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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]