太湖流域典型农用地表层土壤重金属空间分异潜在风险因子识别

Potential Risk Factors Identification of Heavy Metals Spatial Variation in Typical Agricultural Land Topsoil of Taihu Basin

土壤是人类生存、生活最基本的生产资料。识别农用地表层土壤重金属空间分异潜在风险因子对区域重金属污染农用地安全利用具有重要意义。以太湖流域典型农用地土壤为研究对象,运用Normal Score Transformation(NST)变换法处理表层土壤6种重金属非正态分布数据,分析构建表层土壤重金属空间分异的潜在风险因子集,利用地理探测器识别法(GeoDetector Model, GDM)识别潜在风险因子及其交互作用。结果表明:通过因子探测器识别发现6种表层土壤重金属的前5大潜在风险因子既有自然条件因子,也有社会经济因子。交互探测器识别发现自然与社会经济潜在风险因子对表层土壤6种重金属空间分异均随着因子的线性叠加而呈现出1+1>2的非线性增强的交互作用。风险探测器可识别出表层土壤6种重金属各自的潜在风险因子类别。基于NST和GDM的表层土壤重金属空间分异潜在风险因子识别思路可为准确识别与监测重金属污染区域提供更准确和详细的科学依据。

The soil is the most fundamental means of production for human survival and life. It is of great significance to identify the factors of the potential risk because of heavy metals spatial variation in typical agricultural land for the safe utilization of heavy metals contaminated agricultural land. Normal Score Transformation was used to transfer six heavy metals data into Gaussian distribution. In this study, we identified the potential risk factors of six heavy metals spatial variation in typical agricultural land of Taihu watershed by using GeoDetector Model(GDM). Factor detector results showed that the top five potential risk factors of these six heavy metals distribution included both natural factors and social-economic factors. Interaction detector results showed that interactions of potential risk factors all enhance nonlinearly. In other words, the influence of these six heavy metals distribution were an enhance nonlinearly interaction effect, which were based on potential risk factors. The specific categories of each potential risk factor could be identified by risk detector for each heavy metal. GDM results can help identify and monitor the heavy metal contaminated areas.