A consensus that was reached by previous studies was that soil HM concentrations in polluted cities generally increase with an increase in urbanization level (Bai et al., 2016 Foti et al., 2017), thereby suggesting correlations between soil HMs and the urban expansion process. Many cities around the world have suffered different levels of soil HM pollution (Manta et al., 2002 Luo et al., 2012). The concentrations of HMs are particularly high in urban soils because of the excess emissions caused by rapid urbanization and industrialization (Ha et al., 2014). As major contaminants in soils, heavy metal (HM) enrichment has attracted considerable public attention in recent decades owing to its trend of increase and its posing as a potential threat to food security and human health (Li, 2001 Wei and Yang, 2010 Taghipour et al., 2011 Dankoub et al., 2012). Soil plays an important role in sequestering contamination and provides a medium for the transportation of contaminants to the biosphere, hydrosphere, and atmosphere (Alloway, 2013). Furthermore, the performance of the ANN, RF, SVM models were expected to be improved by introducing variables that can reflect the sources, transport, and retention of HMs in urban soils. By using independent predictors for soil HM prediction, ANN, RF, and SVM also produced significant predictions. In comparison, the SVM and RF model revealed higher R 2 and lower error indices than those of the ANN model, suggesting that SVM and RF have the ability to predict urban soil HMs satisfactorily. The influence of Fe 2O 3, Al 2O 3, and SiO 2 on soil As, Ni, and Cr indicates their primary origin from natural processes. The level of Hg in the soil was also likely related to human emissions because of the importance of urbanization history and the surrounded constructing area (CA) in governing the spatial distribution of Hg. According to the RF model, soil CaO, OM, sulfur, phosphorus, and surrounded built-up area were identified as the most important factors for soil Zn, Pb, Cu, and Cd, indicating a predominant anthropogenic control of these HMs. However, the highest concentrations of Ni and Cr were observed in soils between the 2nd and 3rd ring road. The results showed that the concentrations of As, Zn, Pb, Hg, Cu, and Cd increased significantly with an increase in urbanization history. The overall distribution of soil HMs were then predicted using random forest (RF), artificial neural network (ANN), and support vector machine (SVM) models. The areas of different land use types in a specific grid, urbanization history, and soil properties of the site were used as predictors. The concentrations of As, Zn, Pb, Hg, Ni, Cu, Cr, and Cd in the soil, as well as some attributes of soil that were impacted by urbanization were determined. In this study, 251 topsoil samples (0–20 cm) were collected using the grid-sampling method (2 km × 2 km) in a rapid urbanization area (Hefei City, China). However, accurate predictions of urban soil HMs based on predictors associated with urbanization are still lacking. It is important to identify correlations between the urbanization process and HM accumulation in the soil and predict the spatial distribution of soil HMs based on variables related to urban expansion, so that strategies for urban soil management can be created. Accelerated urbanization has resulted in the accumulation of considerable amounts of heavy metals (HMs) in urban soils.
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