Source Apportionment and Risk Assessment of Heavy Metals in Agricultural Soils in a Typical Mining and Smelting Industrial Area

Table 2 shows the Pearson’s correlation coefficients of the investigated heavy metals in farmland soils. The results indicated that the correlation coefficients between Cr and Ni (0.558), Cu and Zn (0.502), Zn and Pb (0.696), and Cd and Pb (0.507) were relatively high, while those between Mn (or Hg) and other heavy metals were low. The results demonstrated that Cu, Zn, Pb, and Cd could be contributed by the same or correlated sources, and Ni and Cr had similar provenances. In contrast, Mn and Hg had different origins from other heavy metals. In addition, hierarchical cluster analysis was also performed to identify groups of heavy metals with similar distribution patterns, as shown in Figure 3. It reveals that Cr and Ni formed a cluster, and the cluster of Zn and Pb linked with Cd and Cu at later stages, while Mn and Hg were lack of connections with the other soil heavy metals. PCA was also conducted to identify the source of soil heavy metals, and the outcomes of KMO (0.622) and Bartlett’s test (p 45,46]. As shown in Table S5, five principal components (PCs) with a cumulative proportion of 79.9% were kept for further analysis. To make the results more explainable, VARIMAX normalized rotation was performed. Table 3 summarizes the factor loadings of heavy metals at the rotated principal components. In general, factor loadings greater than 0.71 are considered outstanding, while those less than 0.32 are very poor [47]. As is shown in Table S5, Cu (0.418), Zn (0.842), Pb (0.846), and Cd (0.747) had high factor loadings in PC1, indicating that PC1 can explain most of their variances. High factor loading values of Ni (0.868) and Cr (0.842) were found in PC2. PC3 was dominated by As (0.924), followed by Cu (0.495). Mn (0.885) and Hg (0.939) had extremely high factor loadings in PC4 and PC5, respectively. The outputs of PCA were consistent with those of cluster analysis.

In this study, APCS-MLR was applied to quantitatively apportion the contributions of heavy metals in soils from various potential sources. Table 4 presents that R² of investigated heavy metals varied from 0.612 to 0.916, and the differences between the observed and predicted mean values of heavy metals were minor. It suggested that the APCS-MLR models were reliable for source interpretation [13]. Based on the receptor models simulation, the spatial distribution of APCS is depicted in Figure 4.

Factor 1 had higher contributions to Cu (34.3%), Zn (67.2%), Pb (75.1%), and Cd (67.9%). In general, the elevated content of Pb, Zn, Cu, and Cd in soils would be contributed by industrial activities, such as mining and steel processing, battery production, waste incineration, and coal combustion [19,30,48,49]. According to Figure 4a and Figure S1, the high values of Factor 1 were mainly observed in the southern mountainous region and Guixi County, which featured intensive mining and smelting–processing activities, respectively. In contrast, the central basin with a high density of road network, together with the western part of the central basin with heavy agricultural production activities, had relatively lower levels of contributions from the factor. Therefore, factor 1 could mainly consist of industrial sources associated with mining and smelting processes.

Factor 2 was heavily loaded with Ni (68.0%) and Cr (66.6%) in agricultural soils. Previous studies have recognized that the natural processes, including rock erosion, weathering, and the degradation and mineralization of sediments in wetlands, are the main drivers for their accumulation [50]. In this study, the high contributions of factor 2 were mainly distributed in Yujiang County (Figure 4b), where the industrial system was featured with carving and glass production [51], which hardly released substantial Cr and Ni into soils. Therefore, Cr and Ni mainly originated from the lithogenic process. The results were consistent with the findings in other study areas in China and the world [52,53,54,55]. Thus, factor 2 was mainly attributed to a natural source.

Factor 3 and factor 4 had a main loading of As (57.4%) and Mn (49.1%), respectively. Generally speaking, Mn in soil mainly comes from geological origin, and human activities would not cause significant changes in the content of Mn in the surface soils [56,57], whereas mining activities, coal burning, wood preservative usage, elements smelting, and agrochemical application are major anthropogenic sources contributing to As contamination in soils [58,59]. According to Figure 4c,d, and Figure S1, Guixi and the southern region with intensive mining and smelting activities have relatively low levels observed, indicating that industrial emission would not account for the main component of the factor. In addition, Table 1 shows that Mn and As had lower concentrations than the background value in most samples, indicating that they had little likelihood of being contaminated by anthropogenic sources. Therefore, factors 3–4 could be attributed to natural sources.

The weight of Hg (71.0%) in factor 5 was extremely high. Studies have shown that the concentration of Hg may be significantly elevated by industrial emissions such as non-ferrous metal smelting, coal mining and coal combustion, waste landfill, paper-making, and chemical production [60,61,62]. Table 1 indicates that Hg concentrations in more than 60% of the samples were below the corresponding background value in the study area, suggesting the important role of natural sources. High contributions from factor 5, as well as the samples with Hg concentrations exceeding 0.1 mg/kg, were frequently found in the areas concentrated with mining and smelting activities, including the mining area located in the southern and northern region, as well as the eastern part of the central basin (Figure 4e, Figures S1 and S3), demonstrating the significant impacts of anthropogenic inputs to the elevated contents of Hg in soils. Thus, factor 5 would be a mixed source of natural origin and industrial emissions.

According to the source identification, the heavy metals in soil were contributed by natural (factor 2–4), industrial (factor 1), and mixed sources (factor 5), which accounted for 34.0%, 49.5%, and 6.73% of total metal contents, respectively (Figure S4). The contribution of unknown sources occupied 9.71%, which may include agricultural sources and traffic sources. The results revealed that the industrial origin exhibited a significant impact on soil quality due to high contribution to the toxic elements input (e.g., Cu, Zn, Pb, and Cd).