Student Publications

Evolution of financial sustainability of Canadian waste management industries in government and private sectors

There is a lack of government and private sector-specific analysis on the economic sustainability of waste management services in Canada. This study addresses that gap by conducting a comprehensive 23-year analysis of waste management industry data across four Western Canadian provinces, examining both sectors separately. This distinction enhances understanding of how economic and employment factors uniquely influence waste disposal, diversion, and revenue growth. The study reveals a predominantly private sector led management system, with the highest national revenue in 2018 ($221.9/cap). The private sector’s substantial investment in waste diversion significantly impacts its robust revenue growth and consistently higher profit margins. In contrast, the government sector exhibits fluctuating operating revenue, primarily supported by income and property taxes, reflecting an inconsistent financial structure. Lower waste diversion rates in some provinces may be linked to higher proportion of part-time employees in the government sector, impacting financial sustainability. However, recent upward trends in government capital investment suggest a shift toward long-term development goals rather than short-term revenue gains. Findings highlights distinct differences in business and employment characteristics between sectors. The study provides an analytical framework for optimized financial and resource planning within Canada’s waste management landscape.

Mim, S. J., Tasnim, A., Chowdhury, R., Ng, K. T. W., & Richter, A. (2025). Evolution of financial sustainability of Canadian waste management industries in government and private sectors. Cleaner Environmental Systems, 100298.

https://doi.org/10.1016/j.cesys.2025.100298

Decoding spatiotemporal dynamics of post-consumer textile waste generation and management using ternary plots

This study aims to understand the post-consumer textile waste (PCTW) management dynamics of a southeastern US state from 2014 to 2022 using time-series ternary diagrams. Multiple linear regression models were developed to assess the impact of various factors on PCTW generation and management practices. During the period, the study revealed a 33 % increase in PCTW generation, averaging 40 kg per capita in 2022, with significant variability influenced by demographic and socioeconomic factors. A shift towards PCTW recycling and reuse across regional levels are observed, probably due to advanced waste sorting systems and improved recycling program accessibility. Population density, land area, household structure, and education were significant predictors for the predictive models (p < 0.05, and 0.39 < R2 < 0.97) of PCTW generation, landfilling, recycling, and reuse. Recycling was preferred over landfilling more often by individuals with higher education levels, with the lowest disposal rate at 27 %. Smaller household sizes favored reuse and donation, underscoring the need for custom PCTW management strategies. Higher recycling rates, reaching up to 74.2 % are found in households with fewer females, and in areas with less employers. The proposed visualization framework helps to facilitate development of evidence-based waste policies for a sustainable PCTW management in diverse regional contexts.

Mim, S. J., Chowdhury, R., Richter, A., & Ng, K. T. W. (2025). Decoding spatiotemporal dynamics of post-consumer textile waste generation and management using ternary plots. Environmental Technology & Innovation, 104331.

https://doi.org/10.1016/j.eti.2025.104331

An analytical framework to decode socioeconomic interplays in pesticides and fertilizer container collection patterns using land dynamics metrics

This study analyzes pesticide and fertilizer container collection trends across Canadian agricultural regions over a seven-year period from 2016 to 2022 through an analytical framework and proposed two land metrics. A 28.3 % decrease in the collection of small empty pesticide and fertilizer containers (EPFCs) coincides with a 41.4 % increase in the collection of non-refillable bulk containers (NRBCs) among associated businesses, indicating a trend toward larger containers, influenced by economic incentives and regulatory guidelines. Nine Canadian provinces were into two regions (developed and emerging) based on their agricultural activities. The agricultural stewardship organization’s spatial collection coverage ratios were notably higher in the developed regions (0.003 to 0.010) than in the emerging ones (0.001 to 0.006), suggesting that recycling services are more efficient in areas with intense agricultural activity. The median EPFC collection rates varied significantly, with the developed regions showing more stability and higher densities (0.24 to 0.41 containers per million CAD) than the emerging ones (0.12 to 0.27 containers per million CAD). The emerging regions exhibited higher land use collection ratios, while the developed regions reported significantly lower ratios, reflecting the challenges posed by larger farm landscapes. The developed collection regression models (R2 = 0.82 to 0.89 and p < 0.0001) highlighted labor and economic factors as predictors of collection efficiency in both regions. These findings indicate that stronger economic incentives and focused infrastructure upgrades could enhance EPFC collection efficiency, especially in the less developed agricultural areas. Targeted policies that enhance collection infrastructure and integrate labor and economic factors to improve stewardship efficiency and support environmental sustainability are recommended.

Chowdhury, R., Karimi, N., Xu, X., An, C., Gitifar, A., & Ng, K. T. W. (2025). An analytical framework to decode socioeconomic interplays in pesticides and fertilizer container collection patterns using land dynamics metrics. Waste Management, 206, 115062.https://doi.org/10.1016/j.wasman.2025.115062

Integrated machine learning and hydrodynamic modeling for agricultural land flood under climate change scenarios

Floods can cause significant damage to land, infrastructure and individual well-being. In Canadian prairies, flood is a recurring natural disaster for farmers and ranchers. The flat terrain and extensive agricultural lands make the region vulnerable to flooding. Climate change could alter hydrological processes, leading to an increase in both frequency and intensity of flood events. In this study, machine learning and hydrodynamic models were combined to predict flood risks on agricultural lands based on various possible climate change scenarios. For this research, outputs from CanESM2, SDSM, ANN, HEC-GEORAS, and HEC-RAS were integrated to generate 2D flood simulation outputs. Climate change models CanESM2, SDSM was used to simulate the possible future temperature and precipitation regimes (RCP 8.5 and RCP 4.5). Artificial Neutral Network (ANN) model was used to predict possible future snowfall levels based on simulated precipitation and ambient air temperature regimes. Second ANN was further trained with first ANN data to predict possible flow rates in the river. A flood-frequency analysis was conducted using 10, 50, and 100 years flood return periods. The collective data output was used in HEC-RAS to flood simulation under respective return periods. The georeferenced vector and raster data were generated using ArcGIS and HEC-GEORAS. A comparative flood simulations outputs were generated using historical data. The flood simulation results using historical data were compared to climate change conditions.  The results indicate that climate change could potentially exacerbate the severity of floods in agricultural lands across the prairies. The greater return periods correspond to the greater flood depths, velocities, and inundation areas, with RCP 8.5 creating the most extreme conditions. In addition, climate change could potentially accelerate peak flows in the river and increase hydrological pressure.

Hassanjabbar, A., Zhou, X., Han, T., McCullum, K., & Wu, P. (2025). Integrated machine learning and hydrodynamic modeling for agricultural land flood under climate change scenarios. Journal of Flood Risk Management, 18(3), e70114.

https://doi.org/10.1111/jfr3.70114

Dynamic energy system risk management under the pressures of GHG-and pollutant-emission mitigation for Hebei Province, China

This study develops dynamic energy system risk management model (DERM) to mitigate emission in Hebei Province energy system and support decision making under uncertainty. The DERM integrates modified fuzzy chance-constrained programming, interval linear programming, and mixed-integer programming into an energy system planning model. Nine scenarios of attitude for decision-makers (γ = 0.9, …, 0.1) and three credibility levels (λ = 0.9, 0.8, 0.7) for the environmental loading capacity scenarios are provided in the case study. Weather conditions (mildly, moderately, and severely smoggy weather) and environmental loading capacity of different emission (NOx, SO2, dust, and CO2) are concurrently considered in this model. The proposed DERM effectively captures both the uncertainties and dynamics characteristics of energy systems. Its application in Hebei Province demonstrates its practical value, and the results align with the long-term planning objective of Hebei. The obtained result indicates that a pessimistic attitude of policymakers toward energy availability can significantly improve the energy system and the air quality compared to a positive attitude. Such pessimism will promote the development of new energy, especially wind power generation, resulting in a [106, 236]% increase in renewable capacity during planning period. Meanwhile, the diminutive degrees of credibility would result in a slight reduction in overall system cost, while leading to a sharp increase in the risk of system failure to the maximum acceptable environmental pollutant capacity. These findings could help to investigate uncertainty features for a domestic energy system and identify desirable attitude alternatives from decision markers under the trade-off between economic and environment.

Zhang, C., Huang, G., & Zhang, C. (2025). Dynamic energy system risk management under the pressures of GHG-and pollutant-emission mitigation for Hebei Province, China. Energy, 137913.

Landfill footprint geometrical design evolution and land surface thermal heterogeneity

The evolution of landfill footprint design and the associated environmental impacts have been mostly ignored in literature. This study examines landfill footprint geometrical shape using four different shape factors, including compactness (CS), rectangularity (RS), stretch (SS), and ease of access (ES) on 99 active landfills in UK. ES had the highest mean value of 0.69, while SS had the lowest mean value of 0.24. RS and CS showed similar mean values of 0.55 and 0.53. The footprints are categorized based on the landfill's age into “young”, “intermediate” and “old” groups to assess the evolution of landfill design. Younger landfills exhibit the lowest compactness (median CS = 0.52), followed by intermediate (median CS = 0.55) and older sites (median CS = 0.62). In terms of RS, all the sites showed median values around 0.55. The young landfills have the lowest SS with a median of 0.20, while the old and intermediate groups had medians of 0.23 and 0.24 respectively. The variabilities of sites' land surface temperature were also assessed. This study underscores the role of landfill footprint design on landfill operation and introduces new aspects of design strategies to mitigate environmental risks.

Gitifar, A., Karimi, N., Mim, S. J., Naghibalsadati, F., & Ng, K. T. W. (2025). Landfill footprint geometrical design evolution and land surface thermal heterogeneity. Journal of Cleaner Production, 145763.

https://doi.org/10.1016/j.jclepro.2025.145763

Influence of geometrical shape on thermal heterogeneity in closed landfill sites

Thermal heterogeneity assessment in landfill sites is essential for identification of potential hazards. The relationship between landfill geometrical shape and land surface thermal heterogeneity is not well understood. This study examines the association between landfills’ shape configuration and thermal heterogeneity by using two mathematical shape factors on thirty-eight closed landfills. Three different multiple linear regression models were developed for landfill sites of various sizes. Geometrical shape analysis of the sites shows that all landfills surpass the 0.5 threshold, suggesting a tendency toward regular shapes and a systematic approach in their design and operation, with a mean elongation and compactness shape factor of 0.819, and 0.724, respectively. This pattern likely accommodates land use constraints and proximity to neighboring properties, with boundaries confined by the surrounding road network. In larger landfill sites, the elongation shape factor exhibits a higher coefficient (− 0.46) than the compactness shape factor (− 0.35), indicating its stronger association on thermal heterogeneity of the site. This finding helps to develop strategies for better thermal management and environmental safety of large landfill sites. The negative coefficients for all the site groups (small, medium, and large) suggest that a more compact and regular shape may promote thermal homogeneity in closed landfills. The proposed method improves monitoring of closed landfills and contributes to the development of evidence-based landfill design guidelines and regulations.

Gitifar, A., Naghibalsadati, F., Karimi, N., Abha, A. T., Chowdhury, R., & Ng, K. T. W. (2025). Influence of geometrical shape on thermal heterogeneity in closed landfill sites. Ecological Informatics, 103219.

Evaluating ENSO-driven risks to strengthen transportation system resilience of Canadian provinces

This paper examines the effects of El Niño/La Niña Southern Oscillation (ENSO) on Canadian transportation networks, including road, rail, transit, and active transportation systems. It highlights hazards such as flooding, drought, wildfires, and storm surges, particularly in regions like British Columbia, Alberta, and the Maritimes. The study discusses the challenges for emergency managers, transportation operators, and planners in developing mitigation and adaptation strategies. As climate change intensifies ENSO impacts, understanding these effects is crucial for strengthening infrastructure resilience. While focused on Canada, the findings also have implications for other Northern Hemisphere regions. The study emphasizes the need for further research on ENSO-climate change linkages and enhanced training for transportation professionals. ENSO applications provide a strategic approach to bridging short-term weather events with long-term climate trends, offering valuable insights for improving the adaptability of Canada’s transportation systems in the face of increasing climate variability.

Islam, M., Kabir, G., & Anis, M. R. (2025). Evaluating ENSO-driven risks to strengthen transportation system resilience of Canadian provinces. Canadian Journal of Civil Engineering, 52(9), 1–15. https://doi.org/10.1139/cjce-2025-0118

https://cdnsciencepub.com/doi/10.1139/cjce-2025-0118

Analyzing meteorological risks to highway infrastructure in Saskatchewan

Highway infrastructure is essential to Canada's transportation system, supporting economic activity and regional connectivity. However, its sustainability is increasingly challenged by meteorological hazards. This study conducts a detailed spatial risk assessment of Saskatchewan's major highways by analyzing six climate-related factors: flood-prone areas, precipitation mm/day), snowfall (cm/day), extreme temperatures (minimum and maximum in °C), and wind (maximum gust speed in km/h). Using ArcGIS, hazard maps were developed and reclassified through three methods: equal-weighting, score-based assessment, and the Analytical Hierarchy Process (AHP). Seasonal variations were also addressed by generating separate risk layers for winter and summer conditions. The results indicate that southern and south-central Saskatchewan especially around Regina and Saskatoon faces the highest cumulative climate risk. Conversely, northern regions show isolated high risks but minimal infrastructure impact due to sparse networks. The integrated risk maps provide actionable insights for transportation authorities to prioritize climate-resilient planning, reduce service interruptions, and improve long-term road network reliability across varying seasonal extremes.

Islam, M., Kabir, G., & Anis, M. R. (2025). Analyzing meteorological risks to highway infrastructure in Saskatchewan. Environmental Research: Infrastructure and Sustainability, 5(3), 1-38. https://doi.org/10.1088/2634-4505/adf87f

Integrated system dynamics with deep learning to determine regional policy approach for building economic resilience: A case study of Khulna City of Bangladesh

This study has developed a system dynamics (SD) framework for investigating the economic resilience policy of Khulna City of Bangladesh with six significant indexes of income diversity, industrial diversity, foreign reserve, physical capital, human capital, and proximity to illustrate economic resilience and the withstand capability to shock situations through numerical simulation. The baseline simulation indicated a normalized economic resilience value of 0.475 with a shock withstand value of 0.395. Though the regional economy suggests the capacity to absorb sudden shocks for a limited period, it comes at the cost of external debt, which rises to 755 billion BDT by 2040. Two consecutive policy scenarios are being considered, and Policy 2 has shown encouraging results with a reduced debt amount of 502 billion BDT by 2040 from the initial debt of 76.1 billion BDT in 2023. This study represents a novel integration process of SD, machine learning algorithms for simulating dynamic and multi-scale parameters associated with regional economic resilience. For model robustness and parametric validity, calibration with the IMF dataset, sensitivity analysis, optimization of sensitivity results, F-test, and t-test are conducted. An optimized resilience value of 0.9269 is observed in SD while using a solver a value of 0.8756 is obtained with p-value <0.001. Employment rate and foreign reserve extensively affect the economic resilience of the region alongside passive factors such as road density, accessibility, and industrial diversity. The integrated SD model can be utilized as a simulation laboratory for identical regions, specifically southwest Asia, to determine the best possible policy planning and implementation tiers.

Rafew, S. M., Hossain, N. U. I., & Hossain, M. (2025). Integrated system dynamics with deep learning to determine regional policy approach for building economic resilience: A case study of Khulna City of Bangladesh. Cities, 167, 106298. https://doi.org/10.1016/j.cities.2025.106298

Theoretical analysis of Trombe wall performance: Evaluating key parameters for system efficiency

With rising energy consumption and greenhouse gas emissions particularly carbon dioxide (CO₂) optimizing fossil fuel use and improving passive heating/cooling systems in buildings has become crucial. Trombe walls, as a sustainable solar heating solution, can significantly reduce energy demand by storing and releasing heat effectively. This study investigates the influence of thermal storage wall materials on the performance of Trombe wall systems through numerical analysis. Different multi-layer wall configurations incorporating brick, adobe, stone, and plaster-concrete-insulation composites were evaluated under varying solar radiation conditions (100-620 W/m²) over an 8-hour period (9 AM-5 PM). Results demonstrate that brick-based walls achieved superior room temperature regulation (21.25 °C vs. 20.53 °C for adobe at 620 W/m²), with thermal resistance proving more critical than material thickness. Comparative analysis revealed that plaster-concrete-insulation walls outperformed traditional materials in heating efficiency. Additionally, the study examined modified heat transfer equations for air ducts, finding that existing theoretical models (15.12 °C prediction at 11 AM) aligned more closely with experimental data (17.5 °C) than the proposed modifications (14.06 °C). The study provides clear design principles for Trombe wall optimization: prioritizing thermal-resistant materials (e.g., brick, insulated composites) over thickness and using validated heat transfer models. These insights enable more effective passive heating systems that lower energy demands in buildings. By implementing these strategies, construction professionals can significantly improve thermal performance while contributing to climate change mitigation through reduced carbon footprints.

Meghdadi, H., & Khodadadi, A. (2025). Theoretical analysis of Trombe wall performance: Evaluating key parameters for system efficiency. Journal of Integrated Environmental Solutions. Advance online publication.

Sustainable CO2 Storage Assessment in Saline Aquifers Using a Hybrid ANN and Numerical Simulation Model Across Different Trapping Mechanisms

The study represents an innovative method to utilize the strong computational power of CMG-GEM, a numerical reservoir simulator coupled with artificial neural networks (ANNs) to predict carbon storage capacity in saline aquifers. The key parameters in geological storage formations are identified by generating a diverse dataset from CMG-GEM simulation runs by varying the different geological and operational parameters. Robust data analysis was performed to understand the effects of these parameters and access the different CO2 trapping mechanisms. One of the significant novelties of this model is its ability to incorporate additional inputs not previously considered in similar studies. This enhancement allows the model to predict all CO2 trapping mechanisms, rather than being limited to just one or two, providing a more holistic and accurate assessment of carbon sequestration potential. The generated dataset was used in MATLAB to develop an ANN model for CO2 storage prediction across various trapping mechanisms. Rigorous testing and validation are performed to optimize the model, resulting in an accuracy of 98% using the best algorithm, which reflects the model’s reliability in evaluating the CO2 storage. Therefore, the number of simulation runs was significantly reduced, which saves great amounts of computational power and simulation running time. The integration of machine learning and numerical simulations in this study represents a significant advancement in sustainable CO2 storage assessment, providing a reliable tool for long-term carbon sequestration strategies.

Hamed, M., & Shirif, E. (2025). Sustainable CO2 Storage Assessment in Saline Aquifers Using a Hybrid ANN and Numerical Simulation Model Across Different Trapping Mechanisms. Sustainability, 17(7), 2904.

https://www.mdpi.com/3239816

Drilling mud requires reliable lubricity evaluation to minimize drill string torque. Conventional EP-lubricity meters like the BAROID model lack precise speed control and torque measurement capabilities, relying on subjective operator judgment. This study presents an upgraded system featuring three technical enhancements: (1) PWM-based speed regulation for consistent rotation, (2) closed-loop motor control using dynamic modeling, and (3) torque estimation through current-speed correlation algorithms. The modernized device connects via USB to a dedicated interface, enabling real-time monitoring of critical parameters including lubricant film failure thresholds and seizure points—previously assessed through auditory methods. Validation tests demonstrated 95% agreement with the industry-standard FANN model, confirming the system's accuracy while eliminating human interpretation errors. These improvements provide objective, quantitative EP-lubricity data essential for drilling fluid optimization and quality control. The upgraded system maintains backward compatibility while offering research-grade measurement precision, particularly valuable for extreme pressure lubricant development and field fluid monitoring. This advancement represents a significant step toward standardized, reproducible lubricity evaluation in drilling operations.

Afsharpour, S., Din Mohammad, M., Hashemi, S. et al. (2025) Accurate torque prediction in drilling operations: an enhanced EP lubricity evaluation methodology. Model. Earth Syst. Environ. 11, 358. https://doi.org/10.1007/s40808-025-02546-1

Machine Learning-Driven Prediction of CO2 Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach.

The solubility of CO2 in brine systems is critical for both carbon storage and enhanced oil recovery (EOR) applications. In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior. Among the tested kernels, the ARD Matern 3/2 and ARD Matern 5/2 kernels achieved the highest predictive accuracies, with R2 values of 0.9961 and 0.9960, respectively, on the test data. This demonstrates superior performance in capturing CO2 solubility trends. The GWO algorithm effectively tuned the hyperparameters for all kernel configurations, while the ARD capability successfully quantified the influence of key physicochemical parameters on CO2 solubility. The outstanding performance of the ARD Matern 3/2 and ARD Matern 5/2 kernels suggests their particular suitability for modeling complex thermodynamic behaviors in brine systems. Furthermore, this study integrates fundamental thermodynamic principles into the modeling framework, ensuring all predictions adhere to physical laws while maintaining excellent accuracy (test R2 > 0.98). These results highlight how machine learning can improve CO2 injection processes, both for underground carbon storage and enhanced oil production.

Hashemi, S. H., Torabi, F., & Tontiwachwuthikul, P. (2025). Machine Learning-Driven Prediction of CO2 Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach. Energies, 18(15), 4205.

https://doi.org/10.3390/en18154205

Fault Tolerance in Service Function Chains: A Taxonomy, Survey and Future Directions

The rise of Network Function Virtualization (NFV) has revolutionized the network services, by enabling the delivery of flexible and scalable solutions through Service Function Chains (SFCs). However, the virtualized deployment of SFCs increases their susceptibility to various types of failures in comparison to their physical deployment. Hence, a thorough understanding of the failure types, their reasons of occurrence and management methods is important. This will help us to achieve high fault tolerance in virtualized SFCs equivalent or perhaps higher to their physical counter parts while reaping the benefits of virtualization. This article attempts to present a survey of fault tolerance practices for SFCs. As an outcome, we propose a comprehensive taxonomy that categorizes the state-of-the-art in fault tolerance in SFCs based on failure types, and their pattern of occurrence, failure management approaches, and their evaluation metrics. A critical analysis of the state-of-the-art is performed to identify the key trends and research gaps. A conceptual framework to achieve high fault tolerance in SFCs is presented along with a discussion on future research directions.

Shahab, M. H., & Sharma, Y. (2025). Fault Tolerance in Service Function Chains: A Taxonomy, Survey and Future Directions. Journal of Network and Systems Management, 33(3), 71.

https://link.springer.com/article/10.1007/s10922-025-09955-8

Drilling mud requires reliable lubricity evaluation to minimize drill string torque. Conventional EP-lubricity meters like the BAROID model lack precise speed control and torque measurement capabilities, relying on subjective operator judgment. This study presents an upgraded system featuring three technical enhancements: (1) PWM-based speed regulation for consistent rotation, (2) closed-loop motor control using dynamic modeling, and (3) torque estimation through current-speed correlation algorithms. The modernized device connects via USB to a dedicated interface, enabling real-time monitoring of critical parameters including lubricant film failure thresholds and seizure points—previously assessed through auditory methods. Validation tests demonstrated 95% agreement with the industry-standard FANN model, confirming the system's accuracy while eliminating human interpretation errors. These improvements provide objective, quantitative EP-lubricity data essential for drilling fluid optimization and quality control. The upgraded system maintains backward compatibility while offering research-grade measurement precision, particularly valuable for extreme pressure lubricant development and field fluid monitoring. This advancement represents a significant step toward standardized, reproducible lubricity evaluation in drilling operations.

Afsharpour, S., Din Mohammad, M., Hashemi, S. et al. (2025) Accurate torque prediction in drilling operations: an enhanced EP lubricity evaluation methodology. Model. Earth Syst. Environ. 11, 358. https://doi.org/10.1007/s40808-025-02546-1

Machine Learning-Driven Prediction of CO2 Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach.

The solubility of CO2 in brine systems is critical for both carbon storage and enhanced oil recovery (EOR) applications. In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior. Among the tested kernels, the ARD Matern 3/2 and ARD Matern 5/2 kernels achieved the highest predictive accuracies, with R2 values of 0.9961 and 0.9960, respectively, on the test data. This demonstrates superior performance in capturing CO2 solubility trends. The GWO algorithm effectively tuned the hyperparameters for all kernel configurations, while the ARD capability successfully quantified the influence of key physicochemical parameters on CO2 solubility. The outstanding performance of the ARD Matern 3/2 and ARD Matern 5/2 kernels suggests their particular suitability for modeling complex thermodynamic behaviors in brine systems. Furthermore, this study integrates fundamental thermodynamic principles into the modeling framework, ensuring all predictions adhere to physical laws while maintaining excellent accuracy (test R2 > 0.98). These results highlight how machine learning can improve CO2 injection processes, both for underground carbon storage and enhanced oil production.

Hashemi, S. H., Torabi, F., & Tontiwachwuthikul, P. (2025). Machine Learning-Driven Prediction of CO2 Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach. Energies, 18(15), 4205.

https://doi.org/10.3390/en18154205

The use of efficiency metrics for cross-jurisdictional assessment of household hazardous waste collection and recycling

Household hazardous waste (HHW) has proliferated with the growing consumption of household products, highlighting the importance of an effective management program. Although industrialized nations have employed collection programs for HHW, their efficiencies are not appropriately assessed. North America currently lacks comprehensive studies on the efficiency of HHW programs. This study introduces two metrics: Collection Ratio (CollectRat) and Recycling Ratio (RecylRat), to analyze the efficacy of HHW collection and recycling. The study develops predictive models for these metrics to identify key household characteristics influencing HHW management practices. Management practices are shifting towards recycling, although reuse remains low, peaking at 20.9 % in California and 10.8 % in Texas. By examining the metrics using American and Canadian datasets, results show that collection rates are higher in highly populated regions, unlike recycling rates. Most Canadian HHW programs have adopted the Extended Producer Responsibility (EPR) framework, while California has recently introduced EPR for certain household products, leading to increased public awareness and improved waste management practices. Findings suggest HHW collection ratio alone does not represent waste recycling well. The rate of collection and recycling depends on household characteristics such as family size, educational attainment, and other factors. The use of efficiency metrics in forecasting models helps to understand trends in HHW management in North America and can be applied to other jurisdictions.

Mim, S. J., Richter, A., Gitifar, A., Chowdhury, R., Ng, K. T. W. (2025) “The use of efficiency metrics for cross-jurisdictional assessment of household hazardous waste collection and recycling”. Journal of Cleaner Production, 503, 145420.

Supply-disposition storage of fresh fruits and vegetables and food loss in the Canadian supply chain

Analyzing transportation and storage inefficiencies at the initial stages of the food supply chain is crucial for minimizing early-stage losses and enhancing food lifecycle efficiency. However, most food system studies,focused on retail and consumer stages. This study delves into the intricate dynamics of fresh fruit and vegetable waste generation at the supply-disposition storage stage, aiming to identify distinct waste generation patterns and predict food loss in Canada using regression analysis. Total food waste generation for 64 fresh fruits and vegetables exhibited a notable increase over a 22-year study period from 2000 to 2022, and fresh vegetables consistently surpassed fresh fruits in average waste generation by 25.9 %. Despite a higher per capita waste generation for fresh vegetables (1.26 kg∙cap-1∙year−1), the steeper growth rate in fruit waste emphasizes the need for nuanced strategies for each category at the supply-disposition storage. The waste generation showed a positive linear relationship with supply, imports, and domestic disappearance in the food supply chain (R2 = 0.80 to 0.99, p < 0.0001), denoting a significant potential impact of supply-disposition parameters on individual waste generation. Two distinct regression models were developed to forecast fresh fruits and vegetables waste generation, and both demonstrated high predictability (0.924 ≤ R2 ≤ 0.975) and low RMSE values (1.571 ≤ RMSE ≤ 3.318). Imports and exports appear crucial to minimize food loss at supply and disposition storage. The proposed analytical approach can be beneficial elsewhere to enhance fresh food supply inventory management and minimize food loss at a global level.

Chowdhury, R., Mim, S. J., Tasnim, A., Ng, K. T. W., & Richter, A. (2025). Supply-disposition storage of fresh fruits and vegetables and food loss in the Canadian supply chain. Ecological Indicators, 170, 113063.

Exploring the role of negative emission technologies in regional power system planning toward carbon net zero--A Case Study for the Province of Saskatchewan, Canada

Negative emission technologies (NETs) such as bioenergy with carbon capture and storage (BECCS) and direct air capture (DAC) are essential for offsetting difficult-to-reduce greenhouse gas (GHG) emissions, critical for achieving a net-zero carbon future. Therefore, an optimization-driven negative emission technologies (ODNET) model has been first developed in this study for deploying NETs in Saskatchewan's power system using a mixed-integer fractional chance-constrained programming approach with Sustainable Development Goals (SDG) assessment. Results indicate that renewable energy including solar, wind, and Small Modular Reactors (SMRs) will dominate future power generation, with natural gas-fired equipped with CCS supporting the low-carbon transition. Applying BECCS and DAC together not only reduces total system costs by 9 %, but also helps achieve carbon net zero ahead of schedule. DAC is expected to deliver 4.28 Mt of negative emissions, while BECCS could provide over 48 Mt of negative emissions and generate over 40,000 GWh of electricity. Moreover, the power system's low carbon transition will enhance SDG indicators in renewable energy share, fossil fuel reduction, and CO2 emissions mitigation. This study highlights optimized strategies for regional power system planning toward carbon net zero and clarifies NETs' roles and potential applications in future power systems.

Xu, Y., Huang, G., Liu, Y., & Chen, L. (2025). Exploring the role of negative emission technologies in regional power system planning toward carbon net zero--A Case Study for the Province of Saskatchewan, Canada. Energy, 136351.

Availability and Sustainability Aware Service Function Chains (SFC) Allocation and Embedding in Edge-Cloud Continuum

The rapid evolution of network services, driven by innovations like virtualization and edge computing, has transformed the way modern applications are deployed and managed. Service Function Chains (SFC) formed by putting Virtual Network Functions (VNFs) in a particular order enable flexible and scalable network solutions. However, their virtualized nature introduces new challenges, including their availability and sustainability. Their joint balancing in SFCs deployment is crucial to meet the stringent requirements of next-generation networks. This study presents a novel approach for availability and sustainability-aware SFCs allocation and embedding in the edge-cloud continuum. It introduces embedding policies tailored to prioritize availability, reduce carbon footprint, or achieve a tradeoff between the two. Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are employed to optimize VNF redundancy strategies. Simulation results demonstrate the effectiveness of the proposed methods, achieving robust fault tolerance while minimizing carbon footprint. The tradeoff-aware policy and PSO based redundancy strategy achieves 95.88% availability while cutting the carbon footprint by 37.6%.

Shahab, M. H., & Sharma, Y. (2025). Availability and Sustainability Aware Service Function Chains (SFC) Allocation and Embedding in Edge-Cloud Continuum. Procedia Computer Science, 257, 637-644.