Life cycle GHG emissions of MSW landfilling versus Incineration: Expected outcomes based on US landfill gas collection regulations

Anshassi, Malak; Smallwood, Thomas J.; Townsend, Timothy G.

doi:10.1016/j.wasman.2022.01.040

Cited by 41 publications

(12 citation statements)

References 48 publications

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“…Landfills have become a popular way for the treatment and disposal of MSW with the advantages of low investment and easy operation (Anshassi et al, 2022). However, the organic waste or wet waste that ends up in landfills would increase the emission of greenhouse gases and also take up a larger number of land, which is posing a great threat to megacities (Tahmoorian and Khabbaz, 2020).…”

Section: Introductionmentioning

confidence: 99%

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Lin²

2022

Front. Environ. Sci.

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It is crucial to realize the municipal solid waste (MSW) classification in terms of its treatments and disposals. Deep learning used for the classification of residual waste and wet waste from MSW was considered as a promising method. While few studies reported using the method of deep learning with transfer learning to classify organic waste and residual waste. Thus, this study aims to discuss the effect of the transfer learning on the performance of different deep learning structures, VGGNet-16 and ResNet-50, for the classification of organic waste and residual waste, which were compared in terms of the training time, confusion matric, accuracy, precision, and recall. In addition, the algorithms of PCA and t-SNE were also adopted to compare the representation extracted from the last layer of various deep learning models. Results indicated that transfer learning could shorten the training time and the training time of various deep learning follows this order: VGGNet-16 (402 s) > VGGNet-16 with TL (272 s) > ResNet-50 (238 s) > ResNet-50 with TL (223 s). Compared with the method of PAC, waste representations were better separated from high dimension to low dimension by t-SNE. The values of organic waste in terms of F1 score follows this order: ResNet-50 with transfer learning (97.8%) > VGGNet-16 with transfer learning (97.1%) > VGGNet-16 (95.0%) > ResNet-50 (92.5%).Therefore, the best performance for the classification of organic and residual waste was ResNet-50 with transfer learning, followed by VGGNet-16 with transfer learning and VGGNet-16, and ResNet-50 in terms of accuracy, precision, recall, and F1 score.

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Section: Introductionmentioning

confidence: 99%

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Lin²

2022

Front. Environ. Sci.

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“…47 This study does not consider CH 4 recovery and utilization in the landfill baseline, following the Intergovernmental Panel on Climate Change (IPCC) practice of setting the national methane recovery factor to be zero to provide the worst scenario baseline for quantifying the maximum potential of an intervention strategy. 47 If CH 4 recovery and utilization is included, the baseline GWP may still be higher than the incineration GWP, because (1) CH 4 recovery efficiency is not 100%, commonly ranging from 35% to 90%, [48][49][50][51][52] and (2) currently, landfill CH 4 recovered is either directly burned to generate electricity/heat or used as renewable natural gas that is still combusted in their end use. 53 Given the high GWP and eutrophication potential of the incineration option compared with other utilization pathways, it is unlikely that including CH 4 recovery and utilization will change the comparative conclusions of this study.…”

Section: National-level Life-cycle Resultsmentioning

confidence: 99%

“…Currently, the US Environmental Protection Agency (EPA) has a voluntary program, Landfill Methane Outreach Program, targeting reducing methane emissions from landfill sites. 53 Given the significant variations of recovery efficiency, operational status, and final products (electricity, renewable natural gas, or heat), [48][49][50][51][52] it is a valuable future research direction to quantify the site-specific GWP of landfills with CH 4 recovery and utilization.…”

Section: National-level Life-cycle Resultsmentioning

confidence: 99%

Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication

2022

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“…Traditional incineration and landfilling can cause serious resource waste and environmental pollution problems. [15][16][17] From the point of view of material recycling, thermoplastic materials seem to be a good choice. When thermoplastics are heated to a certain temperature, intermolecular forces (e.g., molecular chain entanglement, van der Waals forces, and hydrogen bonding) are disrupted, resulting in fluidity and thus repeatability.…”

Section: Introductionmentioning

confidence: 99%

Preparation of lignin-based vinylogous urethane vitrimer materials and their potential use as on-demand removable adhesives

Liu,

Pich,

Bernaerts

2024

Green Chem.

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Life cycle GHG emissions of MSW landfilling versus Incineration: Expected outcomes based on US landfill gas collection regulations

Cited by 41 publications

References 48 publications

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication

Preparation of lignin-based vinylogous urethane vitrimer materials and their potential use as on-demand removable adhesives

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