Time filter

Source Type

Chen F.,South China Agricultural University | Chen F.,Guangdong Province Key Laboratory for Land Use and Consolidation | Chen F.,Guangdong Province Engineering Research Center for Land Information Technology | Hu Y.,South China Agricultural University | And 13 more authors.
Journal of Information and Computational Science | Year: 2015

The soil heavy metal contamination and relevant eco-environmental problems have been concerned for a long time. The assessments of soil heavy metal pollution usually fails to reflect the real pollution level because of the uncertainty process of predicting spatial distribution of soil properties, which is based on the observation data of sampling sites. However, the use of Sequential Indication Simulation (SIS) method can effectively evaluate the uncertainty of heavy metal in soil. The purpose of this study is to evaluate the high soil lead content in a county scale based uncertainty evaluation in Zengcheng District, Guangzhou. It demonstrates that the local uncertainty evaluation value of the soil lead content is very high while the spatial uncertainty evaluation value is relatively low. Hence, more attention should be paid to the misclassification rate of simulation when evaluating the uncertainty of high risk areas. In order to provide contamination prevention measures, this study gives several feasible resolutions for the prevention administrative department of heavy metal in this county: 1) Pay attention to the classification error rate in the simulation when making the decision of fertilization and prevention of pollution risk and evaluating the uncertainty of soil nutrients and areas of high risk of heavy metal in soil, then rationalize the final decision; 2) The delineation of high-standard farmland should make full use of the accurate and detailed spatial results of nutrients and heavy metal contents in soil, and take the spatial uncertainty assessment values of different attributes into consideration to make the results more scientific. Copyright © 2015 Binary Information Press.

Discover hidden collaborations