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
SCC-PG:废物互联网:通过循证教育优化固体废物管理并提高居民回收效率的低成本地理空间传感器网络
基本信息
- 批准号:2341996
- 负责人:
- 金额:$ 14.98万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-04-01 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
固体废物管理,特别是可回收物的管理,对于社区来说仍然是一个重大挑战,因为只有一小部分可回收物被收集用于回收,并且全球范围内只有 5% 的管理不善的固体废物最终被送往垃圾填埋场和焚烧炉,其中 80% 的垃圾填埋场和焚烧厂都建在低矮的环境中。 -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正。因此,实现固体废物循环经济会带来巨大的回报,而失败则会带来环境、公共卫生和环境方面的灾难性成本。拟议的股权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的社区对回收的参与,同时提高公众在废物减少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,有潜力释放可操作回收数据的新回收经济。地方政府。该项目旨在创建一个城市废物的多层模型,将居民、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,以减少密苏里州圣路易斯的废物并提高回收效率。该项目将进行初步社区参与和探索性设计,以创建传感器网络。通过将垃圾箱转变为边缘设备,填补了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,识别影响废物管理操作的数据空白,并描绘公民对废物管理的期望。与收集住宅固体废物数据相关的回收服务透明度和隐私问题,初期工作还将涉及致力于减少食物浪费的非营利组织和社区团体。除了传感器网络之外,该项目还将制作三种人工智能模型的原型:一种用于优化。第二个用于量化各种回收外展对回收率的影响,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外展策略。该奖项是 NSF 的法定使命,并已被视为值得通过使用评估来支持基金会的智力价值和更广泛的影响审查标准。
项目成果
期刊论文数量(0)
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Orhun Aydin其他文献
Artificial Intelligence Approaches
人工智能方法
- DOI:
10.22224/gistbok/2019.3.4 - 发表时间:
2019-07-01 - 期刊:
- 影响因子:0
- 作者:
Yingjie Hu;Wenwen Li;D. Wright;Orhun Aydin;Daniel Wilson;Omar Maher;Mansour Raad - 通讯作者:
Mansour Raad
Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations
通过随机生成树作为弱空间表示的证据积累进行概率区域化
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:3.6
- 作者:
Orhun Aydin;Mark V. Janikas;R. Assunção;Ting - 通讯作者:
Ting
Environmental and demographic factors on childhood academic performance in Los Angeles county: A generalized linear elastic net regression model
环境和人口因素对洛杉矶县儿童学业成绩的影响:广义线性弹性网络回归模型
- DOI:
10.1016/j.rsase.2023.100942 - 发表时间:
2023-02-01 - 期刊:
- 影响因子:0
- 作者:
Bita Minaravesh;Orhun Aydin - 通讯作者:
Orhun Aydin
Sensitivity Analysis for COVID-19 Epidemiological Models within a Geographic Framework
地理框架内的 COVID-19 流行病学模型的敏感性分析
- DOI:
10.1145/3423459.3430755 - 发表时间:
2020-11-03 - 期刊:
- 影响因子:0
- 作者:
Zhongying Wang;Orhun Aydin - 通讯作者:
Orhun Aydin
An improved localization algorithm to replace faulty nodes for an IoT network using weighted grey wolf optimization
一种改进的定位算法,使用加权灰狼优化来替换物联网网络的故障节点
- DOI:
10.1002/dac.5853 - 发表时间:
2024-05-27 - 期刊:
- 影响因子:2.1
- 作者:
V. Kanwar;Orhun Aydin - 通讯作者:
Orhun Aydin
Orhun Aydin的其他文献
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