Gyan Chhipi-Shrestha

‘Socializing’ sustainability: a critical review on current development status of social life cycle impact assessment method

Social life cycle assessment (S-LCA) is a technique to assess the potential social impacts of a product or service caused by its life cycle. The aim of this paper is to critically review the methodologies applied in S-LCIA and establish its current development status by highlighting areas for improvement. The UNEP/SETAC Guidelines published in 2009 provided general procedures for conducting S-LCA, but lack S-LCIA methods. Many new S-LCIA methods have been proposed but these are inherently different, indicating a scientific and well-accepted S-LCIA method is yet to be developed. Broadly, two types of S-LCIA methods, i.e. performance reference point and impact pathways methods are in use. A direction for future research could be the refinement of the social hotspots database and the social hotspot index calculation method. Moreover, the S-LCIA method could be developed by combining the performance reference point and impact pathways methods.

Stable strategies analysis based on the utility of Z-number in the evolutionary games

Evolutionary games with the fuzzy set are attracting growing interest. While among previous studies, the role of the reliability of knowledge in such an infrastructure is still virgin and may become a fascinating issue. Z-number is combined with “restriction” and “reliability”, which is an efficient framework to simulate the thinking of human. In this paper, the stable strategies analysis based on the utility of Z-number in the evolutionary games is proposed, which can simulate the procedure of human’s competition and cooperation more authentically and more flexibly. Some numerical examples and an application are used to illustrate the effectiveness of the proposed methodology. Results show that total utility of Z-number can be used as an index to extend the classical evolutionary games into ones linguistic-based, which is applicable in the real applications since the payoff matrix is always determined by the knowledge of human using uncertain information, e.g., (outcome of the next year, about fifty thousand dollars, likely).

Microbial quality of reclaimed water for urban reuses: Probabilistic risk-based investigation and recommendations

Although Canada has abundant freshwater resources, many cities still experience seasonal water shortage. Supply-side and demand-side management is a core strategy to address this water shortage. Under this strategy, reclaimed water, which the Canadian public is willing to use for non-potable purposes, is an option. However, no universal guidelines exist for reclaimed water use. Despite the federal governments long-term goal to develop guidelines for many water reuse applications, guidelines have only been prescribed for reclaimed water use in toilet and urinal flushing in Canada. At the provincial level, British Columbia (BC) has promulgated guidelines for wide applications of reclaimed water but only at broad class levels. This research has investigated and proposed probabilistic risk-based recommended values for microbial quality of reclaimed water in various non-potable urban reuses. The health risk was estimated by using quantitative microbial risk assessment. Two-dimensional Monte Carlo simulations were used in the analysis to include variability and uncertainty in input data. The proposed recommended values are based on the indicator organism E. coli. The required treatment levels for reuse were also estimated. In addition, the recommended values were successfully applied to three wastewater treatment effluents in the Okanagan Valley, BC, Canada. The health risks associated with other bacterial pathogens (Campylobacter jejuni and Salmonella spp.), virus (adenovirus, norovirus, and rotavirus), and protozoa (Cryptosporidium parvum and Giardia spp.), were also estimated. The estimated risks indicate the effectiveness of the E. coli-based water quality recommended values. Sensitivity analysis shows the pathogenic E. coli ratio and morbidity are the most sensitive input parameters for all water reuses. The proposed recommended values could be further improved by using national or regional data on water exposures, disease burden per case, and the susceptibility fraction of population.

Sustainability assessment framework for low rise commercial buildings: life cycle impact index-based approach

The building industry has regularly been criticized for resource exploitation, energy use, waste production, greenhouse gas emissions, and impacts on the landscape. The growing population demands more built environment to accommodate the socioeconomic wellbeing. Adopting conventional construction practices would continue the aforementioned issues. Therefore, it is important to integrate life cycle thinking into building construction to minimize its social, environmental, and economic impacts. The objective of this study is to assess the life cycle impact of commonly used wall–roof systems for low rise commercial building construction in Canada. A framework is developed to assess different building alternatives using the triple bottom line of sustainability. Identified environmental and socioeconomic impact indicators are eventually aggregated to develop a life cycle impact index. Material quantities of six wall–roof combinations for a single-storey commercial building were obtained from industrial partners. State-of-the-art life cycle assessment software is used to assess the life cycle impacts of different wall–roof systems. To accommodate decision makers’ preferences of sustainability, wall–roof combinations are assessed for three potential scenarios namely, eco-centric, neutral, and economy-centric using multi-criteria decision analysis. The framework has also been implemented on a case study of low rise building in Calgary (Alberta, Canada) to evaluate its practicality. The study results revealed that the concrete–steel building is the most sustainable alternative in neutral and economy-centric scenario while steel–wood building is the most sustainable building in eco-centric scenario.

Development of a predictive model for Clostridium difficile infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory

Clostridium difficile infection is one of the major patient safety concerns in hospitals worldwide. Clostridium difficile infection can have high economic burden to patients, hospitals, and government. Limited work has been done in the area of predictive modeling. In this article, A new predictive model based on Gaussian mixture model and Dempster–Shafter theory is proposed to predict Clostridium difficile infection incidence in hospitals. First, the Gaussian mixture model and expectation–maximization algorithms are used to generate explicit probability criteria of risk factors based on the given data. Second, Dempster–Shafter theory is used to predict the Clostridium difficile infection incidence based on the generated probability criteria that have different beliefs attributing to their different credits. The main procedure includes (1) generate the probability criteria model using Gaussian mixture model and expectation–maximization algorithm; (2) determine the credit of the probability criteria; (3) generate the basic probability assignment; (4) discount the evidences; (5) aggregate the evidences using Dempster combining rule; (6) predict Clostridium difficile infection incidence using pignistic probability transformation. Results show that the model has a higher accuracy than an existing model. The proposed model can generate the criteria ratings of risk factors automatically, which would potentially prevent the imprecision caused by the subjective judgement of experts. The proposed model can assist risk managers and hospital administrators in the prediction and control of Clostridium difficile infection incidence with optimizing their resources.

Characterizing hydraulic fracturing fluid greenness: application of a hazard-based index approach

Growth of the unconventional gas industry is predicted to continue to be an important component of the global energy landscape. The rapid expansion of shale and tight gas development has raised many environmental and human health concerns, particularly in regards to ground and surface water contamination. The unconventional gas industry has begun to transition toward the use of hydraulic fracturing chemicals that pose minimal environmental and human health hazards in order to mitigate the risks associated with possible chemical containment failure. Integrated chemical hazard evaluation has been facilitated by an adapted index-based approach to combine noncommensurate multiparameter chemical hazard data into a single score value. Comparative analysis of existing chemical hazard index scoring systems as well as the formulation of a novel hydraulic fracturing fluid greenness assessment system revealed several important considerations for index development and application. Index scores calculated using the investigated index systems highlighted the need for informed, optimized hazard class selection as input for score determination, the maintenance of hazard category intensity during parameter transformation, as well as representative hazard class and chemical component mathematical weightings, and robust aggregation techniques for final score calculation. Continued research should work to model the combined hazard posed by individual chemicals while considering the effect of dilution as well as incorporate additional index metrics beyond hazard intensity. Fully disclosed index systems, applied with complete knowledge of their strengths and weaknesses, provide useful monitoring and communication tools to promote environmental-best practices in the unconventional gas industry.

Water–Energy–Carbon Nexus Modeling for Urban Water Systems: System Dynamics Approach

AbstractA comprehensive water-energy-carbon (WEC) nexus model for an urban water system (UWS) using system dynamics is proposed to assist municipalities, urban developers, and policy makers for nei...

Fit-for-purpose wastewater treatment: Conceptualization to development of decision support tool (I)

This article is the first in a series of two papers. Paper I focuses on model conceptualization and development, and Paper II in the series focuses on model validation and implementation. The amount of water reuse has been increasing across the globe. Wastewater can be treated based on the intended end use of reclaimed water. Fit-for-purpose wastewater treatment (WWT) simultaneously considers intended end use, economic viability, and environmental sustainability. WWT technologies differ mainly in terms of treatment efficiency, cost, energy use, and associated carbon emissions. The planning and evaluation of water reuse projects requires a decision support tool (DST) to evaluate alternative WWT trains and water reuse applications. However, such a DST is not available in the publically accessible literature. A DST, FitWater, has been developed for the evaluation of WWT for various urban reuses. The evaluation is based on the following criteria: amount of reclaimed water production, health risk of water reuse, cost, energy use, and carbon emissions. The cost is estimated as annualized life cycle cost and health risk is estimated using quantitative microbial risk assessment. The uncertainty analysis has been performed using probabilistic and fuzzy-based methods. A multi-criteria decision analysis, using fuzzy weighted average, is employed to aggregate different criteria and generate a final score. FitWater ranks alternative WWT trains based on the resulting final score. The proposed FitWater DST is user-friendly, and its application is demonstrated using an example. The DST can be enhanced to include additional treatment technologies and carbon emissions of different treatment processes.

Evaluating water reuse applications under uncertainty: generalized intuitionistic fuzzy-based approach

Water reuse is a viable option to increase urban water supply, especially under new realities of climate change and increasing anthropogenic activities. A sustainable water reuse application should be cost-effective and have acceptable health risk to consumers. Water reuse application evaluation is complex because data acquisitions are usually associated with the problems of uncertainty, hesitancy, and parameterization. In this paper, a generalized intuitionistic fuzzy soft set (GIFSS)-based decision support framework is proposed to provide an effective approach to describe uncertainty and hesitancy in an intuitionistic fuzzy number. In addition, the modified measures of comparison and similarity are proposed to compare water reuse applications. Then, the proposed framework is applied to the City of Penticton (British Columbia, Canada) to evaluate seven water reuse applications. The evaluation results show that the applications of garden flower watering and public parks watering are the most preferred alternatives, which are consistent with the existing practice in the city. Furthermore, the results are highly affected by the generalized parameter and the weights of evaluation criteria. Both the comparison measure-based and similarity measure-based evaluations within the same GIFSS-based framework produce consistent results, indicating an applicable and efficient methodology.

Fit-for-purpose wastewater treatment: Testing to implementation of decision support tool (II)

This paper is the second in a series of two papers. In Paper I, a decision support tool (DST), FitWater, was developed for evaluating the potential of wastewater treatment (WWT) trains for various water reuse applications. In the present paper, the proposed DST has been tested and implemented. FitWater has been tested with several existing WWT plants in Canada and the USA, demonstrating FitWaters effectiveness in estimating life cycle cost (LCC), health risk, and energy use. FitWater has also been implemented in a newly planned neighbourhood in the Okanagan Valley (BC, Canada) by developing 12 alternative WWT trains for water reuse in lawn and public parks irrigation. The results show that FitWater can effectively rank WWT train alternatives based on LCC, health risk, amount of reclaimed water, energy use, and carbon emissions. Moreover, functions have been developed for the variation of unit annualized LCC and energy intensity per unit log removal of microorganisms in different treatment technologies with varying plant capacities. The functions have power relations, showing the economies of scale. FitWater can be applied to identify a cost-effective, risk-acceptable, and energy efficient wastewater treatment train with a plant capacity of 500m3/day or more. Furthermore, FitWater can be used to assess potential economic impacts of developing microbiologically stringent effluent standards. The capability of FitWater can be enhanced by including physio-chemical quality of wastewater, additional treatment technologies, and carbon emissions from wastewater decomposition processes.