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Surface soils of five representative land-use types were collected in Yellow River Delta (YRD) and determined for concentrations of heavy metals and soil properties. These data were used to assess the heavy metal pollution status, and discuss the effects of heavy metals and soil properties on microbial community structure. Results showed that overall heavy metal pollution status in YRD was not serious except for the oilfield area. At the phylum level, the dominant species in the soil samples of YRD were Proteobacteria, Actinobacteriota, and Chloroflexi. The bacterial community structures varied significantly among different land-use types, and TN, TP, pH, Ni, and As had the greatest impact on microbial community structure. Moreover, the phylum Actinobacteriota and Chloroflexi were positively correlated with As, Ni, Cu, Zn, and Cd, indicating possible tolerance to these heavy metals. These results could be helpful for understanding the variations of soil microbial communities in YRD. [ABSTRACT FROM AUTHOR]

Abstract Fildes Peninsula, with a high density of scientific stations, has been significantly impacted by anthropogenic activities. However, the contamination from penguins, a biovector that transports pollutants from ocean to land, has seldom been assessed. In this study, 32 lacustrine surface sediment samples on Fildes Peninsula and 8 lacustrine surface sediment samples on Ardley Island were collected to determine Cu, Zn, Pb, Ni, Cr, Cd, Co, Sb, Hg and P levels. The results showed that the heavy metal contents of lacustrine sediments on Ardley Island are significantly higher than those on Fildes Peninsula. The contaminants on Fildes Peninsula are mainly derived from anthropogenic activities, while the contaminants on Ardley Island are transported to the lacustrine sediments in the form of penguin guanos after a series of biomagnification in the food chain. The results indicated that the impact of penguin-transported contamination on Antarctic environment outweighs human activities near scientific stations in some areas. Therefore, more attention should be paid to the impacts of Antarctic animals on the Antarctic environment. Graphical abstract Unlabelled Image Highlights • The contamination sources in Antarctica mainly include: penguins transport, human activities and bedrock weathering. • The impact of penguin-transported contamination on Antarctic environment outweighs human activities in some areas. • Penguins transport anthropogenic contaminants to Antarctica in the form of penguin droppings through marine food web. [ABSTRACT FROM AUTHOR]

Ny-Ålesund has been significantly impacted by anthropogenic activities (e.g. coal mining, scientific research, tourist shipping) over the past 100 years. However, the studies of potential toxic elements (PTEs) contamination in Ny-Ålesund currently mainly focus on surface soil or surface fjord sediments, and little is known about the history and status of PTEs contamination over the past 100 years. In this study, we collected a palaeo-notch sediment profile YN, analyzed the contents of six typical PTEs (Cu, Pb, Cd, Hg, As, Se) in the sediments, and assessed the historical pollution status in Ny-Ålesund using the pollution load index, geo-accumulation index and enrichment factor. The results showed that the contents of PTEs over the past 100 years increased rapidly compared with those during the interval of 9400-100 BP. In addition, Pb, Cd and Hg showed a clear signal of enrichment and were the main polluters among the PTEs analyzed. The contamination was likely linked to gas-oil powered generators, coal mining, research station, tourist shipping and long-range transport of pollutants. Image 1 • Historical records of potential toxic elements (PTEs) in Ny-Ålesund was reconstructed. • The PTEs contents over the past 100 years increased rapidly compared with historical periods. • Pollution was mainly caused by gas-oil powered generators, coal mining, scientific research activities. We reconstructed the history and pollution status of PTEs contamination over the past 100 years in Ny-Ålesund and proposed its potential sources and transport pathways. [ABSTRACT FROM AUTHOR]

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. Image 1093 • Heavy metal contamination levels of topsoils from Mirror Peninsula, East Antarctica, are within the controllable range. • The observed spatial distribution of heavy metal concentrations is related to logistical activities and vehicle use. • Biological activities, including biomagnification along the food chain, are another source. • Enrichment of heavy metal induced by weathering process should be eliminated as natural process. Heavy metal contamination in Mirror Peninsula falls within the controllable range and various factors contributed to the enrichment of heavy metals. [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]