Microbes live throughout the soil profile. Microbial communities in subsurface horizons are impacted by a saltwater–freshwater transition zone formed by seawater intrusion (SWI) in coastal regions. The main purpose of this study is to explore the changes in microbial communities within the soil profile because of SWI. The study characterizes the depth-dependent distributions of bacterial and archaeal communities through high-throughput sequencing of 16S rRNA gene amplicons by collecting surface soil and deep core samples at nine soil depths in Longkou City, China. The results showed that although microbial communities were considerably impacted by SWI in both horizontal and vertical domains, the extent of these effects was variable. The soil depth strongly influenced the microbial communities, and the microbial diversity and community structure were significantly different (p < 0.05) at various depths. Compared with SWI, soil depth was a greater influencing factor for microbial diversity and community structure. Furthermore, soil microbial community structure was closely related to the environmental conditions, among which the most significant environmental factors were soil depth, pH, organic carbon, and total nitrogen.
Landslides - Natural soils often exhibit an anisotropic fabric pattern as a result of soil deposition, weathering, or filling. This paper aims to investigate the effect of soil interdependent... 相似文献
Landslides - According to local villagers, the main Beiguo landslide on the Heyang Loess Tableland in China occurred several decades ago (the specific time is unknown). After the initial occurrence... 相似文献
Landslides - Landslide run-out modeling is a powerful model-based decision support tool for landslide hazard assessment and mitigation. Most landslide run-out models contain parameters that cannot... 相似文献
It is known that a lot of uncertainties are involved in geotechnical design of energy piles. In this paper, a Bayesian updating framework is presented to characterize those uncertainties. The load-transfer model is developed to predict the thermomechanical response of energy piles. Considering the cross-case variability of the uncertainty in the axial strains of pile, the global model bias is firstly calibrated by establishing a comprehensive database consisting of 12 energy pile cases. Furthermore, the uncertainty in input parameters is considered in the Bayesian updating of model bias in a specific case. The variability of the uncertain parameters is effectively reduced after updating. The coefficient of variation of prediction is decreased from 0.34 to 0.13. The present framework can well quantify uncertain factors and improve the accuracy and reliability of the prediction model.
