Biological pretreatment of Yellow River water

Bio-ceramic filter(BF) and moving-bed biofilm reactor(MBBR) were used for biological pretreatment of Yellow River water in this study. The BF only had slight advantage over MBBR for TOC and ammonia removal. However, like UV254, the average removal rate of THMFP in the BF was much higher than that in the MBBR. UV254 removal did not show obvious correlation with trihalomethane formation potential(THMFP) removal. Hexachlorocyclohexane could be effectively removed in both BF and MBBR. As for diatom and cyanobateria removal the MBBR had better performance than the BF, which was contrary to the average chlorophyll-a(Chl-a) removal rate. The proposal was made in this study that biological flocculation and sedimentation of sloughed biofilm should play a more important role on algae removal in the MBBR than in the BF. The BF and MBBR could effectively remove microcystins. Moreover, MBBR could be a promising technology for biological pretreatment.     

UBAF与纤维球过滤结合工艺应用于污水回用的试验研究

利用升流式曝气生物滤池 (UBAF)与纤维球过滤相结合的工艺对城市污水进行深度处理 ,使其达到回用的要求。结果表明 :对于经过二级处理后的可生物降解性较差的城市污水 ,应用UBAF和纤维球过滤相结合的处理工艺能够取得很好的效果 ,处理后的污水完全可以达到污水排放的一级标准和城市生活杂用水的标准     

Fe(Ⅲ,Ⅱ)/H2O2体系中Fe(Ⅲ)水解特征的对比

利用Fe Ferron逐时络合比色法对类Fenton与Fenton体系中Fe(Ⅲ )水解形态的转化规律进行了比较 .结果表明 ,水解度越高 ,两体系中Fe(Ⅲ )的水解速度也越快 ,但水解度相同时 ,与Fenton体系相比 ,类Fenton反应生成的Fe(Ⅲ )的水解速率要低 ,且Feb 所占比例也较少 .与Fenton体系一致 ,类Fenton体系中的Fe(Ⅲ )水解速率随H2 O2 浓度的增加而减缓     

天津地区表层土壤多环芳烃含量的中尺度空间结构特征

应用地统计学技术对全天津地区表层土壤中 16种优控多环芳烃含量和土壤理化参数进行了空间结构分析 .结果显示 ,各组分浓度均存在中尺度的空间自相关性 .多环芳烃浓度的空间结构存在明显的各向异性 .大气运移和土壤TOC含量可能是影响土壤多环芳烃浓度空间结构特征的重要环境因素 .     

魔芋葡甘聚糖在污水处理中的应用

该文就魔芋葡甘聚糖的发展研究状况作了概述，并探讨其与蛋白质共混胶应用于水处理的可能性。     

人工湿地处理污水的研究

简要介绍了人工湿地的发展历史、原理及优点；并对沙井镇人工湿地进行探讨和检测，其ＣＯＤｃｒ去除率可达８３％，总氮去除率达４５％，出水水质达国家排放标准。     

滇池二维分层水质模拟研究

将三维问题“二维化”的分层迭代计算方法引入滇池水质模拟中，建立了湖泊二维分层水质模型，对滇池流场和总氮浓度场分两层进行了模拟计算，计算结果表明，该模型应用于滇池是成功的。     

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.     

北京市机动车行驶周期的建立方法研究

提出了一种北京市机动车行驶周期的建立方法。行驶周期的确定是建立在准确的行驶工况数据之上的，通过结合使用GPS与OEM两种先进的技术，收集机动车行驶工况数据，为北京市行驶周期的建立提供了新的方法。并通过初步的实路测量试验得到了一个北京市的简易机动车行驶周期，最后与国外的一些著名行驶周期作了对比。     

北京市冬季公共场所室内空气中TSP, PM10, PM2.5和PM1污染研究

在北京市的海淀区、朝阳区、丰台区和昌平区选择了 49个公共场所 (包括办公室、宾馆、图书馆、超市等等 ) ,分别对其室内空气中TSP ,PM10 ,PM2 5 和PM1的浓度进行了测定 ,并且对室内空气中粉尘含量的影响因素进行了分析和探讨 .研究结果表明 ,繁忙的交通状况和建筑施工将明显增加公共场所室内空气中TSP ,PM10 ,PM2 5 和PM1浓度 .频繁的室内清扫有助于降低室内空气中颗粒物的浓度 .在室内空气中 ,PM10 浓度与TSP浓度呈现明显的正向线性相关性 ,而PM2 5 和PM1的浓度与PM10 浓度的相关性较差