A review on modern and smart technologies for efficient waste disposal and management

Smart waste management 4.0: The transition from a systematic review to an integrated framework

Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research.

Evaluation of a data-driven intelligent waste classification system for scientific management of garbage recycling in a Chinese community

Waste classification management is effective in addressing the increasing waste output and continuous deterioration of environmental conditions. The waste classification behaviour of resident is an important basis for managers to collect and allocate resources. Traditional analysis methods, such as questionnaire, have limitations considering the complexity of individual behaviour. An intelligent waste classification system (IWCS) was applied and studied in a community for 1 year. Time-based data analysis framework was constructed to describe the residents' waste sorting behaviour and evaluate the IWCS. The results showed that residents preferred to use face recognition than other modes of identification. The ratio of waste delivery frequency was 18.34% in the morning and 81.66% in the evening, respectively. The optimal time windows of disposing wastes were from 6:55 to 9:05 in the morning and from 18:05 to 20:55 in the evening which can avoid crowding. The percentage of accuracy of waste disposal increased gradually in a year. The amount of waste disposal was largest on every Sunday. The average accuracy was more than 94% based on monthly data, but the number of participating residents decreased gradually. Therefore, the study demonstrates that IWCS is a potential platform for increasing the accuracy and efficiency of waste disposal and can promote regulations implementation.

Smart textile waste collection system - Dynamic route optimization with IoT

Increasing textile production is associated with an environmental burden which can be decreased with an improved recycling system by digitalization. The collection of textiles is done with so-called curbside bins. Sensor technologies support dynamic-informed decisions during route planning, helping predict waste accumulation in bins, which is often irregular and difficult to predict. Therefore, dynamic route-optimization decreases the costs of textile collection and its environmental load. The existing research on the optimization of waste collection is not based on real-world data and is not carried out in the context of textile waste. The lack of real-world data can be attributed to the limited availability of tools for long-term data collection. Consequently, a system for data collection with flexible, low-cost, and open-source tools is developed. The viability and reliability of such tools are tested in practice to collect real-world data. This research demonstrates how smart bins solution for textile waste collection can be linked to a dynamic route-optimization system to improve overall system performance. The developed Arduino-based low-cost sensors collected actual data in Finnish outdoor conditions for over twelve months. The viability of the smart waste collection system was complemented with a case study evaluating the collection cost of the conventional and dynamic scheme of discarded textiles. The results of this study show how a sensor-enhanced dynamic collection system reduced the cost 7.4% compared with the conventional one. We demonstrate a time efficiency of -7.3% and that a reduction of 10.2% in CO2 emissions is achievable only considering the presented case study.

Artificial intelligence for waste management in smart cities: a review

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

Sustainable development goals for industry, innovation, and infrastructure: demolition waste incorporated with nanoplastic waste enhanced the physicomechanical properties of white cement paste composites

The COVID-19 pandemic significantly impacts the increase in plastic waste from food packaging, masks, gloves, and personal protective equipment (PPE), resulting in an environmental disaster, if collected, processed, transported, or disposed inappropriately. Plastic waste has a very long deterioration time in the environment (soil and water), cheap, and plentiful. Additionally, construction waste disposal is a process that transfers debris to a state that does lead to any sustainable or environmental problems. The core objective of this current research work is to provide safety and efficacy by partial substitution of both ultrafine demolition waste (UDW), incorporated with nanoplastic waste (NPW), for eco-white cement (E-WC) composition. E-WC is designed by partially substituted WC with UDW (1.0, 5.0, 10.0, 15.0, and 20.0 wt.%); incorporated with NPW (1.0 and 3.0 wt.%); to adequately protect people and the environment over long periods. The context examines the high performance, physicomechanical properties and high durability of blends as presences of silica in UDW proposed a hydraulic filler material, plus; high surface area of NPW. The microstructure and workability are characterized by X-Ray Fluorescence (XRF), Scanning Electron Microscope (SEM), and Transmission Electron Microscope (TEM) measurements. The record results show greatly enhanced in the mechanical strength due to the combination of NPW and UDW (active silica). With the presence of NPW and UDW in WC matrix, the highest level of crystallization formed consequently a decrease in whiteness reflection (Ry) and total porosity. In summary, WC blend with NPW and UDW reflects better workability and energy saving qualities, which are economical and environmentally beneficial and may result in decreased construction budget and improve a long-term raw material sustainability.

Effective mitigation of ammonia in sewage-sludge-derived fermentation liquid using flow-electrode capacitive deionization

Due to the high toxicity of ammonia to organisms and its contribution to eutrophication in surface water, the risk of emission of ammonia and other nitrogenous ions to the environment and ecosystems has aroused wide concerns. Therefore, the discharge criterion on nitrogen in effluent from conventional wastewater treatment plants (WWTP) is very stringent. Furthermore, during the conventional denitrification processes, the relatively costly external carbon source is usually required. Nowadays production of volatile fatty acids (VFAs) from sewage sludge by alkaline anaerobic fermentation has regarded as an attractive carbon source. However, usually ammonia is quite abundant in the fermentation liquid and thus effective mitigation of ammonia in the fermentation liquid is also a significant step for its further utilization. In the present study, the flow electrode capacitive deionization (FCDI) was applied to remove ammonia in the fermentation liquid of sewage sludge. Firstly, response surface method (RSM) was employed to optimize parameters and then the performance of the FCDI in ammonia removal were examined. Results showed that optimal flow rates, carbon content and ammonia concentration were 8.0 mL min^-1, 4.0 wt% and 110 mg N·L^-1 and the ammonia removal efficiency (ARE) reached 42.7%, while treating the alkaline fermentation liquid. Seemingly the presence of Na⁺ and polypeptides in the liquid with their average RE of 53.3% and 11.1% substantially compromised ammonia removal probably due to the competition of adsorption sites. This present study serves as a proven concept for the feasibility of the application of the FCDI system in ammonia separation from the VFAs, which could realize economic and ecological benefits.

Design and development of smart Internet of Things-based solid waste management system using computer vision

Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision-based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet V2 models trained through transfer learning for image classification. ResNet V2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The proposed ResNet-based CNN performs waste classification with 19.08% higher accuracy and 34.97% lower loss than the performance metrics of other existing models.

Characterization of contaminant leaching from asphalt pavements: A critical review of measurement methods, reclaimed asphalt pavement, porous asphalt, and waste-modified asphalt mixtures

In recent years, the pavement industry has been seeking sustainable development through recycling reclaimed asphalt pavement and reusing other waste materials as replacements for asphalt mixture constituents. Incorporating waste material into asphalt mixture and the presence of pollutants such as exhaust fumes and gasoline due to vehicle traffic may lead to contaminants leaching from asphalt pavements to underlying soil layers and groundwater aquifers, posing serious risks to ecosystems and the environment. To cast light on contaminant leaching from asphalt pavements, this article presents a comprehensive review of the literature that is divided into four research areas: evaluation of leaching measurement methods, leaching from recycled asphalt materials, leaching characteristics of porous asphalt pavements, and waste-modified asphalt mixtures. Moreover, a critical discussion of bibliometric data, literature content and knowledge gaps in this domain is provided to help highway agencies and environmental scientists address contaminant leaching from asphalt pavements. Finally, some potential research directions are suggested for future research works.

Anaerobic co-digestion of paper sludge: Feasibility of additional methane generation in mechanical-biological treatment plants

In this work, the feasibility of the anaerobic digestion of paper sludge as a co-substrate in anaerobic digestion mechanical-biological treatment (MBT) plants is investigated. In the first phase, the biochemical properties, biomethane potential (BMP), and pollutant contents of 20 different industrial paper sludges are determined. Following the general evaluation in the BMP tests, the second phase of the project involves the semi-continuous co-digestion of six paper sludges in continuous stirred reactors (CSTR). Paper sludges are categorized according to their origin within the pulp and paper mills: Deinking Sludge (DS), Primary Sludge (PS) and Biological Sludge (BS). The analysis of potentially inhibiting elements shows that the concentrations of chlororganic compounds, mineral oil and some heavy metals are highest in DS, while the mean heavy metal loads in all paper sludges are relatively low compared to other industrial sludges. Large differences in total solids (TS) and volatile solids (VS) contents are observed among the different paper sludges investigated, with DS having the highest TS due to the high inorganic contents. The BMP of the investigated sludges ranges from 90 to 355 NL CH₄ kg^-1 VS. In subsequent semi-continuous co-digestion experiments simulating MBT conditions, three DS and two fiber sludges (a mixture of PS and BS) show good methane generation rates, while one fiber sludge causes inhibition and indicates an increase in viscosity. In general, co-digestion of paper sludge in anaerobic digestion MBT plants can be a viable option for energy production and also facilitates a safe disposal of the paper sludge digestates.

Mixed Contaminants: Occurrence, Interactions, Toxicity, Detection, and Remediation

The ever-increasing rate of pollution has attracted considerable interest in research. Several anthropogenic activities have diminished soil, air, and water quality and have led to complex chemical pollutants. This review aims to provide a clear idea about the latest and most prevalent pollutants such as heavy metals, PAHs, pesticides, hydrocarbons, and pharmaceuticals—their occurrence in various complex mixtures and how several environmental factors influence their interaction. The mechanism adopted by these contaminants to form the complex mixtures leading to the rise of a new class of contaminants, and thus resulting in severe threats to human health and the environment, has also been exhibited. Additionally, this review provides an in-depth idea of various in vivo, in vitro, and trending biomarkers used for risk assessment and identifies the occurrence of mixed contaminants even at very minute concentrations. Much importance has been given to remediation technologies to understand our current position in handling these contaminants and how the technologies can be improved. This paper aims to create awareness among readers about the most ubiquitous contaminants and how simple ways can be adopted to tackle the same.

Environmental Hazards of an Unrecultivated Liquid Waste Disposal Site on Soil and Groundwater

Disposal sites without adequate engineering controls pose a significant risk to the environment. In the present study, the environmental hazards of an abandoned and unrecultivated liquid waste disposal are investigated with a special focus on soil and shallow groundwater contamination. After a period of operation from 1994 to 2010, when the wastewater collection of the municipality was regulated, the disposal site was subsequently decommissioned without further action. Eight monitoring wells have been established in the disposal basins and in the surrounding area to determine the contamination of the site. Sampling took place in the summers of 2020 and 2021. The results of the analysis of the soil and water samples collected showed a high level of contamination in the area. In the borehole profile of the infiltration basin, a well-developed leachate nitrate profile was observed, with a concentration above 3000 mg/kg NO3−. The soil phosphate content was also significant, with a value of over 1900 mg/kg in the upper 40 cm layer. Extremely high concentrations of ammonium (>45 mg/L) and organic matter (>90 mg/L) were detected in the groundwater of the basins, indicating that contaminated soil remains a major source of pollutants more than 10 years after closure. For all micro- and macroelements present in detectable concentrations, a significant increase was observed in the infiltration basin. Our results have revealed that the surroundings are also heavily contaminated. NO3− concentrations above the contamination limit were measured outside the basins. Recultivation of liquid waste disposal sites of similar characteristics is therefore strongly recommended.