电化学还原-氧化工艺降解4-氯酚的毒性研究

采用紫外光还原法制备了Pd-Fe/石墨烯催化阴极,并以Ti/IrO_2/RuO_2为阳极,构成三电极体系(双阴极)和两电极体系(单阴极)的电化学还原-氧化降解工艺,分别对4-氯酚进行降解.采用离子色谱、高效液相色谱、TOC仪对4-氯酚降解过程中中间产物及其浓度进行测定.根据公式计算降解过程中理论计算毒性值,应用发光细菌法测定降解过程中的实际毒性值,对理论计算毒性值与实际毒性值进行比较,分析不同体系下降解过程中毒性的变化规律.结果表明,两种工艺体系在最佳降解条件下,阴极室毒性均呈下降的趋势,由于降解过程中在阳极室生成高毒性的苯醌,阳极室毒性均先升高后降低.通过相关性分析得到,两种体系理论计算毒性与实际毒性在P=0.01水平下,相关性系数均为1,显著相关,表明降解过程中实际毒性的测定结果真实可靠.降解至120 min时,三电极体系毒性小于两电极体系,表明三电极体系优于两电极体系.据此提出实际毒性测定方法在电化学还原-氧化工艺降解氯酚类有机废水毒性测试的工业应用中有着广泛的前景.     

基于多年遥感数据分析长江河口海岸带湿地变化及其驱动因子

研究河口海岸带湿地长时间演变对湿地保护管理和海岸带资源评估具有重要意义.本文获取长江口1979—2015年10景Landsat-MSS/TM/OLI影像和2015年13景GF1-PMS高空间分辨率数据,对比两个典型实验区分类算法,选用最优的决策树算法应用到长江口Landsat影像中,得到沿岸湿地要素近40年的面积变化情况.研究表明,2015年长江河口海岸带湿地总面积为4725 km2,自然湿地占63.5%,人工湿地占21.2%,湿地总面积相比1979年增加了662 km2,自然湿地面积减少了163 km2,而人工湿地面积增加了766 km2.长江口自然湿地面积在1979—2000年减少幅度较大,2000年后由于保护管理加强而减少幅度变小;人工湿地和建筑面积增加较为明显,主要是由于大型水库的修建和人工鱼塘开发及港口建设.湿地总的变化趋势为河口区不断淤积,自然湿地转变为人工湿地,人工湿地转变为建筑用地等非湿地;其中,滩涂面积减少283 km2,水库、养殖鱼塘和水稻田面积分别增加了92、355和319 km2,主要发生在崇明东滩和启东沿岸;非湿地中建筑用地面积增加154 km2,灌木草场面积减少147 km2,主要发生在上海和启东沿岸.同时比较分析长江口3个区域湿地驱动因子发现,北岸启东沿岸和南岸南汇东滩湿地因经济快速发展和港口水利工程修建,以及过度开垦滩涂等自然湿地使人工湿地增加明显;而长江上游径流量、区域降水和海平面上涨等自然因素控制着中支河道区(如崇明东滩)自然湿地的变化.     

林丹短期暴露下的斑马鱼(Brachydanio rerio)组织学变化

以斑马鱼(Brachydaniorerio)为受试生物,初步探讨了林丹对斑马鱼的急性毒性以及不同浓度(0.01μg·L-1、1.0μg·L-1、100.0μg·L-1)的林丹暴露36d后对斑马鱼鳃和肝组织结构变化的影响.结果表明,林丹对斑马鱼96h-LC50为97.98μg·L-1,95%可信限为94.38￣101.72μg·L-1.当林丹浓度为0.01μg·L-1时,斑马鱼的鳃和肝组织结构未发生明显变化,而暴露在1.0μg·L-1和100.0μg·L-1林丹溶液中的斑马鱼的鳃和肝组织结构变化显著.斑马鱼鳃组织变化为:上皮细胞残损、脱落,鳃小片上皮细胞水肿,柱细胞变形.斑马鱼肝组织变化为:部分肝细胞肿大,细胞核萎缩变形或偏离细胞中心,胞质疏松,空泡明显增加,细胞质中可见脂沉积.与1.0μg·L-1林丹暴露相比,暴露在100μg·L-1林丹溶液中的斑马鱼鳃和肝组织结构受到的损害更为严重.     

拟除虫菊酯杀虫剂对水生态系统的毒性作用

拟除虫菊酯杀虫剂主要用于防治棉田、菜地、果树和茶叶上的农业害虫以及卫生害虫,也用于渔业生产上杀灭寄生虫。近几年来其使用量越来越大,是传统有机磷农药的替代品。它具有高效广谱、低残留的特点,对光稳定,在哺乳动物体内代谢、排泄迅速,但是对水生态系统危害较大。报道了拟除虫菊酯杀虫剂对水生生物的急、慢性毒性和中毒机理的研究进展,阐述了有待研究的方向。     

基于大数据挖掘技术的地震舆情感知研究

对地震舆情信息的深入感知和有效管理,能够保障社会和谐发展.提出一个基于大数据技术和深度学习的地震舆情感知平台,基于Hadoop和MongoDB大数据技术实现对海量实时地震舆情数据的处理和存储.基于Word2vec和LSTM的融合模型能够有效实现震后网民的情感识别,为舆情预警提供支持.以台湾5.8级地震舆情数据为例,对该...     

数据挖掘在预测组织事故防控效果中的应用*

为进一步探索数据挖据技术在组织事故预防工作中的融入性与适用性,基于24Model构建事故预控基础模型,通过预测准确率数值及接受者操作特性曲线（ROC曲线）对比分析随机森林（RF）、支持向量机(SVM)、决策树（DT）与神经网络（NN）4种方法对组织事故防控效果的预测性能。结果表明:针对事故率控制（Y1）、职业危害预防（Y2）、财产损失3类预测目标（Y3）,RF方法均能达到较高的准确率及稳定性,具有较优的预测性能。根据特征重要度（FI）排序,明确对组织事故水平影响最显著的因素为安全实践活动认知（SC5）及安全管理程序文件（SMS3）,FI值均大于0.150 0。研究结果可为有效预测组织事故防控效果提供方法依据,同时为企业安全工作的规划设计提供思路。     

基于第二次全国污染源普查质量保证的思考及体系建设初探

通过对清查数据的结果分析,确定第二次全国污染源入户普查的对象,基于企业填报、现场入户、资料收集审核、数据核查等具体工作的实施与开展,思考和归总普查工作中影响数据质量的现实问题和难点,初步构建污染源普查质量保证体系,以期为全国污染源普查数据质量保证提供有益参考。     

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

A critical overview of driver recording tools

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.