利用卫星遥感数据估算PM2.5浓度的应用研究进展

近年来,PM_(2.5)已成为中国大气污染的首要污染物,危害人体健康。为弥补地基监测站点在空间分布上的局限性,借助卫星遥感技术估算PM_(2.5)浓度已成为研究热点。文章总结了利用卫星估算PM_(2.5)浓度的各种研究方法,探讨了不同方法的优势和不足,指出不同方法对不同应用目的的选择性差异较大。提出,应针对不同应用目的选择相应的方法,从而取得满足各方面需求的研究成果,为未来PM_(2.5)浓度估算应用工作提供参考。     

中国可持续社会养老保险的综合评价体系和实证分析

我国可持续社会养老保险的综合评价指标体系包括四个层次和三级子系统，内容涉及养老保险人数、机构设置、基金管理和代际平衡四个子目标。应用因子分析模糊综合评价方法对我国1990-2001年养老保险建设状况进行评价，本文认为基金管理，行政管理和代际失衡是影响我国可持续养老保险发展的主要因素。     

pH值对亚硝酸盐氧化菌动力学及功能基因的影响

为探究pH值对亚硝酸盐氧化菌（NOB）活性动力学影响,本试验采用序批式活性污泥（SBR）反应器,以富含NOB的活性污泥为对象,基于Monod模型考察不同pH值对NOB活性动力学的影响并进行统计学分析.结果表明,Monod方程可较好地反映不同pH值条件下基质底物浓度对NOB比亚硝态氮氧化速率（SNiOR）的影响,且pH=7.0时动力学参数Ks为（6.167mg/L）,r_max为[1.134g/（g·d）],此时NOB活性最好.利用钟形经验模型进行非线性回归拟合,最大比降解速率（r_max）随pH值的增大呈钟形变化,本试验NOB的最佳pH值为（6.9±0.1）,其中r_max维持在r_opt一半以上的pH值范围（ω）为（3.26±0.4）.以亚硝酸盐氧化还原酶类基因（nxrA、nxrB）为引物,基于荧光定量PCR技术分析结果显示,在不同pH值条件下nxrA基因和nxrB基因拷贝数的变化趋势均与动力学参数（Ks、r_max）的规律一致,且nxrA和nxrB基因在系统的降解过程中起协同作用.     

profiling areas of ground water contamination by pesticides in california: phase ii – evaluation and modification of a statistical model

A well sampling study was conducted to evaluate anempirical approach to classifying areasof land in California as vulnerable to ground watercontamination by pesticides (Troiano et al., 1994). Wells were sampled from sections of land that had noprevious detections of pesticideresidues. The sections had been classified into vulnerablesoil clusters or into a not-classified groupusing a procedure based on Principal Components Analysis(PCA). Grape, citrus, and olive growingareas of Fresno and Tulare Counties were targeted, areas wherepre-emergence herbicide residues hadbeen detected in well water. Overall, herbicide residues weredetected in 75 of 176 sampled wells, ahigh frequency of detection in relation to results fromprevious targeted well sampling studies. Sinceresidues were also detected in the not-classified group, theclassification procedure was modified usingan approach based on Canonical Variates Analysis (CVA). Moresections were classified intovulnerable soil clusters with the CVA approach than with thePCA method. Data from two otherexplanatory variables, depth to ground water and amount ofpesticide used per section, were includedto illustrate how additional information can be incorporatedinto this approach of identifying vulnerable areas.     

河流枯水流量特征研究

本文以泾、洛、渭三河流干流在陕西境内的主要控制断面为例,分析了１４个时段枯水流量的概率分布特征,选定了理论分布线型,揭示了各枯水序列的时段平均流量均值、变差系数和偏态系数的变化规律,建立了推求不同频率不同时段的枯水流量的经验公式。绘制了相应的关系曲线,应用十分方便。     

Environmental Ethics Beyond Principle? The Case for a Pragmatic Contextualism

Many nonanthropocentric environmental ethicists subscribe to a ``principle-ist' approach to moral argument, whereby specific natural resource and environmental policy judgments are deduced from the prior articulation of a general moral principle. More often than not, this principle is one requiring the promotion of the intrinsic value of nonhuman nature. Yet there are several problems with this method of moral reasoning, including the short-circuiting of reflective inquiry and the disregard of the complex nature of specific environmental problems and policy arguments. In the present paper, we advance an alternative, pragmatic contextualist approach to environmental ethics, one grounded in the moral theory of John Dewey. We present the results of an empirical study of public environmental ethics and natural resource management attitudes to support our position, and we conclude with a few recommendations for future inquiry in the field of environmental ethics.     

Heavy metal availability and impact on activity of soil microorganisms along a Cu/Zn contamination gradient

All the regulations that define a maximum concentration of metals in the receiving soil are based on total soil metal concentration. However, the potential toxicity of a heavy metal in the soil depends on its speciation and availability. We studied the effects of heavy metal speciation and availability on soil microorganism activities along a Cu/Zn contamination gradient. Microbial biomass and enzyme activity of soil contaminated with both Cu and Zn were investigated. The results showed that microbial biomass was negatively affected by the elevated metal levels. The microbial biomass-C （Cmic）/organic C （Corg） ratio was closely correlated to heavy metal stress. There were negative correlations between soil microbial biomass, phosphatase activity and NH4NO3 extractable heavy metals. The soil microorganism activity could be predicted using empirical models with the availability of Cu and Zn. We observed that 72% of the variation in phosphatase activity could be explained by the NH4NO3-extractable and total heavy metal concentration. By considering different monitoring approaches and different viewpoints, this set of methods applied in this study seemed sensitive to site differences and contributed to a better understanding of the effects of heavy metals on the size and activity of microorganisms in soils. The data presented demonstrate the relationship between heavy metals availability and heavy metal toxicity to soil microorganism along a contamination gradient.     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction

ABSTRACT

In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective.