Waste Management & Research最新文献

Due to global population growth and living standards improvements, textile production and consumption are increased. Textile solid waste has become challenging issue for waste management authority. It is reported that textile materials are discarded daily, representing approximately 1.5% of the generated waste around the world. Over the past few decades, special attention has been given to the used clothes in all regions globally, which can reduce energy costs by 80% and also represent a source of raw materials economically profitable and environmentally responsible. This review article attempted to address different topics including: source of solid textile waste, environmental impact of textile waste as a result of massive consumption of clothing, textile waste management processes such as recycling, reuse of textile waste, landfill and incineration and energy recovery from textile waste. Narrative review with collection of recent quantitative information was carried to reflect the status of textile solid waste. In this article, the possibilities of bio-ethanol production from textile waste as valuable cellulosic raw material are investigated and presented. Results show that developing countries lack of systematic waste management. On another side of the globe, some countries are trying to recover energy these days by incineration. The heat and power that recovered from this process can be used instead of other energy sources. Throughout the incineration process, flue gases (CO₂, H₂O, O₂, N₂) are generated so it should be properly designed to avoid pollution. During energy recovery, different pre-treatment methods and different enzymatic hydrolysis parameters are recommended to be implied for better results.

Due to increased urbanization, the development of new areas, construction of new houses and buildings and uncontrolled dumpsites (UDSs) are becoming a challenge facing local authorities in Saudi Arabia. UDSs pose health risks to the public, potentially deteriorating the environment around them and reducing the value of ongoing development areas. The local municipalities rely on field surveys and citizen reports. This can be inefficient because UDSs are often discovered too late, and remediating them can be costly. This study aimed to assess the conditions of UDSs in two cities in the Eastern Province of Saudi Arabia, Dammam and Hafer Al-Batin, using satellite image classification assessment techniques. The assessment included mapping the UDS locations and studying the spectral reflectance of the materials found in these dumpsites. The study provided a mapping of 62 UDS locations totalling around 13.01 km² in the broader study area. UDS detections using remote sensing were followed by ground truthing and in situ measurements using a spectroradiometer. In addition, the spectral reflectance of 21 commonly deposited UDS materials was studied, and a spectral library was created for these materials for future use by local authorities.

Due to increasing consumption and urbanisation, urban waste management and recycling are a primary concern in Italy. Italian waste collection underwent significant reform with the introduction of a sorted collection target of 65% of total collected waste in Legislative Decree No. 152/2006. In this article, we analyse the effect of this regulatory target on the efficiency of waste collection in 275 Italian municipalities in the years 2016-2019. We estimate the coefficients of the cost efficiencies of the sorted and unsorted waste without assuming functional forms for the efficient frontier or the distribution of efficiency. Our findings suggest that municipalities that met the 65% sorted waste target demonstrated higher efficiency as costs increased, whereas those that failed to meet the target demonstrated higher inefficiency as costs increased. Strong effects emerged for population and urban economic development on the success of waste collection, whereas only marginal effects were observed for population density and city size. To improve the situation of municipalities that are not meeting the 65% target, we propose several policy measures, including 'neighbourhood solidarity'.

Mandatory waste classification has been widely considered as an effective solution for reducing the production and treatment amount of municipal solid waste. However, there is limited evidence regarding whether and how waste classification can affect the composition of residual waste (RW) and its environmental economic impacts. Here, an accounting method recommended by the Intergovernmental Panel on Climate Change, field surveys and cost-benefit analysis was utilized to investigate the changes in RW composition, environmental impacts and economic benefits under the waste classification policies implementation in Xiamen, China. This study found that: (1) The implementation of waste classification policies led to a significant increase in recyclable content from 17% to 51% and a decrease in organic content from 56% to 32%. (2) Waste classification effectively reduces greenhouse gas emissions from landfilling and incineration by an additional 0.34 tCO₂-eq t^-1 RW. (3) The introduction of mechanical recycling achieves a saving of 0.47 tCO₂-eq t^-1 RW at 40% recycling efficiency, a 4.5-fold increase compared to business as usual (BAU). (4) The operational benefits (900 yuan t^-1 RW) from the recyclables sorting system offset the total expenses of investment, operation and waste disposal. The study successfully demonstrated that RW source-classified management can optimize the structure of waste composition, reduce environmental emissions and offer detailed guidance for the development of solid waste management systems in other cities in China.

Grape stem is a winery by-product that it is currently disposed as waste or at best as soil conditioner. However, it is rich in fibres and polyphenols which makes it interesting for animal feeding. In this regard, rabbit farming emerges as a target livestock farming since fibre content is essential in rabbit's diets for preventing digestive troubles and polyphenols are associated with improved performances in animals due to their antimicrobial and antioxidant activities. This study aims to assess the suitability of a grape stem-based ingredient for rabbit feeding. The stem was dried using flash drying technology to prevent rapid spoilage and stabilise the ingredient. Then, its nutritional value was evaluated resulting in a high fibre (>40%) and polyphenol (>6%) content ingredient with antioxidant and antimicrobial activity against Staphylococcus aureus. A feed efficiency trial was conducted and inclusion rates of up to 10% of grape stem-based ingredient did not affect animals' mortality, average daily feed intake, daily gain or feed conversion ratio. In conclusion, grape stem-based ingredient arises as a secondary feedstuff for cuniculture reducing the dependence on other fibre sources, such as cereals or sunflower hulls. This could also contribute to reduce the environmental footprint of the wine sector by giving a second life to an existing waste, while generating a new activity based on circular economy.

Continuous emission monitoring system is commonly employed to monitor NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significantly impact the accurate and stable control of NOx emissions. Therefore, developing a soft NOx emission sensor to complement hardware monitoring becomes imperative. Considering data noise, dynamic nonlinearity, time series characteristics and volatility in the MSWI process, this article introduces a soft sensor model for NOx emission prediction utilizing the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN)-wavelet threshold (WT) method and bidirectional long short-term memory (Bi-LSTM). Firstly, the original data signal is decomposed into a group of intrinsic mode functions (IMFs) using the CEEMDAN. Subsequently, the WT processes the high-frequency IMFs that are noise-dominant. Then, all IMFs are reconstructed to obtain the denoized signal. Finally, the Bi-LSTM model is employed to predict NOx emissions. Compared to conventional modelling approaches, the model proposed in this article demonstrates the best predictive performance. The mean absolute percentage error, root-mean-squared error and average absolute error on the test set of the proposed model are 3.75%, 5.34 mg m^-3 and 4.34 mg m^-3, respectively. The proposed model provides a new method to soft sensing NOx emissions. It holds significant practical value for precise and stable monitoring of NOx emissions in MSWI processes and provides a reference for research on modelling key process parameters.

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.