Assessing soil Cu content and anthropogenic influences using decision tree analysis.
Recent enhanced urbanization and industrialization in China have greatly influenced soil Cu content. To better understand the magnitude of Cu contamination in soil, it is essential to understand its spatial distribution and estimate its values at unsampled points. However, Kriging often can not achieve satisfactory estimates when soil Cu data have weak spatial dependence. The proposed classification and regression tree method (CART) simulated Cu content using environmental variables, and it had no special data requirements. The Cu concentration classes estimated by CART had accuracy in attribution to the right classes of 80.5%, this is 29.3% better than ordinary Kriging method. Moreover, CART provides some insight into the sources of current soil Cu contents. In our study, low soil Cu accumulation was driven by terrain characteristic, agriculture land uses, and soil properties; while high Cu concentration resulted from industrial and agricultural land uses.
Zhang
Xiuying
X
Institution of Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China.
Lin
Fenfang
F
Jiang
Yugen
Y
Wang
Ke
K
Wong
Mike T F
MT
D000383:Agriculture; D002681:China; D003300:Copper / Q000032:analysis*; D003663:Decision Trees; D004787:Environmental Pollution / Q000706:statistics & numerical data*; D007221:Industry; D008669:Metallurgy; D008906:Mining; D012037:Refuse Disposal; D012722:Sewage; D012987:Soil / Q000032:analysis*; D012989:Soil Pollutants / Q000032:analysis*
D012722:Sewage; D012987:Soil; D012989:Soil Pollutants; D003300:Copper