SCC-PG: Internet of Waste: A Low-Cost Geospatial Sensor Network for Optimizing Solid Waste Management and Fostering Resident's Recycling Effectiveness Through Evidential Education

固体废物管理，特别是可回收物品的管理，对社区来说仍然是一个巨大的挑战，因为只有一小部分可回收物品被收集用于回收，全球最终只有5%的可回收物品被回收。管理不善的固体废物被送往垃圾填埋场和焚烧炉，其中80%建在低收入社区和有色人种社区，造成严重的公共卫生问题和环境不公平。因此，实现固体废物的循环经济会带来巨大的回报，而失败则会在环境、公共健康和公平方面付出灾难性的代价。拟议的项目将开发一个物联网系统，通过在公民、地方政府、废物服务承包商和政策制定者之间建立数据驱动的联系，将人们和社区与他们的废物的命运联系起来。该项目将产生针对社区的教育和宣传，增加圣路易斯服务不足社区对回收的参与，同时提高普通公众在减少废物和回收方面的科学素养。本研究中初始化的传感器网络是通用的，并有可能揭示出一种新的循环经济的运营回收数据，可以造福于地方政府。该项目旨在创建一个多层次的城市垃圾模型，将密苏里州圣路易斯市的居民、政策制定者、非营利组织、教育工作者和废物管理承包商联系起来，减少废物，提高回收效率。该项目将进行初步的社区参与和探索性设计，以创建一个传感器网络，通过将垃圾箱转化为边缘设备来填补进入的可回收和不可回收固体废物的数据空白。这项规划拨款将磨练传感器的设计参数，找出影响废物管理运作的数据差距，并勾勒出市民对回收服务透明度的期望，以及与收集生活固体废物数据有关的隐私问题。初期工作还将吸引致力于减少食物浪费的非营利组织和社区团体的参与。除了传感器网络，该项目还将制作三个人工智能模型的原型：一个用于优化全市的回收操作，第二个用于量化各种回收扩展对回收率的影响，第三个用于心理测量分析，以评估导致回收和减少废物行为变化的扩展策略。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。

Solid waste management, particularly for recyclables, remains a significant challenge for communities with only a small fraction of recyclables collected for recycling and only 5% eventually recycled globally. Mismanaged solid waste is routed to landfills and incinerators, 80% of which are built in low-income communities and communities of color, causing substantial public health problems and environmental injustice. Thus, enabling a circular economy of solid waste comes with great rewards, and failure comes at a catastrophic cost regarding the environment, public health, and equity. The proposed project will develop an IoT system to connect people and communities to the fate of their waste by forming data-driven links between citizens, local government, waste service contractors, and policymakers. The project will yield community-tailored education and outreach, increasing participation in recycling by underserved communities of St. Louis while increasing the general public’s scientific literacy in waste reduction and recycling. The sensor network initialized in this study is general and has the potential to unlock a new recycling economy of operational recycling data that can benefit local governments.This project aims to create a multilayered model for municipal waste that connects residents, policymakers, non-profits, educators, and waste management contractors in reducing waste and increasing recycling efficiency in St. Louis, MO. The project will undertake initial community engagement and exploratory design for creating a sensor network that fills the data gap on incoming recyclable and non-recyclable solid waste by transforming waste bins into edge devices. This planning grant will hone the sensors’ design parameters, identify data gaps that impact waste management operations, and delineate citizens’ expectations of recycling service transparency and privacy concerns related to collecting residential solid waste data. Initial work will also engage non-profits and community groups that work on reducing food waste. In addition to the sensor network, the project will prototype three AI models: one for optimizing city-wide recycling operations, the second for quantifying the impact of various recycling outreach on recycling rates, and a third for psychometric analysis to assess outreach strategies that result in recycling and waste reduction behavior change.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.