Pollution Assessment of Heavy Metals in Soils Based on RBF Neural Network
Lei Li, Jian Li, MA Jian-hua · Environmental Science & Technology · 2010
RBF network which was established by using K-means algorithm of MATLAB and combing with neural network toolbox was used to assess soil quality of Putian-Xinghuaying Section,Zhengzhou-Kaifeng Highway. Results showed that peak of heavy metal contamination appeared in the range of 50m away from the roadbed,with Cd and Zn content in the high level. With distance increasing,the degree of pollution decreased gradually. Degree of heavy metal pollution on both sides of the Highway was affected by wind and adjacent railway seriously. The transport of road operator earlier,soil pollution will be more serious. Soil quality of Xinghuaying Section was mainly in Grade II,and most parts of north side were in Grade III. Comprehensive evaluation of various points about Putian Section was in Grade II. The method is advantageous over faster calculation and objectively assessment.