【摘要】 对湖北省重点区域行业企业周边土壤理化指标和重金属含量进行监测;利用监测数据建立含有13输入、1个隐含层和6输出的3层BP神经网络模型,预测监测区域Mn、Co、V、Ag、Tl、Sb含量,综合重金属监测结果和预测结果,采用内梅罗指数对研究区域进行污染评价。结果表明,研究区域重金属存在不同程度超标情况,最大超标倍数范围为1.8～156.1倍;Mn、Co、V、Ag、Tl、Sb等6项重金属预测结果与实际测试结果相对误差范围在0.3%～19.9%,Mn、V、Ag、Tl、Sb在置信度为99%时均呈显著性相关(P<0.01,n=11),Co在置信度为95%时呈显著性相关(P<0.05,n=11),构建的BP神经网络预测模型具有良好的精准度;基于BP神经网络模型预测结果的内梅罗污染指数未超过警戒限的比例为77.3%,达轻度污染比例17.4%,达中度、重度污染比例均为4.0%。更多还原

【Abstract】 The physical and chemical indicators and heavy metal content in the soil around the enterprises in key areas of Hubei Province were monitored. The monitoring data were used to establish a 3-layer BP neural network model with 13 inputs,1 hidden layer and 6 outputs.The content of Mn,Co,V,Ag,Tl,Sb in the monitoring area were predicted. The Nemerow index based on the monitoring and prediction results of heavy metals was used to evaluate the pollution of the area studied. The results showed that there were different levels of exceeding standard of heavy metals in the area studied. The maximum over-standard range was 1.8-156.1 times. The relative error between the prediction results of six heavy metals including Mn,Co,V,Ag,Tl and Sb and the actually tested results was ranged from 0.3% to 19.9%. Mn,V,Ag,Tl,and Sb were significantly correlated with the confidence of 99%(P<0.01,n=11).Co was significantly correlated with confidence of 95%(P<0.05,n=11). The BP neural network prediction model constructed had good accuracy. Based on the BP neural network model,the Nemerow pollution index not exceeding the warning limit took over a proportion of 77.3%,with the proportion of light pollution of 17.4% and the ratio of moderate to severe pollution of 4.0% each.更多还原