Determination of aliphatic amines from soil and wastewater of a paper mill by pre-column derivatization using HPLC and tandem mass spectrometry (HPLC-MS/MS)

A pre-column derivatization method for the sensitive determination of aliphatic amines using the labeling reagent 1,2-benzo-3,4-dihydrocarbazole-9-ethyl chloroformate (BCEOC) followed by HPLC with fluorescence detection and APCI/NIS identification in positive-ion mode has been developed. The chromophore of 2-(9-carbazole)-ethyl chloroformate (CEOC) reagent was replaced by the 1,2-benzo-3,4-dihydrocarbazole functional group, which resulted in a sensitive fluorescence derivatizing reagent, BCEOC, that could easily and quickly label amines. Derivatives were stable enough to be efficiently analyzed by HPLC and showed an intense protonated molecular ion corresponding m/z [M + H](+) with APCI/MS in positive-ion mode. The collision induced dissociation of the protonated molecular ion formed characteristic fragment ions at m/z 264.1, m/z 246.0 and m/z 218.1, corresponding to the cleavages of CH2CH2O-CO, CH2CH2-OCO, and N-CH2CH2O bonds. Studies on derivatization conditions demonstrated that excellent derivatization yields close to 100% were observed with a 3 to 4-fold molar reagent excess in acetonitrile solvent, in the presence of borate buffer (pH 9.0) at 40 degrees C for 10 min. In addition, the detection responses for BCEOC derivatives were compared with those obtained with CEOC and FMOC as labeling reagents. The ratios I-BCEOC/I-CEOC and I-BCEOC/I-FMOC were, respectively, 1.40-2.76 and 1.36-2.92 for fluorescence responses (here, I was the relative fluorescence intensity). Separation of the amine derivatives had been optimized on an Eclipse XDB-C-8 column. Detection limits calculated from an 0.10 pmol injection, at a signal-to-noise ratio of 3, were 18.65-38.82 fmol (injection volume 10 mu L for fluorescence detection. The relative standard deviations for intraday determination (n = 6) of standard amine derivatives (50 pmol) were 0.0063-0.037% for retention times and 3.36-6.93% for peak areas. The mean intra-and inter-assay precision for all amines were <5.4% and 5.8%, respectively. The recoveries of amines ranged from 96 to 113%. Excellent linear responses were observed with correlation coefficients of >0.9994. The established method provided a simple and highly sensitive technique for the quantitative analysis of trace amounts of aliphatic amines from biological and natural environmental samples.

地理信息系统支持下的滑坡灾害空间分析预测-以云南小江流域为例

As a typical geological and environmental hazard, landslide has been causing more and more property and life losses. However, to predict its accurate occurring time is very difficult or even impossible due to landslide's complex nature. It has been realized that it is not a good solution to spend a lot of money to treat with and prevent landslide. The research trend is to study landslide's spatial distribution and predict its potential hazard zone under certain region and certain conditions. GIS(Geographical Information System) is a power tools for data management, spatial analysis based on reasonable spatial models and visualization. It is new and potential study field to do landslide hazard analysis and prediction based on GIS. This paper systematically studies the theory and methods for GIS based landslide hazard analysis. On the basis of project "Mountainous hazard study-landslide and debris flows" supported by Chinese Academy of Sciences and the former study foundation, this paper carries out model research, application, verification and model result analysis. The occurrence of landslide has its triggering factors. Landslide has its special landform and topographical feature which can be identify from field work and remote sensing image (aerial photo). Historical record of landslide is the key to predict the future behaviors of landslide. These are bases for landslide spatial data base construction. Based on the plenty of literatures reviews, the concept framework of model integration and unit combinations is formed. Two types of model, CF multiple regression model and landslide stability and hydrological distribution coupled model are bought forward. CF multiple regression model comes form statistics and possibility theory based on data. Data itself contains the uncertainty and random nature of landslide hazard, so it can be seen as a good method to study and understand landslide's complex feature and mechanics. CF multiple regression model integrates CF (landslide Certainty Factor) and multiple regression prediction model. CF can easily treat with the problems of data quantifying and combination of heteroecious data types. The combination of CF can assist to determine key landslide triggering factors which are then inputted into multiple regression model. CF regression model can provide better prediction results than traditional model. The process of landslide can be described and modeled by suitable physical and mechanical model. Landslide stability and hydrological distribution coupled model is such a physical deterministic model that can be easily used for landslide hazard analysis and prediction. It couples the general limit equilibrium method and hydrological distribution model based on DEM, and can be used as a effective approach to predict the occurrence of landslide under different precipitation conditions as well as landslide mechanics research. It can not only explain pre-existed landslides, but also predict the potential hazard region with environmental conditions changes. Finally, this paper carries out landslide hazard analysis and prediction in Yunnan Xiaojiang watershed, including landslide hazard sensitivity analysis and regression prediction model based on selected key factors, determining the relationship between landslide occurrence possibility and triggering factors. The result of landslide hazard analysis and prediction by coupled model is discussed in details. On the basis of model verification and validation, the modeling results are showing high accuracy and good applying potential in landslide research.