IMPLEMENTING AI-DRIVEN WASTE MANAGEMENT SYSTEMS IN UNDERSERVED COMMUNITIES IN THE USA

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903-Article Text-2377-1-10-20240316.pdf (236.83 KB)

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https://www.fepbl.com/index.php/estj/article/view/903

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https://doi.org/10.51594/estj.v5i3.903
 http://hdl.handle.net/11603/32745

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UMBC Chemical, Biochemical & Environmental Engineering Department
UMBC Student Collection

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Author/Creator ORCID

https://orcid.org/0009-0006-4335-5428

Date

2024-03-17

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journal articles
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Citation of Original Publication

Zamathula Queen Sikhakhane Nwokediegwu, and Ejike David Ugwuanyi. 2024. “IMPLEMENTING AI-DRIVEN WASTE MANAGEMENT SYSTEMS IN UNDERSERVED COMMUNITIES IN THE USA”. Engineering Science & Technology Journal 5 (3):794-802. https://doi.org/10.51594/estj.v5i3.903.

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Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

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Abstract

The integration of Artificial Intelligence (AI) technologies holds immense potential for revolutionizing waste management systems in underserved communities across the United States. This concept paper explores the feasibility, benefits, challenges, and implications of implementing AI-driven waste management systems in these communities. By leveraging AI capabilities such as predictive analytics, optimization algorithms, and IoT sensors, innovative solutions can be developed to enhance waste collection, recycling efficiency, and environmental sustainability. However, successful implementation requires careful consideration of socioeconomic factors, community engagement, privacy concerns, and infrastructure limitations. This paper aims to provide a comprehensive overview of the opportunities and considerations associated with deploying AI-driven waste management systems in underserved communities, ultimately striving to promote equitable access to efficient and sustainable waste management solutions. This concept paper provides a comprehensive framework for implementing AI-driven waste management systems in underserved communities in the USA. It examines various aspects including socioeconomic considerations, community engagement, privacy concerns, infrastructure requirements, policy frameworks, financing options, and sustainability measures. Through careful planning, collaboration, and innovation, AI technologies can be harnessed to address the unique challenges faced by underserved communities, ultimately leading to more efficient, equitable, and sustainable waste management practices.