The fractionation and geochemical characteristics of rare earth elements measured in ambient size-resolved PM in an integrated iron and steelmaking industry zone

Environmental Science and Pollution Research - Improved understanding of the fractionation and geochemical characteristic of rare earth elements (REEs) from steel plant emissions is important due...     

Combined effects of nitrogen addition and organic matter manipulation on soil respiration in a Chinese pine forest

The response of soil respiration (Rs) to nitrogen (N) addition is one of the uncertainties in modelling ecosystem carbon (C). We reported on a long-term nitrogen (N) addition experiment using urea (CO(NH₂)₂) fertilizer in which Rs was continuously measured after N addition during the growing season in a Chinese pine forest. Four levels of N addition, i.e. no added N (N0: 0 g N m⁻² year⁻¹), low-N (N1: 5 g N m⁻² year⁻¹), medium-N (N2: 10 g N m⁻² year⁻¹), and high-N (N3: 15 g N m⁻² year⁻¹), and three organic matter treatments, i.e. both aboveground litter and belowground root removal (LRE), only aboveground litter removal (LE), and intact soil (CK), were examined. The Rs was measured continuously for 3 days following each N addition application and was measured approximately 3–5 times during the rest of each month from July to October 2012. N addition inhibited microbial heterotrophic respiration by suppressing soil microbial biomass, but stimulated root respiration and CO₂ release from litter decomposition by increasing either root biomass or microbial biomass. When litter and/or root were removed, the “priming” effect of N addition on the Rs disappeared more quickly than intact soil. This is likely to provide a point of view for why Rs varies so much in response to exogenous N and also has implications for future determination of sampling interval of Rs measurement.

     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Decoupling of nonferrous metal consumption from economic growth in China

Nonferrous metal is an important basis material for the development of the national economy, and its consumption directly affects economic development. It has great significance in the effective utilization of nonferrous metals, development of an environment-friendly society, and investigation of the decoupling of nonferrous metal consumption and GDP growth. The decoupling indicators for nonferrous metal consumption and GDP growth (D _r) in China from 1995 to 2010 were calculated in this study, and the results were analyzed. A productive model based on BP neural network was established. Then, the decoupling indicators for nonferrous metal consumption and GDP growth in China for the period of 2011–2020 were predicted. For the period of 1995–2010, the annual average decoupling indicators were <1 for copper, aluminum, zinc, lead, and nickel, except for tin, which was 0.21. The analysis showed that the decoupling of nonferrous metal consumption and GDP growth is in a less optimistic situation to copper, aluminum, zinc, lead, and nickel in China from 1995 to 2010. The annual average decoupling indicator for tin was 0.21, which indicates relative decoupling. For the period of 2011–2020, the predicted decoupling indicators for copper, aluminum, zinc, lead, nickel, and tin were between 0 and 1. This finding indicates the implementation of relative decoupling. However, the total consumption of nonferrous metals did not decouple from GDP growth.     

Toxicity of sediment cores from Yangtze River estuary to zebrafish (<Emphasis Type="Italic">Danio rerio</Emphasis>) embryos

Toxicity evaluation is an important segment in sediment quality monitoring in order to protect aquatic organisms and human health. The purpose of this study is to assess the toxicity of sediments from three sediment cores in Yangtze River Estuary, China, using the zebrafish (Danio rerio) embryo tests. Fertilized zebrafish eggs were exposed to both whole sediments and sediment organic extracts prepared from collected sediments, in order to provide a comprehensive and realistic insight into the bioavailable toxicity potential of the sediments. As end points, development parameters (mortality, hatching rate, and abnormality) in the developing embryos were recorded during the 96-h exposure. The results showed that some samples increased mortality, inhibited the hatching of embryos, and induced morphological abnormalities. The embryonic toxicities presented serrated changes and irregular distribution with depth, which may be related to hydrodynamic effect and unstable environmental input. However, lethal and sub-lethal effects were more significant at the sub-surface sediments (10～40 cm), which indicated that the pollution is more serious in recent decades.     

分子筛吸附法脱除废水中的聚丙烯酰胺

聚丙烯酰胺(PAM)用于油田驱油产生大量难处理的含聚废水。以分子筛为吸附剂处理含聚废水,研究分子筛类型(Y、Beta和ZSM-5,H型和Na型)和物化性质对其吸附PAM性能的影响,并测定吸附等温线。结果表明,吸附性能顺序为BetaYZSM-5,H型优于Na型。H-Beta对PAM的吸附来自分子筛中阳离子与PAM中阴离子的静电作用,Si—O和Al—OH与PAM中酰胺基的氢键作用。H-Beta开放的通道结构,较高的介孔比例和表面积,较强的酸性和良好的酸中心可接近性使其具有优异的吸附性能。Si O2/Al2O3=26的H-Beta对浓度为200 mg/L的PAM溶液,PAM脱除率可达95.2%。在低PAM平衡浓度时,PAM在H-Beta上的吸附符合Langmuir单层吸附特征,饱和吸附量达70.2 mg/g,在高浓度区域则由于PAM疏水缔合作用加强呈现多层吸附。