黄土丘陵区土地利用变化对深层土壤有机碳储量的影响

通过研究黄土丘陵子午岭林区5种典型土地利用类型土壤剖面有机碳分布特征,分析了天然乔木林转变为人工乔木林、天然乔木林转变为农田、天然灌木林转变为农田及撂荒后土壤有机碳变化特征.同时,以浅层(0~100 cm)土壤为对照,探讨了土地利用变化对深层(100~200cm)土壤有机碳储量的影响.结果表明,在0~200 cm剖面上,天然乔木林、天然灌木林、人工乔木林、撂荒地、农田土壤有机碳含量分别为5.85、3.96、4.98、3.09、3.20 g·kg-1,天然乔木林、人工乔木林土壤有机碳含量显著高于天然灌木林、撂荒地和农田(p0.05).各土地利用类型下浅层和深层土壤有机碳含量分别占0~200 cm土壤有机碳含量的58%~73%和27%~42%,不同土地利用类型间浅层土壤有机碳含量差异显著,但深层土壤有机碳含量差异不大.土地利用变化对土壤有机碳储量影响显著.天然乔木林转变为人工乔木林、天然乔木林转变为农田、天然灌木林转变为撂荒地、天然灌木林转变为农田4种土地利用转变方式0~200 cm土壤有机碳储量分别减少了9.68、52.90、20.20、12.49 t·hm-2,减幅为7%、39%、21%、13%,其中,浅层土壤减少了2%~48%,深层土壤减少了12%~22%.相对于林地开垦为农田而言,农田退耕还林后土壤有机碳的恢复要慢得多.研究结果揭示了浅层和深层土壤有机碳对土地利用变化的敏感性,反映了深层土壤有机碳具有较大的稳定性.     

基于模拟-优化方法的地下水污染源溯源辨识

以抚顺市某煤矸石堆放场为研究区,根据研究区的实际条件建立地下水污染质运移模拟模型,预测地下水污染质未来时空变化特征.基于正演预报结果构建了假想例子,应用模拟-优化方法对地下水污染源源强及场地的渗透系数进行反演识别.为减小优化模型反复调用模拟模型所产生的计算负荷,分别采用Kriging方法和BP神经网络方法建立了模拟模型的替代模型.最后运用模拟退火法求解优化模型,得到反演识别结果.研究表明：应用Kriging方法建立的替代模型输出结果的平均相对误差为0.3%;应用BP神经网络方法建立替代模型的输出结果平均相对误差为1.5%,应用两种替代模型对污染源源强识别的相对误差均小于0.5%,对场地两个参数分区渗透系数识别的相对误差均不大于5%.综上,应用Kriging方法建立的替代模型精度高于BP神经网络方法,利用基于两种替代模型的模拟-优化方法对污染源源强和渗透系数进行同步识别精度可以满足实际需求,是有效可行的.     

基于贝叶斯网络分类的土壤盐渍化遥感监测

贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的概率模型。采用贝叶斯网络分类对具有典型干旱特征的库车县土壤盐渍化情况进行监测，首先应用条件独立性测试原理建立贝叶斯网络结构，把研究区遥感数据进行离散化，然后应用贝叶斯定理作为分类原则，将每个像元分为像元最大概率的类别。研究结果表明该方法分类6种地类的整体分类精度达到96％，为该区盐渍地面积、空间分布等特征监测提供了较好的依据。     

高效藻类塘与水生植物塘联用深度净化受污染河水研究

采用高效藻类塘与水生植物塘联用工艺对劣Ⅴ类河水进行深度净化,通过对出水中CODCr,NH4+-N,TP和DO等指标的分析测定,研究了其对沈阳某河流水体的净化效果。结果表明,在HRT为4 d,水深0.5 m,进水CODCr为70~100 mg/L,DO,NH4+-N,TN和TP浓度分别为0.89~1.65,1.6~2.9,2.3~4.5和0.6~1.2 mg/L的条件下,联用工艺出水CODCr为35.40 mg/L,DO,NH4+-N,PO43--P,TN和TP浓度分别为5.88,0.66,0.17,1.51和0.16mg/L,水体中各项指标均达到GB 3838—2002《地表水环境质量标准》中规定的Ⅴ类标准。水生植物塘对藻类塘出水中的藻类有很好的去除效果,平均去除率为94.5%。联用工艺对CODCr,NH4+-N和PO43--P的去除效果均表现为一级藻类塘>二级藻类塘>水生植物塘,其中藻类塘起主要作用。     

海洋环境中材料腐蚀数据采集处理网络系统的研究

介绍了利用电话公网为传输栽体建立以海洋腐蚀与防护国防科技重点实验室为中心辐射青岛、厦门和三亚3个海水试验站的材料腐蚀数据采集计算机网络平台。研发了具有自主知识产权的多通道电偶腐蚀数据自动采集系统,将该系统与网络平台相结合可实现远程实验数据自动采集。同时为了有效地管理、存储和实现数据的共享,研究建立了海洋环境材料腐蚀与防护数据库,收集和整理了大量的材料腐蚀数据。在分析研究大量腐蚀数据的基础上建立了误差反传（BP）人工神经网络预测模型和灰色GM（1,1）腐蚀预测模型。从而形成一套较完整的数据采集、处理和分析网络系统。     

Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model

A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia (BC), Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases, damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment approach for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on the corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline's mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF have been obtained and visualized with the aid of the Geographic Information System (GIS). The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.     

A neuro-fuzzy algorithm for assessment of health,safety, environment and ergonomics in a large petrochemical plant

Process plants such as petrochemical units have been continuously trying to improve Health, Safety, Environment and Ergonomics (HSEE) programs. This study proposes an adaptive network-based fuzzy inference system (ANFIS) for assessment of HSEE programs in a petrochemical plant. The proposed neuro-fuzzy approach is applied to a set of operators in the petrochemical unit to show its applicability and superiority. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and accomplishment of HSEE programs is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSEE. Finally, the algorithm identifies efficient operators with respect to HSEE. This is the first study that introduces an intelligence algorithm for assessment and improvement of HSEE program in a petrochemical plant.     

长江口拦门沙及水下三角洲的动力沉积和演变

分析了长江河口拦门沙和水下三角洲及河口和邻近水域的水系和水团的特征，论述了河口混浊带羽状锋系的分布移动规律，并对河口拦门沙的物理，化学，生物及地质屏障效应作了评估。通过对泥沙分布，沉积率和沉积程序的研究，探明了河口拦门沙及水下三角洲的泥沙交换和演变。根据以上所述，探讨了长江河口深水航道的选址问题，为有关管理部门提供了科学的数据。     

SUBSURFACE QUALITY TRANSFORMATIONS DURING THE INITIATION OF A NEW STABILIZATION LAGOON1

ABSTRACT Raw sewage was metered into a newly-constructed lagoon of the Pima County Department of Sanitation, Tucson, Arizona. Seepage losses were calculated from data on inflow, evaporation and change in storage. Water samples were obtained from shallow suction cups, a 40 ft. and a 60 ft. PVC well and a 100 ft. access tube, all located inside the lagoon. Samples from these wells, together with water samples from the lagoon, were examined for conform organisms and various physical and chemical constituents. Of special concern were transformations in nitrogen. Estimated seepage rates in the lagoon during inundation ranged from 0.20 ft. per day to 0.10 ft. per day. Water level observations in wells reflected the percolation of effluent to the water table, 70 ft. below land surface. Initially, the nitrate ion levels in the suction cup samples were high, manifesting the leaching of indigenous soil nitrogen. With the onset of anaerobic conditions at the base of the lagoon, nitrification was inhibited at the soil surface and ammonia became the predominant form of nitrogen in the soil solution. Sorption of ammonium ion appeared to occur on clay particles in a soil zone of high cation exchange capacity. There were no undesirable microbial or chemical effects of recharge from lagoon seepage on native groundwater quality.     

非线性理论在开采沉陷中的应用

简要介绍了近年来把非线性理论应用于矿山开采沉陷领域的一些研究成果,主要包括两大部分：（１）神经网络理论在开采沉陷中的应用;（２）离散单元法在开采沉陷中的应用