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. Furthermore, multivariate fuzzy k-means clustering analysis facilitated the identification of fuzzy-based clusters within the dataset, providing insights into spatial patterns and potential sources of arsenic pollution. The pairwise comparisons indicated the strongest correlation of 0.62 between soil total arsenic and pH, while the weakest correlation of 0.13 was observed between soil-soluble arsenic and soil iron, providing valuable insights into their relationships and impact on water arsenic levels. The associated uncertainties in the relationships among the variables were determined based on the degree of belongingness of each data point to various fuzzy sets. Three distinct clusters emerged from the analysis: Cluster 1 comprised Points 5, 6, and 7; Cluster 2 included Points 1, 2, 3, 4, 8, and 9; and Cluster 3 consisted of Points 10, 11, 12, and 13. The findings enhance our understanding of the factors influencing arsenic contamination to provide an effective mitigation strategy in acid rock drainage scenarios. This research also demonstrates the applicability and effectiveness of fuzzy regression and fuzzy multivariate clustering in the analysis of arsenic-polluted water samples.

Human health risk model for microplastic exposure in the Arctic region.

Microplastics enriched with carcinogens like heavy metals, polycyclic aromatic hydrocarbons (PAHs), and their derivatives are ubiquitous in Arctic waters. They contaminate the local land and sea-based food sources, which is a significant health hazard. It is thus imperative to assess the risk posed by them to the nearby communities, which primarily rely on locally available food sources to meet their energy requirements. This paper proposes a novel ecotoxicity model to assess the human health risk posed by microplastics. The region's geophysical and environmental conditions affecting human microplastic intake, along with the human physiological parameters influencing biotransformation, are incorporated into the developed causation model. It investigates the carcinogenic risk associated with microplastic intake in humans via ingestion in terms of incremental excess lifetime cancer risk (IELCR). The model first evaluates microplastic intake and then uses reactive metabolites produced due to the interaction of microplastics with xenobiotic metabolizing enzymes to assess cellular mutations that result in cancer. All these conditions are mapped in an Object-Oriented Bayesian Network (OOBN) framework to evaluate IELCR. The study will provide a vital tool for formulating better risk management strategies and policies in the Arctic region, especially concerning Arctic Indigenous peoples.

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.e., leach) chemical compounds that adversely affect aquatic species. To better understand the ecological risk of TRWPs, an acute, probabilistic ecological risk assessment model was developed and applied. This was a screening-level, conceptual ecological risk assessment (ERA) based on secondary data from published scientific studies. The model was demonstrated using British Columbia (BC) Highway 97 (TRWP source) and Kalamalka Lake (receiving water) in Canada, considering two spatial scenarios with varied highway (HWY) lengths and lake volumes. TRWP-derived chemical leachates considered for ERA were aniline, anthracene (ANT), benzo(a)pyrene (B(a)P), fluoranthene (Fl), mercaptobenzothiazole (MBT), and zinc (Zn). An assumed 'total TRWP-derived leachate set' was also assessed, representing all compounds present in tire-derived leachate test solutions. The results indicated the risk to aquatic species in two spatial scenarios. In scenario 1, ecotoxicity risk was high from exposure to TRWP-derived zinc and the total TRWP-derived leachate set. Scenario 2 results indicated acute risk was high from all TRWP-derived chemicals examined, except MBT. This preliminary ecological risk screening provides an early signal that freshwater lakes adjacent to busy highways may be at risk from TRWP contamination, indicating a need for further research. This research is the first ERA of TRWPs in Canada, and the results and methodology provide a foundation for future research and solutions development.

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. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R2 =0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R2 =0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints.

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. An integrated framework combining life cycle thinking and water quality assessment techniques was developed in this study to evaluate the DWMSs' performance in terms of water quality, environment, and economics. Six DWMSs were assessed using the integrated framework as a case study. The environmental impacts in terms of human health, ecosystem, and resource use ranged from 1.46E-06 to 4.01E-06 DALY, 9.35E-10 to 3.80E-09 species.yr, and 0.0025-0.0071 USD-$, respectively. Pollution water index (PWI) and cost-benefit analysis (CBA) were used as decision-making techniques to assess the overall performance and suitability of DWMSs under given settings. The DWMSs with surface water as a source or ones providing relatively more degree of treatment have a relatively high PWI score (i.e., ≈0.31), reflective of high environmental impacts and water pollution compared to other alternatives. The CBA scores of selected alternatives ranged between 0.22 and 1.0. Furthermore, it was identified that DWMSs applied on relatively bigger water distribution systems can outweigh their costs (i.e., environmental and economic impacts). The proposed framework and approaches are flexible as they can incorporate different criteria in evaluating the performance and applicability of DWMSs.

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. Consequently, the new occupancy times will impact the performance of energy efficiency measures. To address these gaps, this work uses a real case study, a two-story residential building in the Okanagan Valley (British Columbia, Canada). Further, steady-state building energy simulations are performed on the HOT2000 tool to evaluate the resiliency of energy efficiency measures under a full lockdown. Three-year monitored energy data is analyzed to study the implications of COVID-19 lockdowns on HVAC and non-HVAC loads at a monthly temporal scale. The results show a marked change in energy use patterns and a higher increase in May 2020 compared to the previous two years. Calibrated energy models built on HOT2000 are then used to study the impacts of pre-COVID-19 (old normal occupancy) and post-COVID-19 (new normal occupancy) on energy upgrades performance. The simulations show that under higher occupancy times, the annual electricity use increased by 16.4%, while natural gas use decreased by 7.6%. The results indicate that overall residential buildings following pre-COVID-19 occupancy schedules had higher energy-saving potential than those with new normal occupancy. In addition, the variation in occupancy and stakeholder preferences directly impact the ranking of energy efficiency measures. Furthermore, this study identifies energy efficiency measures that provide flexibility for the decision-makers by identifying low-cost options feasible under a range of occupancy schedules.

An ecotoxicological risk model for the microplastics in arctic waters.

The risk posed to Arctic marine life by microplastics, a Contaminants of Emerging Arctic Concern (CEAC), is poorly known. The reason is the limited understanding of the dose-response relationship due to the region's peculiar environmental and geophysical properties and the unique physiological properties of the species living there. The properties of microplastics in the region and their distribution across the oceanic profile further complicate the problem. This paper addresses the knowledge gap by proposing a novel comprehensive ecotoxicity model. The model uses oxidative stress caused by the Reactive Oxygen Species (ROS) to assess cell mortality. Cell mortality has been used as an indicator of ecological risk. The model is implemented in the Bayesian Network (BN) framework to evaluate the cytotoxicity, measured as the probability of causing mortality. The work enhances the understanding and assessment of the cytotoxicity of microplastics in polar cod and associated risks.

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. Tire and road wear particles (TRWPs) are a dominant group among MPs, and it is vital to estimate their occurrence in the environment. This study proposed a conceptual framework to estimate the occurrence and emissions of TRWPs in the environment. The proposed framework developed a vehicle emission model combined with a previously developed freshwater transport model and was demonstrated using a region in Okanagan Valley, British Columbia, as a case study. A sensitivity analysis was performed to address the uncertainty in TRWP emissions. Furthermore, scenarios were developed considering various environmental, management, and treatment factors to forecast the TRWP emissions under different situations. The total TRWPs emission estimated on the road ranged between 25 and 167 t/year, the estimated TRWPs emission to surface water ranged between 4 and 32 t/year, and the estimated TRWPs emission entering lakebed ranged between 4 and 23 t/year. Furthermore, the scenarios analysis showed that selected management and treatment strategies under given environmental conditions can reduce the total emission on-road (from >130 t/year to <60 t/year); reduce emission to surface water (from >35 t/year to ≈ 12 t/year); and reduce lakebed emissions (from 25 t/year to <8 t/year). Therefore, these management and treatment strategies could reduce the annual per-capita TRWP emissions from >4 kg/c/year to <2 kg/c/year. The proposed framework is flexible and can be adapted to forecast TRWP emissions in different regions. The developed model and framework can be improved by collecting more data and considering other contributing factors.

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. In this study, we used various regression-based machine learning techniques, including artificial neural networks (ANNs), Gaussian process regression (GPR), and support vector regression, to develop decision-support models for OSRM selection. Using a small hypothetical oil spill dataset, the modelling performance was thoroughly compared to find techniques working well under data constraints. The regression-based machine learning models were also compared with integrated and optimized fuzzy decision trees models (OFDTs) previously developed by the authors. OFDTs and GPR outperformed other techniques considering prediction power (> 30 % accuracy enhancement). Also, the use of the Bayesian regularization algorithm enhanced the performance of ANNs by reducing their sensitivity to the size of the training dataset (e.g., 29 % accuracy enhancement compared to an unregularized ANN).

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. We developed a new integrated "Regression-QMRA method" by examining the relationship between pathogens' concentration and environmental variables. Applying of this methodology to a planned LID train shows that the predicted disease burden of diarrhea from Campylobacter is highest (i.e. 16.902 DALYs/1000 persons/yr) during landscape irrigation and playing on the LID train, followed by Giardia, Cryptosporidium, and Norovirus. These results illustrate that the risk of microbial infection can be predicted during the planning phase of LID treatment train. These predictions are of great value to municipalities and decision-makers to make informed decisions and ensure risk-based planning of stormwater systems before their development.

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. In the proposed staged approach, Stage 1 uses a risk-based approach to optimally locate the chlorine boosters in a large distribution system based on residual chlorine failures and the associated consequences in different land uses of the service area. Integrating EPANET and CPLEX software, Stage 2 uses mixed integer goal programming for optimizing the day-ahead pump scheduling. The objective function minimizes the pumping energy cost as well as the undesirable deviations from goal constraints, such as expected water demand. Stage 3 evaluates the combined hydraulics and water quality performances at the network level. The implementation of the proposed approach on a real-time large-sized network of Al-Khobar City in Saudi Arabia, with 44 groundwater wells, 12 reservoirs, 2 storage tanks, 191 mains, 141 junctions, and 17 pumps, illustrated the practicality of the framework. Simulating the network with an optimal pumping schedule and chlorine boosters' locations shows a 40% improvement in water quality performance, desired hydraulics performance with optimal pump scheduling, and an average 20% energy cost reduction compared to the normal (unoptimized) base case scenario.

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review.

COVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the 'One-Health' concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements.

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. Hypothetical scenarios based on oily waste compositions are developed, and the associated environmental impacts and operational costs are evaluated. The LCA results show that oily waste composition accounts for < 5% of the total environmental impacts. Chemical demulsification has the highest total impacts due to high marine ecotoxicity and human toxicity, followed by incineration and transportation. The cost analysis reveals that the strategy comprised of centrifugation and landfilling is most preferable while the combination of chemical demulsification and incineration is least favorable. The strategy of combined use of centrifugation and landfilling is ranked as the most suitable in the MCDA. Regression models are developed to predict environmental impacts based on important factors. The framework can help waste management practitioners select low-impact strategies for handling offshore OSRW.

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). The framework integrated Adverse Outcome Pathway in risk assessment and was applied to a Canadian lake system for seven CECs: salicylic acid, acetaminophen, caffeine, carbamazepine, ibuprofen, drospirenone, and sulfamethoxazole. Altogether 264 water samples were collected and analyzed from 15 sites May 2016 to September 2017. Phytoplankton, zooplankton, and fish were also sampled and analyzed. The results show ecological risk estimates (Risk Quotient, RQ) varied considerably indicating a range of uncertainty. Based on the conservative estimate, the central tendency estimate of the ecological risk of mixture compounds was medium (RQ = 0.6) including drospirenone. However, the reasonably maximum estimate of the risk was high (RQ = 1.4) for mixture compounds including drospirenone. The high risk is primarily due to drospirenone as its individual risk was high (RQ = 1.1) to fish. The specific site and time of high drospirenone exposure was identified for implementing control measures. Classification of ecotoxicity values based on environmental parameters such as climate and water quality, can reduce uncertainty in the risk estimate.

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. Cancer, non-cancer, and microbial risks assessed, as well as both quantitative and qualitative aesthetic impact assessment outcomes, were aggregated into synthetic water quality indices for water quality ranking. The probabilistic health risk assessment results revealed significant health risks for a community with relatively high arsenic concentrations (mean value = 7.0 µg/L) in the water supply. The microbial risks were also found significant (disability-adjusted life years >1 × 10-6) for all communities because of the presence of coliforms in the water. The FSE results indicated that the drinking water quality of five RRCs was associated with high aggregated impacts, which concurred with the "poor" water quality ratings according to the Canadian Water Quality Index. The water quality of the five RRCs was ranked based on the synthetic water quality evaluation indices. The PFSE approach can help decision-makers prioritize RRCs in effective resource allocation for addressing drinking water quality issues.

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. It is found that FIs for buildings in Canada can be distributed into four categories: tax, loans, grants, and rebates. Among these, rebates from utility providers are the most common and are administered in all provinces. In addition to these, special incentives are available for three end-users (low-income, aboriginal people, landlords and tenants) and for three types of buildings (heritage, non-profit and energy rated). A clear contrast is observed on FIs offered in three regulatory regimes (Federal, provincial and municipal). Four provinces (Alberta, British Columbia, Ontario and Quebec) are leading in green building efforts. The in-depth literature review was also used to develop an understanding on the criteria used in effectiveness evaluation and the factors impacting effectiveness. Based on the findings of different studies on FIs effectiveness, a generic approach for evaluation of FIs is proposed that can help in deploying successful FIs programs. The results of this review are of importance to the policymakers, government authorities, and utilities engaged in designing and improving FIs for energy efficient buildings.

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. GCA and HHRA were performed using soil and groundwater sampling data collected in 2016 and 2019. Benzene, chlorobenzene, dichlorobenzenes, 2-nitrochlorobenzene, and α-hexachlorocyclohexane were determined to be critical contaminants in soil. The 2019 sampling data revealed new contaminated locations that were not found in the 2016 sampling campaign. High concentrations (89.81-386.55 mg/L) of vinyl chloride were also found in groundwater samples. Several critical location clusters of high concentrations of dichlorobenzenes, chlorobenzene, and α-hexachlorocyclohexane were found within the site according to the GCA outcomes. These contaminants could pose significant cancer and non-cancer risks to onsite workers. The critical areas were ranked according to cancer and non-cancer risks estimated by HHRA, respectively, for informed remediation planning. Among the critical contaminants, α-hexachlorocyclohexane, 2-nitrochlorobenzene, and 1,4-dichlorobenzene in soil, as well as vinyl chloride in groundwater, contributed a predominant part to the total health risk. The integrated approach can be used to assess the contamination of other similar abandoned industrial complex sites.

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. Applying robust computational techniques based on real-time data (e.g., satellite and aerial observations) and historical records of oil spill incidents may considerably facilitate decision-making processes. Various soft-computing and artificial intelligence-based models and mathematical techniques have been used for the implementation of MOSM's components. This study presents a review of literature published since 2010 on the application of computational techniques in MOSM. A statistical evaluation is performed concerning the temporal distribution of papers, publishers' engagement, research subfields, countries of studies, and selected case studies. Key findings reported in the literature are summarized for two main practices in MOSM: spill detection, characterization, and monitoring; and spill management and response optimization. Potential gaps in applying computational techniques in MOSM have been identified, and a holistic computational-based framework has been suggested for effective MOSM.

Antifouling and Flux Enhancement of Reverse Osmosis Membrane by Grafting Poly (3-Sulfopropyl Methacrylate) Brushes.

A commercial thin film composite (TFC) polyamide (PA) reverse osmosis membrane was grafted with 3-sulfopropyl methacrylate potassium (SPMK) to produce PA-g-SPMK by atom transfer radical polymerization (ATRP). The grafting of PA was done at varied concentrations of SPMK, and its effect on the surface composition and morphology was studied by Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), optical profilometry, and contact angle analysis. The grafting of hydrophilic ionically charged PSPMK polymer brushes having acrylate and sulfonate groups resulted in enhanced hydrophilicity rendering a reduction of contact angle from 58° of pristine membrane sample labeled as MH0 to 10° for a modified membrane sample labeled as MH3. Due to the increased hydrophilicity, the flux rate rises from 57.1 L m-2 h-1 to 71.2 L m-2 h-1, and 99% resistance against microbial adhesion (Escherichia coli and Staphylococcus aureus) was obtained for MH3 after modification.

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. A hierarchical-based framework arranged 81 performance indicators to appraise the key components, including water management, indoor environment, personnel, service quality, energy, social, and operations. Fuzzy AHP and fuzzy mean clustering methods evaluated the identified PIs based on the opinion of experts (from Canadian aquatic centres) on their importance, measurability, and understandability. Finally, the selection process ranked a set of 63 most suitable PIs under 14 sub-criteria. Fuzzy-based methods efficiently handled the subjective scoring process and the difference of opinion among the experts. The criteria performance indices inform the top-level management while the sub-indices stipulate the operations management for honing in the lacking indicators. Using the selected PIs, the AC's management can allocate the available resources for both the short-term (e.g., efficient response to complaints) and long-term (e.g., replacing failed manually operated fixtures with the sensor-operated ones) improvement actions. The selected PIs will enhance the sustainability of ACs in Canada and other cold regions around the globe through a structured benchmarking process.