新冠疫情期间德黑兰城市固体废弃物的生命周期评估及基于机器学习的环境影响预测
Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning.
作者信息
Shekoohiyan Sakine, Hadadian Mobina, Heidari Mohsen, Hosseinzadeh-Bandbafha Homa
机构信息
Department of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
出版信息
Case Stud Chem Environ Eng. 2023 Jun;7:100331. doi: 10.1016/j.cscee.2023.100331. Epub 2023 Mar 10.
Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R. Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated.
生命周期评估与机器学习相结合，以找出德黑兰未来大流行期间废物管理的最佳方案。ReCipe结果表明，由于废物成分变化，新冠疫情后废物的破坏影响比之前更大。塑料废物占比已从7.5%变为11%。各情景的环境负担为：情景1（堆肥增加至50%）>情景3>情景4>情景b2>情景5>情景2（回收率从9%提高到20%）。人工神经网络和梯度提升回归树能够以较高的决定系数预测环境影响。根据结果，应调查新冠疫情后固体废物的环境负担。
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