摘要
为了克服国标(GBl5618—1995)内梅罗综合污染指数评价土壤环境质量时存在的缺点,借助BP神经网络模型,并结合GIS技术对包头土壤重金属污染的空间分布进行研究。实地调研获得221个土壤样,利用原子荧光光谱法和等离子体质谱法测试得到8种重金属含量数据。统计结果表明,研究区主要为Pb、Zn污染,考虑研究区特异性构造神经网络学习样本,建立基于特征模式的BP神经网络土壤环境质量评价模型。根据采样点评价结果,利用Kriging插值法绘制包头土壤环境质量专题图,分析得出包头土壤环境呈沿昆都伦河被污染的条带特点。结果表明,根据采样统计量信息利用BP神经网络模型,能够有效建立特殊研究区土壤中各种重金属含量与环境质量之间的非线性映射关系,为土壤污染的来源、分布、累积效应和主要影响因素以及污染链的阻断提供依据。
In order to apply the new method in the evaluation of soil environmental quality, which can overcome the disadvantages of the national environmental quality standards (GB 15618-1995, China) and Nemerow comprehensive pollution index. The method is based on the BP neural network model combined GIS technology. The author analyzed the spatial distribution of soil heavy metal pollution in Baotou City of China. The concentrations of 8 heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) were measured in 221 plough layer (0-20 cm) soil sampling in Baotou. The field investigation of 221 topsoil samples were statistically analyzed to show that the study areas were mainly Pb, Zn pollution. It was important to consider the study area geographical features. So the author made the learning samples of BP neural network based on the statistical results and data specificity in study areas. The BP soil environmental quality evaluation model was designed by using the pollution value. According to the evaluation results obtained by the Kriging interpolation method, the author drew Baotou soil environmental quality thematic charts, also the spatial characteristics of Baotou soil environment analysis was included. It was found that the enrichment of heavy metals in topsoil was very obvious in industrial areas and regions near both sides of the Kundulun River. The results indicated that, according to sampling statistics information, the method using BP neural network model could effectively establish special research area soil through non-linear mapping relation between heavy metal contaminate and environmental quality.
出处
《中国农学通报》
CSCD
2012年第2期250-256,共7页
Chinese Agricultural Science Bulletin
基金
国家自然科学基金(41001235)
河南省教育厅自然科学研究(2011A630043
2011B520038)