Hydrocarbon contamination may affect the soil microbial community, in terms of both diversity and function. A laboratory experiment was set-up, with a semi-arid control soil and the same soil but artificially contamin...Hydrocarbon contamination may affect the soil microbial community, in terms of both diversity and function. A laboratory experiment was set-up, with a semi-arid control soil and the same soil but artificially contaminated with diesel oil, to follow changes in the dominant species of the microbial community in the hydrocarbon-polluted soil via proteomics. Analysis of the proteins extracted from enriched cultures growing in Luria-Bertani （LB） media showed a change in the microbial community. The majority of the proteins were related to gIycolysis pathways, structural or protein synthesis. The results showed a relative increase in the complexity of the soil microbial community with hydrocarbon contamination, especially after 15 days of incubation. Species such as Ralstonia solanacearum, Synechococcus elongatus and different Clostridium sp. were adapted to contamination, not appearing in the control soil, although Bacillus sp. dominated the growing in LB in any of the treatments. We conclude that the identification of microbial species in soil extracts by culture-dependent proteomics is able to partially explain the changes in the diversity of the soil microbial community in hydrocarbon polluted semi-arid soils, but this information is much more limited than that provided by molecular methods.展开更多
The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial ...The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial information (STMB) versus refined spatial information map (RSIM)) of soil physical properties, including field capacity, soil porosity and saturated hydraulic conductivity are used respectively as input data for Water Flow Model for Lake Catchment (WATLAC) to determine their effectiveness in simulating hydrological processes and to expound the effects on model performance in terms of estimating groundwater recharge, soil evaporation, runoff generation as well as partitioning of surface and subsurface water flow. The results show that: 1) the simulated stream flow hydrographs based on the STMB and RSIM soil data reproduce the observed hydrographs well. There is no significant increase in model accuracy as more precise soil physical properties information being used, but WATLAC model using the RSIM soil data could predict more runoff volume and reduce the relative runoff depth errors; 2) the groundwater recharges have a consistent trend for both cases, while the STMB soil data tend to produce higher groundwater recharges than the RSIM soil data. In addition, the spatial distribution of annual groundwater recharge is significantly affected by the spatial distribution of soil physical properties; 3) the soil evaporation simulated using the STMB and RSIM soil data are similar to each other, and the spatial distribution patterns are also insensitive to the spatial information of soil physical properties; and 4) although the different spatial information of soil physical properties does not cause apparent difference in overall stream flow, the partitioning of surface and subsurface water flow is distinct. The implications of this study are that the refined spatial information of soil physical properties does not necessarily contribute to a more accurate prediction of stream flow, and the selection of appropriate soil physical property data needs to consider the scale of watersheds and the level of accuracy required.展开更多
基金Supported by the JAE-Program for Ph.D. Students of Spanish Research Council
文摘Hydrocarbon contamination may affect the soil microbial community, in terms of both diversity and function. A laboratory experiment was set-up, with a semi-arid control soil and the same soil but artificially contaminated with diesel oil, to follow changes in the dominant species of the microbial community in the hydrocarbon-polluted soil via proteomics. Analysis of the proteins extracted from enriched cultures growing in Luria-Bertani （LB） media showed a change in the microbial community. The majority of the proteins were related to gIycolysis pathways, structural or protein synthesis. The results showed a relative increase in the complexity of the soil microbial community with hydrocarbon contamination, especially after 15 days of incubation. Species such as Ralstonia solanacearum, Synechococcus elongatus and different Clostridium sp. were adapted to contamination, not appearing in the control soil, although Bacillus sp. dominated the growing in LB in any of the treatments. We conclude that the identification of microbial species in soil extracts by culture-dependent proteomics is able to partially explain the changes in the diversity of the soil microbial community in hydrocarbon polluted semi-arid soils, but this information is much more limited than that provided by molecular methods.
基金Under the auspices of Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (No. IWHR-SKL-201111)National Natural Science Foundation of China (No. 41101024)
文摘The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial information (STMB) versus refined spatial information map (RSIM)) of soil physical properties, including field capacity, soil porosity and saturated hydraulic conductivity are used respectively as input data for Water Flow Model for Lake Catchment (WATLAC) to determine their effectiveness in simulating hydrological processes and to expound the effects on model performance in terms of estimating groundwater recharge, soil evaporation, runoff generation as well as partitioning of surface and subsurface water flow. The results show that: 1) the simulated stream flow hydrographs based on the STMB and RSIM soil data reproduce the observed hydrographs well. There is no significant increase in model accuracy as more precise soil physical properties information being used, but WATLAC model using the RSIM soil data could predict more runoff volume and reduce the relative runoff depth errors; 2) the groundwater recharges have a consistent trend for both cases, while the STMB soil data tend to produce higher groundwater recharges than the RSIM soil data. In addition, the spatial distribution of annual groundwater recharge is significantly affected by the spatial distribution of soil physical properties; 3) the soil evaporation simulated using the STMB and RSIM soil data are similar to each other, and the spatial distribution patterns are also insensitive to the spatial information of soil physical properties; and 4) although the different spatial information of soil physical properties does not cause apparent difference in overall stream flow, the partitioning of surface and subsurface water flow is distinct. The implications of this study are that the refined spatial information of soil physical properties does not necessarily contribute to a more accurate prediction of stream flow, and the selection of appropriate soil physical property data needs to consider the scale of watersheds and the level of accuracy required.
