The use of microbes to change the concentration of heavy metals in soil and improve the ability of plants to deal with elevated metals concentrations has significant economic and ecological benefits. This paper reviews the origins and toxic effects of heavy metal pollution in soil, and describes the heavy metal accumulation mechanisms of microbes, and compares their different bioconcentration abilities. Biosorption, which depends on the special structure of the cell wall, is found to be the primary mechanism. Furthermore, Escherichia coli are found to adsorb more heavy metals than other species. Factors influencing microbial treatment of wastewater and soil containing heavy metals include temperature, pH, and different substrates. Finally, problems in the application of microbial treatment of heavy metal contamination are considered, and possible directions for future research are discussed.

We conducted dendroclimatological study on three dominant conifer tree species, Pinus koraiensis, Larix olgensis, and Picea jezoensis, in northeastern China for a better understanding of climate change impacts on temperate forest growth, by discussing the radial growth relationships of these tree species and projecting their radial growth trends under the future climate change scenarios. Based on the tree-ring samples collected from the upper altitude of Changbai Mountain, ring width chronologies were built to examine the growth relationships, and regression equations were established to project the future growth of the species under future climate change projected by the five general circulation models (GCMs) and four representative concentration pathway (RCP) scenarios. Although both temperature and precipitation showed varying degrees of relationships with growth of these three tree species, the limiting climate factors were species-specific. The tree-ring growth of P. koraiensis was limited by the summer temperature and precipitation at the end of growth, namely, significant positive correlations with the current July temperature and the previous September precipitation. Growth of L. olgensis was limited by the temperature before growing season, for its chronology was negatively correlated with the current February and previous December temperature (p < 0.05). The climatic conditions before and after growing season seemed to be the limiting factors of P. jezoensis growth, which was negatively correlated with the current February to April temperature and the current September temperature (p < 0.05), and positively correlated with the current August precipitation (p < 0.05). Under the gradual increasing of temperature predicted by the five GCMs and four RCP scenarios, the radial growth of P. Koraiensis will relatively increase, while that of L. olgensis and P. jezoensis will relatively decrease comparing to the base-line period (1981–2010). The specific growth–climate relationships and the future growth trends are species dependent. P. Koraiensis was the more suitable tree species for the forestation to maintain the sustainable forest in Changbai Mountain.

water consumption of plants is a key parameter for formulating irrigation system, and the precise prediction play a important role in improving the use efficiency of limited water resources. In this experiment, by using the method of artificial neural network and MATLAB DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of p.xeuramericana cv."74176", and through the experiments it has been examined that two neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.07, the General Regression Neural Network (GRNN) prediction model was 0.05, moreover, the latter had good stability, while that of the former was poor.Therefore, we propose that GRNN model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of GRNN predication model was 0.106, the minimum relative error was 0.015. The GRNN model is superior to the BP neural network model that the former performs a higher forecasting accuracy with relatively shorter time consumption and faster speed in training.

No abstract

No abstract