Characteristics of ammonium removal by a newly isolated heterotrophic nitrification-aerobic denitrification bacterium Agrobacterium sp. LAD9 were systematically investigated. Succinate and acetate were found to be the most favorable carbon sources for LAD9. Response surface methodology (RSM) analysis demonstrated that maximum removal of ammonium occurred under the conditions with an initial pH of 8.46, C/N ratio of 8.28, temperature of 27.9 degrees C and shaking speed of 150 rpm, where temperature and shaking speed produced the largest effect. Further nitrogen balance analysis revealed that 50.1% of nitrogen was removed as gas products and 40.8% was converted to the biomass. Moreover, the occurrence of aerobic denitrification was evidenced by the utilization of nitrite and nitrate as nitrogen sources, and the successful amplifications of membrane bound nitrate reductase and cytochrome cd(1) nitrite reductase genes from strain LAD9. Thus, the nitrogen removal in strain LAD9 was speculated to comply with the mechanism of heterotrophic nitrification coupled with aerobic denitrification (NH4+-NH2OH-NO2--N2O-N-2), in which also accompanied with the mutual transformation of nitrite and nitrate. The findings can help in applying appropriate controls over operational parameters in systems involving the use of this kind of strain. (C) 2012, The Society for Biotechnology, Japan. All rights reserved.
Blue-green algae (BGA) bloom is a typical phenomenon in eutrophied lakes. However, up to now, no environmental mechanism has been commonly accepted. Systematic and complete data sets of BGA blooms and environmental factors without any missing data are rare, which seriously affected previous studies. In this study, a bootstrapping based multiple imputation algorithm (EMB) was first applied to reconstruct a complete data set from the available data set with missing data, hence forming a basis for quantitatively relating BGA bloom to contributing factors. Then, the probability of RCA bloom outbreak was simulated using a binomial (or binary) logistic regression model, which is an effective tool for recognizing key contributing factors. The results suggest that 1) the outbreak frequency or probability of BGA bloom tends to first increase and then decrease with a turning point between June and September each year: 2) air temperature, relative humidity, and precipitation were significant positive factors correlated with outbreak frequency, whereas wind speed and the number of sunshine hours were negative factors: 3) water temperature had a strong positive effect on the probability of BGA bloom outbreak, whereas other water quality factors, such as concentrations of organics and nutrients, were not so significant. However, water quality factors, such as NO3-N, SD, pH, NH4-N. COD and DO, still need to be concerned, which had a potential to aggravate the outbreak of BGA bloom in Dianchi Lake, if they were out of control. (c) 2012 Elsevier B.V. All rights reserved.