Evaluation of antibody-based preventive alternatives for respiratory syncytial virus: a novel multi-criteria decision analysis framework and assessment of nirsevimab in Spain.

BACKGROUND: Respiratory syncytial virus (RSV) is a highly infectious disease that poses a significant clinical and medical burden, as well as social disruption and economic costs, recognized by the World Health Organization as a public health issue. After several failed attempts to find preventive candidates (compounds, products, including vaccines), new alternatives might be available, one being nirsevimab, the first and only option approved for RSV prevention in neonates and infants during their first RSV season. The objective of this study was to develop a novel multi-criteria decision analysis (MCDA) framework for RSV antibody-based preventive alternatives and to use it to assess the value of nirsevimab vs. placebo as a systematic immunization approach to prevent RSV in neonates and infants during their first RSV season in Spain. METHODS: Based on a pre-established model called Vaccinex, an ad-hoc MCDA framework was created to reflect relevant attributes for the assessment of current and future antibody-based preventive measures for RSV. The estimated value of nirsevimab was obtained by means of an additive linear model combining weights and scores assigned by a multidisciplinary committee of 9 experts. A retest and three sensitivity analyses were conducted. RESULTS: Nirsevimab was evaluated through a novel framework with 26 criteria by the committee as a measure that adds value (positive final estimated value: 0.56 ± 0.11) to the current RSV scenario in Spain, by providing a high efficacy for prevention of neonates and infants. In addition, its implementation might generate cost savings in hospitalizations and to the healthcare system and increase the level of public health awareness among the general population, while reducing health inequities. CONCLUSIONS: Under a methodology with increasing use in the health field, nirsevimab has been evaluated as a measure which adds value for RSV prevention in neonates and infants during their first RSV season in Spain.

Preferences as fairness judgments: a critical review of normative frameworks of preference elicitation and development of an alternative based on constitutional economics.

Preference elicitation is widely used within health economic evaluations to inform coverage decisions. However, coverage decisions involve questions of social justice and it is unclear what role empirical evidence about preferences can play here. This study reviews the prevalent normative frameworks for using population-based preference elicitation and the criticisms they face, and proposes an alternative based on constitutional economics. The frameworks reviewed include a supposedly value-neutral framework of preferences as predictors of choice, preference utilitarian frameworks that aim to maximize preference satisfaction, and substantive consequentialist frameworks that aim to maximize happiness, health, or capabilities. The proposed alternative implements the idea that indices of social value are tools for conflict resolution, rather than tools for maximization. Preference elicitation is used for validating values generated by multi-criteria decision analysis results within representative processes of stakeholder deliberation.

Exploring health preference heterogeneity in the UK: Using the online elicitation of personal utility functions approach to construct EQ-5D-5L value functions on societal, group and individual level.

A new method has recently been developed for valuing health states, called 'Online elicitation of Personal Utility Functions' (OPUF). In contrast to established methods, such as time trade-off or discrete choice experiments, OPUF does not require hundreds of respondents, but allows estimating utility functions for small groups and even at the individual level. In this study, we used OPUF to elicit EQ-5D-5L health state preferences from a (not representative) sample of the UK general population, and then compared utility functions on the societal-, group-, and individual level. A demo version of the survey is available at: https://eq5d5l.me. Data from 874 respondents were included in the analysis. For each respondent, we constructed a personal EQ-5D-5L value set. These personal value sets predicted respondents' choices in three hold-out discrete choice tasks with an accuracy of 78%. Overall, preferences varied greatly between individuals. However, PERMANOVA analysis showed that demographic characteristics explained only a small proportion of the variability between subgroups. While OPUF is still under development, it has important strengths: it can be used to construct value sets for patient reported outcome instruments such as EQ-5D-5L, while also allowing examination of underlying preferences in an unprecedented level of detail. In the future, OPUF could be used to complement existing methods, allowing valuation studies in smaller samples, and providing more detailed insights into the heterogeneity of preferences across subgroups.

Application of hybrid multi-criteria decision-making approach to analyze wastewater microalgae culture systems for bioenergy production.

Bioenergy generation from microalgae can significantly contribute to climate mitigation and renewable energy production. In this regard, several multi-criteria decision-making method were employed to prioritize appropriate microalgae culture system for bioenergy production. Entropy weight, Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the employed MCDA method. Fourteen microalgae culture systems were selected as a case study, which contain teen monoculture and four dual-culture. Initially, through ans in-depth review of the literature and expert views, four categories total eight indicators were selected as the evaluation indices of the study, namely 1) Proliferation: Half growth cycle and Max growth rate,2) Biomass output: Bio-crude yield and Lipid yield, 3) Nutrient utilization: residual concentration of total Nitrogen and total Phosphorus, and, 4) Stability: coefficient of variation of Bio-crude yield and Lipid yield. The result indicated that "Pediastrum sp. & Micractinium sp." was identified as the most bioenergy potential microalgae culture system, and the evaluation results of entropy weight method and CRITIC method are similar. It is pertinent to note that 1)the entropy weight method exhibits lower sample size requirements, 2) the critic method excels when dealing with larger sample sizes, and 3) the TOPSIS method necessitates the incorporation of appropriate weighting methods to ensure credible results. In the application stage, the key indicators related to cost can be further included in the evaluation indices.

A holistic framework of biochar-augmented cementitious products and general applications: Technical, environmental, and economic evaluation.

In the context of the circular economy, the development of innovative and low-carbon concrete that incorporates different kinds of waste materials is gaining attention among the research community, regulatory agencies, and policymakers. These materials can be incorporated into concrete mixtures as aggregates or as fillers for improvement of product properties. This study aims to identify reliable designs for biochar-augmented cementitious products and general applications through technical, environmental, and economic assessments. The outcomes demonstrate that 5 wt% biochar addition could enhance the compressive strength of the final products. Using biochar, together with other recycled materials, can enormously reduce the environmental impacts, especially for global warming, enabling biochar-augmented cementitious products and general application as carbon-negative resources. The highest GWP reduction reached -720 kg CO2/tonne, equal to a 200% saving. A high quantity of biochar could be included in several specific applications (up to 60 wt%). The economic assessment highlights that the proposed designs are cost-effective and carbon tax can be significantly reduced. Carbon credits can also be earned for some carbon-negative designs. These findings can serve to mitigate GHG emissions and provide decision-makers with a reliable and holistic framework towards the goal of carbon neutrality.

Spatial multi-criteria decision analysis for the selection of sentinel regions in tick-borne disease surveillance.

BACKGROUND: The implementation of cost-effective surveillance systems is essential for tracking the emerging risk of tick-borne diseases. In Canada, where Lyme disease is a growing public health concern, a national sentinel surveillance network was designed to follow the epidemiological portrait of this tick-borne disease across the country. The surveillance network consists of sentinel regions, with active drag sampling carried out annually in all regions to assess the density of Ixodes spp. ticks and prevalence of various tick-borne pathogens in the tick population. The aim of the present study was to prioritize sentinel regions by integrating different spatial criteria relevant to the surveillance goals. METHODS: We used spatially-explicit multi-criteria decision analyses (MCDA) to map priority areas for surveillance across Canada, and to evaluate different scenarios using sensitivity analyses. Results were shared with stakeholders to support their decision making for the selection of priority areas to survey during active surveillance activities. RESULTS: Weights attributed to criteria by decision-makers were overall consistent. Sensitivity analyses showed that the population criterion had the most impact on rankings. Thirty-seven sentinel regions were identified across Canada using this systematic and transparent approach. CONCLUSION: This novel application of spatial MCDA to surveillance network design favors inclusivity of nationwide partners. We propose that such an approach can support the standardized planning of spatial design of sentinel surveillance not only for vector-borne disease BDs, but more broadly for infectious disease surveillance where spatial design is an important component.

Multi-criteria decision making to validate performance of RBC-based formulae to screen [Formula: see text]-thalassemia trait in heterogeneous haemoglobinopathies.

BACKGROUND: India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies. METHODS: We compared the performance of a recently developed formula SCS[Formula: see text] and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden's Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures. RESULTS: MCDM methods revealed that the Shine & Lal and SCS[Formula: see text] were the best-performing formulae. Further, a modification of the SCS[Formula: see text] formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS[Formula: see text] along with the condition MCV[Formula: see text] 80 fl was recommended for a higher heterogeneous population set. It was found that SCS[Formula: see text] can classify all BTT samples with 100% sensitivity when MCV[Formula: see text] 80 fl. CONCLUSIONS: We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS[Formula: see text] and its web application SUSOKA can provide 100% sensitivity when MCV[Formula: see text] 80 fl.

A data-driven and cost-oriented FMEA-MCDM approach to risk assessment and ranking in a fuzzy environment: A hydraulic pump factory case study.

In today's highly competitive business environment, firms strive to maximize profitability by minimizing or eliminating disruptions and failures to maintain a competitive edge. This study focuses on evaluating risks in a hydraulic pump factory as a means to achieve sustainable growth. To accomplish this, a team of experts was formed to identify potential errors, utilizing a combination of risk priority number criteria weighted by Fuzzy Shannon's entropy and a fusion of multi-criteria decision-making and failure mode and effects analysis for evaluating and ranking failures. Moreover, the study emphasizes the importance of considering the interaction among risk assessment indicators, the inclusion of cost of failure, and modeling under fuzzy uncertainty circumstances, as they have a notable impact on the final ranking of failures to be processed for risk mitigation action planning. This research brings a new dimension to enhance the overall effectiveness of risk assessment by aggregation, as evidenced by a novel use of data classification in machine learning and correlation in statistics. The findings indicate that the aggregated ranking data series is best matched and most influenced by the weighted aggregated sum product assessment method, with the highest rate of recall and precision accomplished.

A priory allocation of ecosystem services to forest stands in a forest management context considering scientific suitability, stakeholder engagement and sustainability concept with multi-criteria decision analysis (MCDA) technique: A case study in Turkey.

Various research endeavours are designed to identify ecosystem services, assess their spatial distribution, and prioritize them in a given forest landscape. The Turkish State Forest Organization has introduced an ecosystem-based multiple-use forest management philosophy since 2008, which emphasizes the need for identifying and allocating ecosystem services to each forest planning unit. This paper aims to investigate the use of Multiple Criteria Decision Analysis (MCDA) techniques and explores their effectiveness and suitability in identifying and allocating ecosystem services to forest units, considering scientific suitability, stakeholder engagement and the sustainability concept in the context of ecosystem-based forest management decision-making processes in a case study area of Turkey. We propose a framework that entails an iterative process comprising various stages, starting from identifying ecosystem services (ES) to allocating them to forest stands with a participatory approach. We employed the Analytical Hierarchy Process (AHP) and the Delphi method to determine stakeholder preferences and allocate ecosystem services to forest stands. This was achieved through an equation newly developed using scientific suitability, stakeholder preferences, and the sustainability concept. The landscape percentage allocated primarily to ES was as follows: water regulation (55.44%), soil protection (16.47%), biodiversity conservation (14.03%), wood production (13.08%), and aesthetic-recreation (0.84%). Notably, no allocations were made for national defence and climate regulation services. In conclusion, the stratification of Posof forests into zones was efficiently achieved a priori, considering both scientific-technical and socio-cultural criteria through MCDA techniques based on stakeholder preferences. This study streamlines the decision-making process involved in spatially allocating ecosystem services and provides crucial information instrumental in determining management objectives and optimal forest activities.

Spatiotemporal variations and priority ranking of emerging contaminants in nanwan reservoir: A case study from the agricultural region in huaihe river basin in China.

The presence of emerging contaminants (ECs) in drinking water sources is an increasing concern, yet limited data exists on their occurrence and risk in the upper Huaihe River Basin, an important agricultural region in Central China. This study investigated 70 ECs, including pesticide and antibiotics in surface water from drinking water source areas in Nanwan Reservoir along the upper reaches of the Huaihe River Basin to prioritize the ECs based on ecological risk and health risk assessment. A total of 66 ECs were detected in the surface water at least once at the selected 38 sampling sites, with concentrations ranging from 0.04 to 2508 ng/L. Ecological risk assessment using the risk quotient (RQ) method revealed high risks (RQ > 1) from 7 ECs in the dry season and 15 ECs in the wet season, with triazine pesticides as the main contributors. Non-carcinogenic risks were below negligible levels, but carcinogenic risks from neonicotinoid and carbamate pesticides and macrolide antibiotics were concerning for teenagers. Ciprofloxacin exhibited a high level of resistance risk during the wet season. A multi-indicator prioritization approach integrating occurrence, risk, and chemical property data ranked 6 pesticides and 3 antibiotics as priority pollutants. The results highlight EC contamination of drinking water sources in this agriculturally-intensive region and the need for targeted monitoring and management to protect water quality.

Bottom-up formulations for the multi-criteria decision analysis of oil and gas pipeline decommissioning in the North Sea: Brent field case study.

Many Oil and Gas (O&G) fields in the North Sea have produced their economically recoverable reserves and have entered the decommissioning phase or are close to cessation of production. The subsequent O&G decommissioning process involves a range of stakeholders with specific interests and priorities. This range of inputs to the process highlights the necessity for the development of multi-criteria decision frameworks to help guide the decision-making process. This study presents bottom-up formulations for the economic, environmental, and safety risk criteria to support the multi-criteria decision analysis within the Comparative Assessment (CA) of O&G pipeline decommissioning projects in the North Sea. The approach adapts current guidelines in the O&G industry and considers a range of parameters to provide estimations for the costs, energy usage, greenhouse gas emissions, and safety risks. To verify the effectiveness of the proposed bottom-up formulations, the longest oil export pipeline in the Brent field, PL001/N0501 is selected as a case study. The numerical results revealed the consistency of the results obtained from the proposed approach with those reported in the technical documents by industry. In most cases, the formulations provide estimates with less than 10% differences for the costs, energy usage, emissions, and safety risks. Based on the proposed multi-criteria formulations, the study also presents the use of an immersive decision-making environment within a marine simulator system to help inform the decision-making process by stakeholders.

Integrating meteorological and geospatial data for forest fire risk assessment.

Understanding the factors that cause fire is crucial for minimizing the fire risk. In this research, a comprehensive approach was adopted to recognize factors influencing forest fires. Golestan National Park (GNP) was considered as a representative area with a humid climate in this study. Initially, using the Multi-Criteria Evaluation Method, a fire risk map was created by analyzing natural and human factors, and vulnerable areas were identified. Then, the relationship between key elements such as meteorological conditions, Land Surface Temperature (LST), and precipitation, with the occurrence of fire in different years was investigated. CHIRPS and Landsat data were utilized to assess LST changes and precipitation. 23-year changes in fire occurrence areas in GNP were acquired using MODIS products. The results of the data analysis showed that the highest number of fires occurred in forest areas, and in the fire risk prediction map, the extremely high-risk class is completely consistent with the ground truth data. The assigned weights, derived from expert opinions, highlight the substantial significance of elevation, and distance from roads and settlements. Additionally, the effectiveness of the model in providing reliable forecasts for fire risks in GNP is highlighted by the ROC curve with an AUC value of 0.83. Forest fires within GNP exhibit a distinct seasonality, predominantly occurring from July to December. During the warmer months, by coinciding with summer excursions, human activities may contribute to the ignition of fires. In 2013 and 2014, rising fire incidents correlated with elevated temperatures, hinting at a potential connection. GNP fires showed an upward trend with higher monthly LST and a downward trend with increased annual precipitation. The results showed that there is a relationship between LST, precipitation, and the occurrence of fire in GNP. Approximately 176.15 ha of GNP's forest areas have been destroyed by fires over the last two decades. This research demonstrated that there is a dynamic interaction between environmental conditions and fire incidents. By considering these factors, managers and environmental planners can develop effective strategies for managing and preventing forest fire risks.

Multi-criteria sustainability assessment of solid waste management in Jordan.

This study comprehensively evaluates Jordan's municipal solid waste (MSW) management sector from 2022 to 2030, in alignment with Jordan Vision 2030. This study introduces new sustainability indicators and innovative waste management alternatives to address the challenges of rapid industrialization and population growth. Four strategic scenarios-1) recycling, composting, and sanitary landfilling; 2) recycling, anaerobic digestion, and sanitary landfilling; 3) incineration and sanitary landfilling; and 4) sanitary landfilling alone-were assessed against the business-as-usual scenario. Using multi-criteria decision analysis (MCDA) and sensitivity analysis, this study evaluates net greenhouse gas emissions, annual operating expenses, revenue streams, and employment rates to measure environmental, economic, and social sustainability. The results indicate that Scenario 1 is the optimal scenario for integrating a material recovery facility (MRF) with a composting plant and sanitary landfill, achieving the lowest greenhouse gas emissions, annual costs, and employment opportunities. This study offers practical and sustainable solutions to Jordan's waste management challenges, provides novel insights through the developed MCDA and sensitivity analysis, and significantly contributes to sustainability research.

Urban greening in Dhaka: Assessing rooftop agriculture suitability using GIS and MCDM techniques.

Dhaka ranks among the world's most densely populated cities, with built-up areas expanding to accommodate the demands of a growing population. The rapid urbanization has reduced green space and exacerbated urban heat and pollution in the city. In the quest for a greener and healthier urban environment, rooftop agriculture has emerged as a promising solution, offering opportunities for the restoration of the environment and safe food production. Despite its potential, limited studies have explored the viability of this alternative greening solution for Dhaka. Therefore, this study aims to assess the suitability of rooftops for agricultural activities employing Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) techniques. First, seven criteria were selected based on the literature, such as building age, height, rooftop size, building utility, property value, sunlight, and water availability. Second, an expert opinion survey was conducted using the Best Worst Method (BWM) to calculate the criteria's weights. Finally, the suitability map for Dhaka was derived by combining the criteria layers and was subsequently validated. Rooftop area and property value were identified as the most and least important criteria. Approximately 9% (6.27 km2), 68% (46.59 km2), 22% (15.15 km2), and a negligible portion (0.1 km2) of Dhaka city has been classified as highly suitable, suitable, moderately suitable, and not suitable, respectively, for rooftop agriculture. By identifying and promoting the most suitable locations for rooftop agriculture and highlighting existing opportunities, this research will help to initiate and expand sustainable agriculture practices that can contribute to climate change adaptation and urban resilience.

District heating system's development decarbonization strategy assessment by system dynamics modeling and multi-criteria analysis.

The study introduces a hybrid model that integrates system dynamics modeling and multi-criteria analysis. Through the system dynamics model, the study examines energy, economic, and environmental indicators of a District Heating (DH) company, assessing its dynamics until 2050. Various decarbonization scenarios are explored, involving the transition of the DH system to a 4th generation DH (4GDH) system based on four strategies: utilizing at least (a) 50% Renewable Energy Sources (RES), (b) 50% waste heat, (c) 75% cogenerated heat, or (d) 50% of the combined aforementioned energy and heat. Additionally, development scenarios incorporate measures to enhance energy efficiency on the consumer side and within the heating networks. The sustainability of each scenario is evaluated using the multi-criteria analysis method TOPSIS. The hybrid model establishes a ranking of the transition pathways based on their sustainability scores and benchmarks the results of the developed scenarios against a carbon-neutral DH system. This model serves as a valuable guide for DH system developers and decision-makers. The study focuses on Riga as a practical case study.

Multi-Criteria Decision-Making Approach for Pre-Synthesis Selection of the Optimal Physicochemical Properties of TiO2 Photocatalytic Nanoparticles for Biomedical and Environmental Applications.

Nanomaterials are widely used in several biomedical and environmental applications, due to their ideal properties. However, the synthetic and characterization procedure requires significant costs and has a negative environmental impact. Various methods are available in order to control the pre-synthesis design of the produced materials, predicting their behavior and minimizing the series of experiments. Multi-Criteria Decision-Making is proposed in this study in order to determine the best combination of the physicochemical parameters and to define the best alternative among fifteen different samples of nanostructured titanium dioxide. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to achieve a final ranking of the available alternatives by avoiding several of the trials that would follow testing the biological effect and the photocatalytic degradation of organic pollutants. Thus, this approach helps us to stay environmentally and ethically correct, saving time, money, and energy and also providing an optimization of the nanomaterials that are developed.

Improve the satisfaction of medical staff on the use of home nursing mobile APP by using a hybrid multi-standard decision model.

OBJECTIVE: To identify critical satisfaction gaps in a home nursing mobile application (APP) using a systematic decision-making model. METHODS: Initially, the decision-making trial and evaluation laboratory method was used to analyze the relationship structure and corresponding weights among the indicators. The Importance-Performance Analysis (IPA) method was used to identify the categories of all indicators and their corresponding strategic directions. Twenty-six home nursing specialists currently providing home nursing services were recruited for this study. RESULTS: The IPA results revealed that "Assurance," "Reliability," and "Personal security protection" are critical satisfaction gaps. From the influence network and weight results, "information quality" and "system quality" were the critical quality factors in the home nursing mobile APP. The influence of the network relationship structure and weight demonstrated a 98.12% significance level, indicating good stability. CONCLUSION: Continuous improvement in information and system quality is recommended to optimize the overall quality of the home nursing mobile APP. Additionally, user demands should be considered, and personal safety guarantee functions should be developed and integrated into the system to ensure the safety of home nursing workers.

Determining the most appropriate stormwater management strategies within the development of urban landscape infrastructure using the TOPSIS method: applications to Rize city.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Application of multi-criteria decision-making on low-impact development practice selection for the Kinyerezi River sub-catchments in Dar es Salaam, Tanzania.

The impermeable areas in catchments are proportional to peak flows that result in floods in river reaches where the flow-carrying capacity is inadequate. The high rate of urbanization witnessed in the Kinyerezi River catchment in Dar es Salaam city has been noted to contribute to floods and siltation in the Msimbazi River. The Low-Impact Development (LID) practices that includes bio-retention (BR) ponds, rain barrels (RBs), green roofs (GRs), etc. can be utilized to mitigate portion of the surface runoff. This study aims to propose suitable LID practices and their sizes for mitigating runoff floods in the Kinyerezi River catchment using the Multi-Criteria Decision-Making (MCDM) approach. The results indicated that the BR and RBs were ranked high in capturing the surface runoff while the sediment control fences were observed to be the best in reducing sediments flowing into the BR. The proposed BR ponds were greater than 800 m2 with 1.2 m depth while RB sizes for Kinyerezi and Kisungu secondary schools and Kinyerezi and Kifuru primary schools were 2,730; 2,748; 1,385; and 1,020 m3, respectively. The BR ponds and RBs are capable of promoting water-demanding economic activities such as horticulture, gardening, car washing while reducing the school expenses and runoff generation.

Application of multi-criteria group decision-making for water quality management.

As waste discharge into numerous river systems escalates, the pollution of water bodies typically rises. Given the limited capacity of rivers to withstand pollution and their constrained self-cleaning capabilities, treated pollutants from waste discharge must be released into the river. Despite numerous models and algorithms proposed for managing river water quality to meet standards, literature, to our awareness, lacks the utilization of a comprehensive multi-criteria group decision-making approach for water quality management, particularly in river systems. Therefore, this research introduces a new, comprehensive multi-criteria group decision-making for the management of water quality in the Haraz River basin, located in Iran. To do so, the water quality of the basin, a one-dimensional water quality model, QUAL2Kw, was employed to simulate and calibrate the water quality along the river. The simulation results revealed that the downstream water quality violates the water quality standards. To mitigate this issue, various scenarios for waste load allocation (WLA) were evaluated, including no wastewater treatment, primary wastewater treatment, advanced secondary wastewater treatment utilizing the activated sludge (AS) method, and advanced wastewater treatment via the membrane bioreactor (MBR) method. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS group decision-making model, it was determined that the optimal solution was the implementation of secondary wastewater treatment utilizing the activated sludge method for the 11 PS of pollution, while still adhering to Iranian water quality standard. In addition, the findings of the present study indicate that the implementation of primary wastewater treatment, advanced secondary wastewater treatment utilizing AS, and advanced wastewater treatment through MBR within the study area led to a significant enhancement in water quality. This enhancement ranged from 35 to 105% across various scenarios when compared to conditions where no actions were taken to the treatment of water.