环境中的药物——LC-MS/MS分析地表水和地下水

成百上千的活性物质正被用于人药和兽药处方中.由于药物的广泛应用,它们的残留物可通过多种途径进入环境.虽然主要通过尿和粪便的排泄,但是药品生产中的排放也应被考虑.当抗生素用于水产业,就会产生更直接的影响.污水处理设备不能完全除去药物,因此药物会出现在地表和地下水样品中.近年来,人们对环境中药物的兴趣正在增加,需要建立一种快速、灵敏、有选择性的方法来分析水样.     

Multivariate analysis of heavy metals concentrations in river estuary

Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.     

Analysis of heavy metal concentrations in sediments of selected estuaries of Malaysia—a statistical assessment

Statistical analysis of heavy metal concentrations in sediment was studied to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status of selected estuaries. Concentrations of heavy metals (Cu, Zn, Cd, Fe, Pb, Cr, Hg and Mn) were analyzed in sediments from Juru and Jejawi Estuaries in Malaysia with ten sampling points of each estuary. The results of multivariate statistical techniques showed that the two regions have different characteristics in terms of heavy metals selected and indicates that each region receives pollution from different sources. The results also showed that Fe, Mn, Cd, Hg, and Cu are responsible for large spatial variations explaining 51.15% of the total variance, whilst Zn and Pb explain only 18.93 of the total variance. This study illustrates the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets to get better information about the heavy metal concentrations and design of monitoring network.     

Cross-sectional biomonitoring study of pesticide exposures in Queensland,Australia, using pooled urine samples

A range of pesticides are available in Australia for use in agricultural and domestic settings to control pests, including organophosphate and pyrethroid insecticides, herbicides, and insect repellents, such as N,N-diethyl-meta-toluamide (DEET). The aim of this study was to provide a cost-effective preliminary assessment of background exposure to a range of pesticides among a convenience sample of Australian residents. De-identified urine specimens stratified by age and sex were obtained from a community-based pathology laboratory and pooled (n = 24 pools of 100 specimens). Concentrations of urinary pesticide biomarkers were quantified using solid-phase extraction coupled with isotope dilution high-performance liquid chromatography–tandem mass spectrometry. Geometric mean biomarker concentrations ranged from <0.1 to 36.8 ng/mL for organophosphate insecticides, <0.1 to 5.5 ng/mL for pyrethroid insecticides, and <0.1 to 8.51 ng/mL for all other biomarkers with the exception of the DEET metabolite 3-diethylcarbamoyl benzoic acid (4.23 to 850 ng/mL). We observed no association between age and concentration for most biomarkers measured but noted a “U-shaped” trend for five organophosphate metabolites, with the highest concentrations observed in the youngest and oldest age strata, perhaps related to age-specific differences in behavior or physiology. The fact that concentrations of specific and non-specific metabolites of the organophosphate insecticide chlorpyrifos were higher than reported in USA and Canada may relate to differences in registered applications among countries. Additional biomonitoring programs of the general population and focusing on vulnerable populations would improve the exposure assessment and the monitoring of temporal exposure trends as usage patterns of pesticide products in Australia change over time.     

Assessment of surface water quality of selected estuaries of Malaysia: multivariate statistical techniques

Multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) were used for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two selected estuaries of Malaysia. The two locations of interest with 10 sites in each location were Kuala Juru (Juru estuary) and Bukit Tambun (Jejawi estuary). Cluster analysis showed that some sites in both locations have similar sources of pollution from point or non-point sources whereas FA yielded four factors which are responsible for water quality variations explaining more than 80% of the total variance of the data set and allowed to group the selected water quality. Correlation analysis of the data showed that some parameters have strong association with other parameters and they share a common origin source. This study illustrates the usefulness of multivariate statistical analysis for evaluation and interpretation of complex data sets to get better information about the pollution sources/factors and understanding the behavior of the parameters in water quality for effective river water quality management.