红壤丘陵区典型流域地表水水环境特征研究——以浏阳河流域为例

选取浏阳河流域为例,根据近十年河流断面监测数据,采用改进的综合污染指数法来评价水质污染程度,研究了该红壤丘陵区典型河流水质的时空变化特征,并结合土地利用和土壤特征等分析地表水质变化原因。结果表明,改进的综合污染指数法有较好的适用性;从时间特征上看,由于面源污染加剧,使得浏阳河近十年的水质污染呈增长趋势;从空间特征上看,浏阳河从上游到下游,河流污染呈增长趋势,上游水质较好,中游表现为重金属铅和汞的污染较大,而下游则是氨氮污染加剧。     

大气环境质量综合评价加权灰色关联模型的建立与应用

利用灰色理论建立大气环境质量综合评价加权灰色关联模型 ,运用污染贡献率确定权重系数。与其它评价模型相比 ,加权灰色关联模型还具有排序功能 ,实例表明该模型是简便有效的     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

石墨炉原子吸收光谱法测定海河下游水中痕量镉

原子吸收光谱法直接测定高盐水中痕量镉时，有很大背景吸收和误差。本文采用络合—萃取技术使共存元素与待测元素分离，既消除了基体干扰，又达到了富集作用，使测定结果准确可靠。     

高灵敏XRF测定废水中痕量砷

本文提出了在硫酸介质中，以锌粒还原产生氢化物，溴化铜溶液吸收，微孔滤膜过滤制成薄样，Ｘ射线荧光测定水中痕量砷的分析方法。     

安徽省淮河流域水污染分析与环境目标可达性探讨

介绍了安徽省淮河流域水污染的历史与现状,分析了淮河流域水污染的特点,对实现《淮河流域水污染防治“十五”计划》的可能性进行了探讨。     

肿瘤病房医用直线加速器对周围环境电离辐射的调查分析

通过对医院肿瘤病房医用直线加速器机房周围环境的电离辐射监测调查 ,并计算分析各相关人群的年有效剂量当量 ,结果低于《辐射防护规定》(GB870 3- 88)中的标准限值。表明医用直线加速器机房的辐射防护设施设计是安全合理的 ,具有重要的现实意义 ,该辐射防护设施具有推广价值。     

Desert Water Harvesting to Benefit Wildlife: A Simple, Cheap, And Durable Sub-Surface Water Harvester for Remote Locations

A sub-surface desert water harvester was constructed in the sagebrush steppe habitat of south-central Idaho, U.S.A. The desert water harvester utilizes a buried micro-catchment and three buried storage tanks to augment water for wildlife during the dry season. In this region, mean annual precipitation (MAP) ranges between about 150–250 mm (6″–10″), 70% of which falls during the cold season, November to May. Mid-summer through early autumn, June through October, is the dry portion of the year. During this period, the sub-surface water harvester provides supplemental water for wildlife for 30–90 days, depending upon the precipitation that year. The desert water harvester is constructed with commonly available, “over the counter” materials. The micro-catchment is made of a square-shaped, 20 mL. “PERMALON” polyethylene pond liner (approximately 22.9 m × 22.9 m = 523 m²) buried at a depth of about 60 cm. A PVC pipe connects the harvester with two storage tanks and a drinking trough. The total capacity of the water harvester is about 4777 L (1262 U.S. gallons) which includes three underground storage tanks, a trough and pipes. The drinking trough is refined with an access ramp for birds and small animals. The technology is simple, cheap, and durable and can be adapted to other uses, e.g. drip irrigation, short-term water for small livestock, poultry farming etc. The desert water harvester can be used to concentrate and collect water from precipitation and run-off in semi-arid and arid regions. Water harvested in such a relatively small area will not impact the ground water table but it should help to grow small areas of crops or vegetables to aid villagers in self-sufficiency.     

新疆河流洪水水污染分析

洪水期间的水污染主要来源于面源。其特点是污染物的浓度高、负荷量大。给工业、渔业和人民生活都会带来不同程度的危害。悬浮物是洪水过程中的主要面污染产物，同时还作为载体吸附和携带大量的有机物、无机盐、重金属类和微生物进入水体，致使河流水质下降。在研究洪水灾害的同时。不应忽略洪水期间的水污染问题。     

多氯联苯的气相色谱/质谱/质谱(GC/MS/MS)法测定

采用气相色谱/质谱/质谱(GC/MS/MS)法测定多氯联苯(PCB),具有良好的灵敏度和选择性,在地表水样品检测时,样品只须进行简单的液液萃取-浓缩,不须任何净化过程即可上机分析,方法检出限低于ng/L级。