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Hasan MM, Ng KTW, Ray S, Assuah A, Mahmud TS. Prophet time series modeling of waste disposal rates in four North American cities. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-33335-5. [PMID: 38632194 DOI: 10.1007/s11356-024-33335-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 04/11/2024] [Indexed: 04/19/2024]
Abstract
In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.
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Affiliation(s)
- Mohammad Mehedi Hasan
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada.
| | - Sagar Ray
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
| | - Anderson Assuah
- University College of the North, Box 3000, 436 - 7th Street East, The Pas, Manitoba, R9A 1M7, Canada
| | - Tanvir Shahrier Mahmud
- Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
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2
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Kumari P, Mahmud TS, Ng KTW, Chowdhury R, Gitifar A, Richter A. Variability of the treated biomedical waste disposal behaviours during the COVID lockdowns. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:24480-24491. [PMID: 38441741 DOI: 10.1007/s11356-024-32764-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
Literature review suggests that studies on biomedical waste generation and disposal behaviors in North America are limited. Given the infectious nature of the materials, effective biomedical waste management is vital to the public health and safety of the residents. This study explicitly examines seasonal variations of treated biomedical waste (TBMW) disposal rates in the City of Regina, Canada, from 2013 to 2022. Immediately before the onset of COVID-19, the City exhibited a steady pattern of TBMW disposal rate at about 6.6 kg∙capita-1∙year-1. However, the COVID-19 pandemic and its associated lockdowns brought about an abrupt and persistent decline in TBMW disposal rates. Inconsistent fluctuations in both magnitude and variability of the monthly TBMW load weights were also observed. The TBMW load weight became particularly variable in 2020, with an interquartile range 4 times higher than 2019. The average TBMW load weight was also the lowest (5.1 tonnes∙month-1∙truckload-1) in 2020, possibly due to an overall decline in non-COVID-19 medical emergencies, cancellation of elective surgeries, and availability of telehealth options to residents. In general, the TBMW disposal rates peaked during the summer and fall seasons. The day-to-day TBMW disposal contribution patterns between the pre-pandemic and post-pandemic are similar, with 97.5% of total TBMW being disposed of on fixed days. Results from this Canadian case study indicate that there were observable temporal changes in TBMW disposal behaviors during and after the COVID-19 lockdowns.
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Affiliation(s)
- Preeti Kumari
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regain, SK, S4S 0A2, Canada
| | - Tanvir Shahrier Mahmud
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regain, SK, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regain, SK, S4S 0A2, Canada.
| | - Rumpa Chowdhury
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regain, SK, S4S 0A2, Canada
| | - Arash Gitifar
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regain, SK, S4S 0A2, Canada
| | - Amy Richter
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regain, SK, S4S 0A2, Canada
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Wen C, Lin X, Ying Y, Ma Y, Yu H, Li X, Yan J. Dioxin emission prediction from a full-scale municipal solid waste incinerator: Deep learning model in time-series input. WASTE MANAGEMENT (NEW YORK, N.Y.) 2023; 170:93-102. [PMID: 37562201 DOI: 10.1016/j.wasman.2023.08.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/02/2023] [Accepted: 08/04/2023] [Indexed: 08/12/2023]
Abstract
The immeasurability of real-time dioxin emissions is the principal limitation to controlling and reducing dioxin emissions in municipal solid waste incineration (MSWI). Existing methods for dioxin emissions prediction are based on machine learning with inadequate dioxin datasets. In this study, the deep learning models are trained through larger online dioxin emissions data from a waste incinerator to predict real-time dioxin emissions. First, data are collected and the operating data are preprocessed. Then, the dioxin emission prediction performance of the machine learning and deep learning models, including long short-term memory (LSTM) and convolutional neural networks (CNN), with normal input and time-series input are compared. We evaluate the applicability of each model and find that the performance of the deep learning models (LSTM and CNN) has improved by 36.5% and 30.4%, respectively, in terms of the mean square error (MSE) with the time-series input. Moreover, through feature analysis, we find that temperature, airflow, and time dimension are considerable for dioxin prediction. The results are meaningful for optimizing the control of dioxins from MSWI.
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Affiliation(s)
- Chaojun Wen
- Polytechnic Institute, Zhejiang University, Hangzhou 310027, China
| | - Xiaoqing Lin
- Polytechnic Institute, Zhejiang University, Hangzhou 310027, China; State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China.
| | - Yuxuan Ying
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
| | - Yunfeng Ma
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
| | - Hong Yu
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
| | - Xiaodong Li
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China; Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, Jiaxing Research Institute, Zhejiang University, Jiaxing 314031, China
| | - Jianhua Yan
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
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Mahmud TS, Ng KTW, Hasan MM, An C, Wan S. A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19. SUSTAINABLE CITIES AND SOCIETY 2023; 96:104685. [PMID: 37274541 PMCID: PMC10225168 DOI: 10.1016/j.scs.2023.104685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/18/2023] [Accepted: 05/27/2023] [Indexed: 06/06/2023]
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.
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Affiliation(s)
- Tanvir Shahrier Mahmud
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada
| | - Mohammad Mehedi Hasan
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada
| | - Chunjiang An
- Department of Building, Civil, and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec H3G 1M8, Canada
| | - Shuyan Wan
- Department of Building, Civil, and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec H3G 1M8, Canada
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Requena-Sanchez NP, Carbonel D, Demel L, Moonsammy S, Richter A, Mahmud TS, Ng KTW. A multi-jurisdictional study on the quantification of COVID-19 household plastic waste in six Latin American countries. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:93295-93306. [PMID: 37505388 DOI: 10.1007/s11356-023-28949-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023]
Abstract
This study examines urban plastic waste generation using a citizen science approach in six Latin American countries during a global pandemic. The objectives are to quantify generation rates of masks, gloves, face shields, and plastic bags in urban households using online survey and perform a systematic cross-jurisdiction comparisons in these Latin American countries. The per capita total mask generation rates ranged from 0.179 to 0.915 mask cap-1 day-1. A negative correlation between the use of gloves and masks is observed. Using the average values, the approximate proportion of masks, gloves, shields, and single-use plastic bags was 34:5:1:84. We found that most studies overestimated face mask disposal rate in Latin America due to the simplifying assumptions on the number of masks discarded per person, masking prevalence rate, and average mask weight. Unlike other studies, end-of-life PPE quantities were directly counted and reported by the survey participants. Both of the conventional weight-based estimates and the proposed participatory survey are recommended in quantifying COVID waste. Participant' perception based on the Likert scale is generally consistent with the waste amount generated. Waste policy and regulation appear to be important in daily waste generation rate. The results highlight the importance of using measured data in waste estimates.
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Affiliation(s)
- Norvin Plumieer Requena-Sanchez
- Integrated Waste Management for Sustainable Development (GIRDS), Faculty of Environmental Engineering, National University of Engineering, Av. Túpac Amaru 210, Rímac, 15333, Lima, Peru
| | - Dalia Carbonel
- Integrated Waste Management for Sustainable Development (GIRDS), Faculty of Environmental Engineering, National University of Engineering, Av. Túpac Amaru 210, Rímac, 15333, Lima, Peru
| | - Larissa Demel
- United Nations Development Program, Apartado, 0816-1914, Panama, Panama
| | - Stephan Moonsammy
- Department of Environmental Studies, Faculty of Earth and Environmental Sciences, University of Guyana, RV6J+XV8, Turkeyen Campus, Georgetown, Guyana
| | - Amy Richter
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
| | - Tanvir Shahrier Mahmud
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada.
- Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada.
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Mensah D, Karimi N, Ng KTW, Mahmud TS, Tang Y, Igoniko S. Ranking Canadian waste management system efficiencies using three waste performance indicators. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:51030-51041. [PMID: 36808539 PMCID: PMC9937868 DOI: 10.1007/s11356-023-25866-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 02/07/2023] [Indexed: 04/16/2023]
Abstract
Three waste management system (WMS) efficiency indicators are adopted to systematically assess WMS efficiency in Canada from 1998 to 2016. The study objectives are to examine the temporal changes in waste diversion activities and rank the performance of the jurisdictions using a qualitative analytical framework. Increasing Waste Management Output Index (WMOI) trends were identified in all jurisdictions, and more government subsidiaries and incentive packages are recommended. With the exception of Nova Scotia, statistically significant decreasing diversion gross domestic product (DGDP) ratio trends are observed. It appears that the increases in GDP from Sector 562 were not contributing to waste diversion. On average, Canada spent about $225/tonne of waste handled during the study period. Current spending per tonne handled (CuPT) trends are decreasing, with S ranging from + 5.15 to + 7.67. It appears that WMSs in Saskatchewan and Alberta are more efficient. The results suggest that the use of diversion rate alone to evaluate WMS may be misleading. The findings help the waste community to better understand the trade-offs between various waste management alternatives. The proposed qualitative framework utilizing comparative rankings is applicable elsewhere and can be a useful decision support tool for policy-makers.
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Affiliation(s)
- Derek Mensah
- Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada
| | - Nima Karimi
- Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada.
| | - Tanvir S Mahmud
- Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada
| | - Yili Tang
- Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada
| | - Sotonye Igoniko
- Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada
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Mahmud TS, Ng KTW, Karimi N, Adusei KK, Pizzirani S. Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by World Health Organization guidelines. SUSTAINABLE CITIES AND SOCIETY 2022; 87:104219. [PMID: 36187707 PMCID: PMC9515004 DOI: 10.1016/j.scs.2022.104219] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/26/2022] [Accepted: 09/26/2022] [Indexed: 06/06/2023]
Abstract
This study aims to identify the effects of continued COVID-19 transmission on waste management trends in a Canadian capital city, using pandemic periods defined from epidemiology and the WHO guidelines. Trends are detected using both regression and Mann-Kendall tests. The proposed analytical method is jurisdictionally comparable and does not rely on administrative measures. A reduction of 190.30 tonnes/week in average residential waste collection is observed in the Group II period. COVID-19 virulence negatively correlated with residential waste generation. Data variability in average collection rates during the Group II period increased (SD=228.73 tonnes/week). A slightly lower COVID-19 induced Waste Disposal Variability (CWDW) of 0.63 was observed in the Group II period. Increasing residential waste collection trends during Group II are observed from both regression (b = +1.6) and the MK test (z = +5.0). Both trend analyses reveal a decreasing CWDV trend during the Group I period, indicating higher diversion activities. Decreasing CWDV trends are also observed during the Group II period, probably due to the implementation of new waste programs. The use of pandemic periods derived from epidemiology helps us to better understand the effect of COVID-19 on waste generation and disposal behaviors, allowing us to better compare results in regions with different socio-economic affluences.
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Affiliation(s)
- Tanvir S Mahmud
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan, Canada, S4S 0A2
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan, Canada, S4S 0A2
| | - Nima Karimi
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan, Canada, S4S 0A2
| | - Kenneth K Adusei
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan, Canada, S4S 0A2
| | - Stefania Pizzirani
- School of Land Use and Environmental Change, University of the Fraser Valley, British Columbia, Canada, V2S 7M8
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8
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Requena-Sanchez N, Carbonel D, Moonsammy S, Demel L, Vallester E, Velásquez D, Toledo Cervantes JA, Díaz Núñez VL, Vásquez García R, Santa Cruz M, Visbal E, Ng KTW. COVID-19 impacts on household solid waste generation in six Latin American countries: a participatory approach. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:155. [PMID: 36441286 PMCID: PMC9702680 DOI: 10.1007/s10661-022-10771-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
The COVID-19 pandemic has greatly impacted the Americas, the continent with the highest number of COVID-related deaths according to WHO statistics. In Latin America, strict confinement conditions at the beginning of the pandemic put recycling activity to a halt and augmented the consumption of plastic as a barrier to stop the spread of the virus. The lack of data to understand waste management dynamics complicates waste management strategy adjustments aimed at coping with COVID-19. As a novel contribution to the waste management data gap for Latin America, this study uses a virtual and participatory methodology that collects and generates information on household solid waste generation and composition. Data was collected between June and November 2021 in six countries in Latin America, with a total of 503 participants. Participants indicated that the pandemic motivated them to initiate or increase waste reduction (41%), waste separation (40%), and waste recovery (33%) activities. Forty-three percent of participants perceived an increase in total volume of their waste; however, the quantitative data showed a decrease in household waste generation in Peru (-31%), Honduras (-25%), and Venezuela (-82%). No changes in waste composition were observed. Despite the limited sample size, this data provides a much-needed approximation of household waste generation and composition in the pandemic situation during 2021.
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Affiliation(s)
- Norvin Requena-Sanchez
- Integrated Waste Management for Sustainable Development Group, Faculty of Environmental Engineering, National University of Engineering, 210 Túpac Amaru Ave, Rímac, Lima, Peru
| | - Dalia Carbonel
- Integrated Waste Management for Sustainable Development Group, Faculty of Environmental Engineering, National University of Engineering, 210 Túpac Amaru Ave, Rímac, Lima, Peru
| | - Stephan Moonsammy
- Department of Environmental Studies, Faculty of Earth and Environmental Sciences, University of Guyana, Turkeyen Campus, P. O. Box 10 1110, Georgetown, Guyana
| | - Larissa Demel
- United Nations Development Program, Casa de las Naciones Unidas, Edificio # 129, Ciudad del Saber, Panama City, Panama
| | - Erick Vallester
- Technological University of Panama, Avenida Universidad Tecnológica de Panamá, Vía Puente Centenario, Campus Metropolitano Víctor Levi Sasso, Panama City, Panama
| | - Diana Velásquez
- National Autonomous University of Honduras, Bulevar Suyapa, Tegucigalpa, Honduras
| | | | | | - Rosario Vásquez García
- Daniel Alcides Carrion National University, Av. Los Próceres 703, Yanacancha, Cerro de Pasco, Peru
| | - Melissa Santa Cruz
- Intercultural National University Fabiola Salazar Leguia From Bagua, Jirón Ancash N° 520 Bagua, Amazonas, Peru
| | - Elsy Visbal
- Litoral Headquarters, Simón Bolívar University, Camurí Grande, Edo. Vargas Parroquia Naiguatá, La Guaira, Venezuela
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK Canada
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9
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Impacts of nested forward validation techniques on machine learning and regression waste disposal time series models. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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10
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Adusei KK, Ng KTW, Karimi N, Mahmud TS, Doolittle E. Modeling of municipal waste disposal behaviors related to meteorological seasons using recurrent neural network LSTM models. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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11
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Mahyari KF, Sun Q, Klemeš JJ, Aghbashlo M, Tabatabaei M, Khoshnevisan B, Birkved M. To what extent do waste management strategies need adaptation to post-COVID-19? THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 837:155829. [PMID: 35561899 PMCID: PMC9087148 DOI: 10.1016/j.scitotenv.2022.155829] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 05/02/2023]
Abstract
The world has been grappling with the crisis of the COVID-19 pandemic for more than a year. Various sectors have been affected by COVID-19 and its consequences. The waste management system is one of the sectors affected by such unpredictable pandemics. The experience of COVID-19 proved that adaptability to such pandemics and the post-pandemic era had become a necessity in waste management systems and this requires an accurate understanding of the challenges that have been arising. The accurate information and data from most countries severely affected by the pandemic are not still available to identify the key challenges during and post-COVID-19. The documented evidence from literature has been collected, and the attempt has been made to summarize the rising challenges and the lessons learned. This review covers all raised challenges concerning the various aspects of the waste management system from generation to final disposal (i.e., generation, storage, collection, transportation, processing, and burial of waste). The necessities and opportunities are recognized for increasing flexibility and adaptability in waste management systems. The four basic pillars are enumerated to adapt the waste management system to the COVID-19 pandemic and post-COVID-19 conditions. Striving to support and implement a circular economy is one of its basic strategies.
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Affiliation(s)
- Khadijeh Faraji Mahyari
- Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Iran
| | - Qiaoyu Sun
- Center for Science and Technology Personnel Exchange and Development Service, Ministry of Science and Technology of the People's Republic of China, No.54 Sanlihe Road, Xicheng District, Beijing, PR China
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Mortaza Aghbashlo
- Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Iran
| | - Meisam Tabatabaei
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries (AKUATROP), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia; Henan Province Engineering Research Center for Biomass Value-added Products, School of Forestry, Henan Agricultural University, Zhengzhou 450002, China
| | - Benyamin Khoshnevisan
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Denmark.
| | - Morten Birkved
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Denmark.
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12
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Mookkaiah SS, Thangavelu G, Hebbar R, Haldar N, Singh H. Design and development of smart Internet of Things-based solid waste management system using computer vision. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:64871-64885. [PMID: 35476273 PMCID: PMC9045024 DOI: 10.1007/s11356-022-20428-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 04/20/2022] [Indexed: 05/17/2023]
Abstract
Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision-based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet V2 models trained through transfer learning for image classification. ResNet V2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The proposed ResNet-based CNN performs waste classification with 19.08% higher accuracy and 34.97% lower loss than the performance metrics of other existing models.
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Affiliation(s)
| | | | - Rahul Hebbar
- Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India
| | - Nipun Haldar
- Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India
| | - Hargovind Singh
- Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India
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Singh M, Karimi N, Ng KTW, Mensah D, Stilling D, Adusei K. Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:50780-50789. [PMID: 35239117 PMCID: PMC8892816 DOI: 10.1007/s11356-022-19487-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/24/2022] [Indexed: 06/06/2023]
Abstract
In this study, the hospital waste generation rates and compositions in Delhi were examined temporally and spatially during the first COVID-19 wave of April 2020. A total of 11 representative hospitals located in five districts were considered. The pre-COVID hospital waste generation rates were relatively consistent among the districts, ranging from 15 to 23 tonne/month. It is found that the number of hospital beds per capita may not be a significant factor in the hospital waste quantity. Strong seasonal variations were not observed. All districts experienced a drastic decrease in generation rates during the 1-month lockdown. The average rates during the COVID period ranged from 12 to 24 tonne/month. Bio-contaminated and disposable medical product wastes were the most common waste in Delhi's hospitals, representing 70-80% by weight. The changes in waste composition were however not spatially consistent. The lockdown appeared to have had a higher impact on hospital waste generation rate than on waste composition. The findings are important as the design and operation of a waste management system are sensitive to both waste quantity and quality. Waste records at source helped to minimize waste data uncertainties and allowed a closer examination of generation trends.
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Affiliation(s)
- Mayank Singh
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada
| | - Nima Karimi
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada.
| | - Derek Mensah
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada
| | - Denise Stilling
- Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada
| | - Kenneth Adusei
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada
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ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania. SUSTAINABILITY 2022. [DOI: 10.3390/su14074122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.
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Vu HL, Ng KTW, Richter A, An C. Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 311:114869. [PMID: 35287077 DOI: 10.1016/j.jenvman.2022.114869] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 03/01/2022] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
The use of machine learning techniques in waste management studies is increasingly popular. Recent literature suggests k-fold cross validation may reduce input dataset partition uncertainties and minimize overfitting issues. The objectives are to quantify the benefits of k-fold cross validation for municipal waste disposal prediction and to identify the relationship of testing dataset variance on predictive neural network model performance. It is hypothesized that the dataset characteristics and variances may dictate the necessity of k-fold cross validation on neural network waste model construction. Seven RNN-LSTM predictive models were developed using historical landfill waste records and climatic and socio-economic data. The performance of all trials was acceptable in the training and validation stages, with MAPE all less than 10%. In this study, the 7-fold cross validation reduced the bias in selection of testing sets as it helps to reduce MAPE by up to 44.57%, MSE by up to 54.15%, and increased R value by up to 8.33%. Correlation analysis suggests that fewer outliers and less variance of the testing dataset correlated well with lower modeling error. The length of the continuous high waste season and length of total high waste period appear not important to the model performance. The result suggests that k-fold cross validation should be applied to testing datasets with higher variances. The use of MSE as an evaluation index is recommended.
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Affiliation(s)
- Hoang Lan Vu
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada
| | - Kelvin Tsun Wai Ng
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada.
| | - Amy Richter
- Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada
| | - Chunjiang An
- Department of Building, Civil, and Environmental Engineering, Concordia University, 1455 Boulevard de Maisonneuve O, Montréal, Quebec, H3G 1M8, Canada
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