The aim of this study is to analyze the effect of salinity on polycyclic aromatic hydrocarbons (PAHs) biodegradation, community structure and naphthalene dioxygenase gene (ndo) diversity of a halophilic bacterial consortium with the denaturing gradient gel electrophoresis (DGGE) approach. The consortium was developed from oil-contaminated saline soil after enrichment for six times, using phenanthrene as the substrate. The prominent species in the bacterial consortium at all salinities were identified as halophilic bacteria Halomonas, Alcanivorax, Marinobacter, Idiomarina, Martelella and uncultured bacteria. The predominant microbes gradually changed associating with the saline concentration fluctuations ranging from 0.1% to 25% (w/v). Two ndo alpha subunits were dominant at salinities ranging from 0.1% to 20%, while not been clearly detected at 25% salinity. Consistently, the biodegradation occurred at salinities ranging from 0.1% to 20%, while no at 25% salinity, suggesting the two ndo genes played an important role in the degradation. The phylogenetic analysis revealed that both of the two ndo alpha subunits were related to the classic nah-like gene from Pseudomonas stutzeri AN10 and Pseudomonas aeruginosa PaK1, while one with identity of about 82% and the other one with identity of 90% at amino acid sequence level. We concluded that salinity greatly affected halophilic bacterial community structure and also the functional genes which were more related to biodegradation.
Atrazine is one of the most widely applied and persistent herbicides in the world. In view of limited information on the regional contamination of atrazine in soils in China, this study investigated the spatial distribution and environmental impacts of atrazine in agricultural soils collected from the Yangtze River Delta (YRD) as an illustrative analysis of rapidly developing regions in the country. The results showed that the concentrations of atrazine in the YRD agricultural soils ranged from <1.0 to 113 ng/g dry weight, with a mean of 5.7 ng/g, and a detection rate of 57.7 % in soils. Pesticide factory might be a major source for the elevated levels of atrazine in Zhejiang Province. The contamination of atrazine was closely associated with land use types. The concentrations and detection rates of atrazine were higher in corn fields and mulberry fields than in rice paddy fields. There was no significant difference in compositions of soil microbial phospholipids fatty acids among the areas with different atrazine levels. Positive relationship (R = 0.417, p < 0.05, n = 30) was observed between atrazine and total microbial biomass. However, other factors, such as soil type and land management practice, might have stronger influences on soil microbial communities. Human health risks via exposure to atrazine in soils were estimated according to the methods recommended by the US EPA. Atrazine by itself in all the soil samples imposed very low carcinogenic risks (<10?6) and minimal non-cancer risks (hazard index <1) to adults and children. 相似文献
Observed effects of metal mixtures on animals and plants often differ from the estimates, which are commonly calculated by adding up the biological responses of individual metals. This difference from additivity is commonly referred to as being a consequence of specific interactions between metals. The science of how to quantify metal interactions and whether to include them in risk assessment models is in its infancy. This review summarizes the existing predictive tools for evaluating the combined toxicity of metals present in mixtures and indicates the advantages and disadvantages of each method. We intend to provide eco-toxicologists with background information on how to make good use of the tools and how to advance the methods for assessing toxicity of metal mixtures. It is concluded that statistically significant deviations from additivity are not necessarily biologically relevant. Incorporation of interactions between metals in a model does not on forehand mean that the model is more accurate than a model developed based on additivity only. It is recommended to first use a relatively simple method for effect prediction of uninvestigated metal mixtures. To improve the reliability of toxicity modeling for metal mixtures, further efforts should focus on balancing the relationship between the significance of statistics and the biological meaning, and unraveling the toxicity mechanisms of metals and their mixtures.
