超滤处理洗涤污水循环利用的中试研究

采用不同材质国产超滤膜聚丙烯（PP）、聚丙烯腈（PAN）和聚砜（PS）,进行了洗涤污水处理循环利用的现场中试研究.结合超滤工艺出水水质和膜污染分析,3种膜材质中PAN膜较优,有效去除了水中浊度、悬浮物、油脂等污染物,一定程度保留了游离阴离子表面活性剂（LAS）,长期循环洗涤对衣物的白度无不良影响.根据相关性分析,超滤出水较高COD值很大程度上是由水中LAS引起的.超滤膜对细菌、大肠菌群的去除率较低,出水通过紫外消毒,当紫外线密度≥3?750 J/m²时,微生物水平能够达到国家饮用水水质标准.根据不同水力反冲洗条件下膜渗透通量和净产水量比较,PAN膜最佳水力反冲洗条件为0.5 h反冲洗2 min.长期运行时超滤膜化学清洗方法采用碱洗法方便有效.     

丙酸的加入对厌氧-低氧同时生物除磷脱氮系统的影响

2个实验室规模的序批式反应器（SBRs）在厌氧-低氧（0.15～0.45 mg·L^-1）条件下运行,以比较丙酸的加入对同时生物除磷脱氮系统的影响.结果表明,无论是丙酸与乙酸的混合酸（碳摩尔比为1.5/1）作为碳源（SBR1）,还是乙酸作为单独碳源（SBR2）,系统都发生同步硝化反硝化和磷的去除（SNDPR）,并且氨氮被全部氧化,系统中没有亚硝酸盐的大量累积.与SBR2相比,SBR1中厌氧阶段磷释放量少,聚羟基戊酸（PHV）合成量高,好氧末磷剩余量少,硝态氮累积少,因此SBR1中总氮和总磷的去除率（分别为68%和95%）比SBR2（分别为51%和92%）高,加入丙酸有助于SNDPR系统保持较好的除磷、脱氮效果.     

颗粒污泥SBR处理生活污水同步除磷脱氮的研究

采用厌氧-好氧的SBR运行方式,以人工配水培养的好氧颗粒污泥为接种污泥,处理碳、氮、SS浓度均较高的生活污水,研究了系统中颗粒污泥的稳定性及其去除有机物和同步除磷脱氮的效果.经过1个月的驯化培养,颗粒污泥即可呈现出良好的污染物去除性能并趋于稳定,反应器中颗粒污泥含量始终占污泥总量的68%以上.颗粒污泥系统污泥浓度为5 000～6000mg/L,SVI值为20～35 mL/g.经过3个月的运行后,反应器中颗粒污泥由原来以粒径0.6～0.9 mm的中等大小颗粒占主体变为粒径＞1.25 mm的大颗粒占主体.稳定运行阶段颗粒污泥系统对COD、TOC、磷酸盐、氨氮、总氮和SS的平均去除率分别为83.04%、70.41%、94.30%、86.51%、41.82%和85.89%.对反应器运行过程中典型周期的分析,反映出颗粒污泥良好的同步除磷脱氮效果.     

转炉煤气净化回收技术发展现状

介绍了氧气转炉煤气的干法和湿法净化回收技术的发展现状,着重二者的技术经济比较,阐述干法(LT法)的发展优势.     

底泥中营养物质及其他污染物释放机理综述

水体底泥(沉积物)污染,是世界范围内的一个重要环境问题.其污染物主要通过大气沉降、废水排放、水土流失、雨水淋溶与冲刷进入水体,最后沉积到底泥中并逐渐富集,使底泥受到严重污染.总结了底泥中氨氮、重金属的影响因素及释放机理,也指明了持久性有机污染物释放机理研究不足的现状,提出对其释放机理研究的重要性.     

首钢除尘灰特性分析及综合利用技术研究

通过对首钢烧结、炼铁、炼钢等生产过程产生的各种除尘灰进行理化特性的测定分析,研究了除尘灰直接回用烧结原料对烧结生产及机头电除尘器的影响,提出了除尘灰的科学分类和综合利用技术方案.     

CO2对砷酸钙稳定性影响的热力学分析

通过沉淀和溶解两方面的实验数据,应用PHREEQC程序模拟了砷酸钙在不同CO2分压条件下溶解度的变化情况,发现环境中的CO2可使砷酸钙盐在酸度较高(pH大于8.3)的条件下发生不一致溶解,使其溶解度升高;CO2分压越大,砷酸钙盐发生不一致溶解的酸度越低;CO2主要影响3∶2和5∶3的砷酸钙盐,而对4∶2砷酸钙盐的影响较小。这对不同类型砷酸钙盐废物的处置是否应考虑CO2的因素提供了依据。     

我国电子废弃物的管理及发展道路研究

我国电子废弃物的处理处置正向产业化方向稳步推进。本文概括了电子废弃物的特点,以及我国目前处理电子废弃物的现状。遵照循环经济理念,并基于我国国情,提出了我国在电子废弃物处理处置过程中应遵循的主要原则和今后的发展方向。     

Decommissioning of offshore oil and gas facilities: a comparative assessment of different scenarios

A material and energy flow analysis, with corresponding financial flows, was carried out for different decommissioning scenarios for the different elements of an offshore oil and gas structure. A comparative assessment was made of the non-financial (especially environmental) outcomes of the different scenarios, with the reference scenario being to leave all structures in situ, while other scenarios envisaged leaving them on the seabed or removing them to shore for recycling and disposal. The costs of each scenario, when compared with the reference scenario, give an implicit valuation of the non-financial outcomes (e.g. environmental improvements), should that scenario be adopted by society. The paper concludes that it is not clear that the removal of the topsides and jackets of large steel structures to shore, as currently required by regulations, is environmentally justified; that concrete structures should certainly be left in place; and that leaving footings, cuttings and pipelines in place, with subsequent monitoring, would also be justified unless very large values were placed by society on a clear seabed and trawling access.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.