Spatial distribution analysis of heavy metals in soil and atmospheric dust fall and their relationships in Xinjiang Eastern Junggar mining area

Abstract: Heavy metal pollution is the representative environmental pollution which is caused by heavy metals or their compounds. It has wide range, long duration, non-degradability, and some other features. The accumulation of heavy metals in soil will destroy the physical and chemical properties of soil, cause irreversible pollution of environment, and further threaten human health. For mining area, influenced by human exploitation, the top soil is thin and is easily affected by atmospheric dust fall. The relationship between soil heavy metals and atmospheric dust fall could reflect the sources of heavy metals pollution. Furthermore, the transport, migration and diffusion characteristics of heavy metals in the soil-atmosphere system could be revealed through the analysis of it. Existing work has mostly focused on only top soil, which might ignore the influence of dust reduction. Therefore, this study aimed to investigate the pollution status of heavy metals in the surface soil and their relevance with atmospheric dust fall. With 51 surface soil samples and 51 atmospheric dust fall samples collected from Eastern Junggar coal mining area in Xinjiang Uygur Autonomous Region, China, the contents of 6 kinds of heavy metals (As, Cu, Cr, Hg, Pb, and Zn) in the samples were measured in the laboratory. The geo-accumulation could be used to evaluate the pollution degree of heavy metals in soil. The potential ecological risk index takes into account the difference of the background values of heavy metals and the migration and transformation of heavy metals in the environment. The descriptive statistics of heavy metals in surface soil were carried out. Secondly, the spatial distribution, geo-accumulation, and potential ecological risk index of heavy metal from surface soil and atmospheric dust fall were analyzed. The outcomes of the analysis of them were described by ArcGIS 10.1 spatial analysis with inverse distance weighted (IDW) interpolation method. Subsequently, the correlation between surface soil and atmospheric dust fall was analyzed by Pearson correlation analysis and grey correlation analysis. Pearson correlation coefficient has been widely used to measure the linear relationship between fixed distance variables, and the value range is from -1 to 1; the greater the absolute value, the stronger the correlation. For the grey relational analysis, if the correlation degree was greater than 0.6, the 2 factors could be regard as being correlated significantly. The results showed that: 1) There were significant spatial distinctions between 6 kinds of heavy metal elements (As, Cu, Cr, Hg, Pb, and Zn) in surface soil. The pollution level of Hg was the most serious, which could be regard as the strong polar pollution, and its potential ecological risk index reached a high ecological risk. The Zn and Cu were basically in a state of no pollution, and they were at a slight ecological risk. 2) There were obvious differences in the spatial distribution of heavy metals in atmospheric dust fall. The Zn in atmospheric dust fall was in pollution with extreme intensity, while Hg was in no pollution state. 3) Pearson correlation coefficients showed that Hg and As in the atmospheric dust fall were significantly related to the As in soil, and the correlation coefficients reached 0.72 and 0.66, separately. The results of grey correlation degree showed that the relevance between heavy metal in surface soil and atmospheric dust was notable, but varied among 6 kinds of heavy metal elements. The amount of local dust was related to human production activities and local climatic factors closely, i.e., prevailing winds and precipitation. Therefore, in further research, the size of samples will be expanded, and this important factor will be used to analyze the spatial distribution, geo-accumulation, and potential ecological risk index of heavy metals, and explore the impact of dust on it.