Exploratory study on the metabolic similarity of denitrifying carbon sources.

Mixed carbon sources have been developed for denitrification to eliminate the "carbon dependency" problem of single carbon. The metabolic correlation between different carbon sources is significant as guidance for the development of novel mixed carbon sources. In this study, to explore the metabolic similarity of denitrifying carbon sources, we selected alcohols (methanol, ethanol, and glycerol) and saccharide carbon sources (glucose, sucrose, and starch). Batch denitrification experiments revealed that methanol-acclimated sludge improved the denitrification rate of both methanol (14.42 mg-N/gMLVSS*h) and ethanol (9.65 mg-N/gMLVSS*h), whereas ethanol-acclimated sludge improved the denitrification rate of both methanol (7.80 mg-N/gMLVSS*h) and ethanol (22.23 mg-N/gMLVSS*h). In addition, the glucose-acclimated sludge and sucrose-acclimated sludge possibly improved the denitrification rate of glucose and sucrose, and the glycerol-acclimated sludge improved the denitrification rate of volatile fatty acids (VFAs), alcohols, and saccharide carbon sources. Functional gene analysis revealed that methanol, ethanol, and glycerol exhibited active alcohol oxidation and glyoxylate metabolism, and glycerol, glucose, and sucrose exhibited active glycolysis metabolism. This indicated that the similarity in the denitrification metabolism of these carbon sources was based on functional gene similarity, and glycerol-acclimated sludge exhibited the most diverse metabolism, which ensured its good denitrification effect with other carbon sources.

Microbial community day-to-day dynamics during a spring algal bloom event in a tributary of Three Gorges Reservoir.

The microbial food-loop is critical to energy flow in aquatic food webs. We tested the hypothesis that species composition and relative abundance in a microbial community would be modified by the development of toxic algal blooms either by enhanced carbon production or toxicity. This study tracked the response of the microbial community with respect to composition and relative abundance during a 7-day algal bloom event in the Three Gorges Reservoir in May 2018. Chlorophyll a biomass, microscopic identification and cell counting of algae and algal abundance (ind. L-1) and carbon, nutrient concentrations (total phosphorus and nitrogen, dissolved total phosphorus and nitrogen), and DNA high throughput sequencing were measured daily. Algal density (1.2 × 109 ind. L-1) and Chlorophyll a (219 µg L-1) peaked on May 20th-21st, when the phytoplankton community was dominated by Chlorella spp. and Microcystis spp. The concentrations of both dissolved total nitrogen and phosphorus declined during the bloom period. Based on DNA high throughput sequencing data, the relative abundance of eukaryotic phytoplankton, microzooplankton (20-200 µm), mesozooplankton (>200 µm), and fungal communities varied day by day while the prokaryotic community revealed a more consistent structure. Enhanced carbon production during the bloom was closely associated with increased heterotrophic microbial composition in both the prokaryotic and eukaryotic communities. A storm event, however, that caused surface cooling and deep mixing of the water column greatly modified the composition and relative abundance of species in the microbial loop. The high temporal variability and dynamics observed in this study suggest that many factors, and not just algal blooms, were interacting to determine the composition and relative abundance of species of the microbial loop.

Experimental Investigation of the Physical Properties and Microstructure of Slate under Wetting and Drying Cycles Using Micro-CT and Ultrasonic Wave Velocity Tests.

Cyclic wetting and drying processes have been considered as important factors that accelerate the weathering process and have deteriorative effects on rock properties. In the present study, a fully nondestructive and noninvasive testing approach utilizing micro-CT and ultrasonic wave velocity tests was employed to investigate the microstructure of slate under wetting and drying cycles. We studied variations in the physical properties, including the dry weight and the velocities of P- and S-waves versus the number of wetting and drying cycles. The internal microstructural distributions were visualized and quantified by the 3D reconstruction and hybrid image segmentation of CT images. The degree of deterioration caused by wetting and drying cycles was reflected by exponential decreases of physical properties, including dry weight and velocities of the P- and S-waves. Parameters relating to the microfracture diameter, volume, etc. were quantified. The nondestructive and noninvasive testing approach utilizing micro-CT and ultrasonic wave velocity tests has potential for the detection and visualization of the internal microstructure of rock under wetting and drying cycles.

Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique.

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006-2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure.

A laboratory model test is an effective method for studying landslide risk mitigation. In this study, thermal infrared (TIR) imagery, a modern no-contact technique, was introduced and integrated with terrestrial laser scanning (TLS) and particle tracking velocimetry (PTV) to characterize the failure of a landslide model. The characteristics of the failure initiation, motion, and region of interest, including landslide volume, deformation, velocity, surface temperature changes, and anomalies, were detected using the integrated monitoring system. The laboratory test results indicate that the integrated monitoring system is expected to be useful for characterizing the failure of landslide models. The preliminary results of this study suggest that a change in the relative TIR signal (ΔTIR) can be a useful index for landslide detection, and a decrease in the average value of the temperature change ( Δ T I R ¯ ) can be selected as a precursor to landslide failure.