黄土风化成土过程中主要元素迁移序列

主要研究了渭南黄土剖面中主要化学组分的分布特征、影响因素以及它们在风化成土过程中的迁移富集规律。结果表明：（1）在剖面中随着黄土与古土壤层的交替叠复，各组分含量由低-高或由高-低呈周期性变化。（2）碳酸盐含量的剧烈变化是影响黄土元素分布特征极为重要的因素，古土壤中一些组分的含量增高，主要是碳酸盐被强烈淋滤造成的相对富集。（3）在黄土风化成土过程中，CaCQ3、SiQ2、FeO、MgO、K2O、Na2O等本身的地球化学行为以迁移为主，而Al2O3、Fe2O3、TiO2等均以淀积为主。它们在黄土中的迁移能力有如下顺序：CaCO3＞FeO＞MgO＞Na2O＞K2O＞SiO2＞Al2O3＞TiO2＞Fe2O3。（4）除碳酸盐外，黄土风化成土过程中铁的变化比其它组分都明显，表现为价态以及形态的转化。铁的转化主要为就地完成．本身没有发生明显迁移。     

小型硫磺回收装置的设计与试运

介绍寒冷地区小型硫磺回收的设计特点，包括选用一、二、三级冷凝器同壳、低噪音鼓风机；试生产中的改进经验，包括过程气用蒸汽夹套保温，带有硫封的被硫储罐．     

利用硫铁矿渣（灰）制取海绵铁

结合硫酸生产的特点，充分利用废热的方法，提出一种采用流态化技术，还原回收硫铁矿渣（灰）中铁元素的工艺流程，并对流程中的流态化还原炉进行了初步的物料，热理衡算，同时对该工艺流程技术经济比较作了小结。认为此工艺流程技术可靠，有直接经济效益，对综合利用硫铁矿渣（灰）中的铁元素，消除渣（灰）对环境的污染，有着深远的意义。     

海洋环境中纳米金属的生物吸收及生物效应

当前随着纳米科技的发展,纳米材料,特别是纳米金属,因其独特的物化性质,在各行各业中的使用量呈指数增长,致使其在大气、水域、土壤环境中的安全性问题引起公众关注。尤其是在受到人类活动密切影响的近岸海洋环境中,纳米金属的潜在生态效应成为当前国内外研究的热点之一。本文重点综述了由于海洋环境的理化因子以及纳米金属独特的物化性质导致的纳米金属的环境行为,海洋生物对纳米金属的吸收,以及纳米金属的生物效应和可能的致毒机制,旨在为评估海洋环境中纳米金属的潜在生态危害,完善纳米材料的监管机制及保障纳米科技的可持续发展提供思路。     

蜡状芽孢杆菌WXZ-8的异养硝化/好氧反硝化性能研究

通过聚合酶链式反应(PCR)扩增、16S rDNA测序并结合同源性分析,鉴别出菌株WXZ-8为蜡状芽孢杆菌(Bacillus ce-reus).对菌株WXZ-8进行异养硝化/好氧反硝化性能测定,并通过正交实验进行培养条件的优选,选取的因素为COD/N、温度、转速和pH.结果表明,对菌株WXZ-8影响最大的因素是转速,其次为COD/N;在最优条件(即温度=30℃、COD/N=25、pH=9.0、转速=180 r/min)下,菌株WXZ-8的氨氮去除率最高达到96.06%.在最优条件下,提高初始氨氮质量浓度为211.52、429.16、897.29mg/L时.菌株WXZ-8在前48 h的氨氮去除速度均达到最大,分别为119.04、94.76,142.21 mg/(L·d),其氨氮去除率最高分别为94.41%、73.43%、51.08%.菌株WXZ-8具有良好的异养硝化/好氧反硝化性能.     

火焰原子吸收光谱法测定电镀废水中高浓度的锡

采用盐酸消解样品,用火焰原子吸收光谱法测定电镀废水中高浓度的锡的质量浓度,方法简单可靠。通过实验样品的分析,验证了方法的准确度和精密度。该方法干扰少,数据准确,适合废水分析。     

超重力技术治理火炸药行业氮氧化物的初步研究

火炸药行业排放氮氧化物(NOX)具有浓度高、排放点不集中和间歇排放的特点,采用超重力技术对尾气中高浓度的NOX进行湿法吸收.以清水为吸收剂,研究了进气量、液气比和超重力因子等因素对NOX吸收率的影响.结果表明,单级NOX吸收率可达64.69%(β＝90).超重力旋转填料床具有占地面积小、投资低和操作方便等优点,适宜于火炸药行业吸收NOX.     

Sub-lethal toxicity of the antiparasitic abamectin on earthworms and the application of neutral red retention time as a biomarker

The antiparasitic abamectin has been proven effective against both endo- and ectoparasites of farm animals and hence used widely in animal husbandry. It may enter the soil environment with the excreta of treated animals. Very little information is available with regard to the sub-lethal effects of abamectin on soil invertebrates, such as earthworms. The objective of this study was to evaluate the toxic effect of abamectin on earthworms, using Eisenia fetida, by analyzing changes in the survival, growth, reproduction and cocoon hatchability of exposed earthworms. Furthermore, a biomarker of the lysosomal membrane stability, measured by neutral red retention time (NRR-time), was also applied. Abamectin showed significant toxicity on the growth of earthworms with increasing concentrations up to 5mg/kg. The most sensitive parameter was reproduction (cocoons production and hatchability) and NRR-time. The number of cocoons was reduced at concentrations above 0.25mg/kg and no cocoons were present at the highest concentration of 5mg/kg. Cocoons exposed to abamectin exhibited a reduced hatching success at concentrations above 1.5mg/kg. The NRR-time was reduced significantly at exposure concentrations of abamectin above 0.25mg/kg. The change in lysosomal membrane stability showed a good correlation with reproduction and may hence be a potential predicator of the effects on earthworm populations.     

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.