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.     

微波辐照热解废印刷电路板产物的分析研究

为了减少电子废弃物对环境的危害,实现其资源化回收利用,研究了微波辐照热解废印刷电路板的效果,并采用红外光谱、气相色谱质谱和X荧光光谱等方法对热解产物的组成及性质进行了分析.结果显示:微波热解得到的气体、液体、固体的产率分别为7%～33%、26%～45%、31%～51%,其中气体主要由CO、CO2、H2及有机烃类组成,可燃性气体占70%(体积分数)左右,可作为燃料气加以利用;液体分为水相及油相,经常压蒸馏后得到的120～250 ℃馏分主要为单酚化合物,苯酚高达50%(质量分数)左右,甲基苯酚和邻甲基苯酚为25%(质量分数)以上,是良好的化工原料;固体中除炭外,还含有许多金属如铅、锡和铜等,可以回收利用.说明微波热解技术处理电子废弃物可实现资源化回收利用.     

反硝化条件下有机碳低丰度河床沉积层中的苯胺降解

采集渭河河床沉积物,研究了反硝化条件下有机碳低丰度的河床沉积层中苯胺降解.结果表明,反硝化条件下,苯胺在有机碳低丰度的河床沉积层中可生物降解.使该环境中苯胺(约50 mg/L)降解近95%,当硝酸盐为30.69、184.16、245.54 mg/L时,降解时间分别约为20、45、70 d.在上述环境中另加35.98 mg/L乙酸盐后,苯胺降解速度在硝酸盐为184.16 mg/L时最大,硝酸盐为30.69 mg/L时最小.当硝酸盐为30.69 mg/L,不加乙酸盐,27 d苯胺降解约95%;添加35.98 mg/L乙酸盐后,实验进行了47 d还仍有近13 mg/L苯胺残留,说明外加碳源(乙酸盐)对苯胺降解具有抑制作用.但当硝酸盐为184.16、245.54 mg/L时,外加碳源(乙酸盐)则强化苯胺降解.水合金属氧化物对苯胺降解具有促进作用.     

β射线衰减法和重量法测定环境空气中PM10的对比研究

用β射线衰减法和重量法同步测定了2005年乐清市环境空气中PM10日均浓度,实验覆盖了乐清市的各种气象条件和PM10的浓度变化范围,具有一定的代表性.实验结果表明:在同一监测条件下,两种方法的测定结果具有一致性和可比性,β射线衰减法测得的PM10比重量法高,年均值偏高23%;两种方法测得的PM10高度相关,相关系数为0.994,能用回归直线来描述两者之间的关系.