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. 2023 Sep:96:104685.
doi: 10.1016/j.scs.2023.104685. Epub 2023 May 28.

A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19

Tanvir Shahrier Mahmud  1 , Kelvin Tsun Wai Ng  1 , Mohammad Mehedi Hasan  1 , Chunjiang An  2 , Shuyan Wan  2
Affiliations

A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19

Tanvir Shahrier Mahmud et al. Sustain Cities Soc. 2023 Sep.

Abstract

There is currently a lack of studies on residential waste collection during COVID-19 in North America. SARIMA models were developed to predict residential waste collection rates (RWCR) across four North American jurisdictions before and during the pandemic. Unlike waste disposal rates, RWCR is relatively less sensitive to the changes in COVID-19 regulatory policies and administrative measures, making RWCR more appropriate for cross-jurisdictional comparisons. It is hypothesized that the use of RWCR in forecasting models will help us to better understand the residential waste generation behaviors in North America. Both SARIMA models performed satisfactorily in predicting Regina's RWCR. The SARIMA DCV model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA BCV). The skewness of overprediction ratios was noticeably different between jurisdictions, and modeling errors were generally lower in less populated cities. Conflicting behavioral changes might have altered the residential waste generation characteristics and recycling behaviors differently across the jurisdictions. Overall, SARIMA DCV performed better in the Canadian jurisdiction than in U.S. jurisdictions, likely due to the model's bias on a less variable input dataset. The use of RWCR in forecasting models helps us to better understand the residential waste generation behaviors in North America and better prepare us for a future global pandemic.

Keywords: COVID-19; Municipal solid waste management; North America; Quantitative waste forecasting; Residential waste collection rate; SARIMA.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image, graphical abstract
Graphical abstract
Fig. 1
Fig. 1
Study approach (Note: “BCV” represents before COVID-19 and “DCV” represents during COVID-19).
Fig. 2
Fig. 2
Performance of the Seasonal Autoregressive Integrated Moving Average models using Regina dataset.
Fig. 3
Fig. 3
Effects of seasonality on model accuracy and precision using (a) SARIMA BCV and (b) SARIMA DCV.
Fig. 4
Fig. 4
Accuracy and precision of SARIMA DCV in four North American cities.
Fig. 5
Fig. 5
Model performance of SARIMA DCV across North American cities.
Fig. 6
Fig. 6
Correlation between statistical variables, climatic variables, and performance indicators of SARIMA DCV.
See this image and copyright information in PMC

References

    1. Adelodun B., Kim S.H., Choi K.S. Assessment of food waste generation and composition among Korean households using novel sampling and statistical approaches. Waste Management. 2021;122:71–80. doi: 10.1016/j.wasman.2021.01.003. - DOI - PubMed
    1. Adusei K.K., Ng K.T.W., Karimi N., Mahmud T.S., Doolittle E. Modeling of municipal waste disposal behaviors related to meteorological seasons using Recurrent Neural Network LSTM models. Ecological Informatics. 2022;72 doi: 10.1016/j.ecoinf.2022.101925. - DOI
    1. Argentiero A., D'Amato A.W., Zoli M. Waste recycling policies and Covid-19 pandemic in an E-DSGE model. Waste Management. 2022;141:290–299. doi: 10.1016/j.wasman.2021.12.036. - DOI - PMC - PubMed
    1. Adusei K.K., Ng K.T.W., Mahmud T.S., Karimi N., Lakhan C. Exploring the use of astronomical seasons in municipal solid waste disposal rates modeling. Sustainable Cities and Society. 2022;86 doi: 10.1016/j.scs.2022.104115. - DOI - PMC - PubMed
    1. Ayeleru O., Fajimi L., Oboirien B., Olubambi P. Forecasting municipal solid waste quantity using artificial neural network and supported vector machine techniques: A case study of Johannesburg, South Africa. Journal of Cleaner Production. 2021;289 doi: 10.1016/j.jclepro.2020.125671. - DOI

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