江苏省太湖流域水生态健康评估的初步实践及展望

阐述了水生态健康的内涵与意义,从江苏省率先在太湖流域开展水生态环境功能分区管理的顶层设计,构建以水生态健康指标为核心的水生态健康评估技术体系,地方对照水生态环境功能区划的水生态分级管控目标开展的应用与实践结果等3个方面,回顾了江苏省太湖流域水生态健康评估工作的主要进展,提出了完善水生态健康评估技术体系和推进流域水生态健康评估工作的建议。     

Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

基于支持向量回归机的南黄海浒苔分布面积预测模型

绿潮作为一种新型的海洋灾害,已经引起了各个国家的重视.依据2012年南黄海海域浒苔遥感监测分布面积数据,选取了温度、天气状况、风向、风力、浪高5种影响浒苔扩散的气候因子,建立了基于SVR的浒苔分布面积预测模型,并与经典的最近邻点插值模型、线性插值模型、3次样条函数插值模型和分段3次Hermite插值模型进行了回归效果的对比.分析结果表明,基于SVR的浒苔分布面积预测模型能够为浒苔遥感数据的插补提供一种方法,且回归效果优于传统的回归方法,为浒苔的防治提供辅助决策信息.     

环境保护在学习实践科学发展观活动中的地位与作用——从环境哲学角度看学习实践科学发展观活动

环境保护是科学发展观的题中之意，科学发展观关于环境保护的论述，是党的环境保护理论的精髓。学习实践科学发展观活动，是一个哲学内涵非常丰富的课题，是十分难得的研究环境哲学的大好机遇。试点经验显示了环境保护在学习实践科学发展观活动中的重要作用。他们一方面在科学发展中通过转变发展方式，调整经济结构，解决环境难题；一方面坚持环境保护与经济发展并重，在保护环境中求发展，努力实现环境与经济“双赢”。其共性是，注重治本、注重生态、注重政策。深入学习实践科学发展观，关键在实践，要深入一线，深入调研，融人群众的实践，总结来自一线的新鲜经验。要适应新形势，了解新情况，解决新问题。在学习实践科学发展观活动中，不断提高落实科学发展观，在科学发展中，提高解决环境问题的能力，实现环境与发展、环境与经济的“双赢”。     

Economies of scale in global iron-making

In this paper, we investigate the presence of economies of scale in the global iron-making industry for integrated steel plants, iron making being the first stage in the steel-making process. Iron making depends on basic commodities, such as iron ore, coke and various forms of energy, required in the operation of the blast furnace, which can be classified as essential inputs and used in fixed proportions to produce iron. A generalized Leontief cost function is estimated using panel data for 69 integrated plants, such a specification being appropriate for technologies with essential inputs that are used in fixed proportions in production. A significant scale effect is observed due to the existence of fixed costs and a linear dependence of the cost function on production. Under a simple linear cost function, a rough estimate of the breakeven scale of plant, where costs equal revenue, is 4.5 Mt per year. Competitiveness, as measured by the ratio of plant average cost per tonne to best practice cost per tonne, can be shown to be positively related to the scale of production as well as the cost of essential inputs. Therefore, low-cost producers are also often producers with low raw material costs and production levels below the estimated breakeven scale of operation. Labor costs, although significant, are comparatively less important as a driver towards low costs.     

三峡库区几种林下苔藓的保水功能

通过对三峡库区三种森林类型(松栎混交林、栓皮栎纯林、马尾松纯林)林下苔藓储量调查分析及其持水特性试验，得到不同森林类型林下苔藓储量、最大持水量、吸水速率等水文特征参数。结果表明，马尾松纯林林下苔藓储量最大(12．93t／hm^2)，松栎混交林和栓皮栎纯林林下苔藓储量相同(9．47t／hm^2)。松栎混交林林下苔藓最大持水量为5．36mm，栓皮栎纯林为5．73mm，马尾松纯林为6．26mm。研究结果还表明，三峡库区林下苔藓持水量随时间变化过程与森林类型无关。在只有苔藓覆盖情况下，不同森林类型林内不产生水分下渗和地表径流时的最大降雨量和最大降雨雨强分别为：松栎混交林林地5．36mm，5．820mm／h；马尾松纯林林地6．26mm，7．420mm／h；栓皮栎纯林林地5．73mm，6．060mm／h。     

Prenatal prediction of childhood-onset spinal muscular atrophy (SMA) in Turkish families

Childhood-onset spinal muscular atrophy (SMA) is one of the most common neurodegenerative genetic disorders. SMN1 is the SMA-determining gene deleted or mutated in the majority of SMA cases. There is no effective cure or treatment for this disease yet. Thus, the availability of prenatal testing is important. Here we report prenatal prediction for 68 fetuses in 63 Turkish SMA families using direct deletion analysis of the SMN1 gene by restriction digestion. The genotype of the index case was known in 40 families (Group A) but unknown in the remaining 23 families (Group B). A total of ten fetuses were predicted to be affected. Eight of these fetuses were derived from Group A and two of these fetuses were from Group B families. Two fetuses from the same family in Group A had the SMNhyb1 gene in addition to homozygous deletion of the NAIP gene. One fetus from Group A was homozygously deleted for only exon 8 of the SMN2 gene, and further analysis showed the presence of both the SMN1 and SMNhyb1 genes but not the SMN2 gene. In addition, one carrier with a homozygous deletion of only exon 8 of the SMN1 gene was detected to have a SMNhyb2 gene, which was also found in the fetus. To our knowledge, these are the first prenatal cases with SMNhyb genes. Follow-up studies demonstrated that the prenatal predictions and the phenotype of the fetuses correlated well in 33 type I pregnancies demonstrating that a careful molecular analysis of the SMN genes is very useful in predicting the phenotype of the fetus in families at risk for SMA. Copyright © 2002 John Wiley & Sons, Ltd.     

Decision making under uncertainty in case of soil remediation

Decisions on soil remediation are one of the most difficult management issues of municipal and state agencies. The assessment of contamination is uncertain, the costs of remediation are high, and the impacts on the environment are multiple. This paper presents a general, transparent, and consistent method for decision making among the remediation alternatives. Soil washing, phytoremediation, and no remediation are exemplarily considered. Multi-criteria utility functions including (a) the cost of remediation (b) the impact on human health and agricultural productivity, and (c) the economic gain after remediation are constructed using probability density functions representing contamination for all site coordinates. Herewith, the probability of different types of (i) correct decisions such as a hit or a true rejection and (ii) erroneous decisions such as a false alarm or miss are examined. The decision theoretic model is applied to a case study on heavy metal contaminated soil. This case study reveals the non-linear structure of multi-criteria-decision making. The case study shows that the geostatistical uncertainties of the log-normal distributed soil contamination must be taken into account: When uncertainties are not considered and the utilities are assessed according to the estimated value for a spatial unit, only few (N=26) spatial units result where the utility score of the alternative soil washing are higher than the utility score to the no remediation alternative. However, when taking into account geostatistical uncertainties of the log-normal soil distribution this number is about ten times greater (N=237). Furthermore, the use of 'maximizing expected utility' as decision rule is critical in that it may lead to a high probability of misses.     

人工神经网络在预测深基坑周边地表沉降变形中的应用研究

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。     

北京南部城区PM2.5中碳质组分特征

为了解《大气污染防治行动计划》实施后北京市大气PM_2.5中碳质组分特征,于2017年12月至2018年12月在北京污染较重的南部城区进行了PM_2.5连续采样,对其中的有机碳（OC）和元素碳（EC）进行了全面研究.结果表明,北京大气PM_2.5、OC和EC浓度变化范围分别为4.2~366.3、0.9~74.5和0.0~5.5 μg ·m^-3,平均浓度分别为（77.1±52.1）、（11.2±7.8）和（1.2±0.8）μg ·m^-3,碳质组分（OC和EC）整体占PM_2.5的16.1%.OC质量浓度季节特征表现为：冬季[（13.8±8.7）μg ·m^-3] > 春季[（12.7±9.6）μg ·m^-3] > 秋季[（11.8±6.2）μg ·m^-3] > 夏季[（6.5±2.1）μg ·m^-3],EC四季质量浓度水平均较低,范围为0.8~1.5 μg ·m^-3.二次有机碳（SOC）年均质量浓度为（5.4±5.8）μg ·m^-3,四季贡献比例范围为45.7%~52.3%,年均贡献为48.2%,凸显了二次形成的重要贡献.随污染加重,尽管OC和EC贡献比例均降低,但浓度水平却成倍升高,OC和EC浓度在严重污染天分别是空气质量为优天的6.3和3.2倍.与非供暖时段相比,供暖时段PM_2.5、OC和SOC浓度分别增加了14.4%、47.9%和72.1%,体现了OC对供暖季PM_2.5污染的重要贡献.PSCF分析表明,位于北京西南的山西省和河南省部分区域是PM_2.5和OC的主要潜在源区,且PM_2.5潜在源区更为集中;EC的PSCF高值（>0.7）区域较少,主要位于北京南部,如山东省和河南省部分地区,且北京市及周边地区贡献明显.