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Several countries worldwide are funding large-scale programs to mitigate landslide risk by implementing engineering remedial works. However, the overall effectiveness of such measures is rarely monitored, and they are typically performed at the slope scale without fully exploiting the wide-area capabilities of remote sensing technologies. A multi-scale and multi-source monitoring procedure for evaluating the slope stability and the effectiveness of related remedial works was proposed in this study and applied in the middle section of the Three Gorges Reservoir Area (TGRA), China. The area is highly exposed to landslide hazards, and a massive program of engineering remedial works was recently implemented. Satellite interferometric synthetic aperture radar (InSAR)-based techniques were first exploited at the regional scale with the objective to provide a general overview of the deformative scenario and to highlight localized problems (active landslides or high deformation zones) to be further investigated; then, local-scale field investigation and multi-source ground monitoring data were employed to verify the deforming states of active landslides and to evaluate the effectiveness of the landslide engineering remedial works. The results indicated that, among the 310 mapped landslides in the study area, 52 were identified to be active and in a slow-moving state by satellite InSAR; Among the 58 controlled landslides, 9 of them were suspected to be active in a slow-moving state and require further concern. Particular attention was paid to two controlled landslides that were found in a continuously and progressively deforming state. We observed that the regional-scale program of slope stabilization was highly successful; however, the variation of the surrounding environmental setting could have led to landslide reactivation or partial invalidation of the landslide remedial works. The proposed multi-scale and multi-source monitoring framework is low-cost, easy to perform, and very straightforward to communicate to citizens and authorities. It can be easily implemented with very wide areas to assess the slope stability and to investigate the effectiveness of large-scale governmental risk mitigation programs, identifying precursor signals that could allow for intervention before reaching critical conditions. [ABSTRACT FROM AUTHOR]

The volatile organic compounds (VOCs) generated during remediation of contaminated soils in northern China during atypical site management were investigated. After collecting soil and gas samples, VOC concentrations in soil and gas particles were determined. Health risk during remediation of contaminated sites was evaluated using the National Academy of Sciences four guidelines for risk assessment; subsequently, the risk zone of each pollutant for the respiratory exposure pathways was drawn. Our results showed that restoration of VOC-contaminated sites may have serious risks for operating staff with respiratory exposure being the main route of VOC contamination. Regarding respiratory exposure pathways, the greatest noncarcinogenic toxicity was caused by 1,1,2-trichloroethane (noncarcinogenic risk 2.27 E + 03); the maximum carcinogenic toxicity was caused by 1,2-dichloroethane (carcinogenic risk 1.05 E – 01). 1,2-Dichloroethane posed the greatest noncarcinogenic and carcinogenic risks when exposure was via particulate matter inhalation, showing risk of 3.52 E – 04 and 2.23 E – 08, respectively. Regarding skin contact exposure pathways, carbon tetrachloride was the most significant noncarcinogenic risk (2.68 E – 07) and 1,1-dichloroethylene was associated with the maximum carcinogenic risk (4.31 E – 11). Based on the regional risk map of pollutants, higher health risk was determined at a certain distance from the excavation site of the contaminated location in the downwind. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors ("cases"). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model's results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior. [ABSTRACT FROM AUTHOR]

One of the major challenges of species distribution models (SDMs) is incorporating the influence of prey on the predator. An ensemble modeling approach, the prey-predator species distributabion model (PPSDM), uses environmental variables (sea surface temperature, bathymetry, sea surface salinity, and sea surface current) and a biotic variable (predicted prey distribution: Japanese anchovy) to predict a piscivorous fish (Japanese Spanish mackerel) habitat in the China Seas. PPSDM comprised selected preferable single algorithms from nine algorithms, including tree-based models, machine learning models, regression models, an envelope-style method, and a classification model. Compared with that obtained when using habitat without the Japanese anchovy distribution, the regression models, machine learning models and a classification model have significant improvements. However, tree-based models have only slightly improved. The PPSDM results well reflected the ontogenetic migration of Japanese Spanish mackerel. We found that the wintering ground was distributed in regions with temperatures between 11 and 17 °C and salinities between 33.6 and 34.1. The bottom topography was also closely correlated with Japanese Spanish mackerel, and the suitable habitat was at 50–100 m isobaths as topography determined the position of isotherms (11, 17 °C), salinity isolines (33.6, 34.1), and fronts (temperature, salinity) in winter. The relationships among different climatic indices, temperature, and Japanese Spanish mackerel were analyzed. We found that the East Asian monsoon significantly influenced the Japanese Spanish mackerel habitat by affecting temperature variation. In the future, the suitable habitat of Japanese Spanish mackerel is projected to decrease by 21%, 28%, 36%, and 42% under RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The Japanese Spanish mackerel in the northern Yellow Sea is projected to experience the 'cul de sac' effect, which would drive this species toward local extinction. The Japanese Spanish mackerel in the China Seas will probably shift northeastward through the Tsushima Strait, and the Sea of Japan may form the main habitat at the end of the century. • Prey-predator species distribution model (PPSDM) was developed by an ensemble model. • The prediction accuracy of predator distribution was improved by PPSDM with prey. • Spanish mackerel is closely related with temperature and salinity isolines in winter. • Spanish mackerel experience a 'cul de sac' effect in the northern Yellow Sea. • Spanish mackerel suitable habitat was projected to shrink by 21–42% in the future. [ABSTRACT FROM AUTHOR]

The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models' accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation. [ABSTRACT FROM AUTHOR]

Abstract Niuhuang Shangqing tablet (NHSQT), a well-known traditional Chinese medicine preparation, has been used as an over- the- counter drug for the treatment of headache, dizziness in China. The flavonoids are the main active components in NHSQT, however, there have no reports about their distribution and metabolic fate in vivo after oral administration of NHSQTs so far. An novel UHPLC-QTOF-MS/MS method combined with in silico approach was applied to identify the flavonoids and metabolites profiling in biological samples following oral administration NHSQTs for the first time. As a result, 127 compounds including 34 original compounds of flavonoids and 93 metabolites were identified. There were 20 flavones, 9 flavonols, 4 flavanones and 1 flavan-3, 4-diol found in biological samples. Rutin, wogonoside, apigenin, baicalein, wogonin, oroxylin A, quercetin and acacetin were considered as the potential flavonoids in NHSQT against brain diseases. The docking-based metabolism models were established and applied to propose the sites of hydroxylation of flavonoids, which indicated baicalin was engaged in dihydroxylation at C2′, C3′, tilianin was engaged in hydroxylation at C3, wogonin and wogonside were engaged in dihydroxylation at C3′, C4′. Some novel metabolic pathways were discovered for oroxylin A, acacetin, diosmetin, tilianin. The metabolic spots and pathways of flavonoids vary as much between flavones, flavonols and flavanones. The results presented here would be helpful for the further study of pharmacokinetics and quality control of NHSQT. Highlights • An novel UHPLC-QTOF-MS/MS method combined with in silico approach was applied to identify the compounds in vivo. • The docking-based metabolism models were established and applied to propose the sites of hydroxylation of flavonoits. • 127 compounds, including 34 flavonoid prototypes and 93 metabolites in biological samples were identified. • Some novel or unusual metabolites were discovered for oroxylin A, acacetin, diosmetin, tilianin. [ABSTRACT FROM AUTHOR]