改性蜂窝煤渣吸附Cr（VI）的动力学和热力学性能

利用碳酸钡对蜂窝煤渣进行改性,通过粉末X射线衍射光谱（XRD）和扫描电镜（SEM）对改性前后蜂窝煤渣的物质组成和形貌进行表征,研究了改性蜂窝煤渣吸附Cr（VI）的动力学和热力学性能。实验结果表明,改性蜂窝煤渣对Cr（VI）具有良好的吸附能力,吸附过程符合准二级吸附动力学模型。分别用Langmuir、Freundlich、D-R和Tempkin等4种吸附等温线模型对实验数据进行拟合,Langmuir方程拟合效果最好,25、35和45℃下的饱和吸附量分别为2．985、2．562和2．630mg／g。D—R和Tempkin模型研究表明,吸附过程属于物理吸附。热力学研究表明,改性蜂窝煤渣对Cr（VI）的吸附是一个自发的、放热过程。     

花生壳活性炭对水中荧光素钠的吸附及动力学

H3PO4活化花生壳炭(750℃)为吸附剂,荧光素钠为吸附质.考察了炭化温度、投加量、pH、离子强度、吸附时间以及初始浓度对花生壳活性炭吸附荧光素钠的影响.重点研究了花生壳活性炭对荧光素钠的等温吸附特征,并利用准一级、准二级动力学模型和颗粒内扩散方程对花生壳活性炭吸附荧光素钠的动力学过程进行了分析.结果表明,本实验条件下,Langmuir等温吸附模型能很好地描述花生壳活性炭对荧光素钠的吸附效果,吸附强度因子a为正值,表明吸附过程在本实验条件下可自发进行.通过带入实验数据校对得出准二级动力学方程能更好地描述花生壳活性炭对水中荧光素钠的吸附动力学过程.     

采用分层采样技术对场地地下水污染物进行三维空间描述

地下水单一混合层采样所需建井及采样技术较简单,曾被广泛应用于地下水监测,但根据该技术所得地下水污染物数据进行污染场地地下水污染羽和污染程度描述,常常存在较严重的偏差。以某氯代烃污染场地为例,详细阐述了巢式监测井的建井及分层采样技术,并以该场地主要污染物氯乙烯和氯仿为例,对比了来自不同采样技术的两个主要污染物数据,证实了采用分层采样技术对污染场地地下水进行调查,具有更好的准确性和可靠性。     

预处理-超滤-反渗透工艺深度处理炼油厂污水

采用预处理-超滤-反渗透工艺深度处理某炼油厂污水场的二沉池出水。预处理单元出水COD为40mg/L,ρ(NH3-N)为1mg/L,SS为6.3mg/L,能够满足超滤-反渗透系统的进水要求。超滤单元进水压力在0.04~0.08MPa波动,反渗透单元进水压力在0.92~1.12MPa波动,运行均稳定,化学清洗周期均可超过一个月。预处理-超滤-反渗透工艺产水回用于二级脱盐装置脱盐水补水的成本为每吨产水2.04元,低于企业现有的新鲜水每吨3.45元的费用。该优质产水取代新鲜水回用,每年可为企业节约100多万元。     

柴油机Urea-SCR系统喷射雾化的数值模拟

为研究柴油机Urea-SCR系统喷射雾化规律,应用CFD软件建立喷射雾化模型,对喷嘴喷射距离、喷射位置与方向、喷孔数目与孔径对催化剂载体入口处的浓度分布情况的影响进行了数值模拟。结果表明,喷嘴距反应器距离为排气管直径5倍为宜;采用四喷孔、孔径为0.5 mm、喷嘴处于管道中心,喷射方向为径向时喷射雾化均匀性最好。通过台架实验,验证了采用喷距5D、径向、四孔、小孔径喷嘴方案可使NOx的转化率达到95%以上。     

铁碳微电解预处理汽车电泳涂装废水

采用铁碳微电解法对汽车电泳涂装废水进行处理,研究其处理效果及COD降解动力学。结果表明,进水pH≤4时,铁碳微电解的处理效果影响因素依次为铁碳反应次数、反应时间、进水pH。COD降解率随微电解反应时间的增加而升高,随反应次数的增加而降低,之后趋向稳定。当进水pH=3,反应时间90~150 min时,铁碳微电解的处理效果可稳定在40%左右。同时通过模拟分析,汽车电泳涂装废水COD降解动力学符合三级反应动力学模型。     

清洁生产方案实施成效分析

清洁生产方案的质量及可实施性直接关系到企业清洁生产审核的成效。汇总江苏油田推行清洁生产审核单位实施的清洁生产方案380项,从方案的组成、分类、投资、经济效益等方面进行统计分析,阐明了多种无、低费方案,不仅资金投入的回报率高,而且投资风险少,有明显的效益。通过"节能、降耗、减污、增效"这一主导思想产生的各类清洁生产方案的实施,达到了较可观的经济、环境和社会效益。     

Impact of climate fluctuations on deposition of DDT and hexachlorocyclohexane in mountain glaciers: Evidence from ice core records

How do climate fluctuations affect DDT and hexachlorocyclohexane (HCH) distribution in the global scale? In this study, the interactions between climate variations and depositions of DDT and HCH in ice cores from Mt. Everest (the Tibetan Plateau), Mt. Muztagata (the eastern Pamirs) and the Rocky Mountains were investigated. All data regarding DDT/HCH deposition were obtained from the published results. Concentrations of DDT and HCH in an ice core from Mt. Everest were associated with the El Nino-Southern Oscillation. Concentrations of DDT in an ice core from Mt. Muztagata were significantly correlated with the Siberia High pattern. Concentrations of HCH in an ice core from Snow Dome of the Rocky Mountains responded to the North Atlantic Oscillation. These associations suggested that there are some linkages between climate variations and the global distribution of persistent organic pollutants.     

An efficient approach of aerosol thermodynamic equilibrium predictions by the HDMR method

In this paper, we develop a new and efficient approach for high dimensional atmospheric aerosol thermodynamic equilibrium predictions. The multi-phase and multi-component aerosol thermodynamic input–output systems are solved by the high dimensional model representation (HDMR) method combining with the moving multiple cut points. The developed approach improves the accuracy of numerical simulations for the general high dimensional input–output systems compared with the standard cut-HDMR method. It can simulate efficiently the atmospheric aerosol thermodynamic equilibrium problems in a large range of aerosol concentrations from 10^?10 to 10^?6 mol m^?3. Numerical experiments show that the approach has great computational efficiency and the CPU-time of the approach is much less than that of ISORROPIA. The method does excellent performance in predicting high dimensional aerosol thermodynamic components as well as particulate matters (PMs).     

Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories. Evaluation of individual model and ensemble forecasts of surface ozone and particulate matter (PM) was performed using data from 119 EPA AIRNow ozone sites and 38 PM sites during a 50-day period in August and September of 2006. From the original set of models, two new bias-corrected model data sets were built, either by applying a simple running mean average to the past 7 days of data or by a Kalman-Filter approach. From the original and two bias-corrected data sets, three ensembles were created by a simple averaging of the seven models. For further improvements three additional weighted model ensembles were created, where individual model weights were calculated using the singular value decomposition method. All six of the ensembles are compared to the individual models and to each other in terms of root mean square error, correlation, and contingency and probabilistic statistics. In most cases, each of the ensembles show improved skill compared to the best of the individual models. The over all best ensemble technique was found to be the combination of Kalman-Filtering and weighted averaging. PM_2.5 aerosol ensembles demonstrated significant improvement gains, mostly because the original model's skill was very low.