GIS-based optimization of solid waste collection points and routes: A case study of Majengo Ward, Sumbawanga Municipality, Tanzania

IF 3.9
Muhajir Mussa Kwikima , Fortunata Ngole
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引用次数: 0

Abstract

This study investigates the potential of Geographic Information Systems (GIS) and optimization techniques to improve municipal solid waste management in Sumbawanga Municipality, Tanzania. While GIS applications in waste management are established, scalable models for secondary cities in resource-limited African contexts remain understudied. The research focuses on Majengo Ward, a rapidly urbanizing area with an inadequate waste collection system. Through a mixed-methods approach, the study bridges this gap by proposing solutions to enhance waste collection coverage, reduce environmental impact, and optimize operational efficiency. Spatial analysis reveals significant inefficiencies in the existing system, with only 40 % of the 3.5 tonnes of daily waste collected. Using GIS and suitability modeling, seven new collection points are strategically identified, considering population density, road proximity, and environmental risks. Optimization algorithms (p-median and p-center) design efficient routes, minimizing distances and travel times. The proposed system is projected to increase waste collection coverage by 90 %, reduce fuel consumption by 25 %, and decrease driver working hours by 30 %. The cost-benefit analysis demonstrates significant economic and environmental benefits, with a payback period of 3.5 years. This study highlights the effectiveness of data-driven approaches in addressing waste management challenges in developing cities. The findings offer valuable insights for policymakers, planners, and waste management practitioners, contributing to sustainable urban development in Tanzania and similar contexts.
基于gis的固体废物收集点和路线优化:坦桑尼亚松巴旺加市马金戈区案例研究
本研究探讨了地理信息系统(GIS)和优化技术在坦桑尼亚Sumbawanga市改善城市固体废物管理方面的潜力。虽然地理信息系统在废物管理方面的应用已经确立,但在资源有限的非洲情况下,二级城市的可扩展模型仍未得到充分研究。这项研究的重点是Majengo区,这是一个快速城市化的地区,但垃圾收集系统不完善。通过混合方法,该研究提出了提高废物收集覆盖率、减少环境影响和优化运营效率的解决方案,从而弥合了这一差距。空间分析显示,现有系统效率低下,每天收集的3.5吨垃圾中只有40% %。利用地理信息系统和适宜性建模,考虑到人口密度、道路邻近性和环境风险,战略性地确定了七个新的收集点。优化算法(p-median和p-center)设计有效的路线,最小化距离和旅行时间。该系统预计将增加90% %的垃圾收集覆盖率,减少25% %的燃料消耗,减少30% %的司机工作时间。成本效益分析表明,该项目具有显著的经济效益和环境效益,投资回收期为3.5年。这项研究强调了数据驱动方法在解决发展中城市废物管理挑战方面的有效性。研究结果为政策制定者、规划者和废物管理从业者提供了宝贵的见解,有助于坦桑尼亚和类似国家的可持续城市发展。
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CiteScore
2.60
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0.00%
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