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

废物管理部门正在从“场外、头脑之外”的方法过渡到资源回收方法，在这种方法中，有价值的能源和化肥可以被回收。这种资源回收方法是由与人口增长、气候变化和资源稀缺相关的国家和全球挑战推动的。农村农业区是资源回收的最佳地点，因为它们通常盛产有机废物流，如动物粪便和农作物残渣，同时也需要化肥来生产作物。农村农业地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走，而是被用作回收能源和养分的原料，这些地区可以获得更多的经济效益和减少对环境的影响。然而，由于技术和经济资源有限、无法获得或无法获取数据以及缺乏背景政策支持，这些农村地区在实施资源回收技术方面面临挑战。因此，这一职业项目的总体目标是促进农村地区可持续的、对环境有敏感认识的资源回收。成功完成数据科学、生命周期建模、政策和利益攸关方参与等专题的研究和教育目标，将提供一个数据驱动、成本效益高的框架，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促进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，从而对环境产生负面影响。然而，从有机废物中回收能源和养分等资源可以减少这种负面影响。这一职业项目的目标是开发、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、背景敏感的资源回收。最近出现的强大的数据科学工具可以有效地预测恢复效率、经济影响和环境影响等结果。虽然较大的污水处理公司开始使用数据科学方法来提高处理效率，减少化学和能源消耗，但在农村有机废物管理中使用数据科学还没有探索。因此，存在利用数据科学工具的机会，数据科学工具具有可访问的数据集和与利益攸关方参与和背景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和实施之间的差距。实施这一框架可以使国内和国际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、政治和地理环境。长期的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，提高土木工程专业学生的“影响能力”。为了实现这一目标，这项建议的教育目标包括整合本科生和研究生的学习模块，并在土木工程硕士课程中创建一条途径，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源于利益相关者在本科和研究生课程中与社区合作伙伴的参与，并向本科生提供必要的但未得到充分体现的数据科学和政策方面的技能，他们可以用来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既定的激励竞争研究计划(EPSCoR)联合资助。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。

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.