Dye Removal from Contaminated Water Through PES Membranes Enhanced with the Incorporation of Switchable Polyacrylic Acid Grafted on Graphene Oxide.

Membrane fouling remains a critical challenge in wastewater treatment that ultimately reduces flux, compromised water quality, and higher operational costs. This study addresses such fouling issues by grafting polyacrylic acid (PAA) polymer brushes onto graphene oxide (GO) via surface-initiated atom transfer radical polymerization (SI-ATRP) and incorporating the functionalized GO-PAA into polyether sulfone (PES) membranes. The functionalized graphene oxide with poly-(acrylic acid) (GO-PAA) was blended into a poly-(ether sulfone) (PES) membrane at different concentrations, and its effects on nanocomposite membrane performance were systematically analyzed.

Life Cycle Assessment: A Primer for Kidney Professionals.

Background: The growing emphasis on low-carbon, sustainable health care systems is driving the integration of environmental sustainability into clinical practice and research. This shift necessitates clinician literacy in health care sustainability, particularly in methodologies for assessing environmental impacts.

Objective: To introduce health care professionals to life cycle assessment (LCA) as a tool for evaluating environmental impacts in clinical contexts and to illustrate its application through a case study on hemodialysis therapies.

Sustainability assessment of perioperative patient warming devices: a comparative life cycle assessment and costing.

Background: Perioperative patient warming is an integral component of quality surgical care, subject to highly variable clinical practices. We evaluated the environmental and economic impacts of currently available patient warming methods.

Methods: We conducted a comparative life cycle assessment (LCA) and life cycle costing (LCC) of three active warming devices (forced air warmer, resistive blanket, and circulating water garment) using a functional unit of 1 h of active patient warming.

Disinfection by-products in indoor swimming pools: A critical review to prioritize commonly occurring species and influencing factors.

Despite the reported occurrence of several disinfection by-products (DBPs) in swimming pools, it is challenging to identify important DBP species due to strewn and conflicting information about their occurrence and importance in the literature. There is a need to provide clear direction and decisive information to support regulators' and pool facilities' efforts to control DBPs. In this review, alongside providing detailed occurrence data, we have developed a novel prioritization approach to classify DBPs as Tier-1 (critical priority), Tier-2 (medium priority), and Tier-3 (low-priority) DBPs in chlorinated indoor swimming pools (ISPs).

Robust prediction of water arsenic levels downstream of gold mines affected by acid mine drainage using hybrid ensemble machine learning and soft computing.

Water pollution from hazardous materials, particularly arsenic, downstream of gold mines poses severe environmental and health risks. This study employs a systematic approach to predict water arsenic (WA) levels downstream of gold mines affected by acid mine drainage. WA data from the affected region were collected and preprocessed to standardize the dataset and mitigate overfitting risks.

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.