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.

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.

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.

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 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 D_CV model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA B_CV). 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 D_CV 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands

Information on the spatial extent of potential impact areas near disposal sites is vital to the development of a sustainable natural resource management policy. Eight Canadian landfills of various sizes and shapes in different climatic conditions are studied to quantify the spatial extent of their bio-thermal zone. Land surface temperature (LST) and normalized difference vegetation index (NDVI) are examined with respect to different Land Use Land Cover (LULC) classes. Within 1500 m of the sites, LST ranged from 18.3 °C to 29.5 °C and 21.3 °C-29.7 °C for forest land and agricultural land, respectively. Linear regression shows a decreasing LST trend in forest land for five out of seven landfills. A similar trend, however, is not observed for agricultural land. Both the magnitude and the variability of LST are higher in agricultural land. The size of the bio-thermal zone is sensitive to the respective LULC class. The approximate bio-thermal zones for forest class and agricultural classes are about 170 ± 90 m and 180 ± 90 m from the landfill perimeter, respectively. For the forest class, NDVI was negatively correlated with LST at six out of seven Canadian landfills, and stronger relationships are observed in the agricultural class. NDVI data has a considerably larger spread and is less consistent than LST. LST data appears more appropriate for identifying landfill bio-thermal zones. A subtle difference in LST is observed among six LULC classes, averaging from 23.9 °C to 27.4 °C. Geometric shape makes no observable difference in LST in this study; however, larger landfill footprint appears to have higher LST.

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.

Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation

To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models are required for more precise prediction of the amount and recovery time of methane gas from landfills. When the study associates to greenhouse gas emissions problems, time series prediction models are of considerable interests, in which significant past records of gas data are required. This study is the first to specially impute the missing methane (CH₄) data for applying in time series artificial neural network (ANN) model in an attempt to predict daily CH₄ generation rate from a landfill in Regina, SK, Canada. Pre-processing was conducted on data to evaluate independent and significant meteorological input variables and provide suitable dataset for developing CH₄ generation models. A two-stage time series model proposed in this study was performed by missing data imputation at the first stage, followed by a neural network auto-regressive model with exogenous inputs (NARX) at the second stage. The model with 3 layers, 5 climatic variables and 9 neurons in the hidden layer was the optimal structure. This model shows the high performance in CH₄ prediction with the average index of agreement of 0.92 and the average mean absolute percentage error (MAPE) of 3.03% during the testing stage. Missing data imputation coupled with NARX method decreased the mean squared error (MSE) of the model by 84% (compared to Multilayer Perceptrons neural network model) in the testing period representing the effectiveness of missing data estimation coupling with time series ANN models in daily CH₄ generation prediction.

Siting and ranking municipal landfill sites in regional scale using nighttime satellite imagery

In 2016, about 24.9 million tonnes of solid waste were disposed of in Canadian landfills, where landfill technology is a common choice. This study aims to develop a data-driven GIS-based method that considers spatial, environmental, and economic constraints using study regions derived from night time light data for a 40 km buffer around Regina, Saskatchewan, Canada. Unlike other similar studies, this site suitability study assumes no political or administrative boundaries as inputs. Road network stands as the most decisive factor that accounts for 0.239 of entire weight, followed by protective areas with a total weight of 0.220. The regions that ranked the best for siting new landfills were generally located far from predominant water resources and protected areas, but are in the vicinity of major road networks, but are also far from urbanized regions. The sensitivity analysis showed that, overall, road network and protected areas are the most essential layers in this analysis. For the environmental group, protected areas and water resources are major layers. For the economic group, road network and surface temperature are the most important. The method presented in this study can easily accommodate other data sets based on importance in any given area.

Interactions of residential waste composition and collection truck compartment design on GIS route optimization

Waste collection is an important functional element in a modern waste management system; and may account for up to half of the total expenditure on waste management in industrialized nations. Most optimization of waste collection studies include truck route distance and fuel consumption considerations without explicitly considering the inter-relationships of the model parameters. This study however delineates the complex inter-relationships of waste composition, collection frequency, collection type, and truck compartment configurations in a small waste collection zone in Austin, Texas. A total of 48 different scenarios are modelled and investigated. Truck travel distances are found sensitive to collection frequency, truck capacity, volume ratio of truck compartment, and waste density. The results showed that the increase in waste density and waste collection frequency helped to save up to 18.2% in travel distances and 41.9% in travel time. Waste composition is significant in travel distance, regardless of truck design. Increasing truck capacity by 25% helped to save 4.1-24.4% of truck travel distances. Optimal volume ratio of truck compartments was 50:50 (50% volume for garbage and 50% volume for recyclables); a finding that is different than what is currently reported in the literature; pointing to the site-specific nature of studies of this type. The use of dual compartment trucks helps to reduce travel distances by up to 23.0% and travel time by up to 14.3%. It appears that the minimization of operation time within the collection area is key to an efficient system.

Effects of groundwater metal contaminant spatial distribution on overlaying kriged maps

Groundwater is a major source of drinking water for many Canadians, and contamination by heavy metals poses a significant risk to people and the environment. In this study, three water quality indices are studied in the vicinity of an unlined landfill in a semiarid climate. The study investigates indices using geostatistical analysis and ordinary kriging. This study employs a novel coupling technique in order to compare the index-based maps to a groundwater quality map from overlapping heavy metal kriged maps. A total of 11 heavy metals were evaluated in preliminary analysis, but only four (Mn, As, Fe, and U) had higher concentrations than allowable limits in some or all of the monitoring wells at the site. Results from mean-based classification of indices suggest the aquifer in proximity to the landfill has been impacted by metal contaminants. Kriged maps show that the spatial variations of Mn and U are similar, while results of Fe and As are also similar. However, the two sets of maps have distinctly different patterns. Maps for indices show an elevated plateau extending from the unlined landfill to the southeast corner, implying that the landfill may have negatively impacted groundwater quality. A groundwater quality map is developed by overlaying the heavy metal maps. The resulting map shows that the north and west parts of the study have lower groundwater pollution with respect to metal contaminants. The groundwater quality map may be more applicable for practitioners who need comprehensive water quality measurement.

Assessment of waste characteristics and their impact on GIS vehicle collection route optimization using ANN waste forecasts

Combining an artificial neural network (ANN) waste prediction model with a geographic information system (GIS) waste collection route optimization, the paper shows how the compositional features of waste materials affect the optimized truck route time, distance, and air emissions. Using data from Austin, Texas, USA, a nonlinear autoregressive ANN model is used to predict the waste generation rate of the recycling and garbage streams for the year 2023 in four sub-areas of the city. This ANN model resulted in mean absolute percentage errors ranging from 10.92% to 16.51%. Modified compositions of the recycling and garbage streams are then used as inputs, along with the year 2023 generation rates, to create 6 modified and 3 non-modified scenarios that reflect possible future changes in waste composition. These waste stream scenarios are then used as input parameters to determine optimal waste collection routes with minimal travel distance in each of the four sub-areas using the GIS vehicle routing problem network analysis tool. Results of these 36 scenarios yield changes in travel distance of up to 19.9%, when compared to the non-modified composition. Further, dual compartment trucks were compared to single compartment trucks and found to save between 10.3 and 16.0% in travel distance and slightly reduce emissions but had a 15.7-19.8% increase in collection time. Results suggest temporal changes in waste composition and characteristics are important in GIS route optimization studies.

An integrated multivariate statistical approach for the evaluation of spatial variations in groundwater quality near an unlined landfill

Groundwater is a major resource for water supply in Canada, and 43 of 68 Saskatchewan municipalities rely on groundwater or combined groundwater and surface water sources. The Regina landfill is built on top of the Condie aquifer, without an engineered liner. Missing data and inconsistent sampling make a traditional groundwater assessment difficult. An integrated statistical approach using principle component analysis, correlation analysis, ion plots, and multiple linear regression is used to study groundwater contamination at the Regina landfill. Geological locations of the water samples were explicitly considered. The abundance of cations in the groundwater was Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ > Mn²⁺; and for anions SO₄^2- > HCO₃^- > Cl^-. Correlation analysis and ion plots pointed to gypsum and halite dissolution being the main factors affecting groundwater chemistry. Principal component analysis yielded three principal components, responsible for 80.7% of the total variance. For all monitoring well groups, the sodium absorption ratio was generally less than one. The variation in the ratio from monitoring well groups suggests possible groundwater contamination from landfill operation. Wilcox diagrams indicate groundwater near the landfill is unsuitable for irrigation. A two-step multiple linear regression was used to develop a model for total hardness prediction.

Time-lagged effects of weekly climatic and socio-economic factors on ANN municipal yard waste prediction models

Efficient and effective solid waste management requires sufficient ability to predict the operational capacity of a system correctly. Waste prediction models have been widely studied and these models are always being challenged to perform more accurately. Unlike waste prediction models for mixed wastes, variables for yard waste are time sensitive and the effects of lag must be explicitly considered. This study is the first to specifically look at lag times relating to variables that attempt to predict municipal yard waste generation using machine learning approaches. Weekly averaged climatic and socio-economic variables are screened through correlation analysis and the significant variables are then used to develop yard waste models. These models then utilize artificial neural networks (ANN) where the variables are time lagged for a different number of weeks. This helps to realize a reduction in the error of the predicted weekly yard waste generation. Optimal lag times for each model varied from 1 to 11 weeks. The best model used both the ambient air temperature and population variables, in an ANN model with 3 layers, 11 neurons in the hidden layer, and an optimal lag time of 1 week. A mean absolute percentage error of 18.72% was obtained during the testing stage. One model saw a 55.4% decrease in the mean squared error at training, showing the value of lag time on the accuracy of weekly yard waste prediction models.

Evaluation of the bias and precision of regression techniques and machine learning approaches in total dissolved solids modeling of an urban aquifer

TDS is modeled for an aquifer near an unlined landfill in Canada. Canadian Drinking Water Guidelines and other indices are used to evaluate TDS concentrations in 27 monitoring wells surrounding the landfill. This study aims to predict TDS concentrations using three different modeling approaches: dual-step multiple linear regression (MLR), hybrid principal component regression (PCR), and backpropagation neural networks (BPNN). An analysis of the bias and precision of each models follows, using performance evaluation metrics and statistical indices. TDS is one of the most important parameters in assessing suitability of water for irrigation, and for overall groundwater quality assessment. Good agreement was observed between the MLR1 model and field data, although multicollinearity issues exist. Percentage errors of hybrid PCR were comparable to the dual-step MLR method. Percentage error for hybrid PCR was found to be inversely proportional to TDS concentrations, which was not observed for dual-step MLR. Larger errors were obtained from the BPNN models, and higher percentage errors were observed in monitoring wells with lower TDS concentrations. All models in this study adequately describe the data in testing stage (R² > 0.86). Generally, the dual-step MLR and hybrid PCR models fared better (R²_avg = 0.981 and 0.974, respectively), while BPNN models performed worse (R²_avg = 0.904). For this dataset, both regression and machine learning models are more suited to predict mid-range data compared to extreme values. Advanced regression methods (hybrid PCR and dual-step MLR) are more advantageous compared to BPNN.

Parameter interrelationships in a dual phase GIS-based municipal solid waste collection model

Geographic information systems are a valuable tool for waste collection and optimization, but they have been underutilized in helping to understand the complex interrelationships that exist within a dual phase solid waste collection system. A GIS-based dual phase model integrating the handcart pre-collection phase and truck collection phase for a study area located in Hai Phong, Vietnam was proposed, and a resulting total system cost was estimated. Temporary collection points were first identified using both the maximize coverage and minimize facility location-allocation tools from a list of candidate temporary collection points and constraints. Two vehicle routing problems were then separately modeled for handcart and truck routes. A total of 30 scenarios were considered in order to investigate the interrelationships between the model parameters, with respect to the total operation costs and maintenance system costs. The scenario with 11 temporary collection points and a maximum handcart collection distance of 500 m gave the lowest overall cost in the study area. The results suggest a single temporary collection point in the study is able to serve about 2,590 people in an area of 0.11 km². Compared to the status quo condition, a 13.76% reduction in truck travel distances is attainable using the proposed model. It is found that the number and distribution of temporary collection points greatly affected the cost effectiveness in both pre-collection and collection phases.

Use of seasonal parameters and their effects on FOD landfill gas modeling

Temporal and spatial variations in landfill gas generations and emissions have been observed and reported by others. Real-time gas data between 2008 and 2014 from a municipal landfill located in a cold, semi-arid climate were consolidated to fit a linear-interpolated form of LandGEM. Seasonal variations in gas collection were observed in the landfill. LandGEM's default decay rate k was not applicable for this Canadian landfill due to significant overestimation (32.2% error). Optimal seasonal k and L_o collection parameters had 8.1% error compared to field data, compared to 8.3% error using optimal annual parameters. The optimal k_winter was 0.0118 year^-1 and the k_summer was 0.0141 year^-1 (14.7% difference), with a corresponding L_o of 100.0 m³/Mg which changed negligibly between the sets. Three pseudo-second order iterative methods were considered, and evaluated using RSS and generation parameters in the literature. A simple application study was conducted using LFGcost-Web, and found the increased precision of seasonal k's resulted in negligible differences with annual optimized k. The default parameters overestimated the net present worth by 12-155% for three of the four common LFG energy projects.

Optimization of first order decay gas generation model parameters for landfills located in cold semi-arid climates

Canada has one of the highest waste generation rates in the world. Because of high land availability, land disposal rates in the province of Saskatchewan are high compared to the rest of the country. In this study, landfill gas data was collected at semi-arid landfills in Regina and Saskatoon, Saskatchewan, and curve fitting was carried out to find optimal k and L_o or DOC values using LandGEM, Afvalzorg Simple, and IPCC first order decay models. Model parameters at each landfill were estimated and compared using default k and L_o or DOC values. Methane generation rates were substantially overestimated using default values (with percentage errors from 55 to 135%). The mean percentage errors for the optimized k and L_o or DOC values ranged from 11.60% to 19.93% at the Regina landfill, and 1.65% to 10.83% at the Saskatoon landfill. Finally, the effect of different iterative methods on the curve fitting process was examined. The residual sum of squares for each model and iterative approaches were similar, with the exception of iterative method 1 for the IPCC model. The default values in these models fail to represent landfills located in cold semi-arid climates. The use of site specific data, provided enough information is available regarding waste mass and composition, can greatly help to improve the accuracy of these first order decay models.

Alternative carbon dioxide modelling approaches accounting for high residual gases in LandGEM

High Canadian waste disposal rates necessitate landfill gas monitoring and accurate forecasting. CO₂ estimates in LandGEM version 3.02 currently rest on the assumptions that CO₂ is a function of CH₄, where the two gases make up nearly 100% of landfill gas content, leading to overestimated CO₂ collection estimates. A total of 25 cases (five formulas, five approaches) compared annual CO₂ collection at four western Canadian landfills. Despite common use in literature, the 1:1 ratio of CH₄ to CO₂ was not recommended to forecast landfill gas collection in cold climates. The existing modelling approach significantly overestimated CO₂ production in three of four sites, resulting in the highest residual sum of squares. Optimization resulted in the most accurate results for all formulas and approaches, which had the greatest reduction in residual sums of squares (RSS) over the default approach (60.1 to 97.7%). The 1.4 Ratio approach for L _o:L _o-CO2 yielded the second most accurate results for CO₂ flow (mean RSS reduction of 50.2% for all sites and subsection models). The annual k-modified LandGEM calculated k's via two empirical formulas (based on precipitation) and yielded the lowest accuracy in 12 of 20 approaches. Unlike other studies, strong relationships between optimized annual k's and precipitation were not observed.