Integrated optimization of marine oil spill response and liquid oily waste management using mathematical programming and evolutionary metaheuristic techniques.

Marine oil spill response is a time sensitive and complex task, in which the management of oily wastewater generated from response operations could be a bottleneck limiting the response capacity and efficiency. This study developed a multi-agent decision support system to effectively coordinate mechanical containment and recovery (MCR) of spilt oil and oily wastewater management (OWM) operations. The system aims to minimize the overall response time, cost, and the volume of weathered oil by applying evolutionary optimization, oil weathering process, and response operational agents.

Chemical composition of arsenic-based acid mine drainage in the downstream of a gold mine: Fuzzy regression and clustering analysis.

This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis.

Ecological risk assessment of tire and road wear particles: A preliminary screening for freshwater sources in Canada.

Abrasion of tires on road surfaces leads to the formation of tire and road wear particles (TRWPs). Approximately 5.9 million tonnes/year of TRWPs are emitted globally, and 12-20% of emissions generated on roads are transmitted into surface waters, where they can release (i.

Appraisal of machine learning techniques for predicting emerging disinfection byproducts in small water distribution networks.

Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups.

Drinking water management strategies for distribution networks: An integrated performance assessment framework.

Due to rapid population growth, urbanization, water contamination, and climate change, global water resources are under increasing pressure. Water utilities apply drinking water management strategies (DWMS) to ensure that water is safe for drinking. However, in recent years, due to increased inclination towards climate change, environmental emissions, and sustainable development goals; the environmental and economic performance of DWMSs is getting attention.

Energy efficiency in residential buildings amid COVID-19: A holistic comparative analysis between old and new normal occupancies.

Stringent lockdowns have been one of the defining features of the COVID-19 pandemic. Lockdowns have brought about drastic changes in living styles, including increased residential occupancy and telework practices predicted to last long. The variation in occupancy pattern and energy use needs to be assessed at the household level.

An estimation of tire and road wear particles emissions in surface water based on a conceptual framework.

Freshwater sources have been contaminated with toxic and unwanted substances worldwide. Among these toxic substances, microplastics (MPs) are becoming prominent. There is already a debate on the impact of MPs on the aquatic environment.

Evaluation of machine learning techniques to select marine oil spill response methods under small-sized dataset conditions.

Oil spill incidents can significantly impact marine ecosystems in Arctic/subarctic areas. Low biodegradation rate, harsh environments, remoteness, and lack of sufficient response infrastructure make those cold waters more susceptible to the impacts of oil spills. A major challenge in Arctic/subarctic areas is to timely select suitable oil spill response methods (OSRMs), concerning the process complexity and insufficient data for decision analysis.

Developing an Integrated "Regression-QMRA method" to Predict Public Health Risks of Low Impact Developments (LIDs) for Improved Planning.

Worldwide Low Impact Developments (LIDs) are used for sustainable stormwater management; however, both the stormwater and LIDs carry microbial pathogens. The widespread development of LIDs is likely to increase human exposure to pathogens and risk of infection, leading to unexpected disease outbreaks in urban communities. The risk of infection from exposure to LIDs has been assessed via Quantitative Microbial Risk Assessment (QMRA) during the operation of these infrastructures; no effort is made to evaluate these risks during the planning phase of LID treatment train in urban communities.

Evaluation of offshore oil spill response waste management strategies: A lifecycle assessment-based framework.

This study presents a novel life cycle assessment-based framework for low-impact offshore oil spill response waste (OSRW) management. The framework consists of design of experiment, life cycle assessment (LCA), multi-criteria decision analysis (MCDA), operational cost analysis, and generation of regression models for impact prediction. The framework is applied to four OSRW management strategies as different combinations of solid and liquid oily waste collection, segregation, transportation, and treatment/disposal technologies.

Staged energy and water quality optimization for large water distribution systems.

Simultaneous optimization of energy and water quality in real-time large-sized water distribution systems is a daunting task for water suppliers. The complexity of energy optimization increases with a large number of pipes, scheduling of several pumps, and adjustments of tanks' water levels. Most of the simultaneous energy and water quality optimization approaches evaluate small (or hypothetical) networks or compromise water quality.

Integrated probabilistic-fuzzy synthetic evaluation of drinking water quality in rural and remote communities.

An integrated probabilistic-fuzzy synthetic evaluation (PFSE) approach was developed for assessing drinking water quality in rural and remote communities (RRCs) through the lens of health risks and aesthetic impacts. The probabilistic health risk assessment can handle aleatory uncertainty raised by the variation of contaminant concentrations, and fuzzy synthetic evaluation (FSE) can address vagueness and ambiguity in human perception of risks and aesthetic impacts. The PFSE approach was applied to five RRCs in British Columbia, Canada where different drinking water quality issues, including high metal(loids) concentrations, the presence of coliforms, and poor aesthetics were reported.

Probabilistic framework for assessing ecological risk of Contaminants of Emerging Concern: Application to a Canadian lake system.

Contaminants of Emerging Concern (CECs) in natural water pose risks to ecosystems. The concentration of CECs varies spatially and temporally, and their estimated ecotoxicities differ widely by toxicological studies. This study extensively reviewed literature on ecological risk assessment and proposed a probabilistic framework for assessing ecological risk and its uncertainties (aleatory and epistemic).

An integrated geospatial correlation analysis and human health risk assessment approach for investigating abandoned industrial sites.

An integrated geospatial correlation analysis (GCA)-human health risk assessment (HHRA) approach was developed to investigate abandoned industrial sites featured by heterogeneous contamination data. Critical areas of high health risk concerns can be prioritized for remediation using the integrated approach. An abandoned chemical complex site in Hubei, China was investigated as a case study.

Evaluation of financial incentives for green buildings in Canadian landscape.

Financial Incentives (FIs) for green buildings are a major component of energy policy planning and play a vital role in the promotion of sustainable development and carbon mitigation strategies. Despite the presence of numerous FIs in Canada, there is still a lack of understanding on their distribution and effectiveness. This review first investigates the FIs available for residential and commercial buildings in Canada, and then performs a comprehensive review of studies related to FIs' effectiveness evaluation.

Intelligent computational techniques in marine oil spill management: A critical review.

Effective marine oil spill management (MOSM) is crucial to minimize the catastrophic impacts of oil spills. MOSM is a complex system affected by various factors, such as characteristics of spilled oil and environmental conditions. Oil spill detection, characterization, and monitoring; risk evaluation; response selection and process optimization; and waste management are the key components of MOSM demanding timely decision-making.

Segmentation of COVID-19 pneumonia lesions: A deep learning approach.

Lung CT scan has a pivotal role in diagnosis and monitoring of COVID-19 patients, and with growing number of affected individuals, the need for artificial intelligence (AI)-based systems for interpretation of CT images is emerging. In current investigation we introduce a new deep learning-based automatic segmentation model for localization of COVID-19 pulmonary lesions. A total of 2469 CT scan slices, containing 1402 manually segmented abnormal and 1067 normal slices form 55 COVID-19 patients and 41 healthy individuals, were used to train a deep convolutional neural network (CNN) model based on Detectron2, an open-source modular object detection library.

Low-temperature thermal desorption and secure landfill for oil-based drill cuttings management: Pollution control, human health risk, and probabilistic cost assessment.

Oil-based drill cuttings (OBDCs) were managed in two scenarios including low-temperature thermal desorption (LTTD) and secure landfill through a case study. The removal of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in OBDCs by LTTD under different conditions was investigated. Probabilistic human health risk assessment was performed to quantify the health risk posed to waste management workers under the two scenarios, while the associated costs were also analyzed.

Life cycle assessment of low-temperature thermal desorption-based technologies for drill cuttings treatment.

The life cycle impacts of treatment of typical oil-based drill cuttings (OBDCs) using three low-temperature thermal desorption (LTTD)-based systems, including thermomechanical cuttings cleaner (TCC), screw-type dryer (STD), and rotary drum dryer (RDD), were explored with a case study in British Columbia, Canada. Two energy supply scenarios, including diesel generator-based onsite (scenario i) and hydropower-based offsite (scenario ii) treatments, were considered in the assessment. The results show that RDD generated the lowest life cycle impacts in terms of damages to human health, ecosystems, and resources in scenario i.

Analyzing present and future availability of critical high-tech minerals in waste cellphones: A case study of India.

Critical high-tech minerals (CHTMs) are raw materials that are essential for a future clean-energy transition and the manufacture of high-end products. Cellphones, one of the fastest growing electronic products, contain various CHTMs. Since 2019, India has surpassed the United States to become the second largest smartphone market in the world.

Selection of oil spill response method in Arctic offshore waters: A fuzzy decision tree based framework.

A fuzzy decision tree (FDT) based framework was developed to facilitate the selection of suitable oil spill response methods in the Arctic. Hypothetical oil spill cases were developed based on six identified attributes, while the suitability of three spill response methods (mechanical containment and recovery, use of chemical dispersants, and in-situ burning) for each spill case was obtained based on expert judgments. Fuzzy sets were used to address the associated uncertainties, and FDTs were then developed through generating: i) one decision tree for all three response methods (FDT-AP1) and ii) one decision tree for each response method and the development of linear regression models at terminal nodes (FDT-LR).

Performance indicators for aquatic centres in Canada: Identification and selection using fuzzy based methods.

Aquatic centres (ACs) are becoming exceedingly popular in the urban agglomerations of cold climate countries like Canada but functioning without assessing the state of their sustainability performance. Previous studies examined health and safety, water and indoor air quality, and energy consumption aspects without aiming at the holistic sustainability performance assessment. The present research is the first systematic effort for benchmarking of ACs.

Occupant-based energy upgrades selection for Canadian residential buildings based on field energy data and calibrated simulations.

Occupant behavior in residential buildings has a direct impact on the effectiveness of energy-saving measures. In order to realize a buildings' carbon mitigation targets, the impact of individual occupancy profiles needs to be integrated with building simulation models. This paper introduces a decision support framework as a potential solution to make energy performance upgrade choices based on different occupancy profiles.

Investigating the public health risks of low impact developments at residential, neighbourhood, and municipal levels.

Low Impact Developments (LIDs) employ a series of vegetative techniques to retain rainfall close to the site of origin. Although LIDs offer sustainable runoff management, these infrastructures can be considered a risk to public health due to the presence of pathogens in the runoff and human exposure to contaminated water held in and transported by LIDs. The objective of this study is to examine the disease burden of Gastrointestinal illness (GI) from exposure to LIDs at the residential, neighbourhood, and municipal levels.