Retracted: Design and development of an integrated environmental waste management system with a sustainable solution Open Access

As the environment transitions towards environmental sustainability, intelligent cities incorporate cyber technology to facilitate economic expansion, including increased life expectancy and effective resource management (Gupta & Hall 2020). Owing to inadequate and inefficient waste generation process and management, increased waste quantity and control are critical issues for many cities and regions (Zeb et al. 2020). Increased efforts by smart-enabled technology such as the ‘internet-of-things' (IoT), big data, and artificial intelligence (AI) are still progressively focused on creating solutions that address these problems (Jacobsen et al. 2018). This technology will transform the urban environment and foster a circular economy (CE). CE is now generally recognized as a competitive alternative to the prevailing economic paradigm of the carrier (Muthu et al. 2020). Waste management refers to the different recycling management and disposal arrangements. They can be used to discard, destroy, process, recycle, reuse, or control waste. Waste management is the primary objective of reducing unusable materials and preventing potential dangers for health and the environment (Esmaeilian et al. 2018).

A transition to the CE calls for an innovating and productive ecosystem in the supply chain (Maram et al. 2019). The hierarchy of waste management gives priority to waste prevention, and however, the priority is given when waste is created to prepare it for reuse, compost, and afterwards recycling (Batista et al. 2018). At the microeconomic scale, waste management should be based on a standard basis for the inclusive definition of sustainable growth (Nie et al. 2020). One of the paths to this sustainability has been applying smart technology (Taelman et al. 2019; Nie et al. 2020). In current literature, intelligent waste management has been used (Liu et al. 2019).

However, considering its reported potential, the definition received minimal attention and was not explicitly defined (Sai et al. 2019). This research team describes smart waste management as using smart technology to make waste management more efficient, productive, and renewable (Fuss et al. 2018). Intelligent technologies such as IoT, big data analytics, cloud computing, ubiquitous computing networks, and AI are all but are not limited to them (Goyal et al. 2021).

These techniques will help track, gather, isolate, and transfer waste effectively for recycling and proper disposal (Ogudo et al. 2019; Fatimah et al. 2020). Data acquisition and networking systems have also been drawn by curiosity in recent years. They help track the truck's loading status, optimize lorries, and change the collection schedule with adaptive models (Aljerf 2018). Smart waste management schemes are currently in the earliest deployment stage (Mathews et al. 2018). It is undeniably an ever more complicated issue affecting people, product creators, suppliers, and decision-makers and ultimately involves facets of the technology's operationalization in infrastructure and management. Basic managerial functions will have to change as a result of technological advancements. As a result, management will be held to an even higher standard regarding planning, decision making, controlling, and coordinating business activities. The system comprises incinerators, increased biogas, and treating the core cause of the issue by educating the population, among other treatments (Khalaf et al. 2019). But, in contrast to other waste management methods, modern technologies are comparatively recent. Some concerns, for example, data protection, can place limitations on the capacity of intelligent water quality and treatment systems (Bilal et al. 2020).

Recently there have been many studies concentrating on water quality protection and management. It is critical for the policymaker in charge of water policies to evaluate potential water quality. Water quality assessment is an indispensable part of water policy for high-quality water quality management. Water quality deterioration is far-reaching, adversely influencing the environment, health, and infrastructure. Water quality engineering addresses the transport, sources, and treatment of microbiological and chemical contaminants that affect water. Water quality is defined by physical, chemical, and biological features of water that identify its fitness for various uses and protect the health and reliability of aquatic ecosystems. Reclaimed water, or water recycled from human usage, can be a sustainable source of water supply. It is essential to decrease stress on main water resources like groundwater and surface.

In the meantime, many scholars have called for the study of all facets of waste management by research activities: social analysis, natural science, architecture, ecology, economics, and ethics. There has been little discussion of the policy decisions, revenue streams, and management choices that drive or hinder suitable technology use in recent surveys (Thota et al. 2018). The emphasis is now on defining the technologies involved and their applications (Bai et al. 2020). Literature does not provide reports on obstacles to using intelligent waste management technologies. The key purpose of sustainable waste management is to address public health issues, environmental pollution, land uses, resource management, and the socio-economic influences of incorrect waste disposal. The volume of e-waste and hazardous waste has steadily increased (Kumar et al. 2018).

However, a smooth and productive CE transformation worldwide faces several obstacles. An effective means of exploring unique challenges to introducing efficient and sustainable sewage treatment schemes is interesting (Ghaffar et al. 2020). This paper has also been planned and implemented to advise performance and decision-making (Elhoseny et al. 2018). In this framework, the following two study goals were tackled:

• Recognize primary challenges for transforming a safe and green world in intelligent waste management and water quality management.

• To consider the key challenges to successful improvements to physically, financially, and morally sustainable waste management processes and how all participants interact and participate.

The study's task is diverse and needs knowledge in waste management, water quality management logistics, decision-making, public policy, regulation, environmental science, and technology. The topic in practice, including many states, suppliers, customers, technology companies, and waste treatment organizations, is highly complicated (Sekito et al. 2020). Their problem is very complex. A permanent approach involves behavioural transformation and a complete redesign of traditional waste management programs and the prevalent economic paradigm. Proper waste disposal improves the quality of air and water and reduces greenhouse gas emissions. People can also save natural resources, including minerals, water, and timber, by smartly managing their waste. Therefore this results in a reduction, reuse, and recycling. The significant contributions of this study were focused on exploring the organizational obstacles to intelligent waste disposal (Heidari et al. 2019). The established interactions between cause and effect offered a systemic view of the challenges to adopting smart waste management strategies. The results suggest overcoming challenges for all stakeholders, including suppliers of technology resources, consumers, states, inspection organizations, business organizations, and the community. They also outlined the main inputs to strengthen policy mechanisms on waste management. Uncontrolled waste management may result in household waste mixing with health and medical waste. It increases the risk of toxicity or damage to children and adults who use waste. Huge air pollution and increased greenhouse emissions can be caused by indiscriminate waste combustion. The research was contextualized in China, including environmental growth in its domestic plan (Rabbani et al. 2019). However, this study area also concerns several other nations, particularly those with a CE vision that uses smart technology to develop waste management systems.

The remainder of the article has been structured as follows: Section 2 reviews the related research on integrated environmental waste management systems with a sustainable solution. Section 3 describes the Multi-Criteria Generic Evaluation Sustainable Approach (MCGESA). Section 4 provides the results and analysis utilizing the recommended MCGESA model. Conclusion, limitations of the present study, and scope for further improvement have been given in section 5.

An ecologically sustainable solid waste management, economic, and socially appropriate management scheme must be implemented (Mohsenizadeh et al. 2020). The accountable entities remain sensitive to these primary facets of sustainability by increasing solid waste (SW) management services. SW management is a multifaceted subject that includes political, institutional, social, ecological, and economic considerations. The primary objective of pre-incident waste management is to ensure that a community is prepared to manage garbage, debris, and complex and influenced by a homeland security crisis, including minimizing the quantity of waste generated at the beginning. Therefore, it cannot be entirely satisfactory to plan an SW management scheme based only on economic considerations. An analysis of the latest SW management literature shows many published studies in developing emerging economies, such as India and South Africa (del Carmen Munguía-López et al. 2020). The paper has discussed the more relevant studies following our work. The best places for collection stations are explored to optimize real SW management. In the proposed model, implicitly, these stations’ environmental consequences on the local area were included. It indicates that cost-effectiveness was measured based on continuous washing, pesticide monitoring, and air-curtains installation at the centres.

In addition to the cost analyses, (Cremiato et al. 2018) authors built an exhaustive MILP model that considers the economics and four separate environmental mitigation parameters. In developing the reliable waste stream logistic chain's interim phase, authors regarded daily fuel usage as a foundation for calculating the grid's ecological impact. Earth's most significant environmental issues are greenhouse gas emissions (GHG), leading to universal heating and global environmental variation (Cremiato et al. 2018). Therefore, they tested a whole world SW system in a carbon-controlled climate. Evaluating multiple scenarios found that conventional accounting methods are almost challenging to cope with sustainable schemes. Authors recently discussed environmental challenges within the SW management system in Tanzania in (Singh & Basak 2018).

Full application of the structure proposed in reality resulted in a significant decrease of 78 percent and 57.5 percent of waste to landfill and GHG pollution. Any researchers focused on the conception and optimization of the delivery of SW network processing facilities. In this respect, the authors (Hrabec et al. 2018) have implemented an adaptive SW management network with four leadership equivalents to leverage a nonlinear paradigm, including recycling products, incineration, compost, and anaerobic digestion (AD), as well as landfilling. In literature, it should be noted that it is popular to compare these alternative technologies to use decision-making processes grounded on an industrial ecology approach and life cycle assessment (LCA) (Khalaf et al. 2019). The LCA was used, for instance, to classify and quantify all of the energy consumed and the pollution and waste emitted into the atmosphere as a renewable waste management option as a form of sustainability evaluation. A study has been made of the economic and environmental implications of four methods used to turn the organic component of SW into electricity based on the industrial ecological approach.

The authors in Mohammadi et al. 2019 compared multiple production options for SW with their consequences of treating and eluding variables in financial and ecological characteristics as a recent attempt to adopt the industrial environmental perspective. However, these experiments are notable examples since this paper focuses on studies using multi-objective methods. Authors found that approaching the environmental goal relative to the amount of SW handled and the community subjected to risk moves toward an environmental, balanced approach to the proposed model's performance. In SW management, a new paradigm has been recommended to promote policy decisions in which the targets are focused both on costs and pollution minimization (Delfani et al. 2020). The expenditures for waste reduction and waste processing ads are regarded among the elements of the cost function. The most suitable sites for transmission stations should be calculated using the intermediate waste disposal plant and the temporary waste deposit to save recovery costs. Apart from the plant's location, the built model will decide the appropriate waste compressing technologies at each transmission station. The core theory driving condensing technology equipment is that almost 70% of SW maintenance costs are contributed by storage and transport activities (Yadav et al. 2020). Therefore, the benefits of compaction processes go beyond enhanced preservation, and increased SW density will play an essential role in the transfer step of SW administration.

The studies above reveal that none directly attempted to discuss the third dimension of sustainable development. In comparison to the above works, authors have established a multi-target model that can balance three sustainability dimensions: fiscal, ecological, and communal – their model is based on reutilizing and disposal for safe use of SW. Moreover, the percentage of the waste consumed illustrates the work's sustainable development elements (Pramanik et al. 2018). The explanation is that the waste deposited in the sites reduces the processed SW's better recovery and lowers the environmental issues surrounding soil and water. Similarly, SW's reuse has a beneficial impact on society, owing to the work development required for various reuse processes and increased life quality. The societal sustainability viewpoint was also discussed regarding virtual emissions related to SW manufacturing facilities.

Kumar et al. (2021) proposed a novel framework for risk assessment and resilience of critical infrastructure to improve our critical infrastructure's resilience to resist these events today and in the future. Evaluation of the time distribution in all major river basins of Indian meteorological drought and comprehensive risk assessment framework and improvement of critical infrastructure resilience is designed.

Al-Qerem et al. (2020) introduced cloud–fog environments to prevent the use of the upstream communication channel between customers and the cloud server all the time; the conventional competition control protocols can fluctuate. The results show that IoT users benefit from using the proposed mechanism.

Gupta & Quamara (2020) introduced radio frequency identification (RFID) and wireless sensor networks (WSN), imposing specific requirements, including the most prominent safety requirements. The research will investigate some of the protocols appropriate for developing IoT infrastructure and open-source instruments and platforms.

Jha et al. (2021) proposed a novel analysis of COVID 19 in 623 pandemic areas affected by India. Rural, urban, and total (rural and urban) populations have incorporated socio-economic vulnerability. Wind velocity, pressure, relative humidity, and temperature were the dominating climatic factors. The research offers high-risk maps of the COVID 19 district pandemic and is designed to assist decision-makers in identifying climatic and socio-economic factors in increasing the risk.

Stergiou et al. (2018) introduced sustainable cloud computing for big data and IoT to provide more ‘green’ computing and sustainable computing to process fog data processing. In addition, the integration of IoT and cloud computing is an attempt to present the security challenges to provide architectures that relay network security to improve security.

Bansal & Singh (2020) introduced the Efficient Cloud Computing Model to show that the different forms of effective online learning are accountable and lead to many sustainable livelihoods for unemployed young people. This article presents a strategy for an efficient cloud computing system for active online education in sustainable livelihoods for unemployed young people.

Farjoudi et al. (2021) suggested the probabilistic water quality management model utilizing bankruptcy rules for resolving conflicts among the Environmental Protection Agency and polluters in river systems. Thus, a simulation–optimization model involving QUAL2Kw and particle swarm optimization is utilized to optimize the bankruptcy technique's waste load allocation. In the probabilistic model, latin hypercube sampling and Monte Carlo investigate the effect of river flow ambiguity on the optimal resolution.

The competing fiscal, environmental, and economic goals are a central challenge in sustainability. Another difficulty is that such functional issues typically require a high degree of ambiguity regarding the device parameters for SW management. Displaying the personal preferences on criteria as fluorescent sets by policymakers and facilities administrators is the preferred approach to resolve the confusion. Many unsure optimization models in the literature promote SW management with the disorder. Multiple insecurities have been raised regarding fuzzy sets that could affect cost management and hostile environmental effect mitigation. SW produced values, facilities capability, and individual costs parameter as incorrect data in fluid locations or intervals were fed into the mathematical model.

Besides the quantity of waste, there are questions regarding the operating capacity of each facility. Moreover, modern fuzzy variables have been applied in SW and water quality management instead of the conventional ones modelling fuzzy possibility distributions. Authors have suggested a multifactor SW development problem to promote SW administration, which expressed the model's unreliable cost parameters as flow-through variables. To build an interval/stochastic model for local SW management optimization, the authors defined variables that display vast differences such as hetero variables. In contrast, interval numbers were assumed for parameters with small fluctuations. The best way to express the quality of water resources for consumption is the water quality index (WQI). Utilizing the water quality data is useful for modifying the strategies for sustainable development.

This paper suggests MCGESA to improve the quality of the resultant product, support environmental-friendly practices, and reduce the globe's temperature to develop a sustainable solution to the environmental waste system and water quality management. Partial sustainable product analysis is implemented to replace the disposal items with reusable items and maintain recycled eco-friendly products to develop the ecological waste management system.

The study discusses three critical components of an SW management scheme: waste generation centres, geographic dividing centres fitted with compaction technology, and a collection of processing plants and detection centres. This device is seen in Figure 1 as a schematic view. As seen in Figure 1, waste is classified into paper, plastic, glass, biological, and remaining waste after shipment of the gathered SW from housing and viable areas to separate elements. While e-waste is a precious part of the SW, it is prevalent to vend their e-waste to reserved gatherers in economically developed countries. E-waste comprises precious metal elements that can be reclaimed as co-products. The collection, sorting, treatment, and recycling of waste can provide energy and resources if done correctly. Consequently, governmental and private sector organizations must use this enormous economic potential to their advantage. By enhancing water quality and cutting greenhouse gas emissions, sustainable waste management greatly influences the environment. Food waste reduction also helps decrease the significant environmental costs of producing more. Because the mixture can produce high temperatures or pressure, fire or explosion, violent reaction, poisonous dust, fumes, mists, gases, flammable fumes or gases, or flammable gases, incompatible waste is considered hazardous waste. Source waste prevention, animal feeding and feeding and recycling are subcategories that fall under waste reduction. Begin some of these strategies at home, such as reducing waste and recycling.

Therefore, most e-waste was managed by unregulated industries, and the last type of urban waste is just a limited proportion of e-waste. Indeed, for the detailed preparation and control of this portion of waste, we cannot provide reliable statistics on e-waste output volume. After being compacted in separators, there is a range of handling waste components. In other words, various manufacturing methods may be used for compacted waste. Due to the heterogeneity of the waste flow, it is necessary to recognize the consistency between garbage and processing technologies when allocating each type of waste. Every kind of waste can only be processed using such methods by certain restrictions. In other words, waste is inconsistent with some forms of manufacturing technology. For example, reusable SW flow elements, like paper, cannot be composted. The suggested system includes four critical elements: the information collection module (ICM), the recycling methodology (RM), economic and eco-friendly design module (EEDM), and calculation module (CM).

The current technique of identifying WQI, which is in practice, uses a statistical method and is not precise most of the time. Environmental protection and water quality management have become vital public policies worldwide. With the complexity of water quality data sets, more and more governments are worried about their natural resources. Monitoring and evaluation of river water quality have been implemented in several nations, focusing on the chemical, biological, and nutritional elements and the overall aesthetic state of each river segment. Data on water quality is combined with expert knowledge about its significance and weights to produce a WQI for categorization purposes. Defining water quality for specific purposes is full of uncertainty. Environmental trends and river water quality may be effectively communicated using indices. Based on a formal evaluation approach, the Delphi technique was used to suggest the aggregated indices that indicate the combined influence of individual water quality criteria.

Figure 2 demonstrates the dissemination of knowledge between the different components of the suggested MCGESA system. The data modelling module has three main entities: MCGE, Stored records (related to energy, location, carbon emission, and cost), and consumer interface. The RM and EEDM module input is obtained from MCGE and record entities. Manufacturing activities must be evaluated in light of growing sustainability concerns. Because of this, imperative to measure manufacturing's sustainability level to plan for improvements. Material, resource, and energy efficiency must be improved, exposure to toxic substances; stable, rewarding, and meaningful employment opportunities must be provided; better safety and effective maintenance must be implemented, and production planning and scheduling must be improved the production operation level.

The ICM is in charge of monitoring the system information for financial and ecological success validation. The data modelling module has three main entities: MCGE, stored records (related to energy, location, carbon emission, and cost), and consumer interface.

Alongside a range of predefined goods, geometric, and modelling capacities in standard MCGE implementations, numerous leading MCGE products are commonly used by users to build sections through predefined attributes. The device proposed user-defined parameters to store the strength and maximum volume of the different building components’ standard aggregates. These properties can affect the status of CR products significantly. It should also take note of the recycling procedures required to produce the desired quality. In this step, the number of diverse recyclable SW groups is measured using quantitative deviations of the MCGE. Insets of different SW classes will be defined when evaluating several parent SW severity differences and the maximal RA scale.

This database includes information from the PEC unit involved in different recycling activities, such as compressing, transportation, mixing, etc. However, the PEC database also provides information on the latest SW recycling (SWR) and the PEC for various transport types. This knowledge is necessary if practical conservation techniques are tested efficiently and socially by suppliers of machinery collected from current assets. It should be remembered that PEC data will differ significantly across regions and manufacturers, and thus, the PEC dataset must be developed through the compilation of the useable data stores. One of the conflicting demands for surrounding natural space is the carbon footprint; hence it is an important part of the ecological footprint. Without enough biocapacity to absorb carbon emissions from fossil fuel combustion, these emissions will build in the atmosphere. Due to their role in trapping heat, they play a role in global warming and respiratory problems caused by smog and other air pollutants. Other consequences of human-caused climate change include more frequent and intense wildfires, interruptions to food supplies, and other weather-related catastrophes.

The RM collects data given by the ICM to determine the recycling operations designed to accomplish a specific SWR standard, including the appropriate recycling technology and adhered SW properties.

As depicted in Figure 3, RM starts with minimum standards for various SWR quality standards. The overall SWR quality was appropriate for three unique features: water quality, absorption, volume, and scratch loss. Criteria may be determined by customers’ input or existing standards and recommendations for different SWR attributes. The SW content is compatible with the maximum water absorption allowed, minimum recommended density, and the group-specified maximum allowed scratch loss. For other SW grades and specified SWR classes, the loop is repeated.

The next RM step consists of SWR methods that will deliver SWRs with a minimum material than those necessary to ensure consistency if used on the available SW. Moreover, an experimental assessment should be carried out to filter SWRs provided by various SW recycling ratios. A tile's ability to retain moisture is determined by its ability to absorb water continuously, and cracking in certain types of tile can be caused by moisture infiltration. Water absorption, volume, and scratch loss are more likely to occur if the tile has a low water absorption. The ability of an aggregate to absorb water indicates its strength. Water-absorbent aggregates are highly porous and are generally considered undesirable until they pass strength, impact, and hardness testing and are deemed acceptable.

is the total energy consumption during the process of SWR. n is the total number of recycling processes. denotes the equipment identity involved in SWR. is the working energy of the equipment i to produce SWR. is the assumed reduction in power of the equipment to produce SWR.

is the total cost incurred in the process of SWR. is the cost of the equipment i. assumed reduction in the value of the equipment to produce SWR. The total price includes the fee from various incinerators’ components, burning of SW, transportation to landfilling locations, and the cost incurred for the recycling process. Assumed reduction in carbon emission of the equipment to produce SWR. The assumed carbon reduction was decided by the government or enterprise authority, a typical value. If ⁠, then the EEDM and the overall operation of the MCGESA system would be considered successful. For optimal performance of the proposed MCGESA system, there should be a trade-off between and ⁠. The proposed method will provide optimum results if the carbon emission has been reduced at a comparatively reduced cost. Hence, this proposed system's main objective is to reduce carbon emissions at a low price. Recycling programme effectiveness can be evaluated using a multi-criteria evaluation and selection technique when uncertain about the programme’s performance. Accordingly, decision makers’ subjective and imprecise judgments of evaluation criteria and specific e-waste recycling programmes are effectively represented by intuitionistic EEDM numbers, which are employed to represent the subjective and imprecise evaluations of the decision-makers adequately.

Figure 4 shows the hierarchy structure of water quality and supply management. It must be noted that the assessment criteria, at every level, are aggregated to make evaluations for their greater level criterion. For example, aesthetic and emission rate considerations are first aggregated to assess environmental influences. This process is repeated until all the evaluation criteria are aggregated to make overall evaluations for all alternatives. The second part is modelling the chosen evaluation criteria using the evidential reasoning distributed framework. To conduct a water management evaluation, the influences of the chosen criteria can be estimated utilizing the grades.

The recycling combinations for the proposed MCGESA system to achieve sustainability are shown in Table 1. For each fusion method from R1 to R6, specific processes like split, transfer, sieve, and transport remain common. R1 with single recycling yields low-quality SWR. R2 with double recycling yields medium quality SWR. R3, R4, R5, and R6 with landfill impact, heating, and anaerobic absorption yield high-quality SWR. The energy required to build the equipment is far less than the amount necessary to run it. The heaters and the motors were mostly powered by electric energy. As a result of these findings, it can be concluded that heat insulation and motor efficiency are more important factors in reducing energy consumption than equipment size reduction.

The past sections’ outcomes have provided decision-makers with cost-effectiveness and environmental performance measures for various sustainable SWR practices, conventional recycling, and waste disposal approaches. However, trade-offs will probably dominate a specific option between fiscal, environmental, and social requirements. Considering other eco-related problems, such as the effects of the Initiative on resource depletion and social indicators conditions, it can be more challenging to choose the most successful end-of-life solution. To sum it up, designing and developing the integrated waste management system were humbling, and understanding clients instead of theoretical beliefs was a breakthrough in thinking. Even though human-made systems will never be perfect, avoiding project failures does not need faultless design. The lesson gained here is that successful initiatives build solutions focused on the needs of the consumers. Moreover, decision-makers priorities play an essential role in selecting the right EoL waste management approach. If, for example, local natural resources are lacking, an alternative supply of SWR aggregates may be deemed necessary. Every organization relies on decision-making as a means of improving its overall performance. To make an effective decision, you must make one that has the desired outcome. The ability to make judgments swiftly and consistently is a requirement for those in positions of decision-making authority. In this research, a water quality index value has been found to express the sorting of the river to make SWM, water quality evaluation more understandable, particularly in public consideration and sustainable development.

The proposed model's implementation is seen in this section by real statistics with the Tehran SW administration scheme and WQI. In 2016, the gross daily waste production in this metropolitan area with about 9.1 million was around 6,198 tonnes, according to the Waste Management Agency records. Most of this number refers to municipal SW, including residential and commercial waste produced in 18 metropolitan cities. The city government's primary duties are waste recycling and its distribution to the transfer stations and locations. Depositing is an ordinary way of treating SW. At present, more than 55% of garbage is discarded immediately in waste sites without isolation. More than 62 million tonnes of solid garbage are generated each year, with over 5.6 million tonnes of plastic waste, 0.17 million tonnes of biological waste, hazardous waste generation at 7.90 million tonnes yearly, and e-waste totalling 1.5 million tonnes. About 70–75% of municipal solid garbage is still unprocessed, making it one of the most polluted countries in the world. The majority of these 31 million tonnes of unprocessed trash is deposited in landfills. It must be remembered that specific areas are allocated to waste management and destruction while much SW is dumped in the other centres in the north and west of the town. Besides, twelve active transfer stations are included in the SW management scheme. These stations currently only gather and send the garbage to the dumping grounds. In other terms, the locations do not carry out any manufacturing activities. The primary objective of this research is to quantify the impacts of climate change on the ecological status of the basin and evaluate the effectiveness of alternative management options because of restoring or improving the water quality status of the basin. The secondary objective is to build a process-based hydrologic and water quality model: the Soil and Water Assessment Tool (SWAT) for this cold climate region watershed.

Figure 5 shows the total waste collected from various cities regarding low, best and high approximation levels. All areas covered and the volumes of waste collected by these stations are given in Figure 4. It should have been remembered that the isolation of the source from solid waste and scavenging operations are essential to dipping the useful modules of waste such as paper, plastics, and crystals that have been extended at the distributions. The quantity of garbage transported to the transmission stations mentioned in the figure varies from the maximum volume of waste produced. To limit climate change, lowering greenhouse gas emissions also reduces air pollutants, such as fine particulate matter, emitted from the same sources (PM2.5). Air quality is improved, and human health is improved by reducing these co-emitted air pollutants.

Awareness of the physical structure of SW is essential for optimum and sustainable usage of SW. The proportional share of each form of waste transmitted to the sites is shown in Figure 5. Apart from the physical form dependent on weight, the third dimension of Figure 6 is the volume-based waste composition. Each portion's volume value can be attained by splitting the value of the average undrained density by the weight percentage. As per Figure 5, SW comprises three different materials in the city, including organic, paper, and plastic, if weight (79.3 percent) or volume analysis is performed (80.88 percent). With this in mind, since SW incorporates several useful elements that could be used in industrial applications as raw materials or energy supplies, current SW system facilities are insufficiently suited, and upgrading the system by more effective installations is necessary. In this respect, scientists find that modern waste management plants at the existing transmission stations increase the grid's efficiency to achieve sustainable objectives. Possible locations for fresh waste disposal plants, with incineration, composting, and anaerobic decomposing, are picked from current transmission stations.

Figure 7 presents the total price paid (in $) for various operations used in different SWR methods of the proposed MCGESA system to achieve sustainability. It can be observed the recycling cost has been incurred for all the SWR methods from R1 to R6. The landfill and anaerobic absorption processes pertain only to R3, R4, R5, and R6. The total cost and energy consumption incurred for these operations are R5 and R6 methods ($402). These methods involve double recycling with incineration, landfill, and anaerobic absorption processes.

By employing the proposed system, it has been observed that only six SWR elucidations have a non-control representative. Figure 8 lists the candidate SWR combinations for multi-criteria generic evaluation. Finally, we must decide the most common SWR combination of all candidate SWR combinations. The six substitutes are to be compared with three characteristics: EEDM, ecological, and social. MCGESA is a standard assessment multi-criteria method that can be used for this intent, as explained earlier. According to the results collected, SWR combination number 4 is listed at position one in sustainability. With full EEDM and social advantages, R6 obtained positive results but was impaired by efficiency damage due to low environmental impact.

Figure 9 shows the combined revenue (based on R1 and recycling input) in different SWR methods for the proposed MCGESA system. It can be observed that the recycling cost has been incurred for all the SWR methods from R1 to R6. R1 with single recycling yield low-quality SWR. R2 with double recycling yields medium-quality SWR. R3, R4, R5, and R6 with incineration, landfill, and anaerobic absorption process yield high-quality SWR. R4 and R6 gave the best returns in both R1 and recycling input. Solid waste management's main issues include non-scientific treatment, incorrect waste collection, and ethical problems, resulting in risks such as environmental degradation, water pollution, soil pollution, and air.

Figure 10 shows the water quality prediction ratio. Four designated water quality variables are utilized in this research, i.e., exact conductance, chlorophyll, turbidity, and dissolved oxygen. This study aims to develop effective models to forecast water quality values based on their current values. The information utilized in this research comes in the category of continuous-time series, as it contains the values of water quality factors perceived with the time intermission of 6 minutes.

An MCGESA has been proposed to improve the resulting product quality, support environmentally friendly practices, and reduce the globe's temperature to develop a sustainable solution to environmental waste and water quality management system. An MCGESA method for assessing the environmental impact of manufacturing operations using multiple criteria. Many renewable energy technologies and energy plans undergo multi-criteria analyses (MCGESA) to evaluate and compare their long-term viability to aid in selecting the most environmentally friendly and cost-effective solutions. Multi-dimensional and complicated sustainability assessments make MCGESAs attractive because of the competing criteria and varied forms of information commonly used in these evaluations. Collection, monitoring, control, and disposal are various activities. The municipal government often provides waste collection services at no cost. Different processes, for example, by compacting and incinerating the sites, eliminate the waste.

SW is mostly incinerated to lower its volume by 80–95% and transform it into gas, steam, ash, and heat. However, the disposal of wastes by utilizing incineration is a concern for air pollution. Partial sustainable product analysis is implemented to replace the disposal items with reusable items and maintain recycled eco-friendly products to create the environmental waste management system. Municipal life cycle assessment is integrated with MCGESA to strengthen uniformity in waste management practices that help to develop integrated environmental waste management systems with a sustainable solution. Sustainable development involves mainly people, welfare, and equity in relations with one another, in a context where imbalances in nature's society can put economic and social stability at risk. The type of waste to be separated, as well as the treatment and disposal methods that are most appropriate, must guide the process. Composition, recycling, and waste combustion are the most straightforward processes for waste implementation. As a community, we must take responsibility for waste management and recycling. It can be predicted that the multivariate data visualization method can boost the research and design of WQI, which helps sustainable water quality management.

The total amount of waste collected from various cities regarding low, best, and high approximation levels has been reduced up to 70%. Further, the incineration process was used in the landfill location with 50% weight percentage and uncompacted density for different SW types. The total cost and energy consumption incurred for these operations are R5 and R6 methods ($402). In addition, SWR combinations for MCGE to achieve 85% sustainability in the proposed MCGESA method.

This section of the proposed model is seen by a real data set with the Tehran SW management system a. It can be observed the recycling cost has been incurred for all the SWR methods from R1 to R6. The landfill and anaerobic absorption processes pertain only to R3, R4, R5, and R6. The total cost and energy consumption incurred for these operations are R5 and R6 methods ($402). These methods involve double recycling with incineration, landfill, and anaerobic absorption processes.

This work is supported by the doctoral scientific research initial funding project of Baoji University of Arts and Sciences (ZK2018062). Key Research and Development Program in Shaanxi Province(2020GY-078).

This article does not contain any studies with human participants or animals performed by any of the authors.

Conception and design of study: Xinman Wang

Acquisition of data: Xinman Wang

Analysis and/or interpretation of data: Xinman Wang

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.