Waste Management & Research最新文献

As the Internet of things (IoT) continues to transform modern technologies, innovative applications in waste management and air pollution monitoring are becoming critical for sustainable development. In this manuscript, a novel smart waste management (SWM) and air pollution forecasting (APF) system is proposed by leveraging IoT sensors and the fully Elman neural network (FENN) model, termed as SWM-APF-IoT-FENN. The system integrates real-time data from waste and air quality sensors including weight, trash level, odour and carbon monoxide (CO) that are collected from smart bins connected to a Google Cloud Server. Here, the MaxAbsScaler is employed for data normalization, ensuring consistent feature representation. Subsequently, the atmospheric contaminants surrounding the waste receptacles were observed using a FENN model. This model is utilized to predict the atmospheric concentration of CO and categorize the bin status as filled, half-filled and unfilled. Moreover, the weight parameter of the FENN model is tuned using the secretary bird optimization algorithm for better prediction results. The implementation of the proposed methodology is done in Python tool, and the performance metrics are analysed. Experimental results demonstrate significant improvements in performance, achieving 15.65%, 18.45% and 21.09% higher accuracy, 18.14%, 20.14% and 24.01% higher F-Measure, 23.64%, 24.29% and 29.34% higher False Acceptance Rate (FAR), 25.00%, 27.09% and 31.74% higher precision, 20.64%, 22.45% and 28.64% higher sensitivity, 26.04%, 28.65% and 32.74% higher specificity, 9.45%, 7.38% and 4.05% reduced computational time than the conventional approaches such as Elman neural network, recurrent artificial neural network and long short-term memory with gated recurrent unit, respectively. Thus, the proposed method offers a streamlined, efficient framework for real-time waste management and pollution forecasting, addressing critical environmental challenges.

Landfill leachate characteristics vary depending on the type of waste facilities accept, such as municipal solid waste (MSW), construction and demolition debris (CDD) and MSW incineration (MSWI) ash. Optimizing disposal and treatment practices requires a thorough understanding of the behaviour of leachates from different classifications of refuse. This study provides a critical analysis of variation in leachate quality among over 80 sites based on landfill category: MSW, bulky debris, MSWI ash and MSW-MSWI ash co-disposal. Alkalinity was highest in leachates from facilities accepting MSW (average 2,810 mg L^-1), and the average pH from sites disposing of only ash (7.04) was lower than anticipated. As expected, all leachates were observed to have much greater concentrations of chemical oxygen demand compared to biochemical oxygen demand and require advanced secondary treatment to remove this recalcitrant organic matter. Unsurprisingly, leachates from facilities accepting only ash had elevated concentrations of salts (32,400 mg L^-1 TDS), and those from MSW disposing sites reported high ammonia-nitrogen (381 mg L^-1); co-disposal of MSW with ash resulted in elevated concentrations of both TDS and ammonia-nitrogen (19,400 mg L^-1 TDS, 543 mg L^-1 NH₃-N). Metal concentrations among all leachate types were similar, though arsenic was elevated in landfills accepting only CDD. Trace organic chemicals like benzene were much higher in leachates from sites disposing of unburned residuals compared to those only accepting ash. Variation among landfill types were attributed to leachate flow characteristics, pH, degradation, waste composition and other biogeochemical interactions. The results demonstrate co-disposal practices can potentially require more leachate treatment than separate disposal scenarios.

This research determines the potential impact of reducing food waste on future energy consumption and pollutant emissions. The study uses system dynamics modelling to simulate the complex link between population, food demand, food waste output and their interactions with energy consumption in the food system and carbon dioxide (CO₂) emissions. Scenarios are developed by considering two elements: a reduction in food waste and an increase in energy output. Based on a case study of Delhi, food demand and energy consumption are expected to rise by 6% and 35% every year, respectively, from 2023 to 2033. The model predicts that a 20% reduction in food waste, combined with a 20% increase in energy efficiency, could reduce CO₂ emissions by 23.17% by 2033. The combination scenario proved to be the most efficient in reducing carbon emissions and energy consumption. This significant reduction in emissions highlights the potential of integrated food waste and energy management strategies in mitigating environmental impact.

Effective management of healthcare waste (HCW) imposes a great challenge to all countries. Specially in the developing countries, it is often mixed with municipal waste, adversely affecting the health and safety of the medical personnel, general public and environment. Healthcare waste management (HCWM) basically deals with segregation, collection and storage, routing and transportation, treatment and safe disposal of HCW, while obeying some national legislation. In every stage of HCWM, there are several alternative choices/strategies to be evaluated against a set of conflicting criteria. Numerous multi-criteria decision-making (MCDM) methods have appeared to resolve the issue. This article reviews 101 articles available in Scopus and other scholarly databases on applications of MCDM techniques in solving HCWM problems. Those articles are classified into six groups: (a) selection of the most effective HCW treatment technology, (b) identification of the best HCW disposal site, (c) assessment of the best-performing healthcare unit adopting ideal HCWM strategies, (d) selection of third party logistics providers, (e) identification of HCWM barriers and (f) evaluation of specific HCWM plans. It is observed that the past researchers have mostly preferred to apply MCDM tools for solving HCW treatment technology selection problems, whereas analytic hierarchy process, decision-making trial and evaluation laboratory and best-worst method and fuzzy set theory have been the mostly favoured MCDM tool, criteria weight measurement techniques and uncertainty model, respectively. The outcomes of this article would help the healthcare personnel/policymakers in unveiling the current status of HCWM research, exploring extant research gaps and challenges and providing future directions leading to sustainable environment.

The oceans are facing global and irreversible pollution from microplastics, and China is not immune. In this mini-review, information on microplastics in four coastal waters of China and the natural and social environment of key basins were compiled. The results showed that microplastics were ubiquitous in the coastal waters, and the abundance and spatial distribution of microplastics varied significantly under different sampling methods. For trawl samples, microplastic abundance ranged from 0.045 to 1170.8 items m^-3, among which the coastal waters of the East China Sea were the most polluted. For filtered samples, microplastic abundance ranged from 46 to 63,600 items m^-3, and the coastal waters of the Yellow Sea were the most polluted. Meanwhile, human activities in basin were the key factors affecting microplastic pollution in coastal waters. The main terrestrial source in the coastal waters of the South China Sea was express packaging loss, whereas the main source in the other coastal waters was tyres and road markings wear from vehicle trip. The decoupling results of analytic hierarchy process showed that there was spatial heterogeneity in the impact of socio-economic and natural environmental factors in the basin on the distribution of microplastics in coastal waters. Among the five major basins, the impact weights of the latter were 20.00%, 83.34%, 66.66%, 50.00% and 25.00%, respectively. This study provides the first perspective of land-sea linkage to summarize the characteristics, sources and influencing factors of microplastics in Chinese coastal waters, providing ideas for reducing marine microplastic pollution from the source.

Microplastics are characterized by strong hydrophobicity, large specific surface area. In addition to the pollutant they contain, the heavy metals adsorbed on the surface of microplastics can migrate or be transformed with them into the environmental medium, which is potentially harmful to humans. The distribution characteristics of microplastics in contaminated soil at the e-waste dismantling site were studied. The study investigated the adsorption characteristics of polyvinyl chloride (PVC), polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) on copper (Cu), zinc (Zn) and lead (Pb). It analysed the influence of various factors on the adsorption process of heavy metals, the adsorption law of microplastics on some of the heavy metals in the environment, and the risk of heavy metal release from microplastics to soil. The results showed that ABS and PP were the main microplastics in the contaminated soil. Among them, black, white and transparent microplastics accounted for 89.91%. The shape of microplastics is mainly granular, and microplastics with a particle size of 1-2 mm accounted for the largest proportion. Further studies showed that plastic particles made of ABS, PP and PVC also have the adsorption capacity for different types of heavy metals in soil, and the trends of adsorption capacity are: PP>PVC>ABS. When PP does not reach adsorption equilibrium in the adsorption process, the smaller the particle size and the more added amount, the greater the adsorption capacity. This is because the smaller the particle size of the microplastic is, the more adsorption points it can provide, increasing its ability to adsorb heavy metal ions.

Phosphorus (P) is a key component in agricultural fertilizers, but it is also a scarce resource, why its recycling has been thoroughly investigated and one promising resources is sewage sludge. Because of stricter regulations in terms of sludge disposal, thermal treatment (e.g. incineration) has become an attractive option. The incineration process alters the chemical speciation of P in favour to calcium-associated (apatite, apatite phosphorus (AP)) species, which is preferred for P recovery. In order to achieve qualitatively transformation, it is important to identify limiting or promoting factors. This study reports on the impact of iron, aluminium and calcium on the transformation of iron- and aluminium-phosphate (NAIP) to AP species, assessed by studying sludge and ash from 10 municipal wastewater treatment plants in Sweden. The effect of iron and aluminium added in the treatment processes was also evaluated. The obtained results show that high calcium concentration favours formation of AP species in both sludge and ashes, whereas high concentration of iron and aluminium favours formation of NAIP species in the sludge. The transformation from NAIP to AP species is hampered by aluminium, irrespectively of its origin, whereas no such correlations could be seen for iron. Therefore, in order to enable efficient P recovery from sewage sludge ash, the amount of aluminium added in the treatment process, as well as its concentration in influent streams to the treatment plants, must be limited.

Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapid and contactless classification of metals in electronic waste (e-waste) management. In the present research work, five types of aluminium alloys, because of their extensive use in structural, electrical and thermotechnical functions in the electronics industry, were taken. Laser-induced breakdown spectroscopy (LIBS), a spectral identifier technique, was employed in conjunction with machine learning (ML) classification models of AI. Principal component analysis (PCA), an unsupervised ML classifier, was found incapable to differentiate LIBS data of alloys. Supervised ML classifier was then trained (for 10-fold cross-validation) on randomly selected 80% and tested on 20% spectral data of each alloy to assess classification capacity of each. In most of the tested variants of K nearest neighbour (kNN) the resulting accuracy was lower than 30% but kNN ensembled with random subspace method showed improved accuracy up to 98%. This study revealed that an AI-based LIBS system can classify e-waste alloys rather effectively in a non-contactless mode and could potentially be connected with robotic systems, hence, minimizing manual labour.

Biomethane production by anaerobic digestion (AD) of sludge from municipal wastewater treatment is a viable practice to valorise the residues of these plants. However, although the relevant literature is abundant, no comprehensive reviews have been recently published on this topic. Detailed information concerning the factors influencing the AD process and values of biomethane production from the sludge from municipal wastewater treatment plants (MWWTPs) on the global scale may support technicians and researchers in both the planning and the design steps of an AD process. This study proposes a systematic review and a meta-analysis of the factors that noticeably influence biomethane yield deriving from AD of sludge from MWWTP. The reported values were systematically analysed compared to the main factors driving AD, including publication year, geographical area of each study, type of digested sludge, treatment in the water line of the MWWTP, possible sludge pre-treatments, type of digestion process, hydraulic retention time (HRT) and temperature regime of the AD process. A higher biomethane production was registered in North American plants compared to countries in other continents. Older studies published between 2001 and 2005 reported lower mean values compared to the more recent experiments. A gradient of 'primary sludge' > 'mixed sludge' > 'wastewater activated sludge' was found for the mean biomethane yield in relation to the digested sludge type. The mean biomethane yields for different types of sludge on a global scale are 0.425, 0.296 and 0.176 Nm³ kg _VS^-1 for primary sludge, mixed sludge and waste activated sludge, respectively. Overall, the study demonstrates: (i) the very large variability of biomethane yields from AD of the residues from MWWTPs (mainly due to the different characteristics of sludge) and (ii) the non-significance of some factors (i.e. treatment in the water line, pre-treatments, type of process, HRT and temperature regime) on energy yields from the AD process.

An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.