Search

Slope units (SUs) are sub-watersheds bounded by ridge and valley lines. A slope unit reflects the physical relationship between landslides and geomorphological features and is especially useful for landslide sensitivity modeling. There have been significant algorithmic advances in the automatic delineation of SUs. But the intrinsic difficulties of determining input parameters and correcting for unreasonable SUs have hindered their wide application. An improved method of the evaluation and local multi-scale optimization for the automatic extraction of SUs is proposed. The Sus' groups more consistent with the topographic features were achieved through a stepwise approach from a global optimum to a local refining. First, the preliminary subdivisions of multiple SUs were obtained based on the r.slopeunit software. The optimal subdivision scale was obtained by a collaborative evaluation approach capable of simultaneously measuring objective minimum discrepancies and seeking a global optimum. Second, under the selected optimal scale, unreasonable SUs such as over-subdivided slope units (OSSUs) and under-subdivided slope units (USSUs) were further distinguished. The local average similarity (LS) metric for each SU was designed based on calculating the SU's area, common boundary and neighborhood variability. The inflection points of the cumulative frequency curve of LS were calculated as the distinguishing intervals for those unrealistic SUs by maximum interclass variance threshold. Third, a new effective optimization mechanism containing the re-subdivision of USSUs and merging of OSSUs was put into effect. We thus obtained SUs composed of terrain subdivisions with multiple scales, which is currently one of the few available methods for non-single scales. The statistical distributions of density, size and shapes demonstrate the excellent performance of the refined SUs in capturing the variability of complex terrains. Benefiting from the sufficient integrating approach of diverse features for each object, it is a significant advantage that the processing object can be transferred from general entirety to each precise individual. [ABSTRACT FROM AUTHOR]

The ferromanganese (Fe Mn) deposits could be found in many shallow water and deep-ocean places. Lots of studies have been performed to explore the occurrence, formation and economic value of Fe Mn nodules or crusts on the deep ocean floor. However, the occurrence and formation of Fe Mn beachrocks at the coastal zones has rarely been reported. In the present study, we collected two types of dendritic Fe Mn beachrock samples from an intertidal zone of Zhoushan Archipelago, East China Sea, where was significantly amended by an ancient wood layer formed during 5900–5600 cal. yr B.P. Then the microscale analysis including electron microprobe analyzer (EMPA), X-ray powder diffraction (XRD), and scanning electron microscopy-energy dispersive X-ray spectrometer (SEM-EDS) were performed to study the microscale characteristics on these Fe Mn beachrocks. Combined with our previous studies, a possible formation process was proposed: (1) Buried ancient woods produced enough organic acids to accelerate the weathering of nearby bedrock via chemical or biological reactions; (2) The seepage water could be regarded as a bridge to connect the ancients woods and the intertidal zones, and large amounts of dissolved Fe2+ and Mn2+ can be transported to the sandy beach and intertidal zones; (3) Precipitation of insoluble Fe Mn oxides occurred with the involvement of microbial processes and direct inorganic chemical sorption under mixing of seepage water and seawater; (4) After that, these Fe Mn oxides would experience nucleation, crystallization growth, and then diagenetic process to form the Fe Mn beachrocks. The present study spotted a light on the microscale features, formation process and biogeochemical cycling of nearshore/coastal diagenetic Fe-Mn deposits. • Accelerately formed ferromanganese rocks were discovered in an intertidal zone influenced by buried ancient wood layers; • The microscale characteristics on the intertidal ferromanganese rocks were described; • A complete formation processes on the present beachrocks were proposed. [ABSTRACT FROM AUTHOR]

• Onshore deposits of the 1000-year-old tsunami were identified in Dongshan Bay, northeastern South China Sea. • This study further north is mutually corroborated with previous studies in the SCS. • This research provides examples and methods for the study of onshore tsunami deposits. A giant tsunami occurred in the South China Sea (SCS) approximately a thousand years ago, and various types of tsunami deposits were identified on the Xisha Islands, coastline of South China and Badoc Island, the Philippines. However, little is known about the extent of the affected SCS coastlines and the history of other giant tsunamis. Here, we identify tsunami deposits in the coastal sediment of Dongshan Bay, northeastern SCS. The tsunami deposit contains marine shell debris and coarse quartz grains, distinct from the muddy deposits in the upper and lower layers. Geochemical proxies, including Sr/Ba, Ca, Ti, Ca/Ti, and total organic carbon, indicate the marine intrusion of the event deposit. Radiocarbon ages of shells of the event layer are 970–1179 and 1137–1462 cal. yr BP (2σ range of calibrated 14C age). We suggest that the event layer was possibly deposited by the 1000-year-old tsunami. We found onshore tsunami deposits further north than previous studies, providing a new research example of onshore tsunami deposits and a reference for tsunami deposit research and tsunami disaster prevention. [ABSTRACT FROM AUTHOR]

As an emerging environmental contaminant, di (2-ethylhexyl) phthalate (DEHP) is widely present in the aquatic environment, however, the effects and underlying mechanisms of DEHP on the aquatic organisms are poorly understood. This study systematically investigated the ecotoxicity induced by chronic exposure to environmental relevant concentrations of DEHP (0.03 mg/L, 0.1 mg/L, and 0.3 mg/L) on zebrafish brain. Results indicated that DEHP exposure significantly increased the levels of ROS and disturbance of the antioxidant enzymes activities in the brain, which may further enhance lipid peroxidation and DNA damage. Furthermore, acetylcholinesterase activity was first stimulated and inhibited by exposure to DEHP, and the antioxidant and apoptosis related genes were mainly upregulated. Risk assessment indicated that the ecotoxicity of DEHP on the zebrafish showed an "enhancement-reduction" trend as the exposure time was prolonged. Overall, these results provided new insights and useful information to ecological risk assessment and environmental management of DEHP pollution. • DEHP elevated the ROS levels and disturbed antioxidant homeostasis in zebrafish brain. • DEHP induced lipid peroxidation and DNA damage in brain of zebrafish. • DEHP caused hazardous effects on brain at the neural and genetic levels. • The ecotoxicological risk of DEHP to zebrafish brain first increased and then decreased with the increase of exposure time. [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]

• DEHP presented low acute toxicity to adult zebrafish (Danio rerio) • DEHP induced oxidative stress, lipid peroxidation and DNA damage in zebrafish hepatic tissue. • Acetylcholinesterase (AChE) activity of zebrafish liver was significantly inhibited after DEHP exposure. • The ecotoxicological risk of adult zebrafish increased with increasing DEHP concentrations. Di-(2-ethylhexyl) phthalate (DEHP) has become a ubiquitous contaminant in aquatic environments due to its increasing utilization in recent years. Although its environmental risks to aquatic organisms have attracted widespread attention, the ecotoxicological effects and toxic mechanism of DEHP on zebrafish (Danio rerio) liver remain poorly understood. Herein, this study investigated the acute toxicity, oxidative damage, neurotoxicity, and DNA damage of DEHP on zebrafish at the individual, tissue, physiological and biochemical levels. Results showed that no evident lethal effects of DEHP on zebrafish were observed in all testing concentrations. But in the chronic toxicity test (28 days), superoxide dismutase (SOD) and catalase (CAT) activities in zebrafish liver mainly increased during the pre-and mid-exposure experiment and subsequently decreased after 28 days exposure, whereas the acetylcholinesterase (AChE) activities were always inhibited. By comparison, reactive oxygen species (ROS), malondialdehyde (MDA), and glutathione S-transferase (GST) levels generally increased throughout the experiment, indicating that DEHP enhanced oxidative stress lipid peroxidation, and cytotoxicity of zebrafish liver. DEHP induced DNA damage in hepatocyte, which was positively correlated with changes in ROS levels (0.9874 ≥ R2 ≥ 0.7374). Based on the risk assessment results, the integrated biomarker response (IBR) values were positively correlated with the DHEP concentrations, suggesting that higher DHEP concentrations may cause much more serious detrimental effects to zebrafish liver. Overall, this study is of great significance for understanding the ecotoxicity and mechanism of DEHP on zebrafish. [ABSTRACT FROM AUTHOR]