Investigation of chlorinated phenols sorption mechanisms on different layers of the Danube alluvial sediment

The characteristics of the Danube river alluvial sediment are of great importance in assessing the risk for transport of pollutants to drinking water sources. Characterization of the sediment column layers has shown that the alluvial sediment, sampled near the city of Novi Sad, is a mesoporous sandy material with certain differences in the properties of individual layers. In order to investigate the sorption mechanisms of four chlorinated phenols (CPs) on the alluvial deposit, static sorption experiments were performed at pH 4, 7 and 10. The results of sorption experiments, confirmed by principal components analysis sugest different mechanisms govern the sorption process at different pH conditions. This can be attributed to the molecular characteristics of CPs, geosorbent properties and to variations in the surface charge of the sorbent at different pH conditions.     

郑州市氧化铝工业炉窑污染物及源谱特征

采用稀释通道采样法对郑州市一家拜耳法氧化铝企业及一家α-氧化铝企业的PM_2.5、PM₁₀样品进行采集,利用烟气分析仪检测SO₂和NO_x气体的质量浓度.对颗粒物样品中的38种化学组分进行分析,构建相应的源成分谱,运用分歧系数（CD）和组分差异权重分布函数R/U对其特征进行对比研究.结果表明：α-氧化铝源谱中以Cl^-为主（质量分数18.29%~25.21%）,拜耳法氧化铝源谱中以OC和NO₃^-为主（质量分数9.01%~41.87%）.源谱间的分歧系数（CD）显示,同一种生产工艺两种粒径间的源谱比较相似,相同粒径的两种生产工艺之间的源谱差异较大.R/U值对比结果显示,EC、Ca、F^-和Cl^- 4种元素可作为区分两种氧化铝生产工艺的标识组分.     

重金属离子对肺表面活性物质单层膜的影响

为了探究重金属对肺表面活性物质（PS）膜的影响,利用Langmuir–Wilhelmy膜天平,研究了不同种类和浓度的重金属离子与PS单层膜的相互作用.发现Cr³⁺、Cu²⁺、Hg²⁺、Pb²⁺、Cd²⁺、Ni²⁺6种重金属离子均使PS单层的π-A等温线G-LE相变阶段外扩,而Fe³⁺可诱导单层缩合.随后,选取Cd²⁺,Cu²⁺,Hg²⁺,Fe³⁺4种离子进一步探究了重金属浓度对PS单层膜表面活性的影响规律,进行了可压缩模量分析,并结合红外光谱（FTIR）和原子力显微镜（AFM）探究其作用机理,发现Cu²⁺,Fe³⁺和Hg²⁺均会以浓度依赖的方式与PS单层的极性头部发生络合作用,形成复合物,改变膜的稳定性;而Cd²⁺的影响明显弱于其他3种金属离子,但也能使得PS单层有序性增强.     

Relationship between physicochemical properties and dewaterability of hydrothermal sludge derived from different source

Sewage sludge (SS) and deinking sludge (DS) were used to comparatively study the hydrothermal dewatering of sludge with different components. For a better overview, an insight into the relationship between physicochemical properties and dewaterability of hydrothermal sludge was provided. Results found that not all kinds of sludge were suitably conditioned by hydrothermal treatment (HT) in term of the elevation of dewaterability. Higher hydrothermal temperature tended to enhance the dewaterability of SS rather than DS, which was supported by the variation of their physicochemical properties (including water distribution, bonding energy, extracellular polymeric substance (EPS), particles size, acid functional groups and zeta potential in this study). In addition, the changes in surface morphology suggested that the reverse effect of HT on sludge dewaterability was mainly due to their dewatering behavior. For SS, the destruction of EPS structure leaded to the release of bound water, thereby strengthening sludge dewatering. Conversely, “Bridging effect” generated by lignocellulose in DS was beneficial for sludge dewatering; however, the increasing hydrothermal temperature degraded part of lignocellulose and weakened “bridging effect”, finally resulting in worse dewaterability of DS.     

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.     

ASM1中废水和污泥组分浓度的测定方法研究

对呼吸计量法应用于校准ASM1作了简明的阐述。在呼吸速率与模型组分关系的基础上,介绍了ASM1中废水与污泥各组分浓度的测定方法,并指出模型组分细化的意义。     

主成份分析法在大气环境质量综合评价中的应用

介绍了主成份分析法评价大气环境质量的基本原理，并对某市的大气环境质量进行了综合评价。最后，与其它方法的评价结果进行了对比分析，进一步证实了主成份分析法的科学性、合理性。     

水环境监测点位优化数学模型探讨

根据雨山湖的环境特征和水质状况,在加密监测获取更多的信息的基础上应用多元统计定量分析,选用主成份分析统计模式,用编制计算机程序处理数据,获得优化点数和确定点位.经可靠性检验证明优化方案完全能满足环境监测和环境管理的要求.     

用主成分分析法研究评价地下水质量——以邯郸市为例

运用主成分分析法(PCA)对邯郸市地下水主要污染指标年均值进行了探索性评价研究。分析结果表明,前5个主成分综合携带了全部信息的91.374%。通过对这5个新的综合指标进行分析,确定了地下水的主要污染物,同时根据其综合得分对地下水进行分级,分析了其变化趋势,为地下水资源的有效利用和保护提供了科学依据。     

海面溢油的荧光光谱鉴别法述评

介绍了荧光光谱技术在鉴别海面溢油中的应用和进展。根据荧光光谱的三维特性 ,分别阐述了普通荧光光谱、同步荧光扫描光谱、可变角同步扫描光谱、三维荧光光谱等不同荧光光谱的“指纹”信息特征 ,并介绍了导数荧光法、低温荧光法、磷光光谱法等辅助鉴别方法