ArticleThe Science of the total environment2021
Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models.
Article in The Science of the total environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Promiscuous nitrilase from Bacillus subtilis for herbicide degradation and plant growth promotion.Scientific reports · 2026Article
- Sustainability, profitability, and resiliency of the fast fashion industries during a pandemic.Energy & environment (Brentwood, England) · 2026Article
- Review
- A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19.Sustainable cities and society · 2023Article
- The Impact of COVID-19 on Waste Infrastructure: Lessons Learned and Opportunities for a Sustainable Future.International journal of environmental research and public health · 2023Review
- Waste management beyond the COVID-19 pandemic: Bibliometric and text mining analyses.Gondwana research : international geoscience journal · 2023Article
- Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by World Health Organization guidelines.Sustainable cities and society · 2022Article
- COVID-19 impacts on household solid waste generation in six Latin American countries: a participatory approach.Environmental monitoring and assessment · 2022Article
- To what extent do waste management strategies need adaptation to post-COVID-19?The Science of the total environment · 2022Review
- Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi.Environmental science and pollution research international · 2022Article
- Virtual Methodology for Household Waste Characterization During The Pandemic in An Urban District of Peru: Citizen Science for Waste Management.Environmental management · 2022Article
- Mechanism of Undergraduate Students' Waste Separation Behavior in the Environmentally Friendly Higher Education Mega Center of Guangzhou.Journal of environmental and public health · 2022Article
- The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19.Sustainable cities and society · 2021Article
- A hybrid mathematical modelling approach for energy generation from hazardous waste during the COVID-19 pandemic.Journal of cleaner production · 2021Article
- The impact of successive COVID-19 lockdowns on people mobility, lockdown efficiency, and municipal solid waste.Environmental chemistry letters · 2021Article
- Characterization of perceived biohazard exposures, personal protective equipment, and training resources among a sample of formal U.S. solid waste workers: A pilot study.Journal of occupational and environmental hygieneArticle
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Municipal waste disposal behaviors in Regina, the capital city of Saskatchewan, Canada have significantly changed during the COVID-19 pandemic. About 7.5 year of waste disposal data at the Regina landfill was collected, verified, and consolidated. Four modeling approaches were examined to predict total waste disposal at the Regina landfill during the COVID-19 period, including (i) continuous total (Baseline), (ii) continuous fraction, (iii) truncated total, and (iv) truncated fraction. A single feature input recurrent neural network model was adopted for each approach. It is hypothesized that waste quantity modeling using different waste fractions and separate time series can better capture disposal behaviors of residents during the lockdown. Compared to the baseline approach, the use of waste fractions in modeling improves both result accuracy and precision. In general, the use of continuous time series over-predicted total waste disposal, especially when actual disposal rates were less than 50 t/day. Compared to the baseline approach, mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were reduced. The R value increased from 0.63 to 0.79. Comparing to the baseline, the truncated total and the truncated fraction approaches better captured the total waste disposal behaviors during the COVID-19 period, probably due to the periodicity of the weeklong data set. For both approaches, MAE and MAPE were lower than 70 and 22%, respectively. The model performance of the truncated fraction appears the best, with an MAPE of 19.8% and R value of 0.92. Results suggest the uses of waste fractions and separated time series are beneficial, especially if the input set is heavily skewed.
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