost-effective framework to bridge the gap between research and implementation of resource recovery technologies in rural agricultural regions. Stakeholder engagement and policy dissemination will facilitate increased adoption of best practices for organic waste management.Current organic waste management practices such as landfilling and incineration negatively impact the environment by emitting greenhouse gases, harmful contaminants, and pathogens. However, recovery of resources such as energy and nutrients from organic waste can reduce such negative impacts. The goal of this CAREER project is to develop, apply, and assess a data-driven framework that integrates data science, life cycle modeling, and policy analysis to promote sustainable, context-sensitive resource recovery in rural agricultural regions. The recent emergence of powerful data science tools can effectively predict outcomes such as recovery efficiency, economic impacts, and environmental impacts. While larger wastewater utilities are beginning to use data science methods to improve treatment efficiency and reduce chemical and energy use, the use of data science in rural organic waste management is unexplored. Therefore, an opportunity exists to utilize data science tools with accessible datasets and generalized methods integrated with stakeholder engagement and contextual policy support. This strategy can provide a data-driven, cost-effective framework to bridge the gap between research and implementation of resource recovery technologies in rural farming regions. Implementation of this framework could allow rural farming regions nationally and internationally to strategically utilize their organic waste to best reflect their environmental, economic, social, political, and geographical context. The long-term educational goal is to increase the “impact competencies” in civil engineering students by providing training and practice on sustainable development, data science, and policy. In pursuit of this, the educational objectives of this proposal include integrating undergraduate and graduate learning modules and creating a pathway within the civil engineering MS curriculum that includes one year of stateside community-engaged service integrated with a research thesis. Broader impacts of the educational plan result from stakeholder engagement with community partners in undergraduate and graduate courses and providing needed yet underrepresented skills in data science and policy to undergraduate students that they can use to transition to the marketplace or graduate school.This project is jointly funded by the CBET/ENG Environmental Sustainability program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

废物管理部门正在从“不在现场，不在头脑中”的方法过渡到资源回收方法，可以回收宝贵的能源和肥料。这种资源回收方法是由与人口增长、气候变化和资源稀缺有关的国家和全球挑战推动的。农村农业地区是资源回收的主要地点，因为这些地区通常有丰富的有机废物流，如动物粪便和农作物残留物，同时还需要肥料用于作物生产。农村农业区可能会产生如此多的有机废物，以至于多余的废物被运往其他流域。因此，如果多余的废物不被运出，而是用作回收能量和营养物质的原料，这些地区可以获得更多的经济利益，减少对环境的影响。然而，由于技术和经济资源有限、数据不可用或无法获取以及缺乏相关政策支持，这些农村地区在实施资源回收技术方面面临挑战。因此，该职业项目的总体目标是促进农村地区可持续的、对环境敏感的资源回收。成功完成关于数据科学、生命周期建模、政策和利益相关者参与等主题的研究和教育目标，将提供一个数据驱动的、具有成本效益的框架，以弥合农村农业地区资源回收技术研究和实施之间的差距。利益攸关方的参与和政策传播将有助于更多地采用有机废物管理的最佳做法。目前的有机废物管理做法，如填埋和焚烧，会排放温室气体、有害污染物和病原体，对环境产生负面影响。然而，从有机废物中回收能源和营养素等资源可以减少这种负面影响。该项目的目标是开发、应用和评估一个数据驱动的框架，该框架集成了数据科学、生命周期建模和政策分析，以促进农村农业地区可持续的、对环境敏感的资源回收。最近出现的强大的数据科学工具可以有效地预测结果，如恢复效率，经济影响和环境影响。虽然大型废水处理公司开始使用数据科学方法来提高处理效率并减少化学品和能源的使用，但数据科学在农村有机废物管理中的应用尚未得到探索。因此，有机会利用数据科学工具和可访问的数据集以及与利益攸关方参与和相关政策支持相结合的通用方法。这一战略可以提供一个数据驱动的、具有成本效益的框架，以弥合农村农业地区资源回收技术研究与实施之间的差距。这一框架的实施可以使国内和国际上的农村农业地区战略性地利用其有机废物，以最好地反映其环境，经济，社会，政治和地理背景。长期教育目标是通过提供可持续发展，数据科学和政策方面的培训和实践来提高土木工程学生的“影响力”。为了实现这一目标，该提案的教育目标包括整合本科和研究生学习模块，并在土木工程硕士课程中创建一个途径，其中包括一年的美国社区参与服务与研究论文相结合。教育计划的更广泛影响来自利益相关者与社区合作伙伴在本科和研究生课程中的参与，并为本科生提供必要但代表性不足的数据科学和政策技能，他们可以使用这些技能过渡到市场或研究生院。该项目由CBET/ENG环境可持续发展计划和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF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。