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

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.