红萍净化水产养殖水体的研究

利用红萍(Azolla)对富营养化水产养殖水体进行净化研究。结果表明,红萍能显著增加水体中的DO,增氧幅度随流量在16.88%～70.46%之间变化,随着养殖水体流经的层数增多或者处理的时间延长,红萍对水体的增氧量加大,水中DO最终趋向一个常数值K;同时,红萍对水体中的NH₃-N和TP都有明显的去除效果,NH₃-N去除率随流量在9.86%～38.90%间波动,TP的去除率则随流量的变化在5.80%～38.43%之间波动。可见,红萍是净化水质的良好材料,能有效改善水产养殖水环境。利用红萍净化水体将为解决高密度集约化水产养殖的瓶颈提供新途径,实现水产养殖用水的封闭循环利用。     

聚硅酸锌铝的制备及其性能研究

以硅酸钠、硫酸锌、硫酸铝为原料,通过共聚法制备了无机高分子絮凝剂聚硅酸锌铝（PSZAS）,研究了Na₂SiO₃的摩尔浓度、活化时间、Al/Si、Zn/Si 配比及絮凝剂的投加量对絮凝效果的影响,用X-射线衍射（XRD）和电子扫描电镜（SEM）对该絮凝剂的结构及形貌进行了表征。结果表明：当硅酸钠的浓度为0.4 mol/L,硅酸活化时间为2 h,Si∶Al∶Zn=1∶1∶1.5,絮凝剂投加量为20 mL/L废水时,絮凝剂的电中和能力和吸附架桥能力最强,絮凝剂对含H-酸染料模拟废水的絮凝效果最好。     

CASS处理技术的原理和应用

介绍了CASS工艺的原理、特点、核心构筑物和设计、运行中应注意的主要问题以及常见故障的排除方法。     

硅酸盐细菌解钾兼拮抗活性菌株的筛选

从紫色土壤中分离筛选出具有解钾拮抗双重活性的硅酸盐细菌CS1和CS29菌株。接菌处理,其水溶性钾分别增加75％和65％,转化率达0．82％和0．72％。两株菌对革兰氏阳性的蜡样芽孢杆菌,金黄色葡萄球菌,藤黄八叠球菌等和革兰氏阴性的大肠杆菌均有较强的抑菌活性。筛选菌株中91．2％具有解钾活性,但拮抗活性菌株只占总数的11．6％,表明二性状之间无相关关系。表4参7     

气相色谱-质谱法分析污水中溶解态有机物

采用0.45μm玻璃纤维滤膜将污水分离为悬浮态和溶解态有机物质,以二氯甲烷为萃取剂,采用液液萃取-气质联用法对污水中溶解态有机物进行测定,同时还对萃取条件(萃取次数,萃取时间,无机盐加入量)进行优化.结果表明:萃取回收率最高的是每个pH范围各萃取2次、静置5 min,氯化钠加入15g.该方法分析时间短,准确度好(样品平均加标回收率为91.1％ ～ 103％),精密度高(相对标准偏差小于5％),易于操作.     

渤海湾入海溶解无机氮总量控制研究

基于渤海湾近岸海域的实际调查结果,采取生态物理耦合模型,对渤海湾的主要污染物-溶解无机氮(DIN)的基准环境容量和极小剩余海洋环境容量进行了计算。结果表明,渤海湾DIN的极小剩余海洋环境容量在Ⅰ类和Ⅱ类水质标准下均为负值,渤海湾的DIN已经超标。结合实际的海水功能区水质管理目标,应重点控制非点源的排放,加强上游携带入境污染物的处理,从总量上控制DIN入海污染通量,改善渤海湾水质。     

基于白碳黑的水化硅酸钙制备及其磷回收特性简

以白碳黑、硅灰、硅藻土和硅胶筛选硅质原料,并与钙质原料电石渣制备了水化硅酸钙。借助XRF、BET、FTIR等表征手段,通过多次重复除磷实验,研究了硅质原料特性对水化硅酸钙回收磷性能的影响。结果表明,白碳黑具有极高的反应活性,因此可作为制备具有磷回收特性的水化硅酸钙的硅质原料。结合XRD等表征发现,白碳黑的有效利用率是影响水化硅酸钙回收磷性能的关键,该利用率取决于白碳黑与电石渣的摩尔配比以及水热反应温度。当电石渣与白碳黑的摩尔比为1.6：1,反应温度为170℃时,白碳黑具有最佳的利用效率。该条件制备的水化硅酸钙可作为晶种,在其表面结晶形成羟基磷灰石,从而达到磷回收的目的,磷回收后固体物质中的磷含量为19.05%。     

Effects of the organic matter from swine wastewater on the adsorption and desorption of alachlor in soil

The application of swine wastewater to the soil for agricultural purposes results in the addition of total and dissolved organic matter to the soil, which may interfere with the dynamics of pesticides in the soil. The objective of this study was to evaluate the effects of the application of total and dissolved organic matter from a biodigester and a treatment lagoon of swine wastewater in the adsorption and desorption of alachlor [2-chloro-2,6-diethyl-N(methoxymethyl acetamide)]. The assay was performed by the batch equilibrium method, and the results were fitted to the Freundlich model. The curve comparison test revealed a greater adsorption of alachlor in the soil treated with swine wastewater from the biodigester. The adsorption and desorption of alachlor increased in the soils where swine wastewater was added, and hysteresis was observed in all of the treatments.     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

纳米生态基对水产养殖污水的处理效果

采用三因子四水平的正交设计,实验研究了纳米生态基在不同温度、溶解氧和水力停留时间下对水产养殖污水的处理效果,确定了纳米生态基处理养殖污水的最佳条件。结果表明,含氨氮和亚硝氮浓度较高的模拟养殖污水用纳米生态基挂膜,所需时间约为22 d。纳米生态基对氨氮的去除效果明显,平均去除率达到93.5%。对氨氮去除率的影响程度,水力停留时间>温度>溶解氧。当温度为30℃,DO为5.43 mg/L,HRT为0.33 h时,纳米生态基对氨氮的处理能力最佳,去除率达到94.6%。纳米生态基对亚硝氮的平均去除率为69.3%。对亚硝氮去除率的影响程度,水力停留时间>溶解氧>温度。当温度为21℃,DO为6.40 mg/L,HRT为0.33 h时,纳米生态基对亚硝氮的处理能力最佳,去除率为71.5%。纳米生态基处理养殖污水的最佳条件:温度为30℃,DO为6.40 mg/L,HRT为0.33 h。