对新出现的固体废物流的因素和预测技术进行了深入分析
Amim Altaf Nabi1, Arvind Kumar Nema1
1Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India.
分析了电子废物等新出现的固体废物流 (ESWS) 的预测模型. 本研究对40种方法进行了分类,并确定了关键变量,有助于制定更好的废物管理政策.
科学领域:
- 环境科学 环境科学
- 废物管理工程 废物管理工程
- 数据科学数据科学数据科学
背景情况:
- 技术进步产生了新的新兴固体废物流 (ESWSs).
- 对ESWS的管理政策不充分,这给ESWS带来了重大的环境和经济挑战.
- 准确预测ESWS数量对于有效制定政策和资源分配至关重要.
研究的目的:
- 系统地审查和分析主要ESWS的预测模型:光伏废物,电子废物,电池废物和生物医学废物.
- 根据方法,变量,规模和数据类型对已识别的模型进行分类.
- 为数据源选择提供指导,并突出ESWS预测中的未来研究差距.
主要方法:
- 对四个关键ESWS的预测模型进行系统的文献审查.
- 将40种已识别的建模方法分为不同的类别.
- 分析了100多个独立变量和用于预测的数据源.
主要成果:
- 确定和分类40种不同的ESWS预测方法.
- 分析影响预测准确性的关键独立变量和数据类型.
- 评估数据不确定性,并为选择合适的数据源提供建议.
结论:
- 对ESWS预测模型的结构化分类有助于选择合适的方法.
- 了解数据不确定性和来源选择对于准确的废物量预测至关重要.
- 这项研究为为新出现的固体废物制定强有力的废物管理政策提供了基本指南.
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