Influence of invasive plant Coreopsis grandiflora on functional diversity of soil microbial communities

Biological invasions are increasingly attracting the ecologists' attention. Invasive plants threaten the natural ecosystems not only by competing with the native plants, but also by altering the structure and function of soil microbial communities belowground. In this study, we studied the effects of the invasive plant Coreopsis grandiflora (C. grandiflora) on the functional diversity of soil microbial communities in Laoshan mountain in the province of Shandong, North of China. We sampled soil from plots that were invaded or not invaded by C. grandiflora. The functional diversity of microbial communities in the sampled soils was assessed by the Biolog procedure test. By the ANOVA analysis of average well color development (AWCD), Shannon index (H'), Shannon evenness (E), principle components analysis of the level physiological profiles (CLPP) and correlation analysis between the studied parameters, we found that the invasive species C. grandiflora enhanced the functional diversity of soil microbial communities where the habitat was invaded by the C. grandiflora. The study indicated thatthe successful invasive plants have profound effects on the function of soil microbial communities.     

Soil organic carbon of degraded wetlands treated with freshwater in the Yellow River Delta, China

Supplying freshwater is one of the important methods to help restore degraded wetlands. Changes in soil properties and plant community biomass were evaluated by comparing sites with freshwater treatment versus reference sites following freshwater addition to wetlands of the Yellow River Delta for 7 years. The results indicated that soil organic carbon (SOC) was significantly increased in all wetland sites that were treated with freshwater compared to the reference sites. The treatment wetlands had greater total nitrogen (TN), lower pH and electrical conductivity and higher water content in the soil compared to the reference wetlands. In general, the upper soil layer (0-20 cm) had greater SOC than the lower soil layer (20-40 cm). The increase of SOC in the freshwater reintroduction wetlands was higher in the Suaeda salsa plant community (mean ± standard error) (6.89 ± 0.63 g/kg) and Phragmites communis plant community (4.11 ± 0.12 g/kg) than in the Tamarix chinensis plant community (1.40 ± 0.31 g/kg) in the upper soil layer. The differences were especially marked between the treated and reference wetlands for SOC and TN in the P. communis plant communities. The C:N ratio of the soil was significantly greater in the treated compared to the reference wetlands for the S. salsa plant community. Although the C: N ratios increased after treatment, they were all <25 suggesting that N availability was not limiting soil organic matter decomposition. Our results indicate that freshwater addition and the concomitant increase in soil moisture content enhances the accumulation of SOC in the Yellow River Delta.     

La0.8Ca0.2FeO3/MgAl2O4丝网整体催化剂用于低浓度甲烷催化燃烧

开发了涂覆在金属丝网基体上的La_(0.8)Ca_(0.2)FeO_3/MgAl_2O_4复合整体催化剂,用于低浓度甲烷(0.5%,体积分数)催化燃烧反应.首先用沉积-沉淀法制备了La_(0.8)Ca_(0.2)FO_3(LCF)钙钛矿与Mg Al_2O_4尖晶石复合的粉末催化剂LCF/Mg Al2O4,用程序升温微反应器(TPRS)测定了粉末活性,结合XRD、BET、SEM、TPR的表征结果,筛选出了最佳复合物质的量比和最适焙烧温度.当LCF与Mg Al_2O_4复合后,抗烧结能力增强,以拟一级反应速率常数(k,L·g~(-1)·s~(-1))计的活性提高了1倍左右.然后采用浆料涂覆法,在表面改性的金属丝网蜂窝基体上分层涂覆了作为第二载体的MgAl_2O_4和LCF/Mg Al_2O_4复合粉体涂层,考察了复合粉体球磨前焙烧的温度、第二载体及活性层涂覆量的影响.最后在空速GHSV=40000 h~(-1)条件下测试了甲烷催化燃烧反应的活性.与陶瓷基体相比,涂覆在金属丝网基体上的LCF/MgAl_2O_4活性在600℃时提升了48.1%.500 h长运转试验发现,催化剂活性在初期的200 h下降了20.3%,然后基本保持稳定.     

Efficiencies of different microbial parameters as indicator to assess slight metal pollutions in a farm field near a gold mining area

In order to monitor changes in the concentrations of metals in the soil, different microbial indices such as BIOLOG^®, microbial carbon (C_mic), basal respiration, and culturable microbe’s most probable number were used. We compared these methods and wanted to discover which method was the best at measuring slight changes in the amounts of heavy metals. Factor analyses were applied to the BIOLOG^® data and metal concentrations so the combined effects of heavy metals on microbes could be analyzed via statistical data reduction and the distribution patterns of metal concentration could also be revealed. The results showed that the BIOLOG^® method could barely detect subtle characteristic changes in the soil samples, while the C_mic method was more sensitive. Furthermore, different heavy metals did not have the same origin/source, and their effects on microbial indices should be analyzed separately. Significant positive correlations between C_mic and metals were observed and suggested the limitation of using traditional microbial parameters as metal pollution indicators. Among all the soil characteristics in our study, pH seemed to be the most active abiotic factor that affected microorganisms.     

The development and practices of Strategic Environmental Assessment in Shandong Province, China

Strategic Environmental Assessment (SEA), a newly-developed decision-making support tool, has been used in many developed and developing countries for predicting and evaluating potential environmental impact of policies, plans, and programs (PPPs), as well as for providing alternatives to avoid, mitigate, or compensate for these impacts. Unfortunately, due to the complexity and flexibility of SEA, to date there has been no consensus on a system which could be suitable for the contexts of different regions or countries. Different requirements and practices are observed in the different typical stages of SEA. Controversial areas include the appropriate indicators to apply in the early baseline setting stage, suitable methodologies for the impact assessment, and appropriate procedures for the SEA process. Given this, it is important to review and refine the SEA system specific to the context but informed by internationally agreed norms. As an illustration, this study reviews and proposes steps to refine the SEA system in Shandong Province, an economically powerful province of China, aiming to achieve sustainable development. Supported by the Environmental Impact Assessment Law of the People's Republic of China, Shandong Province employed SEA to reform the traditionally economy-oriented decision-making and incorporate consideration of environmental consequences into government deliberation on proposed PPPs. This paper illustrates the developmental process, procedures, and legal support for SEA in Shandong Province. By analyzing five SEA cases carried out by Shandong Province Environment Protection Bureau (SEPB) and Shandong University, problems in the SEA system were identified, and recommendations were made for improving the SEA system not only in Shandong Province but also other similar regions or countries.     

Using variances in hydrocarbon concentration and carbon stable isotope to determine the important influence of irrigated water on petroleum accumulation in surface soil

Hunpu is a wastewater-irrigated area southwest of Shenyang. To evaluate petroleum contamination and identify its sources at the area, the aliphatic hydrocarbons and compound-specific carbon stable isotopes of n-alkanes in the soil, irrigation water, and atmospheric deposition were analyzed. The analyses of hydrocarbon concentrations and geochemical characteristics reveal that the water is moderately contaminated by degraded heavy oil. According to the isotope analysis, inputs of modern C3 plants and degraded petroleum are present in the water, air, and soil. The similarities and dissimilarities among the water, air, and soil samples were determined by concentration, isotope, and multivariate statistical analyses. Hydrocarbons from various sources, as well as the water/atmospheric deposition samples, are more effectively differentiated through principal component analysis of carbon stable isotope ratios (δ¹³C) relative to hydrocarbon concentrations. Redundancy analysis indicates that 57.1 % of the variance in the δ¹³C of the soil can be explained by the δ¹³C of both the water and air, and 35.5 % of the variance in the hydrocarbon concentrations of the soil can be explained by hydrocarbon concentrations of both the water and the air. The δ¹³C in the atmospheric deposition accounts for 28.2 % of the δ¹³C variance in the soil, which is considerably higher than the variance in hydrocarbon concentrations of the soil explained by hydrocarbon concentrations of the atmospheric deposition (7.7 %). In contrast to δ¹³C analysis, the analysis of hydrocarbon concentrations underestimates the effect of petroleum contamination in the irrigated water and air on the surface soil. Overall, the irrigated water exerts a larger effect on the surface soil than does the atmospheric deposition.     

Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)

Spatial autocorrelation (SAC) is frequently encountered in most spatial data in ecology. Cellular automata (CA) models have been widely used to simulate complex spatial phenomena. However, little has been done to examine the impact of incorporating SAC into CA models. Using image-derived maps of Chinese tamarisk (Tamarix chinensis Lour.), CA models based on ordinary logistic regression (OLCA model) and autologistic regression (ALCA model) were developed to simulate landscape dynamics of T. chinensis. In this study, significant positive SAC was detected in residuals of ordinary logistic models, whereas non-significant SAC was found in autologistic models. All autologistic models obtained lower Akaike's information criterion corrected for small sample size (AICc) values than the best ordinary logistic models. Although the performance of ALCA models only satisfied the minimum requirement, ALCA models showed considerable improvement upon OLCA models. Our results suggested that the incorporation of the autocovariate term not only accounted for SAC in model residuals but also provided more accurate estimates of regression coefficients. The study also found that the neglect of SAC might affect the statistical inference on underlying mechanisms driving landscape changes and obtain false ecological conclusions and management recommendations. The ALCA model is statistically sound when coping with spatially structured data, and the adoption of the ALCA model in future landscape transition simulations may provide more precise probability maps on landscape transition, better model performance and more reasonable mechanisms that are responsible for landscape changes.