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

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.

COVID-19 impacts on household solid waste generation in six Latin American countries: a participatory approach.

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.

Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by World Health Organization guidelines.

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.

Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by World Health Organization guidelines

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.

Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi.

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.

Virtual Methodology for Household Waste Characterization During The Pandemic in An Urban District of Peru: Citizen Science for Waste Management.

The Covid-19 pandemic has caused the alteration of many aspects of the solid waste management chain, such as variations in the waste composition, generation and disposal. Various studies have examined these changes with analysis of integrated waste management strategies; qualitative studies on perceived variations and statistical evaluations based on waste collected or disposed in landfills. Despite this information there is a need for updated data on waste generation and composition, especially in developing countries. The objective of this article is to develop a data sampling and analytical approach for the collection of data on household waste generation and composition during the pandemic; and, in addition, estimate the daily generation of masks in the study area. The proposed methodology is based on the principles of citizen science and utilizes virtual tools to contact participants, and for the training and collection of information. The study participants collected the information, installed segregation bins in their homes and trained their relatives in waste segregation. The article presents the results of the application of the methodology in an urban district of Lima (Peru) in August 2020. The results suggest an apparent decrease in household waste per capita and a slight increase in plastics composition in the study area. It is estimated that each participant generates 0.124 masks per day and 0.085 pairs of gloves per day. The method developed and results presented can be used as a tool for public awareness and training on household waste characterization and segregation. Furthermore it can provide the necessary evidence to inform policy directives in response household waste issues and Covid-19 restrictions.

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19.

A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socioeconomic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both 'Temp-Hum' (Temperature-Humidity) and 'Temp-New Test' (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.

A New Challenge for the Management and Disposal of Personal Protective Equipment Waste during the COVID-19 Pandemic

To prevent the transmission of the Coronavirus (SARS-CoV-2) in the public, the demand and consumption of personal protective equipment (PPE) increased drastically. Such wide use of PPE has brough a new challenge to waste management and disposal. It is difficult to sort PPE waste before further treatment, and such waste will often end up being processed by some traditional disposal methods. During the pandemic, incineration and landfill facilities are currently under significant pressure. In addition, a certain amount of PPE waste is discarded into the environment rather than going to landfills and incinerators. It not only directly affects the ecosystem, but also indirectly threatens human health through various routes of exposure. PPE waste is also the source and carrier of pathogens and chemical contaminants, causing a secondary pollution. Therefore, it is necessary to establish appropriate strategies to deal with the PPE problems related to energy, environment and health, requiring the collaborative efforts of researchers, practitioners, policymakers, and governments.

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 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.

Identification of behaviour patterns in waste collection and disposal during the first wave of COVID-19 in Regina, Saskatchewan, Canada.

The novel coronavirus (2019-nCov) has had significant impacts on almost every aspect of daily life. From 'stay-at-home' orders to the progressive lifting of restrictions, the COVID-19 pandemic has had unprecedented effects on consumer behaviours and waste disposal habits. The purpose of this short communication is to examine time series waste collection and disposal data in a mid-sized Canadian city to understand how behavioural changes have affected municipal waste management. The results suggest that private waste disposal increased during the pandemic. This may be due to people doing home renovations in order to accommodate working from home. Furthermore, it appears that changes in consumer habits destabilized the consistency of waste disposal tonnage when compared to the same time period in 2019. When considering curbside residential waste collection, there was also an increase in tonnage. This may be the result of more waste being generated at home due to changes in eating and cooking habits, and cleaning routine. Finally, the ratio of residential waste collection to total disposal is examined. More residential waste is being generated, which may have environmental and operational effects, especially related to collection and transportation. The results from this study are important from an operational perspective, and will help planners and policy makers to better prepare for changes in the waste stream due to pandemics or other emergencies.

Waste disposal characteristics and data variability in a mid-sized Canadian city during COVID-19.

COVID-19, declared a global pandemic by the World Health Organization, has caused governments to react swiftly with a variety of measures to quell the spread of the virus. This study investigates changes in waste disposal characteristics and the relationship between the mass of biomedical waste disposed and new COVID-19 tests performed in Regina, Canada. Results suggest that between May and September 2020, significant differences in the median amount of waste disposed exist. The amount of monthly waste disposed was slightly lower to about 450-550 tonnes/month. Monthly waste data variability, however, was significantly lower. Seasonal effects on total waste disposal is observed, but is less obvious than pre-COVID time. Furthermore, the distribution of different waste fractions varies, probably due to operational and industrial characteristics. A non-linear relationship exists between the number of COVID-19 tests performed and the mass of biomedical waste disposed, perhaps due to a lagged relationship between biomedical waste generation and disposal.