使用热处理污泥吸附剂去除废水中重金属铜的研究

基于重金属铜的高度危害性和污泥的高效重复利用的考虑,研究了在污泥吸附剂上去除合成废水中重金属铜的实例。将污泥干燥,研磨并在各种温度下进行热处理,使用SEM和FTIR技术分析了污泥的表面形貌和化学结构。实验所研究的参数是铜离子的初始浓度、反应时间、污泥用量以及污泥的热处理温度。实验数据显示随着污泥用量的增加,Cu+2的去除率增加,并根据使用的浓度范围,研究了Cu+2的初始浓度对重金属铜去除率的影响。对实验数据使用不同等温线进行模拟,获得线性拟合性能较好的等温模型。     

低碳经济发展中物流绿色发展趋势研究

文中主要对低碳经济发展中物流绿色发展趋势进行了研究,介绍了对低碳经济发展物流绿色发展评价,探讨了低碳经济发展中的不同问卷的调查结果,对物流绿色发展的认知、态度和行动进行了论述,在论述过程中,主要包括对低碳经济发展中的物流认知、对低碳经济发展中的物流态度、对低碳经济发展中的物流行动和对发展低碳经济物流的认知、态度与行动的比较,最后给出了低碳经济发展中物流绿色发展趋势,对低碳经济发展中物流绿色发展给出了研究基础。     

阳极溶出伏安法、石墨炉原子吸收法测定海水中总铬的方法比较研究

铬是海洋水体中的痕量重金属之一,海水的高盐基体对铬的测定有严重的干扰,而且海水中同时存在Cr(Ⅲ)和Cr(Ⅵ),两种不同价态给测定也带来了一定难度。文章从Cr(Ⅲ)的氧化率、方法检出限、精密度、实际样品加标等方面对过氧化氢氧化-阳极溶出伏安法与传统的MIBK萃取-无火焰原子吸收分光光度法进行了比较研究,发现两种方法在测试结果上无显著差异,但是过氧化氢氧化-阳极溶出伏安法具有更高的精密度,更低的检出限,且对Cr(Ⅲ)的回收率略有提升。     

A study on the interactions among several plant secondary compounds and aphids

The research, focused on the specific interactions among seven plant secondary compounds and aphids, was carried out and the preliminary results showed: (1) "T-typed tube method" was regarded as the best method compared with others designed to observe the interactions. (2) Cabbage aphid was able to be attracted by laurolene while it was not susceptible to α-pinene, β-pinene and diamylene. (3) Gossypol, a major secondary substance in cotton, was able to be implicated as feeding attractant to cotton aphid. (4) Rutin might be implicated repellent to cotton aphid.     

8-氨基喹啉-5-偶氮-对苯酚荧光光度法测定微量铬(Ⅵ)

在盐酸介质中,铬(Ⅵ)与AQAPOH形成可被氯仿萃取的荧光化合物,其λex/λem=295nm/400nm.线性范围为0-1.0μg Cr(Ⅵ)/25ml(水相),检测限为0.7μg/L.除Mn(Ⅶ)外,常见金属离子无干扰,可在数千倍Cr(Ⅲ)存在下测定废水中微量铬(Ⅵ).初步探讨了荧光反应的机理.     

青铜峡铝厂氟污染对广武乡玉米影响的调查分析

监测了青铜峡铝厂东南方向４．５ｋｍ的广武乡玉米叶，得知含氟量为４３．９４－１６６．１ｍｇ／ｋｇ，对照样品树新林场玉米叶的含氟均值为１３．１５ｍｇ／ｋｇ，广武乡的玉米受到了严重的氟污染，农作物减产严重。     

发展我国环境监测技术产业的探讨

本文从发展我国环境监测技术产业的角度论述了环境监测技术产业的主要内容，分析了我国环境监测技术产业的发展和存在的问题，对发展我国环境监测技术产业提出了建议。     

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。     

浅谈烟道气现场监测的工作管理

烟道气现场监测，易出差错。监测人员必须有良好的专业理论水平、丰富的工作经验、高度的敬业精神。本文就现场监测工作易出现问题进行了分析，列举了解决问题的方法     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.