To study the effects of metal mineralization and mine operation on surrounding ecological environment and human health, 69 farmland soil samples of study area were collected, tested with seven heavy metals and analyzed with environmental risk assessment, spatial distribution, and source identification. The I geo values of Cu and Pb indicated light-moderate pollution level and Co belonged to moderate pollution level. The EF values of Co represented moderately severe enrichment level, and Cu and Pb belonged to moderate enrichment level. The E r i values were in decreasing order of Co>Cu>Pb>Cr>Zn>Mn>Fe, moreover both E r i and RI values of seven heavy metals were at low ecological risk level. The spatial distribution of Fe, Zn, Cr and Mn showed a trend "high in northwest and low in southeast", and Co and Cu were similar with the characteristics "the closer to Chating copper ore, the higher the value", and points with higher content for Pb were distributed along the roads beside Gucheng Lake and irrigation canal. Pearson correlation analysis (CA), hierarchical cluster analysis (HACA), and principal component analysis (PCA) were adopted to gradually identified the characteristics, interactions, classifications and possible sources. PC1 was explained by Fe, Cr, Mn and Zn and might be the natural source influenced by the crust and soil parent material; PC2 was dominated by Pb and Zn and might be derived from transportation and mechanical operations; PC3 was loaded by Cu and might be greatly affected by copper mining activities.

In this study, to clarify the optical characteristics of dissolved organic matter (DOM) in the surface water around the metal mine to be exploited and its relationship with heavy metals, 11 pond water samples and 21 river water samples were collected around the typical to be exploited metal mine in southern Anhui Province, China. The optical properties of DOM in surface water were studied by ultraviolet-visible (UV-Vis) spectroscopy and excitation-emission matrix (EEM) spectroscopy. Co-occurrence network analysis revealed the intrinsic relationship among UV-Vis spectral parameters, fluorescent components, and heavy metals. The results showed that the DOM in the river had higher content, but its molecular weight was smaller than in the pond. EEM coupled with parallel factor analysis (EEM- PARAFAC) revealed humic-like components (C1 and C2) and protein-like components (C3), and the average content of each fluorescent component in the river was higher than that in the pond. However, except for As, the average content of other heavy metals (Cr, Cu, Cd, Pb, and Zn) in ponds was more significant than in rivers. The co-occurrence network analysis result revealed that there might be different relationships between heavy metals and the DOM due to the various land-use.

To investigate the impact of industrial activities on the environmental accumulation and the health risks to humans of heavy metals in urban soils, the cultivated soil samples around different industrial areas were collected and analyzed. The heavy metal concentrations in the soil samples were in the decreasing order Mn>Zn>Cr>Ni>Cu>Pb>As>Sn>Cd>Hg, and the average concentrations of all heavy metals exceeding the corresponding background values. In addition to Cd and Hg, all the other eight metals were classified as low ecological risk; Cd had low, moderate, and considerable ecological risk and Hg were in low, high, and very high ecological risk. The proportion of RI in the four ecological risk levels of low risk, moderate risk, considerable risk and high risk were 69.6%, 13%, 8.7% and 8.7%, respectively. All Mn and some Cr pose non-carcinogenic risk to children primarily through inhalation exposure. Carcinogenic risk is Cr>Ni>As>Pb>Cd, and the exposure route is mainly by ingestion. For children, Cr, Ni and As were high carcinogenic risk and Pb, Cd were acceptable carcinogenic risk; for adults, Cr and Ni were high risk, As was acceptable risk, and Pb and Cd were no risk. The results of the APCS-MLR receptor model showed that the percentages of vehicle emission sources, coal transport industrial sources, coal-fired power plant sources and natural sources were 27.8%, 25.2%, 8.7% and 38.3%, respectively.