查看更多>>摘要：China consumes 28% of the world's N fertilizer and produces 17% of global wheat, and is currently selfsufficient. At issue is whether it will remain self-sufficient with the increases in wheat demand and the decreases in fertilizer N usage and harvested area. Here we quantified the detailed benefits of N management technique for wheat production and predicted the potential of China's wheat production in 2030 based on largescale farmer surveys and N management technique. Results showed that applying individual N management techniques increased wheat yield by 3-11%, reduced greenhouse gas (GHG) emissions by 1-39%, and increased net ecosystem economic budget (NEEB) by 1-17%. Applying comprehensive N management techniques (combining optimized N rate with agronomic measures) showed better benefits. The findings of large-scale farmer survey showed that the national average (range at the county scale) wheat yield and N application rate was 5.7 (1.7 to 8.2) t ha(-1) and 210 (32 to 398) kg N ha(-1), respectively. The huge variations provided an opportunity to further improve the national wheat production. Scenario analysis indicated that applying comprehensive N management techniques increased wheat production by 7% (9 Mt), reduced GHG emissions by 17% (15 Mt CO2 eq.), and increased NEEB by 9% (US$ 3 Billion). Such improvements are critical for China's response to the multiple challenges from food demand, environmental protection, and farmers' livelihoods. These insights on N fertilizer management and sustainable wheat production have important implications for countries and regions facing the dilemma of N management.
查看更多>>摘要：Due to the intensive use of fertilisers, soil degradation has become a global problem, leading to the depletion of organic matter and soil fertility. Meanwhile, the intensification of agriculture accompanied by urbanisation and industrialisation has drastically accelerated the waste generation rate. For instance, coal mining produces wastes in a large quantity globally, the majority of which end up in landfills or dump into storage dams. Accordingly, sustainable food production is driving global innovations to better utilise various waste materials to make valueadded products, such as soil conditioners. Nowadays, soil conditioners are of great importance to improve plant growth and soil health and reduce chemical fertiliser use. This paper comprehensively reviews the soil conditioners derived from various agro-wastes and coal by-products. The process of producing soil conditioners and their sustainable applications in agriculture are also reviewed. Furthermore, sustainable approaches to recycle coal wastes are gaining increasing interest, and co-pelletisation of coal waste with agro-waste as a value-added soil conditioner to supplement soil nutrients in the agro-ecosystem has been proposed to improve the productivity of lands towards sustainable agricultural applications. This review highlights the possibility of turning coal wastes and organic wastes into revenue-earning products of environmental and economic values in the form of pellets for soil conditioning. But a multidisciplinary approach should be adopted to utilise the natural resources eco-friendly and cost-effectively, contributing to the United Nations Sustainable Development Goals.
查看更多>>摘要：With significant efforts made to consider water reuse in cities, a robust and replicable framework is needed to quantify the degree of urban water circularity and its impacts from a systems perspective. A quantitative urban water circularity framework can benchmark the progress and compare the impacts of water circularity policies across cities. In that pursuit, we bring together concepts of resource circularity and material flow analysis (MFA) to develop a demand-and discharge-driven water circularity assessment framework for cities. The framework integrates anthropogenic water flow data based on the water demand in an urban system and treated wastewater discharge for primary water demand substitution. Leveraging the water mass balance, we apply the framework in evaluating the state of water circularity in Singapore from 2015 to 2019. Overall, water circularity has been steadily increasing, with 24.9% of total water demand fulfilled by secondary flows in 2019, potentially reaching 39.6% at maximum water recycling capacity. Finally, we discuss the wider implications of water circularity assessments for energy, the environment, and urban water infrastructure and policy. Overall, this study provides a quantitative tool to assess the scale of water circularity within engineered urban water infrastructure and its application to develop macro-level water systems planning and policy insights.
查看更多>>摘要：Heavy metal contaminated spent adsorbents are of great environmental concern due to their hazardous effects and large-scale accumulation in the natural environment. Converting massive spent adsorbents into efficient electrocatalysts with a facile strategy can address the challenge of growing energy demand and achieving carbon neutral goal. Herein, we demonstrated a "spent adsorbents to heterostructured electrocatalysts" conversion strategy based on the "waste-to-wealth" principle. Via a facile boriding process, the metal ions laden biocharbased spent adsorbents (SA) have been totally transformed into magnetic metal borides/biochar heterostructures, which exhibit excellent activities towards oxygen evolution reaction. The optimized NiCuFeB/SA catalyst takes a low overpotential of 251 mV to drive a current density of 10 mA cm(-2), outperforming many Ni/Fe-based catalysts synthesized from commercial material resources. Comprehensive analyses suggest the high catalytic efficiency mainly attributes to the porous biochar confined well-dispersed nano-sized metallic borides, the in-situ evolved active metal (oxy)hydroxides, favourable charge-transfer kinetics, as well as the heterostructure and amorphous feature. This work offers a general strategy to efficiently reutilize the spent metal bearing biochar-based adsorbents, which can be extended to advanced energy applications-oriented reutilization of other metal-contaminated solid wastes in an economically and environmental-benign manner.
Zisopoulos, Filippos K.Schraven, Daan F. J.de Jong, Martin
33页
查看更多>>摘要：Considering its relatively low circularity rate (11.8% in 2019), the EU set several waste management targets as part of its roadmap to a circular economy yet the decision about which transition pathway to follow is not trivial. The maximization of circularity in human made systems is intended to function as a catalyst for this transition albeit at the risk of establishing fragile techno-economic systems. To provide insights for a balanced transition to a circular economy its link with the ecological concepts of "resilience" and "robustness" is illuminated by assessing the theoretical robustness of the material and energy flow networks of the EU27 countries between 2010-2018 using Eurostat data. Results show that despite the high degrees of order (efficiencies) which all European countries developed over the years studied, none of them achieved near-maximum robustness. The identified relationships between the average circularity rate and the average energy efficiency with the theoretical robustness of these material and energy flow networks (for the years studied), respectively, suggest that ascendency analysis is a credible tool for supporting policy making. Both on a national and on a local level for developing circular and robust urban waste management systems given data availability. The contribution to the underlying theory of ascendency analysis is the introduction of the concepts of "technological boundaries" and "windows of efficiency" of these human-made networks which are juxtaposed with the "window of vitality" that is often used to describe healthy natural ecosystems. Finally, the limitations of ascendency analysis and directions for future research are presented.
查看更多>>摘要：Timely and accurate recognition of construction waste (CW) composition can provide yardstick information for its subsequent management (e.g., segregation, determining proper disposal destination). Increasingly, smart technologies such as computer vision (CV), robotics, and artificial intelligence (AI) are deployed to automate waste composition recognition. Existing studies focus on individual waste objects in well-controlled environments, but do not consider the complexity of the real-life scenarios. This research takes the challenges of the mixture and clutter nature of CW as a departure point and attempts to automate CW composition recognition by using CV technologies. Firstly, meticulous data collection, cleansing, and annotation efforts are made to create a high-quality CW dataset comprising 5,366 images. Then, a state-of-the-art CV semantic segmentation technique, DeepLabv3+, is introduced to develop a CW segmentation model. Finally, several training hyperparameters are tested via orthogonal experiments to calibrate the model performance. The proposed approach achieved a mean Intersection over Union (mIoU) of 0.56 in segmenting nine types of materials/objects with a time performance of 0.51 s per image. The approach was found to be robust to variation of illumination and vehicle types. The study contributes to the important problem of material composition recognition, formalizing a deep learning-based semantic segmentation approach for CW composition recognition in complex environments. It paves the way for better CW management, particularly in engaging robotics, in the future. The trained models are hosted on GitHub, based on which researchers can further finetune for their specific applications.
查看更多>>摘要：Due to the ever-increasing worldwide interest in the exploitation of waste vegetable oils, the development of analytical tools able to detect their adulteration with edible oils, is considered a priority for the scientific and industrial community. In this work, edible and waste vegetable oils have been analysed by Fourier TransformInfraRed (FT-IR) and Ultraviolet-Visible (UV-VIS) spectroscopies and the corresponding spectral data subjected to statistical multivariate analysis for classification purposes. In particular, Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were performed in order to develop an analytical tool which is able to distinguish between edible and waste vegetable oil. Qualitative analysis of the spectra suggested FT-IR and UV-VIS as the more suitable techniques to distinguish between wastes and edible samples. Also, statistical multivariate analysis revealed that FT-IR-based methodology is more adequate for the target, even if the elevated sensibility of the method produces an undesired distinction between edible oils of the same type. Finally, further attempts on UV-VIS data obtained in reflection mode allowed to produce a good dataset which after statistical treatment gave a clear differentiation between edible and waste oil samples.
查看更多>>摘要：Garbage classification technology is not only an important basis for the harmless treatment of waste and resource recovery, but also the inevitable trend of social development. The current garbage classification methods rely on manual classification in the garbage collection stage, and it is difficult to achieve satisfying results in consistency, stability, and sanitary conditions. For this reason, this study designs and develops a garbage classification system based on deep learning that can recognize and recycle domestic garbage. Focusing on the problems of low accuracy and poor real-time performance, a lightweight garbage classification model GCNet (Garbage Classification Network) is proposed. GCNet contains three improvements on ShuffleNet v2, including the design of parallel mixed attention mechanism (PMAM), the use of new activation functions, and transfer learning. The experimental results show that the average accuracy of GCNet on the self-built dataset is 97.9%, the amount of model parameters is only 1.3M, the single inference time on Raspberry Pi 4B is about 105ms, and the classification system needs only 0.88 seconds to complete the classification and collection of a single object. The method proposed in this paper is an effective attempt at machine vision in garbage classification and resource recovery. With the improvement of technology, it will effectively promote academic exploration and engineering application in the field of resources and environment.
查看更多>>摘要：The transition of our economy towards a bioeconomy is likely to increase the demand for wood in the future. Because the roundwood supply is limited, wood cascading is a promising concept for meeting the growing demand. In this context, it is necessary to map the current timber market for analyzing potential options for the cascading of recovered timber, and for quantifying future amounts of recovered timber, differentiated by the type of semi-finished wood product and sectoral origin. Therefore, a material flow analysis (MFA) for Germany during 2019 is performed and a model for the prediction of the recovery of timber volumes (PRecTimber) is developed. This model is based on a distributed decay approach which considers sectoral lifetimes. Historical data for the domestic consumption of timber products are used to calculate the annual decay of various timber products entering consumption. The MFA results in about 62 Mm(3) solid wood equivalents (SWE) of various wood raw material assortments being required in the domestic production of wood products. An increasing amount of recovered timber with a minimum of 26.6 Mm(3) (13.1 Mt) for 2019 to 29.5 Mm(3) (14.2 Mt) in 2050 can be expected. In 2050, the recovered timber is derived from the sectors construction with 52%, furniture with 30%, packaging with 15%, and others with 2% (mainly consisting of sawn wood and particleboard products). The results of the model can be used, to derive estimates of the dimension and quality of the future recovered timber accompanying the potentials for cascading.
NSTL
Elsevier