Artificial Neural Network Assisted Spatial Distribution and Pollution Grade Evaluation of PAHs in Soils in a Typical Oilfield of China

摘要

Spatial distribution of polycyclic aromatic hydrocarbons (PAHs) in soils collected from six oil wells was investigated in a typical oilfield in China based on the actual sampling and analysis. All the missing data were completed by artificial neural network (ANN) method. It could be seen that the levels of ∑16PAHs for the six oil wells followed order 6 >5 >3 >1 >2 >4. Relevant effect factors of the concentrations level were concluded as condition of ground oil, exploitation scale, and surrounding environment. Soil pollution grade of the six oil wells were also evaluated through the method of a modified Nemerow Index Method. The evaluation result showed that the percentages of heavy pollution, moderate pollution, and slight pollution were 8.02%, 8.02%, and 2.29%, respectively, and No. 6 and NO. 1 sampling wells were the two most serious polluted wells of the six wells due to the long-term exploitation.