螯合树脂在线富集——火焰原子吸收光度法测定水中微量Cu,Pb,Zn,Cd

对螯合树脂在线富集，火焰原子吸收光度法进行了条件试验。试验证实方法对Ｃｕ、Ｐｂ、Ｚｎ、Ｃｄ４个元素检出浓度可达μｇ／Ｌ级，校准曲线性、样品重现性及准确度可满足测定要求。     

Analysis of BTEX and other substituted benzenes in water using headspace SPME-GC-FID: method validation

The analysis of BTEX and other substituted benzenes in water samples using solid phase microextraction (SPME) and quantification by gas chromatography with flame ionization detection (GC-FID) was validated. The best analytical conditions were obtained using PDMS/DVB/CAR fibre using headspace extraction (HS-SPME) at 50 [degree]C for 20 min without stirring. The linear range for each compound by HS-SPME with GC/FID was defined. The detection limits for these compounds obtained with PDMS/DVB/CAR fibre and GC/FID were: benzene (15 ng L(-1)), toluene (160 ng L(-1)), monochlorobenzene (54 ng L(-1)), ethylbenzene (32 ng L(-1)), m-xylene (56 ng L(-1)), p-xylene (69 ng L(-1)), styrene (35 ng L(-1)), o-xylene (42 ng L(-1)), m-dichlorobenzene (180 ng L(-1)), p-dichlorobenzene (230 ng L(-1)), o-dichlorobenzene (250 ng L(-1)) and trichlorobenzene (260 ng L(-1)). This headspace SPME-GC-FID method was compared with a previously validated method of analysis using closed-loop-stripping analysis (CLSA). The headspace SPME-GC-FID method is suitable for monitoring the production and distribution of potable water and was used, in field trials, for the analysis of samples from main intakes of water (surface or underground) and from the water supply system of a large area (Lisbon and neighbouring municipalities).     

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.     

The organic composition of diesel particulate matter, diesel fuel and engine oil of a non-road diesel generator

Diesel-powered equipment is known to emit significant quantities of fine particulate matter to the atmosphere. Numerous organic compounds can be adsorbed onto the surfaces of these inhalable particles, among which polycyclic aromatic hydrocarbons (PAHs) are considered potential occupational carcinogens. Guidelines have been established by various agencies regarding diesel emissions and various control technologies are under development. The purpose of this study is to identify, quantify and compare the organic compounds in diesel particulate matter (DPM) with the diesel fuel and engine oil used in a non-road diesel generator. Approximately 90 organic compounds were quantified (with molecular weight ranging from 120 to 350), which include alkanes, PAHs, alkylated PAHs, alkylbenzenes and alkanoic acids. The low sulfur diesel fuel contains 61% alkanes and 7.1% of PAHs. The identifiable portion of the engine oil contains mainly the alkanoic and benzoic acids. The composition of DPM suggests that they may be originated from unburned diesel fuel, engine oil evaporation and combustion generated products. Compared with diesel fuel, DPM contains fewer fractions of alkanes and more PAH compounds, with the shift toward higher molecular weight ones. The enrichment of compounds with higher molecular weight in DPM may be combustion related (pyrogenic).     

Modeling photocatalytic destruction of the organic contaminants in continuous stir flow reactor

Thin-film technique is becoming an industry standard for the preparation of TiO₂-based photocatalyst for organic destruction. The catalyst provides several advantages over the conventional powder TiO₂ in the treatment of wastewater and groundwater. In this study, a continuous stir flow reactor model is developed capable of describing the photocatalytic process. The model incorporates the following fundamental mechanisms: adsorption, diffusion, liquid-film transfer, UV attenuation, and photocatalytic reaction. All of the simulation results indicate that there exists a highly nonlinear relation between each of these parameters and the destruction rate. Various incident light intensities also are incorporated to simulate the energy efficiency. The simulations illustrate that the photocatalytic model can be used to elucidate the effect of process variables. It is also possible to “custom-design” a catalyst for the treatment of a particular waste stream.     

林格曼数监测存在的问题及时间——林格曼数计算方法的探讨

分析并讨论了目前国内关于锅炉烟尘林格曼数（黑度级）监测计算几种取值方法存在的问题，对时间－林格曼数这一新的取值方法进行了探讨。     

上海汽车制造业涂装废气VOCs排放特征

于2015年10月对上海市嘉定区4家(A、B、C、D)汽车制造企业涂装废气ρ(VOCs)和组成特征进行了调查分析。结果表明,A、B、C、D厂涂装废气排放口ρ(VOCs)总为0. 743～6. 11 mg/m~3,主要检出物和最高检出值分别为:A厂二甲苯2. 06 mg/m~3、B厂二甲苯0. 578 mg/m~3、C厂甲苯2. 59 mg/m~3、D厂庚烷0. 274 mg/m~3;芳香烃类是A、B、C厂排放比例最高的VOCs组分,烷烃类是D厂排放比例最高的VOCs组分。指出,原、辅料种类影响排放物的主要成分,废气处理工艺类型影响排放物主要成分和浓度。     

铁路运输业污水治理系统分析

研究了乌鲁木剂铁路局系统污水治理污染控制方法及途径，提出了主要污染物为石油类，其最佳治理对策是：确定重点污染单位、污染源、用最低的治理投入，选择斜板隔油气浮工艺治理重点污染和次重点污染部门，得到较好的治理效果，否定盲目追求“零排放”的高投入方案。     

环境样品中痕量锰的催化动力学测定法

对环境样品中痕量锰的催化动力学测定方法进行简要评述，引述文献１６篇。     

Application of PCR and probe hybridization techniques in detection of airborne fungal spores in environmental samples

Specific PCR amplification and probe hybridization techniques were applied to examine the compositions of airborne fungi in samples from three different environments. The results from microscopic and CFU counting were compared to those of the molecular-based detections. The detection sensitivity for PCR amplifications was 9 to 73 spores and 1.3 to 19.3 CFUs per PCR reaction. The hybridization detection limit was 2 to 4 spores and 0.2 to 1.2 CFU. The hybridization method was more sensitive than PCR amplification and showed less variation among samples. Using specific PCR primers and probes we identified the presence of several fungal groups and species in the air samples. Specific detections through probe hybridization to PCR products amplified with universal or group-specific fungal primers have promising applications in the examination of air samples for environmental monitoring.