化工废水中邻苯二甲酸酯的研究

对北京市五个化工厂的废水中8种邻苯二甲酸酯类环境激素进行分析和调查研究.在化工废水中普遍检出的邻苯二甲酸酯有邻苯二甲酸二丁酯(DBP)、邻苯二甲酸二(2-乙基己基)酯(DEHP)、邻苯二甲酸丁基苄基酯(BBP)、邻苯二甲酸二环己酯(DCHP),其中邻苯二甲酸二乙酯(DEP)、邻苯二甲酸二正丙酯(DPrP)、邻苯二甲酸二戊酯(DAP)和邻苯二甲酸二正己酯(DHP)4种物质未检出.废水中DEHP和DBP的浓度均低于排放标准.     

激光诱导击穿光谱技术在环境领域中的应用动态

激光诱导击穿光谱技术是一种通过分析物质等离子体发射光谱而获取物质成分和含量的分析技术。近些年,该技术已经成为光谱学领域的研究热点且发展迅速,涌现出多种新的检测手段和仪器。从它的基本原理、发展历史及其在土壤、水和大气等生态环境保护领域的应用情况为基础,分析总结其优点以及所面临的问题和挑战。     

结构局部损伤诊断的应变模态方法--分析与应用

监测和诊断大型工程结构的健康状况，评估其安全性已成为未来工程的必然要求。基于利用局部反应监测结构局部性能的设想，利用应变模态的局部性特点进行结构的局部损伤诊断。首先由传递函数对应变模态进行贝叶斯区间估计，通过一混凝土框架模型的模态试验，分析应变模态测试的不确定性；然后对混凝土框架柱进行局部加强，考察应变模态界定损伤的能力。结果显示，固有频率的不确定性程度最小，应变模态振型与位移模态振型相比，各测点的变异程度差异较大，而阻尼比受不同类型测试系统影响较大；在高阶应变模态对结构局部损伤发生误判的情况下，一阶应变模态可以界定损伤，准确指示损伤部位。     

流动注射免疫分析及其在环境中农药残留分析研究综述

介绍了流动注射免疫分析、顺序流动注射免疫分析方法及其流动注射脂质体、荧光、化学发光、电化学等检测技术分析环境中的农药残留     

结合态雌激素的检测方法及污染现状

结合态雌激素是天然雌激素物质的一种重要赋存形态,在环境中可水解为自由态,产生内分泌干扰效应,因此具有潜在的环境风险,并且会影响天然雌激素污染调查和毒性评估的准确性.本文详细探讨了结合态雌激素特定的化学结构对其检测、转化及环境风险等方面的影响,对比分析了目前常用的几类检测方法的优缺点,并初步总结了其在污水处理设施和环境水体中的污染浓度及归趋,以期为我国开展相关研究工作提供参考.     

基于TNPE的智能电网虚假数据注入攻击检测

为实现智能电网中虚假数据注入攻击的实时检测,提高电力系统运行的安全性,采用1种基于时序近邻保持嵌入的方法,对正常状态下采集到的电网历史量测数据建立离线模型,得到T2统计限,将实时数据通过模型获得的T2统计量与离线模型的统计限进行对比,若超过统计限,则说明存在虚假数据注入攻击.该方法在提取局部空间结构特征的基础上,可同时...     

填埋场渗漏检测偶极子法的影响因素分析

偶极子法属于高压直流电法的一种,具有操作简便、费用低廉,可操作性强,并能对防渗层进行百分之百的无损检测,已成为防渗层施工验收采用的主要方式.分析了影响偶极子法灵敏度的主要因素.在回路电压一定的情况下,膜上介质电阻率、膜上介质厚度、偶极子与HDPE膜的距离、偶极子间距等因素皆可对偶极子法的灵敏度产生影响.结果表明:①膜上介质电阻率越大,偶极子检测灵敏度越高.②膜上介质厚度越大,偶极子检测灵敏度越低.③偶极子与HDPE膜的距离越小,偶极子检测灵敏度越高.④偶极子间距越大,检测灵敏度越高.但偶极子间距过大,会有漏检情况发生.⑤对于多漏洞情况,当漏洞之间的距离大于偶极子的间距时,偶极子法可准确分辨出多个漏洞.      

A simple spectroscopic method to determine dimethoate in water samples by complex formation

Abstract

A simple and rapid method for the determination of dimethoate in water was developed based on the monitoring of the complex formation between bis 5-phenyldipyrrinate of nickel (II) and the herbicide dimethoate. The method showed a short response time (10?s), high selectivity (very low interference from other sulfate and salts), high sensitivity (limit of detection (LOD) 0.45?µM, limit of quantitation (LOQ) of 1.39?µM), and a K_d of 2.4?µM. Stoichiometry experiments showed that complex formation occurred with a 1:1 relation. The method was applied to different environmental water samples such as lagoon, stream, urban, and groundwater samples. The results indicated that independently from the water source, the method exhibited high precision (0.25–2.47% variation coefficient) and accuracy (84.42–115.68% recovery). In addition, the method was also tested using an effluent from a wastewater treatment plant from Mexico; however, the results indicated that the presence of organic matter had a pronounced effect on the detection.     

水体监测中氨氮测定的影响因素分析

目前,水质监测的范围非常广泛,通常包括易受污染水体和未受污染水体的监测。一般在水质监测过程中,首先需要确定的是科学合理的检测方法,然后对影响该检测方法精度的因素进行综合分析并进行有效监测,当今水质监测中氨氮浓度是评价水质好坏的重要指标之一。基于此,文章综合分析了水体中氨氮浓度检测的基本原理并重点探讨了水体监测中影响氨氮测定的主要因素。     

The detection of drowsiness using a driver monitoring system

Abstract

Objective: Drowsiness is a major cause of driver impairment leading to crashes and fatalities. Research has established the ability to detect drowsiness with various kinds of sensors. We studied drowsy driving in a high-fidelity driving simulator and evaluated the ability of an automotive production-ready driver monitoring system (DMS) to detect drowsy driving. Additionally, this feature was compared to and combined with signals from vehicle-based sensors.

Methods: The National Advanced Driving Simulator was used to expose drivers to long, monotonous drives. Twenty participants drove for about 4?h in the simulator between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic was sparse and semirandom, with both slower- and faster-moving vehicles. Observational ratings of drowsiness (ORDs) were used as the ground truth for drowsiness, and several dependent measures were calculated from vehicle and DMS signals. Drowsiness classification models were created that used only vehicle signals, only driver monitoring signals, and a combination of the 2 sources.

Results: The model that used DMS signals performed better than the one that used only vehicle signals; however, the combination of the two performed the best. The models were effective at discriminating low levels of drowsiness from moderate to severe drowsiness; however, they were not effective at telling the difference between moderate and severe levels. A binary model that lumped drowsiness into 2 classes had an area under the receiver operating characteristic (ROC) curve of 0.897.

Conclusions: Blinks and saccades have been shown to be predictive of microsleeps; however, it may be that detection of microsleeps and lane departures occurs too late. Therefore, it is encouraging that the model was able to distinguish mild from moderate drowsy driving. The use of automation may make vehicle-based signals useless for characterizing driver states, providing further motivation for a DMS. Future improvements in impairment detection systems may be expected through a combination of improved hardware, physiological measures from unobtrusive sensors and wearables, and the intelligent integration of environmental variables like time of day and time on task.