Assessing soil Cu content and anthropogenic influences using decision tree analysis

• Published in: Environmental pollution (1987) Vol. 156; no. 3; pp. 1260 - 1267
• Main Authors: Zhang, Xiuying, Lin, Fenfang, Jiang, Yugen, Wang, Ke, Wong, Mike T.F.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-12-2008; Elsevier
• Subjects: Agriculture; Analysis methods; Applied sciences; CART; China; Copper - analysis; Cu concentration; Decision Trees; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental factors; Environmental Pollution - statistics & numerical data; Exact sciences and technology; Fuyang; Industry; Metallurgy; Mining; Pollution; Pollution, environment geology; Refuse Disposal; Sewage; Soil - analysis; Soil and sediments pollution; Soil Pollutants - analysis; Asia; China; China, People's Rep; Cu concentration; Environmental factors; Fuyang; China; CART; Geostatistics; Environmental factor; Data mining; Soil pollution; Decision tree; Land use; Heavy metal; Statistical method; Spatial distribution; Human activity; Classification; Pollution source; Copper; Concentration distribution; Anthropogenic factor
• ISSN: 0269-7491; 1873-6424
• DOI: 10.1016/j.envpol.2008.03.009

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.
Classification and regression tree (CART) analysis provides insight into sources of soil Cu and rightly predicted Cu concentration classes for 80.5% of the test data.