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Accurate dynamic modeling of landslides could help understand the movement mechanisms and guide disaster mitigation and prevention. Discontinuous deformation analysis (DDA) is an effective approach for investigating landslides. However, DDA fails to accurately capture the degradation in shear strength of rock joints commonly observed in high-speed landslides. In this study, DDA is modified by incorporating simplified joint shear strength degradation. Based on the modified DDA, the kinematics of the Baige landslide that occurred along the Jinsha River in China on 10 October 2018 are reproduced. The violent starting velocity of the landslide is considered explicitly. Three cases with different violent starting velocities are investigated to show their effect on the landslide movement process. Subsequently, the landslide movement process and the final accumulation characteristics are analyzed from multiple perspectives. The results show that the violent starting velocity affects the landslide motion characteristics, which is found to be about 4 m/s in the Baige landslide. The movement process of the Baige landslide involves four stages: initiation, high-speed sliding, impact-climbing, low-speed motion and accumulation. The accumulation states of sliding masses in different zones are different, which essentially corresponds to reality. The research results suggest that the modified DDA is applicable to similar high-level rock landslides. [ABSTRACT FROM AUTHOR]

Comprehensive assessment of heavy metals and environmental factors on soil bacterial communities in YRD are still limited. In this study, the heavy metals and bacterial community compositions of soil samples from different land use types in YRD were analyzed. The EF, Igeo and PLI assessment results indicated that severe heavy metal pollution was observed in crude oil contaminated soil samples. Moreover, the highest bacterial community richness was observed in soil samples of cotton field, while the richness was lowest for soils around a chemical factory. The bacterial community composition and structure were different among different land use types in YRD, which was consistent with beta-diversity indices. Results showed that the dominant bacterial phyla in YRD soils were Proteobacteria, Chloroflexi, Actinobacteria and Acidobacteria. Redundancy analysis (RDA) showed that the nutrients (SOM, TC, TN and TK) and heavy metals (Cd, Zn, Cu and As) had important effects on soil bacterial communities. Furthermore, the resistant bacteria strains may have great potential for remediation of contaminated soils. Therefore, the present study contributes to a deeper insight into the crucial influencing factors on bacterial community structure in soil samples of YRD, which may provide early warnings of environmental pollution and ecological destruction of ecosystems. [ABSTRACT FROM AUTHOR]

Rapid development of industry and agriculture over the past decades have caused serious environmental impacts on the local environment. However, the spatial distribution, sources and risk assessment of seldom monitored trace elements (SMTEs) in the surface soils of Yellow River Delta (YRD) have rarely been reported. In this study, thirty surface soil samples in YRD were collected, analyzed to determine concentrations of 17 SMTEs, and evaluated based on enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF) and pollution load index (PLI). Spatial distribution results indicated that relatively high contents of SMTEs in YRD mainly occurred in the western part and oil field pumping units, which was very likely related to petroleum exploitation and refining industries. In line with spatial distributions, the assessment results using EF, Igeo, CF and PLI observed potential contamination for sampling site f4, f5, f8, f13, f14, f18, f23, f30, f32, f36, f37 and f38, and reported obvious enrichment of Cs, Sn, Th, Tl and U for the surface soils of YRD. According to the multivariate statistical analysis results, 17 kinds of SMTEs could be divided into three clusters and the origins of SMTEs may differ for different clusters. Our results indicated that PC1 scores could be regarded as an "anthropogenic impact factor" and an important proxy to represent possible contamination from SMTEs in YRD. This study provides important information for the contamination status of SMTEs in the surface soils of YRD. [ABSTRACT FROM AUTHOR]

The reactivation of coüuviai Landslides in reservoir banks poses a serious threat to the safety of hydropower projects and nearby towns. This study aims to research the morphological evolution of this type of landslides under the action of reservoir water and the impact of morphological evolution on landslide stability. The study focused on the Yulinerzu landslide, a large reactivated colluvial landslide in the Xiluodu Reservoir, China. Field surveys were conducted to analyze the geological structure of the landslide. In situ monitoring and surveys were used to obtain the deformation characteristics and morphological evolution of the landslide. A combined seepage-slope stability analysis was conducted to reveal the deformation mechanism. The results show that the reactivation of the Yulinerzu landslide is dominated by reservoir water fluctuations rather than rainfall. The underlying geological condition of the colluvial landslide is its hydrogeological structure, which causes the landslide to deform in a step-like manner during reservoir operation. With the accumulation of displacement and morphology evolution, the landslide displayed self-stabilizing characteristics. Therefore, in the stability analysis and risk assessment of large deformation landslides, it is essential to take into account not only the hydraulic effects of reservoir fluctuation but also the evolution of landslide morphology. [ABSTRACT FROM AUTHOR]

The distribution of polycyclic aromatic hydrocarbons (PAHs) and halogenated PAHs (HPAHs) in surface soils from the petroleum industrial area of the Yellow River Delta (YRD) in China were investigated. The total concentrations of 16 PAHs ranged from 19.6 to 1560 ng/g, while 22 HPAHs ranged from 2.44 to 14.9 ng/g. Moreover, a high degree of spatial distribution heterogeneity was observed for both PAHs and HPAHs, which is likely attributed to the distinct industrial activities in studied area. The combustion of biomass and petroleum were identified as primary sources of soil PAHs and HPAHs in the YRD. Furthermore, benzo[ b ]fluoranthene, benzo[ k ]fluoranthene, and benzo[ g , h , i ]perylene exhibited high ecological risks (with risk quotients of 1.47, 1.44, and 1.02, respectively) in specific sites within the YRD. Considering the high toxicity of HPAHs and their potential joint environmental effects with PAHs, continuous attention should be directed towards the environmental risks associated with both PAHs and HPAHs. [Display omitted] • Co-occurrence of PAHs and HPAHs was found in soil in Yellow River Delta. • Industrial activities resulted in spatial heterogeneity of PAHs and HPAHs in soil. • Biomass and petroleum combustion are primary sources of soil PAHs and HPAHs. • Benzo[ b ]fluoranthene poses high ecological risks in studied areas. [ABSTRACT FROM AUTHOR]

The Yellow River Delta (YRD) is the second largest petrochemical base in China and the impact of human activities has been continuously increasing in recent decades, however, the contamination status of seldom monitored trace elements (SMTEs) in YRD has rarely been reported. This study evaluated the levels, vertical distributions, contamination status and sources of SMTEs in soil samples of three different land use types in YRD. The results indicated that the vertical distributions of SMTEs contents showed a gradually upward increasing trend for the soil profiles of black locust forest, while the SMTEs contents displayed a gradually upward decreasing trend for the soil profiles of cotton field. However, the SMTEs contents in the oil field area showed no significant difference among different depths. The vertical distributions of SMTEs were very likely related to the anthropogenic disturbance in the later stage. The environmental pollution status assessment of SMTEs showed obvious enrichment of Cs, Sn, and U in the soils of YRD. Moreover, the potential source analysis based on multivariate statistical methods indicated that Ga, Rb, Cs, Sc, Sn, Tl, Be, Bi, Ca and Mo were clustered together and positively correlated with Al, Fe, Mg and K, and may be mainly associated with geochemical weathering process, while the Ce, La, Th, U, Nb, Ta, and Hf may be impacted by both natural process and human activities. Though the SMTEs pollution status was not very serious, our results highlighted the non-negligible influence of anthropogenic activities on vertical distributions of SMTEs in three different land use types from YRD. Our results provide valuable information for understanding the vertical distribution and pollution status of SMTEs in YRD. • Non-negligible influence of anthropogenic activities on vertical distributions of SMTEs in YRD • SMTEs displayed an upward decreasing trend for the soil profiles of cotton field. • Obvious enrichment of Sn, Cs and U was observed in YRD soils. • Sources of most SMTEs are mainly related to the geochemical weathering process. [ABSTRACT FROM AUTHOR]

Lanzhou New District is the first and largest national-level new district in the Loess Plateau region of China. Large-scale land creation and rapid utilization of the land surface for construction has induced various magnitudes of land subsidence in the region, which is posing an increasing threat to the built environment and quality of life. In this study, the spatial and temporal evolution of surface subsidence in Lanzhou New District was assessed using Persistent Scatterer Interferometric Synthetic Aperture radar (PSInSAR) to process the ENVISAT SAR images from 2003-2010, and the Small Baseline Subset (SBAS) InSAR to process the Sentinel-1A images from 2015-2016. We found that the land subsidence exhibits distinct spatiotemporal patterns in the study region. The spatial pattern of land subsidence has evidently extended from the major urban zone to the land creation region. Significant subsidence of 0-55 mm/year was detected between 2015 and 2016 in the land creation and urbanization area where either zero or minor subsidence of 0-17.2 mm/year was recorded between 2003 and 2010. The change in the spatiotemporal pattern appears to be dominated mainly by the spatial heterogeneity of land creation and urban expansion. The spatial associations of subsidence suggest a clear geological control, in terms of the presence of compressible sedimentary deposits; however, subsidence and groundwater fluctuations are weakly correlated. We infer that the processes of land creation and rapid urban construction are responsible for determining subsidence over the region, and the local geological conditions, including lithology and the thickness of the compressible layer, control the magnitude of the subsidence process. However, anthropogenic activities, especially related to land creation, have more significant impacts on the detected subsidence than other factors. In addition, the higher collapsibility and compressibility of the loess deposits in the land creation region may be the underlying mechanism of macro-subsidence in Lanzhou New District. Our results provide a useful reference for land creation, urban planning and subsidence mitigation in the Loess Plateau region, where the large-scale process of bulldozing mountains and valley infilling to create level areas for city construction is either underway or forthcoming. [ABSTRACT FROM AUTHOR]

[Display omitted] • A machine learning method assisted by auto hyperparameter optimization was developed. • TPE-XGBoost model has the best performance for spatial prediction of soil heavy metals. • Air quality and hyperspectral variables have the high contributions for prediction accuracy. • There is a source-receptor coupling path for the accumulation of soil heavy metals. • Soil heavy metals with high concentrations are concentrated around rivers. Machine learning (ML) models have been widely used for predicting spatial variability of soil heavy metals. However, it is impossible to explore the entire hyperparameter space of ML models by artificially trial-and-error experimentation. Here, an auto hyperparameter optimization-based machine learning (HPO-ML) method with three search algorithms and random forest (RF) and extreme gradient boosting (XGBoost) models was developed to predict the heavy metal content in soil with multiple environmental variables. The tree-structured Parzen estimator (TPE) algorithm outperformed other search algorithms in identifying the optimal hyperparameters of RF and XGBoost models. The model prediction results showed that the TPE-XGBoost had the highest accuracy for predicting the As (RMSE = 3.06 mg kg−1 and R2 = 70.35%), Cd (RMSE = 0.10 mg kg−1 and R2 = 75.43%), Cr (RMSE = 13.86 mg kg−1 and R2 = 82.11%), Ni (RMSE = 3.19 mg kg−1 and R2 = 75.20%), Pb (RMSE = 3.75 mg kg−1 and R2 = 74.79%), and Zn (RMSE = 6.83 mg kg−1 and R2 = 70.05%) contents. The TPE-XGBoost mapping result showed that areas with high concentrations of soil heavy metals were concentrated in the central and eastern areas (As), the mainstream of the Yellow River (Cd), the northeast area (Cr), the ancient watercourse of the Yellow River (Ni and Pb), and the central and northeastern areas (Zn). The SHapley additive explanation (SHAP) and structural equation model (SEM) were used to interpret the drivers of environmental variables. It is found that the variables with the highest contributions were CO, PM 2.5 , O 3 , PC3, PC1, and PC4 for predicting the As, Cd, Cr, Ni, Pb, and Zn contents, respectively, and there was a significant source-receptor coupling path. The results demonstrate the feasibility of using the HPO-ML approach in hyperparameter-limited conditions, which providing data-driven pathways and options to support the high-quality development of agriculture and the protection of farmland soil ecosystem. [ABSTRACT FROM AUTHOR]

Air quality is a vital environment variable which determines spatial accumulation of soil heavy metals. It is very important to estimate the contribution of air quality for soil heavy metals in oil mining area. For the end, we collected 116 samples from surface soil of oil mining in the Yellow River Delta (YRD) of China, and analyzed the content of As, Cr, Ni, Pb, and Zn. Furthermore, 40 monitoring stations data of air quality were collected in study area, including CO, NO 2 , SO 2 , O 3 , PM 2.5 , and PM 10. Spatial estimation and mapping of heavy metals in soil were carried out by hybrid geostatistical models, including multiple linear regression-ordinary kriging (MLROK), support vector machine-ordinary kriging (SVMOK) and random forest-ordinary kriging (RFOK). RFOK exhibited the highest estimation accuracy (R2) for As (65.76%), Cr (77.85%), Ni (61.47%), Pb (74.64%), and Zn (71.35%) in comparison with other models. And relative R2 of RFOK improved 30%, while MLROK and SVMOK increased over 100% for Zn (RI o = 121.90% and RI o = 121.64%) based on their original R2 of machine learning models. In addition, mapping results by RFOK showed the high concentrations of heavy metals were focused in the central and northeastern (As), northern (Cr), northeastern and northwestern (Ni), central and eastern (Pb), and northern (Zn). Especially, compared with vegetation index and topographic factors, PM 2.5 is the highest driving variable for As (18.34%) and Zn (12.91%), and CO is the most important variable for Cr (18.22%), Ni (14.28%). The above results indicated that there is a mechanism of sources-receptor relationship between air quality and soil heavy metals, that is, oil well and factory in study area discharge heavy metal particles into the atmosphere, and then enter the soil through atmospheric deposition and precipitation. Enlightened by this study, variable selection should be focused on important sources for the accumulation of heavy metals in study area, who must take decisions to prevent and to early warn heavy metals pollution in mine soil. [Display omitted] • Air quality data are used to predict the content of heavy metals in oil mining soils. • Random forest-ordinary kriging has the best predicted performance for soil heavy metals. • The high concentrations of soil heavy metals are the northern of study area. • Air quality variables have effective feasibility for predicting soil heavy metals. • PM (for As and Zn) and CO (for Cr and Ni) is the highest driving variable. [ABSTRACT FROM AUTHOR]

The environmental status of seldom monitored trace elements (SMTEs) has rarely been reported in the North Yellow Sea (NYS). This study investigated the levels, sources and ecological risks of 18 SMTEs in a 209-cm-long sediment core from NYS. The concentrations of SMTEs exhibited a gradual increasing trend in the upper 70 cm. Based on the assessment results of enrichment factor (EF), geo-accumulation index (I geo ) and contamination factor (CF), obvious enrichment of Cs, Li, and U was observed for the NYS sediments, indicating possible anthropogenic sources, which are consistent with the geochemical background normalized patterns. Moreover, the pollution load index (PLI) values ranged from 0.93 to 1.24 and showed a steadily increasing trend in the upper 70 cm part, indicating gradual deterioration of environment in NYS. Combined with the multivariate statistical analysis results and PLI variations, the first principal component (PC1) with high positive loading on Be, Cs, Ga, Hf, In, Li, Nb, Rb, Sc, Ta and Tl was very likely an "anthropogenic factor". Therefore, the historical anthropogenic impact record in the NYS was reconstructed based on the PC1 scores, which indicated significant anthropogenic influence over the past 300 years. This study provides valuable information for understanding the pollution history of SMTEs and historical record of anthropogenic impact in the NYS., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The marine environment of coastal Shandong Peninsula has been significantly influenced by anthropogenic activities due to the rapid industrialization and economic development in the past decades. However, the sedimentary records of PTEs in the North Yellow Sea have rarely been reported. In this study, a 209-cm-long sediment core was collected off the northern coast of Shandong Peninsula, analyzed for grain size and elemental compositions, and assessed using EF, I geo and several numerical Sediment Quality Guidelines (SQGs). The EF and I geo results suggested that sediment profile could be slightly to moderately polluted with As and Sb, while ecological risk assessment using SQGs showed that As, Cr, Sb and Ni in the sediment profile may have a moderate incidence of toxicity. Our results highlighted the nonnegligible ecological risk of Sb in sediments of North Yellow Sea, and great importance should be attached to the fact that many PTEs may also pose a potential ecological risk to the aquatic organisms, even though their concentrations meet the standards of the Marine Sediments Quality (MSQ). Moreover, the reconstructed PTEs record showed a dramatic increase over the past 250 years, which could be related to the intense anthropogenic activities since the Industrial Revolution. The multivariate statistical analysis results indicated that Co, Cr, Cu, Pb, Ni and Zn may be mainly related to the natural origin, while As and Sb could be influenced by both natural weathering sources and anthropogenic activities. This study provides more insights into the historical record of PTEs in the North Yellow Sea, and lays foundation for future comparison of PTEs sedimentary records., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Heavy metal contaminants in Mirror Peninsula, East Antarctica, have rarely been studied and the source and influencing factors are poorly understood. We sampled a grid of 189 topsoil samples from Mirror Peninsula and analyzed the concentrations of Zn, Cu, U, Cr, Ga, Pb, Hg, Se and As; we also calculated the chemical index of alteration (CIA), a proxy of weathering. The results show that the distributions of Cr, Ga, Cu, and Zn are associated with weathering; the distributions of As and Pb are related to vehicle use and unloading activities at the wharfs, respectively; and the distribution of Hg is likely associated with both anthropogenic impacts and biological activity. The contamination level of these heavy metals in Mirror Peninsula is relatively low and within the controllable range. Both weathering processes and anthropogenic impacts can cause the enrichment of heavy metals; thus reliable source apportionment is crucial in studying heavy metal enrichment and contamination., (Copyright © 2019 Elsevier Ltd. All rights reserved.)