中南大学学报（自然科学版）2024，Vol.55Issue(6)：2143-2152,10.DOI:10.11817/j.issn.1672-7207.2024.06.008
基于地理最优相似性的土壤重金属镉含量空间预测
Spatial prediction of soil heavy metal cadmium content based on geographically optimal similarity
摘要
Abstract
Based on the law of geographic similarity,40 soil samples collected in the field were used to construct a geographically optimal similarity(GOS)model by combining environmental auxiliary variables to predict the heavy metal cadmium content and its spatial distribution in the study area,and the prediction results were compared and analyzed with those of partial least squares regression(PLSR),random forest(RF)and universal kriging(UK)models.The results show that the mean cadmium content of soil samples in the study area(0.432 mg/kg)is greater than the background value,close to the moderate pollution level(pollution index of 2.18),and the regional soil ecology is under some threat.The GOS prediction results have a coefficient of determination of 0.668,and the root-mean-square error and the mean absolute error are 0.096 and 0.080,which are the best among the four prediction models.The spatial prediction results of the GOS show that the regional content of the heavy metal Cd decreases from the northeast to southwest,and the high values are distributed along rivers and in areas with intensive human activities,reflecting that human activities are the main factors leading to soil heavy metal differentiation in the study area.关键词
地理相似性/土壤属性/重金属Cd/空间预测Key words
geographical similarity/soil properties/heavy metal Cd/spatial prediction分类
信息技术与安全科学引用本文复制引用
廖秀英,王波,余昕,梁继,程辉,田茂军..基于地理最优相似性的土壤重金属镉含量空间预测[J].中南大学学报（自然科学版）,2024,55(6):2143-2152,10.基金项目
国家自然科学基金资助项目(42074219) （42074219）
洞庭湖区生态环境遥感监测湖南省重点实验室开放课题资助项目(2022.11) （2022.11）
国家环境保护重金属污染监测重点实验室开放基金资助项目(SKLMHM202228)(Project(42074219)supported by the National Natural Science Foundation of China （SKLMHM202228）
Project(2022.11)supported by the Open Topic of Hunan Provincial Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area （2022.11）
Project(SKLMHM202228)supported by State Environmental Protection Key Laboratory of Monitoring for Heavy Metal Pollutants （SKLMHM202228）
