8-氨基喹啉-5-偶氮-对苯酚荧光光度法测定微量铬(Ⅵ)

在盐酸介质中,铬(Ⅵ)与AQAPOH形成可被氯仿萃取的荧光化合物,其λex/λem=295nm/400nm.线性范围为0-1.0μg Cr(Ⅵ)/25ml(水相),检测限为0.7μg/L.除Mn(Ⅶ)外,常见金属离子无干扰,可在数千倍Cr(Ⅲ)存在下测定废水中微量铬(Ⅵ).初步探讨了荧光反应的机理.     

盐酸萘乙二胺比色法测定大气中硝基苯、苯胺浓度的改进

本文探讨了盐酸萘乙二胺比色方法测定大气中硝基苯、苯胺浓度的最佳反应条件,如酸度、试剂加入量、反应温度、反应时间等。简化了过滤、洗涤操作,精密度高、显色稳定。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Microbiological characteristics in a zero-valent iron reactive barrier

Zero-valent iron (Fe⁰)-based permeable reactive barriertreatment has been generating great interest for passivegroundwater remediation, yet few studies have paid particularattention to the microbial activity and characteristics withinand in the vicinity of the Fe⁰-barrier matrix. The presentstudy was undertaken to evaluate the microbial population andcommunity composition in the reducing zone of influence byFe⁰ corrosion in the barrier at the Oak Ridge Y-12 Plantsite. Both phospholipid fatty acids and DNA analyses were usedto determine the total microbial population and microbialfunctional groups, including sulfate-reducing bacteria,denitrifying bacteria, and methanogens, in groundwater andsoil/iron core samples. A diverse microbial community wasidentified in the strongly reducing Fe⁰ environment despitea relatively high pH condition within the Fe⁰ barrier (up topH 10). In comparison with those found in the backgroundsoil/groundwater samples, the enhanced microbial populationranged from 1 to 3 orders of magnitude and appeared to increase from upgradient of the barrier to downgradient soil. Inaddition, microbial community composition appeared to change overtime, and the bacterial types of microorganismsincreased consistently as the barrier aged. DNA analysisindicated the presence of sulfate-reducing and denitrifyingbacteria in the barrier and its surrounding soil. However, theactivity of methanogens was found to be relatively low,presumably as a result of the competition by sulfate/metal-reducing bacteria and denitrifying bacteria because of the unlimited availability of sulfate and nitrate in the site groundwater. Results of this study provide evidenceof a diverse microbial population within and in the vicinity ofthe iron barrier, although the important roles of microbial activity, either beneficially or detrimentally, on the longevityand enduring efficiency of the Fe⁰ barriers are yet to be evaluated.     

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).     

Soil Lead and Other Metal Levels in Industrial, Residential and Nature Reserve Areas in Singapore

The use of pressure feedback microwave digestion technique has permitted rapid and efficient digestion of soil and sediment samples. The evaluation of different acid mixtures to digest soil samples were studied by mixed-level orthogonal array design. The selected acid mixture of HCl-HNO3-HF was employed in the survey of Pb, Zn, Cu, Cr and Mn in soil samples. Surface soil samples were collected from industrial, residential and nature reserve areas in Singapore. The five metal concentrations were determined by flame atomic absorption spectrometry and graphite furnace atomic absorption spectrometry. The lead and other metal contents in NBS SRM 1645, NIES CRM No. 2 and NRCC BCSS-1 sediment standard references were determined concurrently with the survey samples. Five measured metal loading on the surface soils was in the order: industrial area > residential area > nature reserve area. The trace metal concentrations in surface soil from areas of heavy traffic are higher than those from residential areas. The main sources of trace metal pollution are vehicular exhaust and industrial activities.     

Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social–economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.     

发光细菌法评价工业废水的毒性

用发光细菌测定法估算了两城市污水对河道水体污染的分担率，评价了工厂排放废水的毒性，并据此确定了污染源监督管理中应优先治理的工厂（车间）废水。