对应分析在CFIT事故致因研究中的应用

为揭示可控飞行撞地(CFIT)事故致因规律,根据Reason-SHEL模型得出的10个CFIT事故致因因子,分析从飞行安全基金会数据库中整理出的2006—2011年间40起CFIT事故。在此基础上建立CFIT事故年份-成因列联表,再利用SPSS软件对表格进行对应分析。将事故成因和年份同时反映在二维散点图中,实现数据可视化。最后对图形进行向量、圆心接近点以及区域化等3项分析。根据结果得出,CFIT事故的主成因是人因操作和外界环境;随着年份增加,飞行员状态(药物/疲劳、经验不足)对造成CFIT事故的重要性越来越明显;不同年份有不同的重要致因因子。     

页岩气压裂作业人因失误结构化辨识方法研究

为提高页岩气压裂作业人因失误辨识结果的全面性和针对性,建立一种结构化的人因失误辨识方法。通过概括辨识阶段的作业流程,建立作业人员行为模型,概述作业人员的行为阶段。利用筛选出的引导词辨识行为阶段人因失误。将该方法应用于页岩气压裂过程的替液阶段。结果表明,用引导词能明确辨识方向,减少头脑风暴时间以及对专家知识的依赖;辨识过程的动态调整能改善认知可靠性,使更多人因失误模式(与传统CREAM方法相比)被辨识出来。     

岩溶塌陷倾向性等级的KPCA-SVM预测模型

为了快速、有效地预测岩溶塌陷倾向性等级,在统计分析大量观测实例的基础上,选取岩性系数、岩体结构系数、地下水系数、覆盖层系数、地形地貌系数和环境条件系数作为特征指标。利用核主成分分析(KPCA)方法在高维空间提取岩溶塌陷影响因子的主成分,将获取的主成分作为支持向量机(SVM)的特征向量,建立基于KPCA的岩溶塌陷倾向性等级的SVM预测模型。将12组观测数据作为学习样本对模型进行训练。采用回代估计法进行回检,误判率为0。利用训练好的模型对2组待判样本进行预测。结果表明:经KPCA后指标个数减少,相关性降低,SVM运算的复杂度降低。用该模型所得预测结果的准确率为100%。     

Reducing Fertilizer‐Nitrogen Losses from Rowcrop Landscapes: Insights and Implications from a Spatially Explicit Watershed Model

We present conceptual and quantitative models that predict changes in fertilizer‐derived nitrogen delivery from rowcrop landscapes caused by agricultural conservation efforts implemented to reduce nutrient inputs and transport and increase nutrient retention in the landscape. To evaluate the relative importance of changes in the sources, transport, and sinks of fertilizer‐derived nitrogen across a region, we use the spatially explicit SPAtially Referenced Regression On Watershed attributes watershed model to map the distribution, at the small watershed scale within the Upper Mississippi‐Ohio River Basin (UMORB), of: (1) fertilizer inputs; (2) nutrient attenuation during delivery of those inputs to the UMORB outlet; and (3) nitrogen export from the UMORB outlet. Comparing these spatial distributions suggests that the amount of fertilizer input and degree of nutrient attenuation are both important in determining the extent of nitrogen export. From a management perspective, this means that agricultural conservation efforts to reduce nitrogen export would benefit by: (1) expanding their focus to include activities that restore and enhance nutrient processing in these highly altered landscapes; and (2) targeting specific types of best management practices to watersheds where they will be most valuable. Doing so successfully may result in a shift in current approaches to conservation planning, outreach, and funding.     

柠檬酸对重金属复合污染土壤的浸提效果研究

以浙江富阳某炼铜厂附近受Cd、Cu、Pb和Zn复合污染的土壤为研究对象,采用振荡、静置和超声3种不同浸提方式,通过研究柠檬酸在不同浸提方式下对重金属的去除效果,筛选出最佳柠檬酸浓度、处理时间及功率,并采用Tessier连续提取法分析浸提前后4种重金属形态变化.结果表明,柠檬酸对4种重金属均具有较好的去除效果,其中对Zn的去除效果最好,Cd的去除效果最差,而Cu和Pb的去除效果相当.Pb和Zn的去除率均随着柠檬酸浓度的增加而增加,但增幅随之减小,Cu的去除率则先增加后基本保持不变,而Cd的去除率变化不明显.综合考虑成本和浸提效果后,选用0.2 mol·L-1柠檬酸浸提2次为最优条件,最佳振荡时间和静置时间分别为4 h和24 h,最佳超声时间和功率分别为30 min和500 W.柠檬酸能够在很大程度上改变土壤中重金属的形态分布,主要去除以可交换态和碳酸盐结合态形式存在的Cd及以碳酸盐结合态和铁锰氧化物结合态形式存在的Cu、Pb和Zn.通过对比3种浸提方式,在同等去除效果的前提下,静置浸提相比振荡浸提需要更长的时间,而将超声波应用于重金属污染土壤淋洗,所需时间较少,因此,超声波辅助浸提土壤中重金属是可行的,具有良好的应用前景.     

1957—2012年中国参考作物蒸散量时空变化及其影响因子分析

根据1957—2012年全国608个气象站的逐日气象资料,利用Penman-Monteith公式计算作物潜在蒸散量,对全国及水资源一级分区潜在蒸散量时空分布特征、变化趋势进行分析;基于Arc GIS及SPSS软件,采用主成分分析方法,对潜在作物蒸散量的影响因子及其分布特征进行探讨。结果表明:近56 a来,全国年潜在蒸散量在616~2 128 mm之间,河西走廊、南部岭南地区、海南岛以及华南沿海作物潜在蒸散量较大,而在黑龙江一带、四川盆地及西南地区东部,潜在蒸发量较小。各分区年均潜在蒸发量均呈现减少趋势,西北诸河区倾向率最大,为-12.22 mm/10 a;影响潜在蒸散量的因子中,第1主成分为热力学因子,第2主成分为水分因子和辐射因子,第3主成分为地理因子和空气动力学因子,第4主成分为高程因子。     

Could wastewater analysis be a useful tool for China? — A review

Analysingwastewater samples is an innovative approach that overcomesmany limitations of traditional surveys to identify and measure a range of chemicals that were consumed by or exposed to people living in a sewer catchment area. First conceptualised in 2001, much progress has been made to make wastewater analysis (WWA) a reliable and robust tool for measuring chemical consumption and/or exposure. At the moment, the most popular application of WWA, sometimes referred as sewage epidemiology, is to monitor the consumption of illicit drugs in communities around the globe, including China. The approach has been largely adopted by lawenforcement agencies as a device tomonitor the temporal and geographical patterns of drug consumption. In the future, themethodology can be extended to other chemicals including biomarkers of population health (e.g. environmental or oxidative stress biomarkers, lifestyle indicators or medications that are taken by different demographic groups) and pollutants that people are exposed to (e.g. polycyclic aromatic hydrocarbons, perfluorinated chemicals, and toxic pesticides). The extension of WWA to a huge range of chemicals may give rise to a field called sewage chemical-information mining (SCIM) with unexplored potentials. China has many densely populated cities with thousands of sewage treatment plants which are favourable for applying WWA/SCIM in order to help relevant authorities gather information about illicit drug consumption and population health status. However, there are some prerequisites and uncertainties of the methodology that should be addressed for SCIM to reach its full potential in China.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

桂林市公共场所空气污染的检测与分析

采用甲醛检测仪、有机气体检测仪等测试分析了桂林市宾馆、商场、候车室、家具城、文化娱乐场等公共场所的甲醛和TVOC浓度。结果显示:候车室和文化娱乐场所的甲醛、TVOC合格率均为100%;宾馆、商场、家具城的TVOC合格率分别为98.7%、98.9%、95.0%,甲醛合格率分别为78.8%、91.1%、85.0%;皮革制品、家具板材等为主要的甲醛和TVOC污染源。     

阜新市空气监测点位优化的聚类分析

基于聚类分析法利用数据之间距离系数进行分类的原理,建立空气监测点位聚类分析优化模型,结合阜新市地形、气象及历史监测数据,进行阜新市空气监测点位布设优化应用,优化结果表明:距离相似水平取d=0.3时,环保局(B点)与人民公园(C点)监测点空气污染物浓度分布相似性最高,合并为1点,增设气象台监测点位作为清洁背景点,4个点位构成阜新市空气监测新网络;利用CALPUFF模型模拟对优化后监测点位进行相关性检验。检验结果表明:监测点位优化后SO2浓度与实测值相关系数为0.984,PM10相关系数为0.968,NO2相关系数为0.973。CALPUFF模型模拟值与实际监测值之间相关系数均大于0.75,表明优化后的阜新市空气监测点位具有客观环境代表性;监测点位优化与检验方法具有一定应用价值。