[Assessing soil Zn content using decision tree analysis].
Taking Fuyang county of Zhejiang Province as the study area, the present study estimated soil Zn concentration (divided by its local background value into G1, G2, G3, G4 and G5) using CART methods, based on 184 soil samples (0-20 cm). The environmental factors used to infer the Zn concentration rules included soil types, pH, organic matter, agricultural land uses, industry plant types, road and village density. The other 41 soil samples were used to test the prediction results. The Zn concentration classes estimated by CART have accuracy in attribution to the right classes of 80.49%. This is a 21.95% improvement on Zn classes estimated by ordinary Kriging method. Concretely, it improved the precision much for G1, G3 and G4, while obtained similar precision for G2 and G5. Moreover, CART provided some insights into the sources of current soil Zn contents. The categories of industrial plants play the most important role in separating the high and low level of Zn concentration (G1, G2 and G3, G4, G5), and the pH value, soil types and agricultural types play important roles in differentiation among G1 and G2, and among G3, G4 and G5.
Zhang
Xiu-ying
XY
Institute of Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China.
Sun
Qi
Q
Wang
Ke
K
Jiang
Yu-gen
YG
Lin
Fen-fang
FF
Han
Ning
N
D002681:China; D003663:Decision Trees; D004784:Environmental Monitoring; D012987:Soil / Q000032:analysis*; D012989:Soil Pollutants / Q000032:analysis*; D015032:Zinc / Q000032:analysis*
D012987:Soil; D012989:Soil Pollutants; D015032:Zinc