土压平衡矩形顶管施工引起的地表沉降规律研究

为了分析与掌握矩形顶管施工引起的地表沉降变形规律,提出基于MIDAS-GTS有限元软件的分析方法,首先,通过工程实例验证MIDAS-GTS有限元软件在分析顶管施工引起地表沉降变形的有效性;其次,通过影响因素的敏感性分析确定不同影响因素作用下的地表沉降变形特性,这些影响因素主要包括开挖面支护压力、侧摩阻力、地层结构特性等;最后,通过地表沉降变形特性分析得出矩形顶管的适用范围。研究结果表明:基于MIDAS-GTS有限元软件的地表沉降规律分析方法和结果,可为矩形顶管施工过程提供相关参考和借鉴。     

遥感技术在灰霾监测中的应用综述

介绍了灰霾遥感监测的基本原理,论述了基于图像变换的TC法、HOT法和基于反演气溶胶光学厚度的灰霾遥感监测技术,以及造成灰霾天气的秸秆燃烧遥感监测方法.指出卫星遥感技术可以提供大尺度、长时间序列的污染物时空分布特征和变化趋势,以及全球性的大气环境综合遥感数据,是灰霾监测与综合治理的重要途径.     

水体重金属污染生物监测的研究进展

综述了重金属的毒性效应和水体重金属污染的现状,介绍了利用水生藻类、浮游动物群落和底栖动物监测水体重金属污染的研究进展及发展趋势。     

开展建设项目施工期的环境管理与环境监测

分析了建设项目施工期的环境影响，阐述了开展施工期环境管理与环境监测的必要性，介绍了施工期环境管理的实施手段，施工单位的环境管理制度，施工期环境监测的主要内容及对环境监测部门的要求，建议将施工期的环境管理与环境监测纳入正常的环保管理渠道之中，实现建设项目的全过程环境管理。     

供水管网震后流量监测点的动态分级优化布局研究

为了提高管网地震监测点布局的准确性和合理性,基于管网微观水力计算模型和动态分级法,提出供水管网震后流量监测点的动态分级优化布局模型。首先,利用管网微观水力计算模型计算管段流量的影响系数,构建管段的影响系数矩阵,并利用信息熵确定管段权重;其次,标准化处理影响系数矩阵,通过聚类迭代提出供水管网地震流量监测点优化布局的动态分级方法,对供水管网震后流量监测点进行优化布置分级评定;最后,根据工程实例进行方法实践,结果表明:供水管网中的管线分类较为科学合理,地震监测点在供水管网上分布也比较均匀,而且该模型在一定程度上消除了人为因素的影响,保障了震时管网的监控效果和日常建设的合理性。     

基于机器视觉的鱼类行为特征提取与分析

近年来,水污染问题备受关注。生物式水质监测成为目前国家环境保护工作的重要任务之一。为准确监测水质污染情况,本文以青鳉鱼(Oryzias latipes)为研究对象,采用非接触式的机器视觉监测技术,提取青鳉鱼的生理特征(呼吸频率)和运动特征(胸鳍和尾鳍的摆动频率),并分析这些特征与水质之间的关系。本文采用支持向量机(Support Vector Machine,SVM)准确提取鱼鳃,并根据鱼鳃呼吸面积大小变化计算出鱼的呼吸频率。基于形态学细化算法提取青鳉鱼骨架,求出胸鳍和尾鳍的摆动频率。结果显示:不同浓度铜离子暴露实验测得的青鳉鱼生理特征和运动特征与实际情况一致;通过对不同铜离子浓度下的毒性实验数据对比,发现了青鳉鱼的生理特征和运动特征会随不同的铜离子浓度发生相应变化,可以作为水质监测的评价标准。     

Occurrence and distribution of trifluralin,ethalfluralin, and pendimethalin in soils used for long-term intensive cotton cultivation in central Greece

In the present study, a soil monitoring program was undertaken in Greek cotton cultivated areas in 2012. Twenty-seven soil samples were collected from the entire Thessaly plain in early summer of 2012, corresponding to approximately three months (current use of pendimethalin), up to one year (for the banned ethalfluralin), and three years (for the also banned trifluralin), after the last dinitroaniline application. Low but not negligible levels of dinitroanilines were detected, ranging from 0.01 to 0.21 μg g^?1 d.w. for trifluralin and 0.01–0.048 μg g^?1 d.w. for pendimethalin, respectively. Trifluralin was the herbicide most frequently detected (44.4%). The high historic application of trifluralin and its high persistence and accumulation potential is in line with the abundance of the detected residues. The present data indicate that soil samples contain extractable residues of banned trifluralin, but based on the comparison of the theoretical PECplateau for trifluralin (0.277 µg g^?1) and the maximum Measured Environmental Concentration, it was concluded that the detected residues should be attributed to previous years’ application. The latter suggested the need for continual monitoring of the dinitroaniline family of pesticides, including the banned substances, aiming thus to an improved environmental profile for agricultural areas.     

基于MSPA与MCR模型的余江县生态网络构建基于MSPA与MCR模型的余江县生态网络构建

在我国社会经济快速发展的背景下,城市化进程的不断推进导致城市生态斑块日益减少,生态环境问题正在逐年增加,构建生态网络是改善城市生态环境与服务功能的有效方法之一。以余江县为研究区,采用形态学空间格局分析(MSPA)方法和景观指数法,提取对生态网络具有重要意义的生态源地,并基于MCR模型构建综合阻力面,采用最小成本路径方法生成潜在廊道,再基于重力模型、中介中心度等对关键廊道及踏脚石斑块进行识别并提取,从而构建研究区潜在生态网络。结果表明：MSPA方法能够与MCR模型有机的结合,通过定量的分析识别出研究区潜在生态廊道,并根据斑块的中介作用选定踏脚石斑块,明确研究区景观要素的保护优先度,将景观中的潜在生态源地及廊道作为生态网络构建的主要依据,能够更加科学地为余江县生态网络的构建提供指导,同时也可为其他区域提供参考。     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

Water resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions (GAMUT) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH, nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.