Stimulating effects of reduced graphene oxide on the growth and nitrogen fixation activity of nitrogen-fixing bacterium Azotobacter chroococcum.

Graphene has found important applications in various areas and hundred tons of graphene materials are annually produced. It is crucial to investigate both the negative and positive environmental effects of graphene materials to ensure the safe applications and develop environmental applications. In this study, we reported the stimulating effects of reduced graphene oxide (RGO) to nitrogen-fixing bacterium Azotobacter chroococcum. RGO stimulated the cell growth of A. chroococcum at 0.010-0.500 mg/mL according to the growth curves and the colony-forming unit (CFU) increases. RGO wrapped over the A. chroococcum cells without inducing ultrastructural changes. RGO decreased the leakage of cell membrane, but slight oxidative stress was observed in A. chroococcum. RGO promoted the nitrogen fixation activity of A. chroococcum at 0.5 mg/mL according to both isotope dilution method and acetylene reduction activity measurements. Consequently, the increases of soil nitrogen contents were evidenced, in particular about 30% increase of organic nitrogen occurred at 0.5 mg/mL of RGO. In addition, RGO might possibly benefit the plant growth through enhancing the indoleacetic acid production of A. chroococcum. These results highlighted the positive environmental effects of graphene materials to nitrogen-fixing bacteria in nitrogen cycle.

Nonstationary flood coincidence risk analysis using time-varying copula functions.

The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising from climate changes. The coincidence probabilities considering flood occurrence dates and flood magnitudes were calculated using nonstationary multivariate models and compared with those from stationary models. In addition, the "most likely" design based on copula theory was used to provide the most likely flood coincidence scenarios. The Huai River and Hong River were selected as case studies. The results show that the highest probabilities of flood coincidence occur in mid-July. The marginal distributions for the flood magnitudes of the two rivers are nonstationary, and time-varying copulas provide a better fit than stationary copulas for the dependence structure of the flood magnitudes. Considering the annual coincidence probabilities for given flood magnitudes and the "most likely" design, the stationary model may underestimate the risk of flood coincidence in wet years or overestimate this risk in dry years. Therefore, it is necessary to use nonstationary models in climate change scenarios.