%0 Journal Article
%T Simulation of distribution pattern of heavy metals in road verge soils using neural network: a case study of modern Yellow River Delta<br>道路边际土壤重金属分布格局的神经网络模拟——以现代黄河三角洲为例
%A WANG Tian-Wei
%A CAI Chong-Fa
%A LI Zhao-Xia
%A SHI Zhi-Hua
%A LIU Gao-Huan
%A <br>王天巍
%A 蔡崇法
%A 李朝霞
%A 史志华
%A 刘高焕
%J 生态学报
%D 2009
%I 
%X Heavy metals concentration was simulated in strip area around three different types of roads on Modern Yellow River Delta (YRD) using GIS aided MLP neural network based on filed samples data. The results showed that the concentrations of heavy metals were relatively lower in the nature reserve areas and coastal region locating in the east and north parts of the modern YRD, but relatively higher in the south and west parts of the area where higher population density were observed. The average metal concentrations of Mn, Zn, Cr, Cu, Pb and Cd in soils beside road verge were 509.57, 61.26, 58.55, 32.40, 26.07mg/kg and 0.27mg/kg respectively. Except the slight pollution of Cd, concentrations of other metals were not exceeding the national environmental quality standard level of soils. This study revealed that the road in the study area played an important role in heavy metal accumulation, but did not bring broad and notable environment contamination yet. For different types of roads, the concentrations of the tested metals were in the order of Express Way (RG)> Provincial Road (RS)> Rural Road (RX), except Pb. The concentrations of heavy metals besides road verge were reached at peak value within 30m, and the distribution was mainly dependent on many factors such as wind direction, soil properties and vegetation cover. The different changing patterns of heavy metals were observed in individual metal elements because of their different diffusion mechanism.
%K heavy metal
%K Modern YRD
%K Road
%K Neural Network<br>重金属
%K 现代黄河三角洲
%K 道路
%K 神经网络
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=FE163E5DB2274E5937319DE98913EC37&aid=542DDA6A3D3B3150705583C215CCC1E6&yid=DE12191FBD62783C&vid=771469D9D58C34FF&iid=B31275AF3241DB2D&sid=E76E83BE92E4D2E5&eid=A276FE41129984FA&journal_id=1000-0933&journal_name=生态学报&referenced_num=1&reference_num=35