An investigation on the operational resilience of the Canadian electronic product stewardship program and the recycling business characteristics.

Electronic waste recycling companies have proliferated in many countries due to valuable materials present in end-of-life electronic and electrical equipment. This article examined the business characteristics and management performance of Electronic Products Recycling Association (EPRA), a Canadian nationwide electronic product stewardship organization. The organization's annual performance reports, from 2012 to 2020, for nine Canadian provinces in which it currently operates were aggregated and analyzed. Temporal analysis using regression and Mann-Kendall tests were employed, and five characteristics of EPRA's business were analyzed, including e-waste products collected, number of drop-off locations, efforts to build public awareness, operating expenses, and growth of e-waste stewardship. Results show a decline in the amount of e-waste collected across the provinces, except in New Brunswick, which started its program in 2017. The Mann-Kendall test revealed declining temporal trends in most provinces. Although the collection/drop off sites and stewardship organizations increased astronomically over the study period in Canada, the amounts of e-waste collected decreased. We found that public awareness generally did not increase the amount of e-waste collected, and these campaigns only appeared to be effective in jurisdictions with good accessibility of e-waste recycling. Processing cost accounted for the majority of the e-waste management budget in Canada, and different factors affected the financial success of the stewards differently.

Prophet time series modeling of waste disposal rates in four North American cities.

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.

Influence of Canadian provincial stewardship model attributes on the cost effectiveness of e-waste management.

This paper presents a comprehensive analysis of e-waste collection and management trends across six Canadian provinces, focusing on e-waste collection rates, provincial stewardship model attributes, program strategies and budget allocations from 2013 to 2020. Temporal and regression analyses were conducted using data from Electronic Product Recycling Association reports. A group characterization based on geographical proximity is proposed, aiming to explore the potential outcomes of fostering collaboration among neighboring provinces. The analysis emphasizes the significant impact of stewardship model attributes on e-waste collection rates, with Quebec emerging as a standout case, showcasing a remarkable 61.5% surge in collection rates. Findings from group analysis reveal a positive correlation between per capita e-waste collection rate and the growth of businesses and collection sites in Western Canada (Group A - British Columbia, Saskatchewan, and Manitoba). This highlights the potential benefits of a coordinated waste management approach, emphasizing the importance of shared resources and collaborative policies. Saskatchewan and Manitoba allocated only 6.6% and 7.0% of their respective budgets to e-waste transfer and storage. British Columbia's observed steady decrease of e-waste collection rate. In Group A, stewards handled 2.18-13.95 tonnes of e-waste during the study period. The cost per tonne of e-waste tended to be lower when more e-waste is managed per steward, suggesting the potential benefits of an integrated e-waste collection and management system.

Variability of the treated biomedical waste disposal behaviours during the COVID lockdowns.

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.

Prediction of photovoltaic waste generation in Canada using regression-based model.

The global surge in photovoltaic (PV) installations and the resulting increase in PV waste are a growing concern. The aims of this study include predicting the volume of photovoltaic waste in Canada. The forecasting of solar waste volume employed linear regression, 2nd order polynomial regression, and power regression models. The study's results indicate that Canada is on the verge of facing challenges related to the end-of-life treatment of photovoltaic modules in the coming years due to the significant growth in PV capacity over recent decades. According to the analysis, for early loss, the PV waste volume in 2045 could range from 180,000 MT to 270,000 MT, and for regular loss, it could range from 160,000 MT to 180,000 MT. This research is anticipated to assist relevant government agencies in assessing the prospective volume of PV waste to establish a sustainable and resilient PV waste management plan for Canada. These findings may shed light on the feasibility of a circular economy and advocate for the involvement of all stakeholders in a carefully coordinated strategy to mitigate potential environmental impacts and optimize resource utilization efficiency.

Complex-Exponential-Based Bio-Inspired Neuron Model Implementation in FPGA Using Xilinx System Generator and Vivado Design Suite.

This research investigates the implementation of complex-exponential-based neurons in FPGA, which can pave the way for implementing bio-inspired spiking neural networks to compensate for the existing computational constraints in conventional artificial neural networks. The increasing use of extensive neural networks and the complexity of models in handling big data lead to higher power consumption and delays. Hence, finding solutions to reduce computational complexity is crucial for addressing power consumption challenges. The complex exponential form effectively encodes oscillating features like frequency, amplitude, and phase shift, streamlining the demanding calculations typical of conventional artificial neurons through levering the simple phase addition of complex exponential functions. The article implements such a two-neuron and a multi-neuron neural model using the Xilinx System Generator and Vivado Design Suite, employing 8-bit, 16-bit, and 32-bit fixed-point data format representations. The study evaluates the accuracy of the proposed neuron model across different FPGA implementations while also providing a detailed analysis of operating frequency, power consumption, and resource usage for the hardware implementations. BRAM-based Vivado designs outperformed Simulink regarding speed, power, and resource efficiency. Specifically, the Vivado BRAM-based approach supported up to 128 neurons, showcasing optimal LUT and FF resource utilization. Such outcomes accommodate choosing the optimal design procedure for implementing spiking neural networks on FPGAs.

A multi-jurisdictional study on the quantification of COVID-19 household plastic waste in six Latin American countries.

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.

Analysis of barriers to photovoltaic waste management to achieve net-zero goal of Canada.

Photovoltaic (PV) installations are experiencing a worldwide exponential upsurge, and the subsequent PV waste is a growing concern. This study identifies and analyzes the critical barriers to PV waste management to achieve the net-zero goal of Canada. The barriers are pinpointed through a literature review and examined by formulating a framework integrating three methods: rough analytical hierarchy process, decision-making trial and evaluation laboratory, and interpretive structural modeling. The findings show that the barriers have complex causal interrelationships with the irregular generation of PV waste and waste collection center as the two crucial barriers with the highest driving powers and causal effects on others. The anticipated outcome of this research is to assist relevant government organizations and managers in assessing the connections between obstacles related to photovoltaic (PV) waste management, with the aim of developing a viable net-zero strategy for Canada.

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.

Ranking Canadian waste management system efficiencies using three waste performance indicators.

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.

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.

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level.

More than half of financial resources allocated for municipal solid waste management are typically spent on waste collection and transportation. An optimized landfill siting and waste collection system can save fuel costs, reduce collection truck emissions, and provide higher accessibility with lower traffic impacts. In this study, a data-driven analytical framework is developed to optimize population coverage by landfills using network analysis and satellite imagery. Two scenarios, SC1 and SC2, with different truck travel times were used to simulate generation-site-disposal-site distances in three Canadian provinces. Under status quo conditions, Landfill Regionalization Index (LFRI) ranging from 0 to 2 population centers per landfill in all three jurisdictions. LFRI consistently improved after optimization, with average LFRI ranging from 1.3 to 2.0 population centers per landfill. Lower average truck travel times and better coverage of the population centers are generally observed in the optimized systems. The proposed analytical method is found effective in improving landfill regionalization. Under SC1 and SC2, LFRI percentages of improvement ranging from 58.3% to 64.5% and 22.7% to 59.4%, respectively. Separation distance between the generation and disposal sites and truck capacity appear not a decisive factor in the optimization process. The proposed optimization framework is generally applicable to regions with different geographical and demographical attributes, and is particularly applicable in rural regions with sparsely located population centers.

Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices.

Illegal dump sites (IDS) pose significant risks to human and the environment and are a pressing issue worldwide. Due to their secretive nature, the detection of IDS is costly and ineffective. In this study, an analytical framework was developed to detect probable IDSs in rural and remote areas using nighttime light (NTL) as a proxy for populated areas. An IDS probability map is produced by aggregation of Landsat-8 and Suomi NPP satellite imagery, multiple-criteria decision-making analysis, and classification tools. Six variables are considered, including modified soil adjusted index, land surface temperature, NTL, highway length, railway length, and the number of landfills. Vulnerability of the inhabitants on reserve lands was assessed using three sample regions. The method appears effective in reducing potential IDSs. Only about 7% of the 31,285 km2 study area are identified as probable IDS, being classified as "very high" and "high". Landfills without permit are found more effective in lowering IDS occurrence. Spatial distributions of reserve lands and the maturity of highways network nearby may be more important than the length of railways when assessing the inhabitant vulnerability due to IDS. Highway length is the most decisive factor on IDS probability among all classes, with membership grades ranging from 0.99 to 0.55. Land surface temperature appears less effective for the identification of smaller scale IDS. NTL is more prominent on IDS probability in the "very high" class, with a membership grade of 0.80. The finding suggests that populated areas represented by NTL is a priori of IDS.

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.

Benchmarking Canadian solid waste management system integrating fuzzy analytic hierarchy process (FAHP) with efficacy methods.

Solid waste management is a recent challenge for both developed and developing countries because of urbanization and population growth. This research aims to identify and compare the economic efficiency of Canadian waste management systems integrating the fuzzy analytic hierarchy process (FAHP) with the efficacy method. Six economic indicators-diversion gross domestic product (DGDP) ratio, gross domestic product (GDP) of all industries, GDP of Sector 562, diversion rate, waste management output indicator, and diversion size indicator-are considered in this study. Initially, the FAHP method was used to calculate the indicator weights, and the efficacy method then ranked the DGDP ratio as the most influential factor for the GDP-related indicators. The DGDP ratio and diversion rate were determined to be most critical in the assessment of the economic efficiency of a solid waste management system in Canada. The result also revealed that the economic performance of the waste management systems of Nova Scotia, British Columbia, and Ontario are better compared to those of other provinces. The outcome of this study will aid the government and provincial organizations in establishing an effective solid waste management plan to improve their overall performance.

Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation.

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.

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.

Yard waste prediction from estimated municipal solid waste using the grey theory to achieve a zero-waste strategy.

Yard waste is one of the key components of municipal solid waste and can play a vital role in implementing zero-waste strategy to achieve sustainable municipal solid waste management. Therefore, the objective of this study is to predict yard waste generation using the grey theory from the predicted municipal solid waste generation. The proposed model is implemented using municipal solid waste generation data from the City of Winnipeg, Canada. To identify the generation factors that influence municipal solid waste generation and yard waste generation, a correlation analysis is performed among eight socio-economic factors and six climatic factors. The GM (1, 1) model is utilized to predict individual factors with overall MAPE values of 0.06%-10.39% for the in-sample data, while the multivariable GM (1, N) grey model is employed to forecast the quarterly level of municipal solid waste generation with overall MAPE values of 5.64%-7.54%. In this study, grey models predict quarterly yard waste generation from the predicted municipal solid waste generation values using only twelve historical data points. The results indicate that the grey model (based on the error matrices) performs better than the linear and nonlinear regression-based models. The outcome of this study will support the City of Winnipeg's sustainable planning for yard waste management in terms of budgeting, resource allocation, and estimating energy generation.

What have we known so far about microplastics in drinking water treatment? A timely review.

Microplastics (MPs) have been widely detected in drinking water sources and tap water, raising the concern of the effectiveness of drinking water treatment plants (DWTPs) in protecting the public from exposure to MPs through drinking water. We collected and analyzed the available research articles up to August 2021 on MPs in drinking water treatment (DWT), including laboratory- and full-scale studies. This article summarizes the major MP compositions (materials, sizes, shapes, and concentrations) in drinking water sources, and critically reviews the removal efficiency and impacts of MPs in various drinking water treatment processes. The discussed drinking water treatment processes include coagulation-flocculation (CF), membrane filtration, sand filtration, and granular activated carbon (GAC) filtration. Current DWT processes that are purposed for particle removal are generally effective in reducing MPs in water. Various influential factors to MP removal are discussed, such as coagulant type and dose, MP material, shape and size, and water quality. It is anticipated that better MP removal can be achieved by optimizing the treatment conditions. Moreover, the article framed the major challenges and future research directions on MPs and nanoplastics (NPs) in DWT.