[Research on land use structure optimization based on nonpoint source dissolved nitrogen load estimation in Shuaishui watershed].

Regional Nutrient Management (ReNuMa) was applied to estimate dissolved nitrogen (DN) load and perform source apportionment in Shuaishui watershed during 2000-2010. Satisfactory performance of ReNuMa was revealed by the E(ns) and R2 of greater than 0.9 in calibrating and validating streamflow and DN. The average nonpoint DN load in this watershed was 1.11 x 10(3) t x a(-1), with the load intensity of (0.75 +/- 0.22) t x km(-2). Among all the land uses, paddy field had the largest DN load intensity [28.60 kg x (hm2 x a)(-1)], while forest had the least [2.71 kg x (hm2 x a)(-1)]. Agricultural land (including paddy, grain, cash crop, tea plant and orchard) contributed most to DN load in Shuaishui watershed, indicating that the human dominated agricultural activities was the major contributor of nonpoint source pollution. Land use structure optimization for Shuaishui watershed in 2015 was conducted under the rule of reducing pollutants loads and maximizing the agricultural output value. The results demonstrated that agricultural monetary growth was accompanied with the increasing DN load at the optimal level, although output increment was higher than that of DN load.

Computational identification and analysis of the key biosorbent characteristics for the biosorption process of reactive black 5 onto fungal biomass.

The performances of nine biosorbents derived from dead fungal biomass were investigated for their ability to remove Reactive Black 5 from aqueous solution. The biosorption data for removal of Reactive Black 5 were readily modeled using the Langmuir adsorption isotherm. Kinetic analysis based on both pseudo-second-order and Weber-Morris models indicated intraparticle diffusion was the rate limiting step for biosorption of Reactive Black 5 on to the biosorbents. Sorption capacities of the biosorbents were not correlated with the initial biosorption rates. Sensitivity analysis of the factors affecting biosorption examined by an artificial neural network model showed that pH was the most important parameter, explaining 22%, followed by nitrogen content of biosorbents (16%), initial dye concentration (15%) and carbon content of biosorbents (10%). The biosorption capacities were not proportional to surface areas of the sorbents, but were instead influenced by their chemical element composition. The main functional groups contributing to dye sorption were amine, carboxylic, and alcohol moieties. The data further suggest that differences in carbon and nitrogen contents of biosorbents may be used as a selection index for identifying effective biosorbents from dead fungal biomass.

Cloning and expression in E. coli of an organic solvent-tolerant and alkali-resistant glucose 1-dehydrogenase from Lysinibacillus sphaericus G10.

The gene gdh encoding an organic solvent-tolerant and alkaline-resistant NAD(P)-dependent glucose 1-dehydrogenase (LsGDH) was cloned from Lysinibacillus sphaericus G10 and expressed in Escherichia coli. The recombinant LsGDH exhibited maximum activity at pH 9.5 and 50 °C. LsGDH displayed high stability at a wide pH ranging from 6.5 to 10.0 and was stable after incubation at 30 °C for 1 week in 25 mM sodium phosphate buffer (pH 6.5) in the absence or presence of NaCl. The activity of LsGDH was enhanced by Li+, Na+, K+, NH4+, Mg2+, and EDTA at pH 8.0. LsGDH exhibited high tolerance to 60% DMSO, 30% acetone, 30% methanol, 30% ethanol, 10% n-propanol, 30% isopropanol, 60% n-hexanol and 30% n-hexane. The relationship between stability and chain length of the alcohols fit a Gaussian distribution model (R2≥0.94), and demonstrated lowest enzyme stability in C4-alcohol. The results suggested that LsGDH was potentially useful for coenzyme regeneration in organic solvents or under alkaline conditions.

[Normal alkanes characteristic parameters of Jinzhou Bay surface sediments].

The concentration, composition and characteristic parameters of 18 surface sediment samples collected from Jinzhou Bay were studied. The samples were soxhlet-extracted with a mixture of 1: 1 (V/V) dichloromethane-hexane and analyzed by GC-MS after purification and concentration. Concentrations of n-alkanes vary from 1.9 to 4.2 microg x g(-1) with an average value of 2.6 microg x g(-1) dry weight. n-Alkanes distribution patterns of all positions were characterized by double peak-cluster, which means double sources from terrestrial and marine origin. The sum of nC25 to nC31 accounts for 20%-32% of the total n-alkanes, while the average value of L/H, C31/C19, TAR ratio are 0.67, 3.06, 2.02, respectively. All of these three indices suggest that the terrestrial contributions are higher than marine sources, especially for No. 2, 3 and 7 stations, which were influenced by riverinput nearby. Carbon Preference Index (CPI) ranges from 1.19 to 2.63 with an average value of 1.73, which is close to 1; the ratio of Pristane/Phytane (Pr/Ph) and unresolved/resolved compounds (U/R) range from 0.91 to 1.28, 2.2 to 4.3, respectively. All of these three parameters indicate that No. 13 and 15 stations are influenced by petroleum pollution. Comprehensive analysis of various parameters shows that Jinzhou Bay is threatened by both terrestrial inputs and petroleum hydrocarbons contaminations, which may relate to river discharging and port shipping in Jinzhou Bay.