鸭绿江（丹东段）江水中未知有机污染物分析鉴定与有毒有机物名录筛选

采用美国惠普公司MC/GS联用仪,对鸭绿江（丹东段）江水中有机污染物种类、组成进行了分析鉴定,进而采用高压液相色谱法对多环芳烃类进行定量测定与评价,进行了该江段有毒有机物名录筛选,提出由27种有毒有机污染物组成的名单。     

污水土地处理CAD系统设计

针对污水土地处理设计实际,以提高设计速度和质量为目标,结合计算机辅助设计技术和软件技术的发展,采用成熟而先进的参数化设计技术及ADS开发工具,对土地处理设计中的几个关系问题进行了探讨。     

水环境监测信息集成、共享与决策支持平台构建

以水环境监测数据可靠传输交换与集成、信息规范化处理分析、高效共享与决策支持为应用主线,对水环境监测信息集成、共享与决策支持平台的建设目标、总体架构、主要功能进行了研究,设计了平台的总体架构及主要功能,以实现涉水部门水环境信息的采集、传输、交换、存储、分析、发布、共享、展现等,为环境管理与决策人员、为公众、为各级(国家级流域级省级地市级县级)环境监测业务人员提供信息支撑。     

大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

Spatial modelling of the probability of obtaining a detectable concentration of ammonia in Moreton Bay, Australia

Ammonia is an important water quality variable, which in excess, can be detrimental to waterways and their ecosystems. In the Ecosystem Health Monitoring Program in South-east Queensland ammonia is monitored monthly, however, often more than 50% of the ammonia observations in Moreton Bay are below detection limit, making it difficult to draw useful inferences. In this paper a clipped Gaussian random field is used to spatially model and map the probability of detectable concentration of ammonia. The methodology is applied to the Moreton Bay samples collected in February 2005. The results suggest that for this month the oceanic impacted areas have higher probability of detectable ammonia concentration than the areas closer to the main sources of anthropogenic inputs.     

岛津TNP-4110在线分析仪的日常维护

从数据比对、硬件维护两个方面探讨岛津TNP-4110在线仪的日常维护问题。通过分析各种干扰因素来查找造成数据比对差异的原因并提出相应解决措施。在仪器日常维护上设立不同周期保养计划、共享设备维护经验,结合实践制定了合适的检修流程图。     

二维平流扩散模型下的集合卡尔曼滤波模拟同化研究

为提高环境数值预报水平,构建了一个针对污染物扩散的模拟数据同化系统。采用集合卡尔曼滤波方法对二维平流扩散模型的状态变量进行了实时校正,实现污染物浓度的实时模拟预报,完成了敏感性实验中集合数目变化、观测方差变化和同化窗口长度变化研究。比较考察观测点位置与污染源距离不同时的预报效果,探讨了优化条件下的同化策略,提出一种根据距离远近动态调节卡尔曼增益权重的方法。在集合数目较小时,可降低计算代价,得到优化的同化效果。     

基于PSO-SVM算法的环境监测数据异常检测和缺失补全

针对环境监测数据异常和数据缺失问题,提出了基于支持向量机的粒子群优化数据异常检测和缺失补全算法。利用粒子群优化算法选取较优的支持向量机训练参数组合,以此建立非线性的支持向量机模型,并利用结果模型对测得的真实数据拟合预测。以宁夏回族自治区某污水处理厂的污染物测量数据作为实验数据,结果表明,利用该算法预测数据的准确率可达97.977%,检测异常数据准确度高,缺失数据补全正确。     

Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I _YZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m²) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m² subplots within each of the 1000 m² plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.     

浅淡监测数据的共享与社会效应

结合工作实际对如何有效利用监测数据进行了探讨 ,并引申出监测数据的 4种效应 ,即信息效应、价值效应、广告效应和政治效应。