Cargando…
Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models
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 to...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632937/ https://www.ncbi.nlm.nih.gov/pubmed/34082208 http://dx.doi.org/10.1016/j.scitotenv.2021.148024 |
_version_ | 1784824149979430912 |
---|---|
author | Vu, Hoang Lan Ng, Kelvin Tsun Wai Richter, Amy Karimi, Nima Kabir, Golam |
author_facet | Vu, Hoang Lan Ng, Kelvin Tsun Wai Richter, Amy Karimi, Nima Kabir, Golam |
author_sort | Vu, Hoang Lan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9632937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96329372022-11-04 Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models Vu, Hoang Lan Ng, Kelvin Tsun Wai Richter, Amy Karimi, Nima Kabir, Golam Sci Total Environ Short Communication 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. Elsevier B.V. 2021-10-01 2021-05-26 /pmc/articles/PMC9632937/ /pubmed/34082208 http://dx.doi.org/10.1016/j.scitotenv.2021.148024 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Short Communication Vu, Hoang Lan Ng, Kelvin Tsun Wai Richter, Amy Karimi, Nima Kabir, Golam Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models |
title | Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models |
title_full | Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models |
title_fullStr | Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models |
title_full_unstemmed | Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models |
title_short | Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models |
title_sort | modeling of municipal waste disposal rates during covid-19 using separated waste fraction models |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632937/ https://www.ncbi.nlm.nih.gov/pubmed/34082208 http://dx.doi.org/10.1016/j.scitotenv.2021.148024 |
work_keys_str_mv | AT vuhoanglan modelingofmunicipalwastedisposalratesduringcovid19usingseparatedwastefractionmodels AT ngkelvintsunwai modelingofmunicipalwastedisposalratesduringcovid19usingseparatedwastefractionmodels AT richteramy modelingofmunicipalwastedisposalratesduringcovid19usingseparatedwastefractionmodels AT kariminima modelingofmunicipalwastedisposalratesduringcovid19usingseparatedwastefractionmodels AT kabirgolam modelingofmunicipalwastedisposalratesduringcovid19usingseparatedwastefractionmodels |