摘要
以新疆玛纳斯河流域(简称玛河流域)为研究区域,运用层次聚类分析及自组织映射神经网络(SOM)法,对研究区域119个表层土壤样本中的As、Cd、Cr、Cu、Mn、Ni、Pb、Zn 8种重金属做空间差异性和相似性分析,并对重金属污染空间分布特征作出评价。结果表明,研究区域8种重金属含量平均值分别为4.342、0.115、96.882、44.710、750.235、24.058、8.427、97.007 mg/kg,均低于土壤环境质量国家二级标准值;层次聚类分析及SOM神经网络法的分析结果显示,工业区土壤污染最重,其次是城区土壤,与内梅罗污染指数法相比,神经网络显示出更高的准确性;根据空间差异性分析结果,研究区整体可优化减少43个采样点。
Hierarchical cluster analysis （HCA） and self-organizing maps （SOM） were applied for spatial variability analysis of heavy metals from the 119 topsoil samples of Manasi river basins, Xinjiang. Results showed that the mean concentrations of As, Cd, Cr, Cu, Mn, Pb and Zn were 4.342, 0.115, 96.882, 44.710, 750.235, 24.058, 8.427, 97.007 mg·kg-1, respectively. And those mean contents were lower than their China Environmental Quality Standard values for the Soils. The results of HCA and SOM revealed that the pollution level was highest in the industrial areas, followed by the urban soil. Compared with the Nemerow integrated pollution index, the neural network showed a higher accuracy. According to the spatial difference analysis results, 43 sites could he removed to optimize the spatial location of the monitoring sites.
出处
《石河子大学学报（自然科学版）》
CAS
2017年第1期102-107,共6页
Journal of Shihezi University(Natural Science)
基金
国家自然科学基金项目(21267020
21467026)
